INVESTIGATING THE NEUROPLASTICITY
OF EMOTIONAL MEMORIES.
Angela Jacques BBiomedSc
Submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
School of Clinical Sciences
Faculty of Health
Queensland University of Technology
2019
1
Keywords
Activity-regulated cytoskeleton-associated protein (Arc / Arg3.1), α-amino-3-
hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR), amygdala (AMG),
anxiety, basal amygdala (B), basolateral amygdala complex (BLC), brain derived
neurotrophic factor (BDNF), c-Fos, conditioned response (CR), conditioned stimulus
(CS), consolidation, extinction, extinction training, contextual fear conditioning
(CFC), contextual fear memory, dentate gyrus (DG), fear, hippocampus, fear
memory, fear memory trace, glutamate, hypothalamus–pituitary–adrenal (HPA) axis,
immediate-early genes (IEGs), immunohistochemistry, lateral amygdala (LA), long
term potentiation (LTP), N-methyl-D-aspartate receptor (NDMAR), memory
consolidation, memory maintenance, microglia, neuroanatomy, neurobiology,
neuroplasticity, Pavlovian fear conditioning, phosphorylated mitogen-activated
protein kinase (pMAPK), post-traumatic stress disorder, prefrontal cortex (PFC),
serotonin, synaptic plasticity, threat, unconditioned stimulus (US), vesicular
glutamate transporter 3.
2
Abstract
A central task in contemporary neuroscience is to identify the cellular and molecular
mechanisms underlying cognitive brain functions, and how alterations of these
mechanisms can lead to neuropsychiatric disease states. There are about 100 billion
neurons in the human brain, and approximately the same number of glial or
supporting cells and both types of cells play a unique role in neuroplasticity. The
complex web of connections they form is constantly being remodelled through
genetics, individual behaviour and our environment. This remodelling may involve
neurogenesis, increased dendritic branching and synaptic connections. However,
neoplasticity is biphasic and may take on a maladaptive nature resulting in atrophy,
reduced branching with smaller dendritic spines and decreased synaptic connections,
all referred to as negative plasticity. Negative neuroplasticity has been linked to
depression, stress, anxiety disorders, schizophrenia, phobias and post-traumatic stress
disorder. Although technological advancements abound we are yet to establish many
successful therapeutic interventions for physical and mental neurological disorders.
Only through continued research will we develop a greater understanding of
neuroplasticity, improved methods of quantification and identification of neural
correlates essential to these changes.
3
Table of Contents
Keywords .................................................................................................................................. 1
Abstract ..................................................................................................................................... 2
Table of Contents ...................................................................................................................... 3
List of Figures ........................................................................................................................... 6
List of Tables ............................................................................................................................ 9
List of Abbreviations .............................................................................................................. 10
Statement of Original Authorship ........................................................................................... 12
List of Publications and Presentations .................................................................................... 13
Acknowledgements ................................................................................................................. 18
Introduction .......................................................................................... 19
1.1 The Research Problem: Understanding the role of neuroplastic adaptations in emotional pathophysiology. ..................................................................................................................... 20
1.2 Context of Research: The mechanisms and biomarkers underlying neuroplasticity associated with emotional behaviours ..................................................................................... 28
1.3 Purposes ............................................................................................................................ 36
1.4 Significance and Scope: .................................................................................................... 40
1.5 Thesis Outline ................................................................................................................... 41
Functional Neuronal Topography: A Statistical Approach to Micro Mapping Neuronal Location ................................................................... 44
2.1 Abstract ............................................................................................................................. 46
2.2 Introduction ....................................................................................................................... 46
2.3 Step-By-Step Methods ...................................................................................................... 51
2.4 Statistical Analysis of topographic neuron density data ................................................... 68
2.5 Discussion ......................................................................................................................... 76
2.6 Conclusion ........................................................................................................................ 80
Localization of Contextual and Context Removed Auditory Fear Memory within the Basolateral Amygdala Complex ............................................ 83
3.1 Abstract ............................................................................................................................. 85
3.2 Introduction ....................................................................................................................... 86
3.3 Experimental Procedures .................................................................................................. 90
3.4 Results ............................................................................................................................. 102
3.5 Discussion ....................................................................................................................... 116
Micro-Topography of Fear Memory Consolidation and Extinction Retrieval within Prefrontal Cortex and Amygdala ............................................ 128
4.1 Abstract ........................................................................................................................... 130
4.2 Introduction ..................................................................................................................... 131
4.3 Material and Methods ..................................................................................................... 133
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4.4 Results ............................................................................................................................. 142
4.5 Discussion ....................................................................................................................... 157
Microglial phenotype alters with varied fear memory recall in the prefrontal cortex..................................................................................................... 161
5.1 Abstract ........................................................................................................................... 163
5.2 Introduction ..................................................................................................................... 163
5.3 Methods .......................................................................................................................... 165
5.4 Results ............................................................................................................................. 174
5.5 Discussion ....................................................................................................................... 182
Contextual Fear Conditioning Alter Microglia Number and Morphology in the Rat Dorsal Hippocampus ...................................................... 186
6.1 Abstract ........................................................................................................................... 188
6.2 Introduction ..................................................................................................................... 189
6.3 Method ............................................................................................................................ 193
6.4 Results ............................................................................................................................. 204
6.5 Discussion ....................................................................................................................... 211
Axonal nonsegregation of the Vesicular Glutamate Transporter VGLUT3 within serotonergic projections in the mouse forebrain. ................... 220
7.1 Abstract ........................................................................................................................... 222
7.2 Introduction ..................................................................................................................... 223
7.3 Materials and Methods .................................................................................................... 226
7.4 Results ............................................................................................................................. 228
7.5 Discussion ....................................................................................................................... 240
7.6 References ....................................................................................................................... 243
Fear extinction recall mediated by 5-HT/VGLUT3 colocalisation 248
8.1 Abstract ........................................................................................................................... 250
8.2 Introduction ..................................................................................................................... 251
8.3 Methods .......................................................................................................................... 254
8.4 Results ............................................................................................................................. 262
8.5 Discussion ....................................................................................................................... 268
General Discussion ............................................................................. 274
9.1 Summary of findings ....................................................................................................... 275
9.2 Significance ..................................................................................................................... 277
9.3 Advanced considerations ................................................................................................ 278
9.4 Future directions ............................................................................................................. 280
9.5 Concluding remarks ........................................................................................................ 281
Appendix A: .......................................................................................................................... 283
Supplementary Material for chapter 7. ................................................................................. 283
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Appendix B: .......................................................................................................................... 292
The Impact of Sugar Consumption on Stress Driven, Emotional and Addictive Behaviours. .................................................................................... 292
10.1 Abstract ......................................................................................................................... 293
10.2 Introduction ................................................................................................................... 294
10.3 Common neuronal pathways for sucrose consumption, addiction, emotions and obesity296
10.4 Physiological and neural substrates of sugar consumption ........................................... 302
10.5 Common neurochemistry underlie consumptive behaviours and emotions .................. 320
10.6 Common anatomical structures and neural substrates of stress driven, emotional behaviour .............................................................................................................................. 331
10.7 Sucrose Consumption Investigated ............................................................................... 341
10.8 Therapeutics for obesity, derived from studies of sucrose consumption ...................... 345
10.9 Discussion ..................................................................................................................... 350
Appendix C: .......................................................................................................................... 354
Glucocorticoid Receptor (GR) ......................................................... 354
Appendix D: .......................................................................................................................... 365
Mineralocorticoid Receptor ............................................................. 365
Bibliography ......................................................................................................................... 375
6
List of Figures
Figure 1-1 Auditory Pavlovian fear conditioning and extinction. ....................................... 23
Figure 1-2 Acquisition, consolidation and retrieval of a memory. ...................................... 24
Figure 1-3 MAPK pathway to neuroplasticity. .................................................................... 32
Figure 2-1: Steps for tissue sampling and measurement from behavioural data. ................ 53
Figure 2-2: Steps for producing raw coordinate data from identified neurons. ................... 60
Figure 2-3: Steps for producing and analysing topographical density maps. ...................... 66
Figure 3-1: Experimental design for behavioural training. .................................................. 91
Figure 3-2: Schematic illustration of aligned sections. ........................................................ 98
Figure 3-3: Arc, c-Fos and co-localised labelling of amygdala neurons. ............................ 99
Figure 3-4: Freezing to context and tone. .......................................................................... 104
Figure 3-5: Immediate early gene expression in Bregma coordinate -3.36mm. ................ 107
Figure 3-6: Immediate early gene expression in Bregma coordinate -3.24mm. ................ 109
Figure 3-7: Immediate early gene expression in Bregma coordinate -3.12mm. ................ 111
Figure 3-8: Immediate early gene expression in Bregma coordinate -3.00mm. ................ 114
Figure 3-9: Schematic representation of IEG expression following conditioning. ............ 115
Figure 3-10: Schematic representation of total IEG expression following conditioning. ................................................................................................ 116
Figure 4-1: Experimental design. ....................................................................................... 136
Figure 4-2: Recall of auditory fear consolidation and extended extinction training result in differing levels of freezing. ........................................................... 144
Figure 4-3: Recall of a conditioned fear memory and extinction memory result in spatially different patterns of pMAPK expression in subregions of the amygdala. .............................................................................................. 146
Figure 4-4: Spatial analysis of pMAPK expression in the LA reveals a stable population of neurons specific to the recall of an extinction memory. ....... 149
Figure 4-5: Recall of auditory fear and extinction memory both activate pMAPK expression in the medial prefrontal cortex. ................................................. 150
Figure 4-6: Remote recall of an extinction memory and recent recall of an auditory fear memory both activate pMAPK expression in the infralimbic cortex. .......................................................................................................... 152
Figure 4-7: Spatial analysis of pMAPK expression in the PL reveals a different neuronal distribution between recent and remote recall of an auditory fear memory. ............................................................................................... 154
Figure 4-8: pMAPK expression in the IL cortex following recall of an extinction memory. ....................................................................................................... 156
Figure 5-1 Recall of recent and remote fear and extinction memories result in different levels of freezing. ......................................................................... 169
Figure 5-2 Schematic drawing showing the location of the acquired micrographs. .......... 172
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Figure 5-3 c-Fos and Arc expression in the PFC are greater in the recall of a recent fear memory. ............................................................................................... 178
Figure 5-4 Microglia alter morphologically in response to fear memory recall. ............... 180
Figure 5-5 Morphological analysis of microglia. ............................................................... 182
Figure 6-1. Experimental Design for Behavioural Training. ............................................. 195
Figure 6-2. Illustration of LA and DH subregions and labelling in these Regions. ........... 202
Figure 6-3. Freezing to Context and Tone Data. ................................................................ 205
Figure 6-4. pCREB Expression in LA and DH. ................................................................. 206
Figure 6-5. BDNF Expression in LA and DH. .................................................................. 207
Figure 6-6. IBA-1 Expression in LA and DH. ................................................................... 208
Figure 6-7. IBA-1 Morphology in DG. .............................................................................. 211
Figure 7-1 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the prelimbic cortex. .................................................................................... 230
Figure 7-2 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the nucleus accumbens. ............................................................................... 231
Figure 7-3 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the posterior shell of the nucleus accumbens. ............................................. 233
Figure 7-4 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the striatum and lateral septum. ................................................................... 234
Figure 7-5 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the bed nucleus of the stria terminalis. ........................................................ 235
Figure 7-6 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the basolateral amygdala and central nucleus of the amygdala. .................. 236
Figure 7-7 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the CA1, CA2, CA3, dentate gyrus of the hippocampus. ............................ 238
Figure 7-8 Quantification of 5-HT neurons expressing vesicular glutamate transporter (VGLUT3) in various regions of the mouse forebrain. ............. 239
Figure 7-9 Visual representation of the proportion of 5-HT varicosities expressing the vesicular glutamate transporter VGLUT3 in various regions of the mouse forebrain. .................................................................................... 240
Figure 8-1 The recall of recent and remote fear results in different levels of freezing compared to extinction memory recall. ......................................... 257
Figure 8-2 Schematic drawing showing the location of the acquired micrographs. .......... 260
Figure 8-3 pMAPK labelling in the prefrontal cortex. ....................................................... 265
Figure 8-4 Colocalisation of VGLUT3, 5-HT and pMAPK. ............................................. 267
Figure 10-1 Reward pathway encompassing the mesocorticolimbic distribution of dopaminergic neurons. ................................................................................ 298
Figure 10-2 Regulation of feeding behaviour and food intake by central and peripheral appetite-regulating hormones and peptides. ............................... 308
Figure 10-3 Hypothalamic-pituitary-adrenal axis. Stress causes the release of corticotrophin-releasing hormone and vasopressin from the hypothalamus. ............................................................................................. 316
Figure 11-1 Glucocorticoid Receptor (GR) ....................................................................... 357
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Figure 11-2 Glucocorticoid Receptor (GR) ....................................................................... 359
Figure 12-1 Mineralocorticoid Receptor ............................................................................ 371
9
List of Tables
Table 1 Options for statistical analysis ................................................................................ 76
Table 2 One-way ANOVA analysis of the volumetric density of 5-HT/VGLUT3 in the mouse forebrain. (Significant changes are highlighted in light grey). ........................................................................................................... 283
Table 3 One-way ANOVA analysis of the volumetric density of 5-HT/VGLUT3 in the mouse forebrain (Significant changes are highlighted in light grey). ........................................................................................................... 286
Table 4 One-way ANOVA analysis of the relative density of 5-HT/VGLUT3 in the mouse forebrain (Significant changes are highlighted in light grey). ......... 288
Table 5 The effects of sugar consumption on the reward pathway. ................................... 305
Table 6 The effects of sugar consumption on the orexigenic pathway. ............................. 312
Table 7 Published reports on the effect of sucrose and sweetener consumption on cognition, emotion and stress. ..................................................................... 341
Table 8 Therapeutics used in sugar consumption trials. .................................................... 346
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List of Abbreviations
1dR Recent fear memory recall 5-HT Serotonin 7dR Remote fear memory recall AAFC Adjusted auditory fear conditioning ACTH Adrenocorticotrophic hormone AFC Auditory fear conditioning AMG Amygdala AMPAR α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid
receptor ANOVA One-way analysis of variance Arc / Arg3.1 Activity-regulated cytoskeleton-associated protein BC Box control BDNF Brain derived neurotrophic factor BLA Basolateral amygdala BLC Basolateral amygdala complex BLP Posterior portion of the basolateral amygdala CatFISH Compartmental analysis of temporal gene transcription by
fluorescent in situ hybridization CeA Central nucleus of amygdala CFC Contextual fear conditioning CNS Central nervous system CO Context only CORT Corticosterone CR Conditioned response CREB Cyclic-AMP response element-binding CS Conditioned stimulus CV Coefficient of variance DA Dopamine Den Dorsal endopiriform nucleus DG Dentate gyrus DH Dorsal Hippocampus ER Extinction recall ERK/MAPK Extracellular signal-regulated kinases/mitogen-activated
protein kinases FDR False discovery rate FMT Fear memory test HPA axis Hypothalamus–pituitary–adrenal axis IBA-1 Ionised calcium binding adaptor molecule 1 IEG Immediate early gene IL Infralimbic cortex ISD Immediate shock deficit LA Lateral amygdala LaDL Dorsolateral portion of the lateral amygdala LaVL Ventrolateral portion of the lateral amygdala LaVM Ventromedial portion of the lateral amygdala
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LI Latent inhibition LTP Long term potentiation LV Lateral ventricle MAPK Mitogen-activated protein kinase MDA Multiple discriminant analysis MMTT Multiple memory trace theory mPFC Medial prefrontal cortex MROI Micro regions of interest MTL Medial temporal lobe Nac Nucleus accumbens NDMAR N-methyl-D-aspartate receptor PCA Principal component analysis pCREB Phosphorylated cylic-AMP response element-binding PL Prelimbic cortex pMAPK Phosphorylated mitogen-activated protein kinase PTSD Post-traumatic stress disorder RE Extinction recall ROI Region of interest SCT Standard consolidation theory SEM Standard error of the mean µm Micrometers UFC Unpaired fear conditioning US Unconditioned stimulus Vglut3 Vesicular glutamate transporter VTA Ventral tegmental area
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Statement of Original Authorship
Date: 22-8-2019
QUT Verified Signature
13
List of Publications and Presentations
REFEREED JOURNAL ARTICLES (included in thesis) Chaaya, N., Jacques, A., Belmer, A., Beecher, K., Ali, S.A., Chehrehasa,
F., Battle, A.R., Johnson, L.R., Bartlett, S.E. Contextual fear conditioning
alter microglia number and morphology in the rat dorsal hippocampus.
Frontiers in Cellular Neuroscience.
Published: 14 May, 2019.
https://doi.org/10.3389/fncel.2019.00214
Belmer, A., Beecher, K., Jacques, A., Patkar, O. L., Sicherre, F., Bartlett,
S. E. Axonal nonsegregation of the Vesicular Glutamate Transporter
VGLUT3 within serotonergic projections in the mouse forebrain.
Frontiers in Cellular Neuroscience.
Published: 10th May, 2019.
https://doi.org/10.3389/fncel.2019.00193
Chaaya, N., Jacques, A., Belmer, A., Richard, D., Bartlett, S., Battle, A.,
& Johnson, L. (2018). Localization of Contextual and Context Removed
Auditory Fear Memory within the Basolateral Amygdala Complex.
Neuroscience.
Published: 1 February, 2019.
https://doi.org/10.1016/j.neuroscience.2018.12.004
Jacques, A., Chaaya, N., Hettiarachchi, C., Carmody, M.-L., Beecher, K.,
Belmer, A., Chehrehasa, F., Bartlett, S.E., Battle, A. R. Johnson, L. R.
14
(2019). Microtopography of fear memory consolidation and extinction
retrieval within prefrontal cortex and amygdala. Psychopharmacology.
Published: January, 2019.
https://link.springer.com/article/10.1007%2Fs00213-018-5068-4
Jacques, A., Wright, A., Chaaya, N., Overell, A., Bergstrom, H. C.,
McDonald, C., Battle, A.R., Johnson, L. R. (2018). Functional Neuronal
Topography: A Statistical Approach to Micro Mapping Neuronal
Location. Frontiers in Neural Circuits. Frontiers in Neural Circuits.
Published: 16 October, 2018.
https://doi.org/10.3389/fncir.2018.00084
Jacques, A., Chaaya, N., Beecher, K., Ali, S. O., Patkar, O. L., Battle, A.
R., Johnson, L. R., Belmer, A., Chehrehasa, F., Bartlett, S. E. Microglial
phenotype alters with varied fear memory recall in the prefrontal cortex.
Brain, Behavior and Immunity.
Submitted 27th December, 2018.
In revision.
Jacques, A., Chaaya, N., Belmer, A., Beecher, K., Ali, S. A., Battle, A. R.,
Johnson, L. R., Chehrehasa, F., Bartlett, S. E. (2019) 5-HT and glutamate
co-transmission in PFC during fear extinction recall. Frontiers in
Behavioural Neuroscience.
Submitted: 20 March, 2019.
In revision.
15
REFEREED JOURNAL ARTICLES (not related to this thesis)
Patkar, O. L., Belmer, A., Beecher, K., Jacques, A., Bartlett, S. E., The
therapeutic effects of pindolol’s unique pharmacology on the maladaptive
emotional and neurogenic consequences of alcohol. Frontiers in
Behavioral Neuroscience. Submitted 28th November, 2018.
In revision.
Chehrehasa, F., Jacques, A., St John, J. A., & Ekberg, J. A. (2018). The
Grueneberg olfactory organ neuroepithelium recovers after injury.
Brain research.
Published: 1 June, 2018.
https://doi.org/10.1016/j.brainres.2018.03.020
REVIEW ARTICLE (Completed during candidature – included in thesis appendix)
Jacques, A., Chaaya, N., Beecher, K., Ali, S.O., Belmer, A., Bartlett, SE.
The Impact of Sugar Consumption on Stress Driven, Emotional and
Addictive Behaviours.
Neuroscience and Biobehavioural Reviews.
Published: 21st May 2019
https://doi.org/10.1016/j.neubiorev.2019.05.021
MANUSCRIPTS IN PREPARATION (Completed during candidature – not included in thesis)
Chaaya, N., Jacques, A., Belmer, A., Beecher, K., Ali, S. A., Chaaya, M.,
Chehrehasa, F., Battle, A. R., Johnson, L. R., Bartlett, S. E. (2019)
16
Contextual fear memory maintenance in the pre-limbic cortex: Evaluation of
pMAPK, BDNF and Microglia.
Brain Structure and Function. Submitted 14th May, 2019
Under review
Jacques, A.*, Chaaya, N.*, Beecher, K., Ali, S. A., Chehrehasa, F., Belmer,
A., Bartlett, S. E. (2019). The Role of 5-HT/VGLUT3 Colocalisation in the
Mouse Prefrontal Cortex after Sugar Consumption. * Co-first authors.
Manuscript in preparation.
BOOK CHAPTERS (Completed during candidature – included in thesis appendix)
Jacques, A., Battle, A.R., Johnson, L.R., (2017). The Glucocorticoid
Receptor (GR). In Choi, Sangdun (Ed.) Encyclopedia of Signaling
Molecules [2nd edition]. Springer Reference, New York.
Published: 3 January, 2107.
https://link.springer.com/referenceworkentry/10.1007/978-3-319-67199-
4_101536
Jacques, A., Johnson, L. R., Battle, A. R., (2017). The Mineralocorticoid
Receptor (MR). In Choi, Sangdun (Ed.) Encyclopedia of Signaling
Molecules [2nd edition]. Springer Reference, New York.
Published: 3 January, 2017.
https://link.springer.com/referenceworkentry/10.1007/978-3-319-67199-
4_101537
17
POSTER PRESENTATIONS Jacques, A. Chaaya, N., Battle, A. R., & Johnson, L. R. (2017).
Microanatomy of fear memory consolidation and extinction within sub-
regions of the prefrontal cortex and amygdala revealed by Arc and
pERK/MAPK activity. Society for Neuroscience, Washington DC, USA.
Jacques, A. Chaaya, N., Hettiarachchi, C., Carmody, M-L., Battle, A. R.,
& Johnson, L. R. (2017). Microanatomy of fear memory within subregions
of the prefrontal cortex and amygdala. Winter Conference on Brain
Research, Queenstown, New Zealand.
Jacques, A. Chaaya, N., Hettiarachchi, C., Carmody, M-L., Battle, A. R.,
& Johnson, L. R. (2016). Microanatomy of fear extinction within
subregions of the prefrontal cortex and amygdala revealed by arc and P
erk/mapk activity. Australasian Neuroscience Society 36th Annual
Scientific Meeting 2016, Hobart, Australia.
AWARDS AND ACHIEVEMENTS Australian-German Joint Research Cooperation Scheme, Australian
Universities. 2018 ($6250)
Grant-In-Aid (GIA) QUT. 2017 ($2,000)
Research Training Program Stipend (Domestic) [RTPSD], QUT.
2017- 2018 ($26,682)
Australian Neuroscience Society Student Travel Award 2016 ($215)
18
Acknowledgements
I gratefully acknowledge the unwavering support and guidance of my Principal
Supervisor Professor Selena Bartlett, Associate Supervisors Dr Fatemeh Chehrehasa
Dr Andrew Battle, and Dr Arnauld Belmer and my external supervisor Associate
Professor Luke Johnson. I consider it an honour and pleasure to have worked with
each of these experts in their fields. I would particularly like to thank Professor
Selena Bartlett and Associate Professor Luke Johnson for allowing me the
opportunity to be part of their teams, and to participate in the study of neuroscience.
Special thanks go to Dr Chehrehasa for her unwavering support which began during
my undergraduate degree and has been a constant throughout my PhD.
I am eternally indebted to the members of both the Bartlett and Johnson labs.
Without their tireless assistance, advice, humour and kindness this document would
not have been possible. I would like to express my deepest appreciation to my life
partner Raymond Penny, my sister Antoinette Turner and brother-in-law Scott
Turner and my lab partner Nicholas Chaaya. I extend many thanks to all my family
and friends for their past and continued support and encouragement throughout this
journey. Finally, I wish to acknowledge the sacrifice my rats made in order to assist
this investigation and bring us closer to understanding the neurobiology of
neuroplasticity. I would also like to gratefully acknowledge the financial support I
received from the Australian Government Research Training Program Stipend.
19
Introduction
This chapter provides a brief introduction detailing the research problem of
understanding the role of neuroplasticity in emotional pathophysiology, the context
of the research and the aims followed to advance knowledge in the field of
behavioural neuroscience.
20
1.1 The Research Problem: Understanding the role of neuroplastic adaptations in emotional pathophysiology.
Mental illness and neurological disorders are widespread in Australia, as in
most developed nations and generate a large personal, social and economic burden.
In 2003, 43% of years lost due to disability were reported to be the result of mental
and neurological disorders (Begg et al., 2007). Children suffering anxiety disorders
are twice as likely to attempt suicide, (Weissman et al., 1999) and with 264 million
people reported to be suffering from anxiety disorders in 2016/17 (Organization,
2017a), it is imperative we uncover the links between our emotions and simultaneous
neuroadaptations occurring in the brain.
Since the 1960s brain research has suggested we have a dynamic and ever
changing brain which is constantly being remodelled through genetics, individual
behaviour and our environment (Chambers, Liu, & McCouch, 1973; Kays, Hurley, &
Taber, 2012; Leuner & Gould, 2010; Pang, Short, Bredy, & Hannan, 2017; Wojtalik,
Eack, Smith, & Keshavan, 2018). This remodelling is inclusive of neurogenesis (the
formation of new neurons), greater dendritic branching and increases in synaptic
connections (synaptogenesis). This remodelling is collectively known as
neuroplasticity. Neuroplastic changes may also take on a maladaptive nature
resulting in decreased levels of neurotransmitters, atrophy, reduced branching with
smaller spines and decreased synaptic connections, all referred to as negative
plasticity.
Negative neuroplastic changes have been linked to depression (Fuchs, Czéh,
Kole, Michaelis, & Lucassen, 2004), stress (Pittenger & Duman, 2008), anxiety /
mood disorders and schizophrenia (Krystal et al., 2009), phobias (Reznikov, Fadel,
& Reagan, 2011) and posttraumatic stress disorder (PTSD) (Deppermann, Storchak,
21
Fallgatter, & Ehlis, 2014). Anxiety disorders are often characterised by the avoidance
behaviour attributed to the normal reaction to a conditioned fear cue (Grillon, 2002),
suggesting an abnormally high response to fear is common to anxiety disorders.
Increased amygdala and prefrontal cortex activity have been noted in patients with
PTSD (Shin et al., 2004), obsessive compulsive disorder (Straube, Mentzel, &
Miltner, 2005) and phobic disorders (Straube et al., 2005).
The neuroplasticity of post-traumatic stress disorder
Traumatic events such as war, natural disasters, serious motor vehicle
accidents and physical or sexual abuse may lead to the development of PTSD in
some individuals (Kilpatrick et al., 2013). Witnessing such events and repeated
exposure to trauma, as experienced by many emergency services personnel, may also
lead to the development of pathological fear memories (Luke R Johnson, Jennifer
McGuire, Rachel Lazarus, & Abraham A Palmer, 2012; Weiss, Marmar, Metzler, &
Ronfeldt, 1995). PTSD is a memory-based disorder, clinically defined as the
presentation of behavioural symptoms three months post trauma (Gray & Liotta,
2012; L. R. Johnson, McGuire, Lazarus, & Palmer, 2012). In order to create abiding
treatments for trauma and stressor related disorders a thorough knowledge of the
neural circuits involved in the formation of fear memories must be attained.
Learning and memory pathology have long been associated with PTSD,
where failure to extinguish fearful memories intensifies survival mechanisms to
debilitating levels (Elzinga & Bremner, 2002). The social and economic burden from
comorbid substance abuse, depression, suicide and the high rate of relapse after
therapy substantiate the requirement for development of improved therapeutics for
PTSD (Brown, Recupero, & Stout, 1995; Campbell et al., 2007; Hendin & Haas,
1991; Possemato, Wade, Andersen, & Ouimette, 2010).
22
Originally thought to be part of the natural progression through trauma
processing, PTSD is now known to involve abnormalities in the neurobiology of the
hypothalamic-pituitary-adrenal (HPA) axis (Yehuda, 2001). The HPA axis, as shown
in figure 2.1 is an inhibitory loop involved in the production and release of
glucocorticoids. Glucocorticoids such as cortisol are released into the blood stream
as a normal homeostatic regulation throughout the day. However, during the body’s
response to stress, cortisol release from the adrenal glands is increased. It binds to
mineralocorticoid and glucocorticoid receptors in the brain where it has been shown
to enhance the consolidation of pathological fear memories (Buchanan & Lovallo,
2001; McFarlane, Atchison, & Yehuda, 1997). Disruption of the glucocorticoid
receptors in the amygdala may play a role in the development of pathological
memories (Keller et al., 2016). Genomic studies investigating the mechanisms
involved are attempting to detect an endophenotype for people at higher risk of
developing PTSD and related disorders (Keller et al., 2016; Mehta & Binder, 2012).
Fear memory consolidation
The consolidation of fear memory involves the process of stabilization from
short term to long term memories (Luke R Johnson et al., 2012). During the1920s
Ivan Pavlov developed an animal model of conditioning that entailed learning that
certain cues within our environment can be associated with other memories (Pavlov,
1927). The most acclaimed of these involved dogs salivating when Pavlov rang a bell
but extended to associations of danger which could be used as predictors to avoid
aversive situations (Sevil Duvarci & Denis Pare, 2014; Maren, 2001).
Pavlovian auditory fear conditioning in rodent models is generated by pairing a
conditioned stimulus (CS), such as an auditory tone, with a noxious unconditioned
stimulus (US), such as a low grade electric foot shock to form a consolidated
23
associative fear memory (Luke R Johnson et al., 2012; Pavlov, 1927). This method
as shown in figure 1.1 (a) is often utilised in PTSD studies as the mechanisms
involved are similar to those of the fear memory formation that may progress to
pathological fear (Milad, Rauch, Pitman, & Quirk, 2006). The animals learn to
associate the neutral tone with the aversive shock and when later presented with the
CS (tone) alone, the conditioned response (CR) of freezing is elicited (M. Fendt &
M. S. Fanselow, 1999; Michael T Rogan, Stäubli, & LeDoux, 1997). Freezing is
denoted by a stillness of movement, other than the motion produced by respiration
and is often used as a rudimentary quantification as opposed to measurements of
sympathetic stress responses (Gisquet-Verrier, Dutrieux, Richer, & Doyère, 1999).
(a) (b)
Figure 1-1 Auditory Pavlovian fear conditioning and extinction.
(a) Conditioning a rodent involves the rodent being placed into a chamber where it receives a mild foot shock at the same time it is presented with an auditory tone. (b) Extinction training is performed by repeated presentation of the tone alone. Extinction is said to be complete once the conditioned response of freezing is no longer apparent. Figure adapted from (Johansen, Cain, Ostroff, & LeDoux, 2011).
Watson and Rayner (Watson & Rayner, 1920) demonstrated the process to be
applicable to humans by conditioning a baby ‘Albert B’ to fear a white rat by pairing
the rat with the loud strike of a hammer on metal (Maren & Fanselow, 1996).
Pavlovian fear conditioning has been demonstrated in several other mammalian
species including monkeys, rodents and cats (Hadley C Bergstrom, McDonald, &
24
Johnson, 2011; Brady, Schreiner, Geller, & Kling, 1954; Joseph E LeDoux,
Cicchetti, Xagoraris, & Romanski, 1990).
The stabilisation of the associative memory occurs after the initial acquisition
of the fear memory and is known as memory consolidation (Alberini, 2005;
McGaugh, 2000). Once a fear memory has had time to consolidate it becomes
extremely resistant to change (Flavell, Lambert, Winters, & Bredy, 2013; H. J. Lee,
R. P. Haberman, R. F. Roquet, & M.-H. Monfils, 2016). The assimilation of a short
term memory to a more resilient long term memory (see figure 1.2) occurs through a
process known as long term potentiation (LTP) which is dependent on N-methyl-D-
aspartate receptors (NMDAR) (McGaugh, 2000; Pinel & Barnes, 2017). Studies have
shown this process of stabilisation also requires new protein synthesis (Santini, Ge,
Ren, de Ortiz, & Quirk, 2004; Schafe & LeDoux, 2000). During retrieval of a
consolidated fear memory, both reconsolidation and extinction of the memory may
occur (Monfils, Cowansage, Klann, & LeDoux, 2009; Nader, Schafe, & LeDoux,
2000). When a memory is recalled, it becomes labile and requires a new process of
consolidation or reconsolidation (Wiltgen, Brown, Talton, & Silva, 2004).
Figure 1-2 Acquisition, consolidation and retrieval of a memory.
Once a memory has been acquired it undergoes the process of consolidation. This alters the memory from short term, an easily erased memory to long term where it becomes more stable. The process of memory retrieval may include both deconsolidation and reconsolidation of the memory (Maren, 2011).
25
Fear memory extinction
In order to decrease the heightened reaction to fear, that is characteristic of
PTSD, fear extinction paradigms, based on exposure therapy, are a leading treatment
for PTSD. Exposure therapy involves repeated exposure to the object of fear,
provided within a safe environment (Foa et al., 1999; Marek, Strobel, Bredy, & Sah,
2013) and has proven effective in the reduction of avoidance behaviours
symptomatic of PTSD but less effective in treating other symptoms such as the
recurring memories, anhedonia, dissociation and hyperarousal (Levin, 2008; Tarrier,
2001). Research by Tarrier and colleagues revealed approximately one third of
patients treated with exposure experienced an increase on the Clinician Administered
PTSD Scale, suggesting a worsening of symptoms occurred post treatment (Tarrier,
2001).
Pavlovlovian fear extinction training consisting of fear conditioning followed
by presentation of the CS (tone) alone (figure 1.1 (b)), which is widely used today in
behavioural models to mimic exposure therapy (for a comprehensive review see
(Maren, 2011). To reduce the fear associated with the CS, extinction training is
introduced at least 24 hours after fear conditioning. This initially results in freezing,
regardless of the absence of the shock, demonstrating the animal has learned the
correlation between the tone and the shock (Tronson & Taylor, 2007). After repeated
exposure there is a cessation of the conditioned response.
Pioneering neuroscientist Jerzy Konorsky (Konorski, 1967), who studied the
physiological properties of behaviour, suggested that extinction involved new
learning and the formation of a new association in competition with the original
memory. Bouton led the way through the 80’s and 90’s qualifying this proposal by
showing fear memories could recover spontaneously, renew in context and become
26
reinstated if exposed to the original threat (Bouton & Bolles, 1979; Bouton & Peck,
1989; Brooks & Bouton, 1993).
Extinction training reduces the expression of fearful behaviour, but the
extinction memory is not permanent and the pre-existing fear memory is not
extinguished, as demonstrated by a return of the fear response 24 hours after training
(Milad et al., 2008; Monfils et al., 2009). The new extinction memory may suppress
the original fear memory for a time, but the introduction of certain stimuli may result
in reappearance of the original fear memory (S. Li, Kim, & Richardson, 2012;
Maren, 2011; Onoue, Nakayama, Ikegaya, Matsuki, & Nomura, 2014). Recent
research by Li and colleagues suggests DNA modifications in the brain accompany
the formation of fear extinction memories (X. Li et al., 2018). Therefore, it would
appear that divergent molecular mechanisms and the neural circuits involved in the
extinction process relevant to cognitive extinction based therapies are a long way
from being understood.
Neural pathways
Although investigation of distinct neural pathways is not within the scope of
the research discussed herein, it is relevant to understand the functional brain nuclei
complicit in fear memory formation. The auditory thalamus and auditory association
cortex process the CS, while the somatosensory cortex and thalamus assimilate the
US (Medina, Repa, Mauk, & LeDoux, 2002). These sensory pathways, along with
contextual information processed by the hippocampus, synapse with principle
neurons in the lateral amygdala (LA) (Maren, 2001). Damage to the LA has been
shown to prevent auditory fear conditioning in rodent models (Joseph E LeDoux et
al., 1990). The LA and basolateral amygdala (BLA) consist of approximately 80%
glutamatergic pyramidal neurons with pyramidal shaped somas, long axons and
27
multipolar dendritic trees, often forming excitatory synapses with other principle
cells (H.-C. Pape & D. Pare, 2010).
The reconsolidation process involves protein synthesis to allow the retrieved
memory to continue to exist and to permit information update to existing memories
(Monfils et al., 2009; Nader et al., 2000). It is argued that consolidation and
reconsolidation involve common mechanisms and signalling pathways though
investigations of cellular correlates and specific brain subregions involved in the
process remain incomplete.
Fear extinction paradigms in rodent models show fear conditioning leads to
consolidation in the LA, while extinction learning involves the medial prefrontal
cortex (mPFC), the intercalated cells and the BLA (Hongjoo J Lee et al., 2016;
Onoue et al., 2014). Conflicting results are emerging within the literature regarding
these pathways and the involvement of the intercalated cells (Strobel, Marek, Gooch,
Sullivan, & Sah, 2015). Neural pathways used in extinction have been suggested, but
there is little consistency in the paradigms used to elicit the extinction. It is possible
that temporal differences in the paradigms may contribute to the array of pathways
suggested.
Functional neuroimaging studies in humans have shown results implicating
the amygdala, prefrontal cortex, anterior cingulate and the hippocampus in the
anatomy of patients with PTSD (Ursano et al., 2009). These studies demonstrate how
acquisition and extinction of fear memories is analogous between human and rodent
models (Herry et al., 2010; LaBar, Gatenby, Gore, LeDoux, & Phelps, 1998) and
therefore animal models provide a useful translational model to highlight the
microanatomy of the neurobiological mechanisms of fear memory extinction
(Gunduz-Cinar et al., 2013; Milad & Quirk, 2012).
28
Summary
In conclusion, neuroplasticity, is a fundamental process in the formation of
memories and alteration of behaviours (Berlucchi & Buchtel, 2009). Furthermore,
utilizing a behavioural model of fear memory formation and extinction provides a
robust platform upon which to build, as investigation into this model has detailed the
neuronal circuits, neurochemicals and some of the molecular mechanisms involved.
Quantifying and visualising the densities of neurons involved in neuronal
plasticity is of clinical relevance as the relapse of pathological fear is a leading
clinical obstacle in the treatment of disorders such as anxiety, phobia and PTSD.
Although there have been many technological advancements to increase our
understanding of neuroplastic change we are yet to establish many successful
therapeutic interventions for physical or mental neurological disorders. Electrical
brain stimulation, cognitive and motor training and some neuropharmacological
therapeutics have been identified with limited success. Only by continued research
will we develop a greater understanding of neuroplasticity, improved methods of
quantifying plastic changes in the brain and identification of biomarkers central to
these changes.
1.2 Context of Research: The mechanisms and biomarkers underlying neuroplasticity associated with emotional behaviours
Fight, flight and freeze are mechanisms that evolved to assist survival by
associating an appropriate behaviour to a potential threat. Many of these mechanisms
are conserved between species and now form the basis of the stress response first
described in the 1920’s by development of threat or fear memory pathologies such as
those involved in PTSD, phobias and anxiety disorders (Bouton, Mineka, & Barlow,
29
2001; Cannon, 1935; Hinkle Jr, 1973). To adequately investigate the neuroplasticity
of emotional memories, these mechanisms must be examined.
Acute stress elicits the fight-or-flight response through the activation of
numerous neuropeptide-secreting systems. This mechanism is designed to be
engaged briefly to provide the necessary energy and resources required to respond to
life threatening situations. A hypothesis supported by many studies demonstrating
neural adaptations that vary between acute and chronic stress and cognitive function
(for review see (De Kloet, Joëls, & Holsboer, 2005)), suggests that sustained
activation of this system leads to pathophysiological changes in the brain.
Limbic brain structures such as the hippocampus, prefrontal cortex and
amygdala process incoming sensory information regarding potential threats and if
necessary, trigger the release of corticotropin-releasing hormone (CRH). This
activates the hypothalamic–pituitary–adrenal (HPA) axis to stimulate corticosteroid
secretion from the adrenal cortex. Corticosteroids act through glucocorticoid (A.
Jacques, Battle, & Johnson, 2016) and mineralocorticoid receptors (A. Jacques,
Johnson, & Battle, 2016). The mineralocorticoid receptor moderates initiation of the
stress response while the glucocorticoid receptor terminates it and promotes memory
storage in preparation for future adverse encounters (for further information see
Appendix B and C). Interestingly both receptors are involved in the metabolism of
energy storage, metabolism and appetite, environmentally influenced factors that
affect neuroplasticity. Long-term exposure to adrenal glucocorticoids may lead to
atrophy of neurons, or negative neuroplasticity in the PFC and hippocampus, similar
to the effects of chronic stress (Hu et al., 2016). This effect may be exacerbated
through epigenetics with elevated paternal glucocorticoid exposure having been
shown to modify anxiety traits in offspring (Short et al., 2016). Impairment of the
30
HPA axis negative feedback system and stimulation of the HPA axis have been
observed in over 50% of depressed patients, which highlights the influence this
endocrine system exerts mood disorders (Varghese & Brown, 2001).
Originally thought to be part of the natural progression through trauma
processing, PTSD is now known to involve abnormalities in the neurobiology of the
HPA axis (Yehuda, 2001). The HPA axis is an inhibitory loop involved in the
production and release of glucocorticoids. Glucocorticoids such as cortisol are
released into the blood stream as a normal homeostatic regulation throughout the
day. However, during the body’s response to stress, cortisol release from the adrenal
glands is increased. It binds to mineralocorticoid and glucocorticoid receptors in the
brain where it has been shown to enhance the consolidation of pathological fear
memories (Buchanan & Lovallo, 2001; McFarlane, Atchison, & Yehuda, 1997).
Disruption of the glucocorticoid receptors in the amygdala may play a role in the
development of pathological memories (Keller et al., 2017). Genomic studies
investigating the mechanisms involved are attempting to detect an endophenotype for
people at higher risk of developing PTSD and related disorders (D. Mehta & Binder,
2012).
Technological advancements in whole brain imaging (Gao et al., 2019),
calcium imaging (Mishne & Charles, 2019) and electrical stimulation (Tucker,
Anderson, & Luu, 2019) have significantly improved our understanding of
neuroplastic changes and our ability to visualise generalised plasticity in the brain,
particularly after physical trauma. However, visualisation of precise subregions of
neuronal reorganisation and correlation with behaviour requires precise methods
capable of identifying and quantifying this functional circuitry. Functional magnetic
resonance imaging and positron emission tomography investigations into PTSD,
31
revealed hypo-activation in the ventromedial prefrontal cortex, a region modulating
emotional memories (for review see (Etkin & Wager, 2007).
In addition to a need for improved methods of neuroplastic quantification there
is little known about the underlying interactions between signal transduction
pathways, the effect of negative environmental influences on neuroplasticity and the
subsequent behavioural adaptations. As an inability to suppress a fear reaction and
HPA axis dysregulation have been associated with amygdala over activation in
subjects with PTSD (Jovanovic et al., 2010), factors capable of influencing stress
induced plasticity to circuits modulating the HPA axis require further elucidation and
may provide insight into novel therapeutic targets.
Neurochemical influence: pMAPK, Arc, serotonin and glutamate.
Factors influencing neuroplasticity include genetics, environmental stimuli,
and individual behaviour. From the food we ingest, to learning from our social
interactions, the environment appears to have a great capacity to keep our brains in a
constant state of flux, but what mediates this state? Experience, or how we interact
with our environment, has been shown to generate the greatest plastic neural change
(Kerr, Cheng, & Jones, 2011).
Both the formation and storage of fear and extinction memories are reliant on
neural plasticity i.e. changes within the neuron to allow a greater or lesser number of
connections to form with surrounding neurons (Izquierdo & Medina, 1997;
Rosenzweig & Bennett, 1996). The extracellular signal-regulated kinases / mitogen-
activated protein kinase (ERK/MAPK) pathway (see figure 1.3) has been
investigated and identified as essential during fear memory formation and extinction
learning (Hadley C Bergstrom et al., 2011; Herry, Trifilieff, Micheau, Lüthi, &
Mons, 2006). Phosphorylated mitogen-activated protein kinase (pMAPK) has been
32
well established as playing a role in cell growth and differentiation (G. L. Johnson &
Lapadat, 2002; Lai et al., 2001) and defined as fundamental in the plasticity required
for learning and memory.
Reports also suggest activity-regulated cytoskeleton-associated protein
(Arc/Arg3.1), an immediate-early gene (IEG) and downstream marker within this
pathway, along with serotonin, an inhibitory neurotransmitter, are required for α-
amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor trafficking,
which regulates synaptic plasticity and LTP (Chowdhury et al., 2006; Lesch &
Waider, 2012; Plath et al., 2006). The excitatory neurotransmitter glutamate, released
from the presynaptic neuron binds to NMDA receptors on the postsynaptic
membrane causing the cascade of events that lead to this gene expression and protein
phosphorylation. (Bauer, Schafe, & LeDoux, 2002; Maren, 2011).
Figure 1-3 MAPK pathway to neuroplasticity.
33
Glutamate NMDA receptor activation permits the influx of calcium which activates Ras protein (not shown). This causes consequent activation of phosphorylated MAPK, which conducts the extracellular signal to the nucleus. In the nucleus, transcription factors such as cyclic AMP response element binding (CREB) protein trigger the rapid transcription of Arc mRNA. The synthesized Arc protein is conveyed to dendrites where it moderates α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPAR) regulation and F-actin expansion. Originally adapted from (Rodrigues, Schafe, & LeDoux, 2004).
Serotonin (5-HT), is a monoamine inhibitory neurotransmitter which plays a
role in mood, appetite, learning, memory and cellular metabolism (Hodges,
Cummings, & Pilowsky, 2018; McAllister-Williams, Ferrier, & Young, 1998).
Changes in the levels of 5-HT receptors present within the nervous system have been
noted under various environmental conditions. There are currently sixteen catalogued
5-HT receptor subtypes involved in mediation of either excitatory or inhibitory
neurotransmission. (Hodges et al., 2018). Chemical manipulation of the
serotonergic system has shown that 5-HT2A agonists increase fear learning while 5-
HT1A agonists impair the learning (for review see (Bauer, 2015)), but how they
impact the neuroplasticity of memory is not well understood.
Glutamate signalling also plays a role in processing stressful situations as
demonstrated by use of ketamine (N-methyl-D-aspartic acid receptor antagonist) to
distort emotional processing similar to that seen in patients suffering PTSD (Cortese
& Phan, 2005). As glutamate and 5-HT can be released from the same neuron a
possible relationship may exist between the release of serotonin and glutamate during
the recall of stressful memories.
Neurobiological influence
Recently research across diverse mental pathologies has revolved around
inflammatory conditions (Fourrier, Singhal, & Baune, 2019; N. Mehta, Li,
Woolwine, Haroon, & Felger, 2019; Niraula, Sheridan, & Godbout, 2019). In the
brain, microglia are the immune cells that respond to all pathophysiological events.
They also play a role in neuroplasticity within the healthy brain. Microglia serve as
34
surveillance cells which actively search the brain for unrequired or damaged
synapses, establish contact with that synapse and prune it in preparation for
phagocytic removal (Hong, Dissing-Olesen, & Stevens, 2016). In this manor they
contribute to homeostatic regulation of the brain and the HPA axis (Silverman,
Pearce, Biron, & Miller, 2005). It is widely acknowledged that microglia respond to
stressful environmental challenges, however, their role in the plasticity of fear
memory is as yet undiscovered.
With respect to memory formation, microglia facilitate learning-induced
plasticity in glutamatergic synapses (Parkhurst et al., 2013) through the secretion of
brain-derived neurotrophic factor (BDNF) and constantly undergo morphological
change whilst monitoring their microenvironment(Wake, Moorhouse, Jinno,
Kohsaka, & Nabekura, 2009). Microglia sense environmental change and may use
this information to modulate hippocampal wiring through the formation, elimination
and relocation of synapses, thereby impacting memory function. Microglial-derived
IL-1β has been directly implicated in normal hippocampus-dependent learning
(Williamson, Sholar, Mistry, Smith, & Bilbo, 2011).
Summary
It is well known that fear memories are encoded by neurons in the amygdala,
with the BLA indicated in the acquisition of fear memories (Hadley C Bergstrom,
Craig G McDonald, Smita Dey, Haying Tang, et al., 2013; Herry et al., 2010). The
BLA encompasses the lateral and basal nuclei of the amygdala, with direct and
indirect neuronal connections to the central nuclear group (Maren, 2011). The spatial
allocation, temporal divergence and stability of these neuronal populations within
these nuclei are as yet far from complete.
35
Most of the research implicating the mPFC in extinction learning has come
through lesions which attenuate extinction (Falls, Miserendino, & Davis, 1992).
Several theories have emerged suggesting the mPFC may inhibit the amygdala after
extinction training (Gregory J Quirk, Likhtik, Pelletier, & Paré, 2003; Rosenkranz,
Moore, & Grace, 2003) but while some studies show that extinction learning recruits
glutamate and NMDA receptors (Myers & Davis, 2007; Sotres-Bayon, Cain, &
LeDoux, 2006) others suggest the neuroplasticity may be moderated by γ-
aminobutyric acid transmission (Sotres-Bayon et al., 2006).
One of the most successful ways to study the neuroplasticity of emotion is to
target an emotion with a thoroughly investigated and known circuitry. Fear is such an
emotion as the circuitry involved in the formation, extinction and recall of fear
memories have been extensively researched and defined. The pathways involved
have been silenced through lesion studies (McGaugh et al., 1995), drug infusions
(Miserendino, Sananes, Melia, & Davis, 1990) optogenetics (Do-Monte, Quinones-
Laracuente, & Quirk, 2015) and most recently, chemogenetics (Marek et al., 2018a).
Fear conditioning itself is a well-defined method of creating associative memories
reliant on neuroplasticity for their formation and has been fundamental in the
development of cognitive behavioural therapies for disorders such as phobia, anxiety
and post-traumatic stress (Maren & Holmes, 2016).
Treatment regimens encompassing neuroplastic change have long been
suggested for patients suffering mental illness but are now also being incorporated
into treatment for neurological disorders such as dementia, malnutrition associated
disorders, chronic pain, stroke and traumatic brain injuries (Altman, Richards,
Goldberg, Frucht, & McCabe, 2013; DeFina et al., 2009; Dimyan & Cohen, 2011;
Kass, Kolko, & Wilfley, 2013; Sibille, Bartsch, Reddy, Fillingim, & Keil, 2016).
36
Therefore, investigation of neural substrates and neuroplastic change may facilitate
the development of increasingly effective pharmacotherapeutics.
1.3 Purposes
PTSD represents a form of emotional memory reliant on neuroplastic changes
within the brain and may occur following exposure to a life threatening, traumatic
event. It is defined by symptoms such as intrusive memories, anhedonia (inability to
feel happiness) and avoidance behaviours, which may manifest as permanent
personality alterations (Andreasen, 2011). PTSD is a memory-based disorder,
clinically defined as the presentation of behavioural symptoms three months post
trauma (Gray & Liotta, 2012). In order to create abiding treatments for anxiety
related disorders a thorough knowledge of neuroplasticity must be attained.
Hypothesis
As these neuroplastic adaptations require modulation through neurochemical
and neurobiological factors, it was hypothesised that a new method to map neuronal
density would answer fundamental questions about fear related learning and memory
and its recall. Further to the examination of functional neuronal populations, the role
of the brains immune cells (microglia), known to alter phenotype in stressful
situations, was investigated during memory recall. It was hypothesised that
phenotype changes would occur during the recall of emotional memories, suggesting
reactive microglia play an active cellular level role that may prove to be a therapeutic
target for improved learning techniques. Investigation into the role of specific
neurotransmitters (5-HT and glutamate) was undertaken to establish a molecular
level target for improved treatment of anxiety related disorders.
37
Aim 1
To partially address the research problem surrounding the neuroplasticity of
emotional memories, a new method to map and quantify neuroplastic changes was
developed. Density topography (or heatmaps) generated after several behavioural
paradigms could assist in predicting the amount of cellular change or neuroplasticity
that occurs as a result of a specific environmental influence. Utilizing the expression
of neuroplasticity markers pMAPK and Arc, topographic density maps of the
amygdala and prefrontal cortex were generated.
The initial project was designed to
a) develop an economical approach to statistically map molecular markers
of neuroplasticity in specific neuronal networks for the identification of
new functional micro regions within established nuclei subdivisions
b) be adaptable to all fields of neuroscience and suitable to study any brain
region that could be identified through an anatomical anchor
c) This topographic visualisation was used to distinguish differences in
location derived through varying the type of memory recalled and the
temporal recall of auditory fear memories in the amygdala and
prefrontal cortex
d) illustrate the density of pMAPK expressing neurons and the
microanatomy involved in the encoding process of extinction versus
non-extinction of fear memories in the amygdala and mPFC
e) visualise differential neuronal activation within specific prefrontal
cortex cell layers in auditory fear memory and extinction memory recall
38
f) evaluate amygdala sub region involvement in the neuroplastic creation
and storage of emotional memories such as pure, context-removed and
auditory fear memories
Aim 2
In an attempt to identify novel cellular mechanisms in the prefrontal
cortex, amygdala and hippocampus that may influence the neuroplasticity of fear
memory consolidation and extinction, investigations were conducted into possible
morphological changes of microglia. It is well established that microglia are involved
in the neuroinflammatory response triggered by exposure to psychological stress but
their role in fear is as yet undefined. Arc plays a role in the activity-dependent
neuroplasticity of dendrites and c-Fos is a commonly used marker of neuronal
activation. pCREB and BDNF expression are present in fear memory consolidation.
Brain derived neurotrophic factor (BDNF) is a neurotrophin that modulates neuronal
survival and differentiation and may be released by microglia. Phosphorylated
cyclic-AMP response element binding (pCREB) a marker of plasticity can be
induced by BDNF. The studies undertaken aimed to
a) establish levels of neuronal activity through the expression of the immediate
early genes c-Fos and Arc
b) determine the extent to which memory recall influences the reactivity of
microglia within the prefrontal cortex
c) define microglial phenotypic changes as a consequence of recent and remote
fear memory recall, extinction memory recall
d) investigate the possibility of microglial morphological changes occurring as a
result of fear memories progressing from short term to long term
39
e) observe any alterations to amygdala and hippocampus BDNF and pCREB as
a consequence of different contextual fear memories
f) evaluate the maintenance of two different contextual fear memories in the
prelimbic cortex as demonstrated by pMAPK and BDNF expression and
through the microglia number present and phenotype displayed
Aim 3
To further define possible therapeutic targets capable of altering
negative neuroplasticity, current literature was examined to determine the molecular
mechanisms involved. It is well documented that manipulations of the 5-HT system
are widely used to treat anxiety and phobias; the pMAPK pathway to neuroplasticity
relies on the release of glutamate from the presynaptic neuron; 5-HT neurons express
VGLUT3, a transporter that concentrates glutamate into synaptic vesicles in
preparation for exocytosis; imbalanced levels of astrocytes and microglia has led to a
reduction of serotonin and consequent overabundance of glutamate and deletion of
VGLUT3 has been found to increase anxiety-related behaviours in mice. As
serotonergic neurons and glutamate release are known to be pivotal in anxiety
disorders, involved in fear memory consolidation, induce AMPAR mediated synaptic
plasticity and play a role in the activation of microglia, it seemed pertinent to observe
their role in the neuroplasticity of fear memory recall. Investigations were designed
to
a) map brain region specific contributions of colocalized 5-HT and
glutamatergic inputs specific to serotonergic neurons
a) determine if neuroplastic changes, denoted by pMAPK activation and due to
temporally different varieties of fear recall, were modulated through 5-HT
expression in the presence of the vesicular glutamate transporter type 3
40
1.4 Significance and Scope:
As many as 1.4 million Australians suffer PTSD annually and the estimated
harm, personal, social and economic burden facilitate a dire need for effective
clinical treatments (Maren, 2001; Statistics, 2008). Behavioural therapies targeting
enhancement of positive neuroplasticity have long been used to treat mental illness
however, very few pharmacological interventions have been developed in the last 40
years, in part due to the lack of novel mechanisms identified during this period (Insel
et al., 2013). The extinction of pathological fear is central to the treatment of
phobias, PTSD and anxiety disorders and involves a learning process, which forms
the basis of exposure therapy.
Exposure therapy utilizes fear memory extinction procedures, but the high rate
of relapse is a leading dilemma (Bouton & Peck, 1989; R. Bryant et al., 2008). This
thesis builds on previous research, which has defined a microanatomy of fear
memory by identifying a micro-topography of fear memory encoding in the
amygdala (Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez,
& Luke R Johnson, 2013). Identifying the precise biological mechanism of fear
memory acquisition and extinction is of great clinical relevance as the relapse of
pathological fear is a leading clinical obstacle in the treatment of PTSD and anxiety
disorders. Advances in imaging technology are permitting a greater understanding of
brain region adaptability. Developing a method to quantify neuroplastic change in
microanatomical regions and defining factors, both neurochemical and
neurobiological, that contribute to negative neuroplastic change will enhance our
ability to locate therapeutic targets.
41
1.5 Thesis Outline
This thesis consists of 10 chapters, 5 appendices and a bibliography.
Chapter 1 consists of an introduction, discussing the research problem, the
background behind the research undertaken, the purpose and scope of the research
and the aims of the study.
Chapter 2 is a published methods paper which provides a detailed
documentation of the method developed to locate and quantify specific subregions
involved in neuroplasticity.
Chapter 3 is a published data manuscript utilising the developed method to
anatomically define subregions of the basolateral amygdala undergoing localised
neuroplastic change due to contextual and auditory fear.
Chapter 4 is a published data manuscript expanding our understanding of
the microanatomical subregions of the amygdala and prefrontal cortex involved in
recent and remote fear memory and the formation of a new extinction memory. In
this study the micro regions are mapped and statistically quantified, to provide
insight into the individual cell populations and layers involved in the neuroplasticity
of fear memory formation.
Chapter 5 is a data manuscript in revision. This chapter investigates the
effect of fear memory recall on the number and phenotype of microglia within the
prefrontal cortex. It expands on our knowledge of neurobiological factors that
influence neuroplasticity.
Chapter 6 is a data manuscript investigating morphological change in
microglia in the rat dorsal hippocampus as a result of contextual fear conditioning.
This manuscript is under review.
42
Chapter 7 is a data manuscript mapping the colocalisation of serotonin,
serotonin transporters and vesicular glutamate type 3 transporters. These
neurotransmitters are well known for playing a role in anxiety disorders. This
manuscript is currently in revision.
Chapter 8 is a data manuscript recently submitted which explores the
activation of serotonin and glutamate with regards to the neuroplasticity of fear
memory and extinction memory recall.
Chapter 9 draws conclusions from the preceding chapters to create a cohesive
discussion to improve knowledge surrounding the neuroplasticity of emotional
memories. It increases our understanding of the micro regions involved in the
consolidation and recall of fear memories. Furthermore it discusses the role of
neurobiological and neurochemical factors affecting negative neuroplastic
adaptations.
Appendicies:
Appendix A displays the tables of statistical quantification outlined in chapter
7: Axonal nonsegregation of the Vesicular Glutamate Transporter VGLUT3 within
serotonergic projections in the mouse forebrain.
Appendix B holds chapter 10 a review manuscript discussing the most recent
clinical and pre-clinical work involving the impact of sugar, a common
environmental factor and its complicated relationship with neural adaptation. It
discusses the neuroplastic changes within the reward system, the neurobiology and
the neurochemistry behind consumptive behaviours and how our emotions are
intricately linked to neural adaptations. This review has been published.
43
Appendix C contains chapter 11, a published book chapter detailing the
function of the glucocorticoid receptor and its role in regulation of the HPA axis.
Appendix D is chapter 12, a published book chapter discussing the
mineralocorticoid receptor, its function within the brain and its role in fear, learning
and memory.
Bibliography.
44
Functional Neuronal Topography: A Statistical
Approach to Micro Mapping Neuronal Location
This chapter comprises the following published article: Jacques, A., Wright, A., Chaaya, N., Overell, A., Bergstrom, H. C., McDonald, C., Battle, A.R., Johnson, L. R. Functional Neuronal Topography: A Statistical Approach to Micro Mapping Neuronal Location. Frontiers in Neural Circuits, Published: 16 October, 2018 https://doi.org/10.3389/fncir.2018.00084
The project documented in this chapter encompasses part of aim 1, to develop a
method of mapping neuroplasticity that would be economical, adaptable to all fields
of neuroscience, capable of quantifying neurons, fibres and puncta, suitable to any
brain region and widely accessible to the general scientific public.
45
Statement of Contribution of Co-Authors for Thesis by Published Paper
The authors listed below have certified that:
1. they meet the criteria for authorship in that they have participated in the conception, execution, orinterpretation, of at least that part of the publication in their field of expertise;
2. they take public responsibility for their part of the publication, except for the responsible author who accepts overall responsibility for the publication;
3. there are no other authors of the publication according to these criteria;
4. potential conflicts of interest have been disclosed to (a) granting bodies, (b) the editor or publisher of journals or other publications, and (c) the head of the responsible academic unit, and
5. they agree to the use of the publication in the student’s thesis and its publication on the QUT’s ePrints site consistent with any limitations set by publisher requirements.
In the case of chapter 2:
Chapter 2: Functional Neuronal Topography: A Statistical Approach to Micro Mapping Neuronal Location Publication status: Published
Contributor Statement of contribution*Angela Jacques Contributed to methods development, conducted behavioural and laboratory
experiments, analysed data and wrote and edited the manuscript, created the figures.
Alison Wright Assisted with writing and editing the manuscript, involved in the development of the technique.
Nicholas Chaaya Assisted with writing and editing the manuscript, assisted with data analysis, assisted with behavioural experiments.
Ann Overell Assisted with writing and editing the manuscript.
Hadley Bergstrom Involved in the conception and design of the project, development of the technique, assisted with writing and editing the manuscript.
Craig McDonald Involved in the conception and design of the project, development of the technique.
Andrew Battle Assisted with creation of figures and editing the manuscript.
Luke Johnson Development of the technique, conceptual design of the methods paper, assisted with behavioural experiments, assisted with writing and editing the manuscript.
QUT Verified
Signature
46
2.1 Abstract
In order to understand the relationship between neuronal organization and
behaviour, precise methods that identify and quantify functional cellular ensembles
are required. This is especially true in the quest to understand the mechanisms of
memory. Brain structures involved in memory formation and storage, as well as the
molecular determinates of memory are well-known, however, the microanatomy of
functional neuronal networks remain largely unidentified. We developed a novel
approach to statistically map molecular markers in neuronal networks through
quantitative topographic measurement. Brain nuclei and their subdivisions are well-
defined – our approach allows for the identification of new functional micro regions
within established subdivisions. A set of analytic methods relevant for measurement
of discrete neuronal data across a diverse range of brain subdivisions are presented.
We provide a methodology for the measurement and quantitative comparison of
functional micro- neural network activity based on immunohistochemical markers
matched across individual brains using micro-binning and heat mapping within brain
sub-nuclei. These techniques were applied to the measurement of different memory
traces, allowing for greater understanding of the functional encoding within sub-
nuclei and its behaviour mediated change. These approaches can be used to
understand other functional and behavioural questions, including sub-circuit
organization, normal memory function and the complexities of pathology. Precise
micro-mapping of functional neuronal topography provides essential data to decode
network activity underlying behaviour.
2.2 Introduction
Following Cajal’s identification of the neuron as the fundamental functional
unit of the nervous system (López-Muñoz, Boya, & Alamo, 2006), neuroscience has
47
endeavoured to understand how neurons operates in local groups (ensembles) and
distributed networks to bring about behaviour. In 1894, Cajal (Cajal, 1894) proposed
a theory that memory storage requires the formation of new connections between
neurons in the brain. How neurons and their thousands of synaptic connections act
together to encode a memory was first conceptualized by Donald Hebb (Hebb, 1949)
as neuronal ensembles that both spatially and temporally act together to encode a
component of the memory. Since these foundational anatomical and theoretical
works, newer studies involving fluorescent imaging and electron microscopy have
since provided growing evidence for the modification of neuronal synapses as a
result of information storage, now known as synaptic plasticity (Kandel, 2001; Korb
& Finkbeiner, 2011). Thus, at the sub-cellular level knowledge of mechanisms of
memory encoding is more established, in contrast at the neuronal ensemble level
memory encoding mechanisms are not yet understood. Some functional evidence for
Hebbian reverberatory networks connecting ensembles of neurons (Hebb, 1949) has
been identified in memory circuits (L. R. Johnson et al., 2008; L. R. Johnson,
Ledoux, & Doyere, 2009; Josselyn, Kohler, & Frankland, 2017). However, key
challenges in neuroscience remain around how neurons collectively undergo
plasticity in ensembles to encode memories and behaviours. Aspects of neural
ensemble activity has been demonstrated in Hippocampus (N. H. Nakamura et al.,
2010) and Caudate (Barbera et al., 2016) and in Amygdala (Davis & Reijmers, 2017;
L. R. Johnson et al., 2008; L. R. Johnson et al., 2009; Josselyn & Frankland, 2018;
Josselyn et al., 2017; Rogerson et al., 2014). A key challenge in the neuroscience of
memory is in identifying which neurons have been allocated to the memory trace and
which have not, while some progress has been made (H. C. Bergstrom, 2016; Hadley
C Bergstrom & Luke R Johnson, 2014; Hadley C Bergstrom, Craig G McDonald,
48
Smita Dey, Gina M Fernandez, et al., 2013; Hadley C Bergstrom, Craig G
McDonald, Smita Dey, Haying Tang, et al., 2013; H. C. Bergstrom, McDonald,
French, & Smith, 2008; Hadley C Bergstrom et al., 2011; P. W. Frankland &
Josselyn, 2015; Josselyn & Frankland, 2018; Mayford, 2014; Rogerson et al., 2014),
more details are needed. This aim can be aided by the development of methods and
approaches to help reliably identify and quantify systematic topographies of neurons
allocated to specific memory traces.
Here we developed a method for topographical analysis and measurement of
neurons allocated to memory traces. We have applied this method to study aspects of
the neurobiological encoding of fear memory. We termed this method “neuronal
topographic density mapping” and have devised it to identify and map the degree of
stability within a micro-topography of neurons encoding Pavlovian fear memory
across different animals undergoing fear memory acquisition or extinction. The
methods described in detail below were developed over multiple studies,
investigating the location and distribution of neurons activated in fear memory in
amygdala (Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez,
et al., 2013; Hadley C Bergstrom, Craig G McDonald, Smita Dey, Haying Tang, et
al., 2013; Hadley C Bergstrom et al., 2011; Haranhalli, Massie, Little, LeDoux, &
Johnson, 2007; Luke R Johnson et al., 2012; Joseph E LeDoux, Haranhalli, Massie,
Little, & Johnson, 2006). For illustrative purposes and to expand on the scope of
these techniques, here we employed a small data set drawn from the study of
activity-regulated cytoskeleton-associated protein (Arc) expression in prefrontal
cortex.
In our studies to date, we have investigated the micro-topography of memory
using Pavlovian fear conditioning. In Pavlovian or classical fear conditioning a mild
49
foot shock (unconditioned stimulus, US) is temporally paired with an auditory tone
or comparable visual stimuli (conditioned stimulus, CS) (Hadley C Bergstrom &
Luke R Johnson, 2014; Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina
M Fernandez, et al., 2013; Luke R Johnson et al., 2012). The animal learns to
associate the US with the CS and exhibits typical behaviours including freezing,
typical of fear / threat behaviour (described extensively by other authors; see Johnson
and colleagues (2012) (Luke R Johnson et al., 2012) for details). We measured
neurons expressing plasticity associated proteins identified by
immunocytochemistry. Other functional protein and RNA expression in neurons and
glia can also be used with this approach. We tested for differences in the localization
of neurons among the conditioned memory groups. We have provided a
methodological approach to produce topographic neuron data from brain within
precisely aligned anatomical regions. This approach enables investigation of the
topographic patterns of neurons expressing plasticity associated proteins in the
associative fear memory formation and its extinction. We propose that this method
can also be used in the reproduction of neuronal density maps with regard to many
forms of neuroscience data for example, drug treatments, stress and addiction or
neurodegenerative disorders.
Our methodological approach to neuron topography, described here, provides
useful advantages for localizing function across behavioural conditions. Other
analysis methods to measure topography also provide useful topographic data. For
example, Nakamura and colleagues (N. H. Nakamura et al., 2010) identified that
memory activated neurons formed small anatomical clusters in hippocampus during
place preference formation, which was identified using a cluster analysis approach.
Recent studies by Barbera et al., (2016) (Barbera et al., 2016) used measures of
50
neuronal clustering of medium spiny neurons to predict locomotive states of
behaviour in mice. They reported that behavioural decoding accuracy improved
using spatially distinct neural clusters over single neurons (Barbera et al., 2016).
The brain is a complex interconnection of billions of neurons and decoding
how information is processed and stored by this circuitry requires the ability to
capture specific neuronal populations undergoing plasticity as a result of specific
behaviours. In vivo techniques such as calcium imaging are valuable tools to observe
functional imaging of neuronal populations in awake behaving subjects (Castanares,
Stuart, & Daria, 2019; Ohki & Reid, 2014; Romano et al., 2017). Of particular note
is a method developed by Romano and associates, to analyse neuronal population
dynamics (Romano et al., 2017). However, this method is invasive and not
conducive to the study of multiple brain regions or spatially segregated regions and
sub regions.
Yongsoo Kim and associates used rodent behaviour to develop a spatial IEG-
based mapping technique as a method to view whole-brain activity (Y. Kim et al.,
2015; Y. Kim et al., 2017). Furthermore, whole brain mapping methods have been
developed by Vousden and colleagues and Renier and colleagues (Renier et al.,
2016; Vousden et al., 2015). Each of these in vitro methods provides the advantage
of visualizing patterns of neural activity across brain regions to map distributed brain
networks and could be utilized to generate structure-function hypotheses prior to the
mapping of sub regions and micro circuits involved. The creation of neuronal
topographic density maps, as described here, can be used for a variety of studies to
pinpoint functional microcircuits in the brain.
Using our approach to mapping and measuring topography we have
characterized the microanatomy and topography of neurons involved in different
51
phases of memory, consolidation, reconsolidation and extinction (Hadley C
Bergstrom & Luke R Johnson, 2014; Hadley C Bergstrom, Craig G McDonald,
Smita Dey, Gina M Fernandez, et al., 2013; Hadley C Bergstrom, Craig G
McDonald, Smita Dey, Haying Tang, et al., 2013; H. C. Bergstrom et al., 2008;
Hadley C Bergstrom et al., 2011; N Chaaya, Jacques, Battle, & Johnson, 2017;
Haranhalli et al., 2007; A. Jacques, Chaaya, Battle, & Johnson, 2017; Joseph E
LeDoux et al., 2006). These data have the potential to pinpoint neuronal topography
patterns underlying memory encoding in the mammalian brain in normal and
pathological situations (Luke R Johnson et al., 2012) and thereby facilitate current
treatments for pathological memory disorders (Luke R Johnson et al., 2012). The
generation of neuronal topographic density maps can be used to define and measure
memory allocation within the brain.
Throughout this methodological report we provide details of the rationale,
procedures and equipment needed to produce and analyse topographic neuronal data.
In addition, within each methodological section we provide ‘examples’ from our own
data in order to illustrate how the methods can be applied and used. The
methodological approaches we describe here have wide applications for
understanding and measuring neuronal topography. Applications, include measuring
the topography of neurons encoding different types of memory, different sensory
stimuli and motor behaviours.
2.3 Step-By-Step Methods
2.3.1 Data collection: Behavioural, tissue and neuron analysis in preparation for topographic investigation
52
Run behavioural models
Pavlovian fear conditioning forms associative memories. Synaptic plasticity,
dependent upon phosphorylation of extracellular signal-regulated kinase (pMAPK)
has been established as critical in the formation of these memories in the lateral
amygdala (LA) and medial prefrontal cortex (mPFC) (J. E. LeDoux, 2000).
Example: The sample data set consisted of fear conditioned adult male
Sprague-Dawley rats (RRID:RGD_5508397) (n=40) that underwent behavioural
procedures in standard Pavlovian fear conditioning chambers (Coulbourn
Instruments, Allentown, PA, USA) (see fig. 1A). The unconditioned stimulus, a 0.6
mA foot shock with duration of 500 ms, was paired with the conditioned stimulus, a
tone of 5 kHz and 75 dB (Digitech Professional Sound Level Meter,
https://www.jaycar.com.au/pro-sound-level-meter-with-calibrator/p/QM1592), 20 s
in duration, to produce an associative memory. Three pairings were presented with
an average 180 s inter-trial interval with total time in box of 10 min. Standard
conditioning and behavioural testing procedures were followed (Hadley C Bergstrom
& Luke R Johnson, 2014; Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina
M Fernandez, et al., 2013; Hadley C Bergstrom, Craig G McDonald, Smita Dey,
Haying Tang, et al., 2013; H. C. Bergstrom et al., 2008; Hadley C Bergstrom et al.,
2011; N Chaaya et al., 2017; Haranhalli et al., 2007; A. Jacques et al., 2017; Joseph
E LeDoux et al., 2006). The experimenter was blind to the experimental conditions
when scoring freezing behaviour, which was defined as a lack of movement except
that required for respiration (J. E. LeDoux, J. Iwata, P. Cicchetti, & D. Reis, 1988).
Next, brains were prepared for histological analysis and measurement.
53
Figure 2-1: Steps for tissue sampling and measurement from behavioural data.
(A) Run behavioural models. Any expression of a chosen behaviour can be used as a model. In our example we have used Auditory Pavlovian fear conditioning. Behavioural testing was conducted with adult male Sprague-Dawley rats in acoustic classical fear conditioning chambers. A 0.6 mA foot shock with duration of 500 ms was paired with a tone of 5 kHz and 75 dB, 20 s in duration to produce an associative fear memory. (B) Perform immunocytochemistry. Avidin–biotin peroxidase complex method is demonstrated here. Sections from the lateral amygdala (LA) were labelled for Arc, scanned using a slide scanner and cropped at 2x magnification. Enlarged inset square shows Arc+ neurons in the dorsolateral portion of the LA at 20x magnification. Inverted gray scale images of fluorescent immunocytochemistry would also be suitable. (C) Choose suitable anatomical marker to be used as an anchor. The caudate putamen and lateral ventricle are two examples of anatomical landmarks that we have used previously, and can be differentiated in serial sections for section alignment by Feret length within the ventricle or between anatomical landmarks. Photomicrographs show three consecutive 60
54
µm sections across the rostrocaudal axis of the rat brain, depicting 2.76, 2.70, and 2.64 mm anterior to Bregma in the medial prefrontal cortex (mPFC). Feret diameter is shown – red arrow. Brain sections at Bregma coordinates –3.32, –3.36, and –3.40 mm posterior from Bregma were used to align the LA (Source: see Bergstrom et al., 2011). The maximum Feret length of the caudate putamen in the prefrontal cortex was shown to be statistically different across Bregma coordinates, animals and conditions. (D) Establish section alignment. The Rat Brain Atlas (Paxinos and Watson, 2007) is an important tool to assist alignment of sections. Schematic diagrams are shown depicting the regions of interest. The dorsolateral portion of the lateral amygdala (LAd), the ventromedial portion of the lateral amygdala (LAvm) and the ventrolateral portion of the amygdala (LAvl) are shown in three serial sections caudal from bregma –3.36 mm. The prelimbic (PL) and infralimbic (IL) cortex are represented by three serial sections caudal from bregma 2.52 mm. Brain Atlas diagrams are adapted from Paxinos and Watson (2007).
Perform immunohistochemistry
Rats were transcardially perfused and brains were post-fixed in 4 % PFA
overnight then stored in 0.1 M phosphate buffered saline. Free-floating serial coronal
sections (40 μm) of the medial prefrontal cortex and amygdala were prepared using a
vibratome (M11000; Pelco easiSlicer, Ted Pella Inc, Redding, CA, USA). Sections
from the lateral amygdala and prefrontal cortex were labelled for pMAPK and Arc
activation using the avidin-biotin peroxidase method. Detailed immunocytochemical
methods can be obtained from our previous reports (see (Hadley C Bergstrom, Craig
G McDonald, Smita Dey, Haying Tang, et al., 2013; Hadley C Bergstrom et al.,
2011)). Slides were scanned with an Olympus VS120 slide scanner and cropped at
2x magnification. (see fig. 1B)
Choose anatomical marker
Establishing anatomical alignment between regions of interest (ROI) is
necessary for visual comparison of neuron density in neural images, for sectioning
the ROI into micro regions for analysis, and for both quantitative and visual analysis
of the data. Therefore, choosing an appropriate anatomical anchor is a key step. The
anchor point should: 1) be a readily visible anatomical feature that is close in
proximity to the ROI, 2) be stable across subjects and conditions, and 3) change
55
shape rapidly and distinctly as the viewing plane changes, so that different planes of
view can be discriminated clearly. These characteristics are identifiable
microscopically and importantly can also be quantified (see fig. 1C) .
Example: The amygdala and medial prefrontal cortex (mPFC) have been
implicated in Pavlovian fear conditioning (Michael S. Fanselow & Gale, 2003; Luke
R Johnson et al., 2012; H. J. Lee, Haberman, Roquet, & Monfils, 2015). In a series of
studies, we have focused on the amygdala and have used the opening of the Lateral
Vertical (LV) as an anatomical anchor (Hadley C Bergstrom & Luke R Johnson,
2014; Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez, et
al., 2013; Hadley C Bergstrom, Craig G McDonald, Smita Dey, Haying Tang, et al.,
2013; H. C. Bergstrom et al., 2008; Hadley C Bergstrom et al., 2011; N Chaaya et
al., 2017; Haranhalli et al., 2007; A. Jacques et al., 2017; Joseph E LeDoux et al.,
2006). The LV has proved a useful structure for the purpose because it meets the
criteria outlined above: 1) the LV is close in proximity to the amygdala, 2) the LV
changes rapidly in size along the longitudinal plane, 3) the LV is a stable anatomical
feature and 4) LV changes can be seen clearly, and measured, through the sequence
of planes on which the brains were sectioned, enabling quantitative analysis of the
changes section by section. In order to further demonstrate and measure the
properties of the LV for landmark suitability, in addition to histological
measurements, we made measurements of the LV with MRI. Here, the
morphological properties of the LV, including its increase in diameter along the
rostral-caudal axis, were confirmed in vivo, using 3-dimensional T2-weighted MRI
to quantify its area (Hadley C Bergstrom, Craig G McDonald, Smita Dey, Haying
Tang, et al., 2013). This rapid change from rostral to caudal allows for precise
quantitative section alignment from plane to plane. In our histological studies the
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morphology of the LV was reconstructed from five consecutive planes (Bregma -
3.36 to -3.48). The coronal plane with the least variance between conditions was
found at Bregma -3.36 in the rat (Paxinos & Watson, 2007), the entrance of the LV,
so this was chosen as the most suitable anatomical anchor, in addition, it could be
readily visualized and measured. At -3.36 mm Bregma, in addition to the LV it is
also possible to identify the major anatomical structures of the ROI (the subnuclei of
the lateral amygdala (LA). The choice of the LV as an anatomical anchor was
therefore suitable because it is amygdala-centric, changes shape rapidly and clearly,
and is stable across subjects (Hadley C Bergstrom & Luke R Johnson, 2014; Hadley
C Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez, et al., 2013;
Hadley C Bergstrom, Craig G McDonald, Smita Dey, Haying Tang, et al., 2013; H.
C. Bergstrom et al., 2008; Hadley C Bergstrom et al., 2011; N Chaaya et al., 2017;
Haranhalli et al., 2007; A. Jacques et al., 2017; Joseph E LeDoux et al., 2006).
We used the caudate putamen as an anatomical landmark to align sections in
the prefrontal cortex (described below). Aspects of the caudate putamen met the
criteria we previously set for landmark identification (see fig. 1D). Histological
images were captured as virtual slide images (OlyVia; format.vsi) using a slide
scanner (Olympus VS120). Capturing images with a slide scanner (used in this
example) is an alternative approach to live capturing of neuron data with a
microscope connected directly to Neurolucida as used in our previous published data
(Hadley C Bergstrom & Luke R Johnson, 2014; Hadley C Bergstrom, Craig G
McDonald, Smita Dey, Gina M Fernandez, et al., 2013; Hadley C Bergstrom, Craig
G McDonald, Smita Dey, Haying Tang, et al., 2013; H. C. Bergstrom et al., 2008;
Hadley C Bergstrom et al., 2011; N Chaaya et al., 2017; Haranhalli et al., 2007; A.
Jacques et al., 2017; Joseph E LeDoux et al., 2006). In this example, we used
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OlyVIA XV Image Viewer (Olympus Australia Pty Ltd, Vic, RRID: SCR_014342)
to ascertain and measure images within a Bregma range that showed an alteration in
the size of the caudate putamen. The caudate putamen becomes visible 2.7 mm
anterior to Bregma, distinctly widens and lengthens in serial coronal sections across
the rostrocaudal axis. Three consecutive sections (Bregma 2.7 mm – 2.58 mm) were
aligned and verified across subjects and conditions by statistical comparison
(ANOVA) of the feret length (the maximum feret length or distance between two
perpendicular tangents) was measured with Neurolucida 360 software (Neurolucida,
MBF BioScience, VT, RRID:SCR_001775) and analysed with SPSS, (IBM SPSS
Statistics 23, WA, SCR_002865). A similar comparison of sections was calculated
using z-scores from each maximum feret measurement of the caudate putamen. No
outliers were detected using +/- 3.0 standard deviation (SD). This principle includes
99.9 % of values coming from the same normal distribution). Additionally, outliers
can also be checked using online software tools, e.g. GraphPad Prism. Next, in order
to test each Bregma point assignment was dissimilar and no difference existed
between experimental conditions, paired t-tests were performed on the feret
measures. Each distance was found to be statistically different (example 2.76 mm
Bregma; p = 0.000304). This data was used to help exclude misaligned sections due
to natural or histological induced variations. This quantitative analysis approach can
thus be used to assign sections to distinct groups maximizing alignment accuracy for
subsequent neuronal topography measures.
Section alignment
Quantitative topographical data was produced beginning with neuron
identification and section alignment. While LV and caudate putamen changes can be
observed through a sequence of many planes, the region of interest may be rostral or
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caudal to this point. For this reason, the chosen landmark is used only as a point of
reference. Sections are aligned manually using the landmark and working rostrally or
caudally through the Bregmas using the measurement of width of each section as a
guide. For example, Bregma 2.76mm is 0.48mm away from Bregma 3.24mm;
therefore there will be 8 x 60µm sections or 12 x 40µm sections between the two
Bregma coordinates. This highlights the need for precision when slicing and marking
serial sections. Having mounted sections in the correct order on slides prior to
labelling decreases time taken during this stage.
2.3.2 Generate topography in preparation for analysis
Create contour
In order to ensure consistency and precision in neuron counting across all
subjects, a contour or tracing of the anatomical structure being investigated can be
prepared in Neurolucida (NL) 360 (Neurolucida, MBF Bioscience, VT). Prior to
importing an image into NL for tracing, it is necessary to calibrate the image to
approximate the dimensions of a single brain section bitmap image (cellSens
software, Olympus, Vic, RRID: SCR_014551). Within Neurolucida select >File,
>Image open to allow the image to appear and select x and y calibration pixel size.
These measurements are located in the image properties section in the cellSens
program. Choose >Trace, >Contour Mapping in NL to begin the trace (see fig. 2A).
The image lines may be enlarged using the zoom tool, to increase accuracy of the
trace. Use the curser to trace around the selected area and >Close Contour when
finished each area. This allows delineation of each section of the contour with a
separate colour using ‘User Line’.
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Scale contour
At this point it is essential to align the contour. The size of the tracing can be
adjusted to fit the image using >Tools, >Adjust Scaling. Contour alignment must be
consistent across all groups, prior to neuron counting. It is advisable to open several
images to scale the contour, due to minor variation in dimensions across subjects.
Calibrate contour
Very importantly, the contour is then calibrated to a constant point (0, 0 on the
x, y axis) to preserve consistency of neuron marker coordinates. The reference point
is displayed by selecting >Options, >Display Preferences, >View. In this window,
the radius of the point can be set to a desired diameter. Apply the display grid setting
and enlarge with the magnification tools as required. The contour is moved (using
move tools) such that the 0,0 coordinates are placed in the superior left corner of the
contour. Once in position the contour must not move or be resized for the duration of
neuron counting across all groups to ensure the integrity of the quantitative data.
Save contour as a data file.
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Figure 2-2: Steps for producing raw coordinate data from identified neurons.
(A) Create, scale, and calibrate contour. Neurolucida 360 (or an equivalent program) can be used to produce a nucleus or brain region contour from a rat brain atlas diagram. Using the contour mapping tool in Neurolucida 360 contours (in different colours) can be traced over a figure from an atlas. Lateral amygdala tracing shown was generated from Bregma –3.36 of Rat Atlas (Paxinos and Watson, 2007). (B) Align section to contour and mark immuno positive neurons. Prefrontal cortex section with contour overlaid. Immuno-positive neurons were marked within the contour. Saved data files can be opened in Neurolucida Explorer to gain data file information such as contour areas, Feret length measures, and neuron counts. Prelimbic contour and neurons were marked in aqua, infralimbic contour, and neurons marked in yellow. Once neurons are marked, Neurolucida Explorer (or equivalent) can import the data file to generate a contour and marker analysis, LA example shown. (C) Export marker coordinates. The x, y coordinates produced for each marked neuron are exported to an ASCII file which can be opened in graphing software such as Origin Pro (or equivalent). (D) Produce bin matrix. A data matrix is generated based on the area and density of marked neurons within the contour. Bin size is calculated using twice the area of the contour divided by the total number of neurons (De Smith et al., 2009). Once the x, y coordinates are highlighted in an Origin Pro (or equivalent) workbook, the 2D binning option under descriptive statistics is chosen. The bin ends and size can be manually entered into the dialog box once determined using the standard geospatial formula (De Smith et al., 2009).
Align sections to contour
Once the tracing has been saved >CTRL+S, a scanned and cropped image of a
single neural section may be opened (>File, >Image Open, >calibrate pixel size) and
the tracing can be overlaid using the move tools to move only the image. There may
be some minor variation in the size and properties of each subject, driven by natural
61
variation or variations introduced during tissue processing - therefore the contour
must be aligned to each section. To align the section and the contour, select >Image,
>Image Processing and >Orientation (see fig. 2B). Options are provided for a mirror
image, flip, 90° or 180° rotation of the image. Choose Arbitrary Rotation and use the
arrows to alter the Rotation in Degrees.
Mark immuno positive neurons
Once the section is aligned to the contour (or tracing), begin to mark neurons
by choosing a marker from the marker toolbar located down the length of the left
side of the screen. Right click the mouse button on the selected marker to rename,
recolour or resize the marker. Elect to use a different colour for markers in separate
areas of the contour for ease of analysis at later stages of the process (see fig. 2B).
Markers may be erased at any time during counting by >CTRL Z, or >Edit, >Undo,
to remove the last placed marker. If mapping to determine the organization of
synaptic connection strength the same procedure should be followed for marking
puncta. To map the density of dendrites and axons in the neuropile the coordinate
markers must be placed along their length.
Note: If mapping neurons using NeuroLucida directly connected to a
microscope for live imaging, then, following contour tracing and neuron mapping, a
final alignment of all data to be compared must be made before analysis of neuron
spatial distribution. Contours with mapped neurons are rotated for matched
alignment using the Neurolucida Contour Alignment function.
Example: A digital image of the ROI, the mPFC, was sourced from the rat
brain atlas, 6th edition, 2007 (Paxinos and Watson, 2007, RRID: SCR_006369).
Three locations, 3.3 mm, 3.24 mm and 3.18 mm anterior to Bregma (Paxinos &
62
Watson, 2007) were used for cell counting. This level was chosen as both the
prelimbic and infralimbic cortices were represented at this point. Specific markers
were recoloured and renamed for each subregion to be mapped (fig. 2B).
Export Neurolucida ASCII File into OriginPro (or alternative)
Once all the neurons in the ROI are counted with the aligned contours, the
marker coordinates (x,y,z), which Neurolucida has recorded relative to the nominated
reference point, can be exported as an ASCII (plain text) file (see fig 1C). To
accomplish this, select >File, >Export Marker Coordinates and save the file. At this
point it is also prudent to save the data file you have placed your makers on, by
choosing >File, >Save Data File As. The Data file can be opened in Neurolucida
Explorer >File, >open data file, >contour, >analysis, >markers and region analysis.
This program provides a full synopsis of the contour areas, required for later
mapping, perimeters, feret measures and neuron counts for each designated region.
Once this information has been saved the neuron markers can be cleared in NL 360
using >Edit, >Select Objects. A window will open to the right of the screen where
you can select Any Object, Only Markers, Select All, then press the Delete key.
Choose >File, >Image Open to import a new section and begin the entire sequence
again. Once two or more images are open, select >Image, >Image Organizer, to
choose which images you will Show, Hide or Delete. Files can also be closed by
selecting >File, >Close All Images. To analyse the data obtained the ASCII files can
be opened in Microsoft Excel where the x and y coordinates are quickly accessed and
can be cut and pasted into Origin Pro (see fig. 1C)
(http://www.scientificcomputing.com/product-release/2014/10/origin-and-originpro-
2015-data-analysis-and-graphing-software). Alternatively Origin Pro has the facility
63
to open all files at once by choosing >File, >Import, >Multiple ASCII, and following
the prompts to choose the files you wish to include in one density map. It is
recommended to import only files from one behavioural condition at a time to reduce
human error. Once coordinates are listed, select >Descriptive Statistics, >2D
Frequency Binning, which will require input of bin sizes. (Alternatives to Origin Pro
can also be used – see Discussion below).
Select binned data parameters within Origin Pro (or alternative)
Data binning, also known as discretization, involves grouping data into bins in
order to ascertain a quantitative understanding of neuronal distribution (Kerber,
1992). Developing an appropriate data matrix relies on the optimization of the
dimensions of micro regions of data (bins). This part of the analysis should be well
considered and standardized in order to closely match the bin number and
dimensions with the central experimental question being investigated and also to
ensure the repeatability across subjects and experiments. The number of bins can be
determined based on experimenter determined parameters or alternatively a formula
can be applied to standardize the selection on bin numbers and to reduce any bias in
bin number selection. An established formula for this type of spatial analysis is based
on twice the expected frequency of items identified in a random field (2*sampling
area/n, where n = mean number of items to be counted, e.g. activated neurons)
(Michael J De Smith, Goodchild, & Longley, 2009). This method can be used to
ensure an unbiased estimate of the optimal dimension of bins for sectioning the ROI
into a matrix for data analysis. The neuron counts, and contour area measurements
are obtained from the Neurolucida Explorer data. Once bin number has been
calculated, the minimum bin beginning and maximum bin end for the x axis and y
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axis are adjusted to encompass the smallest and largest coordinates contained within
the ASCII files. In Origin Pro, all Auto windows must be unchecked to allow manual
input of data. The bin size is measured in micrometres squared (m2). Once these
measurements have been entered and the number of bins is calculated by the
program, select >OK (see fig. 2D). This converts the data into an appropriate matrix,
based on the area and density of the marked objects.
Produce bin matrix
The next step is to use the data from the calculated matrix of bins and their
corresponding neuron counts for graphing and statistical analysis. The table of bins
and neurons counts derived from Origin Pro (see fig. 2D) can now be copied into an
Excel spreadsheet (or equivalent program). Repeat this process for each ASCII file
obtained from one section, in one condition across all animals – this will be based on
the section alignment for a specific ‘Bregma” coordinate – as described above. For
validation purposes individual density maps can be produced at this point, for later
comparison to the mean map. For an example see a range of 26 maps produced from
raw values for each subject across 4 experimental conditions in comparison to mean
maps in Figure 2 of (Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina M
Fernandez, et al., 2013).
2.3.3 Topographic neuronal density maps (heat maps) and analysis
Create density maps
Using Excel, an average across all sheets can then be calculated – this is used
to plot a graph of the mean for an experimental condition (see fig. 3A). In addition,
65
from these combined and averaged data a coefficient of variance (CV) and other
measures can be calculated. The mean and CV data can be used to create separate
neuron topographic density ‘heat’ maps using graphing software SigmaPlot or
OriginPro (SigmaPlot v 12.5, Systat Software, San Jose, CA, RRID:SCR_003210)
(or alternatives). For producing a variety of graphs from the now binned data we
have used SigmaPlot, however other programs can be used. The data matrix, using
individual subject data or averaged data from Excel, is transferred beginning in the
third column of SigmaPlot. The x and y coordinates from Origin Pro are copied into
columns one and two of Sigma Plot. In order to produce a coloured neuron
topographic density ‘heat’ map, select >Create Graph, >Contour Tool (see Fig.3A).
The scale can be adjusted using the graph properties tool. The production of a
neuronal topographic density ‘heat’ map is also possible using Origin Pro.
Example: We have used bin matrix data from neurons identified and marked
in the prelimbic and infralimbic cortices and transferred this data to SigmaPlot. This
data was used to produce both prelimbic (PL) and infralimbic (IL) mean neuron
topographic density graph (heat maps). As described above, during the creation and
alignment of the contour the 0, 0 coordinate was aligned to the superior left corner of
the contour. The creation of an overlay was performed by aligning this same superior
left landmark of the contour with the 0,0 coordinates as displayed on the SigmaPlot
contour graph export. This process allowed aligned or registered heat maps from
different animals to be combined into signed maps of mean data for initial qualitative
analysis of the data sets. In our example we identified neurons activated during the
recall of an extinguished fear memory – initial qualitative analysis of this data
reveals increased neuron density within the deep layers of the PL and IL.
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Figure 2-3: Steps for producing and analysing topographical density maps.
(A) Create topographic density map. A neuronal topographic density mean map is produced by transferring binned data from Excel to Sigma Plot (or equivalent software) (X data = x coordinates, Y data = y coordinates). Density maps can be created for each sub region. A coefficient of variance map can be prepared by dividing the standard deviation by the mean across all samples in one condition. Difference maps can also be created between conditions. The data matrix from Origin Pro (or equivalent) is transferred to a spreadsheet. This procedure is followed for each animal from a single condition/group. An average across all sheets produces the data for a mean density map. The standard deviation is calculated and divided by the mean, producing the data required for the coefficient of variance (CV) map. Example – topographic density (mean and CV) maps shown for Bregma –3.36, pMAPKC neurons in the ventrolateral portion of the LA of rats that underwent extinction training (n = 7). (B) Align density map with contour and brain sections. To enhance visualization of specific neuronal subsets, density maps can be inserted into the contours or superimposed over brain sections. Density maps may be edited to change the styles, colours, font sizes, labels etc., providing alternatives conducive to individual requirements. Information regarding cell layers can be determined from visualizing the distribution of activated neurons as shown in the pMAPK labelling of the mPFC of rats that have undergone auditory fear conditioning (n = 7): mean map generated in Sigma Plot (or equivalent), map placed into contour, map overlaid on rat brain section. (C) Quantitative analysis of variance between conditions. A variety of statistical analysis can be performed to compare binned data such as Bonferroni correction, principal component analysis (PCA), false discovery rate (FDR), multiple discriminant analysis and mixed model ANOVA. Example of mean maps for the expression of pMAPK in the LA provides visual comparison between auditory fear conditioned (n = 6) and naïve (n = 7) rats. pMAPK ranks comparing extinction (n = 7) and no extinction (n = 5) groups within the ventrolateral portion of the LA p = 0.0022 (t-test, Mann–Whitney rank and SEM)
Align maps with contours and sections
We recommend two methods to enhance visualization of specific neuronal
subsets and gain visual information regarding distribution of activated neurons with
regards to cell layer. The density maps can be inserted into the contours generated
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from an atlas, or superimposed over the original brain sections (see fig. 3B). To
ensure ease of fit it is prudent to place a marker in the corner of each contour which
can be removed prior to statistical analysis. Density maps may be edited in Sigma
Plot to change the styles, colours, font sizes, labels etc., providing alternatives
conducive to individual requirements.
Example: Information regarding cell layers can be determined from
visualising the distribution of activated neurons as shown in the pMAPK labelling of
the medial prefrontal cortex (see fig. 3B) of rats that have undergone auditory fear
conditioning (n=7): mean map generated in Sigma Plot (Systat Software). The map
was placed into the prefrontal contour and overlaid onto a rat brain section.
Analysis of binned data
Graphing topographic neuron density data is an important step to provide
visual evidence for changes in topography associated with behavioural and other
experimental manipulations, as described above. However, when further evidence is
needed to support conclusions of changes to neuronal topographic patterns then
statistical analysis of the topographic data is required. Quantitative analysis can be
performed with a variety of methods (discussed below) to compare the coefficient of
variance and topographical differences between conditions. GraphPad Prism 7
(GraphPad Software Co., CA, USA) can be utilized for each of the discussed
methods as well as linear regression and Pearson’s r coefficient which can also be
collected for correlation between groups.
Example: To evaluate the bins in each data matrix, two-way ANOVA with a
false discovery rate correction for multiple comparisons was conducted. The
discovered bins were termed micro regions of interest (MORIs) and assigned a
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colour to represent the density of neuronal cell bodies located in that position (see
fig. 3C). Post hoc analysis of MROIs was conducted using t-tests.
2.4 Statistical Analysis of topographic neuron density data
In the next section, we describe statistical methods than can be applied to
binned data sets of topographic data combined with behavioural manipulations to
groups of experimental and control subjects. We also provide examples of
application of statistical analysis from our own behavioural and neuronal topography
data sets. The major challenge with the statistical analysis of multiple topographical
binned data sets, combined with several experimental groups, is statistical error due
to multiple comparisons. In order to best handle the analysis of topographical data
we have investigated and utilized a variety of statistical approaches for large multiple
comparison data sets – these include ANOVA and its variants; principle components
analysis (PCA); and false discovery rate (FDR) correction (see table 1). A very
important step in performing statistical analysis of topographic data is to perform the
statistical analysis in very close consultation with the Data produced from the
topographic maps as described above. Through careful observation and consultation
of the heat maps, derived from both individual animals and importantly behavioural
group mean heat maps together with their measures of variance (CV maps), the most
meaningful analyses can be performed and interpreted.
2.4.1 ANOVA followed by Bonferroni corrected t-tests
A question addressed in topographic data analysis is whether there is a
significant difference in the data (e.g. number of activated neurons in the ROI) across
all experimental conditions and in all ROI. One way to assess the overall difference
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in experimental manipulation is with analysis of variance (ANOVA), followed by a
post hoc t-test with a correction for multiple comparisons (e.g. Bonferroni), among
specific ROI and experimental groups to determine where the significance arises.
Where multiple comparisons are necessary, a Bonferroni-type correction may be
employed (see use in (Hadley C Bergstrom et al., 2011), however it has the risk of
being too strict and likely to sacrifice power in the attempt to exert stringent control
over error. The potential for false negatives (type II errors) can be controlled
effectively, while still retaining sufficient power, with false discovery rate (FDR)
correction (Benjamini & Hochberg, 1995).
Example: We have analysed topographic neuron density data from Pavlovian
fear conditioning experiments in order to determine whether there was a significant
differences in topographic neuron density data across conditions by comparison of
activated neuron density in each of the micro ROIs (46 bins) across all conditions via
multiple comparisons (Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina M
Fernandez, et al., 2013). The mean numbers of activated neurons identified in the
ROI from topographic data were used to conduct ANOVA across all conditions.
Where a significant difference was found, planned contrasts between experimental
and control groups were performed to assess where the differences lay (Hadley C
Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez, et al., 2013).
Multiple comparison tests involved three contrasts using one-way ANOVA. The first
compared the fear conditioned and conditioned stimulus reactivated groups to the
control groups: In this example, we compared box alone and conditioned stimulus
(memory not reactivated groups). The second contrast was between the fear
conditioned and conditioned stimulus reactivated groups and the third compared the
box alone to the conditioned stimulus group. Having established a significant
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difference across conditions and located the main effect between experimental and
control conditions, the next step was to locate the region of greatest variance in the
ROI, requiring assessment of the differences in micro ROIs between groups (Hadley
C Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez, et al., 2013). In
addition in this example, we also ran correlations with behavioural data as additional
analysis (Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez,
et al., 2013).
2.4.2 False Discovery Rate (FDR)
Where the area under investigation has been sectioned into topographical units,
each having its own data set, multiple ANOVAs on all topographical units may
determine more precisely any variance between experimental conditions. FDR
controls the expected rate of false rejection of the null hypothesis, by setting a
parameter, the quotient q, as the “tolerable” FDR (Genovese, Lazar, & Nichols,
2002). The q value is used as an alternative to p value when reporting significance,
and while it may be set at a conventional level (.05), a higher level may be
reasonable (Genovese et al., 2002). FDR has been used effectively in neuroscientific
studies (Hadley C Bergstrom & Luke R Johnson, 2014; Hadley C Bergstrom, Craig
G McDonald, Smita Dey, Gina M Fernandez, et al., 2013; Genovese et al., 2002;
Groppe, Urbach, & Kutas, 2011). Once the region of greatest variance across all
conditions is identified, follow up tests focus the investigation on the variance
between experimental conditions, in those locations.
Example: We have previously successfully applied FDR for type II error
minimization and identification of significance in specific topographic ROI in
behavioural experiments (see (Hadley C Bergstrom & Luke R Johnson, 2014;
Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez, et al.,
71
2013)). In these studies, we conducted mass univariate ANOVAs to assess
differences in neuron activation across all conditions in each of 46 bins. FDR
correction was used, with the tolerable limit set at q=.1. Significant differences
across conditions were found in certain micro ROIs (nine of 46 bins), so comparisons
were performed on those particular data to locate (1) the effect of the experimental
versus control groups and (2) the difference between two experimental groups
(Hadley C Bergstrom & Luke R Johnson, 2014; Hadley C Bergstrom, Craig G
McDonald, Smita Dey, Gina M Fernandez, et al., 2013). The q values were mapped
onto the topographical matrix (bins) to reveal the highly localized topography of
neuronal activation. The spatial distribution of these points of significance was
confirmed on visual analysis of the neuronal topographic density maps compiled
from topographic data, and also reflected earlier findings (Hadley C Bergstrom et al.,
2011). Subsequent correlational analysis was used to confirm the relationship
between the density of marked neurons and behaviour.
2.4.3 Principle Components Analysis (PCA)
Another approach to topographical data with multiple ROI and group
comparisons is principal components analysis (PCA). PCA seeks to identify and rank
combinations of variables that account for variance within the data set. PCA enables
the relationships between these patterns of variables to be identified, tested and
confirmed (Jolliffe, 2002). PCA has been applied by ourselves and others to address
a variety of anatomical questions, for example in morphological studies of microglial
cells (Soltys et al., 2005); and vagus nerves (Horn & Friedman, 2003); localization of
sensory cells in the thalamus in facial recognition (Chapin & Nicolelis, 1999); the
segregation of pyramidal neurons into morphological defined cell populations (H. C.
72
Bergstrom et al., 2008); eye-tracking data (J. C. R. Bergstrom, Olmsted-Hawala, &
Bergstrom, 2016) and extensivley in MRI data (F. Lin et al., 2006).
Example: We have successfully applied PCA for the analysis of topographic
neuronal density data activated in studies of Pavlovian fear conditioning. Activated
neurons were mapped and the area sectioned into micro ROIs (bins) as described
above, to produce a matrix of memory data (Hadley C Bergstrom, Craig G
McDonald, Smita Dey, Haying Tang, et al., 2013; Hadley C Bergstrom et al., 2011) .
Ten components (of spatial data) were revealed, with one of these (SC1) being
associated with the pattern of greatest difference (principal component score) in the
spatial distribution of activated neurons between experimental conditions. SC1
displayed a unique pattern of activated neurons in a particular subnucleus of the
amygdala (the LAd) across all brain samples in the experimental group. This was
confirmed by t-test comparisons (Bonferroni corrected) of the bins with the most
prominent loading values, and these also correlated with the area of highest density
in the topographic analysis outlined above. That is, as described above, the statistical
pattern could be confirmed by visual patterns seen in the neuronal topographic
density maps generated by color-coding neuron densities. PCA has proved a useful
statistical tool to extract meaningful patterns of variance related to the experimental
manipulation, which could be confirmed by both comparison with visual
representations of the data and Bonferroni corrected t-tests (Hadley C Bergstrom,
Craig G McDonald, Smita Dey, Haying Tang, et al., 2013; Hadley C Bergstrom et
al., 2011).
2.4.4 Multiple discriminative analyses (MDA)
73
Multiple Discriminant Analysis (MDA) is a method of visualizing patterns
within complex data sets (L. Lin, Osan, & Tsien, 2006). With complex data, such a
topographic data with many anatomical sub-regions and bins combined with multiple
experimental conditions, where both location and distribution across area, are under
investigation it can be important to identify patterns within this data set, in order to
help understand and interpret the data. MDA can be used to determine how a set of
continuous variables can discriminate groups (Hadley C Bergstrom, Craig G
McDonald, Smita Dey, Haying Tang, et al., 2013), for example, how the pattern of
neuron density in certain subnuclei (the independent or predictor variable) can
predict the experimental condition the subject brain best fits into (the grouping or
independent variable). MDA gives loading values (canonical variate correlation
coefficients) that represent the relative contribution of each variable in a set of
variables (a dimension) that discriminates groups from each other (see (L. Lin et al.,
2006); and (Hadley C Bergstrom, Craig G McDonald, Smita Dey, Haying Tang, et
al., 2013)).
Example: In one topography of Pavlovian fear memory study, we were
interested in the relative contribution of lateral and basal amygdala (LA) subnuclei to
the overall density of activated (pERK/MAPK expressing) neurons among each
experimental condition (Hadley C Bergstrom, Craig G McDonald, Smita Dey,
Haying Tang, et al., 2013). First, MANOVA was performed to examine the
relationship among the subnuclei. Where a significant relationship was found, one-
way ANOVA on each subnucleus tested for significant differences between
conditions. Next, MDA was used to test the relative contribution of each subnucleus
to the overall difference in density of activated neurons between conditions. The
MDA revealed a single underlying pattern in density of activated neurons across
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lateral and basal amygdala subnuclei that discriminated the experimental and control
groups. It also showed the subnucleus (the LAd) that contributed most to the overall
difference between conditions. Having used MDA to help identify the region with
the most significant contribution to the overall pattern of variance between
conditions, it was possible to go further and explore more fine-grained details within
the data. To confirm the pattern identified with MDA, post hoc comparisons with
Bonferroni correction were performed, verifying the findings on the location and
experimental condition of the greatest activation, and reinforcing ours and others
previous findings about the predominance of LAd neural plasticity in fear memory
(Hadley C Bergstrom et al., 2011; Rodrigues et al., 2004).
2.4.5 Mixed Model ANOVA
The Mixed Model ANOVA also known as a Mixed Design ANOVA or a Split-
Plot ANOVA, allows for testing for differences between independent groups (in
functional topography experiment these will be the impendent behavioural groups,
i.e. experiment and control groups) while using repeated measures (bins in
topography experiments). Thus, the Mixed Model ANOVA can be employed for
microanatomy data comprising neuron counts within bins contrasted across several
independent groups. For our studies of functional neuronal topography, we typically
derive 20-80 bins per animal comprising the within-group dependent variable. For
the independent variable, several independent groups of animals are used including
experiment and control groups. Mixed Models allow for the analysis of data from all
locations and all animals in one analysis. Thus, Mixed Models a have strong
potential for analysis of topographic data combined with experimental manipulations
– such as behavioural or pharmacological manipulations. Using a Mixed Model
analysis data between anatomic locations can be compared and no adjustment for
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multiple comparisons is required. Mixed Models can be thought as an advancement
of ANOVA and regression models. One, very important but often overlooked,
assumption of ANOVA/Regression, is that the data are independent of each other.
Thus, the analysis cannot have the same individual represented twice in the same
dataset. For example, measurements on lateral amygdala have to be analysed
separately from infralimbic cortex.
Mixed models ANOVA offers a toolbox to account for the dependence of
measurements taken on the same individual, by accounting for, so called, random
effects. Random effects are variables for which we are not interested in the actual
levels that we have sampled but on what they represent as a sample from a
population. The most usual random effect would be the individual animal (for further
definitions of random effects readers are directed to (Fitzmaurice, Laird, & Ware; A.
Zuur, Ieno, & Smith, 2007; A. F. Zuur, Ieno, Walker, Saveliev, & Smith, 2009).
Methods related to Mixed Model ANOVA that could also be applied to topographic
data sets with is the Generalized Estimated Equations (GEE) and the Generalized
Additive Mixed Models (GAMM) which can accommodate non-linear relationships
(for further information see, Zuur and Ieno, 2016 for GAMM and Fitzmaurice et al.
2004 on Mixed Model ANOVA and GEEs and their differences) (Fitzmaurice et al.;
A. F. Zuur & Ieno, 2016).
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Table 1 Options for statistical analysis
Method
Purpose Advantage
ANOVA followed by Bonferroni corrected t-tests
To define where there is a significant difference in the data across conditions
Stringent control over type II errors
False discovery rate
To locate specific topographic regions of greatest variance across all conditions
- Controls the expected rate of false rejection of the null hypothesis) - Greater power - Can be useful prior to correlational analysis
Principle components analysis
Identifies and ranks combinations of variables that account for variance within the data set
Extract meaningful patterns of variance related to the experimental manipulation
Multiple discriminative analysis
To visualize patterns within complex data sets
Determines how a set of continuous variables can discriminate groups
Mixed model ANOVA
Tests for differences between independent groups while using repeated measures to analyse topographic data combined with experimental manipulations
- No adjustment for multiple comparisons is required - Accounts for random effects - *GEE and **GAMM can be applied after it, to accommodate non-linear relationships
* GEE: generalized estimated equations, **GAMM: generalized additive mixed models
2.5 Discussion
Understanding neural network organization and predicting memory and
behaviour from neural network functionality is a critical goal of neuroscience. While
various imaging techniques are capable of large-scale analysis of functional brain
regions, it is not suitable for imaging the spatial distribution, connectivity and
stability of neurons at the micro-network level. The ability to accurately map,
measure and compare neural network spatial properties, as described here,
contributes to our fundamental awareness of the organization and structure of
functional neural circuits. Classic cellular and molecular analysis of neuronal tissue
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assists in the identification of molecular machinery underlying behaviour but does
not answer questions relative to the fundamental organizational properties and their
functional changes associated with behaviour. We have developed a combined
topographic and statistical approach for producing and analysing micro-topographic
data. This method provides clear visualization of the spatial organization and degree
of consistent neuronal patterns across brains from individual subjects and in different
experimental conditions.
Neuronal material used for topographic mapping can include both exogenously
labelled, such as immunocytochemistry and in situ hybridization, as well as
endogenous genetic labelling with green fluorescent protein (GFP) and other
fluorescent probes. Consistency in labelling is important with regard to whichever
neuron marking system is selected for topographic mapping. The statistical methods
recommended and applied here allow for natural variation in measured populations.
Nonetheless, reduction of variability will improve outcome consistency and
statistical verifications. Marking neurons requires consistent labelling and consistent
identification of neurons. To verify consistency, ideally experimenters blind to the
experimental conditions are employed throughout or for verification checks of large
data sets. The general principles outlined here for micro-topographic mapping can
be applied to sectioned brain material as well as whole brain analysis approaches
using CLARITY, CUBIC or iDISCO. Three-dimensional analysis also requires focus
and comparative measurements on specific anatomic regions of interest. Both 2D and
3D analysis ultimately requires localization and correlation of cellular activity with
behavioural function using the approaches described here.
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2.5.1Topographic Mapping
The first step in the approach to visual and quantitative analysis of functional
neuronal topography between animals is to establish section alignment. Careful
choice of an appropriate and stable landmark or anchor point associated with the
region of interest is essential (Hadley C Bergstrom & Luke R Johnson, 2014; Hadley
C Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez, et al., 2013;
Hadley C Bergstrom, Craig G McDonald, Smita Dey, Haying Tang, et al., 2013;
Hadley C Bergstrom et al., 2011; Haranhalli et al., 2007; Luke R Johnson et al.,
2012; Joseph E LeDoux et al., 2006). Identification of an anchor point which has
rapid and distinct conformational change through sectional view planes will ensure
success at this level. The second stage involves fitting a contour to the region of
interest, which ensures precision of the region in which the neurons will be counted,
as well as consistency in the area across subjects. A limitation at this stage is small
variation between sections from each subject, which can come from animal
variations and also from histological processing, therefore care is needed to minimize
variation. The contour must be fitted to each section with a degree of individual
judgement. Specific brain regions, such as the hippocampus, may also significantly
change in shape along the longitudinal axis and therefore a single contour is not
feasible. An alternate approach entails producing a unique mean contour section for a
specific data set. The rat brain atlas, developed by Paxinos and Watson in the 1980’s
(Paxinos & Watson, 2007), is one of the most established and detailed sources of
anatomical coordinates available at this time. Other brain atlases are available and
can also be used. In the Paxinos and Watson atlases, the depicted brain sections can
appear up to 480 micrometres apart necessitating several brain sections to be mapped
79
to individual atlas plates. Our method is therefore limited in part by the standardized
atlas information currently available (Paxinos & Watson, 2007).
Prior to creating a contour an atlas image generally requires resizing, which
can represent an amount of time spent making adjustments with various software
packages. Due to the number of software packages used to produce the images, it is
essential to note both the accepted file types (as listed in methods above) for
compatibility as images are moved between programs. Furthermore, it is very
important to note the numerical functions involved in any resizing, so that
consistency is maintained. Computer processing speed and memory requirements
must also be considered when using the large data files produced by slide scanning.
Free, open source programs are available for some procedures, making our
described method economically viable to all. For example, Image J and FIJI
(National Institutes of Health) can be substituted for some elements of the
topographic mapping, as it is able to perform cell counts and export x,y coordinate
data. Image J has many plugins available and runs in Java which is editable. Prior to
this the contours must be calibrated to a zero point to facilitate precise individual
comparisons. Once the coordinates have been exported a data matrix may be
developed. Data bins are created using a geospatial analysis formula to establish
unbiased bin dimensions. Open source programs are also available for this step
requiring some degree of coding for specific features. QtiPlot (Free Software
Foundation) is a free replacement for Origin and SigmaPlot. It will enable binning of
x, y coordinates into a two-dimensional matrix and has contour generating
capabilities for producing neuronal topographic density maps. Free online software
for FDR analysis, as described above, is also available (sdmproject.com). While we
have outlined and described our methodical approach using a series of standalone
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commercial software packages for each of the steps descried, free software is also
available making the methodical approaches described here freely available for all
worldwide.
2.5.2 Analysis of Topographic Data
Although we have presented several arguments for the use of binned data for
micro-topographic analysis, there remains the opinion that discretization has
limitations (Langseth, 2008; MacCallum, Zhang, Preacher, & Rucker, 2002). We
have used both PCA as well as Mass Univariate ANOVA with FDR correction as a
useful way to locate areas of most variance in complex data, and to confirm the
qualitative data from our mean heat maps. This method assists in decreasing the
reduction in power generated with Bonferroni procedures (Verhoeven, Simonsen, &
McIntyre, 2005). While we provide general guidance for analysis of binned micro-
anatomical data sets, we advise the reader to liaise with statisticians to evaluate the
methodical approaches described here with the chosen data analysis techniques for
the analysis of unique data sets and research questions.
2.6 Conclusion
Neuronal micro-topographic density maps can assist in defining specific brain
regions involved in behaviour. Statistically verified microanatomical mapping has
the ability to advance our knowledge of the multi layered, complex organization of
the brain and its cognitive systems. Our approach for the measurement and
contrasting of neuronal topographic data in behavioural experiments has been
successfully applied to the study of the microanatomy of memory formation. It has
enabled us to visualize the spatial allocation of neurons activated during the
acquisition of fear memories (Hadley C Bergstrom & Luke R Johnson, 2014; Hadley
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C Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez, et al., 2013;
Hadley C Bergstrom, Craig G McDonald, Smita Dey, Haying Tang, et al., 2013;
Hadley C Bergstrom et al., 2011; Haranhalli et al., 2007; Luke R Johnson et al.,
2012; Joseph E LeDoux et al., 2006). We propose this method will prove
advantageous to other forms of neuroscience, including the cellular basis of
addiction; pathological memory models; pharmacological manipulations and other
forms of functional microanatomy (Holmes & Singewald, 2013; Luke R Johnson et
al., 2012). Existing nuclei catalogued in brain atlases have been defined
histologically, our approach allows for the identification of new functional micro
regions within established brain nuclei. By providing this walk-through tutorial we
encourage further development of these goals.
83
Localization of Contextual and Context Removed
Auditory Fear Memory within the Basolateral Amygdala Complex
This chapter comprises the following published article: Chaaya, N., Jacques, A., Belmer, A., Richard, D. J., Bartlett, S. E., Battle, A. R., & Johnson, L.R. (2019). Localization of Contextual and Context Removed Auditory Fear Memory within the Basolateral Amygdala Complex. Neuroscience. Published 13th December, 2018 https://doi.org/10.1016/j.neuroscience.2018.12.004
Chapters 3 and 4 encompass the remaining objectives listed in aim 1, inclusive
of identifying visual and quantitative differences in functional neuronal population
involved in memory consolidation and recall.
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In the case of chapter 3: Localization of Contextual and Context Removed Auditory Fear Memory within the Basolateral Amygdala Complex Publication status: Published
Contributor Statement of contribution*
Nicholas Chaaya Involved in the conception and design of the project, behavioural and laboratory experiments, analysed the data and wrote and edited the manuscript and produced the figures.
Angela Jacques Assisted with behavioural and laboratory experiments
Arnauld Belmer Assisted with laboratory experiments.
Derek Richard Assisted with laboratory experiments.
Selena Bartlett Assisted in editing the manuscript.
Andrew Battle Assisted in editing the manuscript.
Luke Johnson Assisted with behavioural experiments, assisted in reviewing and editing the manuscript.
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3.1 Abstract
Debilitating and persistent fear memories can rapidly form in humans
following exposure to traumatic events. Fear memories can also be generated and
studied in animals via Pavlovian fear conditioning. The current study was designed
to evaluate basolateral amygdala complex involvement following the formation of
different fear memories (two contextual fear memories and one adjusted auditory
fear memory). Fear memories were created in the same context with five 1.0 mA
(0.50 s) foot-shocks and, where necessary, five auditory tones (5 kHz, 75 dB, 20 s).
The adjusted auditory fear conditioning protocol was employed to remove
background contextual fear and produce isolated auditory fear memories.
Immunofluorescent labelling was utilised to identify neurons expressing immediate
early genes. We found the two contextual fear conditioning procedures to produce
similar levels of fear-related freezing to context. Contextual fear memories produced
increases in basolateral amygdala complex immediate early gene expression with
distinct and separate patterns of expression. These data suggest contextual fear
memories created in slightly altered contexts, can produce unique patterns of
amygdala activation. The adjusted auditory fear conditioning procedure produced
memories to tone, but not to context. This group, where no contextual fear was
present, had a significant reduction in basolateral amygdala complex immediate early
gene expression. These data suggest background contextual fear memories, created
in standard auditory fear conditioning protocols, contribute significantly to increases
in amygdala activation.
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Keywords: Topography, Memory Allocation, Threat Conditioning, Lateral
Amygdala; Fear Conditioning, Immediate Early Genes.
3.2 Introduction
Pathological and persistent fear memories can rapidly form in humans
following exposure to trauma (Boschen, Neumann, & Waters, 2009; Michael S.
Fanselow, 2010; Maren, 2011). In animals, such as rodents, similar fear memories
can be experimentally generated by Pavlovian fear conditioning protocols (Foa,
Zinbarg, & Rothbaum, 1992; L. R. Johnson, J. McGuire, R. Lazarus, & A. A.
Palmer, 2012; Joseph E. LeDoux, 2014; Rothbaum & Davis, 2003). The neuronal
mechanisms of both contextual fear conditioning (CFC) and auditory fear
conditioning (AFC) have been, and continue to be deeply explored (for example, see
(Hadley C. Bergstrom, 2016; Michael S. Fanselow, 2010; Izquierdo, Furini, &
Myskiw, 2016; L. R. Johnson et al., 2012; Joseph LeDoux, 2012)). CFC and AFC are
both naturally occurring phenomenon which can also be replicated experimentally.
In the laboratory, noxious unconditioned stimuli (US), typically mild foot-shocks in
rodent research, are paired with specific contexts (for CFC), or contexts and
conditioned stimuli (CS) such as tones for AFC (Nicholas Chaaya, Battle, &
Johnson, 2018; L. R. Johnson et al., 2012; Joseph LeDoux, 2012). Numerous
investigations have shown abolishment, damage or inhibition of amygdala, either
permanently or temporarily, to result in attenuated or eradicated fear memories
(Michael S. Fanselow, 2010; J. LeDoux, 2000; Joseph LeDoux, 2003; Rudy, Huff, &
Matus-Amat, 2004). Our lab has previously identified specific sub-regions of the
amygdala responsible for auditory fear memory formation (H. C. Bergstrom & L. R.
Johnson, 2014; H. C. Bergstrom, C. G. McDonald, S. Dey, G. M. Fernandez, & L. R.
Johnson, 2013; H. C. Bergstrom et al., 2012; Hadley C Bergstrom et al., 2011). We
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showed CFC and AFC (H. C. Bergstrom et al., 2012) to result in different numbers
of amygdala neurons expressing phosphorylated mitogen-activated protein kinase
(pMAPK), a well-documented marker of neuronal plasticity (Giese & Mizuno, 2013;
S. Peng, Zhang, Zhang, Wang, & Ren, 2010). Data suggested lateral amygdala (LA)
to be essential for auditory fear memory formation, but not for contextual fear
memory formation (H. C. Bergstrom et al., 2012). More recently, we identified
unique and separate patterns of auditory and visual fear memory consolidation in the
amygdala (H. C. Bergstrom & L. R. Johnson, 2014). These studies provided
evidence into the organisation of differential fear memories in the rodent brain. The
current study is designed to further investigate the complex organisation of fear,
focusing on fears of context and auditory tone. Specifically, using novel modified
behavioural approaches, the differences in the neural organisation of contextual fear
memories (created by two separate conditioning protocols) and auditory fear
memories are evaluated here.
Contextual and auditory fear conditioning are often referred to as unsignaled
and signalled fear conditioning, respectively (Russel G Phillips & LeDoux, 1994).
Both fear conditioning protocols produce contextual fear memories (Lehmann,
Lacanilao, & Sutherland, 2007; Russel G Phillips & LeDoux, 1994). “Foreground”
contextual fear memories develop following unsignaled fear conditioning, whereas
“background” contextual fear memories develop following signalled fear
conditioning as the signal (tone) is now in the foreground (Russel G Phillips &
LeDoux, 1994). Evaluations of auditory (signalled) fear memories are, in fact,
evaluations of both (1) auditory fear and (2) background contextual fear (Nicholas
Chaaya et al., 2018). The current study is designed to evaluate amygdala
involvement following the creation of pure, context-removed, auditory fear
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memories. Rats in the AFC group underwent high exposure to the context prior to
conditioning. As per latent inhibition (LI), we hypothesised that prior exposure to the
fear conditioning context would lead to the development of an attenuated, or even
abolished, background contextual fear memory (R. Lubow, Rifkin, & Alek, 1976; R.
E. Lubow, 1965; R. E. Lubow & Moore, 1959). The ability for prior context
exposure to abolish foreground contextual fear memories has been demonstrated
previously (Barot, Chung, Kim, & Bernstein, 2009). By exposing rodents in the AFC
group to the conditioning chamber for 30 minutes a day for 10 days prior to
conditioning, the current study aimed to create pure, context-removed, auditory fear
memories (Barot et al., 2009).
Context and spatial memory research has delineated the influence of single
discrete stimuli (e.g. sound or smell) on the brain (M. I. Anderson & Jeffery, 2003;
D. M. Smith & Mizumori, 2006a, 2006b; Song, Kim, Kim, & Jung, 2005). Inclusion
of single new elements into a context can directly alter hippocampal place cell
activity (M. I. Anderson & Jeffery, 2003). Similarly, presentation of a single new
element (such as an acoustic stimulus) can alter amygdala activity (Romanski,
Clugnet, Bordi, & LeDoux, 1993). Unpaired fear conditioning (UFC) protocols are
identical to standard CFC protocols except for the addition of a single new discrete
element (in many cases, a tone). Following UFC, rodents develop fear memories to
the conditioning context, but not the auditory tone (H. C. Bergstrom et al., 2012).
Therefore, UFC protocols offer a method to evaluate how alterations to the fear
conditioning context alter contextual fear related behaviour and anatomy.
Traditionally, UFC protocols have been utilised as control conditions to paired or
signalled fear conditioning (H. C. Bergstrom et al., 2012; Hadley C Bergstrom et al.,
2011; Majak & Pitkänen, 2003; McKernan & Shinnick-Gallagher, 1997; Radley et
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al., 2006; Michael T. Rogan, Staubli, & LeDoux, 1997). However, the ability for
UFC to produce associative fear memories to context, as demonstrated previously
(H. C. Bergstrom et al., 2012; Hadley C Bergstrom et al., 2011), suggests activation
of amygdala likely occurs. This was demonstrated by Trifilieff and colleagues, who
showed UFC to produce greater amygdala-related plasticity as compared to AFC
(Trifilieff, Calandreau, Herry, Mons, & Micheau, 2007). The current study,
therefore, evaluates if, and how, UFC differs from standard CFC and context-
removed AFC.
The objectives of the current study were to explore the complex neural
organisation of contextual fear memories (created via a standard CFC or an UFC
protocol) and context-removed auditory fear memories in the rodent basolateral
amygdala complex (BLC). While these fear memories were created with a consistent
noxious unconditioned stimulus (US; foot-shocks), the neutral conditioned stimulus
(CS) differs. Specifically, the CS is the context (CFC), the context and tone (UFC) or
just the tone with context removed (adjusted AFC; AAFC). The current study
explored the number of neurons expressing the immediate early genes (IEGs) activity
regulated cytoskeleton-associated protein (Arc) and c-Fos. Co-localisation of these
two IEGs was also explored to determine if a separate distribution of neurons express
both markers following fear memory formation. In order to obtain a large
representation of the BLC, comparisons between IEG expression were conducted at
four accurately aligned Bregma coordinates (see (H. C. Bergstrom et al., 2012;
Hadley C Bergstrom et al., 2011; A Jacques et al., 2018)). We focus on consistent
differences between conditioned groups as compared to two control groups: a context
only (CO) control and a tone alone (TA) control. We found our adjusted AFC
protocol to produce a reduced pattern of Arc and c-Fos expressing neurons as
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compared to the pattern evident in rats that developed contextual fear memories
either via a CFC or UFC protocol. Furthermore, contextual fear memories created
with CFC lead to a different pattern of IEG expression in amygdala when compared
to those created by UFC.
3.3 Experimental Procedures
3.3.1 Animals
Experimentally naïve adult male Sprague Dawley Rats (Animal Resources
Centre, WA, Australia) weighing between 176-200 g at the time of arrival were
housed (two per cage) on a 12-hour light/dark cycle by the University of Queensland
Biological Resources (UQBR) facility. Food and water were provided ad libitum. All
procedures were approved by the University of Queensland (Ethics approval no.
023/17) and Queensland University of Technology (QUT approval number:
1700000295) animal ethics committees and complied with policies and regulations
regarding animal experimentation and other ethical matters, in accordance with the
Queensland Government Animal Research Act 2001, associated Animal Care and
Protection Regulation (2002 and 2008), as well as the Australian Code for the Care
of Animals for Scientific Purposes, 8th Edition (National Health and Medical
Research Council, 2013). Rats were acclimatised to the UQBR Facility for 8 days,
and then underwent 10 days of handling or habituation prior to fear conditioning.
Rats (weighing 324.8 ± 2 g at the time of training) were divided into five separate
groups, three experimental (Contextual Fear Conditioned; CFC n = 18, Adjusted
Auditory Fear Conditioned; AAFC n = 18 and Unpaired Fear Conditioned; UFC n =
18) and two control (Tone Alone; TA n = 18 and Context Only; CO n = 18) groups.
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Following fear conditioning, rats were further divided into an anatomical (n = 12 per
group) and a behavioural (n = 6 per group) group.
3.3.2 Apparatus
Rats were exposed to two Plexiglas conditioning chambers (Coulbourn
Instruments, Lehigh Valley, Pennsylvania, USA). Both chambers (context A and B)
were dimly illuminated with a single house light (2 – 3 lux), sound insulated
(background dB = 55), equipped with a speaker and contained an infrared camera.
Context A was fitted with a metal grid floor which was connected to a shock
generator. Context A was undecorated and cleaned with ethanol (EtOH) 80%
following presentation of each rat. Context B had a flat floor, coloured decoration,
alterations to the roof to change its physical dimensions, and was lightly covered
with bedding. Following the presentation of each rat, context B was cleaned with
orange scented hand soap and the bedding replaced.
3.3.3 Procedure and Design
Behavioural procedures are outlined in Figure 1 and explained in detail below.
Figure 3-1: Experimental design for behavioural training.
Following acclimatisation, rats in the AAFC group underwent nine days of habituation to context A, while rats in all other groups were handled. Following, all rats were habituated to the same context for one day. On day 11, rats underwent their respective conditioning procedures, and then were either perfused 90 minutes later (anatomical rats) or provided with a fear memory test (behavioural rats) to context 24 hours later, and then to tone three days later. CFC: contextual fear conditioning; UFC: unpaired fear conditioning; TA: tone alone; CO: context only; AAFC: adjusted auditory fear conditioning; FMT: fear memory test.
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3.3.4 Acclimatisation and Habituation
All rats had eight days of acclimatisation to the vivarium prior to behavioural
procedures. Rats in all groups, except the AAFC group, were handled for nine days,
and then habituated to context A (30 minutes per rat) for one day. Alternatively, rats
in the AAFC group were habituated to context A for 10 days (30 minutes each). This
adjustment was designed to induce latent inhibition to the fear conditioning context.
Previous investigations into latent inhibition suggest that 10 days is sufficient (Barot
et al., 2009).
3.3.5 Fear Conditioning
One day following habituation, rats in the CFC group were placed into context
A and presented with five non-overlapping and random shocks to the foot (1.0 mA,
0.50 s). Rats in the AAFC group were placed into context A and presented with five
pairings of an auditory tone (5 kHz, 75 dB, 20 s) that co-terminated with the foot-
shocks (1.0 mA, 0.50 s). Rats in the UFC group were placed into context A and
presented with five non-overlapping presentations of the same auditory tones (5 kHz,
75 dB, 20 s) and foot-shocks (1.0 mA, 0.50 s). Rats in the TA control group were
placed into context A presented with five non-overlapping and random auditory
tones (5 kHz, 75 dB, 20 s). Rats in all groups were permitted to explore the context
for 180 seconds prior to the presentation of any stimuli. Rats were allowed 60
seconds following the presentation of the final stimulus before being removed and
returned to their home-cage. Rats in the CO control group were left to explore
context A without any added stimuli. Fear conditioning procedures were 660 seconds
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long for all groups except the UFC group, which lasted 880 seconds to account for
the non-overlapping foot-shocks and tones.
3.3.6 After Fear Conditioning
Behavioural group
Rats in the behavioural group (n = 30) had freezing behaviour manually scored
during training (fear conditioning) and testing (fear memory test; FMT). To avoid
inaccuracies that may occur from manual counting, and to remain consistent with
previous investigations, scoring occurred in 20 second blocks (H. C. Bergstrom & L.
R. Johnson, 2014; H. C. Bergstrom et al., 2012; R. G. Phillips & LeDoux, 1992;
Russel G Phillips & LeDoux, 1994; Gregory J. Quirk, Armony, & LeDoux, 1997;
Radley et al., 2006). During training, freezing was scored prior to (base line), during
(at cue 1, cue 2, cue 3, cue 4 and cue 5), and following fear conditioning (final). This
provided a progressive measure of fear. Immediately following fear conditioning, all
rats in the behavioural group were returned to their home-cage for 24 hours. Rats
were then re-exposed to context A for a 10 minute FMT to context. Neither foot-
shock nor auditory tones were presented during this time. Freezing was scored for
the final 20 seconds of every minute that rats were in context A. Rats were
immediately returned to home-cages for an additional three days and then re-exposed
to context B for a 10 minute FMT to tone. During this time, 10 of the same auditory
tones (5 kHz, 75 dB, 20 s) used for fear conditioning were presented in the final 20
seconds of every minute. Freezing behaviour was scored during this time.
Scoring of freezing
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Freezing behaviour was defined as the inhibition, absence or suppression of
movement, except that necessary for autonomic nervous system functioning, such as
heart rate and respiration (Michael S. Fanselow, 1980). Head scanning and sleeping
were not included as freezing, however, heavy breathing and minimal movement
was. During training, freezing behaviour was scored in the final 20 seconds of the
first minute, in the final 20 seconds of the last minute, and the 20 seconds prior to
each foot-shock (or equivalent time-point for control groups). For rodents in the
AAFC and TA groups, this 20 second period is during tone presentation.
Alternatively, for rodents in the CFC and CO, this 20 second period occurred at the
same time-point, but without the presentation of a tone. Similarly, rodents in the
UFC had freezing scored during the 20 second time point prior to receiving foot-
shocks, as opposed to the 20 second time-point at which auditory tones are presented,
as the development of contextual fear memories is of interest here. During testing,
freezing behaviour was scored in the final 20 seconds of every minute. This was
done in order to account for extinction learning that naturally occurs following
exposure to fear conditioned stimuli (Maren, Phan, & Liberzon, 2013). For rodents
that received CFC and UFC (thus developing contextual fear memories), extinction
learning begins immediately upon re-exposure to context A, and continues until
rodents are removed from said context. This results in the gradual reduction of fear
(Bouton, 1988; Bouton, 2004). Alternatively, during the FMT to tone (in context B),
extinction learning will not occur for rodents that have auditory fear memories
(AAFC group) until they are presented with the tone. Therefore, in an attempt to
equalize extinction during the FMT to context in rodents with contextual fear
memories and during the FMT to tone in rodents with auditory fear memories, the
auditory tone was presented ten times. To obtain an accurate and full measure of
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fear, freezing was scored during each of these presentations, or at identical time-
points during the FMT to context (H. C. Bergstrom et al., 2012; Hadley C Bergstrom
et al., 2011).
Anatomical group
Following fear conditioning, all rats in the anatomical group (n = 60) were
removed from the fear conditioning chamber and placed in their home cages. Ninety-
minutes following, rats were anesthetised and sacrificed via perfusion for double
fluorescent labelling.
3.3.7 Arc and c-Fos Fluorescent Immunohistochemistry
Tissue preparation
Arc and c-Fos protein expression have been shown to peak between 60 and 180
minutes following learning (Ivashkina, Toropova, Ivanov, Chekhov, & Anokhin,
2016; Lonergan, Gafford, Jarome, & Helmstetter, 2010; Morgan & Curran, 1991;
Ramírez-Amaya et al., 2005). We, therefore, aimed for a 90 minute timeframe.
Intraperitoneal (i.p.) injections of Ketamine/Xylazine (100 mg/kg, 10 mg/kg) were
administered. Once anaesthetised, rats were transcardially perfused via the ascending
aorta with ice-cold saline (200 mL per rat), followed by 4% paraformaldehyde/0.1 M
phosphate buffer (PB; pH of 7.4; 400 mL per rat). Brains were subsequently removed
and stored in the paraformaldehyde fixative for 24 hours (4 oC), and then stored in
phosphate buffered saline (PBS)/0.02% Azide for a minimum of three days. Free-
floating sequential coronal brain sections containing the amygdala were sliced on a
vibratome (M11000; Pelco easiSlicer, Ted Pella Inc, CA, USA) at 40 μm per section.
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Left hemisphere sections were stored in PBS/0.02% Azide (4 oC) until
immunohistochemistry commenced.
Immunohistochemistry
Brain sections were removed from PBS/0.02% Azide and washed thoroughly
in PBS at room temperature. All further washes and incubations were conducted at
room temperature. To begin, sections were permeabilised with 1% Triton/0.1%
Tween 20 in PBS for one hour. Sections were washed with PBS, and then blocked
with 0.3% Triton/0.05% Tween 20/3% NGS/1% BSA in PBS for one hour. Blocking
solution was removed, and sections were immediately incubated in a c-Fos (9F6)
rabbit monoclonal antibody (1:300; Cell Signalling Technology, MA, USA) and an
Arc (C-7) mouse monoclonal antibody (1:300; Santa Cruz Biotechnology, TX, USA)
in blocking solution for 24 hours. Brain sections were washed in blocking solution
and immediately incubated in a pre-adsorbed goat anit-rabbit IgG H&L (Alexa Fluor
594) secondary antibody (1:500; Abcam, VIC, Aus) and a pre-adsorbed goat anit-
mouse IgG H&L (Alexa Fluor 488) secondary antibody (1:500; Abcam, VIC, Aus)
in blocking solution for 30 minutes. Brain sections were then washed in blocking
solution, then PBS, and mounted in numerical order on silane coated slides. Mounted
sections were immediately cover-slipped, left to dry and stored at 4 oC.
Brain Scans
Following immunohistochemistry, cover-slipped brain sections were scanned
using the InCell Analyser 2200 (GE Healthcare Life Sciences, NSW, Aus), provided
by Translational Cell Imaging Queensland (TCIQ). The InCell Analyser 2200 is an
imaging system capable of producing confocal-like images of up to two colour
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channels. The InCell Analyser 2200 was set up to obtain 10x magnified consecutive
fields of the amygdala (15% overlap, horizontal acquisition pattern, 10% laser
power) in FITC (Arc protein) and Cy3 (c-Fos protein) channels. The InCell Analyser
2200 does not stitch or merge channels. Individual images are provided per scan
area, with an associated .xdce file. Channels were manually merged in Fiji
(Schindelin et al., 2012) by opening the images via the .xdce file in the Bio-Formats
Import Option. Individual merged images were subsequently stitched (Preibisch,
Saalfeld, & Tomancak, 2009) using Fiji.
Amygdala Brain Section Alignment and Neuron Quantification
Neuron counting was conducted blind to the experimental conditions.
Aligned Bregma coordinates -3.36 mm, -3.24 mm, -3.12 mm and -3.00 mm were
counted. At these levels, the BLC is well represented (Paxinos & Watson, 2006).
Merged and stitched files were imported into Neurolucida 360 (Neurolucida 360,
MBF Bioscience, VT). To separate the various subregions of the BLC, a contour of
the BLC was created using Neurolucida trace option. Subregions (see Figure 2) were
as follows: dorsolateral portion of the lateral amygdala (LaDL); ventromedial portion
of the lateral amygdala (LaVM); ventrolateral portion of the lateral amygdala
(LaVL); anterior portion of the basal amygdala (BLA); and posterior portion of the
basal amygdala (BLP). The resulting contour was scaled and superimposed on
imported amygdala images. Precise stereotaxic alignment is required to accurately
analyse neurons expressing Arc and c-Fos protein. The lateral ventricle (LV) is a
major anatomical landmark which changes rapidly at different rostral to caudal
locations. The LV becomes noticeable at Bregma coordinate -3.32, and consistently
increases in size towards more caudal locations. Bregma coordinate -3.36 mm in the
Rat Brain Atlas depicts the LV as a noticeable tear-drop size, allowing for easy
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identification and matching in all rats (Paxinos & Watson, 2006). Bregma coordinate
-3.36 mm was identified in all rats by this anatomical landmark. Preceding Bregma
coordinates were identified by measuring back from Bregma coordinate -3.36 mm.
Anatomical landmarks were utilised to accurately and consistently align a contour
across all rats. These anatomical landmarks were the LV (at Bregma coordinate -3.36
mm only), rhinal fissure (RF), dorsal endopiriform nucleus (DEn), central amygdala
(CeA), stria terminalis (st) and the optic tract (opt; see Figure 2).
Figure 3-2: Schematic illustration of aligned sections.
Bregma coordinate -3.36 mm was identified following delineation of the LV. Following, Bregma coordinates -3.24 mm to -3.00 mm were identified by measuring back from Bregma coordinate -3.36 mm. To accurately align stencils onto brain images, the following anatomical landmarks were utilised (shaded grey in Bregma coordinate -3.36 mm): opt, st, central amygdala, DEn and RF and the LV in Bregma coordinate -3.36 mm. Neurons expressing the IEGs Arc and c-Fos were quantified in sub-regions (outlined in all Bregma coordinates above) LaDL, LaVM, LaVL, BLA and BLP (Paxinos & Watson, 2006). LaDL: dorsolateral portion of the lateral amygdala; LaVM: ventromedial portion of the lateral amygdala; LaVL: ventrolateral portion of the lateral amygdala; BLA: anterior portion of the basal amygdala; BLP: posterior portion of the basal amygdala; RF: rhinal fissure; Den: dorsal endopiriform nucleus; CeA: central amygdala; st: stria terminalis; opt: optic tract.
Neuron Counting and Identification
Neurons were manually tagged and counted using Neurolucida 360. Neurons
within each subregion were tagged with a different colour. Arc protein expression is
present in neuronal cell bodies and dendrite (Lyford et al., 1995), while c-Fos protein
is only expressed in the nucleus (Curran, Miller, Zokas, & Verma, 1984; Morgan,
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Cohen, Hempstead, & Curran, 1987). Therefore, Arc expressing neurons were
counted if they met the following criteria: triangular or oval shape with a clear
neuronal form, significantly more perceptible compared to the background and
containing at least one dendritic protrusion per cell body (see Figure 3a). c-Fos
expressing neurons were counted if they met the following criteria: clear circular
shape without a neuronal form, significantly more perceptible compared to
background, with no dendritic protrusions (see Figure 3b). Arc expressing and c-Fos
expressing neurons were counted separately. Co-localisation (see Figure 3c) was
obtained using the co-localisation option in Neurolucida 360. Correcting for double
counting was not necessary as sections were non-consecutive.
Figure 3-3: Arc, c-Fos and co-localised labelling of amygdala neurons.
A Arc expressing neurons are represented in green. B c-Fos expressing neurons are represented in red. C Co-localised expressing neurons are represented in yellow. Green arrows indicate single labelled Arc expressing neurons; red arrows indicate single labelled c-Fos expressing neurons; and yellow arrows represent co-localised expression of both Arc and c-Fos.
Raw Neuron Topographic Density Maps (Heat Maps)
Neuron topographic density maps, known as heat maps, allow for the
visualization of neuronal densities in particular brain regions, with neuron densities
depicted in increasingly ‘hot’ (blue to red) colors. In order to build heat maps, data
regarding XY coordinates of individual neurons were provided by Neurolucida. XY
coordinates of neurons were binned by Origin (2018, OriginLab, Northampton, MA).
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Each bin contains data regarding the number of Arc, c-Fos and co-expressing
neurons. Spatial dimension of each bin was 100m x 100m. Consequently, each bin
represents the same portion of amygdala. Values within each bin (average number of
neurons activated within each bin) were represented by different colors (SigmaPlot v
12.5, Systat Software, San Jose, CA), allowing for the creation of the heat maps (for
further details on specific methodology used see (H. C. Bergstrom et al., 2012;
Hadley C Bergstrom et al., 2011; A Jacques et al., 2018))
Data Analysis
Anatomical data from the current experiment explores the difference between
behavioural groups at different (1) BLC sub-regions, (2) Bregma coordinates and (3)
IEG marker. Focus of analyses was dedicated to the differences between behavioural
groups, ANOVAs were utilised to compare differences between behavioural groups
at individual IEG marker, individual sub-region and individual Bregma coordinate
(e.g. difference in Arc expressing neurons between behavioural groups in the LaDL
of Bregma coordinate -3.36 mm). This method of analysis captures differences
following the various forms of conditioning, which is the focus of this study. Prior to
analysis, normality and homogeneity of variance were tested for. Normality was
found to be present in almost all cases, with some breaches noted in subregions BLA
and BLP. As these subregions are not the focus of this paper, analyses continued
with the normality assumption confirmed. Homogeneity of variance was found to be
breached on several occasions – suggesting that inflation of type I errors is possible.
Non-parametric tests are designed to account for these breaches by reducing the
possibility of producing type I errors. Similar to non-parametric tests the Bonferroni
adjustment accounts for type I errors that may arise for other reasons, namely,
multiple comparisons. The data reported here includes both unequal variances and
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multiple comparisons. Nevertheless, we used the Bonferroni correction to account
for both unequal variance and multiple comparisons. This is because non-parametric
tests are strongly recommended for breaches of normality, but are less effective
against breaches of homogeneity (Zimmerman & Zumbo, 1993). Similar to
parametric tests, non-parametric tests can be sensitive to unequal variance, unequal
sample size and outliers (Parra-Frutos, 2013; Zimmerman & Zumbo, 1993).
Alternatively, the Bonferroni correction is robust and effective – and is often cited to
be too severe, thus producing type II errors (Perneger, 1998). For these reasons, we
believe that the level of severity employed by the Bonferroni adjustment accounted
for all possible inflations in type I errors that may have arose (unequal variance and
multiple comparisons). Therefore, all behavioural and anatomical analyses were
conducted using one-way ANOVAs, followed by Bonferroni-corrected post-hoc
tests. All values embedded in the text are expressed as the mean +/- standard error of
the mean. P values at or below 0.05 were considered statistically significant. All
major statistical analysis was conducted using the Statistical Package for the Social
Sciences (SPSS) v22 software (IBM Corporation, NY, USA). GraphPad Prism v7
software (GraphPad, CA, USA) was utilised to create graphs and identify outliers.
Throughout the results section asterisks denote levels of statistical significance (* p ≤
0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001).
Excluded Cases
Anatomical cases were excluded if they served as statistical outliers, or had
significant damage to the brain tissue, which occurred either during the perfusion,
labelling or cover slipping process. Statistical outliers were identified in GraphPad
Prism using the ROUT method, with the maximum false discovery rate set at 1%.
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The ROUT method was used as SPSS does not provide an automatic test for outlier
identification. The ROUT method – as compared to the alternate option in GraphPad:
Grubb’s test – is effective at identifying multiple outliers in large datasets. Outlier
analysis was conducted on individual groups. Identified outliers were excluded on a
pairwise basis. Ten cases were excluded on a list wise basis due to significant
damage to brain tissue. Behavioural cases were excluded if they served as a
statistical outlier (identified using the ROUT method). Further, one behavioural rat
was interrupted during the FMT to tone by the fire alarm. Fear to tone data from this
rat was excluded.
3.4 Results
3.4.1 Behavioural Results
The amount of time rats displayed freezing behaviour was quantified during
training and testing. During training, the mean amount of time (as a percentage) rats
displayed freezing behaviour was quantified as a function of condition and time-
point (baseline, cue 1, cue 2, cue 3, cue 4, cue 5 and final). A two-way mixed design
ANOVA was conducted with condition as the between-subjects factor and time-point
as the within-subjects factor. Mauchly’s test of sphericity was breached, therefore
corrected results from the Greenhouse-Geisser test is reported. The Greenhouse-
Geisser test revealed a significant interaction of freezing behaviour as a function of
condition and time-point F(10.103, 45.466) = 8.921, p < 0.0001. The main effects for
condition (F[4, 18] = 24.424, p < 0.0001) and time-point (F[2.526, 45.466] = 46.573,
p < 0.0001) were significant. Bonferroni correct post-hoc tests (see Figure 4a)
revealed no differences between conditions at baseline, cue 1 or cue 2. At cue 3,
differences between conditions become apparent and significant between rats that
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received CFC (freezing response was 73%) and UFC (69%) as compared to the TA
(0%) group. At cue 4 rats that received CFC (99%), UFC (71%) and AAFC (85%)
exhibited significantly more freezing as compared to both the TA (0%) and CO (7%)
controls. This significant difference between fear conditioned and control groups
remained present at cue 5 and at the final time-point, indicating progressive
development of fear-learning. as a function of condition.
One-way ANOVA of freezing data obtained from the FMT to context revealed
significant differences between groups as seen, F(4, 25) = 20.899, p > 0.0001.
Bonferroni corrected post-hoc tests revealed rats that underwent CFC (freezing
response was 71%) and UFC (56%) exhibited significantly more freezing as
compared to those that underwent AAFC (21%) during the FMT to context. Further,
those that underwent CFC and UFC exhibited significantly more freezing than the
TA (6%) or CO (3%) controls. No further differences between groups were observed
(see Figure 4a). One-way ANOVA of freezing data obtained from the FMT to tone
revealed significant differences between groups as seen, F(4, 24) = 9.113, p > 0.001.
Bonferroni corrected post-hoc tests showed rats that underwent AAFC (49%)
exhibited significantly more freezing as compared to those in all other conditions
(CFC = 11%; UFC = 10%; TA = 1%; CO = 0.3%). No further differences between
groups were observed (see Figure 4b). These data confirm the behavioural
procedures employed in this experiment successfully create fear memories. Further,
these data suggest AAFC, where rodents underwent extreme prior exposure to the
context, significantly attenuated background contextual fear memories, while leaving
auditory fear memories intact.
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Figure 3-4: Freezing to context and tone.
A Freezing behaviour was scored during training to obtain a progressive measure of fear learning. For rodents in the AAFC and TA group, this 20 second period occurred during auditory tone presentation. For rodents in the CFC and CO group, this 20 second period occurred at the same time-point, but without tone presentation. Alternatively, rodents in the UFC had freezing scored during the 20 second time point prior to receiving foot-shocks, as opposed to the 20 second time-point at which auditory tones were presented (as this group develops contextual fear memories, not auditory fear memories). Analysis of mean freezing during training showed that rats in the CFC, UFC and AAFC groups exhibited equivalent freezing to rats in the CO and TA control groups at baseline, cue 1 and cue 2. At cue 3, differences between conditions become apparent and significant between rats that received CFC and UFC as compared to the TA (CFC *, p < 0.05; UFC *, p <0.05) group. At cue 4 rats that received CFC, UFC and AAFC exhibited significantly more freezing as compared to both the TA (CFC ****, p < 0.0001; UFC **, p < 0.01; AAFC ***, p < 0.001) and CO (CFC ****, p < 0.0001; UFC **, p < 0.01; AAFC ***, p < 0.001) controls. This significant difference between fear conditioned and control groups remained present at cue 5 (all differences between the conditioned and control groups were ****, p <0.0001) and at the final time-point (all differences between the conditioned and control groups were ****, p <0.0001). Symbols in figure denote level of statistical significance between average (of all time points) freezing behaviour between all fear conditioned groups compared to TA (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001) and CO (‡ p ≤ 0.05; ‡‡ p ≤ 0.01; ‡‡‡ p ≤ 0.001; ‡‡‡‡ p ≤ 0.0001) control groups B One day post fear conditioning, all rats were re-exposed to context A. Rats that underwent CFC or UFC had significantly more freezing as compared to controls, indicating that these fear conditioning protocols reliably created contextual fear memories. Rats that underwent AAFC did not exhibit any statistical differences in freezing as compared to controls, suggesting a loss of background contextual fear memories. Asterisks denote levels of statistical significance between groups (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001). C Three days following, all rats were exposed to a new context and provided with auditory tones. Rodents that underwent AAFC had significantly more freezing to tone as compared to all other groups. This suggests that only these rodents developed auditory fear memories. Asterisks denote levels of statistical significance between groups (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001). CFC: contextual fear conditioning; UFC: unpaired fear conditioning; AAFC: adjusted auditory fear conditioning; TA: tone alone; CO: context only.
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3.4.2 Anatomical Results
The following section is divided by Bregma coordinate. We focus on
statistical significance between the experimental groups (CFC, AAFC or UFC) to the
control groups (TA or CO). Importance is attributed to significant differences when
compared to both controls, as opposed to just one. Patterns of consistent statistical
significance between behavioural groups are of interest. Differences between said
behavioural groups identified between multiple Bregma coordinates and identified
with multiple IEG markers are most important as they represent clear and stable
involvement of a particular subregion to a behavioural condition. Investigations
revealed little consistent differences between rats that were conditioned and controls
in subregions BLA and BLP. Therefore, while some consistent differences are noted
in text and heat maps include these sub-regions, they are excluded in graphs.
IEG Expression at Bregma Coordinate -3.36 mm
To determine involvement of BLC in CFC, AAFC and UFC, one-way
ANOVAs were conducted. ANOVAs revealed group differences to exist in Arc
expressing neurons in LaDL (F[4, 41] = 5.453, p < 0.01) , LaVM (F[4, 37] = 6.575,
p < 0.001) and LaVL (F[4, 37] = 2.879, p < 0.05), but not BLA (F[4, 42] = 1.853, p
= 0.137) or BLP (F[4, 39] = 1.111, p = 0.365). Group differences were consistently
observed in all BLC subregions in c-Fos expressing neurons (LaDL F[4, 41] = 7.888,
p < 0.0001; LaVM F[4, 43] = 7.048, p < 0.001; LaVL F[4, 41] = 7.845, p < 0.0001;
BLA F[4, 43] = 4.7335, p < 0.01; BLP F[4, 41] = 6.956, p < 0.001) as well as co-
expressing neurons (LaDL F[4, 43] = 7.088, p < 0.001; LaVM F[4, 42] = 7.080, p <
0.001; LaVL F[4, 41] = 6.485, p < 0.00; BLA F[4, 40] = 7.749, p < 0.001; BLP F[4,
37] = 3.225, p < 0.05). Bonferroni corrected post-hoc tests (see Figure 5) revealed
those that underwent CFC expressed significantly more Arc, c-Fos and co-expressing
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neurons in LaDL as compared to both controls. Comparatively, those that underwent
UFC had consistent significant increases in Arc, c-Fos and co-expressing neurons in
LaDL and LaVM. LaVL c-Fos and co-localised, but not Arc, expression was
significantly higher in those that underwent UFC as compared to controls. Further
differences were present in BLA for c-Fos and co-expressing neurons (data visually
present in heat maps). No significant differences were observed between those that
underwent AAFC as compared to both controls.
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Figure 3-5: Immediate early gene expression in Bregma coordinate -3.36mm.
A Arc expression in LaDL was significantly higher in rats that underwent CFC as compared to both controls. Rats that underwent UFC exhibited similar differences as compared to both controls in LaDL. Further differences were noted in those that underwent UFC as compared to controls in subregion LaVM, but not LaVL. No differences were identified in those that underwent AAFC as compared to controls in any lateral amygdala subregion. B Examination of c-Fos expression revealed significant increases in rats that underwent CFC and UFC as compared to both controls in subregion LaDL. Further, those that underwent CFC exhibited a significant increase as compared to the CO group in subregion LaVM, whereas those that underwent UFC exhibited further differences as compared to controls in subregions LaVM and LaVL. No differences were observed in those that underwent AAFC. C Co-localised neuron expression revealed similar differences in rats that underwent CFC and UFC as compared to both controls in subregion LaDL. Those that underwent UFC exhibited further differences as compared to controls in subregions LaVM and LaVL. Once
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again, no differences were observed in those that underwent AAFC. D Frequency of significant differences as compared to control groups in those that underwent CFC, UFC or AAFC. Six significant differences per subregion can exist: three IEG markers and two controls. As seen, those that underwent UFC had the highest number of significant differences as compared to controls in all three LA subregions. Rats that underwent CFC also had many significant differences, mostly contained to LaDL, whereas rats that underwent AAFC had no significant differences. Density heat maps visually depict mean Arc (E), c-Fos (F) and co-expression (G) in all five behavioural groups. Asterisks denote levels of statistical significance (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001). CFC: contextual fear conditioning; UFC: unpaired fear conditioning; AAFC: adjusted auditory fear conditioning; TA: tone alone; CO: context only; LaDL: dorsolateral portion of the lateral amygdala; LaVM: ventromedial portion of the lateral amygdala; LaVL: ventrolateral portion of the lateral amygdala.
IEG Expression at Bregma Coordinate -3.24 mm
Immediate early gene expression in Bregma coordinate -3.24 mm was
compared across the five behavioural groups. Similar to above, ANOVAs were
utilised to reveal group differences in Arc expressing neurons in subregion LaDL
(F[4, 42] = 4.526, p < 0.01), LaVM (F[4, 41] = 3.762, p < 0.01) and LaVL (F[4, 40]
= 3.36 mm, p < 0.05), but not BLA (F[4, 42] = 1.021, p = 0.408) or BLP (F[4, 42] =
0.686, p = 0.606). Alternatively, significant group differences were observed in all
BLC subregions in c-Fos (LaDL F[4, 42] = 3.061, p < 0.05; LaVM F[4, 41] = 4.734,
p < 0.01; LaVL F[4, 41] = 6.587, p < 0.001; BLA F[4, 40] = 4.873, p < 0.01; BLP
F[4, 41] = 2.642, p < 0.05) and co-expressing neurons (LaDL F[4, 40] = 5.901, p <
0.001; LaVM F[4, 41] = 6.017, p < 0.001; LaVL F[4, 40] = 7.374, p < 0.001; BLA
F[4, 40] = 4.843, p < 0.01; BLP F[4, 40] = 3.451, p < 0.05). Bonferroni correct post-
hoc tests (see Figure 6) revealed group differences to primarily exist between UFC
rodents and the two controls. Some differences existed between CFC rodents and
controls in subregion LaVM, and between AAFC and controls in LaVM and LaVL.
Minor differences in subregion BLA between UFC and controls should be noted,
along with smaller differences in subregion BLP (data visually present in heat maps).
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Figure 3-6: Immediate early gene expression in Bregma coordinate -3.24mm.
A Arc expression in LaDL revealed consistent significant differences between rats that underwent UFC as compared to both controls. Further differences were observed in this group in subregions LaVM and LaVL when compared to the TA control. No differences were identified in those that underwent CFC or AAFC as compared to controls in any lateral amygdala subregion. B Examination of c-Fos expression in LaDL revealed no differences in any groups. Alternatively, LaVM expression was significantly higher in all three conditioning groups as compared to the TA control. LaVL specific c-Fos expression was higher in the UFC as compared to both controls, and in the AAFC as compared to the TA control. C Co-localised neuron expression revealed consistent differences in rats that underwent UFC as compared to both controls in subregion LaDL, LaVM and LaVL. Those that underwent CFC exhibited an increase as compared to the TA control in subregions LaVM D As seen, the frequency of significant differences in the LA in those that underwent UFC was highest. Rats that
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underwent CFC and AAFC had some differences in LaVM (both groups) and LaVL (for AAFC group only) as compared to controls. Density heat maps visually depict mean Arc (E), c-Fos (F) and co-expression (G) in all five behavioural groups. Asterisks denote levels of statistical significance (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001). CFC: contextual fear conditioning; UFC: unpaired fear conditioning; AAFC: adjusted auditory fear conditioning; TA: tone alone; CO: context only; LaDL: dorsolateral portion of the lateral amygdala; LaVM: ventromedial portion of the lateral amygdala; LaVL: ventrolateral portion of the lateral amygdala.
IEG Expression at Bregma Coordinate -3.12 mm
Analysis of Bregma coordinate -3.12 mm revealed significant group
differences in Arc expressing neurons in BLC subregions LaDL (F[4, 40] = 6.505, p
< 0.001), LaVM (F[4, 38] = 7.354, p < 0.001), LaVL (F[4, 39] = 3.457, p < 0.05)
and BLA (F[4, 40] = 2.606, p = 0.05), but not BLP (F[4, 40] = 1.965, p = 0.118). In
c-Fos expressing neurons, significant group differences were observed in subregions
LaDL (F[4, 40] = 5.291, p < 0.01), LaVM (F[4, 41] = 4.843, p < 0.01), LaVL( F[4,
41] = 6.481, p < 0.001) and BLP (F[4, 41] = 2.522, p = 0.056), but not BLA (F[4,
41] = 3.542, p < 0.05 ). Alternatively, in co-expressing neurons, significant group
differences existed in all BLC subregions (LaDL F[4, 39] = 6.913, p < 0.001; LaVM
F[4, 39] = 10.411, p < 0.00001; LaVL F[4, 40] = 6.953, p < 0.001; BLA F[4, 39] =
7.997, p < 0.0001; BLP F[4, 38] = 3.941, p < 0.01). Bonferroni corrected post-hocs
(see Figure 7) revealed these differences to, once again, exist primarily between
rodents that underwent UFC as compared to controls. These differences were also
noted (albeit less consistently) in subregion BLA and BLP (data presented visually in
heat maps). Some minor differences were observed between those that underwent
CFC as compared to controls in subregion LaVM. Further, some differences were
present in rodents that underwent AAFC in LaDL (as compared to one control in c-
Fos expressing neurons) and LaVM (as compared to both controls in c-Fos
expressing neurons). Consistent differences were noted in those that underwent
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AAFC in subregion LaVL when compared to both controls in c-Fos and co-
expressing neurons.
Figure 3-7: Immediate early gene expression in Bregma coordinate -3.12mm.
A Arc expression in LaDL and LaVM was significantly higher in rats that underwent UFC as compared to both controls. Furthermore, Arc expression was significantly higher in this group in subregion LaVL when compared to the TA control. B c-Fos expression in LaDL was significantly higher in the UFC and AAFC as compared to the CO control. Alternatively, LaVM expression was
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significantly higher in the AAFC conditioning group as compared to both controls, while LaVL specific c-Fos expression was higher in the UFC and AAFC as compared to both controls. C Co-localised neuron expression was higher in rats that underwent UFC as compared to both controls in subregions LaDL, LaVM and LaVL. In addition, those that underwent CFC exhibited an increase as compared to the both control in subregions LaVM, whereas those that underwent AAFC had a significant increase in LaVL as compared to both controls D The frequency of significant differences in the LA in those that underwent UFC was, once again, highest. Rats that underwent CFC had minimal differences in LaVM, with rats that underwent AAFC exhibiting some differences throughout the LA. Density heat maps visually depict mean Arc (E), c-Fos (F) and co-expression (G) in all five behavioural groups. Asterisks denote levels of statistical significance (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001). CFC: contextual fear conditioning; UFC: unpaired fear conditioning; AAFC: adjusted auditory fear conditioning; TA: tone alone; CO: context only; LaDL: dorsolateral portion of the lateral amygdala; LaVM: ventromedial portion of the lateral amygdala; LaVL: ventrolateral portion of the lateral amygdala.
IEG Expression at Bregma Coordinate -3.00 mm
Investigations of Bregma coordinate -3.00 mm revealed the most number of
group differences. Group differences were present in Arc expression in all
subregions except BLP (LaDL F[4, 41] = 5.867, p < 0.001; LaVM F[4, 37] = 6.643,
p < 0.001; LaVL F[4, 38] = 3.842, p < 0.05; BLA F[4, 39] = 3.362, p < 0.05; BLP
F[4, 40] = 1.275, p = 0.296). Group differences in c-Fos expressing neurons were
present in all subregions except BLA (LaDL F[4, 41] = 6.528, p < 0.001; LaVM F[4,
41] = 4.267, p < 0.01; LaVL F[4, 41] = 3.938, p < 0.01; BLA F[4, 41] = 2.261, p =
0.079; BLP F[4, 41] = 3.139, p < 0.05). Similarly, group differences in co-expressing
neurons were present in all subregions except BLA (LaDL F[4, 41] = 7.620, p <
0.001; LaVM F[4, 41] = 7.552, p < 0.001; LaVL F[4, 40] = 7.546, p < 0.001; BLA
F[4, 41] = 2.144, p = 0.093; BLP F[4, 39] = 2.716, p < 0.05). Bonferroni correct
post-hoc tests (see Figure 8) identified group differences to consistently exist
between rats that were CFC as compared to both controls in LaDL. Some further
differences existed between those that underwent CFC as compared to controls in
LaVM and LaVL. Rodents that underwent UFC had consistent significant increases
as compared to controls in subregions LaDL and LaVM. Further differences were
noted in LaVM. Once again, rodents that underwent AAFC had little amygdala-
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related differences as compared to controls. Those that were present existed in LaVM
(as compared to one control in co-expressing neurons) and LaVL (as compared to
both controls in co-expressing neurons).
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Figure 3-8: Immediate early gene expression in Bregma coordinate -3.00mm.
A Arc expression in LaDL and revealed significant increases between rats that underwent CFC and UFC as compared to both controls. Further differences were observed in the rats that underwent CFC as compared to TA rats in subregion LaVM, and rats that underwent UFC as compared to both controls in LaVM, and as compared to TA controls in LaVL. B Similar to Arc expression, c-Fos expression in LaDL was significantly higher in the CFC and UFC as compared to both controls. LaVM expression was significantly higher in the UFC conditioning group as compared to both controls, while LaVL expression was higher in the UFC as compared to the CO control. C Once again, co-localised neuron expression was, once again, significantly higher in rats that underwent CFC and UFC as compared to both controls in subregion LaDL. Further differences in LaVM were observed in those that received CFC and UFC as compared to both controls, and between those that received AAFC and the TA control. In subregion LaVL significant differences were observed between those that received UFC and AAFC as compared to both controls, and between these that received CFC and the TA controls. D The frequency of significant differences in the LA in those that underwent UFC was, once again, highest. Rats that underwent CFC also exhibited large differences in LaDL, with some minor differences in LaVM and LaVL. AAFC produces, once again, smallest differences. Density heat maps visually depict mean Arc (E), c-Fos (F) and co-expression (G) in all five behavioural groups. Asterisks denote levels of statistical significance (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001). CFC: contextual fear conditioning; UFC: unpaired fear conditioning; AAFC: adjusted auditory fear conditioning; TA: tone alone; CO: context only; LaDL: dorsolateral portion of the lateral amygdala; LaVM: ventromedial portion of the lateral amygdala; LaVL: ventrolateral portion of the lateral amygdala.
3.4.3 Summary of Results
In the current study, we evaluated involvement of the BLC following three
different forms of fear conditioning. The current study extensively evaluates
amygdala IEG expression, focusing on Arc, c-Fos and their co-localisation in
Bregma coordinates -3.36 mm, -3.24 mm, -3.12 mm and -3.00 mm. In this section
results are visually summarised (see Figure 9 and 10). As seen, a role for LaDL and
LaVM exists following CFC. Alternatively, widespread BLC IEG expression is
noted following UFC, with limited role for BLC noted following AAFC, besides a
minor difference in IEG expression at subregion LaVL.
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Figure 3-9: Schematic representation of IEG expression following conditioning.
Within each sub-region, statistically significant differences between conditioned rats as compared to both controls are represented by dots. Stronger significance results in more representative dots (p ≤ 0.05 = five dots; p ≤ 0.01 = 10 dots; p ≤ 0.001= 15 dots; p ≤ 0.0001 = 20 dots). Green dots represent significant difference in Arc expression, red dots represent c-Fos expression and yellow dots represent co-localised expression. As seen, following CFC a specific role for LaDL is present at Bregma coordinate -3.36 mm and -3.00 mm, whereas a more consistent, albeit weaker role for LaVM is present in all four Bregma coordinates. Comparatively, following UFC, a largescale and consistent role for the entire BLC is noted, except at Bregma coordinate -3.00 whereby IEG expression seems mostly localised to the LA. Following AAFC, little consistent differences are noted in BLC, besides at subregion LaVL, whereby a c-Fos and co-localised neuron expression seemed to increase at Bregma coordinates -3.12 mm and -3.00 mm. BLC: basolateral amygdala complex; LA: lateral amygdala; LaDL: dorsolateral portion of the lateral amygdala; LaVM: ventromedial portion of the lateral amygdala; LaVL: ventrolateral portion of the lateral amygdala; IEG: immediate early genes CFC: contextual fear conditioning; UFC: unpaired fear conditioning; AAFC: adjusted auditory fear conditioning.
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Figure 3-10: Schematic representation of total IEG expression following conditioning.
Merged IEG expression across Bregma coordinates -3.36 mm, -3.24 mm, -3.12 mm and -3.00 mm reveal a differing pattern of activation following the three different fear conditioning protocols. IEG expression was seen in LaDL and LaVM following CFC, while widespread IEG was present within the BLC following UFC. IEG expression was found to be much lower following the AAFC protocol. CFC: contextual fear conditioning; UFC: unpaired fear conditioning; AAFC: adjusted auditory fear conditioning; BLC: basolateral amygdala complex; LaDL: dorsolateral portion of the lateral amygdala; LaVM: ventromedial portion of the lateral amygdala.
3.5 Discussion
In this study we examined the behavioural and anatomical changes following
fear conditioning protocols designed to isolate and investigate the component of
contextual memory within the basolateral amygdala. We studied contextual fear
memories by way of two different protocols: a standard CFC protocol and an UFC
protocol. Behavioural and anatomical differences in rodents that underwent CFC and
UFC were compared to a group that underwent an AAFC protocol. Behaviourally,
we show both CFC and UFC to produce fear memories to context but not tone. The
AAFC protocol employed here was successful in creating fear memories to tone with
accompanying background fear memories to context significantly attenuated. This
suggests creation of a context-removed or context-reduced auditory fear memory.
Data reported here suggest differential contribution of BLC subregions
following different fear conditioning protocols. In this investigation of the BLC, we
evaluate differences in the expression of Arc, c-Fos and their co-localisation in five
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amygdala subregions across four rostral to caudal planes identified by consecutive
atlas Bregma coordinates. We demonstrate a role for the LA in the creation of fear
memories to context. Interestingly, LA involvement differed depending on the fear
conditioning protocol. Contextual fear conditioning produced specific changes to
LaDL and LaVM, compared to UFC which produced wide-scale BLC changes. Our
data suggests that the creation of context-removed/context-reduced auditory fear
memories become less dependent upon BLC, with only minor differences being
consistently present in subregion LaVL. Cumulatively, data suggest contextual fear
memories to rely heavily upon BLC. Alternatively, auditory fear memories, with
accompanying background contextual fear memories removed, seem to be less
reliant upon the BLC, and perhaps more reliant upon alternative brain areas.
3.5.1 Contextual Fear Conditioning
Contextual fear memories produced with a standard CFC protocol rely upon
the LaDL and LaVM subregion of the BLC (see Figure 9 and 10). In these
subregions, we found rats that underwent CFC to have significantly more neurons
expressing Arc and c-Fos as compared to the TA and CO control groups. Further,
examinations of co-localising neurons suggest that a unique population of neurons
express both Arc and c-Fos in response to CFC in these regions. Cumulatively, data
highlight contextual fear memory formation to be reliant upon LaDL and LaVM.
Many previous investigations have identified increases in IEG expression following
contextual fear memory formation (Barot et al., 2009; Hall, Thomas, & Everitt,
2001a; Malkani & Rosen, 2000; Perez-Villalba, Mackintosh, & Canales, 2008;
Trogrlic, Wilson, Newman, & Murphy, 2011; Y. M. Wilson & Murphy, 2009;
Zelikowsky, Hersman, Chawla, Barnes, & Fanselow, 2014). However, these
investigations examined the BLC as a whole (Barot et al., 2009; Hall et al., 2001a),
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or only explored or found differences in the LaVL (Trogrlic et al., 2011; Y. M.
Wilson & Murphy, 2009), the BLA (Perez-Villalba et al., 2008), or the basal portion
of the BLC (Zelikowsky et al., 2014). Only one previous study, to our knowledge,
identified a specific role for LaDL following CFC (Malkani & Rosen, 2000). When
these studies, along with new data provided here, are viewed together, little
advancement is made in the ability to identify a specific BLC subregion whereby
contextual fear memories may be located. However, the in-depth and voluminous
analysis of BLC provided here can explain why particular subregion activation
following contextual fear memory formation cannot be consistently identified.
Data presented here provides some insight into the organisation of contextual
fear memories. Reported differences in LaDL between Arc, c-Fos and co-expressing
neurons were consistently present in Bregma coordinates -3.36 mm and -3.00 mm,
but not -3.12 mm or -3.24 mm. Alternatively, LaVM involvement was present in all
Bregma coordinates examined, but the amount of increase in IEG expression differed
depending upon the Bregma coordinate at which analyses occurred. The various
investigations above, demonstrate differential involvement of BLC subregions
following contextual fear memory formation. In these studies, an array of divergent
rostral-caudal locations are examined. Our data may explain the contradictory results
from previous studies. Our data provides evidence for a potential dynamic and
changing role for LaDL and LaVM involvement, depending upon rostral-caudal
location, following contextual fear memory formation.
3.5.2 Unpaired Fear Conditioning
Unpaired fear conditioning resulted in a broad and consistent (across all
Bregma coordinates studied) increase in Arc expressing, c-Fos expressing and co-
expressing neurons at subregions LaDL, LaVM and LaVL. The LA subregion
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changes were accompanied by a less pronounced level of IEG activation in the BLA
and BLP (see Figure 9 and 10). Data suggests UFC leads to consistent and high BLC
activation. Immediate early gene expression was highest in these rats as compared to
rodents that underwent CFC or AFC. Interestingly, behavioural data did not show
UFC to result in stronger fear memories. More importantly, these data are in contrast
to many previously reported studies showing amygdala activation/plasticity to be
comparatively low following UFC, as opposed to AFC (H. C. Bergstrom et al., 2012;
Hadley C Bergstrom et al., 2011; Majak & Pitkänen, 2003; McKernan & Shinnick-
Gallagher, 1997; Radley et al., 2006; Michael T. Rogan et al., 1997). Unpaired fear
conditioning protocols are often utilised as controls for AFC. Leading hypotheses
suggest that, while tone alone or foot-shock alone may activate amygdala neurons,
the pairing of tone and foot-shock leads to plasticity in a unique set of BLC neurons
(Joseph LeDoux, 2003; Romanski et al., 1993). The UFC protocol acts as a
behavioural and anatomical control, as associative fear memories are not produced,
and therefore BLC involvement is reduced (H. C. Bergstrom et al., 2012; Hadley C
Bergstrom et al., 2011; Majak & Pitkänen, 2003; McKernan & Shinnick-Gallagher,
1997; Radley et al., 2006; Michael T. Rogan et al., 1997). Data from this study,
however, contrast these findings showing high amygdala activation following UFC.
Associative learning can occur without explicit presentation of two stimuli.
Foreground CFC is a prime example of this (Michael S. Fanselow, 1980).
Foreground contextual fear memories are formed by association of a foot-shock to a
context (Calandreau, Desmedt, Decorte, & Jaffard, 2005; Calandreau et al., 2006;
Desmedt, Garcia, & Jaffard, 1998; Russel G Phillips & LeDoux, 1994; Trifilieff et
al., 2007; Trifilieff et al., 2006). This context (for example, the chamber) is
constantly present as the environment (or backdrop) under which psychological
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processes occur. Unpaired fear conditioning is identical to CFC, except the ‘context’
now includes further stimuli (in this case, auditory tones). These stimuli are not
paired with the foot-shock, and therefore are a component of the context.
Consequently, amygdala involvement following UFC is not surprising. In fact,
previous research has demonstrated significantly higher levels of pMAPK expression
following UFC, but not AFC, in the BLC (Trifilieff et al., 2007). Despite this,
Trifilieff et al. (2007) is the only paper, to our knowledge, showing significant
amygdala involvement following UFC, but only limited involvement following AFC,
as compared to controls. Previously we showed a non-significant trend level increase
of pMAPK expressing neurons following UFC as compared to controls in LaDL,
LaVM and LaVL (H. C. Bergstrom et al., 2012; Hadley C Bergstrom et al., 2011).
Similar results have been produced by others (Hall, Thomas, & Everitt, 2001b;
Schafe et al., 2000). However, no further research has demonstrated consistently high
levels of amygdala involvement, as compared to controls, following UFC.
Consequently, based on previous evidence, it was unexpected that UFC produced
high BLC IEG expression, while AFC did not. Data presented here, however, can be
explained when the adjustment made to the AFC protocol is considered. Our specific
AAFC protocol, whereby latent inhibition reduced background contextual fear, may
have reduced amygdala involvement. Without such latent inhibition, amygdala
involvement may have been above and beyond that of rats that underwent UFC as
extensively demonstrated (H. C. Bergstrom et al., 2012; Hadley C Bergstrom et al.,
2011; Majak & Pitkänen, 2003; McKernan & Shinnick-Gallagher, 1997; Radley et
al., 2006; Michael T. Rogan et al., 1997).
The UFC protocol here activated nearly the entire BLC, whereas the CFC
protocol did not. This is despite both protocols producing contextual fear memories.
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In a 1993 investigation of LA activity during fear conditioning, subregion LaDL
responded both to somatosensory (foot-shock) and acoustic (clicks) stimuli
(Romanski et al., 1993). In the current study, presentation of both foot-shock and
acoustic tones, although not exclusively paired, may have led to large scale BLC
activation. Alternate explanations for large scale BLC activation include a non-
significant trend level development of auditory fear memories as a result of UFC, as
seen in the current and some previous investigations (H. C. Bergstrom et al., 2012;
Hadley C Bergstrom et al., 2011; Hall et al., 2001b). Nevertheless, UFC seems to
produce complex associative fear memories which may be responsible for the greater
BLC activation.
3.5.3 Auditory Fear Conditioning
In contrast to our contextual fear memory data, we found limited consistent
pattern of BLC IEG activity in rodents that underwent AAFC (see Figures 10 and
11). Countless investigations have consistently identified numerous plasticity and
activity related molecules to be highly expressed in the BLC following auditory fear
memory formation (see reviews by (Hadley C. Bergstrom, 2016; L. R. Johnson et al.,
2012; J. J. Kim & Jung, 2006; J. LeDoux, 2000; Joseph E. LeDoux, 2014; Maren,
2011; Sah, Westbrook, & Lüthi, 2008). Data here, therefore suggests that the
removal of context, by way of latent inhibition, significantly reduces BLC
involvement following auditory fear memory formation to a level whereby some,
albeit limited, LaVL IEG expression is present. Two possible explanations for this
reduction in IEG expression are considered. First, the reduction of background
contextual fear memories may have directly altered the level of IEG expression in the
BLC. This indicates that background contextual fear memories (that are present
following all forms of discrete fear conditioning, such as AFC) may be responsible
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for a major degree of BLC activation/plasticity. Some evidence for this is presented
in our previous research, where we demonstrated visual and auditory fear memories
(both discrete fear memories with the contextual fear memory in the background) to
activate a similar density of plasticity-related pMAPK-expressing neurons in the LA
(H. C. Bergstrom & L. R. Johnson, 2014). Interestingly, the anatomical distribution
of pMAPK expressing neurons differed between these groups (H. C. Bergstrom & L.
R. Johnson, 2014). This suggests that alterations to discrete elements that are paired
with foot-shocks (auditory versus visual in this case) in fear conditioning have less
impact on the overall number of neurons undergoing plasticity in the amygdala when
background context is kept stable. Alternatively, context-related modifications (CFC
group versus UFC in this study) produce clear alterations in the number of neurons
expressing IEGs in the amygdala. This highlights the large and quantitative
importance of context in the amygdala during fear memory consolidation.
An alternative explanation for the reduction in LA IEG expression relates to
the clear reduction in freezing to tone in the AAFC group, as compared to previous
investigations. In our previous reports, when the same number and intensity of foot-
shocks were utilised, rodents froze to tone 96% of the time (Bergstrom et al., 2012).
Only with fewer presentations (two tone-shock pairings) and less severe (0.6mA
foot-shocks) fear conditioning does freezing to tone drop to 55% (Bergstrom et al.,
2014), which is still higher than the 49% reported here. This suggests the removal of
context, by way of latent inhibition, directly affects the ability to effectively create
auditory fear memories. Previous investigations have provided evidence for this
(Balaz, Capra, Hartl, & Miller, 1981; Balaz, Capra, Kasprow, & Miller, 1982;
Urcelay & Miller, 2010). These investigations, pioneered by Miller, demonstrated
some minor reductions to auditory fear memory (testing occurred in training context)
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following attenuation or abolishment of contextual fear via latent inhibition or
extinction. Importantly, these reductions were shown to become severe when fear
memory testing occurred in a new context (Balaz et al., 1981; Balaz et al., 1982).
This suggests that context memory (sever reduction of fear when contextual fear is
attenuated and testing is in a new chamber), but not context fear memory (minor
reductions in fear when contextual fear is attenuated but testing is in the same
chamber) influences the association of discrete stimuli with foot-shocks. Two
primary explanations have attempted to explain how context influences discrete
stimuli and foot-shock associations (Balaz et al., 1981; Balaz et al., 1982; Grahame,
Hallam, Geier, & Miller, 1990; Urcelay & Miller, 2010). First, the conditioning
context is hypothesised to be responsible for retrieving associations between the
discrete stimuli and foot-shock – without context recall via exposure, or other
mechanisms, the discrete stimuli and foot-shock association is not effectively
retrieved (Balaz et al., 1982). Alternatively, attenuation in fear, especially following
latent inhibition, arises due to discrimination. Latent inhibition allows rodents to
more effectively identify and discriminate one context from another. Therefore, upon
placement to a new context, rodents quickly determine that it is not the same as the
training context – resulting in attenuation of fear (Balaz et al., 1982). While these
studies examine the possibility of deficits at recall, anatomical data reported here
(reduction of BLC involvement immediately following AAFC) suggest that latent
inhibition may cause encoding deficits.
Following extreme prior exposure to a discrete or contextual CS, conditioned
fear is attenuated (Barot et al., 2009; R. E. Lubow, 1965; R. E. Lubow & Moore,
1959). This occurs due to an encoding deficit, arising from competition for
associative strength. For contextual fear memory, context pre-exposure leads to the
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development of a neutral associative contextual memory which, following CFC,
competes with the fearful associative memory (Bouton, 1993; Nicholas Chaaya et al.,
2018; Traverso, Ruiz, & De la Casa, 2012). Background contextual fear was
attenuated in the AAFC group via this mechanism. Importantly, we propose that the
(foreground) auditory fear may have also been attenuated by related mechanisms. It
is hypothesised that the auditory tone used to condition rodents became included as
part of the ‘neutral’ context during training. Evidence for this is provided in studies
of the hippocampus. In these studies, the addition of a discrete stimulus (such as an
auditory tone) to a known context largely alters place cell (neurons that fire to form
spatial representations of ‘contexts’) activity (M. I. Anderson & Jeffery, 2003; D. M.
Smith & Mizumori, 2006a). Further investigations have demonstrated a complete
remapping of hippocampal place cell activity to occur following similar
environmental changes to a context that rats have been heavily exposed to (Bostock,
Muller, & Kubie, 1991; Jeffery, Gilbert, Burton, & Strudwick, 2003). In the current
study, it is hypothesised that an encoding deficit occurred due to competition for
associative strength between the ‘neutral’ context – which, following a single
presentation, now includes the auditory tone – and the aversive foot-shock. While the
role of context is still being outlined during discrete fear conditioning (Urcelay &
Miller, 2010), this hypothesis suggests that latent inhibition of background
contextual fear memories, directly alters the efficacy of auditory fear conditioning.
This serves as a limitation of the AAFC protocol. Future research is required to
investigate uninhibited auditory fear memories, with the contextual component
removed.
For the above reason, the data here is reported cautiously. Additionally,
methodological limitations have produced some caveats. Firstly, to correct for
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multiple comparisons, a Bonferroni adjustment was applied to all statistical analyses.
The Bonferroni adjustment has been cited to be too severe and capable of producing
type II errors (Perneger, 1998). Indeed, when evaluated without the Bonferroni
adjustment (data not reported), a pattern of IEG expression arises in this AAFC
group, mostly in c-Fos expressing and co-expressing, but not Arc expressing
neurons. These statistical differences, however, are much weaker than those seen in
the two contextual fear memory groups (CFC and UFC), and lose statistical
significance following minor and less severe adjustments to alpha. Furthermore, LA
involvement was not compared in this group to a non-adjusted AFC group. However,
the involvement of LA following non-adjusted AFC has been previous delineated on
numerous occasions (see reviews by (Hadley C. Bergstrom, 2016; L. R. Johnson et
al., 2012; J. LeDoux, 2000; Joseph E. LeDoux, 2014)). Therefore, we did not
determine a need for this group.
3.5.4 Technical Considerations
Data reported in the current study highlight a dependence on LA activation
following contextual fear memory formation, but a reduced role following auditory
fear memory formation. These conclusions are made with the IEGs Arc and c-Fos,
demonstrating their reliability as indicators of neuronal activity representing
differences in behaviour. Due to the large-scale investigations conducted here,
behavioural differences were found to produce a number of statistically significant
anatomical alterations. In this voluminous exploration of amygdala, we focus on
consistent (across multiple Bregma coordinates and also identified in Arc, c-Fos and
co-expressing neurons) statistical differences between conditioned rats and controls.
Many previous investigations focus primarily on differences in one Bregma
coordinate, utilising a single plasticity or activity marker, and with one behavioural
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control. Here we sought to develop and use a multifaceted and more powerful
approach, as it reduces the chances of obtaining false-positives. For example, a
difference between groups may exist in one Bregma coordinate when measured with
one marker but may not exist in other Bregma coordinates or when measured with
many other related markers. A further advantage to the current approach is the ability
to identify patterns. For example, in the present study, CFC led to an increase in
LaDL IEG expression in two distinct Bregma coordinates (-3.00 mm and -3.36 mm),
suggesting CFC may activate particular areas of LaDL. Evaluation of a single
Bregma coordinate may not have accurately identified the complex involvement of
LaDL following CFC. The ability to highlight the complex involvement of various
brain regions following fear conditioning of various forms is essential. Treatment of
post-traumatic stress disorder (PTSD) relies upon a clear and thorough understanding
of the neural mechanisms involved following fear memory formation (L. R. Johnson
et al., 2012; Maren et al., 2013). Therefore, while we do not discount individual and
less consistent differences between groups, emphasis is on those differences which
were reliably and consistently identified. These highlight a clear pattern of amygdala
involvement which is less likely to have arisen due to sampling and statistical error.
3.5.5 General Discussion
In this study we found modification to fear memory conditioning protocols
leads to differing IEG activation patterns in BLC. While foot-shock presentation, and
corresponding freezing to context, did not differ between these two groups (CFC and
UFC), the ‘context’ did (i.e. the presence or absence of tone in the in the UFC box
versus CFC box, respectively). The hippocampus has high functional significance in
context/spatial memory formation and contextual fear memory formation (see
(Nicholas Chaaya et al., 2018) for review, along with recent chemogenetic and
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optogenetic data (Huff, Emmons, Narayanan, & LaLumiere, 2016; Park et al., 2016;
Sparta et al., 2014; C. Xu et al., 2016)). We, therefore, hypothesise the alterations in
BLC following the two different fear conditioning protocols may be regulated by the
hippocampus. This highlights the interaction between amygdala and hippocampus as
essential brain regions to contextual fear memory formation (see (Nicholas Chaaya et
al., 2018)).
In this study, we found that when auditory fear memories are formed with
background contextual fear memories removed, BLC activation is significantly
reduced. Reduction in BLC activation suggests that background contextual fear
memories, formed by standard and non-adjusted AFC protocols, may be responsible
for a significant portion of amygdala activation found in previous investigations (see
(Hadley C. Bergstrom, 2016; L. R. Johnson et al., 2012; J. LeDoux, 2000; Joseph E.
LeDoux, 2014) for reviews of these investigations). This suggests auditory fear
memories may be modulated by context/spatial memory specific brain regions;
namely the dorsal portion of the hippocampus (Nicholas Chaaya et al., 2018).
Indeed, this was previously demonstrated by Fanselow and Quinn (Pierson, Pullins,
& Quinn, 2015; Jennifer J. Quinn, Loya, Ma, & Fanselow, 2005; Jennifer J Quinn,
Wied, Ma, Tinsley, & Fanselow, 2008). Similar to contextual fear memory
formation, we conclude that the interaction between amygdala and hippocampus may
be important to auditory fear memory formation. This warrants further investigation.
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Micro-Topography of Fear Memory Consolidation and
Extinction Retrieval within Prefrontal Cortex and Amygdala
This chapter comprises the following published article: Jacques, A., Chaaya, N., Hettiarachchi, C., Carmody, M., Beecher, K., Belmer, A., Chehrehasa, F., Bartlett, S., Battle, A.R., Johnson, L. R. Micro-Topography of Fear Memory Consolidation and Extinction Retrieval within Prefrontal Cortex and Amygdala. Psychopharmacology. Published 4th January, 2019 https://link.springer.com/article/10.1007%2Fs00213-018-5068-4
129
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1. they meet the criteria for authorship in that they have participated in the conception, execution, orinterpretation, of at least that part of the publication in their field of expertise;
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In the case of chapter 4: Micro-Topography of Fear Memory Consolidation and Extinction Retrieval within Prefrontal Cortex and Amygdala
Publication status: Published
Contributor Statement of contribution*Angela Jacques Involved in the conception and design of the project, behavioural and
laboratory experiments, analysed data, created the figures and wrote and edited the manuscript.
Nicholas Chaaya Assisted with editing the manuscript, behavioural and laboratory experiments.
Cheimi Hettiarachchi Assisted with laboratory experiments.
Marie Carmody Assisted with behavioural experiments.
Kate Beecher Assisted with creating figures.
Arnauld Belmer Assisted with editing the manuscript.
Fatemeh Chehrehasa Assisted with reviewing and editing the manuscript.
Selena Bartlett Assisted with editing the manuscript.
Andrew Battle Assisted with editing the manuscript.
Luke Johnson Involved in the conception and design of the project, assisted with behavioural experiments and reviewing and editing the manuscript.
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4.1 Abstract
The precise neural circuitry that encodes fear memory and its extinction within
the brain are not yet fully understood. Fearful memories can be persistent, resistant to
extinction and associated with psychiatric disorders, especially post-traumatic stress
disorder (PTSD). Here, we investigated the micro-topography of neurons activated
during the recall of an extinguished fear memory, and the influence of time, on this
micro-topography. We used the plasticity-related phosphorylated mitogen activated
protein kinase (pMAPK) to identify neurons activated in the recall of consolidated
and extinguished auditory Pavlovian fear memories in rats. We used quantitatively
matched brain regions to investigate activity in the amygdala and prefrontal cortex.
Recall of a consolidated, non-extinguished auditory fear memory resulted in a
significantly greater number of activated neurons located in the dorsolateral sub-
division of the lateral amygdala (LADL) when recalled 24 hours after consolidation
but not when recalled seven days later. We found the recall of an extinction memory
was associated with pMAPK activation in the ventrolateral sub-division of the lateral
amygdala (LAVL). Next, we showed the pattern of pMAPK expression in the
prelimbic cortex differed spatially following temporal variation in the recall of that
memory. The deep and superficial layers of the pre-limbic cortex were engaged in
recent recall of a fear memory, but only the superficial layers were recruited if the
recall occurred seven days later. Collectively, our findings demonstrate a functional
micro-topography of auditory fear memory during consolidation and extinction at the
micro-anatomical level within the lateral amygdala and medial prefrontal cortex.
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Keywords:
pMAPK; Topography; Neuroplasticity; Amygdala; Medial prefrontal cortex; Extinction; Fear conditioning.
4.2 Introduction
Individuals with post-traumatic stress disorder (PTSD) have difficulty learning
that cues previously associated with a threat no longer pose danger once removed
from the threatening situation, suggesting deficits in extinction learning (Luke R
Johnson et al., 2012; Yehuda, 2002) Pavlovian, or classical fear conditioning, has
long been used to study an animal’s response to threatening situations (Maren, 2001;
Rescorla, 1988; Schafe, Nader, Blair, & LeDoux, 2001), which involves a form of
associative learning. An animal will exhibit a defensive action such as freezing, in
response to a conditioned stimulus (CS) paired with an unpleasant, unconditioned
stimulus (US) (á. Fendt & M. Fanselow, 1999). The new associative memories
undergo consolidation, a process of stabilization from short-term to long-term
memories (Hebb, 1949; Luke R Johnson et al., 2012; Nader et al., 2000). In auditory
Pavlovian fear conditioning a learned association forms between a mild foot shock
(US) paired with an auditory tone (CS) (S. Duvarci & D. Pare, 2014; Michael S.
Fanselow & Gale, 2003; Luke R Johnson et al., 2012; J. LeDoux & Daw, 2018;
Maren, 2001, 2011; Maren & Quirk, 2004; H. C. Pape & D. Pare, 2010)
It is generally accepted that only a small subset of neurons within a given
population is required to encode a fear memory (Hadley C Bergstrom & Luke R
Johnson, 2014; X. Liu et al., 2012; Rumpel, LeDoux, Zador, & Malinow, 2005). This
has help direct investigations into localizations and mechanisms of memory
consolidation and extinction within amygdala and prefrontal cortex (Baker, Bisby, &
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Richardson, 2016; Hadley C Bergstrom & Luke R Johnson, 2014; Bukalo et al.,
2015; Maren, 2011). Studies on the molecular mechanisms of fear memories have
identified activation of the extracellular signal-regulated kinase-mitogen-activated
protein kinase (ERK/MAPK) pathway as necessary for memory consolidation and
extinction learning (Adams & Sweatt, 2002; Hadley C Bergstrom et al., 2011;
Cestari, Rossi-Arnaud, Saraulli, & Costanzi, 2014; Herry et al., 2006; Schafe et al.,
2000). Molecular and cellular measures to date have provided evidence that the
medial prefrontal cortex (mPFC) and the amygdala engage varied neuronal networks
during fear memory consolidation, extinction and retrieval (Bukalo et al., 2015; H. J.
Lee, R. P. Haberman, R. Roquet, & M. H. Monfils, 2016; Milad & Quirk, 2002;
Shin, Rauch, & Pitman, 2006). This is consistent with studies in human subjects with
PTSD have been shown to have morphological changes and functional abnormalities
in the PFC suggesting compromised extinction circuits associated with the disorder
(Milad & Quirk, 2012).
The micro-topographical organization of neurons encoding fear memories
and their extinction memories remains elusive. Moreover, the degree to which
neuronal architecture that encodes memories is stable over time is not fully known.
Topographic stability, or reproducibility, is a function of repeated reappearance of
activation within a mapped space. We hypothesized that the spatial distribution of
pMAPK within the ERK/MAPK cascade in response to remote fear memory recall
and remote extinction memory recall could be visualised using density heat maps and
would occur in different sub regions of the amygdala. Furthermore pMAPK positive
neurons would be present in both the infralimbic and prelimbic regions of the mPFC,
however, their spatial distribution would be predominantly prelimbic during
conditioning recall and extinction recall. In addition, we predicted the magnitude of
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plasticity would vary between remote and recent fear memory recall. Specifically, we
suggested the neurons expressing pMAPK would be distributed in distinct micro-
anatomical regions dependent on their memory group and be visible within distinct
cortical layers through the generation of density maps. Our objectives included
establishing the microanatomical properties of a marker of neuroplasticity - pMAPK
in subregions of the amygdala and the mPFC during fear memory and remote
extinction memory recall. We sought to define the specific sub regions undergoing
neuroplastic changes due to the recall of a remote extinction memory and define
differences if any, with the spatial distribution of this neuroplasticity after recent and
remote fear memory retrieval. We utilizedclassicalPavlovianconditioningandan
extinction paradigm spanning 3 days, combined with cytochemical
quantifications to determine themicroanatomy of fearmemory consolidation
andremoterecallofanextinctionmemory.Our method of quantitative functional
microanatomy and spatial analysis of micro-anatomical sub-regions involved in fear
and extinction memory recall in rodents was used to measure stable neuronal patterns
and differences among animals.
4.3 Material and Methods
4.3.1 Subjects
Experimentally naive adult male Sprague-Dawley rats (supplied by Animal
Resource Center, ARC, Western Australia), were housed in pairs in temperature (≈
24 °C) and humidity (35 %) controlled clear Plexiglas cages, maintained on a 12-
hour light/dark cycle. Behavioral procedures were conducted during the light cycle
as fear conditioning is documented to be more effective during the nocturnal phase
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for rodents i.e. when the lights are on (Albrecht & Stork, 2017). Food and water were
provided ad libitum. Rats were acclimatized to the vivarium for 7 days prior to
training. At time of memory recall testing rats weighed 326 ± 3.6 g (276-391 g). All
procedures were conducted in compliance with the Animal Welfare Unit, The
University of Queensland Research and Innovation Ethics Committee and the
Research Ethics Committee of the Queensland University of Technology, Australia.
Procedures complied with policies, regulations and ethical standards for animal
experimentation, in accordance with the Queensland Government Animal Research
Act 2001, associated Animal Care and Protection Regulations (2002 and 2008), and
the Australian Code for the Care and Use of Animals for Scientific Purposes, 8th
Edition (National Health and Medical Research Council, 2013).
4.3.2 Apparatus
Two clear Plexiglas conditioning chambers were utilized for behavioral
procedures: Context A and Context B. Each was encased in an acoustic isolation box
(Coulbourn Instruments, Allentown, PA, USA). Background noise level was
measured at 55 dB, using a sound level meter (Digitech Professional Sound Level
Meter QM1592). Each chamber was equipped with a speaker, a low-level house light
(2-3 lux), an infrared light and an infrared camera. Modification to the chambers
occurred to create unique testing environments for fear conditioning or extinction
and to restrict any reaction to context. Context A contained a stainless-steel grid floor
connected to a shock generator and computer (Freeze frame software, Coulbourn
instruments). The novel odorant and cleaning agent, ethanol (70 %) was used in
Context A. To differentiate between contexts, Context B contained a plastic floor
covered with fresh bedding and internal colored decoration on the walls and ceiling.
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Orange scented antibacterial soap was used to produce a smell unique to Context B.
The chambers were cleaned between each animal.
4.3.3 Behavioral procedures
A total of 52 rats were randomized into four groups (Figure 1). Extinction
recall (ER, n = 14), animals underwent auditory fear conditioning followed by
extinction training. Remote fear memory recall (7dR, n = 12), was a no extinction
control group that underwent auditory fear conditioning followed by no extinction
training. A recent fear memory recall (1dR, n = 16) group acted as a control for fear
memory consolidation. This group underwent auditory fear conditioning 24 h prior to
a memory recall test on the final day of behavioral training. Box control (BC, n =
10), a group of naive animals that underwent context training for the same duration
as the extinguished and non-extinguished groups (Figure 1). In addition to providing
a control to the extinction group, the no extinction animals represented the remote (7
d) retrieval of a fear memory in comparison to the fear conditioned group, where
recent memory retrieval occurred 24 h after memory formation. Therefore, the
groups were named accordingly.
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Figure 4-1: Experimental design.
Experimental design of extended extinction paradigm with controls (no extinction, fear conditioned alone and naive). An extinction recall (ER, n = 14) group underwent auditory fear conditioning followed by three days extinction training followed by three days in their home cage and a fear memory extinction retrieval test. A no extinction group underwent auditory fear conditioning, context exposure for three days (no tone) and home cage prior to a remote fear memory recall test performed seven days after conditioning (7dR, n = 12). A box control group followed this protocol but received no stimuli (BC, n = 10). A group of rats remained in their home cages until the seventh day of experimentation when they received auditory fear conditioning. One day later they underwent a recent fear memory recall test (1dR, n = 16).
For two days prior to training, rats were habituated to each context for 30
minutes. On day one the ER and 7dR groups underwent three minutes of acclimation
to context A, followed by a 10-minute conditioning protocol involving three pairings
of an auditory conditioned stimulus (CS, tone - 5kHz, 75 dB, 20 s) that co-terminated
with an unconditioned stimulus (US, foot shock - 0.6 mA, 500 ms). The 1dR fear
conditioning control group underwent the same conditioning on day seven. Stimuli
were controlled through Freeze Frame software (Coulbourn Instruments) and were
separated by a mean inter-trial interval of 180 s. Rats were returned to home cages 60
s after the final stimulus presentation. 24 h, 48 h and 72 h after fear conditioning the
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ER group underwent 30 minutes of extinction training (20 x CS alone, 5 kHz, 75 dB,
20 s) in Context B after which they remained in their home cages for three days to
allow for memory consolidation and a delay before memory was recalled. The 7dR
and BC groups followed this protocol, but received no stimuli in context B. The
intertrial interval throughout training and testing averaged 180s.
On the eighth day the ER, 7dR and 1dR groups underwent a 10-minute fear
memory test consisting of three 20 s CS presentations to test recall of the
consolidated auditory cued fear memory or extinction memory. BC animals were
exposed to context B for the same duration but did not undergo memory testing.
Freezing, an accepted behavioral index to quantitate the formation of a CS-US
association (Blanchard & Blanchard, 1969; Michael S Fanselow, 1984), was scored
during the 20 s CS intervals by an experimenter blind to the conditions. These
intervals were indicated in the recordings by an infrared light. Behaviour of the box
control (BC) and no extinction (7dR) groups were scored for the same 20 s, at the
same time points as each CS interval. Behavioral results were expressed as
percentage time freezing (dependent variable). Due to the low level of foot shock
given, not all animals formed an associative fear memory, therefore only data from
naïve animals and animals shown to have undergone successful fear conditioning
through the display of freezing behaviour when tested for the recall of an associative
fear memory (N = 40) were included in the behavioural analyses (n = 10 in each
group). These animals were identified using the ROUT method of statistical outlier
identification in GraphPad Prism 7 (GraphPad Software Co., CA, USA).
4.3.4 Tissue preparation
Rats were anesthetized with an injection of ketamine-xylazine (200 µl/100 g,
i.p.) and transcardially perfused through the ascending aorta with ice-cold saline (0.9
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% NaCl) followed by 4 % paraformaldehyde in 0.1 M phosphate buffer 60 minutes
post memory recall. Brains were extracted and post-fixed in 4 % PFA overnight then
stored in 0.1 M phosphate buffered saline (PBS). Free-floating serial coronal sections
(40 μm) of the medial prefrontal cortex and amygdala were prepared using a
vibratome (M11000; Pelco easiSlicer, Ted Pella Inc, Redding, CA, USA).
4.3.5 Immunohistochemistry
To visualize pMAPK activation in neurons as a neuroplastic marker of fear
memory consolidation (Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina
M Fernandez, et al., 2013; Brambilla et al., 1997; Schafe et al., 2000) the sections
were immunolabelled with an antibody against pMAPK. Briefly, the sections were
washed three times in PBS (pH 7.4) and were blocked in PBS containing 1 % bovine
serum albumin (BSA, Sigma – Aldrich Pty. Ltd, NSW, Aus), 0.02 % Triton X-100
and 3 % Normal Goat Serum (NGS, Abcam, Vic, Aus) for 1 h. After blocking
sections were incubated in a rabbit monoclonal antibody to phospo-p44/42 MAPK
(Erk 1/2) (Thr 202/Tyr 204) (1:250; #4370, Cell Signaling Technology, MA, USA)
for 24 h at room temperature. After wash, the sections were incubated with
biotinylated goat anti-rabbit IgG (1:100 dilution, Vector Laboratories, Burlingame,
CA, USA) in PBS, 0.02 % Triton X-100 for 3 h. Avidin–biotin HRP complex (ABC
Elite, Vector Laboratories, Burlingame, CA, USA) was applied for 1.5 h. Activated
neurons were developed in SG chromagen (Vector Laboratories, Burlingame, CA,
USA) for 10 minutes. Serial sections were mounted on silane-coated slides, air-dried
then dehydrated in a graded series of alcohol, xylene and cover slipped using DPX
mountant (Sigma-Aldrich). Slides were scanned at 20 x magnification with an
Olympus VS120 bright field slide scanner.
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4.3.6 Section alignment
Topographic spatial analysis relies on precision alignment of brain sections.
This ensures comparison of equivalent neuronal populations across subjects (Hadley
C Bergstrom & Luke R Johnson, 2014; Hadley C Bergstrom, Craig G McDonald,
Smita Dey, Gina M Fernandez, et al., 2013; Hadley C Bergstrom et al., 2011).
Precise alignment was achieved by reference to Paxinos and Watson’s stereotaxic
coordinates (Paxinos & Watson, 2007), and anatomical features easily visualized in
coronal sections. Methods for this process were described in detail for amygdala
studies using the entrance of the lateral ventricle as a land mark (Hadley C
Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez, et al., 2013).
Adopting the same principles, the caudate putamen was used as a landmark for the
mPFC sections. In each animal, three locations, bregma +3.24 mm, +3.00 mm and
+2.76 mm were aligned and verified across subjects and conditions by statistical
comparison of the feret length (distance between two perpendicular tangents).
Sections were identically matched across each animal at specific Bregma locations.
Animals were excluded from a specific location if the section required was missing,
visibly torn, damaged or misshapen. This same animal however, may have supplied a
section for a map generated at a more caudal Bregma location.
4.3.7 Neuron density quantifications
Neuron cell quantifications were performed by an experimenter blind to the
experimental conditions. Sections from the left brain hemisphere were counted at
Bregma -3.36 mm and +3.00 mm, providing suitable representations of the LA and
mPFC (Paxinos & Watson, 2007). Subdivisions selected for quantitative analysis
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included the lateral amygdaloid nucleus dorsolateral (LAd), ventromedial (LAvm)
and ventrolateral (LAvl) parts and the prelimbic and infralimbic regions of the
mPFC. Regional subdivisions were assigned by tracing contours from digital
depictions of the rat brain atlas (Paxinos & Watson, 2007) calibrated, scaled, aligned
and superimposed on the immunolabeled brain sections (Neurolucida 360, MBF
Bioscience, VT). To ensure position of the contour remained stable across
experimental groups, anatomical features used for alignment included the lateral
ventricle, central amygdala, external capsule and rhinal fissure for the LA. Forceps
minor of the corpus callosum, claustrum, olfactory part of the lateral ventricle, the
nucleus accumbens (shell) and the caudate putamen were employed to align the
mPFC. The XY coordinates of pMAPK+ neurons were marked and exported as
ASCII files (Neurolucida 360, MBF Bioscience, VT).
4.3.8 Topographic density maps
An important graphing technique for the visualization of neuronal topography
is the generation of neuron density maps (Hadley C Bergstrom & Luke R Johnson,
2014; Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez, et
al., 2013; Hadley C Bergstrom, Craig G McDonald, Smita Dey, Haying Tang, et al.,
2013; Hadley C Bergstrom et al., 2011; Luke R Johnson et al., 2012). A series of
plotting and analytic measures were employed to generate mean density maps. The
ASCII files containing the XY coordinates were imported into graphing software
where the data was binned (OriginPro v 9, Origin Lab, Northampton, MA). Bin
spatial dimensions (100 µm2) were determined by averaging twice the expected
frequency of points in a random distribution (D) across all subnuclei, calculated by D
= 2 (sampling area/n), where (n) is the mean number of pMAPK+ neurons for all
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subjects (Michael John De Smith, Goodchild, & Longley, 2007). Therefore, each bin
portrayed the total number of pMAPK expressing neurons within an equivalent sized
portion of each sub region. To produce a contoured density map, the value within
each bin was assigned a colour (SigmaPlot v 12, Systat Software, San Jose CA).
Difference maps were generated to localize the neurons specific to fear or
extinction memory formation. Subtraction of the mean neuron density of the control
condition from the experimental condition was used to exclude non-specific neuronal
activation from the associative fear or extinction learning. Coefficient of variance
maps were produced to profile the variation of activated neurons within one
experimental condition, both within groups and across the sampling area. The
coefficient of variance (CV) for each bin was calculated by dividing the standard
deviation by the mean (CV = SD/mean). As the CV is normalized to the mean of the
neurons located within each bin, there is equal comparison of the relative variability
across the bin matrix. Low variation (CV < 1.0) was considered as representative of a
stable neuronal population.
4.3.9 Statistical analysis
Analysis of behavioural conditions and neuron counts were conducted using
one-way and two-way ANOVAs. Post hoc Bonferroni correction was used to reduce
type 1 errors synonymous with multiple comparisons. Outliers were removed from
neuron counts and binned data analysis using the ROUT method with the maximum
false discovery rate (q) set at 1 %. To evaluate all bins in each data matrix, two-way
ANOVA with a false discovery rate (FDR) correction for multiple comparisons was
conducted with the total number of activated neurons in each bin considered the
dependent variable. FDR cut-off was set to q ≤ 0.10. FDR correction has been
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applied to similar data sets previously (Benjamini & Hochberg, 1995; Hadley C
Bergstrom & Luke R Johnson, 2014; Hadley C Bergstrom, Craig G McDonald,
Smita Dey, Gina M Fernandez, et al., 2013). The discovered bins represent the
region of specific interest when defining a population of functional neurons and as
such were termed micro regions of interest (MROIs) and subsequent analysis of
MROIs was conducted using t-tests. A p value ≤ 0.05 was stated as significant, *: p ≤
0.05, **: p ≤ 0.01, ***: p ≤ 0.0001, ****: p ≤ 0.0001. All statistical analysis was
generated with GraphPad Prism 7 and values are expressed as the mean ± standard
error of the mean (SEM).
4.4 Results
4.4.1 Extended extinction training results in decreased freezing in comparison to recent and remote auditory fear memories.
We assessed whether auditory fear conditioning conducted seven days apart
(ER, 7dR and 1dR) would result in comparative levels of freezing to CS/US pairings.
All animals presented similar freezing levels to each pairing with a two-way
ANOVA showing no significant interaction (F4,81 = 0.41,ns, p = 0.8018). Percentage
freezing increased significantly (F2,81 = 42.36, ****, p < 0.0001 ) after each
additional pairing (Figure 2a). This data indicate freezing levels to auditory fear
conditioning were consistent across all groups prior to extinction training. The
association formed between the CS and US was of equivalent strength across groups,
as revealed by the small difference in magnitude of freezing response at each pairing
(F2,81 = 0.55, ns, p = 0.5817). Twenty-four hours following fear conditioning, we
tested the effect of extinction training on freezing levels. The training consisted of 20
presentations of the CS alone. For within-session analyses, five presentations were
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averaged to represent one trial or block of extinction (Figure 2b-d). Subjects showed
higher % freezing during the first block (CS x 5) of extinction trials compared to the
last block (ER: 1st block 73.7 ± 4.8%, 4th block 16.2 ± 4.0%) on the first day of
training, indicating successful recall of auditory conditioned fear at the beginning of
the extinction session. A significant interaction of time was seen between the first
and last block of presentations (beginning vs end of training, first day: t 588 = 11.58,
****, p < 0.0001; second day: t 588 = 8.92, ****, p < 0.0001; third day: t 588 = 1.61,
***, p = 0.0006 ) indicating the effectiveness of extinction training. Together this
data suggests successful extinction learning occurred, but highlights the spontaneous
renewal that can occur after the passage of time (24 h) (Bouton & King, 1983;
Gregory J Quirk, 2002; Rescorla & Heth, 1975) by showing the increase in freezing
at the beginning of each days extinction training.
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Figure 4-2: Recall of auditory fear consolidation and extended extinction training result in differing levels of freezing.
(a) Freezing due to auditory fear conditioning conducted on different days resulted in similar levels of freezing for each pairing of US and CS. F4,81 = 0.41 , ns, p = 0.81, with a significant increase in freezing between the first and last tones: F2,81 = 42.36 ****, p < 0.0001, % freezing on final pairing: ER 54%, 7dR 61%, 1dR 54%. (b-d) Auditory fear conditioning was successfully recalled and extinguished as revealed by blocks of extinction training. A significant difference in freezing levels was seen in the beginning versus end of training each day; Day one: block one = 73.7 ± 4.8 % in comparison to the last block of training, block four = 16.2 ± 4.0 %; t 588 = 11.58, ****, p < 0.0001. Day two: t 588 = 8.92, ****, p < 0.0001 and day three: t 588 = 1.61, ***, p = 0.0006. Extinction training resulted in lower freezing levels at the beginning of each session due to repeated exposure to tone on the previous day: day one = 57.5 ± 7.3 %; day 2 = 35.8 ± 5.9 %; day three = 14.3 ± 4.7 %) and negligible levels of freezing by the third day: ER 2.2 %, 7dR 2.7 %, BC 0.6 %. ER subjects exhibited significantly higher levels of freezing than both the 7dR and BC groups in all four blocks on the first day of training (block 1: ER vs 7dR, ****, p < 0.0001; ER vs BC, ****, p < 0.0001; block 2: ER vs 7dR, ****, p < 0.0001; ER vs BC, ****, p < 0.0001; block 3: ER vs 7dR, ****, p < 0.0001; ER vs BC, ****, p < 0.0001; block 4: ER vs 7dR, **, p = 0.0040; ER vs BC, *, p = 0.0243. In the first two blocks on the second day of training: block 1: ER vs 7dR, ****, p < 0.0001; ER vs BC, ****, p < 0.0001; 7dR vs BC, **, p = 0.0038; block 2: ER vs 7dR, ***, p = 0.0002; ER vs BC, **, p = 0.0030.
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On the third day of extinction training: block 1: ER vs 7dR, ***, p = 0.0008; ER vs BC, ***, p < 0.0005. On each day of training the interaction (indicated by bar) remained significant but decreased on the final day of training (first day: ****, p < 0.0001; second day: ****, p < 0.0001; third day: *, p = 0.0431). (e) Lower freezing levels were observed in the extinction group (ER) compared to animals that did not undergo extinction training (7dR, 1dR); ER = 8.0 ± 2.9 %, 7dR = 53.6 ± 3.1 %, BC = 4.8 ± 4.1 %, 1dR = 38.2 ± 4.6 %. Significant differences were noted between the extinction and fear conditioned groups at the second tone and third tones respectively, (second tone: ER vs7dR, **, p = 0.0012, ER vs 1dR, *, p = 0.0265; third tone: ER vs7dR, **, p = 0.0015). Recent and remote retrieval of a fear memory did not reveal significantly different rates of freezing when compared to each other. Variability among groups as shown by significance bar: F (3,108) = 18.68, ****, p < 0.0001. Data are presented as mean % freezing ± S.E.M; n=10 rats/group, **: p ≤ 0.01, ****: p ≤ 0.0001, two-way ANOVA followed by Bonferroni post hoc analysis.
To evaluate the recall of a consolidated fear or extinction memory, the animals
underwent a 10 minute memory test consisting of 3 x CS only presentations or 10
minutes in context for the box control group (Figure 2e). Freezing levels were
significantly lower in extinction animals compared with those that did not undergo
extinction training (ER vs 7dR, F3,108 = 18.68, ****, p < 0.0001). Bonferroni’s post
hoc comparisons revealed % freezing to be relatively equivalent between the
extinction and box control groups (ER vs BC recall 1: t108 = 1.01, ns, p > 0.9999;
recall 2: t108 = 0.41, ns, p > 0.9999; recall 3: t108 = 0.71, ns, p > 0.9999 ), but higher
in the recent and remote retrieval animals compared to box control (BC vs 7dR recall
1: t108 = 3.57, **, p = 0.0032; recall 2: t108 = 4.26, ***, p = 0.0003; recall 3: t108 =
3.08, ***, p = 0.0158; BC vs 1dR recall 1: t108 = 2.87, *, p = 0.0296; recall 2: t108 =
3.32, **, p = 0.0074; recall 3: t108 = 1.27, ns, p > 0.9999). This suggests the extended
extinction protocol to be highly effective in consolidating an extinction memory.
4.4.2 Spatial patterns of pMAPK expression differ in subregions of the amygdala due to the recall of consolidated or extinguished fear memories.
As pMAPK is implicated in the neuroplasticity required for fear memory
consolidation and extinction learning, we evaluated expression levels within sub
regions of the amygdala and prefrontal cortex. At Bregma -3.36 mm (Figure 3a) the
lateral amygdala is well represented and easily identified by the opening of the lateral
146
ventricle. Across 24 subjects (ER n = 7, 7dR n = 5, BC n = 7, 1dR n = 5) we
observed a significant difference in neuron counts between the box control and all
other experimental conditions, confirming the activation of pMAPK during fear and
extinction memory recall (F3,68 = 4.76, **, p = 0.0045). This was further established
with post hoc analyses ER: t68 = 3.07, *, p = 0.0187; 7dR: t68 = 2.77, *, p = 0.0431
and 1dR: t68 = 3.12, *, p = 0.0161, suggesting pMAPK activation increased in the LA
due to the recall of both fear conditioned and extinction memories.
Figure 4-3: Recall of a conditioned fear memory and extinction memory result in spatially different patterns of pMAPK expression in subregions of the amygdala.
(a) Depiction of the lateral amygdala at Bregma -3.36 mm (Paxinos & Watson, 2007). (b) The LAd was revealed to have a significantly higher number of activated neurons (F3,20 = 14.46, ****, p < 0.0001) due to the recall of both recent and remote auditory fear conditioned memories compared to
147
box control, 7dR: t20 = 4.37, **, p = 0.0018; 1dR: t20 = 6.13, ****, p < 0.0001. The extinction group was also observed to have significantly less activation than the recent auditory fear conditioned group, ER vs 1dR: t20 = 4.08, **, p = 0.0035. Neuron counts in the LAvm revealed a significantly greater number of pMAPK+ neurons in the remote fear memory group (7dR) than the box control, F3,20 = 4.30, *, p = 0.0170; 7dR vs. BC: t20 = 3.37, *, p = 0.0184. Investigation of the LAvl revealed a significantly higher activation after the recall of an extinction memory in comparison to all other groups, F3,20 = 10.99, ***, p = 0.0002; ER vs 7dR: t20 = 4.15, **, p = 0.0030; ER vs BC: t20 = 4.65, ***, p = 0.0009; ER vs 1dR: t20 = 4.78, ***, p = 0.0007, indicating a pattern of activation specific to extinction learning occurred in the LAvl. (c) Topographic density maps were generated using a matrix created from the XY coordinates of pMAPK+ neurons within 100 µm2 bins, to visualize the spatial distribution of the activation. Below each map is its coefficient of variance (CV) map, generated by dividing the standard deviation by the mean. Regions showing < 1 depict stable neuronal populations. Data are presented as mean ± S.E.M; ER n = 7, 7dR n = 5, BC n = 7, 1dR n = 5, *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001, ****: p ≤ 0.0001, one-way ANOVA followed by Bonferroni post hoc analysis.
The LA was divided into three sub regions for further analysis (Figure 3b). The
LAd showed a significantly higher number of activated neurons (F3,20 = 14.46, ****,
p < 0.0001) due to the recall of both recent and remote auditory fear conditioned
memories compared to box control (7dR vs BC **, p = 0.0018, 1dR vs BC ****, p <
0.0001). Significantly less pMAPK activation was observed in the LAd of the
extinction memory recall group compared to the recent fear memory recall group
(ER vs 1dR **, p = 0.0035). In 2011, Bergstrom et al. showed similar findings
regarding the localization of auditory conditioned fear memories within the LAd
(Hadley C Bergstrom et al., 2011). A difference was noted in the LAvm between the
7dR and box control groups (7dR vs. BC *, p = 0.0184), with no differences seen in
the distribution of pMAPK activated neurons in either the 1dR or ER groups when
compared with the naive animals. This may suggest the recruitment of a second sub
region for the remote recall of an auditory fear memory. Analysis of the LAvl
revealed a significantly higher number of pMAPK+ neurons after the recall of an
extinction memory in comparison to all other groups (ER vs 7dR: **, p = 0.0030; ER
vs BC: ***, p = 0.0009; ER vs 1dR: ***, p = 0.0007), indicating a pattern of
activation specific to extinction learning. This can be visualized in the topographic
density maps and CV maps for stability depicted in Figure 3c.
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4.4.3 Spatial analysis of pMAPK expression in the LAvl reveals a stable population of neurons specific to the recall of an extinction memory.
Difference and CV maps were generated to visualize any patterns of pMAPK
expression unique to the extinction condition (Figure 4a). To do this, binned data
from the non-extinguished control animals (7dR) was subtracted from the extinction
matrix (ER). The CV map was generated to show the stability of any neuronal
population identified. CV values < 1 that corresponded to the regions of greatest
difference (ER – 7dR) indicated stable populations. As the LAvl appeared to be the
focal point for pMAPK expression after the recall of an extinction memory, spatial
analyses were conducted on the LAvl, across all subjects, on 263 neurons in 25 bins
at Bregma -3.36 mm. The LAvl data matrix was compared to reveal a significant
difference between conditions (ER vs 7dR, ***, p = 0.0006) (Figure 4c). The
topography of pMAPK+ neurons was further analysed with FDR corrected mass
multiple comparisons, which resulted in a discovery of 2/25 (8 %) significantly
different bins between the ER and 7dR groups (Figure 4b). Post hoc analysis
revealed significantly more activation in the extinguished animals relative to controls
(mean of two sig. bins ***, p = 0.0004, 4:1 ratio) (Figure 4d). Reported CV values of
the MROIs where < 1 suggesting a statistically stable pattern of neurons encoding
extinction memory recall within the LAvl, across individual brains at Bregma -3.36
mm. Together these results indicate a stable neuronal population within the LAvl,
undergo neuroplastic changes to recall extinction memories.
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Figure 4-4: Spatial analysis of pMAPK expression in the LA reveals a stable population of neurons specific to the recall of an extinction memory.
(a) A subpopulation of neurons specific to the recall of an extinction memory was identified within the LAvl. A difference map was generated by subtracting non-extinguished group (7dR) binned data from the extinguished group (ER). The CV map allowed visualization of the stability of the neuronal population within the LAvl. Regions showing < 1 depict stable neuronal populations. (b) A q value matrix revealed a discovery of 2/25 (8 %) bins between the ER and 7dR groups, q < 0.10. (c) The LAvl data matrix was compared to reveal a significant difference between conditions, ER 0.79 ± 0.1 vs 7dR 0.32 ± 0.1, n = 25, R2 = 0.22, t = 3.66, ***, p = 0.0006. (d) Post hoc analysis of discovered bins showed the mean of the two significant bins was 0.4 ± 0.2, ***, p = 0.0004, t10 = 5.18, reporting a 4:1 ratio of neurons between conditions, with a difference between means of 1.29 ± 0.7, R2 = 0.73. CV values of the MROIs where < 0 suggesting a statistically stable neuronal population. Data are presented as mean ± S.E.M; ER n = 7, 7dR n = 5, BC n = 7, 1dR n = 5, ***: p ≤ 0.001, t-tests and two-way ANOVA with FDR correction for multiple comparisons.
150
4.4.4 pMAPK expression was observed in PL cortex following recent and remote recall of an auditory fear memory.
Analysis of pMAPK+ neurons at Bregma +3.00 mm was conducted on the
medial prefrontal cortex of 31 rats: ER n = 10, 7dR n = 6, BC n = 6, 1dR n = 9
(Figure 5a). A between groups one way ANOVA of the mPFC (PL + IL) showed an
effect of memory recall on the number of pMAPK neurons activated (F3,58 = 3.91, R2
= 0.17, *, p = 0.0130) revealing increased densities of pMAPK+ neurons in the
recent recall of an auditory fear memory (BC vs 1dR: t58 = 3.37, **, p = 0.0080).
When analysed across 3 Bregmas of the PFC (Bregma +3.24 mm, +3.00 mm and
+2.76 mm) the difference increased, (F3,164 = 8.36, R2 = 0.13, ****, p < 0.0001)
(Figure 5b). Post hoc tests revealed significantly higher pMAPK+ neuron densities in
two groups (ER and 1dR) suggesting the activation of the ERK/MAP kinase pathway
is necessary for recall of both memory types. This data supports the importance of
mPFC modulation in recent fear memory recall.
Figure 4-5: Recall of auditory fear and extinction memory both activate pMAPK expression in the medial prefrontal cortex.
(a) Quantification of activated pMAPK neurons in the medial prefrontal cortex at Bregma +3.00mm showed significant difference between groups t58 = 3.91, *, p = 0.0130 and increased numbers of pMAPK + neurons were seen due to the recall of recent auditory fear conditioned memories in comparison to box control, 1dR vs. BC: t58 = 3.37, **, p = 0.0080. (b) Across 3 Bregma locations in the mPFC ( +3.24mm, +3.00mm, +2.76mm), the difference between groups increased t164 = 8.36, ****, p < 0.0001. Post hoc tests revealed higher pMAPK+ neuron densities in two groups (ER vs BC: t164 = 3.16, *, p = 0.0113 and 1dR vs BC: t164 = 4.92, ****, p < 0.0001 ) as compared to box control
151
suggesting the activation of the ERK/MAP kinase pathway is activated for recall of both memory types. Data are presented as mean ± S.E.M; ER n = 10, 7dR n = 6, BC n = 6, 1dR n = 9, *: p ≤ 0.05, ***: p ≤ 0.001, one-way ANOVA followed by Bonferroni post hoc analysis.
The mPFC subdivisions used for analysis consisted of the PL cortex and IL
cortex (Figure 6a). The PL is well known for its role in fear memory recall (Corcoran
& Quirk, 2007). One-way ANOVA and post hoc analysis with Bonferroni correction
was conducted on neuron counts within the PL cortex. Our findings supported the
previously noted discovery with greater pMAPK expression in the recent recall
group (1dR vs. ER: *, p = 0.0276; 1dR vs. BC: ***, 0.0008), with no significant
difference between the number of pMAPK+ neurons activated in the PL due to the
recall of a recent or remote fear memory.
2
0
PL
IL
0
50
100
150
200
250
PL + 3.00
No.
of p
MA
PK
+ N
euro
ns
****
IL + 3.00
**
ER
7dR
BC
1dR
Condition
152
Figure 4-6: Remote recall of an extinction memory and recent recall of an auditory fear memory both activate pMAPK expression in the infralimbic cortex.
(a) Depiction of the medial prefrontal cortex at Bregma +3.00mm (Paxinos & Watson, 2007). (b) The PL cortex showed increased numbers of pMAPK + neurons due to the recall of recent auditory fear conditioned memories and extinction memories compared to box control, 1dR vs. ER: t27 = 3.09, *, p = 0.0276; 1dR vs. BC: t27 = 4.44, ***, p = 0.0008. There was a nonsignificant trend level difference between remote recall and box control (ns, p = 0.0684). Significant changes in neuronal numbers were seen in the IL cortex between the extinguished (ER) and recent recall (1dR) groups when compared to the naive animals (BC): ER vs BC: t27 = 3.17, *, p = 0.0224; 1dR vs BC: t27 = 3.33, *, p = 0.0152. (c) Topographic density maps were generated to visualize the spatial distribution of the activation. Below each map is its coefficient of variance (CV) map, regions showing < 1 depict stable neuronal populations. Data are presented as mean ± S.E.M; ER n = 10, 7dR n = 6, BC n = 6, 1dR n = 9, *: p ≤ 0.05, ***: p ≤ 0.001, one-way ANOVA followed by Bonferroni post hoc analysis.
There is an established view that the IL is involved in the recall of extinction
memories (Milad & Quirk, 2002) and more recently, argument for involvement only
in extinction learning and not recall (Do-Monte, Manzano-Nieves, Quiñones-
Laracuente, Ramos-Medina, & Quirk, 2015). Results from our statistical evaluation
of neuron counts in the IL, after the remote retrieval of an extinction memory
indicated no significant difference between the memory recall of extinguished and
non-extinguished animals (F2,22 = 1.71, ns, p = 0.2046). Post hoc tests comparing all
conditions revealed differences between the box control animals and some conditions
(ER vs BC *, p = 0.0276; 1dR vs BC ***, p = 0.0008, see Figure 6b). To test the
veracity of this result, analysis was performed on the number of activated pMAPK
neurons within the IL across 3 Bregmas (+3.24 mm, +3.00 mm and +2.76 mm). The
outcome confirmed the original finding with significant difference only to the box
control animals (ER vs BC: ***, p = 0.0002; 1dR vs BC: ***, p = 0.0004). These
results are in accordance with the later findings on the suspected role the IL cortex
153
plays in fear memory extinction, whereby it only exhorts influence during learning.
However, visualization of the spatial distribution of pMAPK+ neurons in the IL
cortex shows a slightly greater density of expression during extinction recall, in
support of the original findings on IL activation for recall. Figure 6c depicts density
and CV maps for both the PL and IL cortex, where the neuron distribution can be
visualized within specific cortical layers.
4.4.5 Patterns of pMAPK expression in the PL cortex reveal activation of cortical layers differ between the recall of recent and remote auditory fear memories.
A visual difference was noted in the density of neurons in the superficial and
deep layers of the recent (1dR) and remote (7dR) auditory fear memory groups (see
Figure 6c). This prompted further investigation into pMAPK activation in the PL
cortex. Difference and CV maps for recent recall less remote recall are depicted in
Figure 7a. The topographic density matrix of pMAPK+ neurons in the PL was greater
due to recent recall of an auditory fear memory when compared to the remote recall
of a fear memory (1dR vs 7dR: ****, p < 0.0001) (Figure 7c). FDR corrected mass
multiple comparison revealed a discovery of 33/261 (~ 8 %) significantly different
MROIs (q < 0.10) between the 1dR and 7dR groups (Figure 7b). Post hoc analysis of
the 33 MROIs showed significant difference between conditions (1dR vs 7dR: ****,
p < 0.0001) (Figure 7d). A CV was calculated for each MROI and 26 bins reported
densities < 1, defining a stable distribution of neurons encoding recent fear memory
recall within the PL across individual brains at Bregma +3.00 mm.
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Figure 4-7: Spatial analysis of pMAPK expression in the PL reveals a different neuronal distribution between recent and remote recall of an auditory fear memory.
(a) A greater density of pMAPK+ neurons was observed in the deep layers of the PL cortex when remote recall (7dR) was subtracted from recent (1dR) of auditory fear memories (difference map). The CV map showed this region to be highly stable. (b) A q value matrix was developed from the PL cortex binned data. FDR corrected mass multiple comparison revealed a discovery of 33/261 significantly different MROIs (q < 0.10) between the 1dR and 7dR groups. (c) Post hoc analysis of the binned matrix revealed a significant difference between groups, t520 = 5.37, ****, p < 0.0001, difference between means 0.28 ± 0.05 and variance F260 = 1.43. (d) Post hoc analysis of the 33 MROIs showed significant difference, t64 = 15.47, ****, p < 0.0001, with a difference between means of 1.39 ± 0.09, R2 = 0.79 and variance of F32 = 2.37. A CV was calculated for each 26 out of 33 MROIs
7dR 1dR0.0
0.5
1.0
1.5
2.0
PL bin matrix
Condition
No
. of
pM
AP
K+
Neu
ron
s
****
7dR 1dR0.0
0.5
1.0
1.5
2.0
Mean of 33 MROIs PL
Condition
No
. of
pM
AP
K+
Neu
ron
s
****
155
showed values < 1, defining a stable distribution of neurons encoding recent fear memory recall within the deep layers of the PL cortex. Data are presented as mean ± S.E.M; ER n = 10, 7dR n = 6, BC n = 6, 1dR n = 9, ****: p ≤ 0.0001, t-tests and two-way ANOVA with FDR correction for multiple comparisons.
4.4.6 Distribution of pMAPK+ neurons in the IL cortex supports findings of IL activation during extinction memory recall.
Spatial analysis was performed on the mPFC (PL + IL) across subjects, on
3954 neurons in 348 bins. A difference was noted in the IL cortex between the
distributions of pMAPK+ neurons after the recall of memories from extinguished
(ER) and non-extinguished animals (7dR), supported by the CV map displaying
regions of stability (Figure 8a). This may suggest a function for the infralimbic
cortex in the recall of a remote extinction memory. Post hoc analysis of the binned
matrix for the IL cortex resulted in significant difference between conditions (ER vs
7dR: **, p = 0.003) (Figure 8c) and a q value matrix (q < 0.10) revealed a discovery
of three bins (Figure 8b). Analysis of the three MROIs resulted in a significant
difference (ER vs 7dR: **, p = 0.0030), with two of the three bins considered
representative of stable neuronal populations, with CV values < 1 (Figure 8d).
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Figure 4-8: pMAPK expression in the IL cortex following recall of an extinction memory.
(a) A subpopulation of neurons specific to the recall of an extinction memory was identified within the IL cortex. A difference map was generated by subtracting the non-extinguished group (7dR) binned data from the extinguished group (ER). The CV map allowed visualization of the stability of the neuronal population within the IL cortex. Regions showing < 1 depict stable neuronal populations. (b) A q value matrix revealed a discovery of 3/87 (3 %) bins between the ER and 7dR groups, q < 0.10. (c) Post hoc analysis of the binned matrix for the IL cortex resulted in a significant difference ER vs 7dR: t 172 = 3.00, **, p = 0.003, 0.14 ± 0.05, R2 = 0.05. (d) Post hoc analysis of discovered bins showed significant difference between conditions t4 = 6.45, **, p = 0.0030 with the difference between means of three significant bins 1.056 ± 0.16, R2 = 0.91. CV values of 2 of the MROIs where < 1 suggesting a stable neuronal population. Data are presented as mean ± S.E.M; ER n = 10, 7dR n = 6, BC n = 6, 1dR n = 9, **: p ≤ 0.01, t-tests and two-way ANOVA with FDR correction for multiple comparisons, q = 0.10.
ER 7dR0.0
0.5
1.0
1.5
2.0
IL bin matrix
Condition
No
. o
f p
MA
PK
+ N
eu
ron
s
**
ER 7dR0.0
0.5
1.0
1.5
2.0
Mean of 3 MROIs in IL
Condition
No
. of
pM
AP
K+
Neu
ron
s
**
157
4.5 Discussion
This study sought to identify the micro topography of neurons associated with
extinction memory encoding with prefrontal cortex and amygdala. We aimed to
identify the stable topography of neurons undergoing plasticity following extinction
memory recall in amygdala and prefrontal cortex. We identified a population of
pMAPK+ neurons consistently activated in the LAvl and the IL cortex following
extinction memory recall, elucidating the contributions of these sub regions in
extinction memory encoding.
Our results also show fear memory encoding alters between recent and remote
memory recall. We identified a specific role for the dorsolateral portion of the lateral
amygdala in the recent recall of a fear memory, consistent with our previous findings
(Hadley C Bergstrom, Craig G McDonald, Smita Dey, Gina M Fernandez, et al.,
2013; Hadley C Bergstrom, Craig G McDonald, Smita Dey, Haying Tang, et al.,
2013; Hadley C Bergstrom et al., 2011; Luke R Johnson et al., 2012). In contrast,
reduced activation in the LAd was observed following seven-day remote recall of
extinction memory, which coincided with an increased pattern of activation within
the ventromedial portion of the lateral amygdala. Time dependent differential
activation was also identified in the PL involving cell layer diversity as opposed to
sub regional changes. Superficial cortical layers showed consistent activation in both
recent and remote memory recall, whereas deeper layers were activated only during
recent recall of fear memories suggesting changed IL involvement in the remote
recall of fear memories.
Using an innovative and comprehensive methodology we were able to
identify a difference in the spatial allocation of pMAPK+ neurons in the PL cortex
due to the recent and remote recall of fear memories. Our topographic maps revealed
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a direct quantitative comparison of pMAPK activation within specific cell layers of
the mPFC. We showed the recall of a fear memory 24h after conditioning
predominantly required layers V and VI of the PL cortex in conjunction with layers
II and III. In 2004, Vertes used anterograde anatomical tracers to show labelled
neurons in layers V and VI of the PL cortex projected to many limbic regions
(Vertes, 2004). Layers II and III primarily process information from the midline
thalamus and ventral hippocampus (Little & Carter, 2012). When the recall of the
conditioned memory was remote by seven days we found pMAPK activated neurons
restricted to superficial layers (II and III) only, suggesting the possibility of a greater
role for the broader limbic system in recent recall. Together, our data demonstrate a
consistent requirement for both amygdala and PL cortex in recent and remote
auditory fear memory retrieval. Interestingly, we show an internal re-organisation of
both amygdala (from LAd to LAvl) and within PL when recent auditory fear
memories are made remote. This highlights the complexities of systems
consolidation within these micro anatomical regions.
Numerous animal studies revealed the IL, in particular, plays a role in fear
memory extinction and for many years the inference that the IL was required for all
aspects of extinction memory formation remained uncontested (Delamater &
Westbrook, 2014; Milad & Quirk, 2002). With recent advances in technology, Do-
Monte and associates reported optogenetic inactivation of glutamatergic neurons
within the IL did not affect the recall of an extinction memory (Do-Monte, Manzano-
Nieves, et al., 2015). However, in support of our work, earlier studies by Laurent and
Westbrook inactivated the IL resulting in impaired extinction retrieval, suggesting
other glutamatergic neurons may have been affected (Laurent & Westbrook, 2009).
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Our study suggests a possible role for the IL in the recall of remote extinction
memories. Our spatial analysis affirmed this trend by displaying a small pattern of
consistently activated neurons within the IL cortex, specific to the extinguished
group. This is in accord with recent chemogenetic studies showing inhibition of the
IL to BLA pathway at time of extinction learning can reduce the recall of an
extinction memory (Bloodgood, Sugam, Holmes, & Kash, 2018). The relatively
small sample size of the non-extinguished group in the present study may account for
the lack of significance. This was evident by the small observed power size (power =
0.367) between the non-extinguished and extinguished groups. Further work is
therefore needed to confirm this result.
Emotional memories form part of the mechanism required to determine the
appropriate behavioural response to a threatening situation (Faliagkas, Rao-Ruiz, &
Kindt, 2018; Van der Kolk, 1994). PTSD sufferers not only have an impairment to
this mechanism but have an extinction retention deficit after training to extinguish
the fearful memory, for review see (Adams & Sweatt, 2002). Our study shows that a
portion of extinction memory recall is encoded in a stable specific subpopulation of
neurons localized to the LAvl, based on the relative distribution of neurons within
this region after the remote recall of extinction memories. Examination of a protein
synthesis marker such as arc (Arg 3.1) (Mamiya et al., 2009a) could also enhance the
knowledge gained from studying pMAPK, a single molecular marker of neuronal
plasticity. Further to this, a larger number of bregma sites and time points could
expand the current recorded data and identify other regions involved in extinction
memory recall. To further investigate this fundamental molecular finding,
DREADDs (K. S. Smith, Bucci, Luikart, & Mahler, 2016), optogenetics (Do-Monte,
Manzano-Nieves, et al., 2015) or local micro-infusions of pMAPK inhibitors (Herry
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et al., 2006) could further expand our knowledge of the significant role this sub-
region plays in the extinction of fear memories.
Our findings within the mPFC provide new insight into the organization of
laminar activation due to fear conditioning and extinction recall, which enhances our
understanding of the neurobiological substrates of fearful memories. Future studies
involving retrograde tracers may be useful to assist in determining the specific
circuits mediating this behaviour (Bloodgood et al., 2018; Marek et al., 2018b).
Analysis revealed several regions with a four-fold increase in MAPK activated
neurons following extinction memory recall, proposing potential micro-target regions
for future therapeutic interventions. As the neurobiological mechanisms of extinction
are thought to be compromised in PTSD, a thorough understanding of these
mechanisms is necessary for providing potential new therapeutic sites and targets.
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Microglial phenotype alters with varied fear memory
recall in the prefrontal cortex.
This chapter comprises the following article, currently undergoing revision: Jacques, A., Chaaya, N., Beecher, K., Ali, S. A., Patkar, S. O., Battle, A. R., Johnson, L. R. Belmer, A., Chehrehasa, F., Bartlett, S. E. Microglia phenotype alters in the prefrontal cortex with varied fear memory recall. Brain, Behavior and Immunity. Submitted 27th December, 2018. In revision.
Chapters 5 and 6 investigate the role of microglia in fear memory consolidation
and recall, as outlined in aim 2. Briefly this entails establishing levels of c-Fos, Arc,
pMAPK and BDNF, and defining changes to microglia number and phenotype as a
result of differing fear memory types.
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1. they meet the criteria for authorship in that they have participated in the conception, execution, orinterpretation, of at least that part of the publication in their field of expertise;
2. they take public responsibility for their part of the publication, except for the responsible author who accepts overall responsibility for the publication;
3. there are no other authors of the publication according to these criteria;
4. potential conflicts of interest have been disclosed to (a) granting bodies, (b) the editor or publisher of journals or other publications, and (c) the head of the responsible academic unit, and
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In the case of chapter 5
Microglial phenotype alters with varied fear memory recall in the prefrontal cortex.
Publication status: In revision
Contributor Statement of contribution*Angela Jacques Involved in the conception and design of the project, behavioural and
laboratory experiments, imaging, analysed data, created the figures and wrote and edited the manuscript.
Nicholas Chaaya Assisted with editing the manuscript, behavioural and laboratory experiments and imaging.
Kate Beecher Assisted with editing the manuscript.
Syed Aoun Ali Assisted with editing the manuscript.
Omkar Patkar Assisted with laboratory experiments.
Andrew Battle Assisted with editing the manuscript.
Luke Johnson Involved in the conception and design of the project and assisted with editing the manuscript.
Arnauld Belmer Assisted with imaging.
Fatemeh Chehrehasa
Assisted with interpretation of data and editing of manuscript
Selena Bartlett Assisted with reviewing and editing the manuscript.
QUT Verified
Signature
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5.1 Abstract
In the central nervous system, microglia act as immune cells and respond to all forms
of pathophysiological events from infection to traumatic brain injury. In the healthy
brain, resting (or surveillance) microglia actively survey functional brain tissue to
maintain homeostasis. It is accepted that microglia respond to environmental
challenges such as stress but it is unknown if their morphology is altered as a result
of fear. First to establish levels of neuronal activity we investigated the influence of
fear memory recall on the expression of the immediate early genes c-Fos and Arc.
Following, we note any microglial phenotypic changes as a consequence of three
types of fear memory recall: recent fear, remote fear and extinction memory.
Histological analysis of ionized calcium binding adaptor molecule 1 (Iba1), was
employed to identify microglia while cell tracing was utilised to determine
morphological adaptations. Our results demonstrate that microglia alter their
phenotype after the recall of temporally distinct fearful events.
5.2 Introduction
Microglia facilitate learning-induced plasticity of glutamatergic synapses (Parkhurst
et al., 2013) through the secretion of brain-derived neurotrophic factor (BDNF) and
constantly undergo morphological change whilst monitoring their microenvironment
(Wake et al., 2009). They are myeloid cells which continuously monitor the brain
for invading micro-organisms, damaged neurons and to prune synaptic terminals
(Paolicelli et al., 2011) by extension or retraction of their processes. These non-
neuronal cells may respond to pathological brain changes in a similar manner to
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macrophages (Hanisch & Kettenmann, 2007) and are capable of evoking an innate
immune response (Kettenmann, Hanisch, Noda, & Verkhratsky, 2011).
Microglia are capable of morphological and functional adaptation from a
heavily branched or ramified shape to an amoeboid shape. To date, reports show
unramified microglia tend to have a large or occasionally elongated cell body with
short, distended extensions, are said to be “phagocytic” and provide a pro-
inflammatory role (Karperien, Ahammer, & Jelinek, 2013; Stence, Waite, & Dailey,
2001). Microglia with a highly ramified phenotype are called surveillance or resting
microglia (Swanton et al., 2018) and provide neurotrophic factors (Nakajima &
Kohsaka, 2002), mediate pain (Watkins, Milligan, & Maier, 2001), monitor and
prune synapses (Pow, Perry, Morris, & Gordon, 1989). Although many studies
involve the microglial response to inflammatory conditions, structural remodelling of
surveillance microglia can occur as a result of natural environmental challenges
without any concomitant inflammation (Kongsui, Beynon, Johnson, & Walker, 2014)
(Tremblay, Lowery, & Majewska, 2010). Essential to neuroplasticity, surveillance
microglia monitor the synaptic connections within their micro environment at a rate
of once per hour (Wake et al., 2009). Reduced neuronal activity results in a reduced
frequency of this monitoring (Wake et al., 2009). The duration of a microglia-
synapse connection may range from 5 minutes to 1 hour, dependant on the functional
status of the synapse and whether synaptic pruning is required (Wake et al., 2009).
Stress has been shown to facilitate increased proliferation and adaptation to
the activated phenotype of microglia (Tynan et al., 2010; Varvel et al., 2012; Wohleb
et al., 2012). Therefore, we hypothesised that recalling a fear memory would also
alter the phenotype and function of these cells. We examined the prefrontal cortex
(PFC) as studies suggest remote memories are stored here (Knowlton & Fanselow,
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1998; Squire & Alvarez, 1995) with the medial PFC indicated to integrate emotional
memories such as fear (Paul W Frankland, Bontempi, Talton, Kaczmarek, & Silva,
2004). To investigate the changes in neuronal activity we examined immediate early
genes (IEGs) c-Fos and Arc which can provide a measure of neural activity and are
implicated in memory recall (Minatohara, Akiyoshi, & Okuno, 2016; Plath et al.,
2006). c-Fos expression has been shown to be greater in the prelimbic cortex
following fear conditioning than during extinction learning (Morrow, Elsworth,
Inglis, & Roth, 1999), with Arc being considered a contributing factor in the synaptic
plasticity required for memory storage (Plath et al., 2006). Our findings show recent
fear memory recall results in high neuronal activation, as evidenced by increased
expression of c-Fos and Arc. Further to this, we observed microglial phenotype
adaptations present in each case of fear recall. In this study prelimbic cortex tissue is
labelled with markers of immediate early genes (c-Fos and Arc) and microglia
(Iba1). Quantification and tracing of cells demonstrates a distinct morphological
change of microglia in response to temporally diverse fear memory recall.
5.3 Methods
5.3.1 Subjects
Experimentally naive adult male Sprague-Dawley rats (N = 21) were supplied
by Animal Resource Center, ARC, Western Australia and housed 3 per cage, in
temperature (≈ 24 °C) and humidity (35 %) controlled Plexiglas cages. The cages
were maintained on a 12-hour light/dark cycle with the behavioral procedures
conducted during the light cycle, as fear conditioning is reported to be more
effective during the nocturnal phase for rodents i.e. during the light cycle (Albrecht
& Stork, 2017). Rats were acclimatized to the vivarium for 7 days prior to training,
with food and water provided ad libitum. At time of memory recall testing, rats
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weighed 326.4 ± 6.7 g. All procedures were conducted in compliance with the
Animal Welfare Unit, The University of Queensland Research and Innovation Ethics
Committee and the Research Ethics Committee of the Queensland University of
Technology, Australia. Procedures complied with policies, regulations and ethical
standards for animal experimentation, in accordance with the Queensland
Government Animal Research Act 2001, associated Animal Care and Protection
Regulations (2002 and 2008), and the Australian Code for the Care and Use of
Animals for Scientific Purposes, 8th Edition (National Health and Medical Research
Council, 2013).
5.3.2 Apparatus
Two conditioning chambers were utilized for behavioral procedures, referred
to as Context A and Context B. The chambers were modified to create unique testing
environments for fear conditioning or extinction and to restrict any reaction to
context. Context A included a stainless-steel grid floor connected to a shock
generator and computer (Freeze frame software, Coulbourn instruments). Ethanol (80
%) was used as the cleaning agent and odorant in Context A and the chambers were
cleaned between each animal. To create contextual difference, Context B contained a
solid plastic floor with fresh bedding and internal colored decoration on the walls and
ceiling. Orange scented antibacterial soap was used as the cleaning agent and to
provide a unique smell to Context B. The chambers were contained in an acoustic
isolation box (Coulbourn Instruments, Allentown, PA, USA) with the background
noise level measured at 55 dB, using a sound level meter (Digitech Professional
Sound Level Meter QM1592). Each chamber contained a speaker, low-level house
light (2-3 lux), infrared light and infrared camera.
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5.3.3 Behavioral procedures
A total of 21 rats were randomized into three groups. Recent and remote
auditory fear conditioning was run using an extended extinction group as a control.
Both fear to context and extinction of fear are types of learning, therefore any
differences in IEG expression and changes in microglia morphology can be assumed
to be specific to the type of learning undertaken. Changes in microglia morphology
specific to exposure to the context alone in fear conditioning was recently published
by our lab (Nicholas Chaaya et al., 2019), therefore the unique control group of
extinction was chosen to ensure each group of animals had undergone the same fear
conditioning protocol and to highlight the changes in microglia through the recall of
different types of fear memories specifically. For two days prior to training, rats were
habituated to each context for 30 minutes. On the first day of training the RE and 7dF
groups underwent three minutes of acclimation to context A, followed by a 10-
minute conditioning protocol involving three pairings of an auditory conditioned
stimulus (CS, tone - 5kHz, 75 dB, 20 s) that co-terminated with an unconditioned
stimulus (US, foot shock - 0.6 mA, 500 ms). Stimuli were controlled through Freeze
Frame software (Coulbourn Instruments) and training was separated by a mean inter-
trial interval of 180 s. Rats were returned to home cages after final stimulus
presentation. The remote extinction recall (RE, n = 7) group then underwent three
days of extinction training. Each day the animals had 30 minutes of extinction
training (20 x CS alone, 5 kHz, 75 dB, 20 s) in Context B. The average inter-trial
interval for extinction training was 76 s and began after 5 minutes of habituation to
the context. To facilitate memory consolidation they were returned to their home
cage for three days then provided with a fear memory test (FMT) 1 hr prior to
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sacrifice on day 8. The FMT was a 10-minute memory recall test consisting of three
20 s CS presentations to observe the recall of a consolidated auditory cued fear
memory or extinction memory. The remote fear memory group (7dF, n = 7),
followed the same protocol, without exposure to tone during the extinction training
days. The recent fear memory group (1dF, n = 7), remained in their home cages until
the seventh day when they received auditory fear conditioning and 24 hr later
underwent a FMT (see Figure 1A).
Freezing, a behavioral index to quantitate a CS-US association
(Blanchard & Blanchard, 1969; Michael S Fanselow, 1984), was scored during the
20 s CS intervals by an experimenter blind to the conditions. These intervals were
indicated in the video recordings by an infrared light allowing the behaviour of the
7dF group to be scored for the same length of time and at the same time points as
each CS interval. Behavioral results (see Figure 1B) were expressed as percentage
time freezing (dependent variable).
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Figure 5-1 Recall of recent and remote fear and extinction memories result in different levels of freezing.
A. Experimental design. Recent and remote auditory fear conditioning was run using an extended extinction group as a control. Remote extinction recall (RE, n = 7) group underwent auditory fear conditioning followed by three days of extinction training and three days in the home cage, with a retrieval test 1 hr prior to sacrifice. A remote fear memory group (7dF, n = 7), underwent the same conditioning and followed the same protocol as the remote extinction group, however, were not exposed to the tone. The recent fear memory group (1dF, n = 7), remained in their home cages until the seventh day of experimentation when they received auditory fear conditioning and underwent a memory recall test 24 hr later. B. Behavioural results. Habituation: Habituation to context provided a base line freezing measure (0.2 ± 0.1%) with no difference between contexts (0.04 ± 0.2 %, t 34 = 0.23, ns, p = 0.8218). Fear conditioning: Auditory fear conditioning was conducted seven days apart with groups RE and 7dF on day 1 and 1dF on day 7. Percentage freezing increased at each pairing of CS and US (F2,48 = 23.22, ****, p < 0.0001 ). 1st, 2nd and 3rd extinction: The effect of extinction or no extinction training (20 presentations of CS alone or context alone, in context B) is shown as four CS presentations averaged to one block. Higher % freezing in the first block of extinction trials compared to the last block (RE: 1st block 83.5 ± 5.5%, 4th block 10.3 ± 3.3%) on the first day of training. There was a significant temporal interaction between the beginning vs end of training, first day: t 54 = 11.46, ****, p < 0.0001; second day: t 54 = 7.08, ****, p < 0.0001; third day: t 54 = 2.28, *, p = 0.0265). FMT: Recall of fear memory or extinction memory was tested with a 10-minute recall test consisting
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of 3 x CS only presentations. % freezing levels were lower due to remote extinction recall as opposed to fear memory recall (RE vs 7dF and 1dF, **, p = 0.0026). Dataarepresentedasmean%freezing±S.E.M,**:p≤0.01,****:p≤0.0001,two‐wayANOVAfollowedbyBonferroniposthocanalysis.
5.3.4 Tissue preparation
Rats were anesthetized with an injection of lethabarb (400 mg/kg, i.p.) and
transcardially perfused through the ascending aorta with ice-cold 1% (wt/vol)
paraformaldehyde (PFA) with 0.125% (vol/vol) glutaraldehyde followed by 4 %
(wt/vol) PFA with 0.125% (vol/vol) glutaraldehyde in 0.1 M phosphate buffer, 60
minutes post memory recall. Brains were extracted and post-fixed in 4 % PFA with
0.125% (vol/vol) glutaraldehyde overnight then stored in 0.1 M phosphate buffered
saline (PBS). Free-floating serial coronal sections (40 μm) of the medial prefrontal
cortex were prepared using a vibratome (M11000; Pelco easiSlicer, Ted Pella Inc,
Redding, CA, USA). Analysis was performed solely on the prelimbic section of the
medial prefrontal cortex. Tissue from the animals used for this research was also
used in other studies. These studies required the neuronal cell walls to hold a specific
resistance, thus the perfusion was performed without saline solution and with
glutaraldehyde added to the PFA.
5.3.5 Immunohistochemistry
Three prefrontal sections were taken from each subject to visualize Iba1
expression in microglia and cFos / Arc activation in neurons. The sections were
immunolabelled with antibodies against each. It must be noted that Iba-1 staining
may label microglia, infiltrating monocytes, macrophages and blood vessel-
associated macrophages. Briefly, the sections were washed three times in PBS
(containing 0.02 % sodium azide) and blocked in PBS containing, 0.3 % Triton X-
100, 0.05 % Tween 20 and 2 % Normal Horse Serum (NHS, Abcam, Vic, Aus) for 1
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h. After blocking sections were incubated with a goat monoclonal antibody (mAb) to
Iba1 (1:500; ab5076, Abcam, Vic, Australia), rabbit mAb to cFos (9F6) (1:300;
#2250, Cell Signaling Technology, MA, USA) and mouse mAb to Arc (c-7) (1:300;
sc-17839, Santa Cruz Biotechnology Inc., Santa Cruz, CA, USA), for 24 h at room
temperature. After wash, the sections were incubated with Alexa Fluor donkey anti-
sheep IgG 594, donkey anti-rabbit IgG 488 and donkey anti-mouse IgG 647 (1:500;
Invitrogen, Life Technologies, CA, USA) in blocking solution (see above) for 3 h.
Sections were mounted on silane-coated slides and cover slipped using ProLong
Gold antifade reagent (Invitrogen, DR, USA).
5.3.5 Imaging
3 sections per animal, n = 5 animals per group, totalling 15 sections per
condition, were imaged on an Olympus FV3000 confocal laser scanning microscope
(Olympus Australia Pty Ltd., VIC, Australia) using a 30X oil-immersion objective
(NA 1.05) with a 1.5 x zoom and a Z-axis step of 0.8 μm, using sequential scanning.
The sections were taken from the prelimbic cortex around Bregma +2.76mm, and
mosaics of the regions of interest (as depicted in Figure 2) were acquired in OIR file
format. Each mosaic consisted of a data volume of 1452000 µm3.
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Figure 5-2 Schematic drawing showing the location of the acquired micrographs.
A. Images were acquired around bregma +2.76mm (depicted by the blue / purple vertical line) in the medial prefrontal cortex. B. Representation of brain slice showing the prelimbic (PL) and infralimbic (IL) cortex. Mosaic images were captured from the PL, layer II & III (blue / purple square). Drawings depicted from (Paxinos & Watson, 2007).
5.3.6 Cell quantifications
For the purpose of cell quantification, volumes equaling x = 212 µm, y = 614
µm and z = 12 µm were scanned from the medial prelimbic cortex of each subject.
Cell quantifications were performed by an experimenter blind to the conditions,
using IMARIS software (IMARIS 9.1.2, Bitplane, ZH, CHE). Using the measuring
feature in IMARIS an average diameter was defined for each cell type. The cells
were reconstructed in 3D using the ‘spot detection’ function (cFos 4.3 µm, Arc 6 µm
and Iba1 2.7 µm) and automatically counted. Filter intensity was set by the
experimenter and images were batch processed using the same thresholding
parameters across experimental groups. The cell numbers including colocalisation
were obtained from the statistics function in Imaris (see Figure 3A-C, 4A). These
steps have been previously described in (Belmer, Klenowski, Patkar, & Bartlett,
2017; Nicholas Chaaya et al., 2019; Tarren, Lester, Belmer, & Bartlett, 2017).
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Neurolucida 360 software (Neurolucida 360, MBF Bioscience, VT) was used
to trace three microglia from each brain slice (RE n = 42, 7dF = 42, 1dF = 36). These
microglia were chosen by meeting the criteria of being centrally located in both the
middle of the scan and centered within the scan to allow for all branching to be
complete within the scans parameters. The cell body of each microglia was
automatically selected using soma detector sensitivity of 50, an interactive search
region of 20µm and a size constraint of 2 µm. The extensions were traced manually.
The traces were used to analyse the cell body volume and microglial processes,
inclusive of the number of extensions leaving the cell body (trees), the number of
branches off these extensions (branches) and the total length of all extensions (see
Figures 4 and 5). Neurolucida 360 uses the formula (sum of the terminal orders +
number of terminals) ∗ (total process length/number of primary branches) (Pillai et
al., 2012) to determine the ramification of microglia branching.
5.3.7 Statistical analysis
Analysis of behavioral conditions and cell counts were performed using t-
tests, one-way and two-way ANOVAs. Post hoc Bonferroni correction was used in
all cases to reduce type one errors synonymous with multiple comparisons. A p value
≤ 0.05 was stated as significant, *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.0001, ****: p ≤
0.0001. All statistical analyses were generated with GraphPad Prism 7 (GraphPad
Software Co., CA, USA) and values are expressed as the mean ± standard error of
the mean (SEM). As foot shock levels for this protocol were low, not all animals
formed an associative fear memory, therefore only data from animals shown to have
undergone successful fear conditioning through the display of freezing behaviour
when tested for the recall of an associative fear memory were included in the
behavioral analyses (removed: 7dF n = 1, 1dF n = 1). Four animals were removed
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due to inadequate perfusion (RE n = 2, 7dF n=1, 1dF n=1) and significant tissue
damage resulting in an inability to analyze the region of interest resulted in the
removal of several brain slices (RE n = 1, 7dF n = 1, 1dF n = 3). Statistical outliers
were identified and excluded using the ROUT method of statistical outlier
identification in GraphPad Prism 7. The ROUT method used combined robust
regression and outlier removal. It is used to fit a curve that is not influenced by
outliers. The residuals are analysed with a test adapted from the False Discovery
Rate approach for multiple comparisons. Once the outliers are removed ordinary
least-squares regression is performed on the remaining data (Graphpad, 2016).
5.4 Results
5.4.1 Remote extinction memory recall results in decreased freezing compared to recent and remote auditory fear memory recall.
For all behavioral results see Figure 1B. Baseline freezing behavior (0.2 ±
0.1%) was obtained to both fear conditioning and extinction contexts during the
habituation phase. Freezing behaviour to context A and context B was averaged
across both days and then compared. There was no significant difference between
contexts (0.04 ± 0.2 %, t 34 = 0.23, ns, p = 0.8218). Auditory fear conditioning was
conducted seven days apart with groups RE (n = 7) and 7dF (n = 6) on day 1 and 1dF
(n=6) on day 7. A two-way ANOVA was conducted to show freezing behaviour to
be equivalent between groups, while progressively increasing as a function of CS/US
pairing. Results from the two-way ANOVA showed no interaction to exist (F4, 48 =
0.22, ns, p = 0.93). Follow-up main effect confirmed that no differences between
behavioural conditions existed (F2, 48 = 0.01, ns, p = 0.99), while statistical
significant difference between CS/US pairing existed (F2, 48 = 23.22, ****, p <
0.0001), with each pairing resulting in higher fear expression. These data indicate the
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fear conditioning protocol employed here resulted in equivalent freezing to tone
across all groups.
Twenty-four hours following fear conditioning (RE and 7dF), the effect of
extinction training (20 presentations of CS alone in context B) on freezing levels was
tested. For analyses, four CS presentations were averaged to represent one trial or
block of extinction. Higher % freezing was observed during the first block (CS x 4)
of extinction trials compared to the last block (RE: 1st block 83.5 ± 5.5%, 4th block
10.3 ± 3.3%) on the first day of training, an indication of successful recall of auditory
conditioned fear at the beginning of the extinction session, and extinction of fear by
the end of the session. Statistical analyses across all extinction days revealed a
significant temporal interaction between the first and last block of presentations
(beginning vs end of training, first day: t 54 = 11.46, ****, p < 0.0001; second day: t
54 = 7.08, ****, p < 0.0001; third day: t 54 = 2.28, *, p = 0.0265) indicating the
effectiveness of extinction training over a period of three days. Together these data
suggest successful extinction learning occurred and highlights the spontaneous
renewal that occurred after each 24 h period (Bouton & King, 1983; Gregory J
Quirk, 2002; Rescorla & Heth, 1975) as seen by the increase in freezing at the
beginning of each day’s extinction training.
Following extinction training, remote fear conditioning (7dF), or recent fear
conditioning (1dF), recall of fear memory or extinction memory was tested with a
10-minute recall test consisting of 3 x CS only presentations. Two-way ANOVA
revealed no interaction as a function of condition versus CS/US presentation (F4, 48 =
0.80, ns, p = 0.53). Similarly, follow-up main effect of CS/US presentation was not
significant (F2, 48 = 0.97, ns, p = 0.39). Importantly, follow-up main effect of
condition was statistically significant (F2, 48 = 0.29.84, ****, p < 0.0001). Follow-up
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of simple main effect via Bonferroni corrected post hoc comparisons revealed %
freezing levels were significantly lower in extinction animals compared with those
that did not undergo extinction training (RE vs 7dF and 1dF, **, p = 0.0026),
suggesting the extended extinction protocol to be highly effective in consolidating
extinction memories.
5.4.2 Recall of recent fear memory results in increased c-Fos and Arc activation.
Analysis of c-Fos+ and Arc+ neurons and Iba1+ microglia was conducted on
the medial prelimbic cortex of 15 rats, 5 per condition (Figure 3). Following our labs
previous work mapping the location of neuroplastic change produced by the recall of
recent and remote fear memories and remote extinction memories, we noted
activation of the ERK/MAP kinase pathway was evident in the prelimbic cortex (A.
Jacques et al., 2017). These findings provided purpose to investigate microglial
activity in the prelimbic rather than the infralimbic cortex. 3 sample sections, each
with a volume of 2 x 106 µm3, were taken for analysis, from each animal and
averaged across the group. A between groups one way ANOVA showed an effect of
memory recall on the number of cFos+ neurons activated (F2,37 = 3.41, R2 = 0.16, *, p
= 0.0437). Post hoc tests revealed an increased density of cFos+ neurons in the
recent recall of an auditory fear memory in comparison to a remote extinction
memory and no significant difference between recent and remote memory recall (1dF
vs RE: t37 = 2.60, ns, p = 0.0399; 1dF vs 7dF: t37 = 1.20, *, p = 0.7161; 7dF vs RE: t37
= 1.46, ns, p = 0.4578). Analysis of Arc activation correlated these results with the
between groups one-way ANOVA showing memory recall activated Arc expression
(F2,37 = 16.6, R2 = 0.47, ****, p < 0.0001). Post hoc tests revealed an increased
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density of Arc+ neurons in the recent recall of a recent auditory fear memory in
comparison to both remote fear and remote extinction memories (1dF vs 7dF: t37 =
5.00, ****, p < 0.0001; 1dF vs RE: t37 = 5.10, ****, p < 0.0001; 7dF vs RE: t37 =
0.10, ns, p > 0.9999). Together these data are in concurrence with studies suggesting
greater activation of the prelimbic cortex occurs during the recall of recent fear
memories (for review see (Giustino & Maren, 2015)). There is an established view
that Arc mRNA activation colocalizes with c-Fos expressing cells (Fanous et al.,
2013). Our data revealed Arc immunoreactivity was present in c-Fos+ neurons and
this colocalisation was not affected by the type of memory recall tested (F2,37 = 0.9,
R2 = 0.04, ns, p = 0.4361; 1dF vs 7dF: t37 = 0.32, ns, p > 0.9999; 1dF vs RE: t37 =
1.24, ns, p = 0.6733; 7dF vs RE: t37 = 0.96, ns, p > 0.9999).
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Figure 5-3 c-Fos and Arc expression in the PFC are greater in the recall of a recent fear memory.
A. Analysis of c-Fos+ and Arc+ neurons was conducted on 3 sections (volume of 2 x 106 µm3) from each animal and averaged across groups. An increased density of cFos+ neurons was apparent after recent fear memory recall compared to remote extinction memory recall, with no change between recent and remote memory recall (1dF vs RE: t37 = 2.60, ns, p = 0.0399; 1dF vs 7dF: t37 = 1.20, *, p = 0.7161; 7dF vs RE: t37 = 1.46, ns, p = 0.4578). B. Arc expression (F2,37 = 16.6, R2 = 0.47, ****, p < 0.0001) increased due to the recent recall of a recent auditory fear memory in comparison to both remote fear and remote extinction memories (1dF vs 7dF: t37 = 5.00, ****, p < 0.0001; 1dF vs RE: t37
= 5.10, ****, p < 0.0001; 7dF vs RE: t37 = 0.10, ns, p > 0.9999). C. Arc immunoreactivity was present in c-Fos+ neurons however, colocalisation was not affected by memory type (1dF vs 7dF: t37 = 0.32, ns, p > 0.9999; 1dF vs RE: t37 = 1.24, ns, p = 0.6733; 7dF vs RE: t37 = 0.96, ns, p > 0.9999). Dataarepresentedasmean±S.E.M;*:p≤0.05,****:p≤0.0001,one‐wayANOVAfollowedbyBonferroniposthocanalysis.D.Representativeimagesofc‐Fos,ArcandIba1expression.
5.4.3 Microglia alter morphologically in response to recent and remote fear recall.
Social defeat stress is known to increase the number of Iba-1+ microglia in
the hippocampus, prefrontal cortex and amygdala (Wohleb et al., 2012). In line with
this finding fear may also contribute to the number and morphology of microglia.
Quantification of microglia nuclei (Figure 4A) revealed a variation in microglia
density between our memory recall groups (F2,37 = 10.37, R2 = 0.36, ***, p = 0.0003)
with post hoc analysis demonstrating the greatest difference in cell number between
early fear recall and remote extinction recall (1dF vs 7dF: t37 = 2.34, ns, p = 0.0743;
1dF vs RE: t37 = 4.55, ***, p = 0.0002; 7dF vs RE: t37 = 2.30, ns, p = 0.0810). This
data suggests an increased number of microglia are present within the prefrontal
cortex after the recall of recent fear memories. Figure 4 depicts graphically and
pictorially, the number and morphology of microglia found in each condition. The
complexity of the microglia refers to the normalization and comparison of processes
among microglia. It is calculated using the dendritic complexity index, (sum of the
terminal orders + number of terminals) * (total process length / number of primary
branches)(Pillai et al., 2012). Statistical analyses (ANOVA) revealed the degree of
ramification (complexity of the extensions, Figure 4B) was different between groups
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(F2,112 = 10.97, R2 = 0.16, ****, p < 0.0001), with post hoc analysis showing recent
fear memory recall to be significantly different from remote fear and extinction
memory recall (1dF vs 7dF: t112 = 3.02, **, p = 0.0093; 1dF vs RE: t112 = 4.63, ****,
p < 0.0001; 7dF vs RE: t112 = 1.58, ns, p = 0.3545). These data reveal well ramified
cells only in the remote extinction group, as clearly depicted in Figure 4C.
Alongside proliferation and ramification changes, experience dependent
modifications include morphological adaptations of cell body size and shape, and the
number and length of extensions protruding from the cell body (see Figure 5). A
between groups ANOVA of cell body volume was significant (F2,117 = 17.76, R2 =
0.23, ****, p < 0.0001), with Bonferroni corrected post hoc tests revealing
differences between the remote extinction versus both recent and remote fear
memory recall (RE vs 7dF: t117 = 5.18, ****, p < 0.0001; RE vs 1dF: t17 = 5.07, ****,
p < 0.0001; 7dF vs 1dF: t117 = 0.10, ns, p > 0.9999). This finding was repeated in
measurements of the cell body area (F2,117 = 18.94, R2 = 0.25, ****, p < 0.0001; RE
vs 7dF: t117 = 5.18, ****, p < 0.0001; RE vs 1dF: t117 = 5.41, ****, p < 0.0001; 7dF
vs 1dF: t117 = 0.10, ns, p > 0.9999). One-way ANOVAs revealed significant
differences between groups (number of extensions: F2,117 = 17.27, R2 = 0.23, ****, p
< 0.0001; number of branches: F2,117 = 21.38, R2 = 0.27, ****, p < 0.0001; length of
extensions: F2,580 = 11.44, R2 = 0.04, ****, p < 0.0001). Post hoc evaluation revealed
the number of extensions (7dF vs 1dF: t117 = 4.69, ****, p < 0.0001; 1dF vs RE: t117
= 5.51, ****, p < 0.0001; 7dF vs RE: t117 = 0.86, ns, p < 0.9999), their branches (7dF
vs 1dF: t117 = 5.06, ****, p < 0.0001; 1dF vs RE: t117 = 6.21, ****, p < 0.0001; 7dF
vs RE: t117 = 1.20, ****, p < 0.0001) and their overall length (7dF vs 1dF: t117 = 4.21,
****, p < 0.0001; 1dF vs RE: t117 = 4.43, ****, p < 0.0001; 7dF vs RE: t117 = 0.21,
ns, p > 0.9999) were greater in both the extinction and remote fear memory recall
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groups versus recent fear recall. Interestingly, together this data suggests the
morphology of the microglia shows increased ramification reminiscent of resting
state microglia in the remote extinction recall group and a trend towards this
ramification in the remote fear recall subjects.
Figure 5-4 Microglia alter morphologically in response to fear memory recall.
A. Quantification of microglia nuclei revealed density variations between memory recall groups with the greatest difference in cell number between early fear recall and remote extinction recall (1dF vs 7dF: t37 = 2.34, ns, p = 0.0743; 1dF vs RE: t37 = 4.55, ***, p = 0.0002; 7dF vs RE: t37 = 2.30, ns, p = 0.0810). B. The measure of complexity (sum of the terminal orders + number of terminals) * (total process length / number of primary branches) varied across groups with recent fear memory recall significantly different from remote fear and extinction memory recall (1dF vs 7dF: t112 = 3.02, **, p = 0.0093; 1dF vs RE: t112 = 4.63, ****, p < 0.0001; 7dF vs RE: t112 = 1.58, ns, p = 0.3545). Dataarepresented asmean ± S.E.M; **: p ≤ 0.01, ***: p ≤ 0.0001, ****: p ≤ 0.0001, one‐way ANOVAfollowedbyBonferroniposthocanalysis.C. Top panel shows Iba1+ microglia with tracing overlaid and bottom panel shows the tracing alone. Scale bar 50µm.
Alongside proliferation and ramification changes, experience dependent
modifications include morphological adaptations of cell body size and shape, and the
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number and length of extensions protruding from the cell body (see figure 4). A
between groups ANOVA of cell body volume was significant (F2,117 = 17.76, R2 =
0.23, ****, p > 0.0001), with Bonferroni corrected post hoc tests revealing
differences between the remote extinction versus both recent and remote fear
memory recall (RE vs 7dF: t117 = 5.18, ****, p < 0.0001; RE vs 1dF: t17 = 5.07, ****,
p < 0.0001; 7dF vs 1dF: t117 = 0.10, ns, p > 0.9999). This finding was repeated in
measurements of the cell body area (F2,117 = 18.94, R2 = 0.25, ****, p > 0.0001; RE
vs 7dF: t117 = 5.18, ****, p < 0.0001; RE vs 1dF: t117 = 5.41, ****, p < 0.0001; 7dF
vs 1dF: t117 = 0.10, ns, p > 0.9999). One-way ANOVAs for each revealed significant
differences between groups (number of extensions: F2,117 = 17.27, R2 = 0.23, ****, p
> 0.0001; number of branches: F2,117 = 21.38, R2 = 0.27, ****, p > 0.0001; length of
extensions: F2,580 = 11.44, R2 = 0.04, ****, p > 0.0001). Post hoc evaluation revealed
the number of extensions (7dF vs 1dF: t117 = 4.69, ****, p < 0.0001; 1dF vs RE: t117
= 5.51, ****, p < 0.0001; 7dF vs RE: t117 = 0.86, ns, p < 0.9999), their branches (7dF
vs 1dF: t117 = 5.06, ****, p < 0.0001; 1dF vs RE: t117 = 6.21, ****, p < 0.0001; 7dF
vs RE: t117 = 1.20, ****, p < 0.0001) and their overall length (7dF vs 1dF: t117 = 4.21,
****, p < 0.0001; 1dF vs RE: t117 = 4.43, ****, p < 0.0001; 7dF vs RE: t117 = 0.21,
ns, p > 0.9999) were greater in both the extinction and remote fear memory recall
groups versus recent fear recall. Interestingly, together this data suggests the
morphology of the microglia shows increased ramification reminiscent of resting
state microglia in healthy brain tissue in the remote extinction recall group and a
trend towards this ramification in the remote fear recall subjects.
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Figure 5-5 Morphological analysis of microglia.
A. Depiction of cell tracing showing the body, tree (extension from the body) and a branch (extension from a tree). B. Analysis of cell body volume revealed differences between the remote extinction versus both recent and remote fear memory recall (RE vs 7dF: t117 = 5.18, ****, p < 0.0001; RE vs 1dF: t17 = 5.07, ****, p < 0.0001; 7dF vs 1dF: t117 = 0.10, ns, p > 0.9999). C. Significant differences were noted between the number of extensions (7dF vs 1dF: t117 = 4.69, ****, p < 0.0001; 1dF vs RE: t117 = 5.51, ****, p < 0.0001; 7dF vs RE: t117 = 0.86, ns, p < 0.9999), (D) their branches (7dF vs 1dF: t117 = 5.06, ****, p < 0.0001; 1dF vs RE: t117 = 6.21, ****, p < 0.0001; 7dF vs RE: t117 = 1.20, ****, p < 0.0001) and (E) their overall length (7dF vs 1dF: t117 = 4.21, ****, p < 0.0001; 1dF vs RE: t117 = 4.43, ****, p < 0.0001; 7dF vs RE: t117 = 0.21, ns, p > 0.9999). Data are presented as mean ± S.E.M; ****: p ≤ 0.0001, one-way ANOVA followed by Bonferroni post hoc analysis. Scale bar 10 µm.
5.5 Discussion
The purpose of this study was to determine the extent to which fear memory recall
influences neuronal activity, and importantly, the morphology of microglia within
the prefrontal cortex. Our data indicate that recall of recent fear resulted in
significant morphological change in microglia. This finding is consistent with past
substantiation that microglia change due to exposure to psychological stress (Aji Nair
& Robert H Bonneau, 2006; Tynan et al., 2010). We extend on previous evidence
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through demonstration that these morphological adaptations are time and memory
type dependent. As diversity in phenotype equates to diversity of function, this
outcome may suggest that microglial activation could play an important role in the
control of and adaptation to the fear response. If further examination supports this
theory microglia may constitute a novel target for attenuating the emotional and
physiological consequences of pathological fear.
Our behavioural findings provide evidence of the effective training and recall
of each memory category. The extended extinction protocol appeared to be
extremely successful at facilitating the development of a new extinction memory
more robust than the original fear memory. The levels of freezing to tone were
relatively similar in the case of a recent and remote fear memory, suggesting the
level of fear demonstrated by these two groups was equivocal at time of memory
recall.
Reduced neuronal activity has been reported as attenuating monitoring of
synapses by microglia (Wake et al., 2009). Our study reported lower c-Fos and Arc
expression in the extinction memory recall group, alongside smaller densities of
Iba1+ microglia. It appears possible that the lower number of surveillance cells
present may contribute to a lower frequency of monitoring. The recall of a recent
fear resulted in both increased neuronal activity as quantified by increased numbers
of c-Fos and Arc, and correspondent increases in Iba1+ microglia present in the
sample. However, the phenotype of the cells present was not conducive to synaptic
monitoring as they had short, thickened extensions more akin to an active microglia.
In contrast to microglia involved in previously documented studies on stress response
(Hinwood et al., 2012), these cells did not possess the larger cell bodies typical of
active microglia. This may be explained by the fact that the recent fear memory is an
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example of an acute stressor as opposed to chronic. Therefore, the adaptation process
from surveillance to activated microglia may take longer than the 60 minutes we
permitted or that an immune response does not occur as a result of fear memory
recall. The morphology of microglia across all groups are not typical of pro or anti-
inflammatory microglia as the cell body increase observed was consistent with an
increased number of process as opposed to a reduction. Observations of microglia in
the rodent and human prefrontal cortex have identified four divisions of microglia:
ramified, primed, reactive and amoeboid microglia (Kongsui et al., 2014; Torres-
Platas et al., 2014). Unlike ramified microglia that have small cell bodies with large
extensions, the primed microglia have wider cell bodies but retain the standard
ramified extensions. Similar to the remote extinction recall group, the remote
memory group displayed ramified extensions. However, the cell bodies in this group
were smaller (not typical of an active microglia), indicating a morphology that may
have been transitioning towards a more active state, or reverting to a more
surveillance / resting state phenotype. In recent research from our lab examining
microglial morphology after contextual fear conditioning, cell body volumes were
not found to be different between contextually fear conditioned animals versus box
control in the rat dentate gyrus (Nicholas Chaaya et al., 2019), suggesting that
context alone may not generate these changes.
Although the experiments documented in this report reveal a phenotype
change and this change is generally associated with functional alteration
(Kettenmann et al., 2011), it should be noted that a marker of neuroinflammation
(such as MHC II) could provide additional evidence of functional changes between
memory classes. A box control group could define similarities between microglial
morphology and density in a group that had no fear memory to begin with in
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comparison to a fear memory that has been updated with a new extinction memory.
An increase of cell number was found in the recent fear memory recall group.
Labelling with EDU (5-ethynyl-2’-deoxyuridine), a marker of cell proliferation
would assist in determining the nature of the increase as it may occur through
migration as opposed to proliferation. Transmembrane protein 119 (Tmem119)
staining may also provide insight into the proliferation versus infiltration issue as it
labels microglia exclusively. To further investigate the functional role microglia play
in fear memory, molecular analyses of mediators of inflammation could be
conducted at the RNA level, similar to those conducted by (Butovsky et al., 2014).
Colony stimulating factor 1 receptor (CSF1R), could be used after fear conditioning
to decrease microglia proliferation, a function previously shown by (Gerber et al.,
2018).
In conclusion this study is the first to characterise microglial phenotype after
fear memory recall. Our findings are consistent with the observation that microglial
activation could play an important role in facilitating fear responses and further
investigation into the functional changes of microglia and their effect on fear
memory itself may provide new therapeutic targets for phobias and pathological fear
as is present in post-traumatic stress disorder.
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Contextual Fear Conditioning Alter Microglia Number
and Morphology in the Rat Dorsal Hippocampus
This chapter comprises the following published article:
Chaaya, N., Jacques, A., Belmer, A., Beecher, K., Ali, S. A., Chehrehasa, F., Battle, A. R., Johnson, L. R., Bartlett, S. E. Contextual fear conditioning alter microglia number and morphology in dorsal hippocampus. Frontiers in Cellular Neuroscience. Published 14th May, 2019. https://doi.org/10.3389/fncel.2019.00214
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1. they meet the criteria for authorship in that they have participated in the conception, execution, orinterpretation, of at least that part of the publication in their field of expertise;
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In the case of chapter 6: Contextual Fear Conditioning Alter Microglia Number and Morphology in the Rat Dorsal Hippocampus Publication status: Published
Contributor Statement of contribution*
Nicholas Chaaya Design of the project, behavioural and laboratory experiments, analysed data, created the figures and wrote and edited the manuscript.
Angela Jacques Assisted with behavioural experiment, laboratory experiments and imaging.
Arnauld Belmer Assisted with laboratory experiments, imaging and editing the manuscript.
Kate Beecher Assisted with laboratory experiments, assisted with editing the manuscript.
Syed Aoun Ali Assisted with laboratory experiments, assisted with editing the manuscript.
Fatemeh Chehrehasa Assisted with interpretation of data and editing of manuscript.
Andrew Battle Assisted with editing the manuscript.
Luke Johnson Assisted with design of study and behavioural protocols
Selena Bartlett Involved in the conception and design of the study and interpretation of results.
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6.1 Abstract
Contextual fear conditioning is a Pavlovian conditioning paradigm capable of
rapidly creating fear memories to contexts, such as rooms or chambers. Contextual
fear conditioning protocols have long been utilised to study how fear memories are
consolidated, maintained, expressed, recalled and extinguished. This study has
identified the lateral portion of the amygdala and the dorsal portion of the
hippocampus as essential for contextual fear memory consolidation. The current
study was designed to evaluate how two different contextual fear memories alter
amygdala and hippocampus microglia, brain derived neurotrophic factor (BDNF;
which can be released by microglia), and phosphorylated cyclic-AMP response
element binding (pCREB; phosphorylation can be induced by BDNF). We find rats
provided with standard contextual fear conditioning to have more microglia and
express more BDNF in the dentate gyrus as compared to a context only control
group. Interestingly, the unpaired fear conditioning procedure, despite producing
equivalent levels of fear as the standard procedure, did not alter microglia, BDNF or
pCREB number in any dorsal hippocampus or lateral amygdala brain regions. In the
dentate gyrus, standard contextual fear conditioning alters microglia morphology to
become amoeboid in shape, which is a common response to central nervous system
insult, such as traumatic brain injury, infection, ischemia and more. Additionally, the
unpaired fear conditioning protocol produced some alterations in microglia
morphology, also becoming more amoeboid in shape. These data suggest that
contextual fear conditioning is capable of producing large alterations to dentate gyrus
function, whereas unpaired fear conditioning produces minor changes. Cumulatively,
these data suggest that Pavlovian fear conditioning protocols induce similar
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responses as trauma, infection or other direct causes within the central nervous
system.
Key words: Contextual Fear Conditioning, Microglia, BDNF (brain-derived
neurotrophic factor), Hippocampus & Amygdala, Dentate Gyrus
6.2 Introduction
Contextual fear conditioning (CFC) is a Pavlovian conditioning protocol
whereby an animal, typically a rodent, is placed into a context (conditioned stimulus;
CS) and provided with noxious stimuli (unconditioned stimulus; US) (Nicholas
Chaaya et al., 2018; Michael S. Fanselow, 2010; Foa et al., 1992; Joseph E. LeDoux,
2014; Rothbaum & Davis, 2003). CFC, along with similar fear conditioning
protocols are utilised to replicate the behavioural events that lead to the development
of fear-based disorders, namely, post-traumatic stress disorder (PTSD) (Nicholas
Chaaya et al., 2018; Michael S. Fanselow, 2010; Foa et al., 1992; Joseph E. LeDoux,
2014; Maren, 2011; Rothbaum & Davis, 2003). Utilising these conditioning
protocols, researchers have identified various essential brain regions, circuits and
molecules involved in the consolidation, maintenance, expression, recall and
extinction of fear (Michael S. Fanselow, 2010; Joseph E. LeDoux, 2014; Maren,
2011). During CFC, the amygdala and hippocampus, or more specifically, the lateral
amygdala (LA) and dorsal hippocampus (DH), have been identified as two brain
regions critical for its consolidation (Nicholas Chaaya et al., 2018). Numerous
investigations, largely directed by Fanselow, have demonstrated an inability for
contextual fear memories to be consolidated when these regions are inhibited or
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abolished (Anagnostaras, Maren, & Fanselow, 1999; Michael S. Fanselow, 2010;
Maren, Aharonov, & Fanselow, 1997; Maren & Fanselow, 1997). Additional
investigations exploring cellular and molecular alterations following learning in these
regions have also identified a critical role for various plasticity and activity related
proteins and immediate early genes (IEGs) in the LA and DH following CFC (Barot
et al., 2009; Besnard, Laroche, & Caboche, 2014; N. Chaaya et al., 2019; Choi et al.,
2016; Impey et al., 1998; Perez-Villalba et al., 2008; Pignataro, Middei, Borreca, &
Ammassari-Teule, 2013; Sananbenesi, Fischer, Schrick, Spiess, & Radulovic, 2002;
Y. M. Wilson & Murphy, 2009; Zelikowsky et al., 2014; Zheng, Luo, Guo, Cheng, &
Li, 2015). The current study expands on these and our previous work (N. Chaaya et
al., 2019) which demonstrated how minor changes to the context during CFC alter
LA activity. This study explores a variety of cellular and molecular alterations in LA
as well as the DH.
The unpaired fear conditioning (UFC) protocol is an alternate Pavlovian
conditioning procedure capable of robustly producing contextual fear memories (H.
C. Bergstrom et al., 2012; Hadley C Bergstrom et al., 2011; Trifilieff et al., 2007).
Despite this, UFC protocols have traditionally been utilised as controls for cued fear
learning (e.g. auditory fear conditioning; AFC) procedures (H. C. Bergstrom et al.,
2012; Hadley C Bergstrom et al., 2011; Majak & Pitkänen, 2003; McKernan &
Shinnick-Gallagher, 1997; Radley et al., 2006; Michael T. Rogan et al., 1997). While
the CS (e.g. tone) and US (e.g. foot-shock) are explicitly paired in cued fear
conditioning, they are presented at random non-overlapping times during UFC. This
led to the hypothesis that the amygdala would not be activated, as no explicit CS-US
associative memory was formed (H. C. Bergstrom et al., 2012; Hadley C Bergstrom
et al., 2011; Majak & Pitkänen, 2003; McKernan & Shinnick-Gallagher, 1997;
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Radley et al., 2006; Michael T. Rogan et al., 1997). However, as noted above,
contextual fear memories activate both LA and DH. Therefore, the ability for fear
memories to context to be formed following UFC suggest the LA, DH, or other
related brain region are similarly recruited. Indeed, this has been demonstrated
previously in LA by Trifilieff and colleagues (quantified phosphorylated mitogen-
activated protein kinase; pMAPK (Trifilieff et al., 2007)), and more recently by our
lab (quantified IEGs (N. Chaaya et al., 2019)). The current study aims to expand on
this research by exploring additional molecular and cellular alternations in both DH
and LA.
Microglia are functionally and anatomically distinct central nervous system
(CNS) cells that possess macrophage-like function (Walker et al., 2014). They are
crucially involved in responding to infection, trauma, ischemia and other insults of
the brain, and participate in maintaining neuronal integrity (Kettenmann et al., 2011;
Pósfai, Cserép, Orsolits, & Dénes, 2018). Briefly, microglia respond to insult in two
main ways: they increase in number in the affected area and their morphology (cell
body size and extension number/size) alter (Calcia et al., 2016; Kettenmann et al.,
2011). Resting (ramified) microglia in a healthy CNS system have a small cell body
and long, thin extensions, with many processes (Calcia et al., 2016; Dwyer & Ross,
2016; Kettenmann et al., 2011). In this ramified stage microglia search for signals of
insult (Dwyer & Ross, 2016; Kettenmann et al., 2011; Walker et al., 2014). Upon
detection of harmful stimuli, microglia number increase in the affected area, and
morphology alter to become amoeboid, with most notable changes being an increase
in cell body size, and reduced branching, and number of extensions (Dwyer & Ross,
2016; Kettenmann et al., 2011; Walker et al., 2014). Furthermore, in this state,
microglia release a number of factors and compounds, one such being brain derive
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neurotrophic factor (BDNF), an important neurotrophin involved in neuronal
survival and differentiation (Ferrini & De Koninck, 2013; Pósfai et al., 2018). Recent
research has begun to identify how microglia respond to psychosocial stressors, with
various studies (extensively reviewed by (Calcia et al., 2016)) showing alterations
corresponding to that of an injured or insulted brain. To our knowledge, one study
has identified such alterations following chemically induced fear (Vollmer et al.,
2016). However, no research has directly examined how Pavlovian fear conditioning
protocols alter microglia number and morphology. The current study, is therefore
designed to examine how CFC and UFC result in alterations in microglia number and
morphology. Furthermore, we investigate how these protocols alter BDNF
expression (which can be released by microglia), as well as phosphorylated cyclic-
AMP response element binding (pCREB) expression (plasticity marker that can be
activated by BDNF) (Ferrini & De Koninck, 2013; Minichiello, 2009). Both pCREB
and BDNF expression have been demonstrated to be involved in fear memory
formation, making them good candidate proteins for the investigation of differential
fear memory consolidation (Bekinschtein et al., 2007; Hall, Thomas, & Everitt,
2000; Impey et al., 1998; I. Y. C. Liu, Lyons, Mamounas, & Thompson, 2004;
Mamiya et al., 2009b; Mizuno, Dempster, Mill, & Giese, 2012).
The objectives of the current study were to explore how two different
Pavlovian fear conditioning protocols, capable of creating contextual fear memories,
alter LA and DH microglia (identified by labelling for ionized calcium binding
adaptor molecule 1; IBA-1) number and morphology, BDNF number and pCREB
number. The current study provides the first insights into how these Pavlovian fear
conditioning protocols alter microglia number and morphology. We find that
microglia number and BDNF expression increase in the dentate gyrus (DG)
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subregions of the DH, following CFC, as compared to a context only (CO) control
group. Further investigations show that CFC alter microglia morphology to become
amoeboid (responding to insult). Interestingly, we show that UFC does not lead to a
change in BDNF or microglia number. Despite this, investigations of microglia
morphology in UFC suggest they also appear to have some characteristics of
amoeboid microglia; signifying they also responded to insult.
6.3 Method
6.3.1 Animals
Animals were experimentally naïve adult male Sprague Dawley Rats
(Animal Resources Centre, WA, Australia). Data reported here was gathered from
rats that make part of a larger dataset. Rats weighed 176-200 g at arrival and were
housed, two per cage, by the University of Queensland Biological Resources
(UQBR) facility on a 12-hour light/dark cycle. Food and water was provided ad
libitum. All behavioural procedures were approved by the University of Queensland
(Ethics approval no. 023/17) and Queensland University of Technology (QUT
approval number: 1700000295) animal ethics unit. All procedures complied with the
Queensland Government Animal Research Act 2001, associated Animal Care and
Protection Regulation (2002 and 2008), as well as the Australian Code for the Care
of Animals for Scientific Purposes, 8th Edition (National Health and Medical
Research Council, 2013) policies and regulations of animal experimentation and
other ethical matters. Upon arrival, rats were acclimatised to the UQBR Facility for
eight days, handled by the experimenter for nine days, habituated to the fear
conditioning context for one day, and then, 24 hours later, fear conditioned (now
weighing 326.56g +/- 2.8g on the fear conditioning day) as explained previously (N.
Chaaya et al., 2019). There were two experimental (Contextual Fear Conditioned;
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CFC n = 18, Unpaired Fear Conditioned; UFC n = 18) groups and one control
(Context Only; CO n = 18) group. Rats were divided into anatomical (n = 12 per
group) and behavioural (n = 6 per group) subgroups following experimental
procedures.
6.3.2 Apparatus
All procedures occurred in one of two Plexiglas conditioning chambers
(Coulbourn Instruments, Lehigh Valley, Pennsylvania, USA). A single house light (2
– 3 lux) dimly illuminated both chambers (context A and B). Chambers contained an
infrared camera, were equipped with a speaker and sound insulated (background dB
= 55). Context A contained a metal grid floor which connected to an electric shock
generator. This context contained no decorations, and was cleaned with ethanol
(EtOH) 80% following the presentation of each rat. Alternatively, context B was
fitted with a flat floor that was lightly covered with bedding. The walls were
coloured, and alterations were made to the roof which altered its physical
dimensions. Following the presentation of each rat, orange scented hand soap was
used to clean context B. The bedding was also replaced.
6.3.3 Procedures and Design
Figure 1 briefly outlines behavioural procedures. These procedures
are explained in detail below and have been outlined previously (MS1; (N. Chaaya et
al., 2019)).
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Acclimatisation, Habituation and Fear Conditioning
Prior to behavioural procedures, rats in all conditions were acclimatised to the
vivarium for eight days. Rats were handled for nine days by the experimenter, and
then each placed in context A for 30 minutes on the tenth day. After twenty-four
hours, rats in the CFC and UFC group were placed into context A for fear
conditioning. Rats were permitted 180 s to explore the context before receiving any
Figure 6-1. Experimental Design for Behavioural Training. Following an acclimatisation, handling and habituation period, rats were divided into three distinct behavioural groups. Rats in the CFC group were placed into a chamber and provided with five non-overlapping and random electric shocks to the foot. Rats in the UFC were placed in the same chamber and provided with the same foot-shocks. However, five non-overlapping and random auditory tones were also presented during the fear conditioning procedure. Rats in the CO control group were placed in the same chamber and provided with no further stimuli. Following conditioning, rats in all three groups were separated into an anatomy (perfused 90 minutes post-conditioning) and behavioural group (provided with two fear memory test 24 hours after conditioning, and four days after conditioning). CFC: contextual fear conditioning; UFC: unpaired fear conditioning; CO: context only.
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stimuli. Rats in the CFC group were then presented with five non-overlapping and
random electric shocks to the foot (1.0 mA, 0.50 s). Whilst in receiving these foot-
shocks in context A, rats in the UFC group also received five presentations of
auditory tones (5 kHz, 75 dB, 20 s). These auditory tones did not overlap with each
other, or with the foot-shocks. Following presentation of the final stimulus, rats
remained in the context for 60 s. The experimenter then removed the rats, and
returned them to their home-cage. In total, fear conditioning procedures were 660 s
long for rats in the CFC group, and 880 s long for rats in the UFC (extra time was
required to account for the addition of auditory tones). Rats in the CO control group
were placed into context A without any added stimuli, and left to explore for 660 s.
Following Fear Conditioning
Behavioural subgroup.
The behavioural subgroup of rats (n = 6 per group) had freezing behaviour
manually scored during training (fear conditioning) and testing (fear memory test:
FMT). As per previous investigations (H. C. Bergstrom & L. R. Johnson, 2014; H. C.
Bergstrom et al., 2012; R. G. Phillips & LeDoux, 1992; Russel G Phillips & LeDoux,
1994; Gregory J. Quirk et al., 1997; Radley et al., 2006), all scoring occurred in 20
second blocks. During training a progressive measure of fear was obtained by
scoring freezing behaviour before (baseline), during (cue 1 - 5), and after fear
conditioning (final). Following training, rats were returned to their home-cages, and
kept there for 24 hours. Rats were then placed back into context A, and freezing
behaviour was scored for a 10 minute FMT to context (no foot-shocks or auditory
tones provided). Freezing behaviour was scored during the final 20 seconds of every
minute that rats were undergoing their FMT to context. Rats were returned to their
home-cages for 72 hours, until which a FMT to tone was conducted. During the FMT
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to tone, rats were placed in context B for 10 minutes and presented with auditory
tones (5 kHz, 75 dB, 20 s) at the end of each minute. Freezing behaviour was scored
during the 20 s tone presentation periods.
Scoring of freezing.
Freezing behaviour was defined as the inhibition, absence or suppression of
movement, besides from that required for autonomic nervous system functioning
(Michael S. Fanselow, 1980). Head scanning and sleeping were not included as
freezing. Heavy breathing, minimal movement and other movements required for
normal respiration and autonomic function were considered as freezing behaviour.
During training, freezing behaviour was scored in the CFC and UFC groups during
the final 20 s of the first minute, the final 20 s of the last minute, and the 20 seconds
prior to each foot-shock. Freezing behaviour was not scored prior to auditory tone
presentation in the UFC, as the development of contextual fear memories are of
interest here. For rats in the CO control group, freezing behaviour was scored at
identical time points as that of the CFC group (as their trials had identical durations).
During testing, freezing behaviour was scored in the final 20 seconds of every
minute (H. C. Bergstrom et al., 2012; Hadley C Bergstrom et al., 2011).
Anatomical group.
Following completion of fear conditioning, rats in the anatomical group (n =
12 per group) were removed from the conditioning context and immediately returned
to their home cages. Ninety minutes following, rats were anesthetised and sacrificed
via perfusion for fluorescent labelling of pCREB, BDNF and IBA-1.
6.3.4 pCREB, BDNF and IBA-1 Immunohistochemistry
Tissue preparation
To anaesthetise rats, intraperitoneal (i.p.) injections of Ketamine/Xylazine
(100mg/kg, 10mg/kg) were administered. Following anaesthetisation, rats were
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transcardially perfused with ice-cold saline (200 mL per rat) followed by 4%
paraformaldehyde/0.1 M phosphate buffer (PB; pH of 7.4; 400 mL per rat) via the
ascending aorta. Subsequently, brains were removed and stored at 4 oC in the 4%
paraformaldehyde fixative for 24 hours. Following, brains were stored in phosphate
buffered saline (PBS)/0.02% Azide for a minimum of three days, at which point free-
floating sequential coronal brain sections were obtained. These sections, sliced on a
vibratome (M11000; Pelco easiSlicer, Ted Pella Inc, CA, USA) at 40 μm per section,
contained the lateral amygdala and the hippocampus. Sections were stored at 4 oC in
PBS/0.02% azide until immunohistochemistry commenced. Immunohistochemistry
was conducted on right hemisphere sections.
Immunohistochemistry
Brain sections were removed from PBS/0.02% azide and washed thoroughly
with PBS. Optimisation of each antibody required altered protocols. These
alterations are outlined below. First, all sections were post-fixed for an additional
five minutes with the 4% paraformaldehyde fixative used for perfusion. Sections
were then thoroughly washed with PBS. Sections labelled for BDNF and IBA-1 (but
not pCREB) were incubated in 3% H2O2/10% Methanol in PBS for five minutes.
Once washed in PBS, all sections were permeabilised with 1% Triton/0.1% Tween
20 in PBS for one hour, and then washed in PBS again. Labelling for BDNF required
antigen retrieval; whereby sections were incubated in Citrate Buffer (10mM Sodium
Citrate, 0.05% Tween 20, pH 6.0) for five minutes (80 oC). Once sections returned to
room temperature, they were washed in PBS. All sections were then blocked with
0.3% Triton/0.05% Tween 20/2% normal horse serum (NHS) in PBS for one hour.
Blocking solution was removed, and sections were incubated in their respective
primary (either pCREB, BDNF or IBA-1) antibody diluted in the blocking solution
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for 24 hours. Sections labelled for pCREB were incubated in anti-phospho-CREB
(Ser133) rabbit polycloncal antibody (1:500; Merck Milipore, HE, DEU). Sections
labelled for BDNF were incubated in anti-BDNF [EPR1292] (ab108319) rabbit
monoclonal antibody (1:500; Abcam, VIC, Aus). Sections labelled for IBA-1 were
incubated in anti-IBA1 (ab5076) goat polyclonal antibody (1:500; Abcam, VIC,
Aus). Following incubation in primary antibody, all sections were washed with
blocking solution. Sections labelled for pCREB and BDNF were immediately
incubated in a pre-absorbed goat anti-rabbit IgG H&L (Alexa Fluor 594) secondary
antibody (1:500; Abcam, VIC, Aus) in blocking solution, whereas sections labelled
for IBA-1 were immediately incubated in a cross-absorbed donkey anti-sheep IgG
H&L (Alexa Fluor 594) secondary antibody (1:500; ThermoFisher Scientific, VIC,
Aus). Brain sections were then washed in blocking solution and then PBS.
Following, sections were incubated in 4′,6-diamidino-2-phenylindole (DAPI) diluted
in PBS for five minutes (1:1000; D1306 ThermoFisher Scientific, VIC, Aus), washed
a final time, and then mounted on silane coated slides. Mounted sections were
immediately cover-slipped using ProLong Gold antifade reagent (Invitrogen, DR,
USA), left to dry and stored at 4 oC.
Image Acquisition
Cover-slipped brain sections were scanned using a Nikon/Spectral Spinning
Disc Confocal Microscope (Nikon Instruments Inc, NY, USA) to take 20x magnified
tile-scan mosaics of the amygdala and hippocampus. Scan settings are as follows: x =
14, y = 10 consecutive fields (horizontal acquisition pattern) with 10% overlap and 7
z-stacks with 4 μm step-size. Laser channels were 405nm for DAPI (20ms exposure
time) and 561nm (high) for pCREB, BDNF and IBA-1 (300ms exposure). Individual
scans (each z-stack and wavelength/channel as a separate image) were saved as
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separate .tiff files, and manually merged using imageJ (Schindelin et al., 2012).
Merged z-stacks and channels were then stitched (Preibisch et al., 2009) in imageJ.
The Olympus FV3000 Confocal Laser Scanning Microscope (Olympus Australia Pty
Ltd, VIC, Aus) was used to take 40x magnified (1.5x zoom) scans (x = 212 μm, y =
212 μm) with 30 z-stacks of 0.50 μm thickness (z = 15 μm). These scans were only
conducted on IBA-1 sections to allow for the tracing of these cells. Only brain
subregions that were identified to have significant differences in pCREB, BDNF and
IBA-1 number (as identified with initial Spinning Disc Confocal scans) were scanned
with the FV3000.
Amygdala and Hippocampus Subregion Identification
All brain sections contained the LA and its three subregions: dorsolateral
portion of the lateral amygdala (LaDL), ventromedial portion of the lateral amygdala
(LaVM) and ventrolateral portion of the lateral amygdala (LaVL), as well as the
dorsal hippocampus (DH) and its three subregions: dentate gyrus (DG), CA1 and
CA3. The CA2 was excluded from analysis as it is significantly smaller than the
other hippocampal subregions, and is difficult to accurately outline (Caruana,
Alexander, & Dudek, 2012). Stereotaxic alignment was utilised to accurately analyse
pCREB, BDNF and IBA-1 expression in the same rostral-caudal location of each rat
brain. Briefly, the lateral ventricle (LV) – a rapidly changing anatomical landmark –
becomes present at Bregma coordinate -3.32 mm, and consistently grows towards
more caudal locations (Paxinos & Watson, 2006). The LV becomes easily
identifiable at Bregma coordinate -3.36 mm (depicted as a tear-drop size; Paxinos &
Watson, 2006), allowing for accurate identification and alignment. Following
identification of Bregma coordinate -3.36 mm, preceding sections can be identified
by counting back. Identification of Bregma coordinate -3.36 mm allowed for all rat
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brains to be aligned at the same rostral-caudal location. To provide a larger
representation of the LA and DH, two sections per rat (equal distances apart) were
labelled with each antibody. Anatomical landmarks, such as the external capsule
(ec), rhinal fissure (RF), central amygdala (CeA), dorsal endopiriform nucleus
(DEn), optic tract (opt) and stria terminalis (st) were used to identify the three LA
subregions (see Figure 2). The hippocampal subregions were more easily identified
by the clear structural alterations of each subregion (see Figure 2).
6.3.5 Neuron/Microglia Quantification
Neurons and Microglia were automatically counted and tagged using the spot
detection option in IMARIS (IMARIS 9.1.2, Bitplane, ZH, CHE). An average
diameter was obtained for cells labelled with pCREB, BDNF and IBA-1. Filter
intensity was determined by the experimenter, and kept constant across experimental
groups. Cell diameter and filter intensity were altered, if necessary, depending on
amygdala or hippocampus subregion, but remained constant across experimental
groups for each subregion. Representative areas of LA subregions (x = 266.66 μm, y
= 266.66 μm) were selected and the number of cells and microglia quantified (see
Figure 2). Due to the relatively large size of the DH and its subregions, three areas (x
= 333.33 μm, y = 333.33 μm) were counted from each DH subregion (see Figure 2).
Tracing of microglia was dependent upon pCREB, BDNF and IBA-1 quantification.
In identified brain subregions, a maximum of three microglia per section were traced
using Neurolucida 360 (Neurolucida 360, MBF Bioscience, VT, USA). The average
length of microglia extensions, number of trees or ends for these extensions, the cell
body volume and complexity of the microglia branching (measure of ramification
state) were quantified from these traces. The following formula was utilised to
determine the complexity of microglia branching (sum of the terminal orders +
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number of terminals) * (total process length / number of primary branches) (Pillai et
al., 2012).
6.3.6 Data Analysis
The results section is separated into three parts: pCREB expression, BDNF
expression and IBA-1 expression. Within each part, differences as a function of
Figure 6-2. Illustration of LA and DH subregions and labelling in these Regions. Three subregions of interest within the LA, and three subregions of interest within the DH, were identified in the rat brains. (A) To accurately identify LA subregions LaDL, LaVM and LaVL, the CeA, LV (when present), st, opt, Den and RF were used (shaded in grey) as anatomical landmarks. Following identification of subregions, pCREB (B), BDNF (C) and IBA-1 (D) were quantified. (E) The hippocampus has easily distinguishable subregions as a result of its unique anatomical architecture. Therefore, identification of CA1, CA3 and DG relied upon the assistance of the (Paxinos & Watson, 2006) rat brain atlas. Due to the limited research and relative size of the CA2 subregion, this region was utilised as a border between the CA1 and CA3. Following identification of subregions, pCREB (F), BDNF (G) and IBA-1 (H) were quantified. LA: lateral amygdala; DH: dorsal hippocampus; LaDL: dorsolateral portion of the lateral amygdala; LaVM: ventromedial portion of the lateral amygdala; LaVL: ventrolateral portion of the lateral amygdala; RF: rhinal fissure; DEn: dorsal endopiriform nucleus; CeA: central amygdala; st: stria terminalis; opt: optic tract; pCREB: phosphorylated cyclic-AMP response element binding; BDNF: brain derived neurotrophic factor; IBA-1: ionized calcium binding adaptor molecule 1; CA1: hippocampal subregion CA1; CA2: hippocampal subregion CA2; CA3: hippocampal subregion CA; DG: dentate gyrus. Scale bar: 100 µm.
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conditioning (CFC, UFC and CO control) across all LA and DH subregions are
explored. For this reason, analyses of variances (ANOVAs) were utilised to compare
differences between behavioural groups within each subregion (e.g. differences in
pCREB expressing neurons between behavioural groups in the LaDL). Prior to
analysis, normality and homogeneity of variance were tested for. Assumptions of
normality and homogeneity of variance were confirmed in the majority of cases. To
control for possible type I errors arising from these breaches, a Bonferroni
adjustment (Perneger, 1998) was utilised in all follow-up post-hoc tests. The
Bonferroni adjustment also controlled for multiple comparisons that were conducted
in these analyses. Therefore, all analyses to anatomical data were conducted with
one-way ANOVAs, followed by Bonferroni corrected post-hoc tests. All values in
the text and graphs are expressed as the mean +/- standard error of the mean. P
values at or below 0.05 are considered statistically significant. All major statistical
analyses, outlier analyses and graph generation were conducted using GraphPad
Prism v7 software (GraphPad, CA, USA). Asterisks are used to denote levels of
statistical significance within all graphs (* p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; ****
p ≤ 0.0001). Behavioural data is previously reported in MS1 (N. Chaaya et al., 2019),
and therefore summarised briefly at the beginning of the results section.
Excluded Cases
Statistical outliers or significantly damaged brain tissue (occurring from
perfusion, labelling or cover-slipping process) were excluded from analyses.
Statistical outliers were identified via the ROUT method (GraphPad Prism), with the
maximum false discovery rate set to 1%. The ROUT method is capable of effectively
identifying multiple outliers in large datasets. Outlier analysis was conducted on
individual groups, and identified outliers were excluded on a pairwise basis.
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6.4 Results
6.4.1 Behavioural Results
In order to determine confirm that the behavioural procedures produced
differences in fear to context, freezing behavioural during training and testing was
quantified. The percentage of time rats displayed freezing behaviour as a function of
condition (CFC, UFC or CO) and time-point (baseline minute, cue 1 – 5, and final
minute) were quantified as explained previously (N. Chaaya et al., 2019). A two-way
mixed design ANOVA revealed a significant interaction of freezing behaviour as a
function of condition and time-point (p < 0.0001), a significant main effect of
condition (p < 0.0001) and a significant main effect of time-point (p < 0.0001).
Bonferroni corrected post-hoc tests (see Figure 3A) demonstrated a progressive
acquisition of fear to context, becoming significantly different between both
conditioned groups as compared to the CO control group starting from cue 4. One-
way ANOVA of freezing during the FMT to context revealed significant differences
between groups (p < 0.0001). Bonferroni corrected post-hoc tests revealed rats that
underwent CFC and UFC to exhibit significantly more freezing to context as
compared to rats in the CO control group (Figure 3B). Alternatively, one-way
ANOVA of freezing during the FMT to tone revealed no statistical differences to
exist between groups (see Figure 3C).
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6.4.2 Anatomical Results
pCREB Expression Does Not Changed by both CFC and UFC
One-way ANOVA of pCREB (a marker for neuronal plasticity) expression in
LA and DH revealed no group differences to exist (see Figure 4). No differences in
Figure 6-3. Freezing to Context and Tone Data. Previously reported freezing to context and tone (N. Chaaya et al., 2019) reveal both training protocols to robustly create fear memories to context. (A) Analysis of fear-related freezing during conditioning reveal rats that underwent both CFC and UFC to progressively develop fear memories. By the final 20 second period, rats in the CFC and UFC both expressed significantly more fear-related freezing to context as compared to the CO control. (B) During the FMT to context provided 24 hours following conditioning, rats in both the CFC and UFC expressed significantly more fear-related freezing behaviour as compared to the CO control. There was no difference in fear-related freezing between the CFC and UFC group. (C) During the FMT to tone provided three days after the FMT to context, fear-related freezing was equivalent in all groups. CFC: contextual fear conditioning; UFC: unpaired fear conditioning; CO: context only; FMT: fear memory test. Asterisks denote level of statistical significance between groups * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.
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pCREB number were observed as a function of conditioning in LA subregions LaDL
(F[2, 55] = 0.0431, p = 0.9578), LaVM (F[2, 55] = 1.282, p = 0.2856), or LaVL
(F[2, 55] = 0.1709, p = 0.8433). Similarly, no group differences were observed in
DH subregions CA1 (F[2, 61] = 0.5423, p = 0.5842), CA3 (F[2, 60] = 0.7473, p =
0.4780) and DG (F[2, 60] = 0.5430, p = 0.5838).
BDNF Expression in DG is Increased by CFC
One-way ANOVA of BDNF expression in LA and DH subregions CA1 and
CA3 provided mostly similar results as that of pCREB expression. No differences in
BDNF expression were noted in LA (see Figure 5A) subregion LaDL (F[2, 57] =
0.3216, p = 0.7263), LaVM (F[2, 56] = 0.1123, p = 0.8940) or LaVL (F[2, 57] =
1.0571, p = 0.3542). Furthermore, no differences in BDNF expression were noted in
DH subregion (see Figure 5B) CA1(F[2, 56] = 0.6008, p = 0.5519) or CA3 (F[2, 56]
= 0.8187, p = 0.4462). Contrary to pCREB data above, one-way ANOVA revealed a
significant difference in BDNF expression as a function of condition in DH
Figure 6-4. pCREB Expression in LA and DH. Evaluation of pCREB expression in LA (A) and its subregions and DH (B) and its subregions revealed no statistically significant differences as a function of conditioning. LA: lateral amygdala; DH: dorsal hippocampus; pCREB: phosphorylated cyclic-AMP response element binding.
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subregion DG (F[2, 55] = 8.1509, p < 0.001). Bonferroni correct post-hoc tests (see
Figure 5B) found rats that underwent CFC to have significantly more BDNF
expression as compared to the control group. Interestingly, rats that underwent CFC
also exhibited significantly more BDNF expression in DG as compared to those that
underwent UFC.
IBA-1 Number in CA1 and DG is Increased by CFC
Similar to pCREB and BDNF expression, one-way ANOVA of IBA-1
number revealed no differences in the LA (see Figure 6A) as a function of
conditioning. The number of IBA-1 remained the same in subregions LaDL (F[2, 57]
= 0.9217, p = 0.4037), LaVM (F[2, 58] = 0.3137, p = 0.7320) and LaVL (F[2, 58] =
0.9302, p = 0.4003) as a function of conditioning. Alternatively, in DH (see Figure
Figure 6-5. BDNF Expression in LA and DH. (A) Evaluation of BDNF expression in LA and its subregions revealed no statistically significant differences as a function of conditioning. (B) Evaluation of BDNF expression in DH revealed statistically significant differences in subregion DG as a function of condition. Rats that underwent CFC had significantly more BDNF expression as compared to both rats that underwent UFC and the CO control rats. LA: lateral amygdala; DH: dorsal hippocampus; BDNF: brain derived neurotrophic factor; DG: dentate gyrus; CFC: contextual fear conditioning; UFC: unpaired fear conditioning; CO: context only. Asterisks denote level of statistical significance between groups * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.
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6B), one-way ANOVA revealed differences in the number of IBA-1 in subregions
CA1 (F[2, 55] = 3.4574, p < 0.05) and DG (F[2, 54] = 5.928, p < 0.01), but not CA3
(F[2, 53] = 0.8517, p = 0.4324). Bonferroni corrected post-hoc tests (see Figure 6B)
showed rats that underwent CFC to have significantly more IBA-1 as compared to
the CO control in subregions CA1 and DG. Similar to BDNF data, IBA-1 number in
DG was also significantly higher in the CFC group as compared to the UFC group in
DG.
IBA-1 Morphology in DG is Altered by CFC
Due to consistent differences in BDNF expression and IBA-1 number
between groups (CFC versus CO control) following conditioning, IBA-1 was traced
in DG (see Figure 7A). Four measures were obtained from traced IBA-1: average
Figure 6-6. IBA-1 Expression in LA and DH. (A) Evaluation of IBA-1 number in LA and its subregions revealed no statistically significant differences as a function of conditioning. (B) Evaluation of IBA-1 number in DH revealed statistically significant differences in subregions CA1 and DG as a function of condition. Rats that underwent CFC had significantly more IBA-1 as compared to the CO control in subregions CA1 and DG. Furthermore, rats that underwent CFC had significantly more IBA-1 than rats that underwent UFC in the DG. LA: lateral amygdala; DH: dorsal hippocampus; IBA-1: ionized calcium binding adaptor molecule 1; CA1: hippocampal subregion CA1; DG: dentate gyrus; CFC: contextual fear conditioning; UFC: unpaired fear conditioning; CO: context only. Asterisks denote level of statistical significance between groups * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001.
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length of extensions, number of ends, cell body volume and complexity (as a
measure of IBA-1 ramification or activation state). One-way ANOVA revealed
significant differences in IBA-1 morphology in average length of extensions (F[2,
76] = 18.3192, p < 0.0001), number of ends (F[2, 80] = 8.1239, p < 0.001),
complexity (F[2, 70] = 17.2069, p < 0.0001), but not cell body volume (F[2, 80] =
1.493, p = 0.2308) (see Figure 7B – E). Bonferroni corrected post-hoc tests (see
Figure 7B – E) revealed rats in the CFC group to have significantly larger extensions
than both the UFC group and CO control group. Interestingly, despite no difference
in IBA-1 number between UFC and CO controls, rodents in the UFC had
significantly larger extensions than the CO control. Bonferroni corrected post-hoc
tests revealed rats in the CFC group to have significantly more ends than those in the
UFC and CO control group. No differences were observed between the UFC group
and CO control group. Finally, statistical analyses on complexity data revealed rats
in the CFC and UFC group to have less complex (or less ramified), IBA-1 as
compared to the CO control group.
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6.5 Discussion
This study investigated the involvement of amygdala and hippocampus
following the formation of contextual fear memories created via separate protocols.
The primary difference between these two protocols was the amount of time spent in
the fear conditioning context (660 s for rats in the CFC group versus 880 s for rats in
the UFC group), and importantly, the inclusion of five non-reinforced auditory tones
for rats in the UFC group. Expression of pCREB and BDNF, along with the number
of IBA-1 were quantified and compared in these two groups to a CO control group
that explored the same context, but with no stimuli provided. Behavioural results
(previously reported in (N. Chaaya et al., 2019)) demonstrate successful acquisition
of fear to context, but not to tone, in both conditioned groups. Despite these
behavioural changes, investigations of LA pCREB, BDNF and IBA-1 did not reveal
Figure 6-7. IBA-1 Morphology in DG. (A) The 3D projection of scanned microglia were manually traced in each behavioural group. These traces provided data regarding the average length of extensions, the number of trees/endings, the cell body volume and the complexity of the extensions. (B) Rats that underwent CFC were found to have the shortest average length of extensions. Their extensions were significantly shorter than those in the CO control group. Rats that underwent UFC had significantly longer extensions than those in the CFC group, but significantly shorter extensions than those in the CO control group. (C) Rats that underwent CFC were found to have significantly less endings as compared to both the UFC group and the CO control group. (D) No difference microglia cell body volume was identified as a function of fear conditioning. (E) Analysis of the complexity of extensions (providing a measure of microglia ramification state) revealed the CFC and UFC group to both have significantly less complex extensions than the CO control group. This indicates that only the CO group had ramified or resting microglia. (E) Max projection of IBA-1 microglia with tracing overlaid (top) and the tracing alone (bottom). DG; dentate gyrus; CFC: contextual fear conditioning; UFC: unpaired fear conditioning; CO: context only; IBA-1: ionized calcium binding adaptor molecule 1. Asterisks denote level of statistical significance between groups * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001. Scale bar = 10 µm.
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any changes following either CFC or UFC. These data suggest the markers examined
here may not be capable of identifying alterations in the LA that exist following
these forms of conditioning protocols. Contrary to this, differences in BDNF and
IBA-1 number were present in hippocampus following CFC, but not UFC.
Specifically, an increase in the expression of BDNF in hippocampal subregion DG,
and IBA-1 in hippocampal subregions CA1 and DG was present. Interestingly, DG-
specific BDNF and IBA-1 were significantly higher following CFC as compared to
both the CO control group, and the UFC group. These data suggest that contextual
fear memories created with standard CFC protocols rely upon the DG. However,
when contextual fear memories are altered with non-reinforced auditory tones,
dependency for the DG appears to be lost.
6.5.1 Lateral Amygdala
Contextual fear memories created with either the standard CFC protocol or
the altered UFC protocol does not appear to rely upon the LA. Evaluation of pCREB,
BDNF and IBA-1 number in the LaDL, LaVM and LaVL did not reveal any changes
as a function of fear conditioning protocol. These data appear to contradict previous
reports (Barot et al., 2009; Nicholas Chaaya et al., 2018; Michael S. Fanselow, 2010;
Hall et al., 2000, 2001a; Impey et al., 1998; Malkani & Rosen, 2000; Perez-Villalba
et al., 2008; Pignataro et al., 2013; Trogrlic et al., 2011; Y. M. Wilson & Murphy,
2009; Zelikowsky et al., 2014). Furthermore, these results are in direct contrast with
our previous investigations, demonstrating wide scale BLC activation following
UFC, and specific LaDL activation following CFC (N. Chaaya et al., 2019). The
particular proteins and cells explored here may explain this discrepancy. For
example, early research could not identify any changes in LA BDNF expression
following CFC (Hall et al., 2000). Further research found amygdala BDNF to
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increase as a function of cue fear conditioning, but not as a function of contextual
fear conditioning (Rattiner, Davis, French, & Ressler, 2004). Similar to this,
investigations into microglia following fear conditioning appears to be limited, with
only recent research beginning to examine its functional role in psychiatric disorders
(Dwyer & Ross, 2016). For example, a recent study demonstrated the requirement
for microglia activation in chemically induced fear (Vollmer et al., 2016), with
numerous other investigations showing microglia to respond to stressful stimuli
(Calcia et al., 2016; Frank, Baratta, Sprunger, Watkins, & Maier, 2007; Aji Nair &
Robert H. Bonneau, 2006; Tynan et al., 2010). However, no research to our
knowledge has specifically examined the alterations in microglia number and
morphology that occur following fear conditioning protocols. The data here,
therefore, provides evidence that microglia number in LA remains stable following
contextual fear conditioning. Additionally, despite many documented cases of altered
activity and protein expression in LA following CFC (see recent review (Nicholas
Chaaya et al., 2018)), data reported here confirm that BDNF expression in LA is
unchanged.
Contrary to BDNF and IBA-1 data, pCREB expression in amygdala
following CFC has been examined. Early research on CRE-lac Z transgenic mice
investigated CREB (expressed as β-galactosidase in these mice) expression following
contextual, unpaired and cued fear conditioning (Impey et al., 1998). They noted an
increase in amygdala CREB as a function of both CFC and UFC (Impey et al., 1998).
More recent research conducted in two phases demonstrated that (1) oral
administration of DMP696, a corticotropin-releasing factor 1 antagonist significantly
reduced BLC pCREB expression and subsequent contextual fear expression, and (2)
bilateral microinjections of DMP696 in BLC significantly reduce fear-related
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freezing to context (Hubbard, Nakashima, Lee, & Takahashi, 2007). Interestingly,
DMP696 had no effect on CeA pCREB expression, and microinjections of the
antagonist into the CeA did not affect fear-related freezing to context (Hubbard et al.,
2007). This suggests the deficits in fear-related freezing to context occurred due to
disturbances in contextual fear learning, as opposed to fear-expression. Numerous
other investigations have also confirmed a role for amygdala pCREB expression in
CFC (Hagewoud, Bultsma, Barf, Koolhaas, & Meerlo, 2011; Mamiya et al., 2009b;
Stanciu, Radulovic, & Spiess, 2001). Considering these studies, the results reported
here are unexpected, and may be attributed to methodological confounds. These are
explained in detail below.
6.5.2 Dorsal Hippocampus
The data reported here suggest that CFC, but not UFC, rely upon the DG
subregion of the hippocampus. Following CFC, BDNF expression was significantly
increased in DG, while IBA-1 number was significantly increased in DG and CA1 as
compared to the CO control. However, similar to above, no difference in pCREB
expression was present as a function of conditioning. Once again, this pCREB data is
contradictory to previous reports. On numerous occasions, hippocampal pCREB has
been found to be essential to CFC (Hagewoud et al., 2011; Impey et al., 1998; Kudo,
Qiao, Kanba, & Arita, 2004; Mamiya et al., 2009b; Stanciu et al., 2001; Trifilieff et
al., 2006). Nevertheless, the increase in BDNF expression is expected. Several
studies have found a functional role for hippocampal BDNF following CFC (R. M.
Barrientos et al., 2004; Bekinschtein et al., 2007; Hall et al., 2000; I. Y. C. Liu et al.,
2004; Lubin, Roth, & Sweatt, 2008; Mizuno et al., 2012; Takei et al., 2011).
However, the majority of this research found CA1 BDNF to be essential for CFC
(Bekinschtein et al., 2007; Hall et al., 2000; J. L. C. Lee, Everitt, & Thomas, 2004;
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Lubin et al., 2008). Our recent review also concluded that the DH subregions most
involved in CFC appear to be the CA1 and CA3, but not the DG (Nicholas Chaaya et
al., 2018). Nevertheless, some evidence has found increases in BDNF expression in
the DG (R. M. Barrientos et al., 2004), with many other studies showing the
requirement for BDNF in the DH as a whole (I. Y. C. Liu et al., 2004; Mizuno et al.,
2012; Takei et al., 2011). The data here, therefore, suggest that DG BDNF is required
for contextual fear memory consolidation.
This work showed a significant increase in the number of IBA-1 in
hippocampal subregions CA1 and DG. Examination of microglia morphological
states within the DG revealed altered branching and processes. As compared to CO
controls, rats that underwent CFC had significantly shorter extensions, with fewer
ends and less complex extensions, suggesting a clear morphological response to this
fear conditioning protocol. An increase in the number of microglia (via the blood
system or proliferation) typically occurs due to central nervous system response to
harmful stimuli (Calcia et al., 2016; Kettenmann et al., 2011). Additionally,
microglia alter morphology in response to harmful stimuli (Calcia et al., 2016;
Dwyer & Ross, 2016; Kettenmann et al., 2011). During resting phase, without
harmful stimuli, microglia are ramified; when responding to harmful stimuli,
microglia become amoeboid (Calcia et al., 2016). When ramified, or “resting”,
microglia have long, thin extensions with many processes that search for signals of
insult (Dwyer & Ross, 2016; Kettenmann et al., 2011; Walker et al., 2014). When
amoeboid, microglia extensions retract, cell bodies enlarge, and they respond to
insult by release of proinflammatory and immunoregulatory factors and compounds
(Dwyer & Ross, 2016; Kettenmann et al., 2011; Walker et al., 2014). One such factor
that can be released by microglia in response to injury is BDNF (Ferrini & De
216
Koninck, 2013; Pósfai et al., 2018). Microglia in the DG following CFC in the
current study were found to represent the typical morphological state of those
responding to insult. Not only did they represent the morphological structure of an
amoeboid microglia, they may have been responsible for the increase in BDNF.
While further research is required to confirm the direct relationship of microglia and
BDNF in fear, this is the first study to examine and report alterations in the number
and morphological state of microglia following CFC.
Both BDNF and IBA-1 number in DG were significantly higher in the CFC
group as compared to the UFC group. The UFC group has typically been used as a
control to cued fear conditioning groups (H. C. Bergstrom et al., 2012; Hadley C
Bergstrom et al., 2011; Majak & Pitkänen, 2003; McKernan & Shinnick-Gallagher,
1997; Radley et al., 2006; Michael T. Rogan et al., 1997). This is because the UFC
do not have overlapping cue and foot-shock, and therefore no associative memory is
formed (Joseph LeDoux, 2003; Romanski et al., 1993). Nevertheless, during CFC,
the ‘context’ becomes paired with the foot-shock (Calandreau et al., 2005;
Calandreau et al., 2006; Desmedt et al., 1998; Russel G Phillips & LeDoux, 1994;
Trifilieff et al., 2007; Trifilieff et al., 2006). While this context is altered during the
UFC protocol, it still gets paired with the foot-shock. We previously showed the
UFC protocol to result in wide-spread BLC activation, whereas the CFC protocol
only resulted in specific LaDL activation (N. Chaaya et al., 2019). Contrastingly,
despite equivalent levels of fear to context, the UFC protocol here resulted in
significantly less BDNF and IBA-1 number in DG as compared to the CFC protocol.
We hypothesise that this occurred as a result of the auditory tones altering the
contextual fear memories. Trace fear conditioning is a behavioural paradigm similar
to the UFC protocol; with the only difference being that the auditory tones are
217
presented before a foot-shock (the period in between the tone and foot-shock
represent the trace period) in a consistent and ordered manner (e.g. 10 s before each
foot-shock) (Rogers, Hunsaker, & Kesner, 2006). While the DH is well-documented
to be essential to CFC (reviewed in (Nicholas Chaaya et al., 2018)), some research
has shown that it may be less involved in trace fear conditioning (Rogers et al.,
2006). Nevertheless, the majority of research has reported the DH to be essential to
trace fear conditioning (Pierson et al., 2015; Jennifer J. Quinn et al., 2005; Reichelt,
Maniam, Westbrook, & Morris, 2015). This suggests the difference between the UFC
protocol and trace fear conditioning protocol may drastically change the need for
DH. Contrary to BDNF and IBA-1 number data, traced microglia in DG did appear
to have some morphological alterations (significant decrease in length of extensions
and complexity of extensions as compared to CO controls) following UFC. Given
that both protocols successfully produce contextual fear memories, further research is
required to fully delineate DH involvement following CFC and UFC.
6.5.3 Technical Considerations
Evaluation of BDNF and IBA-1, but not pCREB revealed a functional role
for DG following CFC. The stability of pCREB expression in hippocampus, and also
amygdala, was in direct contrast to many previous investigations (Hagewoud et al.,
2011; Impey et al., 1998; Kudo et al., 2004; Mamiya et al., 2009b; Stanciu et al.,
2001; Trifilieff et al., 2006). Methodological considerations may explain these
results. First, no previous research identifying a role for pCREB found a difference
90 minutes post-learning. Various proteins and IEG’s have a peak expression time
following learning (Ivashkina et al., 2016; Lonergan et al., 2010; Morgan & Curran,
1991; Ramírez-Amaya et al., 2005; Schafe et al., 2000). Behavioural differences
cannot be identified if animals are sacrificed before or after this time-point (Schafe et
218
al., 2000). An alternate explanation for the stability of pCREB expression relates to
the quality of labelling. While labelling in previous investigations allow for clear
differentiation between neurons (see (Kudo et al., 2004; Mamiya et al., 2009b;
Stanciu et al., 2001; Trifilieff et al., 2006)), there appear to be more total pCREB
expressing neurons in the current study, which overlap. This made it difficult to
accurately quantify the number of pCREB positive neurons. For this reason, the
pCREB-specific results are reported cautiously.
The further caveat of the current project is the small differences in actual
number of BDNF (mean difference of 16.17 between CFC and CO control) and IBA-
1 (mean difference of 7.167) number. The small standard errors make these
differences statistically significant. Nevertheless, the change in number may limit the
clinical significance of this study. Targeted pharmacological inhibitors can produce
large-scale alterations in protein expression (see pCREB study (Hubbard et al.,
2007)). While large-scale alterations may be beneficial to the treatment of fear-
related disorders, it may have numerous confounding behavioural consequences.
While this particular caveat exists in all neuroscientific research, it appears to be
exaggerated by the atypically small number in differences reported here.
Nevertheless, identification of these differences may be essential to understanding
how the healthy brain can become pathological. For example, the small increase in
BDNF and IBA-1 number lead to more in-depth analyses into microglia morphology,
which were found to be have large and clear alterations.
6.5.4 Conclusion
The current study investigated how two differing contextual fear memories
are represented in the rat brain. While fear to context was relatively constant, we
report differences in BDNF and IBA-1 number between these two conditioned
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groups. Standard CFC leads to an increase in BDNF expression, IBA-1 number,
while UFC did not. Furthermore, we provide some of the first data showing
microglia morphology to become altered as a function of CFC and UFC.
Interestingly, while BDNF expression and IBA-1 number increased only in following
CFC, morphological differences were identified in both the CFC and UFC as
compared to the CO control. While the change in BDNF and IBA-1 number may
have limited clinical significance, the alteration in microglia morphology may be
clinically significant. When active, or amoeboid, microglia can alter neuronal activity
via the BDNF-TrkB pathway (Ferrini & De Koninck, 2013; Pósfai et al., 2018), and
therefore targeted treatment of microglia may affect only the necessary neurons
responding to fear-inducing stimuli. This suggests that targeted drug treatments
aimed at inhibiting microglia activity (Cheng et al., 2015; Greter & Merad, 2013)
may provide a new therapeutic tool for sufferers of fear-based disorders. Further
research is required to investigate how reducing microglia activity influences fear
memory consolidation and maintenance.
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Axonal nonsegregation of the Vesicular Glutamate
Transporter VGLUT3 within serotonergic projections in the mouse
forebrain.
This chapter comprises the following published article: Belmer, A., Beecher, K., Jacques, A., Patkar, O. L., Sicherre, F., Bartlett, S. E. Axonal nonsegregation of the Vesicular Glutamate Transporter VGLUT3 within serotonergic projections in the mouse forebrain. Frontiers in Cellular Neuroscience. Published 10th May, 2019. https://doi.org/10.3389/fncel.2019.00193
Chapters 7 and 8 address the objectives listed in aim 3. The co-release of
glutamate and serotonin are central to the encoding of reward and anxiety type
behaviours, therefore the mapping of colocalized 5-HT and Vglut3 in relation to
brain region and to pMAPK expressing neurons may reveal potential therapeutic
targets.
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Statement of Contribution of Co-Authors for Thesis by Published Paper
The authors listed below have certified that:
1. they meet the criteria for authorship in that they have participated in the conception, execution, orinterpretation, of at least that part of the publication in their field of expertise;
2. they take public responsibility for their part of the publication, except for the responsible author who accepts overall responsibility for the publication;
3. there are no other authors of the publication according to these criteria;
4. potential conflicts of interest have been disclosed to (a) granting bodies, (b) the editor or publisher of journals or other publications, and (c) the head of the responsible academic unit, and
5. they agree to the use of the publication in the student’s thesis and its publication on the QUT’s ePrints site consistent with any limitations set by publisher requirements.
In the case of chapter 7: Axonal nonsegregation of the Vesicular Glutamate Transporter VGLUT3 within serotonergic projections in the mouse forebrain. Publication status: Published
Contributor Statement of contribution*Arnauld Belmer Performed IHC experiments, imaging, design of experiments, data analysis,
drafted and edited the manuscript
Kate Beecher Assisted with imaging, design of experiments, data analysis, drafted and edited the manuscript
Angela Jacques Assisted with data collection, figure formatting and editing the manuscript.
Omkar Patkar Assisted with data collection, figure formatting and editing the manuscript.
Florian Sicherre Assisted with data collection, figure formatting and editing the manuscript.
Selena Bartlett Assisted with the design of experiments and editing the manuscript
QUT Verified
Signature
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7.1 Abstract
A subpopulation of raphe 5-HT neurons expresses the vesicular glutamate
transporter VGLUT3 with the co-release of glutamate and serotonin proposed to play
a pivotal role in encoding reward- and anxiety-related behaviors. Serotonin axons are
identifiable by immunolabelling of either serotonin (5-HT) or the plasma membrane
5-HT transporter (SERT), with SERT labeling demonstrated to be only partially
overlapping with 5-HT staining. Studies investigating the colocalization or
segregation of VGLUT3 within SERT or 5-HT immunolabeled boutons have led to
inconsistent results. Therefore, we combined immunohistochemistry, high resolution
confocal imaging and 3D-reconstruction techniques to map and quantify the
distribution of VGLUT3 immunoreactive boutons within 5-HT vs SERT- positive
axons in various regions of the mouse forebrain, including the prefrontal cortex,
nucleus accumbens core and shell, bed nucleus of the stria terminalis, dorsal
striatum, lateral septum, basolateral and central amygdala and hippocampus. Our
results demonstrate that about 90% of 5-HT boutons are colocalized with SERT in
almost all the brain regions studied, which therefore reveals that VGLUT3 and SERT
do not segregate. However, in the posterior part of the NAC shell, we confirmed the
presence of a subtype of 5-HT immunoreactive axons that lack the SERT.
Interestingly, about 90% of the 5-HT/VGLUT3 boutons were labelled for the SERT
in this region, suggesting that VGLUT3 is preferentially located in SERT
immunoreactive 5-HT boutons. This work demonstrate that VGLUT3 and SERT
cannot be used as specific markers to classify the different subtypes of 5-HT axons.
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7.2 Introduction
Intensive efforts have long been made to understand the complexity of the
serotonin (5-Hydroxytryptamine, 5-HT) system and to identify specific markers for
serotonin neuron diversity. Although it is becoming evident that raphe serotonin
neurons are morphologically, functionally and molecularly heterogeneous (Calizo et
al., 2011; Fernandez et al., 2016; Gaspar and Lillesaar, 2012; Kiyasova et al., 2011,
2013), the diversity of serotonergic axonal projections to the forebrain is not
completely understood.
Pioneer electron or light microscopy and anterograde tracing studies have
revealed the existence of 5-HT axon terminals with different sizes, shapes, contents
of their small vesicles, and the presence or absence of dense-core vesicles (for review
see(Descarries et al., 2010)). In rats, two types of axons were reported, with axons
originating from the dorsal raphe showing fine beaded or fusiform varicosities
separated by smooth axon segments of variable length (type D), while axons
originating from the median raphe displayed large spherical varicosities with fine and
smooth inter-varicosity segments (type M) (Kosofsky and Molliver, 1987). In
primates, two types of axons were also described, with sparse, small, ovoid or large,
spheroidal varicosities (Hornung et al., 1990). However, it is likely that this axonal
morphology classification cannot longer be considered as valid criteria for
distinguishing the cellular origin or the chemical identity of the 5-HT neurons.
Indeed a chemically defined 5-HT neuron can send several types of axonal
projections with different morphologies to different brain regions (Gagnon and
Parent, 2014). Hence, research has been rather devoted to studying the molecular or
physiological diversity of 5-HT neurons, identifying various 5-HT neuronal subtypes
that differentially express the 5-HT1A autoreceptor (Bonnavion et al., 2010;
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Fernandez et al., 2016; Kirby et al., 2003; Kiyasova et al., 2013; Sotelo et al., 1990),
substance P/neurokinin receptor 1 (NK1)(Lacoste et al., 2006), galanin and its
receptor (Larm et al., 2003; Xu and Hökfelt, 1997), neuronal nitric oxide synthase
(nNOS) (Xu and Hökfelt, 1997), gamma-aminobutyric acid (GABA)-synthesizing
enzyme glutamic acid decarboxylase (GAD) (Fu et al., 2010), alpha7 nicotinic
receptor (Aznar et al., 2005), MET receptor tyrosine kinase (Kast et al., 2017) or
display different pharmacological and electrophysiological properties (Calizo et al.,
2011; Hajós et al., 2007; Kirby et al., 2003). This heterogeneity appears to be target-
specific (Fernandez et al., 2016; Prouty et al., 2017) and could therefore be used to
establish a specific anatomy/function cartography of raphe serotonin sub-systems
(Ren et al., 2018).
In addition, a subpopulation of dorsal and median raphe 5-HT neurons was
found to co-express transcripts of the vesicular glutamate transporter type 3
(VGLUT3) (Gras et al., 2002; Hioki et al., 2010), suggesting that 5-HT and
glutamate could be stored in the same vesicles and co-released. VGLUT3 protein
was also reported to be located in the some 5-HT immunoreactive axonal varicosities
in the forebrain (Mintz and Scott, 2006; Schäfer et al., 2002), including the granular
cell layer of the olfactory bulb, cerebral cortex, central amygdaloid nuclei,
hippocampal CA3 field, dorsolateral septum and supra-ependymal plexus of the third
ventricle (Shutoh et al., 2008). A classification of two serotonergic axons subtypes
depending on the presence or absence of VGLUT3 was therefore proposed (Shutoh
et al., 2008). It is likely that the co-expression of VGLUT3 and the vesicular
monoamine transporter 2 (Vmat2) in serotonin terminals (Schäfer et al., 2002)
synergizes the filling of 5-HT and glutamate in the same synaptic vesicles (Amilhon
et al., 2010), with 5-HT/glutamate cotransmission proposed to play a pivotal role in
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the control of reward- and emotion-related neural circuitry (Liu et al., 2014;
Sengupta et al., 2017) and their plasticity/adaptability during development or
pathological processes (Gagnon and Parent, 2014).
However, some discrepancies have emerged from the aforementioned studies,
regarding the total or sparse colocalization of the serotonin transporter SERT in 5-
HT axon varicosities. While Gagnon and Parent (2014) observed that all 5-HT axon
varicosities contain the SERT in rats, two studies have reported a very sparse
colocalization of SERT and 5-HT immunolabeling in mice, with VGLUT3 and
SERT mostly segregrated within 5-HT varicosities (Amilhon et al., 2010; Voisin et
al., 2016), especially in the prefrontal cortex, hippocampus, dorsal striatum and
lateral septum. This data suggests that different subtypes of 5-HT axonal varicosities
(SERT-/VGLUT3+ or SERT+/VGLUT3-) may coexist in the mouse forebrain.
In the present study, we therefore investigated the immunohistological distribution of
SERT and VGLUT3 within 5-HT axon varicosities in various regions of the mouse
forebrain, including the prefrontal cortex, nucleus accumbens core and shell, bed
nucleus of the stria terminalis, dorsal striatum, lateral septum, basolateral and central
amygdala and hippocampus. For this, we imaged ±1.55x108 um3 of tissue and 3D-
reconstructed ±106 5-HT varicosities to determine the volumetric density and the
proportion of varicosities co-labelled with SERT and/or VGLUT3, in each brain
region. We found that the great majority (≈ 90%) of 5-HT varicosities express the
SERT in every brain regions analyzed except the posterior shell of the nucleus
accumbens (50%). We herein report that VGLUT3 is preferentially located in SERT+
5-HT varicosities. Our results demonstrate that VGLUT3 and SERT do not
particularly segregate in 5-HT axonal varicosities of the mouse forebrain.
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7.3 Materials and Methods
7.3.1 Animals
Six 8-10 week-old C57Bl6 mice (3 males, 3 females) were housed in standard
ventilated cages in climate-controlled rooms. Food, water, and environmental
enrichment were available ad libitum. This study was carried out in accordance with
the recommendations of National Health and Medical Research Council (NHMRC)
guidelines to promote the well-being of animals used for scientific purposes and the
Australian code for the care and use of animals for scientific purposes. The protocol
was approved by the Queensland University of Technology Animal Ethics
Committee and the University of Queensland Animal Ethics Committee.
7.3.2 Histology
Mice were transcardially perfused with 4% paraformaldehyde (PFA) prior to
decapitation. Brains were harvested and post-fixed overnight at 4°C. Forty-micron
thick coronal vibratome sections were collected and incubated overnight in blocking
solution (2% normal goat serum, 0.3% Triton and 0.05% Tween 20 in 0.1M
phosphate-buffer saline (PBS)).
7.3.3 Immunohistochemistry
Sections containing the pre-limbic cortex (Bregma +2.46 ± 0.3 mm), the
NAC (Bregma +1.42 ± 0.2 mm), the posterior NAC, the dorsal striatum and the
lateral septum (Bregma +1.00 ± 0.2 mm), the BNST (Bregma -0.22 ± 0.3 mm), the
hippocampus (Bregma -1-70 ± 0.3 mm) or the amygdala (Bregma -1.40 ± 0.2 mm)
were incubated with primary antibodies diluted in the blocking solution: rat anti-5-
HT (Millipore #MAB352, 1:100) for 48 hours at room temperature followed by
rabbit anti-SERT (Millipore #PC177L, 1:1000) and guinea-pig anti-VGLUT3
227
(Synaptic System #135204, 1:500) overnight at 4 degrees. After three washes in the
blocking solution, the slices were incubated for 4 hours at room temperature with
secondary antibodies diluted in the blocking solution: goat anti-rabbit-Alexa 488,
goat anti-guinea pig-Alexa 647 (Thermofisher Scientific, #A11034 and #A21450,
1:500) and goat anti-rat biotinylated (Jackson Laboratory # 112-065-003, 1:200).
After three washes in the blocking solution, slices were incubated for 30 min in
Streptavidin-Cy3 (Thermofisher Scientific #438315, 1:1000), washed 3 times in
PBS, and mounted in Prolong Gold antifade mountant (Thermofisher Scientific,
#P36934).
7.3.4 Imaging and Analysis
Sections (3 sections per animal, n = 6 animals, 18 sections/brain region) were
imaged on an Olympus FV3000 using a 60X oil-immersion objective (NA 1.35) with
a 2.5 x zoom and a Z-axis step of 0.3 µm, using sequential scanning. Mosaics of the
regions of interest were acquired as depicted in A of Figures 1 to 7, in OIR file
format. The 5-HT immunoreactive boutons were reconstructed in 3D using the
surface rendering function with Imaris 9.2.1 (Bitplane), as previously described
(Belmer et al., 2016; Tarren et al., 2017). All the images were processed in batch
using the same surface thresholding parameters. Mean fluorescence intensities of
SERT or VGLUT3 labeling within 5-HT boutons and image volumes were obtained
from the surface statistics in Imaris. Since the level of background of confocal
images can reach as much as 30% of maximum image intensity (Landmann and
Marbet, 2004), we use this threshold as a criteria to define the 5-HT boutons
colocalized and non-colocalized with SERT and/or VGLUT3 (i.e mean intensity
>30% or <30% of the maximum intensity, respectively). For each brain region, the
frequency distribution of the colocalized and non-colocalized boutons within each
228
brain region was analyzed using Excel 365, averaged for each animal, and plotted in
Graphpad Prism 7.0 (Graph Pad Software Co., San Diego, CA, United States) as
replicates (n = 6). 3D representation of the proportion of VGLUT3 immunoreactive
5-HT boutons within each brain region was generated using the online Scalable
Brain Atlas (Bakker et al., 2015) with a custom color-coded scale (Figure 9).
7.3.5 Statistics
Statistical analyses were carried out using GraphPad Prism 7.0. The
proportion of 5-HT or 5-HT/VGLUT3 varicosities immunopositive for SERT were
compared to SERT immunonegative boutons within each brain region using a two-
tailed unpaired t-test and across all studied regions using a one-way ANOVA. The
densities and relative densities of 5-HT/VGLUT3 boutons within all the analyzed
brain regions were compared using one-way ANOVA with Sidak correction for
multiple comparison. A p-value < 0.05 was considered significant, with all values
expressed as the mean ± SEM.
7.4 Results
Previous studies have suggested that VGLUT3 always segregates with SERT
within the varicosities of the 5-HT axons projecting to the prelimbic region of the
prefrontal cortex (Amilhon et al., 2010). Therefore, we first examined the
distribution of VGLUT3 and SERT immunoreactive 5-HT varicosities within the
different layers of the prelimbic cortex (PrL), at bregma +2.46 mm (Fig. 1A). Co-
labeling of the SERT and 5-HT revealed that most of the varicosities reconstructed
were co-labeled for the two markers (Fig. 1B: a-i). Labeling of VGLUT3 showed the
typical scattered punctate fluorescence (Fig. 1B: j) and by combining the 3D-
reconstruction and the masking functions of Imaris, we isolated the VGLUT3
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labeling that was only contained in 3D-reconstructed 5-HT varicosities (Fig. 1B: k).
We observed that almost all the 5-HT/VGLUT3 boutons were co-labeled with SERT
(arrow heads, Fig. 1B: c, f, i, l). Indeed, the absolute quantifications of the proportion
of 5-HT varicosities that were co-labeled with SERT confirmed that 85 % and 91 %
of 5-HT varicosities were co-labeled with SERT in the layers 4-5 and 1-3,
respectively (Fig 1C, a, b; ****: p<0.0001). Similar proportions of 5-HT/VGLUT3
boutons (82 % and 91 %) were also co-labeled with SERT (Fig 1C, c, d). These
results suggest that only a small proportion of 5-HT boutons (9-18 %) do not express
detectable levels of SERT in the PrL and, that VGLUT3 and SERT do not
predominantly segregate within the 5-HT axonal varicosities in this brain region.
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Figure 7-1 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the prelimbic cortex.
A. Schematic drawing showing the location of the acquired micrographs. Prelimbic (PrL) cortex mosaic images of layer I to V were acquired at bregma +2.46 ± 0.2 mm (red vertical line) in the dorsal half of the medial cortex (yellow/red square). B. Micrograph showing the distribution of the SERT (green, a-c), 5-HT (red, d-f), SERT (green) + 5-HT (red) (g-i), and VGLUT3 (magenta, j-l). Left panel shows lower magnification. Scale bar: 50 µm. Middle and right panels show higher magnification of the white dashed box in the left panel. Scale bar: 2 µm. The right panel shows the VGLUT3 boutons co-labeled with SERT (c), 5-HT (f) and SERT+5-HT (i). VGLUT3 puncta located within 5-HT varicosities were isolated using Imaris (l). Arrow heads show 5-HT varicosities collocated with SERT and VGLUT3 (c, f, i and l). C. (a, b) Quantification of the proportion of 5-HT varicosities not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the upper (a) or deeper (b) layers of the PrL, showing a great majority of 5-HT varicosities co-labeled with SERT. (c, d) Quantification of the proportion of 5-HT+/VGLUT3+ not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the upper (a) or deeper (b) layers of the PrL, showing a great majority of 5-HT/VGLUT3 varicosities co-labeled with SERT (t test, ****: p<0.0001).
The co-release of 5-HT and glutamate by 5-HT neurons from the dorsal raphe
has been proposed to play an important role in the regulation of the
neurotransmission in the ventral striatum and the modulation of reward-related
behaviors (Liu et al., 2014). Hence, we next investigated the distribution of SERT
and VGLUT3 immunolabeled varicosities within the NAc core and shell, at bregma
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+1.42 (Fig. 2A). Both in the core (94 %) and the shell (95 %) regions of the NAc, the
great majority of 5-HT boutons were also immunoreactive for SERT (Fig. 2B and
2C: a-i; 2D: a-b, **: p<0.01; ****: p<0.0001). Hence, a great proportion of 5-
HT/VGLUT3 boutons were co-labeled with the SERT, both in the core (92 %) and
the shell (89 %) (arrow heads, Fig. 2B and 2C: c-l; Fig. 2D: c-d, ****: p<0.0001).
These results support a high degree of overlap between 5-HT and SERT
immunoreactive axons in the core and the shell of the rostral NAc (Brown and
Molliver, 2000), and further highlight the absence of any particular segregation
between SERT and VGLUT3 within 5-HT varicosities.
Figure 7-2 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the nucleus accumbens.
A. Schematic drawing showing the location of the acquired micrographs. Nucleus accumbens (NAC) mosaic images were acquired at bregma +1.42 ± 0.2 mm (red vertical line) in the dorsomedial shell and the core (ventrolateral to the anterior commissure) of the nucleus accumbens (green/red areas). B. Micrograph showing the distribution of the SERT (green, a-c), 5-HT (red, d-f), SERT (green) + 5-HT (red) (g-i), and VGLUT3 (magenta, j-i). Left panel shows lower magnification. Scale bar: 50 µm. Middle and right panels show higher magnification of the white dashed box in the left panel. Scale bar: 2 µm. The right panel shows the VGLUT3 boutons co-labeled with SERT (c), 5-HT (f) and
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SERT+5-HT (i). VGLUT3 puncta located within 5-HT varicosities were isolated using Imaris (l). Arrow heads show 5-HT varicosities collocated with SERT and VGLUT3 (c, f, i and l). C. (a, b) Quantification of the proportion of 5-HT varicosities not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the NAC, showing a great majority of 5-HT varicosities co-labeled with SERT. (c, d) Quantification of the proportion of 5-HT+/VGLUT3+ not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the NAC, showing a great majority of 5-HT/VGLUT3 varicosities co-labeled with SERT (t test, **: p<0.01, ****: p<0.0001).
Brown and Molliver also observed that a subset of 5-HT axons lack SERT in
the caudal part of the rat NAc shell(Brown and Molliver, 2000). Therefore, we
investigated the distribution of SERT and VGLUT3 within the 5-HT varicosities in
the posterior NAc shell, at bregma +1.00 mm, at a similar level to the
aforementioned study in rat (i.e “septal pole” or “cone region”) (Fig. 3A).
Interestingly, we found the 5-HT varicosities either co-labeled (arrow heads) or not
labelled (or only weakly/partially labeled, arrows) for the SERT in the posterior NAc
shell (Fig. 3B: a-c, e-g) and both SERT+ and SERT- serotonergic varicosities were
co-labeled for VGLUT3 (Fig. 3B: i-l). The quantification revealed that half of the 5-
HT varicosities do not express the SERT in the posterior shell of the mouse NAc
(Fig. 3C: a) as previously described in rats. Although VGLUT3 was observed in
some SERT- varicosities, this subtype of serotonergic boutons only represents a
minority (11 %), as the great majority (89 %, Fig. 3C: b, ****: p<0.0001) of the 5-
HT+/VGLUT3+ boutons were also co-labeled for SERT. These results further
suggest that VGLUT3 are rather colocalized than segregated with the SERT in 5-HT
varicosities.
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Figure 7-3 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the posterior shell of the nucleus accumbens.
A. Schematic drawing showing the location of the acquired micrographs. The posterior (caudal) shell of the nucleus accumbens (NAC) (green/red area) mosaic images were acquired at bregma +1 ± 0.2 mm (red vertical line). B. Micrograph showing the distribution of the SERT (green, a, e, i), 5-HT (red, b, f, j), SERT (green) + 5-HT (red) (c, g, k), and VGLUT3 (magenta, d, h, l). Top panel shows lower magnification. Scale bar: 50 µm. Middle and bottom panels show higher magnification of the white dashed box in the left panel. Scale bar: 2 µm. The bottom panel shows the VGLUT3 boutons co-labeled with SERT (i), 5-HT (j) and SERT+5-HT (k). VGLUT3 puncta located within 5-HT varicosities were isolated using Imaris (l). Arrow heads show 5-HT varicosities collocated with SERT and VGLUT3 (i-l). C. (a) Quantification of the proportion of 5-HT varicosities not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the posterior shell of the NAC, showing a great majority of 5-HT varicosities co-labeled with SERT. (b) Quantification of the proportion of 5-HT+/VGLUT3+ not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the posterior shell of the NAC, showing a great majority of 5-HT/VGLUT3 varicosities co-labeled with SERT (t test, ****: p<0.0001).
The segregation of SERT and VGLUT3 was also reported in the dorsal
striatum and the lateral septum(Voisin et al., 2016). Hence, we investigated their
distribution at bregma +1.00 mm (Fig. 4A). Again, the great majority of the 5-HT
varicosities express the SERT in both the dorsal striatum (92 %, Fig. 4B: a-i; Fig.
4D: a) and lateral septum (75 %, Fig. 4C: a-i; Fig. 4D: b; ***: p<0.001; ****:
p<0.0001). Therefore, no axonal segregation could be observed, indeed more than 80
% of the VGLUT3 immunoreactive 5-HT boutons were also co-labeled for the
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SERT, in both the striatum and lateral septum (Fig. 4B and 4C: a-i, arrow heads;
Fig. 4D: c-d; ***: p<0.001; ****: p<0.0001). These results further evidence that
SERT and VGLUT3 cannot be used as markers to classify different 5-HTergic
axonal subtypes.
Figure 7-4 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the striatum and lateral septum.
A. Schematic drawing showing the location of the acquired micrographs. Mediolateral dorsal striatum and lateral septum (LS) mosaic images were acquired at bregma +1 ± 0.2 mm (red vertical line) (green/red circles). B. Micrograph showing the distribution of the SERT (green, a-c), 5-HT (red, d-f), SERT (green) + 5-HT (red) (g-i), and VGLUT3 (magenta, j-i) in the striatum. Left panel shows lower magnification. Scale bar: 50 µm. Middle and right panels show higher magnification of the white dashed box in the left panel. Scale bar: 2 µm. The right panel shows the VGLUT3 boutons co-labeled with SERT (c), 5-HT (f) and SERT+5-HT (i). VGLUT3 puncta located within 5-HT varicosities were isolated using Imaris (l). Arrow heads show 5-HT varicosities collocated with SERT and VGLUT3 (c, f, i and l). C. Micrograph showing the distribution of the SERT (green, a-c), 5-HT (red, d-f), SERT (green) + 5-HT (red) (g-i), and VGLUT3 (magenta, j-l) in the LS. Left panel shows lower magnification. Scale bar: 50µm. Middle and right panels show higher magnification of the white dashed box in the left panel. Scale bar: 2µm. The right panel shows the VGLUT3 boutons co-labeled with SERT (c), 5-HT (f) and SERT+5-HT (i). VGLUT3 puncta located within 5-HT varicosities (l). Arrow heads show 5-HT varicosities collocated with SERT and VGLUT3 (c, f, i and l). D. (a, b) Quantification of the proportion of 5-HT varicosities not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the striatum (a) and LS (b), showing a great majority of 5-HT varicosities co-labeled with SERT. (c, d) Quantification of the proportion of 5-HT+/VGLUT3+ not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the striatum (c) and LS (d), showing a great majority of 5-HT/VGLUT3 varicosities co-labeled with SERT (t test, ****: p<0.0001; ***:p<0.001).
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DR 5-HT neurons that send VGLUT3 immunoreactive axons to the NAc also
send collaterals to different brain regions, including the bed nucleus of the stria
terminalis (BNST) and central (CeA) and basolateral (BLA) amygdala. We therefore
investigated the distribution of SERT and VGLUT3 within 5-HT varicosities in those
brain regions (Fig. 5 and 6). In the anterior BNST at bregma -0.22 mm (Fig. 5A), we
again observed a high degree of overlapping between SERT and 5-HT
immunoreactivity (82 %) (Fig. 5B: a-i and 5C: a; ****: p<0.0001), with a high
proportion (89 %) of 5-HT/VGLUT3 varicosities that were co-labeled for SERT
(Fig. 5B: a-i, arrow heads and 5C: b; ****: p<0.0001).
Figure 7-5 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the bed nucleus of the stria terminalis.
A. Schematic drawing showing the location of the acquired micrographs. The bed nucleus of the stria terminalis (BNST) mosaic images were acquired at bregma -0.22 ± 0.3 mm (red vertical line) in the medial posteromedial BNST (BSTMPM) and posterointermediate part of the BNST (BSTMPI) (green/red area). B. Micrograph showing the distribution of the SERT (green, a-c), 5-HT (red, d-f),
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SERT (green) + 5-HT (red) (g-i), and VGLUT3 (magenta, j-l). Left panel shows lower magnification. Scale bar: 50 µm. Middle and right panels show higher magnification of the white dashed box in the left panel. Scale bar: 2 µm. The right panel shows the VGLUT3 boutons co-labeled with SERT (c), 5-HT (f) and SERT+5-HT (i). VGLUT3 puncta located within 5-HT varicosities were isolated using Imaris (l). Arrow heads show 5-HT varicosities collocated with SERT and VGLUT3 (c, f, i and l). C. (a) Quantification of the proportion of 5-HT varicosities not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the BNST, showing a great majority of 5-HT varicosities co-labeled with SERT. (b) Quantification of the proportion of 5-HT+/VGLUT3+ not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the BNST, showing a great majority of 5-HT/VGLUT3 varicosities co-labeled with SERT (t test, ****: p<0.0001).
Similarly, in the amygdala at bregma -1.70 (Fig. 6A), the great majority of 5-
HT boutons were co-labeled for SERT in both the BLA (87 %) and CeA (79 %) (Fig.
6B: a-i; Fig. 6C: a-b, ***: p<0.001; ****: p<0.0001). Consequently, a great
proportion of 5-HT/VGLUT3 boutons were co-labeled with the SERT within the
BLA (89 %) and CeA (73 %) (arrow heads, Fig. 6B: g-l; Fig. 6C: c-d; ****:
p<0.0001; ***: p<0.001).
Figure 7-6 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the basolateral amygdala and central nucleus of the amygdala.
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A. Schematic drawing showing the location of the acquired micrographs. The basolateral amygdala (BLA) and central nucleus of the amygdala (CeA) mosaic images were acquired at bregma -1.70 ± 0.2 mm (red vertical line) in the anterior part of the basolateral amygdala and the lateral CeA (green/red areas). B. Micrograph showing the distribution of the SERT (green, a-c), 5-HT (red, d-f), SERT (green) + 5-HT (red) (g-i), and VGLUT3 (magenta, j-l) in the CeA. Left panel shows lower magnification. Scale bar: 150 µm. Middle and right panels show higher magnification of the white dashed areas (circle = CeA) in the left panel. Scale bar: 2 µm. The right panel shows the VGLUT3 boutons co-labeled with SERT (c), 5-HT (f) and SERT+5-HT (i) in the CeA. VGLUT3 puncta located within 5-HT varicosities were isolated using Imaris (l). Arrow heads show 5-HT varicosities collocated with SERT and VGLUT3 (c, f, i and l) in the CeA. C. (a-b) Quantification of the proportion of 5-HT varicosities not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the BLA (a) and CeA (b), showing a great majority of 5-HT varicosities co-labeled with SERT. (c-d) Quantification of the proportion of 5-HT+/VGLUT3+ not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the BLA (c) and CeA (d), showing a great majority of 5-HT/VG varicosities co-labeled with SERT (t test, ****: p<0.0001; ***: p<0.001).
In the hippocampus, VGLUT3 was shown to modulate 5-HTergic tone, and
to stimulate VMAT2-dependent accumulation of 5-HT in synaptic vesicles (Amilhon
et al., 2010), which further suggests that VGLUT3/5-HT synaptic cross-talk may
play an important role in hippocampal-mediated behaviors such as anxiety and
depression. Since these behaviors are also dependent upon SERT activity/blockade
by serotonergic antidepressant, whether VGLUT3 and SERT segregate or co-localize
within 5-HT varicosities in the hippocampus is question of great interest and could
further help in developing improved therapeutics for the treatment of anxiety- or
depression-related disorders. Therefore, we rigorously investigated the distribution of
SERT and VGLUT3 in 5-HT axonal varicosities within the different subregions of
the hippocampus, at bregma -1.70 ± 0.3 mm (Fig. 7A). Most of 5-HT boutons were
also immunoreactive for SERT in all four regions of the hippocampus, CA1 (90 %),
CA2 (85 %), CA3 (84 %) and DG (86 %) (Fig. 7B: g-i; 7C: a-b, ****: p<0.0001). A
similar proportion of 5-HT/VGLUT3 boutons were co-labeled with SERT in the
CA1 (87 %), CA2 (87 %), CA3 (82 %) and DG (78 %) (arrow heads, Fig 7B: c, f, i,
l; 7C: c-d; ****: p<0.0001; **: p<0.01).
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Figure 7-7 Distribution of VGLUT3+ boutons within 5-HT+ and SERT+ axons in the CA1, CA2, CA3, dentate gyrus of the hippocampus.
A. Schematic drawing showing the location of the acquired micrographs. The hippocampus (HIP) mosaic images were acquired at bregma -1.70 ± 0.3 mm (red vertical line) in the dorsal hippocampus (green/red area). B. Micrograph showing the distribution of the SERT (green, a-c), 5-HT (red, d-f), SERT (green) + 5-HT (red) (g-i), and VGLUT3 (magenta, j-l) in the CA1. Left panel shows lower magnification. Scale bar: 150 µm. Middle and right panels show higher magnification of the CA1 in the white dashed box in the left panel. Scale bar: 2 µm. The right panel shows the VGLUT3 boutons co-labeled with SERT (c), 5-HT (f) and SERT+5-HT (i). VGLUT3 puncta located within 5-HT varicosities were isolated using Imaris in the CA1 (l). Arrow heads show 5-HT varicosities collocated with SERT and VGLUT3 in the CA1 (c, f, i and l). C. (a-d) Quantification of the proportion of 5-HT varicosities not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the CA1, CA2, CA3 and DG of the HIP, showing a great majority of 5-HT varicosities co-labeled with SERT. (e-h) Quantification of the proportion of 5-HT+/VGLUT3+ not co-labeled (SERT-) and co-labeled (SERT+) with SERT in the CA1, CA2, CA3 and DG of the HIP, showing a great majority of 5-HT/VG varicosities co-labeled with SERT (t test, ****: p<0.0001; **: p<0.01).
We have reconstructed a total of 106 serotonergic varicosities from various
brain regions, including the prelimbic cortex (5x104), nucleus accumbens (10x104),
lateral septum (15x104), BNST (25x104), amygdala (27x104), hippocampus (15x104)
and dorsal striatum (5x104). Our quantification of the volumetric density of 5-
HT+/VGLUT3+ boutons revealed a high heterogeneity along the different brain
regions. The, CA1-3, lateral septum, amygdala (BLA and CeA) and NAc shell show
the highest density, and the NAc core, striatum, dentate gyrus and prelimbic cortex
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show the lowest density of 5-HT+/VGLUT3+ varicosities (Fig. 8A) (see table 1 for
detailed statistics). The relative density of 5-HT+/VGLUT3+ boutons, calculated as a
percentage of total 5-HT varicosities within each brain region, shows the largest
proportion is located in CA1-3 (24 to 32 %), followed by the PrL (20-23 %) > DG
(19 %) > CeA (16 %) > LS (14 %) > BLA (13 %) > NAc shell (10 %) > BNST (7 %)
> striatum (6 %) > NAc core (4.2 %) (Fig. 8B and Fig. 9). Correlation analysis shows
that the density of 5-HT+/VGLUT3+ varicosities is totally independent of the density
of 5-HT boutons within each brain region (Pearson’s coefficients r = 0.32 and R2 =
0.10, p = 0.28). This data suggests that the heterogeneous distribution of 5-
HT+/VGLUT3+ varicosities in the forebrain represents a functionally relevant feature
of 5-HT neurons complex topology, rather than a biased detection of those
varicosities in our methodological approach.
Figure 7-8 Quantification of 5-HT neurons expressing vesicular glutamate transporter (VGLUT3) in various regions of the mouse forebrain.
Volumetric quantification of the density of VGLUT3 immunoreactive boutons within 5-HT-labeled fibers in each brain region. The results are expressed as density of boutons per 106 mm3 of 5-HT+ fiber (a), or percent of boutons in 5-HT+ fiber (b) and represented as the mean ± SEM.
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Figure 7-9 Visual representation of the proportion of 5-HT varicosities expressing the vesicular glutamate transporter VGLUT3 in various regions of the mouse forebrain.
The percentage of total 5-HT varicosities that are co-labeled with VGLUT3 is represented with a color coding from 0 % (dark blue) to 32 % (dark red) within each analyzed brain region, from various viewing angle. The highest proportion of VGLUT3 was found in CA2, followed by CA3, CA1, PrL IV-V, PrL I-III, DG, CeA, BLA, NAC shell, BNST and NAC core.
7.5 Discussion
The main finding of the present study is that SERT and VGLUT3 rarely
segregate within 5-HT varicosities, but rather preferentially co-localize in most of the
mouse forebrain regions we analyzed. These results are in agreement with a previous
observation in rats (Gagnon and Parent, 2014), but surprisingly differ from two
previous studies in WT littermates (vglut3+/+) of a transgenic mouse line, obtained by
the breeding of vglut3+/- mice (Amilhon et al., 2010; Voisin et al., 2016). In these
mice, a clear segregation of SERT and VGLUT3 was reported in the varicosities of
the 5-HTergic axons projecting to the prelimbic cortex, the ventral and dorsal CA3
field of the hippocampus, the dorsal striatum and the lateral septum (Amilhon et al.,
2010; Voisin et al., 2016). In our study in wild-type C57Bl6 mice, we did not
observe this segregation between SERT and VGLUT3 in5-HT varicosities. This
suggests that the aforementioned discrepancies may originate from technical issues,
241
probably related to antibody specificity or sensitivity, rather than true interspecies
differences. Further studies using a different methodological approach are therefore
needed to confirm the co-localization or segregation of SERT and VGLUT3 within
5-HT axonal varicosities.
Axonal segregation between SERT and VGLUT3 within 5-HT varicosities
would hence imply that a significant subset of 5-HT varicosities do not express the
SERT. However, our results demonstrate that the great majority of 5-HT boutons
(about 90%) are immunoreactive for the SERT, in almost all the brain regions
studied. The only brain region with an equivalent proportion of SERT-positive and
SERT-negative 5-HT boutons was the posterior part of the nucleus accumbens shell.
This is in line with previous reports in rats showing that only subtle differences could
be detected between 5-HT and of SERT immunostaining in particular brain regions,
with a subset of 5-HT axonal projections that lack the SERT in the posterior part of
the nucleus accumbens (Brown and Molliver, 2000). Since 5-HT could be stored in
dopaminergic neurons of the substantia nigra or the ventral tegmental area (Zhou et
al., 2005), we assessed the dopaminergic nature of these SERT-/5-HT+ varicosities.
The absence of immunoreactivity for the tyrosine hydroxylase has confirmed the
non-catecholaminergic phenotype of these varicosities (data not shown). Retrograde
tracing studies are currently performed in our laboratory to determine the origin of
this particular subset of SERT-/5-HT+ axons, i.e. whether these projections originate
from the dorsal, median or caudal raphe and whether this particular subtype of 5-HT
neurons are restricted to specific raphe subnuclei.
The high density of 5-HT/glutamate co-release sites identified within brain
regions known to be involved in the control of emotion, reward and decision-making
such as the amygdala, NAc shell, hippocampus and prefrontal cortex, evoke new
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potential mechanisms for the control of the neuroplasticity in these brain regions, and
its dysregulation in the development of addictive disorders (Kauer and Malenka,
2007). In line with this, the genetic ablation of vglut3 in mice was shown to
predispose to cocaine abuse (Sakae et al., 2015), however, the authors reported that
this mechanism is likely independent of 5-HT signaling but rather linked to an
increased dopamine and glutamate signaling in the nucleus accumbens, from VTA
dopaminergic and cortical glutamatergic inputs, respectively. Further studies are
hence needed to identify the contribution of serotonin/glutamate co-signaling in this
effect.
Indeed, recent evidence supports a significant role of 5-HT/glutamate co-
transmission in both reward and emotion (Amilhon et al., 2010; Liu et al., 2014).
Further, the activation of dopamine- and cAMP-regulated phosphoprotein of M(r)
32,000 (DARPP-32) by dopamine/glutamate co-transmission has been postulated to
act as a molecular switch that control the reward pathway plasticity that mediates the
behavioral sensitization to various drugs of abuse (Nairn et al., 2004; Valjent et al.,
2005). DARPP-32 also appears to be involved in some biochemical and behavioral
actions of 5-HT (Svenningsson et al., 2002), however, there is no evidence to date
that serotonin-induced DARPP-32 activation is mediated by 5-HT/glutamate co-
transmission. Further studies are thus required to determine the precise role(s) played
by 5-HT and/or glutamate and their different receptors in DARPP-32 activation, the
subsequent neuroplastic changes and associated cognitive/emotional deficits
produced by drugs of abuse. Furthermore, genetic ablation of VGLUT3 in mice
(vglut3-/-) produces increased anxiety (Amilhon et al., 2010; Sakae et al., 2018),
enhanced fear and altered stress axis regulation (Balázsfi et al., 2018), concomitant to
the desensitization of 5-HT1A autoreceptors (Amilhon et al., 2010), suggesting a
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potential cross-regulation between 5-HT1A receptors and glutamate co-release. Since
the desensitization of 5-HT1A autoreceptors is essential for the antidepressant effect
of selective serotonin reuptake inhibitors (SSRIs)(Popa et al., 2010), this further
suggests that selective pharmacological ablation of VGLUT3 might represent a
potential adjunct for antidepressant therapy.
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Fear extinction recall mediated by 5-HT/VGLUT3
colocalisation
This chapter comprises the following submitted article: Jacques, A., Chaaya, N., Belmer, A., Beecher, K., Ali, S. A., Battle, A. R., Johnson, L. R., Chehrehasa, F., Bartlett, S. E. (2019) Fear extinction recall mediated by 5-HT/VGLUT3 colocalisation. Frontiers in Behavioural Neuroscience Submitted: 20 March, 2019.
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Statement of Contribution of Co-Authors for Thesis by Published Paper
The authors listed below have certified that:
1. they meet the criteria for authorship in that they have participated in the conception, execution, orinterpretation, of at least that part of the publication in their field of expertise;
2. they take public responsibility for their part of the publication, except for the responsible author who accepts overall responsibility for the publication;
3. there are no other authors of the publication according to these criteria;
4. potential conflicts of interest have been disclosed to (a) granting bodies, (b) the editor or publisher of journals or other publications, and (c) the head of the responsible academic unit, and
5. they agree to the use of the publication in the student’s thesis and its publication on the QUT’s ePrints site consistent with any limitations set by publisher requirements.
In the case of chapter 8: Fear extinction recall facilitated by 5-HT/VGLUT3 colocalisation Publication status: Submitted
Contributor Statement of contribution*Angela Jacques Designed the project, conducted behavioural and laboratory experiments,
analysed data, created the figures and wrote and edited the manuscript.
Nicholas Chaaya Assisted with behavioural and laboratory experiments.
Kate Beecher Assisted with editing the manuscript.
Syed Aoun Ali Assisted with editing the manuscript.
Andrew Battle Assisted with editing the manuscript.
Luke Johnson Involved in the conception and design of the project.
Arnauld Belmer Involved in the conception and design of the project, and assisted with editing the manuscript and assisted with laboratory experiments, imaging and data analysis.
Fatemeh Chehrehasa Involved in the conception and design of the project, and assisted with editing the manuscript and assisted with editing the manuscript
Selena Bartlett Involved in the conception and design of the project, assisted with reviewing and editing the manuscript.
QUT Verified Signature
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8.1 Abstract
The hallmark of social anxiety and panic disorder is excessive fear. The
neurobiological mechanisms that underpin the recall of fear and extinction memories
may assist in development of strategies to improve the treatment of these disorders.
Extinction memories are particularly labile and the introduction of certain stimuli
may result in a re-emergence of the original fear. As the divergent molecular
mechanisms and complex neural circuits involved in the extinction process relevant
to cognitive extinction based therapies are a long way from being understood, this
study examined two of the essential elements. Serotonin (5-Hydroxytryptamine, 5-
HT) modulates the acquisition and storage of conditioned emotional memories and
glutamate facilitates learning and memory. In addition, the loss of vesicular
glutamate transporter 3 (VGLUT3) expression may lead to anxiety-like behaviour.
Here we tested the hypothesis that the colocalisation of VGLUT3 puncta within 5-
HT varicosities (reported as 5-HT+/VGLUT3+ boutons) may correlate with the recall
of extinction memories. To test this hypothesis, male Sprague Dawley rats underwent
auditory Pavlovian fear conditioning and extinction using an extended protocol. We
quantified the distribution of VGLUT3 puncta within 5-HT immunoreactive
varicosities apposed to pMAPK+ neurons within the prefrontal cortex using
immunohistochemistry, high resolution confocal microscopy and 3D image
reconstruction techniques. Significant increases in the density of pMAPK+ neurons
was observed in the extinction recall group with a corresponding increase in the
percentage of 5-HT+/VGLUT3+ boutons in their vicinity. This data suggests these
VGLUT3+ serotonergic axons may play a role in plasticity and represent a potential
target in the extinction of pathological fear and anxiety.
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8.2 Introduction
Social anxiety disorder, panic disorder, post-traumatic stress disorder and
phobias are characterised by failures of extinction and inhibitory learning
(Cannistraro & Rauch, 2003). Central nervous system serotonin (5-
Hydroxytryptamine, 5-HT) originates from the raphe nuclei, modulates mood,
emotion and behaviours (Jacobs & Azmitia, 1992), and enhances learning and
memory (Dai et al., 2008; Kandel, 2004; Zhang et al., 2013). Pathophysiological
changes in levels of 5-HT transmission occur in cases of memory dysfunction,
anxiety, depression and post-traumatic stress disorder (Owens & Nemeroff, 1994).
Well documented treatment of anxiety related disorders revolve around modulation
of the serotonergic system (Bezchlibnyk-Butler, Aleksic, & Kennedy, 2000; Guiard
& Di Giovanni, 2018) however, the role of the glutamate transporter within
serotonergic projections has not been observed during relevant therapies for these
disorders.
Extinction and inhibitory learning underpin exposure therapy, a cognitive
behavioural therapy often utilised in the treatment of anxiety and pathological fear.
Understanding the functional role of the serotonergic system in extinction and
inhibitory learning can be facilitated through research utilizing a Pavlovian fear
conditioning protocol. In auditory fear conditioning a tone (CS - conditioned
stimulus) is paired with a mild foot shock (US - unconditioned stimulus) resulting in
a behavioural reaction to the associated CS. In rodents, this behaviour is known as
freezing and is depicted by the cessation of movement other than for breathing (J. E.
LeDoux, J. Iwata, P. Cicchetti, & D. J. Reis, 1988). Repeated exposure to the CS
alone generates a new extinction memory whereby the CS no longer elicits a fear
response (Bouton, Westbrook, Corcoran, & Maren, 2006; Haubrich et al., 2017;
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Gregory J Quirk & Mueller, 2008). This is a robust and well validated protocol
capable of modelling the learning and recall of fear and extinction memories and one
in which the neural circuitry has been clearly defined (Bezchlibnyk-Butler et al.,
2000; Wilensky, Schafe, Kristensen, & LeDoux, 2006).
The current study examines the medial prefrontal cortex (mPFC), which is
implicated in the expression of both fear and extinction memories (Bredy et al.,
2007; Angela Jacques et al., 2019) and is densely innervated by 5-HT neurons
(Cornea‐Hébert, Riad, Wu, Singh, & Descarries, 1999). Increased levels of
extracellular 5-HT in the rat PFC have been observed immediately post fear
conditioning (Yoshioka, Matsumoto, Togashi, & Saito, 1995) suggesting its
functional role in the consolidation of fearful memories and correlated behaviours.
As extracellular 5-HT levels increase, activation of the 5-HT2A receptor stimulates
intracellular signalling cascades that lead to the phosphorylation of mitogen-activated
protein kinase (pMAPK), a well-established marker of neuroplasticity (Musazzi et
al., 2010; Orsini & Maren, 2012). Indeed, extensive evidence has demonstrated the
pivotal role of 5-HT in fear conditioning (for review see (Haubrich et al., 2017)). As
neuroplastic change is imperative to the formation of new extinction memories the
goal of our investigation was to discern whether there was a correlation between
certain serotonergic inputs, (specifically those with the capacity to corelease
glutamate) and the recall of extinction memories.
Glutamate N-methyl-d-aspartate (NMDA) receptors in mPFC are activated
during the acquisition of an extinction memory but not the acquisition of a
conditioned fear memory (Burgos-Robles, Vidal-Gonzalez, Santini, & Quirk, 2007).
Glutamate elicits fast excitatory neurotransmission and mediates both memory
consolidation and retrieval (Hassel & Dingledine, 2012). Glutamate is loaded into
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synaptic vesicles through proteins known as vesicular glutamate transporters
(VGLUTs). Of particular note is VGLUT3, a transporter expressed in discrete
neuronal populations including 5-HT neurons projecting to the PFC (Herzog et al.,
2004), which allows for serotonin and glutamate to be co-released from the same
terminals (Gras et al., 2002), a mechanism proposed to play an important role in
neuronal plasticity (Gagnon & Parent, 2014).
This study tests the hypothesis that the colocalisation of VGLUT3 puncta
within 5-HT varicosities (reported as 5-HT+/VGLUT3+ boutons) may correlate with
the plasticity generated through the recall of extinction memories. The search for
molecular markers to strengthen extinction memory formation is essential to the
evolution of anxiety and fear related therapeutics.
Here we used Pavlovian fear conditioning and an extended extinction protocol
whereby the extinction to tone training spanned 3 days, followed by three days with
no training. The protocol was designed to ensure the long-term potentiation of both a
fear memory and an extinction memory prior to reconsolidation. To evaluate whether
VGLUT3 immunoreactive 5-HT varicosities were closely apposed (< 1 µm) to
pMAPK+ neurons in the PFC of male Sprague Dawley rats we combined
immunohistochemistry, high resolution confocal imaging and 3D image
reconstruction (Belmer et al., 2019; Belmer et al., 2017). Our results showed that the
recall of an extinction memory both increased the density of pMAPK neurons and
upregulated the proportion of VGLUT3-containing 5-HT neuronal inputs.
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8.3 Methods
8.3.1 Subjects
Experimentally naive adult male Sprague-Dawley rats (N = 21, 3 groups, n =
7) were supplied by Animal Resource Centre, ARC, Western Australia. They were
housed 3 per cage, in temperature (≈ 24 °C) and humidity (35 %) controlled
Plexiglas cages maintained on a 12-hour light/dark cycle with the behavioural
procedures conducted during the light cycle. Fear conditioning has been observed as
more effective during the nocturnal phase for rodents, equating to the light cycle
(Albrecht & Stork, 2017). Rats were acclimatized to the vivarium for 7 days prior to
training, with food and water provided ad libitum and by end of training protocols
weighed 326.4 ± 6.7 g on average. All procedures were reviewed and approved
(AEC Approval Number: QUT/280/17) by the Animal Welfare Unit, University of
Queensland Research and Innovation Ethics Committee and the Research Ethics
Committee of the Queensland University of Technology, Australia. All procedures
complied with the policies, regulations and ethical standards for animal
experimentation, in accordance with the Queensland Government Animal Research
Act 2001, associated Animal Care and Protection Regulations (2002 and 2008), and
the Australian Code for the Care and Use of Animals for Scientific Purposes, 8th
Edition (National Health and Medical Research Council, 2013).
8.3.2 Apparatus
Behavioural procedures were conducted in two conditioning chambers
referred to as Context A and Context B. The chambers were modified to create
unique testing environments for fear conditioning or extinction thus restricting any
reaction to context. Context A contained a stainless-steel grid floor connected to a
shock generator and computer (Freeze frame software, Coulbourn instruments).
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Ethanol (80 %) was used in Context A to clean the chamber between each animal.
Context B contained a solid plastic floor with fresh bedding and internal coloured
decoration on the walls and ceiling to provide contextual difference. Orange scented
antibacterial soap was the cleaning agent that provided a unique smell to the
chamber. The chambers were housed within an acoustic isolation box (Coulbourn
Instruments, Allentown, PA, USA) with the background noise level measured at 55
dB, using a sound level meter (Digitech Professional Sound Level Meter QM1592).
Each chamber contained a speaker, low-level house light (2-3 lux), infrared light and
infrared camera.
8.3.3 Behavioural procedures
A total of 21 rats were randomized into three groups, a remote extinction
memory recall group (n = 7), remote fear memory recall group (n = 7), and a recent
fear memory recall group (n = 7). Experimental design and behavioural results are
depicted in Figure 1. Each of the behavioural training protocols have been previously
described in (Angela Jacques et al., 2019). For two days prior to training, rats were
habituated to context A and context B for 30 minutes in each. On the third day of
training the remote extinction and remote fear groups underwent three minutes of
acclimation to context A, followed by a 10-minute fear conditioning protocol that
consisted of three pairings of an auditory conditioned stimulus (CS, tone - 5kHz, 75
dB, 20 s) that co-terminated with an unconditioned stimulus (US, foot shock - 0.6
mA, 500 ms). Stimuli were controlled through Freeze Frame software (Coulbourn
Instruments) with training separated by a mean inter-trial interval of 180 s. The rats
were returned to their home cages after final stimulus presentation. On the fourth,
fifth and sixth days of the protocol the remote extinction group underwent 30
minutes of extinction training (20 x CS alone, 5 kHz, 75 dB, 20 s) in context B after
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which they were returned to their home cages for three days (days 7, 8 & 9) to allow
for memory consolidation. The remote fear memory recall group followed the same
training regime, but received no stimuli in context B.
On the ninth day the recent fear memory recall group underwent the same
fear conditioning training the other two groups had received. On the tenth day all
groups underwent a 10-minute memory recall test consisting of three 20 s CS
presentations to test for recall of the consolidated auditory fear memory or extinction
memory. Freezing, a behavioural index used to quantify CS-US association
(Blanchard & Blanchard, 1969; Michael S Fanselow, 1984), was scored during the
20 s CS intervals by an experimenter blind to the conditions. These intervals were
indicated in the video recordings by an infrared light allowing the behaviour of the
remote fear memory group to be scored for the same length of time and at the same
time points as the remote extinction group. Behavioural results were expressed as
percentage time freezing (dependent variable).
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Figure 8-1 The recall of recent and remote fear results in different levels of freezing compared to extinction memory recall. (a) Habituation: Habituation to context provided a base line freezing measure (0.2 ± 0.1%) with no difference noted between freezing levels to each context (0.04 ± 0.2 %, t 34 = 0.23, ns, p = 0.8218). (b) Fear conditioning: Auditory fear conditioning (3 x CS + US) was conducted seven days apart with the
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remote extinction and remote fear groups on day 3 and the recent fear group on day 9. Percentage freezing increased at each pairing of CS and US (F2,4 = 106.3, ***, p = 0.0003 ) but did not differ between groups. (c-e) 1st, 2nd and 3rd extinction session occurred on days 4, 5 & 6: The effect of extinction or no extinction training (20 presentations of CS alone or context alone, in context B) is shown as four CS presentations averaged to one block. Higher % freezing was observed in the first block of extinction trials as compared with the last block (remote extinction: 1st block 83.5 ± 5.5%, 4th block 10.3 ± 3.3%) on the first day of training. There was a significant temporal interaction between the beginning vs end of training, first day: t 54 = 11.46, ****, p < 0.0001; second day: t 54 = 7.08, ****, p < 0.0001; third day: t 54 = 2.28, *, p = 0.0265). (f) FMT: Recall of fear memory or extinction memory was tested via a 10-minute recall test consisting of 3 x CS only presentations. % freezing levels were lower due to remote extinction recall as opposed to fear memory recall (remote extinction vs remote fear and recent fear, F2,4 = 37.11, **, p = 0.0026). Data are presented as mean % freezing ± S.E.M, *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.0001, ****: p ≤ 0.0001, two-way ANOVA followed by Bonferroni post hoc analysis.
8.3.4 Tissue preparation
Rats were anesthetized with lethabarb (400 mg/kg, i.p.) and transcardially
perfused through the ascending aorta with ice-cold 1% (wt/vol) PFA with 0.125%
(vol/vol) glutaraldehyde followed by 4 % (wt/vol) PFA with 0.125% (vol/vol)
glutaraldehyde in 0.1 M phosphate buffer, 60 minutes after the fear memory recall
test. Brains were extracted and post-fixed in 4 % PFA with 0.125% (vol/vol)
glutaraldehyde overnight then stored in 0.1 M phosphate buffered saline (PBS). Free-
floating serial coronal sections (40 μm) of the medial prefrontal cortex were prepared
using a vibratome (M11000; Pelco easiSlicer, Ted Pella Inc, Redding, CA, USA).
8.3.5 Immunohistochemistry
Immunohistochemistry was performed as previously described (Belmer et al.,
2019). Briefly, three prefrontal sections containing the pre-limbic cortex (every 2nd
section caudally from Bregma +3.24 mm), were taken from each subject and
immunolabelled with antibodies against VGLUT3, 5-HT and pMAPK. Briefly, the
sections were washed three times in PBS (containing 0.02 % sodium azide) and
blocked in PBS containing, 0.3 % Triton X-100, 0.05 % Tween 20 and 2 % Normal
goat serum (NGS, Abcam, Vic, Aus) for 1 h. After blocking sections were incubated
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with primary antibody: rat anti-5-HT (Millipore #MAB352, 1:100) diluted in the
blocking solution for 48 hours at room temperature. Primary antibody guinea-pig
anti-VGLUT3 (Synaptic System #135204, 1:500) was then added directly to each
well and incubated for a further 24 hours at room temperature. Sections were
washed 3 times in blocking solution and incubated for 4 hours at room temperature
in secondary antibodies goat anti-guinea pig-Alexa 647 (Thermofisher Scientific,
#A21450, 1:500) and goat anti-rat 568 (Thermofisher Scientific, #A11077, 1:500).
Sections were washed 4 times in PBS and once in blocking solution as listed above
without the Triton X-100, as triton had previously been observed as affecting the
pMAPK labelling quality. Sections were incubated for 24 hours in primary antibody
phosphor-p44/42 MAPK (Erk 1/2) (Thr 202/Tyr 204) (1:250; #4370, Cell Signalling
Technology, MA, USA) diluted in the no-triton blocking solution. After three washes
in the blocking solution, the slices were incubated for 4 hours at room temperature
with secondary antibody goat anti rabbit-Alexa 488 (Thermofisher Scientific,
#A11034; 1:500) diluted in blocking solution. Sections were washed 3 times in PBS,
mounted on silane-coated slides and cover slipped using ProLong Gold antifade
reagent (Thermofisher Scientific, #P36934, Invitrogen, DR, USA).
8.3.6 Imaging
3 sections per animal, n = 5 animals per group, totalling 15 sections per
condition, were imaged on an Olympus FV3000 confocal microscope using a 60X
oil-immersion objective (NA 1.35) with a 1.5 x zoom and a Z-axis step of 0.5 μm,
using sequential scanning. The sections were taken from the prefrontal cortex around
Bregma +3.24mm, and mosaics of the regions of interest were acquired as depicted
in Figure (2 a, b), in OIR file format. Each mosaic consisted of a data volume of 1.95
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x 106 µm3. The pMAPK immunoreactive neurons and 5-HT boutons were
reconstructed in 3D using the surface rendering function with Imaris 9.2.1 (Bitplane),
similarly to our previous work (Belmer et al., 2019; Belmer et al., 2017). The
removal (or masking) of the VGLUT3 fluorescence signal outside of the 5-HT
surface ensured that only intra-fibre VGLUT3 labelling remained, allowing for
quantification of 5-HT boutons containing VGLUT3 puncta. The ‘spot detection’
function was applied for each created mask. Images were batch processed using the
same surface thresholding parameters. Figure (2 c-f) depicts Imaris images. Mean
fluorescence intensities and image volumes were obtained from the statistics function
in Imaris. Each of these steps have been previously described in (Belmer et al., 2017;
Tarren et al., 2017).
Figure 8-2 Schematic drawing showing the location of the acquired micrographs.
(a-b) Prelimbic (PL), infralimbic (IL) cortex mosaic images of layer II & III were acquired around bregma +3.24mm (depicted by the blue / purple vertical line) in the medial prefrontal cortex (blue / purple square) Drawings depicted from (Paxinos & Watson, 2007). (c) Sample of section labelled for
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pMAPK expressing neurons, 5-HT boutons and VGLUT3 puncta and processed using surface and spot detection functions in Imaris. (d) pMAPK surface. (e) 5-HT surface. (f) Masked VGLUT3 (colocalized with 5-HT boutons). Scale bar 50µm.
8.3.7 Analysis
Analyses of behavioural conditions were performed by two-way ANOVAs.
Post hoc Bonferroni correction was used in all cases to reduce type one errors
synonymous with multiple comparisons. A p value ≤ 0.05 was stated as significant,
*: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.0001, ****: p ≤ 0.0001. All statistical analyses
were generated with GraphPad Prism 7 (GraphPad Software Co., CA, USA) and
values are expressed as the mean ± standard error of the mean (SEM). The
quantification of pMAPK labelling, 5-HT-/VGLUT3+, 5-HT+/VGLUT3- and 5-
HT+/VGLUT3+ boutons < 1µm from pMAPK+ neurons (Fogarty, Hammond,
Kanjhan, Bellingham, & Noakes, 2013) was analysed using one-way ANOVAs again
with Bonferroni correction. Excluded cases included subjects that failed to acquire a
fear memory during conditioning (as defined by the fear to tone test) (7dF n = 1, 1dF
n = 1), failed to adequately perfuse (as perfusion without saline may result in
thromboembolism) (RE n = 2, 7dF n=1, 1dF n=1), or sections that sustained
significant damage during processing resulting in an inability to analyse the region of
interest in the PFC (RE n = 3, 7dF n = 1, 1dF n = 3). Furthermore, statistical outliers
were identified and excluded using the ROUT method of statistical outlier
identification in GraphPad Prism 7. The ROUT method combines robust regression
and outlier removal, and is used to fit a curve not influenced by outliers. The
residuals are analysed using a test adapted from the False Discovery Rate approach
of testing for multiple comparisons. The outliers are then removed and ordinary
least-squares regression is performed on the remaining data (Graphpad, 2016).
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8.4 Results
8.4.1 Decreased freezing due to remote extinction memory recall as compared to both recent and remote auditory fear memory recall.
For all behavioural results see Figure 2. Baseline freezing behaviour (0.2 ±
0.1%) was obtained to both context A and B during the first 2 days of the
behavioural protocol (habituation). No significant difference was observed between
contexts (0.04 ± 0.2 %, t 34 = 0.23, ns, p = 0.8218). Auditory Pavlovian fear
conditioning was conducted seven days apart with the remote extinction group and
remote fear recall group on day 3 and the recent fear recall group on day 9. A two-
way ANOVA was conducted to show freezing behaviour to be equivalent between
groups, while progressively increasing as a function of CS/US pairing. Results from
the two-way ANOVA showed no interaction to exist (F4, 48 = 0.22, ns, p = 0.93).
Follow-up main effect confirmed that no differences between behavioural conditions
existed (F2, 48 = 0.01, ns, p = 0.99), while a statistically significant difference between
CS/US pairing existed (F2, 48 = 23.22, ****, p < 0.0001). These data indicate the fear
conditioning protocol employed here produced equivalent freezing to tone across all
three groups regardless of the temporal difference.
Twenty-four hours following fear conditioning, the effect of extinction
training (day 4 - 20 presentations of CS alone in context B) on freezing levels was
tested. For analyses, 4 CS presentations were averaged and presented as one block of
extinction training. Higher % freezing was observed during the first block (CS x 4)
of extinction trials compared to the last block (remote extinction: 1st block 83.5 ±
5.5%, 4th block 10.3 ± 3.3%) on the first day of training, suggesting successful recall
of auditory conditioned fear at the beginning of the extinction session, and extinction
of fear by the end of the session. Statistical analyses across all three extinction days
revealed a significant temporal interaction between the first and last block of
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presentations (beginning vs end of training, first day: t 54 = 11.46, ****, p < 0.0001;
second day: t 54 = 7.08, ****, p < 0.0001; third day: t 54 = 2.28, *, p = 0.0265)
indicating the effectiveness of extended extinction training over a period of three
days. Together these data suggest that extinction learning occurred and highlights the
spontaneous renewal that occurred after each 24 h period (Bouton & King, 1983;
Gregory J Quirk, 2002; Rescorla & Heth, 1975) as seen by the increase in freezing at
the beginning of each day i.e. in the first block of extinction training.
On the tenth day of the training protocol recall of fear memory or extinction
memory was tested with a 10-minute recall test consisting of 3 x CS only
presentations. Two-way ANOVA revealed, a significant difference between groups
(**, p = 0.0026). This was followed with Bonferroni’s post hoc comparisons
revealing % freezing levels were significantly lower in extinction animals as
compared with those that did not undergo extinction training upon presentation of
both the second and third tone (second tone: remote extinction vs remote fear, t 4 =
5.07, *, p = 0.0214, remote extinction vs recent fear, t 4 = 4.69, *, p = 0.0281; third
tone: remote extinction vs remote fear, t 4 = 4.68, *, p = 0.0284, remote extinction vs
recent fear, t 4 = 5.36, *, p = 0.0176) suggesting the extended extinction protocol to
be highly effective in consolidating extinction memories.
8.4.2 pMAPK activation differs for conditions when measured as neuron counts or volumetric density.
The number of neuronal cell bodies expressing pMAPK (fig. 3a) and the
density of pMAPK labelling per cubic centimetre of tissue in the sample (fig. 3b),
were compared as functions of condition. One-way ANOVA revealed no significant
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difference between groups in the number of pMAPK expressing neuronal cell bodies
(ns, p= 0.3545). Paradoxically, one-way ANOVA revealed a significant group
difference (***, p = 0.0002) in the volumetric density of pMAPK immunoreactivity
within the tissue sample. Bonferroni-corrected post-hoc tests revealed significantly
higher density in the remote extinction group as compared to the remote fear (t34 =
2.92, *, p = 0.0187) and recent fear (t34 = 4.74, ***, p = 0.0001) groups. No
significant difference in density was noted in remote versus recent fear recall (t33 =
2.07, ns, p = 0.1393). These data suggest the measurement of volumetric density in
observations of neuroplastic change may provide a more comprehensive data set. In
Figures 3 (c – e) representative images of pMAPK expression are shown for each
condition.
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Figure 8-3 pMAPK labelling in the prefrontal cortex. (a) The number of pMAPK expressing neuronal cell bodies was counted for each sample with no significant difference noted between conditions (F[2, 33] = 1.07, p= 0.3545). (b) A significant difference between groups (F[2, 34] = 11.43, p = 0.0002) was observed in pMAPK immunoreactivity within the tissue sample. Bonferroni-corrected post-hoc tests showed significantly higher density in the remote extinction recall group as compared to the remote fear recall group (t34 = 2.92, *, p = 0.0187) and the recent fear recall (t34 = 4.74, ***, p = 0.0001) group. No significant difference in density was observed between the remote versus recent fear recall groups (t33 = 2.07, ns, p = 0.1393). (c) pMAPK immunoreactivity due to remote extinction memory recall. (d) pMAPK immunoreactivity due to remote fear memory recall. (e) pMAPK immunoreactivity due to recent fear memory recall. Data are presented as mean ± S.E.M, *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.0001, ****: p ≤ 0.0001, one-way ANOVA followed by Bonferroni post hoc analysis. Scale bar 50µm.
8.4.3 Increased levels of colocalized VGLUT3 in 5-HT axon varicosities were located adjacent to pMAPK during extinction memory recall.
Labelling of VGLUT3 was demonstrated by scattered punctate fluorescence
(Fig. 4d-e) and by combining the 3D-reconstruction and masking functions of Imaris,
we isolated the VGLUT3 labelling that was only contained in 3D-reconstructed 5-HT
varicosities (Fig. 4a, h). We were interested in the levels of colocalized VGLUT3, 5-
HT and pMAPK relative to the volume of pMAPK in each condition. We observed a
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significant difference between groups in the % of 5-HT/VGLUT3 boutons located
within 1µm of a pMAPK+ neuron (***, p= 0.0007). Post hoc analysis revealed a
significantly higher % in the remote extinction group as compared to the remote fear
(t34 = 3.04, *, p = 0.0137) and recent fear (t34 = 4.09, ***, p = 0.0007) groups. No
significant difference was observed in remote versus recent fear recall (t34 = 1.28,
ns, p = 0.6325). These data suggest that corelease of serotonin and glutamate may be
involved in the recall of extinction memories, in particular, neuroplasticity in the
medial prefrontal cortex.
To define the level of involvement of 5-HT+/VGLUT3+ fibres, in comparison
to other glutamatergic, cholinergic or serotonergic neurons, we quantified the levels
of 5-HT-/VGLUT3+ puncta and 5-HT+/VGLUT3- boutons separately, relative to the
density of pMAPK expression in each condition. The percentage of 5-HT-
/VGLUT3+ puncta located within 1µm of pMAPK+ neurons was significantly
different between groups (***, p= 0.0023). Once again post hoc analysis revealed a
significantly higher % in the remote extinction group as compared to the remote fear
(t35 = 3.37, **, p = 0.0055) and recent fear (t35 = 3.27, **, p = 0.0073) groups. No
difference was observed in remote versus recent fear recall (t35 = 0.02, ns, p
>0.9999).
The percentage of 5-HT+/VGLUT3- boutons located within 1µm of pMAPK+
neurons was also significantly different between groups (***, p= 0.0007).
Interestingly, post hoc analysis showed a higher % of 5-HT+/VGLUT3- boutons in
the recent fear group (t35 = 4.23, ***, p = 0.0005) when compared to the remote fear
recall group. There was no significant difference between recent fear and remote
extinction (t35 = 1.89, ns, p = 0.2020) nor between remote fear and remote extinction
groups (t35 = 2.27, ns, p >0.0891). This finding suggests greater serotonergic
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innervation was present in the prefrontal cortex during the recall of a recent fear
memory however, this expression did not correlate with increased expression of the
neuroplasticity marker.
Together these data suggest a greater role for the corelease of serotonin and
glutamate in the mPFC due to the recall of extinction memories; however, it would
appear the glutamatergic input may have a larger functional role. Figure 4 (a-i)
shows representative images of pMAPK, VGLUT3 and 5-HT expression before and
after surface and spot functions in IMARIS were utilised.
Figure 8-4 Colocalisation of VGLUT3, 5-HT and pMAPK.
(a) Sample of section labelled for pMAPK expressing neurons, 5-HT boutons and masked VGLUT3 puncta. (b) Immunoreactive pMAPK neurons. (c) Immunoreactive 5-HT boutons. (d) Immunoreactive VGLUT3 puncta (e) IMARIS created surface of pMAPK and 5-HT with VGLUT3 spots (f) pMAPK surface. (g) 5-HT surface (h) masked VGLUT3 spots (colocalized with 5-HT boutons). (i) enlarged view of pMAPK surfaces, 5-HT surfaces and VGLUT3 spots. (j) Post hoc analysis of the percentage of colocalized VGLUT3 puncta in 5-HT boutons near pMAPK expressing neurons, relative to the density of pMAPK within the tissue revealed a significantly higher % in the remote extinction group
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as compared to the remote fear (t34 = 3.04, *, p = 0.0137) and recent fear (t34 = 4.09, ***, p = 0.0007) groups. (k) The percentage of VGLUT3 puncta relative to the density of pMAPK expression in each condition revealed a significantly higher % in the remote extinction recall group as compared to the remote fear recall group (t35 = 3.37, **, p = 0.0055) and recent fear recall group (t35 = 3.27, **, p = 0.0073). (l) The percentage of 5-HT immunoreactive boutons located within 1µm of pMAPK+ neurons was higher in the recent fear recall group (t35 = 4.23, ***, p = 0.0005) when compared to the remote fear recall group. Data are presented as mean ± S.E.M, *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.0001, ****: p ≤ 0.0001, one-way ANOVA followed by Bonferroni post hoc analysis. Scale bars 50µm and 15µm.
8.5 Discussion
Anxiety related illness contributes to the greatest share of global mental health
disorders. Current therapeutics are associated with significant side effects, high rates
of relapse and low efficacy. Neuroplastic changes occur during both the formation
and treatment of anxiety disorders. To further explore the changes that occur during
the recall of noxious memories we investigated the correlation between 5-
HT+/VGLUT3+ boutons and the plasticity generated through the recall of extinction
memories. Research conducted by Amilhon and colleagues showed VGLUT3
increases 5-HT transmission and the loss of the transporter leads to anxiety like
behaviours (Amilhon et al., 2010). In this study, we used Pavlovian fear conditioning
to show that synaptic plasticity in the prefrontal cortex correlates with the presence
of 5-HT+/VGLUT3+ fibres during extinction memory recall, but not with recent or
remote fear recall. We further demonstrated that the density of non-serotonergic
VGLUT3+ varicosities was higher during extinction memory recall and established a
recruitment of 5-HT+/VGLUT3- immunoreactive boutons in the prefrontal cortex
during recent fear recall. These results suggest that 5-HT/glutamate release could
exert a particular type of neurotransmission underlying the recall of extinguished
memories. This study further highlights the potential therapeutic effect of 5-
HT/glutamate co-release to enhance exposure therapy in the treatment of fear-related
anxiety disorders.
Behaviour
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The behavioural findings in this study provide evidence of effective training
and memory recall for each group. The extended extinction protocol (3 days of
exposure to tone) facilitated the development of a new extinction memory. The
percentage of freezing to tone was relatively equivalent between recent and remote
fear memory, suggesting the level of behavioural defence demonstrated by these two
groups was equivocal at time of memory recall, independent of the temporal
variation in conditioning (N. Chaaya et al., 2019; A. Jacques et al., 2017).
pMAPK expression
The behavioural studies were followed by immunohistological observations
where we compared the number of neuronal cell bodies expressing pMAPK and the
density of pMAPK labelling per cubic centimetre of tissue in the sample.
Interestingly, the number of pMAPK expressing neuronal cell bodies did not differ
between groups. In contrast to this finding, a significant group difference was found
in pMAPK immunoreactivity within the tissue sample with a greater density of
pMAPK labelling in the remote extinction group compared to the two fear recall
groups. This finding suggests the measurement of volumetric density may provide
greater scope when observing neuroplastic adaptations in the brain.
5HT+/VGLUT3+ fibres correlate with neuroplasticity-like changes during
extinction memory recall
Glutamatergic neurons transmit sensory information from the thalamus to the
frontal cortex (Romanski et al., 1993) and are therefore complicit in forming an
associative memory involving an unconditioned stimulus such as a tone (as in
auditory fear conditioning). Glutamate elicits depolarization (via sodium influx),
permitting magnesium efflux through NMDA receptors to trigger synaptic plasticity
(Nicoll, Kauer, & Malenka, 1988) and has long been implicated in extinction
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memory retention (Cox & Westbrook, 1994; D. M. Johnson, Baker, & Azorlosa,
2000). Drug infusion studies on glutamatergic AMPA receptors in the amygdala
have been used to demonstrate the involvement of these receptors in extinction
training and retention (M. Kim, Campeau, Falls, & Davis, 1993; H. C. Lin, Mao, &
Gean, 2009). At present, therapeutic targets specific to long-term retention of
extinction memories remain elusive.
Glutamatergic neurons store the excitatory neurotransmitter glutamate in
synaptic vesicles via three isoforms of vesicular glutamate transporter, named
VGLUT1 to 3. VGLUT3 is structurally similar to VGLUT 1 and 2 but differs
through its limited distribution within the brain and its location in cholinergic
neurons in the caudate-putamen and 5-HT neurons in the brainstem raphe nuclei. Of
specific interest to this study was the role 5-HT+/VGLUT3+ axonal varicosities
played in memory recall in the prefrontal cortex.
Serotonergic function in the brain is regulated through its release and reuptake
(Wong-Lin, Wang, Moustafa, Cohen, & Nakamura, 2017). It is both complex and
contradictory in the sense that it is an inhibitory neurotransmitter that can facilitate
both increased and decreased excitability through different receptors, for review see
(Lesch & Waider, 2012). The serotonin 2A receptor has been evidenced facilitating
both consolidation and extinction of fear memories (Zhang et al., 2013) and is widely
spread throughout the rat cortex, in the dendritic spines of pyramidal
neurons (Willins, Deutch, & Roth, 1997; Yoshida et al., 2011). Selective serotonin
reuptake inhibitors (SSRI) increase 5-HT levels by inhibition of 5-HTT (the
transporter responsible for the reuptake of serotonin) and are implicated in extinction
memory formation (Homberg, 2012). It has been shown that fluoxetine treatment (an
SSRI antidepressant) dampens the reactivation of the original fear memory, thereby
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enabling strengthening or stability of the extinction memory (Deschaux, Motanis,
Spennato, Moreau, & Garcia, 2011), suggesting the important role serotonin plays.
This accumulated data on serotonin and glutamate is in accordance with our
findings of increased neuroplasticity (indicated by pMAPK expression) in
conjunction with increased 5-HT+/VGLUT3+ axonal varicosities in the prefrontal
cortex after extinction memory recall. Our data is further supported by previous
research conducted by Karpova and colleagues (Karpova et al., 2011) demonstrating
increased extracellular 5-HT in the IL cortex (of the mPFC), leads to neuroplastic
changes via alterations to the peri neuronal nets, thereby contributing to the erasure
of fear memory (Karpova et al., 2011).
5HT+/VGLUT3- fibres present during recent fear recall
Kawahar and colleagues (Kawahara, 1995) and Suzuki and colleagues
(Suzuki, Ishigooka, Watanabe, & Miyaoka, 2002) undertook micro dialysis studies
that established a role for serotonin in the recall of conditioned fear in the amygdala.
Further to this, serotonin does not interact with NMDA receptors to mediate
plasticity, but has been shown to inhibit glutamate mediated sensory input to the
lateral amygdala (via GABA interneurons) demonstrating its role in defence against
fear, as would be required during recent fear recall (Stutzmann & LeDoux, 1999;
Yokoyama et al., 2005). Our studies expand on this accumulated information by
demonstrating increased serotonergic innervation during recent (but not remote) fear
memory recall in the prefrontal cortex, in association with low levels of the
neuroplasticity marker pMAPK. Previous studies from our lab have shown divergent
cell layer involvement in the prefrontal cortex during recent and remote fear memory
recall (Angela Jacques et al., 2019), therefore it is not surprising that the same
neurotransmitter is not involved at the same level in both temporal recall points.
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5-HT1A and 5-HT2A receptor subtypes are abundant in the prefrontal cortex
and involved in the regulation of mood (Caliendo, Santagada, Perissutti, & Fiorino,
2005; Weisstaub et al., 2006). Highly expressed in the deep layers of the rodent
cerebral cortex, 5-HT2A receptors are particularly located in Layer V (Pazos &
Palacios, 1985), which we previously showed is activated during recent but not
remote fear conditioning (Angela Jacques et al., 2019). As our current findings of
increased serotonergic innervation after fear recall do not correlate with increased
neuroplasticity (as measured through pMAPK activation) we suggest 5-HT is
exerting an inhibitory influence in this instance. However, the current study observed
tissue from layer 2 of the prefrontal cortex only. It may prove interesting to examine
the deeper layers of the prefrontal cortex with regard to the 5-HT+/VGLUT3+ inputs.
Svenningsson and colleagues have reported dopamine- and cAMP regulated
phosphoprotein of M(r) 32,000 (DARPP-32) is involved in both the biochemical and
behavioural consequences of 5-HT activation (Yoshida et al., 2011). Further to this,
serotonin-induced DARPP-32 activation, mediated by 5-HT/glutamate co-
transmission may also warrant investigation.
Technical considerations
Future studies would benefit by examining a no recall group of animals to
establish a baseline for associative learning, a recent extinction group for comparison
to remote extinction and a group that has undergone extinction training without
recall, to establish changes rought through consolidation only. It is possible the
colocalisation correlating with reconsolidation observed here may also occur after
memory consolidation. A control marker for the colocalisation of 5-HT+/VGLUT3+
inputs to markers that do not indicate plasticity may provide further relevant
information.
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Conclusion
In conclusion, it is apparent that 5HT+/glutamate co-release plays a role
during the recall of extinction memories in the prefrontal cortex of rats and further
studies are necessary to determine how neuromodulators drive the associative
learning that encodes extinction memory recall to enhance treatment of anxiety
disorders.
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General Discussion
This dissertation details findings that advance our knowledge of the
neuroplasticity involved in the development of emotional memories. This chapter
presents a summary of results, followed by the limitations of the research conducted
within. Possible future directions of this research are detailed, ensued by concluding
remarks.
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9.1 Summary of findings
Initially, a topographic and statistical method to accurately map, quantify and
compare the spatial properties of neural networks was developed. This technique
contributes to our knowledge of the organization and structure of functional neural
circuits by allowing visualization of the spatial organization of the neuroplastic
changes incurred through memory formation.
The initial aim of identifying precise biological mechanism of fear memory
acquisition and extinction was achieved in part by identifying specific patterns of
neuronal activation. In Chapter 3 the developed method was utilized to anatomically
define subregions of the basolateral amygdala undergoing localised neuroplastic
change due to contextual and context-removed auditory fear. The key finding was
that modification to fear memory conditioning protocols lead to varied spatial
patterns of IEG activation in the BLC. Interestingly this was not represented by
alterations in freezing behaviour, leading us to believe neuroplastic changes in the
BLC may be regulated by the hippocampus. Furthermore, auditory fear memories
that are formed without background contextual fear memories were found to reduce
BLC activation also suggesting hippocampal modulation.
Emotional memories such as the pathological fear suffered by those with PTSD
occur due to impairment in the mechanism required to determine the appropriate
behavioural response to threatening situations. Statistically verified neuronal micro-
mapping can advance our knowledge of the elaborate composition of the brain
during the formation of extinction memories that may ultimately override this
response. Chapter 4 investigated and analysed the spatial and temporal micro
topography of pMAPK+ neurons involved in encoding recent, remote and extinction
memory recall within prefrontal cortex and amygdala. A small consistent (stable)
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population of neurons in the ventrolateral portion of the lateral amygdala (LAvl)
were found to be activated only following extinction memory recall and a reduction
of activation in the dorsolateral portion of the lateral amygdala after remote fear
recall was visualised, in comparison to recent recall. Importantly, time dependent
activation was noted in the PL as expression in diverse cell layers. Superficial layers
of the prelimbic cortex were consistently activated after both recent and remote
memory recall, as opposed to the deeper layers which were only activated through
recent recall of the auditory fear memory. This data suggests changed PL
involvement due to the remote recall of fear memories.
There are a multitude of neurobiological factors that may influence
neuroplasticity due to fear, however, to our knowledge, this is the first time
adaptations to microglia as a result of fear, have been assessed. Therefore chapters 5,
6 and 7 were dedicated to observations of phenotypical changes microglia underwent
when subjected to contextual fear, auditory fear and extinction memories.
Examination of microglial morphology was conducted for short and long-term
consolidation of memories as well as recent and remote recall. Findings included
significant morphological change and an increase in number of microglia present in
the PFC due to recent fear memory recall suggesting some form of activation occurs
to the brains equivalent of immune cells, during fear states. Additionally, reduced
neuronal activity as demonstrated by lower c-Fos and Arc expression was noted in
the extinction memory recall group, correlated with reduced densities of Iba1+
microglia.
Examination of the rat dorsal hippocampus and amygdala as a result of
contextual fear conditioning revealed that microglia released more BDNF in the
dentate gyrus as compared to controls. Surprisingly, unpaired fear conditioning
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resulted in equivalent levels of fear behaviour though did not alter microglia, BDNF
or pCREB number in either the dorsal hippocampus or lateral amygdala. Contextual
fear conditioning resulted in microglia altering morphology to become more
amoeboid-like within the dentate gyrus. This is a common response to traumatic
brain injury and infection. Further to these findings, a spatial relationship between
pMAPK+ neurons and microglia was noted in the prelimbic cortex, in which
microglia located near pMAPK expressing neurons correlated with enhanced group
differences. These data indicate alterations in PL may occur for up to two weeks post
contextual fear conditioning.
The two overarching neurotransmitters reported as contributors to anxiety, fear
memory formation, microglial activation and synaptic plasticity are serotonin and
glutamate with recent evidence suggesting 5-HT/glutamate co-transmission plays a
role in both reward and emotion processing. In chapter 8 the colocalisation of
serotonin, serotonin transporters and vesicular glutamate type 3 transporters were
mapped in brain regions involved in emotional memories. Results revealed that 90%
of 5-HT boutons are immunoreactive for the SERT and furthermore, SERT and
VGLUT3 preferentially co-localize within 5-HT varicosities. Chapter 9 built on this
finding by exploring the impact of 5-HT and Vglut3 with regards to the
neuroplasticity of fear memory, as indicated by pMAPK activation.
9.2 Significance
The extinction of pathological fear is central to the treatment of several anxiety
disorders and involves a learning process, which forms the basis of exposure therapy.
At the present time pharmacological interventions target neurotransmitters in a
whole brain manner, resulting in adverse side effects and high rates of relapse. This
thesis builds on previous research, which has identified a micro-topography of fear
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memory encoding in the amygdala by defining the precise brain subregion necessary
for the extinction of auditory fear. In addition to this anatomical finding corelease of
the neurotransmitters glutamate and serotonin have been correlated with the recall of
these extinction memories. As technology and pharmaceutical treatment advances
this basic science has defined both the precise target region and molecules involved
in the extinction of fear.
9.3 Advanced considerations
The technique developed to define functional neuronal topography has both
auspicious and inauspicious traits. While providing ease with which to identify
patterns of neuronal activation during set behaviours, once identified the clinical
relevance becomes limited as there are currently no pharmacotherapeutics designed
to target such small regions of functionality. Both the time required to produce
comprehensive quantitative results using this method and the current standardised
brain atlases available to guide the process, are somewhat prohibitive. As mentioned
in chapter 2 there is also a degree of brain tissue variability and individual judgment
required to align sections with contours. There is exigency for an improved
methodological approach to contouring brain nuclei and perhaps an atlas detailing
such contours would prove beneficial. Despite these limitations chapters 2, 3 and 4
provide essential and comprehensive conclusions with regards to the spatial and
temporal alterations in functional neuronal populations associated with varied fear
consolidation, recall and extinction.
Conjecture surrounding the translatability of animal models is a constant
technical consideration in behavioural neuroscience. Fear conditioning paradigms are
used to model traumatic events leading to PTSD in humans (J. LeDoux, 2000;
Maren, 2011), however, expression of neuroplasticity markers may differ
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considerably between species and procedures fail to capture the emotional
complexity of PTSD (Beckers, Krypotos, Boddez, Effting, & Kindt, 2013; Newman,
Riggs, & Roth, 1997).
The analysis of microglial cell bodies described in chapters 4, 5 and 6 was
performed through volume and area quantification as opposed to the perimeter and
diameter measurements utilized in most studies to date. This difference may account
for the lack of adherence to the definitive morphological stereotypes previously
described in the literature. The observation that microglia may be phagocytic in
nature suggests the elongation of cell bodies may routinely occur and therefore skew
cell body measurement data. In chapter 4 comparisons were made between microglia
after the recall of fear and extinction memories. Although the variation in
ramification was enlightening, a box control group may have supplemented
informative comparison by establishing a baseline of unreactive microglial
morphology.
The research conducted to generate chapters 8 and 9, highlight the need for
lower cost high resolution imaging. The costs sustained to procure quality images
prohibited the size of the brain region studied. The finding of increased dendritic
presence (as noted by pMAPK expression) in the case of extinction memory recall,
as opposed to the number of actual cell bodies counted, indicates the necessity of
improved quantitative techniques in the study of neuroplasticity. As plasticity occurs
at a synaptic level it does not appear as imperative to know whether cell number
itself has increased. Even in studies measuring the size and number of dendritic
spines present, suggestions pertaining to large brain regions must be inferred from
small samples of tissue.
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9.4 Future directions
Further to the topographic density maps described within this dissertation,
mapping of neurotransmitter release may prove to be more clinically relevant,
particularly in association with addiction and depression studies and research
involving pharmacological interventions. Examination of additional protein synthesis
markers, behavioural models, time points and brain nuclei may also result in
instrumental advances in knowledge surrounding the neuroplasticity of emotional
memories.
Conceivable future avenues of inquiry into the role of microglial activation in
emotional situations are virtually limitless due to the infancy of this field of study.
Markers capable of characterising the activation state of microglia at time of memory
recall should be foremost in the research agenda. Investigation of the inflammatory
status and molecular correlates of microglial activation as a result of various
emotional states would also enhance development of potential therapeutics.
At the opposite end of the spectrum is an almost endless supply of research into
the action of neurotransmitters serotonin and glutamate. However, this research is
often confounded by the complexity of neuronal networks. Serotonin has been touted
as the “mediator that spans evolution” and many scientists have dedicated their life’s
work to uncovering its secrets (Pilowsky, 2018). To date, 16 different serotonin
receptors of varying excitatory and inhibitory transmission have been uncovered and
characterised (Ohno, 2019), with perhaps many more to come. The frequency of 5-
HT/glutamate co-release sites within brain regions involved in emotional regulation
would suggest moderation of neuroplasticity and raise possibilities of potential
mechanisms for the control of that neuroplasticity (Kauer and Malenka, 2007).
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Further research is required to discover the role of co-released serotonin and
glutamate on the neuroplasticity induced by emotional states.
9.5 Concluding remarks
In conclusion, understanding neural network organization may lead to the
ability to predict memory and behaviour from functional neural networks while
microglial activation may play an important role in the facilitation of this neuronal
organisation. Knowledge and understanding of anxiety related disorders and the
neuroplasticity underlying consequential behaviours, expands with each vital new
piece of research conducted.
283
Appendix A:
Supplementary Material for chapter 7.
Table 2 One-way ANOVA analysis of the volumetric density of 5-HT/VGLUT3 in the mouse forebrain. (Significant changes are highlighted in light grey).
Tukey'smultiplecomparisonstest
MeanDiff.
95.00%CIofdiff.
Significant?
Summary
AdjustedPValue
PrL1‐3vs.PrL4‐6 6.418 ‐6.531to No ns 0.9009
PrL1‐3vs.NACc ‐1.444 ‐14.39to No ns >0.9999
PrL1‐3vs.NACs ‐1.718 ‐14.67to No ns >0.9999
PrL1‐3vs.pNACs 41.99 29.04to Yes **** <0.0001
PrL1‐3vs.CPU ‐ ‐13.33to No ns >0.9999
PrL1‐3vs.BNST 10.8 ‐2.149to No ns 0.2037
PrL1‐3vs.LS 19.31 6.363to Yes *** 0.0002
PrL1‐3vs.BLA 6.46 ‐6.489to No ns 0.8965
PrL1‐3vs.CeA 16.48 3.535to Yes ** 0.0026
PrL1‐3vs.CA1 1.724 ‐11.22to No ns >0.9999
PrL1‐3vs.CA2 6.995 ‐5.955to No ns 0.8317
PrL1‐3vs.CA3 8.201 ‐4.748to No ns 0.6304
PrL1‐3vs.DG 6.255 ‐6.694to No ns 0.9165
PrL4‐6vs.NACc ‐7.861 ‐20.81to No ns 0.6925
PrL4‐6vs.NACs ‐8.135 ‐21.08to No ns 0.6426
PrL4‐6vs.pNACs 35.57 22.62to Yes **** <0.0001
PrL4‐6vs.CPU ‐6.8 ‐19.75to No ns 0.8574
PrL4‐6vs.BNST 4.383 ‐8.566to No ns 0.9956
PrL4‐6vs.LS 12.89 ‐0.05477to No ns 0.052
PrL4‐6vs.BLA 0.0428 ‐12.91to No ns >0.9999
PrL4‐6vs.CeA 10.07 ‐2.882to No ns 0.3005
PrL4‐6vs.CA1 ‐4.693 ‐17.64to No ns 0.9916
PrL4‐6vs.CA2 0.577 ‐12.37to No ns >0.9999
PrL4‐6vs.CA3 1.783 ‐11.17to No ns >0.9999
PrL4‐6vs.DG ‐ ‐13.11to No ns >0.9999
284
NACcvs.NACs ‐ ‐13.22to No ns >0.9999
NACcvs.pNACs 43.43 30.48to Yes **** <0.0001
NACcvs.CPU 1.061 ‐11.89to No ns >0.9999
NACcvs.BNST 12.24 ‐0.705to No ns 0.0825
NACcvs.LS 20.76 7.807to Yes **** <0.0001
NACcvs.BLA 7.904 ‐5.045to No ns 0.6848
NACcvs.CeA 17.93 4.979to Yes *** 0.0007
NACcvs.CA1 3.168 ‐9.781to No ns 0.9998
NACcvs.CA2 8.438 ‐4.511to No ns 0.5859
NACcvs.CA3 9.645 ‐3.305to No ns 0.367
NACcvs.DG 7.699 ‐5.25to No ns 0.7211
NACsvs.pNACs 43.7 30.75to Yes **** <0.0001
NACsvs.CPU 1.335 ‐11.61to No ns >0.9999
NACsvs.BNST 12.52 ‐0.431to No ns 0.0682
NACsvs.LS 21.03 8.081to Yes **** <0.0001
NACsvs.BLA 8.178 ‐4.771to No ns 0.6346
NACsvs.CeA 18.2 5.253to Yes *** 0.0005
NACsvs.CA1 3.442 ‐9.507to No ns 0.9996
NACsvs.CA2 8.712 ‐4.237to No ns 0.5342
NACsvs.CA3 9.919 ‐3.031to No ns 0.323
NACsvs.DG 7.973 ‐4.976to No ns 0.6724
pNACsvs.CPU ‐42.37 ‐55.32to‐ Yes **** <0.0001
pNACsvs.BNST ‐31.18 ‐44.13to‐ Yes **** <0.0001
pNACsvs.LS ‐22.67 ‐35.62to‐ Yes **** <0.0001
pNACsvs.BLA ‐35.52 ‐48.47to‐ Yes **** <0.0001
pNACsvs.CeA ‐25.5 ‐38.45to‐ Yes **** <0.0001
pNACsvs.CA1 ‐40.26 ‐53.21to‐ Yes **** <0.0001
pNACsvs.CA2 ‐34.99 ‐47.94to‐ Yes **** <0.0001
pNACsvs.CA3 ‐33.78 ‐46.73to‐ Yes **** <0.0001
pNACsvs.DG ‐35.73 ‐48.68to‐ Yes **** <0.0001
285
CPUvs.BNST 11.18 ‐1.766to No ns 0.163
CPUvs.LS 19.69 6.745to Yes *** 0.0001
CPUvs.BLA 6.843 ‐6.106to No ns 0.8519
CPUvs.CeA 16.87 3.918to Yes ** 0.0018
CPUvs.CA1 2.107 ‐10.84to No ns >0.9999
CPUvs.CA2 7.377 ‐5.572to No ns 0.7746
CPUvs.CA3 8.583 ‐4.366to No ns 0.5585
CPUvs.DG 6.638 ‐6.312to No ns 0.877
BNSTvs.LS 8.512 ‐4.438to No ns 0.572
BNSTvs.BLA ‐4.34 ‐17.29to No ns 0.996
BNSTvs.CeA 5.684 ‐7.265to No ns 0.9582
BNSTvs.CA1 ‐9.076 ‐22.03to No ns 0.4666
BNSTvs.CA2 ‐3.806 ‐16.75to No ns 0.9989
BNSTvs.CA3 ‐2.6 ‐15.55to No ns >0.9999
BNSTvs.DG ‐4.545 ‐17.49to No ns 0.9937
LSvs.BLA ‐12.85 ‐25.8to No ns 0.0537
LSvs.CeA ‐2.827 ‐15.78to No ns >0.9999
LSvs.CA1 ‐17.59 ‐30.54to‐ Yes *** 0.0009
LSvs.CA2 ‐12.32 ‐25.27to No ns 0.0784
LSvs.CA3 ‐11.11 ‐24.06to No ns 0.1702
LSvs.DG ‐13.06 ‐26.01to‐ Yes * 0.0462
BLAvs.CeA 10.02 ‐2.925to No ns 0.3069
BLAvs.CA1 ‐4.736 ‐17.69to No ns 0.9909
BLAvs.CA2 0.5341 ‐12.41to No ns >0.9999
BLAvs.CA3 1.74 ‐11.21to No ns >0.9999
BLAvs.DG ‐ ‐13.15to No ns >0.9999
CeAvs.CA1 ‐14.76 ‐27.71to‐ Yes * 0.0119
CeAvs.CA2 ‐9.49 ‐22.44to No ns 0.3931
CeAvs.CA3 ‐8.284 ‐21.23to No ns 0.6149
CeAvs.DG ‐10.23 ‐23.18to No ns 0.2769
286
CA1vs.CA2 5.27 ‐7.679to No ns 0.9771
CA1vs.CA3 6.477 ‐6.473to No ns 0.8948
CA1vs.DG 4.531 ‐8.419to No ns 0.9939
CA2vs.CA3 1.206 ‐11.74to No ns >0.9999
CA2vs.DG ‐ ‐13.69to No ns >0.9999
CA3vs.DG ‐1.946 ‐14.9to11 No ns >0.9999 Table 3 One-way ANOVA analysis of the volumetric density of 5-HT/VGLUT3 in the mouse forebrain (Significant changes are highlighted in light grey).
Sidak'smultiplecomparisonstest
MeanDiff.
95.00%CIofdiff.
Significant?
Summary
AdjustedPValue
BLAvs.CeA 0.0888 ‐0.2917to No ns 0.9999
BLAvs.BNST 0.6377 0.3401to Yes **** <0.0001
BLAvs.NACc 1.053 0.7212to Yes **** <0.0001
BLAvs.NACs 0.0501 ‐0.3138to No ns >0.9999
BLAvs.CPU 1.131 0.8124to Yes **** <0.0001
BLAvs.CA1 ‐ ‐0.4975to No ns >0.9999
BLAvs.CA2 ‐ ‐0.7175to No ns 0.5757
BLAvs.CA3 0.0161 ‐0.4178to No ns >0.9999
BLAvs.DG 1.022 0.588to Yes **** <0.0001
BLAvs.LS ‐ ‐0.5869to No ns 0.8206
BLAvs.PrL1‐3 0.9156 0.5752to Yes **** <0.0001
BLAvs.PrL4‐6 0.9743 0.634to Yes **** <0.0001
CeAvs.BNST 0.5489 0.206to Yes **** <0.0001
CeAvs.NACc 0.9642 0.5913to Yes **** <0.0001
CeAvs.NACs ‐ ‐0.4405to No ns >0.9999
CeAvs.CPU 1.042 0.681to Yes **** <0.0001
CeAvs.CA1 ‐ ‐0.6185to No ns 0.9959
CeAvs.CA2 ‐ ‐0.8385to No ns 0.2596
CeAvs.CA3 ‐ ‐0.5388to No ns >0.9999
CeAvs.DG 0.9331 0.467to Yes **** <0.0001
287
CeAvs.LS ‐ ‐0.7121to No ns 0.4462
CeAvs.PrL1‐3 0.8267 0.4462to Yes **** <0.0001
CeAvs.PrL4‐6 0.8855 0.5049to Yes **** <0.0001
BNSTvs.NACc 0.4153 0.1275to Yes *** 0.0003
BNSTvs.NACs ‐ ‐0.9119to‐ Yes **** <0.0001
BNSTvs.CPU 0.4931 0.2209to Yes **** <0.0001
BNSTvs.CA1 ‐ ‐1.103to‐ Yes **** <0.0001
BNSTvs.CA2 ‐ ‐1.323to‐ Yes **** <0.0001
BNSTvs.CA3 ‐ ‐1.023to‐ Yes **** <0.0001
BNSTvs.DG 0.3842 ‐0.01709to No ns 0.0742
BNSTvs.LS ‐ ‐1.187to‐ Yes **** <0.0001
BNSTvs.PrL1‐3 0.2779 ‐0.01981to No ns 0.0917
BNSTvs.PrL4‐6 0.3366 0.03895to Yes * 0.0131
NACcvs.NACs ‐1.003 ‐1.359to‐ Yes **** <0.0001
NACcvs.CPU 0.0778 ‐0.2313to No ns 0.9997
NACcvs.CA1 ‐1.117 ‐1.544to‐ Yes **** <0.0001
NACcvs.CA2 ‐1.337 ‐1.764to‐ Yes **** <0.0001
NACcvs.CA3 ‐1.037 ‐1.464to‐ Yes **** <0.0001
NACcvs.DG ‐ ‐0.4583to No ns >0.9999
NACcvs.LS ‐1.259 ‐1.632to‐ Yes **** <0.0001
NACcvs.PrL1‐3 ‐ ‐0.4692to No ns 0.9699
NACcvs.PrL4‐6 ‐ ‐0.4104to No ns 0.9998
NACsvs.CPU 1.081 0.7373to Yes **** <0.0001
NACsvs.CA1 ‐ ‐0.5663to No ns 0.9997
NACsvs.CA2 ‐ ‐0.7863to No ns 0.3808
NACsvs.CA3 ‐ ‐0.4865to No ns >0.9999
NACsvs.DG 0.9718 0.5192to Yes **** <0.0001
NACsvs.LS ‐ ‐0.6582to No ns 0.6123
NACsvs.PrL1‐3 0.8655 0.5016to Yes **** <0.0001
NACsvs.PrL4‐6 0.9242 0.5604to Yes **** <0.0001
288
CPUvs.CA1 ‐1.194 ‐1.611to‐ Yes **** <0.0001
CPUvs.CA2 ‐1.414 ‐1.831to‐ Yes **** <0.0001
CPUvs.CA3 ‐1.115 ‐1.532to‐ Yes **** <0.0001
CPUvs.DG ‐ ‐0.5258to No ns 0.9995
CPUvs.LS ‐1.337 ‐1.698to‐ Yes **** <0.0001
CPUvs.PrL1‐3 ‐ ‐0.5337to No ns 0.5214
CPUvs.PrL4‐6 ‐ ‐0.4749to No ns 0.8996
CA1vs.CA2 ‐0.22 ‐0.7306to No ns 0.9595
CA1vs.CA3 0.0797 ‐0.4308to No ns >0.9999
CA1vs.DG 1.086 0.5749to Yes **** <0.0001
CA1vs.LS ‐ ‐0.6089to No ns 0.9978
CA1vs.PrL1‐3 0.9792 0.5452to Yes **** <0.0001
CA1vs.PrL4‐6 1.038 0.604to Yes **** <0.0001
CA2vs.CA3 0.2998 ‐0.2108to No ns 0.7304
CA2vs.DG 1.306 0.7949to Yes **** <0.0001
CA2vs.LS 0.0772 ‐0.3889to No ns >0.9999
CA2vs.PrL1‐3 1.199 0.7652to Yes **** <0.0001
CA2vs.PrL4‐6 1.258 0.824to Yes **** <0.0001
CA3vs.DG 1.006 0.4952to Yes **** <0.0001
CA3vs.LS ‐ ‐0.6886to No ns 0.9169
CA3vs.PrL1‐3 0.8994 0.4655to Yes **** <0.0001
CA3vs.PrL4‐6 0.9582 0.5243to Yes **** <0.0001
DGvs.LS ‐1.228 ‐1.694to‐ Yes **** <0.0001
DGvs.PrL1‐3 ‐ ‐0.5403to No ns 0.9998
DGvs.PrL4‐6 ‐ ‐0.4815to No ns >0.9999
LSvs.PrL1‐3 1.122 0.7413to Yes **** <0.0001
LSvs.PrL4‐6 1.181 0.8001to Yes **** <0.0001
PrL1‐3vs.PrL4‐6 0.0587 ‐0.2816to No ns >0.9999
Table 4 One-way ANOVA analysis of the relative density of 5-HT/VGLUT3 in the mouse forebrain (Significant changes are highlighted in light grey).
289
Sidak'smultiplecomparisonstest
MeanDiff.
95.00%CIofdiff.
Significant?
Summary
AdjustedPValue
BLAvs.CeA ‐3.801 ‐16.86to No ns >0.9999
BLAvs.BNST 6.116 ‐3.858to No ns 0.9282
BLAvs.NACc 8.216 ‐2.967to No ns 0.5826
BLAvs.NACs 2.695 ‐8.207to No ns >0.9999
BLAvs.CPU 6.465 ‐4.198to No ns 0.9389
BLAvs.CA1 ‐12.17 ‐27.24to No ns 0.3484
BLAvs.CA2 ‐19.15 ‐34.23to‐ Yes ** 0.0017
BLAvs.CA3 ‐17.12 ‐32.2to‐ Yes ** 0.0098
BLAvs.DG ‐7.094 ‐22.17to No ns 0.9997
BLAvs.LS ‐1.215 ‐14.27to No ns >0.9999
BLAvs.PrL1‐3 ‐8.508 ‐20.02to No ns 0.5686
BLAvs.PrL4‐6 ‐11.08 ‐22.6to No ns 0.0762
CeAvs.BNST 9.918 ‐2.585to No ns 0.3881
CeAvs.NACc 12.02 ‐1.469to No ns 0.1614
CeAvs.NACs 6.497 ‐6.759to No ns 0.9992
CeAvs.CPU 10.27 ‐2.793to No ns 0.41
CeAvs.CA1 ‐8.364 ‐25.22to No ns 0.9989
CeAvs.CA2 ‐15.35 ‐32.21to No ns 0.1325
CeAvs.CA3 ‐13.32 ‐30.18to No ns 0.3983
CeAvs.DG ‐3.292 ‐20.15to No ns >0.9999
CeAvs.LS 2.586 ‐12.49to No ns >0.9999
CeAvs.PrL1‐3 ‐4.706 ‐18.47to No ns >0.9999
CeAvs.PrL4‐6 ‐7.283 ‐21.05to No ns 0.9949
BNSTvs.NACc 2.1 ‐8.428to No ns >0.9999
BNSTvs.NACs ‐3.421 ‐13.65to No ns >0.9999
BNSTvs.CPU 0.3483 ‐9.626to No ns >0.9999
BNSTvs.CA1 ‐18.28 ‐32.88to‐ Yes ** 0.0021
BNSTvs.CA2 ‐25.27 ‐39.87to‐ Yes **** <0.0001
290
BNSTvs.CA3 ‐23.23 ‐37.83to‐ Yes **** <0.0001
BNSTvs.DG ‐13.21 ‐27.81to No ns 0.1406
BNSTvs.LS ‐7.331 ‐19.83to No ns 0.9634
BNSTvs.PrL1‐3 ‐14.62 ‐25.51to‐ Yes *** 0.0006
BNSTvs.PrL4‐6 ‐17.2 ‐28.08to‐ Yes **** <0.0001
NACcvs.NACs ‐5.521 ‐16.93to No ns 0.9994
NACcvs.CPU ‐1.752 ‐12.93to No ns >0.9999
NACcvs.CA1 ‐20.38 ‐35.83to‐ Yes *** 0.0008
NACcvs.CA2 ‐27.37 ‐42.82to‐ Yes **** <0.0001
NACcvs.CA3 ‐25.33 ‐40.79to‐ Yes **** <0.0001
NACcvs.DG ‐15.31 ‐30.76to No ns 0.0555
NACcvs.LS ‐9.431 ‐22.92to No ns 0.705
NACcvs.PrL1‐3 ‐16.72 ‐28.72to‐ Yes *** 0.0003
NACcvs.PrL4‐6 ‐19.3 ‐31.3to‐ Yes **** <0.0001
NACsvs.CPU 3.77 ‐7.133to No ns >0.9999
NACsvs.CA1 ‐14.86 ‐30.11to No ns 0.0667
NACsvs.CA2 ‐21.85 ‐37.1to‐ Yes *** 0.0002
NACsvs.CA3 ‐19.81 ‐35.06to‐ Yes ** 0.0011
NACsvs.DG ‐9.789 ‐25.04to No ns 0.8716
NACsvs.LS ‐3.91 ‐17.17to No ns >0.9999
NACsvs.PrL1‐3 ‐11.2 ‐22.94to No ns 0.0833
NACsvs.PrL4‐6 ‐13.78 ‐25.52to‐ Yes ** 0.006
CPUvs.CA1 ‐18.63 ‐33.71to‐ Yes ** 0.0026
CPUvs.CA2 ‐25.62 ‐40.7to‐ Yes **** <0.0001
CPUvs.CA3 ‐23.58 ‐38.66to‐ Yes **** <0.0001
CPUvs.DG ‐13.56 ‐28.64to No ns 0.1489
CPUvs.LS ‐7.68 ‐20.74to No ns 0.9616
CPUvs.PrL1‐3 ‐14.97 ‐26.49to‐ Yes ** 0.0011
CPUvs.PrL4‐6 ‐17.55 ‐29.07to‐ Yes **** <0.0001
CA1vs.CA2 ‐6.988 ‐25.46to No ns >0.9999
291
CA1vs.CA3 ‐4.953 ‐23.42to No ns >0.9999
CA1vs.DG 5.071 ‐13.4to No ns >0.9999
CA1vs.LS 10.95 ‐5.909to No ns 0.8533
CA1vs.PrL1‐3 3.657 ‐12.04to No ns >0.9999
CA1vs.PrL4‐6 1.081 ‐14.61to No ns >0.9999
CA2vs.CA3 2.034 ‐16.43to No ns >0.9999
CA2vs.DG 12.06 ‐6.409to No ns 0.8445
CA2vs.LS 17.94 1.078to Yes * 0.0236
CA2vs.PrL1‐3 10.64 ‐5.05to No ns 0.7728
CA2vs.PrL4‐6 8.068 ‐7.627to No ns 0.9973
CA3vs.DG 10.02 ‐8.444to No ns 0.9911
CA3vs.LS 15.9 ‐0.9559to No ns 0.0939
CA3vs.PrL1‐3 8.61 ‐7.084to No ns 0.989
CA3vs.PrL4‐6 6.034 ‐9.661to No ns >0.9999
DGvs.LS 5.879 ‐10.98to No ns >0.9999
DGvs.PrL1‐3 ‐1.414 ‐17.11to No ns >0.9999
DGvs.PrL4‐6 ‐3.991 ‐19.69to No ns >0.9999
LSvs.PrL1‐3 ‐7.293 ‐21.06to No ns 0.9947
LSvs.PrL4‐6 ‐9.869 ‐23.63to No ns 0.6446
PrL1‐3vs.PrL4‐6 ‐2.577 ‐14.89to No ns >0.9999
292
Appendix B:
The Impact of Sugar Consumption on Stress Driven,
Emotional and Addictive Behaviours.
This appendix comprises the following published article:
Jacques, A., Chaaya, N., Beecher, K., Ali, S.O., Belmer, A., Bartlett, SE. The Impact of Sugar Consumption on Stress Driven, Emotional and Addictive Behaviours.
Neuroscience and Biobehavioral Reviews. Published August, 2019. https://doi.org/10.1016/j.neubiorev.2019.05.021
Owing to the substantial comorbidity of mental illness, neurodegeneration and
the current world obesity epidemic (Lopresti & Drummond, 2013; Spielman, Little,
& Klegeris, 2014) there is also a crucial need to investigate any co-localising
mechanisms of action between neuroplastic changes and high caloric substances. A
growing body of evidence has implicated a critical role for negative neuroplasticity
in the development of these disorders (Bansal, Hellerstein, & Peterson, 2018;
Matikainen‐Ankney & Kravitz, 2018). This review summarizes neural adaptations
that influence emotion following sugar consumption.
293
10.1 Abstract
In 2016 the World Health Organization reported 39% of the world’s adult
population (over 18 y) was overweight, with western countries such as Australia and
the United States of America at 64.5% and 67.9% respectively. Overconsumption of
high fat/sugar containing food and beverages contribute to the development of
obesity. Neural plasticity that occurs as a result of long term sugar consumption has
been shown to reduce impulse control and therefore lower the ability to resist the
high fat/sugar foods contributing to the obesity epidemic. There is significant overlap
between the neural pathways involved in emotions that guide behavioural responses
to survival situations with those regulating overconsumption of highly palatable
food. This suggests that having a clearer understanding of the role of stress and
emotions in the development of obesity will lead to the development of novel
therapeutic strategies. Sucrose consumption activates the mesocorticolimbic system
in a manner synonymous with substances of abuse. There is overwhelming evidence
to support the hypothesis that sucrose consumption results in pathophysiological
consequences such as morphological neuronal changes, altered emotional processing
and modified behaviour in rodent and human models. In this comprehensive review,
we examined >300 studies investigating the interaction between sugar consumption,
stress and emotions. Preclinical and clinical trials investigating highly palatable
foods and stress, anxiety, depression and fear are reviewed. Importantly, the synergy
between sugar consumption and neurobiology is addressed. This review summarizes
the neurochemical changes and neural adaptations – including changes in the
dopaminergic system – that influence emotion and behaviour following sugar
consumption.
294
Keywords: sucrose consumption; stress; anxiety; depression; fear; obesity; addiction;
emotion; behaviour
10.2 Introduction
A sedentary lifestyle combined with a high caloric diet plays a significant role in
obesity(Organization, 2017b). In 2016 the World Health Organization reported more
than 1.9 billion adults were overweight (Organization, 2016). World obesity has
essentially tripled since 1975(Organization, 2016). Excessive sugar consumption has
been shown to be one of the leading contributors to weight gain (Malik, Pan, Willett,
& Hu, 2013). Furthermore, a diet high in sugars has been linked to cognitive
impairments, negative neuroplasticity changes such as hippocampal dysfunction
(Kanoski & Davidson, 2011; Noble, Hsu, Liang, & Kanoski, 2017) and emotional
disorders such as anxiety and depression (S. Kim, Shou, Abera, & Ziff, 2018). High
sugar intake increases the risk of cancer, oxidative stress, inflammation, and obesity
(Makarem, Bandera, Nicholson, & Parekh, 2018), as well as impacting cognitive
function (Barnes & Joyner, 2012) and mental health (Peet, 2004). For example, a diet
higher in refined sugar has been shown to predict a worsening of schizophrenic
behaviour over a two year period (Peet, 2004). Despite the many psychological,
physical and neurological burdens of sugar overconsumption and consequent obesity,
there are no therapies directed at reducing sugar consumption, and few therapies
capable of successfully treating obesity (for review see (Kanoski & Davidson,
2011)).
Obesity, arising from overconsumption of rewarding foods such as those with a
high sugar content, may result in negative consequences, such as a loss of self-
control and subsequent poor decision-making (Beilharz, Maniam, & Morris, 2014).
This loss of control poses a significant challenge for overweight individuals
295
attempting to lose weight. The desire to eat, is regulated by brain regions known as
feeding centers, located in the arcuate nucleus of the hypothalamus. Importantly,
these regions are interconnected with the limbic system and cerebral cortex
(specifically the hippocampus and amygdala), which are responsible for the
modulation of emotions (Ahima & Antwi, 2008; C. Liu, Lee, & Elmquist, 2014;
Simon et al., 2006; Stunkard, Faith, & Allison, 2003; Sweeney & Yang, 2017;
Ulrich-Lai et al., 2010).
Sugar, artificial sweeteners and obesity
Sugar became embedded in the food chain in the late 1960s and replaced fats
to mask bitterness and make food more palatable (Bakke et al., 2018; Moss, 2013).
In the 1970’s a shift toward increased sugar-sweetened beverages became apparent
(Wolf, Bray, & Popkin, 2008). Our early ancestors obtained sugar from either fruit,
limited by seasons, or honey, protected by bees. In the last half century, sugar
consumption has tripled worldwide, partially due to the hidden use of added sugars
in processed food (Lustig, Schmidt, & Brindis, 2012). The first artificial sweetener
(saccharin) was introduced in 1879 with low production costs during wartime,
increasing its popularity (Weihrauch & Diehl, 2004). In the 1950s as sugar became
readily available, the use of sweeteners shifted to so-called ‘diet products’ with low
caloric content.
The development of obesity relies on both the hedonic, sweet taste of food in
conjunction with the negative emotional properties of food consumption (Berthoud,
Münzberg, & Morrison, 2017; Meye & Adan, 2014; Yu & Kim, 2012). Hedonic
reactions to a 10% sucrose solution (and as low as 3.4%) were tested and found to be
significantly higher in adolescent rats when compared to adult rats (Wilmouth &
Spear, 2009).
296
Sugar and emotions
Emotional eating has been show to stem from the desire to mitigate the
effects of stress (Van Strien, Frijters, Bergers, & Defares, 1986) and stress is partially
regulated by the hypothalamic-pituitary-adrenal (HPA) axis. Interestingly, activity of
the HPA axis has been shown to be reduced through the consumption of sugar
containing foods (Ulrich-Lai, Ostrander, & Herman, 2011). Following consumption,
hormones are released to reduce the feelings of stress, which also increase the desire
for comfort foods, thus perpetuating emotional eating habits (Ulrich-Lai et al., 2011;
Ursano et al., 2009).
The objective of this review is to summarize research that examines how the
consumption of sugar leads to changes in neurobiological brain function that alters
emotional states and subsequent behaviours. We will examine findings from studies
at the intersection between the consumption of sucrose and changes in the
performance of tasks with a stressful or emotional component and review
neurobiological and neurochemical mechanisms involved in addiction, stress, fear,
anxiety and depression to determine whether there are overlapping neural
mechanisms. Lastly, we will determine whether there are novel
pharmacotherapeutics and/or interventions that target these brain circuits or
neurochemical pathways to improve the current approaches for the treatment of
obesity.
10.3 Common neuronal pathways for sucrose consumption, addiction, emotions and obesity
Addiction is characterized by a difficulty to control habitual behaviour even in
the face of negative consequences (Lindgren et al., 2018). Early addiction research
focused on drugs of abuse such as alcohol, morphine and nicotine. This has since
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been extended to include gambling, eating and more recently, sugar consumption
(Benowitz, 1988; Benton, 2010; Comings et al., 2001; Gearhardt, Corbin, &
Brownell, 2009; Jellinek, 1952; Klenowski et al., 2016; Weeks, 1962). Addiction to
substances of abuse relies on the drug binding to specific protein targets which elicits
certain physiological and behavioural responses unique to that drug(A. M. Lee &
Messing, 2008). Psychoactive drugs commonly result in rewarding sensations that
lead to repeated use and, depending on genetic susceptibility, environmental factors
and subsequent addiction(A. M. Lee & Messing, 2008). Stress has long been
associated with both the motivation to use rewarding substances and the result of not
attaining those substances (for review see (Sinha, 2008)). The negative symptoms
produced through withdrawal are common to all forms of addictive substances
including highly palatable food. These include prolonged sensitization to the
substance of choice and associative learning where environmental cues become
associated with the pleasure derived from the substance (Hebebrand et al., 2014).
These associative memories, combined with intense cravings, increase the incidence
of relapse even after sustained abstinence. These commonalities may be due to
substances of addiction utilizing the same circuitry within the brain’s
mesocorticolimbic system (Figure 1) (Baron, Blum, Chen, Gold, & Badgaiyan, 2018;
G. F. Koob & Le Moal, 2001; Volkow, Fowler, Wang, & Swanson, 2004).
Food consumption is necessary to regulate homeostatic energy balance.
However, humans also consume food for pleasure or comfort. The hedonistic desire
for palatable food is considered reward-related and may result in maladaptive or
negative neuroplasticity that can override homeostatic regulation and result in
overeating behaviours(Kenny, 2011). Reward is delivered through stimuli that
produce pleasurable or enjoyable experiences, contrasting with addiction, which
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involves compulsive and sometimes painful behaviour, yet both share similar
neuroadaptive responses and overlapping neuronal pathways (Adinoff, 2004). The
reward or mesocorticolimbic pathway plays an influential role in what we choose to
eat as demonstrated by studies where rats appear willing to endure noxious stimuli
such as extreme cold, heat and foot-shock to procure highly palatable foods over
standard rat chow(Cabanac & Johnson, 1983; Foo & Mason, 2005; Oswald,
Murdaugh, King, & Boggiano, 2011).
Figure 10-1 Reward pathway encompassing the mesocorticolimbic distribution of dopaminergic neurons.
The neural regions of the reward pathway include the prefrontal cortex (PFC), amygdala (AMG), ventral-tegmental area (VTA) and nucleus accumbens (NAc). Each anatomical region modulates individual behaviours and contributes to general behaviours through cross-connectivity. Regions shown and behaviours listed are consistent between human and rodent brains(Belin & Dalley, 2012).
The reward pathway consists of the prefrontal cortex (PFC), amygdala (AMG),
ventral-tegmental area (VTA) and nucleus accumbens (NAc) and in accord with
drugs of abuse (Alsiö, Olszewski, Levine, & Schiöth, 2012; Sinha, 2018), is thought
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to be stimulated by the overconsumption of sugar, thus contributing to the
development of obesity (Leigh & Morris, 2016; Murray, Tulloch, Criscitelli, &
Avena, 2016; Shariff et al., 2016). The reward pathway is highly associated with the
efflux of dopamine which regulates the motivational state of wanting or craving that
substance or behaviour (Adinoff, 2004). Once overstimulated, the pathway becomes
primed to require that particular stimuli when presented with contextual cues or
emotional stress (Adinoff, 2004). The VTA induces this sensitization while the NAc
modulates its expression through dopaminergic control (Huang, Wu, Lee, Huang, &
Chen, 2018; Xiong et al., 2018; C. Zhang et al., 2018).
Nucleus accumbens
The NAc consists of two sub regions (core and shell), each containing specific
neuronal populations fundamental in processes of motivation (Reker et al., 2018;
Yang et al., 2018), aversion(Rosas et al., 2018), fear related avoidance (Moscarello
& Maren, 2018), reinforcement learning (E. M. Anderson et al., 2018; Floresco,
Montes, Maric, & van Holstein, 2018), pleasure seeking, addiction (C. Barrientos et
al., 2018; S. Kim et al., 2018; Lam & Jadavji, 2018) and behavioural sensitization
(Nona & Nobrega, 2018). To examine motivation and the role played by α-amino-3-
hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in the NAc, intra-
NAc infusions of AMPAR antagonists were given to obesity prone and obesity
resistant male rats (Derman & Ferrario, 2017). Compared with controls, findings
showed the obesity-prone animals exhibited cue-triggered food seeking and the
behaviour was mediated by increased surface expression of AMPA receptors in the
core of the NAc. This data suggests cue-triggered food seeking may elicit greater
motivational feedback in individuals prone to obesity, and thus play a role in driving
the compulsive over-consumption of food (Berridge, Ho, Richard, &
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DiFeliceantonio, 2010; Derman & Ferrario, 2017). Similarly, consumption of a 10%
sucrose solution resulted in alterations in synaptic strength via AMPA receptor
trafficking, which modulates the compulsive tendencies characteristic of any drug-
seeking (X. X. Peng, Ziff, & Carr, 2011). .
Drug induced neuroplasticity has been observed in the NAc through alterations
in dendritic morphology and altered gene expression (Huang et al., 2018;
Legastelois, Botia, Coune, Jeanblanc, & Naassila, 2014; Y. Li, Acerbo, & Robinson,
2004). Similar to psychoactive substances, sugar binging has been shown to cause
repeated increases in dopamine release and altered expression of NAc Delta FosB as
demonstrated in rats with an increase in Delta Fos B expression after consumption of
a high sugar diet (Avena, Rada, Moise, & Hoebel, 2006; Wallace et al., 2008).
Dopamine release increases as a direct effect of chronic drug use and results in
postsynaptic changes of Delta FosB and CREB accumulation (Nestler & Aghajanian,
1997). The intracellular build-up of Delta FosB can alter the gene production of
receptors which may result in reinstatement during withdrawal (Nestler &
Aghajanian, 1997).This data suggest that neuroadaptations in the brain reward
pathway in obese subjects may contribute to the progression of compulsive eating.
The NAc is considered to be the main region to undergo neuroadaptation after sugar
consumption, but changes have been noted in many brain regions encompassing the
mesocorticolimbic system (De Jong, Vanderschuren, & Adan, 2016).
Amygdala
The AMG plays a key role in negative reinforcement, the progression towards
addiction and extensively in the learned associations that lead to relapse (See, Fuchs,
Ledford, & McLAUGHLIN, 2003). Neuroplastic changes occur in the AMG which
facilitate the level of dependence to a substance of abuse to move from impulsivity to
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compulsion(G. F. Koob, 2009a). This was demonstrated by long term sucrose
consumption in rats which produced maladaptive alterations in the apical dendritic
morphology of AMG principle neurons(Shariff et al., 2017). Optogenetic stimulation
of central AMG neurons was paired with a sucrose reward to test reward incentive in
rats(M. J. Robinson, Warlow, & Berridge, 2014). Stimulation increased incentive to
choose the sucrose reward over a similar sweet alternative and amplified the degree
of effort the rats were willing to exert to obtain the reward(M. J. Robinson et al.,
2014). These findings suggest neuroplastic changes in the AMG may occur due to
sucrose consumption and increased addictive-like compulsive behaviour. The AMG
and PFC share a role in motivation, associative learning (including negative
reinforcement) (G. F. Koob, 2009a), compulsive behaviour and deficits in executive
functioning (Zehra et al., 2018). Relapsing into addictive behaviours is most likely
due to dysregulation of motivational processes. Negative reinforcement, or substance
abuse to alleviate an hedonic, anxious, irritable or dysphoric state, is characteristic of
addiction withdrawal (G. Koob, 2018).
Prefrontal cortex
The orbitofrontal cortex and anterior cingulate are associated with compulsive
cravings for drugs of abuse (Adinoff, 2004). Interestingly, neurons in the
orbitofrontal cortex of rats were shown to encode this same compulsion to seek a
sucrose reward solution (15%) (Moorman & Aston-Jones, 2014). The PFC is the
brain region responsible for executive functions such as planning and decision
making. Addiction and periods of withdrawal from substances of abuse are generally
accompanied by a loss of executive function, which is due to dysfunctional
neurocircuitry in the medial PFC (Molnar et al., 2018). Sucrose and fructose
consumption have also been linked to metabolic and electrophysiological changes in
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the hippocampus (Hsu et al., 2015; Lemos et al., 2016), the thalamus (Jastreboff et
al., 2016) and the hypothalamus (Berthoud et al., 2017; Ulrich-Lai et al., 2011); brain
regions involved in the associative learning of cues that lead to receiving rewards (L.
C. Anderson & Petrovich, 2018; Atkins, Selcher, Petraitis, Trzaskos, & Sweatt,
1998).
10.4 Physiological and neural substrates of sugar consumption
Feeding centers linked to limbic regions relaying emotional information
influence our behaviour towards food(C. Liu et al., 2014). The current view of what
drives us to eat takes into account the drive to maintain an internal balance between
energy expenditure and consumption. However, the influence of external cues that
promise immediate reward may negate this balance(Lowe & Butryn, 2007; Stroebe,
Papies, & Aarts, 2008). Sucrose added to food provides this immediate reward, with
the human desire for sucrose or any sweet taste being comparable to the degree of
yearning and reward produced by drugs of addiction (Ahmed, Guillem, & Vandaele,
2013). Sugar or sucrose (table sugar) is a disaccharide composed of the
monosaccharides glucose and fructose (I. Smith, 1960; Southgate, 1995). Unlike
most drugs of addiction (excluding alcohol), sugar does not cross the blood brain
barrier to bind to molecular substrates/receptors on the cell surface and subsequently
alter neural plasticity. Recent research has observed astrocytes, a type of glial cell
that helps maintain the blood-brain barrier appear to sense and uptake sugar to
regulate neuronal signaling related to appetite(García-Cáceres, 2016). However, as
sugar is a food, ingestion begins in the mouth.
Sugar is initially sensed by heterodimeric G protein-coupled receptors on taste
cells located in the mouth and gut(Brown & Rother, 2012). Once in the small
intestine, sucrose is broken down into glucose and fructose; which are metabolized
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by separate and disparate mechanisms(Southgate, 1995). Glucose increases the
absorption of fructose from the gut, whereas fructose acts as a catalyst for the uptake
and storage of glucose by the liver(Laughlin, 2014). Fructose is absorbed into the
bloodstream at a slower rate and persists for longer(Teff et al., 2004). As opposed to
glucose, fructose is not well absorbed by pancreatic beta cells and therefore
stimulates very little insulin secretion(Kyriazis, Soundarapandian, & Tyrberg, 2012).
Insulin increases satiety and subdues the reward value derived from food, suggesting
fructose may play a more complex role in the development of obesity than glucose
consumption(Grant, Christie, & Ashcroft, 1980). In addition to these observed
features fructose can increase the rate of carbohydrate oxidation after a meal, also
decreasing satiety levels(Laughlin, 2014).
Glucose and fructose have the ability to cross the blood-brain-barrier. However,
as the gut and liver rapidly break down fructose, the blood concentration levels are
generally low, resulting in only small amounts available to cross the
barrier(Gonzalez & Betts, 2018). Once across the barrier glucose signaling
mechanisms are activated. As demonstrated in animal studies(Asnaghi,
Gerhardinger, Hoehn, Adeboje, & Lorenzi, 2003; Kikkawa et al., 1987), emerging
arguments suggest fructose is produced from glucose in the brain via the polyol
pathway (glucose → aldose reductase → sorbitol → sorbitol dehydrogenase →
fructose), driven by hyperglycemia(J. J. Hwang et al., 2017). Glucose consumption
increases functional connectivity between the hypothalamus, thalamus and striatum
where fructose does not affect striatal connectivity(Cha, Wolfgang, Tokutake,
Chohnan, & Lane, 2008). Regional cerebral blood flow patterns also differ post
ingestion of these monosaccharides, with reduced flow in appetite and reward
regions of the brain after glucose consumption inclusive of the hypothalamus,
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thalamus, insula, anterior cingulate, and striatum. Alternate to this, fructose reduces
flow in the thalamus, hippocampus, posterior cingulate cortex, fusiform, and visual
cortex(Page et al., 2013). These differences may assist in clarifying the role of
sucrose consumption in obesity and diabetes mellitus. Both human and rat studies
have shown that fructose and not glucose consumption results in conditions such as
metabolic syndrome and contributes to obesity(Nakagawa et al., 2006).
On the neuroanatomical level, regulation of energy homeostasis is dependent on
the hypothalamus(Benoit, Tracy, Davis, Choi, & Clegg, 2008), however, the NAc,
part of the reward-pathway, is pivotal in the pathophysiology of sugar
consumption(Geiger et al., 2008). Table 1 depicts the findings of several studies
examining the effect of sugar consumption on brain regions involved in the reward
pathway, defining the molecular mechanisms resultant of the neural adaptations and
the subsequent behavioural changes demonstrated by the animals.
To determine separation of neural networks engaged in choosing to eat based on
palatability or nutritional status, striatal dopamine levels were measured in sugar
consuming mice (Tellez et al., 2016). Only when the sweet solution contained energy
was the dorsal striatum, basal ganglia descending pathway recruited. Energy content
drives the release of dopamine in the NAc, however, sweetness modulated this
efflux, inhibiting dopamine release as palatability decreased(Tellez et al., 2016).
Alternatively, suppression of the sweeteners nutritional value inhibited dopamine
release in the dorsal striatum, demonstrating recruitment of different circuitry when
separating energy requirements from pleasurable taste(Tellez et al., 2016).
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Table 5 The effects of sugar consumption on the reward pathway.
Sugar consumption causes neural adaptation to brain regions in the reward pathway
Sugar consumption alters activation of molecular substrates
Sugar consumption alters behaviour
Ventral tegmental area Nucleus accumbens Amygdala Prefrontal cortex
Dopamine (VTA-NAc)
Sensitisation (Sharpe, Clemens, Morris, & Westbrook, 2016; Wyvell & Berridge, 2001) Reward seeking (B. Gosnell, 1987; Spangler et al., 2004) (Avena & Hoebel, 2003)
Dopamine Kir2.1 CREB (NAc)(Leão, Cruz, Marin, & da Silva Planeta, 2012)
Anxiety (S. Kim et al., 2018) Depression (Avena, Rada, & Hoebel, 2008)
Serotonin
Motivation (Mathes, Gregson, & Spector, 2013)
α6β2 nAChRs α4β2 nAChRs
Anticipation of reward Motivation (Shariff et al., 2016)
AMPAR (NAc core)
Motivational feedback Cue-triggered food seeking (Derman & Ferrario, 2017)
IL-6 Leptin IL-6 TNF-α
Anxiety Fear Depression (Santos et al., 2018)
Protein oxidation (PFC)
Executive function Anxiety (Chepulis, Starkey, Waas, & Molan, 2009)
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Delta FosB (NAc) Delta FosB (PFC)
Reward seeking (Wallace et al., 2008) Fear extinction memory retention (Baker & Reichelt, 2016)
10.4.1 The effect of sugar on the neurobiology of food consumption
The hedonic value of food, the reward associated with its consumption and the
learned external cues which trigger the desire to feed are modulated via bidirectional
circuitry between the reward pathway, hippocampus and orexigenic cells ( which
induce appetite and stimulate food intake) in the lateral hypothalamus (Kastin, 2013;
Petrovich & Gallagher, 2003; Petrovich, Hobin, & Reppucci, 2012) (see Figure 2)
and for a comprehensive review see (Murray et al., 2016). Studies activating the
lateral hypothalamus show glutamatergic inhibition of feeding (Jennings, Rizzi,
Stamatakis, Ung, & Stuber, 2013) and GABAergic stimulation of feeding (Jennings
et al., 2015). Furthermore, a subset of these GABAergic neurons project from the
VTA to the hypothalamus and express galanin (a neuropeptide) which enhances
motivation for sucrose consumption (Bocarsly, 2018; Qualls-Creekmore et al., 2017).
Lateral hypothalamic orexin neurons gain input regarding food intake from the
arcuate nucleus of the hypothalamus via endogenous melanocortin receptor
antagonists; neuropeptide Y (NPY) and agouti-related peptide-expressing neurons
(AgRP). Further, metabolic homeostasis is sensed by the lateral hypothalamus
through surrounding glucose, ghrelin and leptin levels, and this drives food seeking
behaviours (Yamanaka et al., 2000).
Hypothalamic orexinergic and anorexinergic pathways are regulated by
NPY/AgRP and POMC/CART peptides and are affected in different ways by the
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consumption of sugar (Murray et al., 2016). The orexigenic pathway is influenced by
two specific neuronal populations within the arcuate nucleus, the first population
expresses NPY and AgRP and stimulates food intake (Benoit et al., 2008; Yamanaka
et al., 2000). Variations in both NPY and AgRP were associated with rats consuming
more chow after being provided with a 30% sucrose solution (Gaysinskaya,
Karatayev, Shuluk, & Leibowitz, 2011). Reduced expression of these peptides was
noticed following sucrose consumption, which then increased thirty to sixty minutes
later, prior to feeding (Gaysinskaya et al., 2011).
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Figure 10-2 Regulation of feeding behaviour and food intake by central and peripheral appetite-regulating hormones and peptides.
Hypothalamic orexinergic and anorexinergic pathways and their regulation by NPY/AgRP and POMC/CART peptides, respectively, are depicted in the right-hand side pannel. Peripheral appettite-stimulating (green) and –inhibiting (red) hormones and peptides crossing the blood-brain barrier (BBB), and the organs their originate from are also represented. The effect of these hormones/peptides on the stimulation or ihibition of the orexigenic (dashed green arrows) and anorixegenic (dashed red arrows) is depicted as (+) or (-), respectively.
The link between p53 (a gene encoding a protein involved in regulating the cell
cycle) in AgRP neurons (which regulate ghrelin-induced appetite) and obesity, has
been demonstrated in mice, with its overexpression resulting in excessive weight loss
(Quiñones et al., 2018). Under normal circumstances, a lack of available nutrients
drives AgRP neurons to initiate feeding behaviour. In mice, it has been shown that
the desire to feed does necessarily involve AgRP neuronal activation when the food
has been enriched with sugar and fat (Denis et al., 2015). In this case dopamine
signaling initiates the desire to eat and feeding behaviour becomes driven by reward,
through the neural circuits involved in emotion, as opposed to the orexigenic
pathway induced by metabolic need. This appears to be the case when eating for
comfort (Denis et al., 2015). Further examinations directly link sucrose
consumption with the orexigenic pathway. In two separate studies sucrose intake was
found to be increased following NPY infusion into the lateral ventricle or AgRP
administration into the NAc shell (Badia‐Elder, Stewart, Powrozek, Murphy, & Li,
2003; Pandit et al., 2015). The effects of AgRP administration into the NAc shell is,
however, halted by pre-treatment of α-flupenthixol, a non-selective dopamine
receptor antagonist (Pandit et al., 2015). Furthermore, chemogenetic and optogenetic
manipulation of AgRP neurons activity modulates the emotional valence of feeding.
Specifically, arcuate nucleus AgRP neurons were shown to regulate the emotional
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aspects of feeding involved with anxiety, fear-like behaviour and aggression (Burnett
et al., 2016; Joly-Amado et al., 2014; Padilla et al., 2016; Sweeney & Yang, 2017).
The second, or anorexigenic pathway contains proopiomelanocortin (POMC), a
pre-cursor to melanocortin receptor agonist α-MS, and cocaine- and amphetamine-
regulated transcript (CART). This pathway is used to inhibit the consumption of food
(Benoit et al., 2008; Lau & Herzog, 2014). Disruption of the genes encoding POMC,
CART or the melanocortin-4 receptor may lead to obesity (Graham, Shutter,
Sarmiento, & Stark, 1997). Mice fed a high fat diet and then provided with sucrose-
sweetened water showed a down-regulation of POMC mRNA expression in the
hypothalamus (Soto, 2015). Prolonged limited access to sucrose lead to decreased
activity of the anorexigenic oxytocin system (associated with satiety and termination
of feeding), in the hypothalamus of rats (Mitra, 2010). Together, these findings
suggest sugar plays a role in limiting the activation of this pathway, thus increasing
the desire to feed.
Neural pathways connecting feeding centres of the brain to the limbic system
have been identified through optogenetic activation of melanin-concentrating
hormone neurons during the intake of an artificial sweetener (sucralose) (Domingos
et al., 2013). Sucralose was found to increase striatal dopamine levels which
transposed the preference to sugar normally shown by mice into a preference for the
sweetener (Domingos et al., 2013). Further to this, Domingos and colleagues showed
that melanin-concentrating hormone neurons projecting to reward areas are necessary
for the rewarding effects of sucrose (Domingos et al., 2013).
Furthermore, the serotonergic system is indicated in the regulation of hedonic
feeding, with increases of serotonin causing decreased food intake and decreased
serotonin increasing motivation to feed (Halford, Boyland, Blundell, Kirkham, &
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Harrold, 2010). The suggested mechanism of action for serotonin in feeding
behaviour is via AgRP and POMC neurons in the arcuate nucleus (Sohn, Elmquist, &
Williams, 2013). The serotonin transporter-linked polymorphic region (5-HTTLPR)
regulates vulnerability to stress, which is increased in cases of pathological fear, and
influences energy intake, suggesting its role on stress related overeating (Capello &
Markus, 2014). Neural adaptations resulting from sugar dependence in rodents
include alterations in dopamine and opioid receptor binding in the mesolimbic
cortex, changed expression of encephalin mRNA and modified NAc release of
dopamine and acetylcholine(Avena et al., 2008).
Leptin and Ghrelin
There are several alternate neuronal populations, pathways and brain nuclei
which drive overeating behaviour and sucrose consumption. Along with
hypothalamic orexin neurons, MCH producing neurons and leptin-receptor cells
projecting from the lateral hypothalamus to the VTA also influence hedonic feeding
and reward seeking behaviour (Bocarsly, 2018). High fructose intake results in lower
insulin levels, decreased levels of leptin and increased concentrations of ghrelin
when compared to meals high in glucose(Teff et al., 2004). Alteration to levels of
these satiety hormones (which result in the feeling of ‘fullness’ following food
consumption) may be a precursor to overeating. These results concur with those
found after intracerebroventricular injection of concentrated fructose or glucose into
the hypothalamus of rats exposed to 2-deoxy-Dglucose (2DG) (Miller, Martin,
Whitney, & Edwards, 2002). DG is an analogue of glucose that cannot be
metabolized and is known to cause increased food intake in rats by interfering with
the process of glycolysis(Fiorentini & Müller, 1975). Rats provided with fructose
injections showed enhanced food intake both in the presence and absence of 2DG,
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where glucose suppressed 2DG induced food intake suggesting a role for brain
glucoreceptors in the control of food consumption(Miller et al., 2002).
Glucoreceptors monitor blood glucose levels and are located within the
hypothalamus. They have been shown to be stimulated by low blood sugar levels to
increase feeding behaviour (Ritter, Slusser, & Stone, 1981).
Dopaminergic neurons projecting from the VTA to the NAc are inhibited by
leptin and insulin and stimulated by ghrelin(Palmiter, 2007). Overconsumption of
sugar has been shown to increase dopamine D1 receptor binding in the NAc core and
shell, decrease dopamine D2 binding in the dorsal striatum and increase binding to
dopamine transporters in the midbrain(N. T. Bello, Lucas, & Hajnal, 2002; C
Colantuoni et al., 2001). Increases in dopamine delays the release of acetylcholine
during feeding, which postpones satiety and paves the way for overconsumption(B.
Hoebel, Avena, Bocarsly, & Rada). Alternatively, opioid receptor binding (mu-1)
was increased in the NAc shell, hippocampus, locus coeruleus and cingulate cortex
after rats were permitted intermittent binging on a 25% glucose solution(C
Colantuoni et al., 2001). Together these findings suggest the overconsumption of
sugar may sensitize dopamine D1 and opioid mu-1 receptors in a similar manner to
drugs of dependence. Table 2 depicts studies examining the effect of sugar
consumption on brain regions, molecular mechanisms and behavioural changes
involved in the orexigenic pathway.
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Table 6 The effects of sugar consumption on the orexigenic pathway.
Sugar consumption causes neural adaptation to brain regions connected to the orexigenic pathway
Sugar consumption alters activation of molecular substrates
Sugar consumption alters behaviour
Hypothalamus Hippocampus
Dopamine Acetylcholine
Satiety (B. Hoebel et al.)
Corticosterone CRH mRNA
Stress (Kinzig, Hargrave, & Honors, 2008)
Orexin / hypocretin
Cue-induced over-consumption (Wise et al., 1995)
BDNF
Spatial learning(Molteni, Barnard, Ying, Roberts, & Gomez-Pinilla, 2002)
AgRP
Stimulation to feed(Badia‐Elder et al., 2003; Pandit et al., 2015)
5-HT2CRs
Incentive motivation(Valencia-Torres et al., 2017)
Mu-1 binding
Overconsumption(C
Colantuoni et al., 2001)
TNF-α
Neurogenesis(Van der Borght et al., 2011)
NPY
Desire to feed Learned cue
associations(Badia‐Elder et al., 2003)
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10.4.2 Compulsive sucrose seeking
Since the 1980’s it has been known that neurons from the lateral hypothalamus
encode reward-associated cues (K. Nakamura, Ono, & Tamura, 1987) and both
feeding (Schwartzbaum, 1988) and drinking (Tabuchi et al., 2002) behaviour. In
2015 an attempt was made to define the neural circuits specific to compulsive
sucrose seeking(Nieh et al., 2015). Photoinhibition was employed to show a selective
pathway from the lateral hypothalamus to the VTA reduced compulsive sucrose-
seeking in mice without affecting normal feeding behaviour (Nieh et al., 2015). The
study found a bidirectional circuit of both inhibitory and excitatory projections from
the lateral hypothalamus to dopaminergic and GABAergic neurons in the VTA (Nieh
et al., 2015). It is possible that this circuit increases the focus and intensity of sucrose
motivation, highlighting it as a potential therapeutic target for the compulsive
overeating of sugar.
As obesity occurs due to excess caloric intake, the incentive value of the food
consumed must be considered in parallel to the satiation of hunger. 5-
hydroxytryptamine 2C receptors (5-HT2CRs) play a role in incentive motivation via
hypothalamic-VTA feeding circuits and have therefore been considered a target for
obesity treatments (Valencia-Torres et al., 2017). Lorcaserin, a 5-HT2CR agonist
was administered to mice and shown to reduce both standard food intake and the
desire for chocolate pellets with a corresponding increase in c-fos expression in VTA
5-HT2CR GABAergic neurons (not dopaminergic neurons) (Valencia-Torres et al.,
2017), validating their role in the inhibition of motivational behaviour. Similar
findings arose from observations of sucrose drinking where leptin administered to the
VTA was found to reduce food intake and knockdown of the leptin receptors
increased sucrose seeking (Clifton, 2017). Interestingly, mediation of the orexigenic
pathway between the VTA and NAc may affect behavioural changes towards reward
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seeking for drugs of abuse, including withdrawal and relapse behaviour (Carr &
Kalivas, 2006; Kastin, 2013).
Decreased dopamine levels and simultaneous increased acetylcholine levels are
associated with behavioural signs of withdrawal from drugs of abuse (Rada &
Hoebel, 2005; Rada, Jensen, & Hoebel, 2001). Rats provided intermittent sugar
access show similar imbalances to initiate withdrawal after provision of the opioid
receptor antagonist naloxone(Carlo Colantuoni et al., 2002). An animal will crave a
substance of abuse it has been deprived, as shown by elevated operant responding
even when unrewarded and increased responding to associated drug cues
(Bienkowski et al., 2004; Lu, Grimm, Hope, & Shaham, 2004). Rats deprived of
sugar after glucose overconsumption (i.e. permitted 25% glucose for 30 min per day
for 28 days and glucose access in their home cages for an additional 11.5 h per
day) responded significantly more in operant chambers compared to controls
suggesting that sensitization of the dopaminergic system and associative learning
leads to increased motivation to seek sugar (Avena, Long, & Hoebel, 2005). The
neural adaptations required to cause the behavioural change appear long lasting as
sugar consumption ceased 2 weeks prior to testing. Similar results were shown in rat
studies of alcohol consumption (Heyser, Schulteis, & Koob, 1997).
10.4.3 Sucrose consumption and the hypothalamic-pituitary-adrenal axis
Long term stress, depending on its severity, appears to correlate with a
preference for high sugar foods, suggesting its contribution to the progression of
obesity. Chronic stress may develop through an accumulation of physical
(traumatic), chemical (dietary), physiologic (painful), psychologic (fear) or social
(lifestyle) stressors (Powers & Howley, 2007). Extended periods of stress result in
hyper activation of the hypothalamic-pituitary-adrenal (HPA) axis, a mammalian
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stress response system involving the endocrine and central nervous systems (S. M.
Smith & Vale, 2006). Hyper activation of the HPA axis leads to increased release of
corticosteroids (steroid hormones produced in the adrenal cortex) see figure 3
(BjÖrntorp, 2000; S. M. Smith & Vale, 2006).
Activation of the HPA axis leads to increased adrenocorticotropic hormone
(ACTH) levels and increased glucocorticoids, which affect the utilization of energy
stores (Ulrich-Lai et al., 2010). This is of particular importance, as people regularly
report choosing to consume sweet tasting food (of higher caloric content) due to its
ability to enhance their mood and relieve negative emotional states (S. M. Smith &
Vale, 2006). In an observation of rats undergoing acute stress, the provision of a
sucrose solution significantly reduced levels of both ACTH and corticosterone
secretion (Ulrich-Lai et al., 2010). Furthermore, rats fed with a sweetener
(Saccharin), also showed lower HPA axis responses to acute stress suggesting the
hedonic nature of sugar may be responsible for the reduced stress response (Ulrich-
Lai et al., 2010).
Contrastingly, a study involving nineteen women found consumption of sucrose
but not the sweetener (Aspartame) resulted in reduced reduced stress-induced
cortisol (Tryon et al., 2015). Sucrose and not Aspartame also produced greater
activity in the left hippocampus (Tryon et al., 2015) suggesting the HPA negative
feedback loop may assist in generating a tendency towards sugar consumption in
people dealing with stress. The following section reviews the complex relationship
between the HPA axis, food intake, energy stores and chronic stress.
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Figure 10-3 Hypothalamic-pituitary-adrenal axis. Stress causes the release of corticotrophin-releasing hormone and vasopressin from the hypothalamus.
These hormones are transported to the anterior pituitary. Adrenocorticotropic hormone (ACTH) is then released from the pituitary gland which stimulates the adrenal glands to release cortisol, catecholamines and aldosterone into the bloodstream. Cortisol released from the adrenal glands targets systemic organs, including the brain where it increases sucrose seeking and exerts a feedback inhibition on the release of CRH by the hypothalamus and ACTH by the pituitary gland, via corticoid receptors (mostly glucocorticoid receptors (GRs), and to a lesser extent mineralocorticoid receptors).
Any discerned acute stressor may cause engagement of the HPA axis and result
in an emotional response eliciting fear, anxiety or defensive behaviours designed to
maximize chances of survival (Ulrich-Lai et al., 2010). The relationship between
stress, cortisol levels and sugar consumption is far from understood, however, it
appears that high sugar diets play a significant role in HPA axis regulation and
binge-like eating associated with stress. When ingested, foods high in sugar release
neuropeptides, elevating mood and reinforcing desire or selective preference towards
greater amounts of high caloric food (Ulrich-Lai et al., 2011). Sucrose and the
317
sweetener saccharin have been shown to dampen the HPA axis response and this
effect is possibly why highly palatable foods are consumed to assuage unpleasant
emotions (Ulrich-Lai et al., 2011). Sugar consumption regulates stress-like behaviour
via the HPA axis however, the precise mechanisms of action are yet to be elucidated.
Contrarily, examples of chronic stress reveal elevated levels of
glucocorticoids with increased risk of developing stress related illnesses such as
depression. Long term stress appears to alter brain function through changes in
negative feedback loops from energy stores and glucocorticoid modulation of neural
circuits (for review see (Dallman et al., 2003)). As glucocorticoid levels are tightly
controlled by the HPA axis, one avenue of investigation has been to examine levels
of mRNA expression of 11βHSD-1(a mediator of glucocorticoid metabolism in the
liver) in cases of early life stress(Maniam, Antoniadis, & Morris, 2015). Interested in
the role of sugar intake on lipid homeostasis, researchers found a 53% increase in
transcriptional levels of 11βHSD-1 in animals permitted chronic sugar (25% sucrose
solution) consumption in contrast to controls revealing a correlation between sucrose
consumption, stress and increased glucocorticoid metabolism(Maniam et al., 2015).
Additionally, when humans are exposed to stress at a young age, they become more
likely to develop anxiety and depression associated with dysregulation of the HPA
axis(Heim & Nemeroff, 2001). Increased levels of cortisol (the resultant product of
the HPA axis in humans) have also been positively correlated with higher visceral fat
deposits and insulin resistance (Misra et al., 2008). Similarly high cortisol levels
(22% higher) were reported in overweight/obese adolescents after 2 or more sugar-
sweetened beverages per day, suggesting significant increases in stress hormone
were not only due to visceral fat deposits but also increased sugar
consumption(Shearrer et al., 2016). One suggested mechanism may include
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morphological alteration of the adrenal glands as shown through consumption of
high sugar content beverages which increased the risk of metabolic syndromes such
as diabetes and lead to dysfunction of the adrenal glands(Díaz-Aguila et al., 2016).
Young adult rats fed a 30% sucrose solution for 12 weeks showed increased visceral
fat deposits and insulin resistance (Díaz-Aguila et al., 2016). Interestingly, the
adrenal glands showed histomorphological changes in the adrenal cortex and medulla
due to sucrose consumption, suggesting hyperplasia and indicators of metabolic
syndrome(Díaz-Aguila et al., 2016).
10.4.4 Developmental neuroadaptation
In rodents prenatal stress can decrease glucocorticoid and mineralocorticoid
receptor levels in the hippocampus thus decreasing receptor availability for the
feedback inhibition of corticosterone, which may explain why novel stressors cause
an increased and longer lasting corticosterone response (Henry, Kabbaj, Simon, Le
Moal, & Maccari, 1994). Pregnant rats underwent restraint stress during the third
week of gestation(Henry et al., 1994). The HPA axis and hippocampal corticosteroid
receptors in the male offspring were investigated. Plasma corticosterone was found
to be significantly higher in the prenatally-stressed rats compared to controls and the
receptor subtypes (hippocampal type I and type II corticosteroid receptors) were
decreased in the hippocampus suggesting long term changes in the HPA axis may
occur following prenatal stress(Henry et al., 1994). Numerous studies have examined
maternal stress and corticosteroids, for a comprehensive review see (Welberg &
Seckl, 2001). Fewer studies involve these neuroadaptations and diet, therefore more
direct investigation of high sugar consumption in mothers may be significant in the
study of childhood obesity.
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Furthermore, evidence exists for the pre-programming of HPA axis hyper
activation during prenatal periods (Welberg & Seckl, 2001). In adolescents deemed
to be at higher risk of becoming obese (by evaluating their parent’s body mass
index), a higher functional magnetic resonance imaging (fMRI) signal was observed
in brain regions associated with reward learning, processing and motivation after the
consumption of high sugar milkshakes as compared to high fat milkshakes (Grace E.
Shearrer, Eric Stice, & Kyle S. Burger, 2018). Although fat provides greater energy
and therefore contributes to obesity by providing excess calories, sugar is more often
associated with modulation of habitual overeating and therefore the addiction-like
behaviour associated with obesity (Grace E. Shearrer et al., 2018).
Overconsumption during adolescence leads to long-lasting changes in the
dopaminergic reward system and may cause stimuli-induced sensitization that is
observed in adulthood (Naneix et al., 2018; T. E. Robinson & Berridge, 2008).
Stimuli-induced sensitization refers to an increased effect of the stimuli, following
repeated exposures eg. repeated exposure to a loud noise may create a sensitization
to noise, generating an enhanced response, the same being true for repeated
exposures to a drug of abuse. In drug addiction, sensitization causes changes in
dopamine transmission and delta FosB expression which contributes to increased
craving and relapse (T. E. Robinson & Berridge, 2001).
Adolescent rats permitted sucrose consumption (5% sucrose solution) for sixteen
days were later tested for motivation to seek either saccharin, maltodextrin or cocaine
(Vendruscolo, Gueye, Darnaudéry, Ahmed, & Cador, 2010). The adult rats showed a
reduction in motivation to procure saccharin and maltodextrin, however this was not
the case for cocaine (Vendruscolo et al., 2010). Further to these findings, rats
presented with a choice between a saccharin solution and intravenous cocaine
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demonstrated the reward provided by the sweetness surpassed the desire for cocaine
(Lenoir, Serre, Cantin, & Ahmed, 2007); an intense debate remains as to whether
sugar is in fact addictive (Benton, 2010; Corsica & Pelchat, 2010; G. T. Wilson,
2010).
It has been hypothesized the addictive-like behaviours present after long-term
sucrose consumption arise from the highly palatable nature of sugar and not the
neurochemical effects of sugar itself (for reviews see (Ahmed et al., 2013; Avena,
Rada, & Hoebel, 2009; Criscitelli & Avena, 2017; B. Hoebel et al.; Kendig, 2014;
Westwater, Fletcher, & Ziauddeen, 2016)). The purpose of this review is not to
debate the argument, but to examine the role of sugar in emotional dysfunction.
10.5 Common neurochemistry underlie consumptive behaviours and emotions
Neuroimaging studies and animal models investigating the mechanisms
involved in the progression to obesity have revealed neurobiological correlates
participating in the neuroadaptations of obesity and sugar consumption (Lindgren et
al., 2018; Novelle & Diéguez, 2018). Preclinical rodent models of consumption are
useful for investigating neural regions and pathways underlying consumptive
behaviours (Bonin et al., 2018; Holgate, Shariff, Mu, & Bartlett, 2017) with
intermittent access to sucrose shown to have an effect on opioid, cholinergic and,
importantly, dopaminergic receptors (Avena et al., 2008; Carlo Colantuoni et al.,
2002; Pratt & Kelley, 2004; Shariff et al., 2016).
10.5.1 Opioids
Opioid receptors are expressed throughout the limbic system and play key roles
in the regulation of fear, happiness, anger, arousal, motivation and reward related
feeding(Levine & Billington, 2004; Nummenmaa & Tuominen, 2017). Opioid-
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induced behaviour includes the modulation of pain, drug addiction and control of the
autonomic nervous system, which includes emotions, cognitive processes and stress
coping (Belzeaux, Lalanne, Kieffer, & Lutz, 2018; Benarroch, 2012). Opioids
influence the way we process rewards through interaction with the dopamine
system(Bray & Bouchard, 2014). The way an animal behaves towards food is also
modulated by endogenous opioid neurotransmitters, which are neural substrates that
create susceptibility to reward (Kavaliers & Hirst, 1987; Wise, 1996). Alterations in
hedonic processing are demonstrated to be highly dependent on endogenous opioid
receptors in the NAc suggesting these changes may result in the development of
reward-related disorders such as obesity (Naneix, Darlot, Coutureau, & Cador,
2016).
Opioid peptides (β-endorphin, enkephalins, and dynorphins), and activation
of their receptor types, μ (mu), δ (delta) and κ (kappa) in the NAc cause inhibition at
both pre and post synapses (Benarroch, 2012). Opioid agonists modulate pleasure,
reward and reinforcement through activation of mu and delta receptor ligands via the
mesolimbic dopamine system whereas the dysphoria associated with withdrawal
relies on kappa receptors (Benarroch, 2012; Herz, 1997; Nummenmaa & Tuominen,
2017). Mu receptor binding in the NAc is significantly increased after
cocaine(Bailey, Gianotti, Ho, & Kreek, 2005), morphine (Vigano et al., 2003) and
sucrose (C Colantuoni et al., 2001).
In the endeavor to define specific peptide and receptor involvement in the
motivation and reward seeking behaviour characteristic of hedonistic feeding, several
animal studies have combined opioid antagonists and agonists with sucrose
consumption (Beczkowska, Bowen, & Bodnar, 1992; Hayward, Schaich-Borg,
Pintar, & Low, 2006; Ruegg, Yu, & Bodnar, 1997). Opioid receptor antagonists
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(naltrexone and naloxone), used to treat drug and alcohol dependence, were shown to
use kappa and mu2 binding sites to inhibit sucrose intake, thus demonstrating the
role of endogenous opioids in regulation of hedonic rewards in rats (Beczkowska et
al., 1992).
Substance P and the NK1 receptor system, interact with opioid receptor systems
to regulate reward related behaviours, suggesting its role in addictive
behaviour(Steensland et al., 2010). Substance P is also a neuropeptide that affects
the orexigenic pathway, via the NK1 receptor(Karagiannides et al., 2008). Its
presence in the brain assists the regulation of feeding, however, it is also located in
the stomach and small intestine suggesting it may be a potential therapeutic target for
obesity. Administration of a substance P antagonist (CJ 012,255) was shown to
prevent weight gain in obese mice following a 2w high fat diet while treatment with
CJ 012,255 in obese mice resulted in a loss of weight loss and reduction improved
insulin sensitivity, partially due to reduced food intake(Karagiannides et al., 2008).
Using a two-bottle choice drinking paradigm, knockout mouse models (lacking
either one or two opioid peptides) were used to identify opioid receptor ligands
modulating sucrose preference (Hayward et al., 2006). Enkephalin and dynorphin
were found to modulate preference to sucrose but were not considered necessary for
its consumption, supporting the role these peptides play in the motivation to consume
unnecessary calories(Hayward et al., 2006). A decrease in enkephalin mRNA
expression was observed in the NAc after rats were provided with intermittent access
to sugar (Spangler et al., 2004). The link between opioids, dopamine and motivation
for hedonic rewards is further reinforced with data showing rats preference for sweet
taste after the introduction of morphine to the NAc (Berridge, 1996; Pecina &
Berridge, 1995).
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10.5.2 Acetylcholine
Neuronal nicotinic acetylcholine receptors (nAChRs) act as biological targets for
ethanol, nicotine (Bito‐Onon, Simms, Chatterjee, Holgate, & Bartlett, 2011) and
sucrose(Löf, Olausson, Stomberg, Taylor, & Söderpalm, 2010; Shariff et al., 2016)
and modulate the neurotransmission of GABA, glutamate, dopamine, serotonin,
acetylcholine and noradrenaline(Wu, 2009). Homeostatic dysregulation of
acetylcholine in the limbic system has been shown to modulate the motivation and
reward seeking behaviours characteristic of addiction and relapse (Aosaki, Miura,
Suzuki, Nishimura, & Masuda, 2010; B. G. Hoebel, Avena, & Rada, 2007; Rahman,
Engleman, & Bell, 2015), neurodegeneration (Alzheimer’s disease, mild cognitive
impairment) and mental illness (anxiety, depression and schizophrenia)(Posadas,
López-Hernández, & Ceña, 2013; Wu, 2009). Within the NAc, cholinergic
interneurons regulate the expression and release of enkephalin(Kelley, Baldo, &
Pratt, 2005) and many rat studies support the theory that they inhibit feeding
behaviou (Avena et al., 2006; B. G. Hoebel et al., 2007; Mark et al., 2006). As
increased serotonin reduces the motivation to feed, a study using rats to perform a
progressive ratio task and paroxetine (a selective serotonin reuptake inhibitor) found
that increased serotonergic activity decreased the appetitive-based responses to the
hedonic taste of sucrose and the aversive taste of quinine (Mathes et al., 2013).
Withdrawal from morphine increases acetylcholine levels in the NAc, while
dopamine simultaneously remains low, leading to the assertion that this mechanism
is involved in the unpleasant aspects associated with withdrawal (Avena et al., 2008;
Rada et al., 2001). Arecholine (a muscarinic agonist) inhibits feeding but can be
blocked by pirenzapine (a muscarinic acetylcholine 1 receptor antagonist),
suggesting a role for acetylcholine in food intake (Avena et al., 2006).
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In 2010, Lof and associates utilized rats to demonstrate the role of nAChRs in
the conditioned reinforcement of lever pressing for a sucrose reward (Löf et al.,
2010). Using a selective antagonist at α7 nAChRs (methyllycaconitine) they showed
reduced lever pressing for the sucrose solution down to the level of the control,
suggesting mediation of the amount of influence the cue had on the desirability of the
sucrose (Löf et al., 2010). More recently our laboratory elucidated the effects of
varenicline (an FDA-approved drug to reduce nicotine cravings) on sucrose
consumption in rats (Shariff et al., 2016). Varenicline, a partial agonist at α4β2* and
antagonist α6β2* nAChRs subtypes, significantly reduced sucrose consumption in
both short and long term binge like (intermittent access) drinking. While α4β2*
nAChR binding sites were increased, α6β2* nAChRs were significantly decreased in
the NAc as a result of both short-term and long-term sucrose consumption (Shariff et
al., 2016). By modulating dopamine release in the NAc (Feduccia, Simms, Mill, Yi,
& Bartlett, 2014) the control of sucrose consumption by nicotinic receptors and their
subtypes may provide promising therapeutic strategies for obesity (Cocores & Gold,
2008).
10.5.3 Dopamine
Sucrose is considered a primary or natural reinforcer that does not require a
learning process to be considered desirable (Corsini, 1999). Primary reinforcers are
known to trigger dopaminergic activation in the VTA(Moore, Sabino, & Cottone,
2018; Olds & Olds, 1963; Wise, 1996), which act on the NAc via the medial
forebrain bundles (Yu & Kim, 2012). Activation of dopaminergic neurons in the
VTA modulates processes of memory, learning and motivation(Adcock, Thangavel,
Whitfield-Gabrieli, Knutson, & Gabrieli, 2006; Otmakhova, Duzel, Deutch, &
Lisman, 2013; Wise, 2004; Yang et al., 2018) as well as the intense emotions
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associated with love, sexual desire, orgasm(Acevedo, Aron, Fisher, & Brown, 2012;
Holstege et al., 2003; Ulrich, Stauß, & Grön, 2018), fear, stress, anxiety (Bouchet et
al., 2018; Moghaddam, 2018; S. Zhang et al., 2018) and psychosis (De Nijs et al.,
2018; Knolle et al., 2018). Rat models of obesity display low levels of dopamine and
impairment in the release of dopamine (Geiger et al., 2008).
The mesocorticolimbic dopaminergic system has been implicated in modulation
of addictive reward seeking behaviour, (for review see (S. Liu, 2018)). Drugs of
abuse such as alcohol, nicotine, cocaine and amphetamines that cause increased
extracellular dopamine and subsequent heightened pleasure levels in humans (T. E.
Robinson & Berridge, 1993; Volkow, Fowler, & Wang, 2002) also cause increases in
dopamine release in the NAc in rats (Di Chiara & Imperato, 1988; Wise et al., 1995).
Commonly abused (opiates, ethanol, cocaine, amphetamine and nicotine) given to
rats resulted in increased extracellular dopamine in the NAc and the dorsal caudate
nucleus(Di Chiara & Imperato, 1988). Similar results were found in rats permitted
intermittent access to sugar with subsequent binge drinking resulting in increased
dopamine release in the NAc (Avena et al., 2006; Rada, Avena, & Hoebel, 2005).
Supporting both the hedonic potency of sucrose and its reliance on dopaminergic
regulation, orosensory stimulation in sucrose sham-fed rats resulted in increased
dopamine release within the NAc, thus increasing reward seeking behaviour (Hajnal,
Smith, & Norgren, 2004; Schneider, 1989). Compelling evidence shows drugs not
routinely abused, such as imipramine, atropine and diphenhydramine do not display
rewarding properties and do not alter synaptic dopamine levels(Di Chiara &
Imperato, 1988). Studies show an attenuated dopamine system in obesity prone rats
in the NAc, PFC and dorsal striatum and suggest the reduction in hedonic response
available to these animals is related to hyperphagia (increased appetite) and resultant
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obesity (Geiger et al., 2008). These findings were similar to those of a human study
showing reduced levels of dopamine D2 receptors correlated with increased body
mass index in 10 obese individuals (Wang et al., 2001).
Dopamine sensitization
In 1993 Robinson and Berridge postulated the incentive-sensitization theory,
which suggested that repeated exposure to rewarding substances sensitizes the
dopaminergic system resulting in exaggerated “cue-triggered wanting” which can
transform into a compulsion to seek out an associated reward (T. E. Robinson &
Berridge, 1993; Wyvell & Berridge, 2001). Cue-reward learning depicts the learned
association between the rewarding effects of substances of abuse and environmental
cues (Bianchi et al., 2018; Tye, Stuber, De Ridder, Bonci, & Janak, 2008). This
learning contributes to obsessive overuse and the tendency to relapse after long
periods of abstinence (Bianchi et al., 2018; Ziauddeen, Farooqi, & Fletcher, 2012).
Repeated or prolonged substance abuse can cause modifications to neurotransmitter
release and alterations in synaptic strengths (Gerdeman, Partridge, Lupica, &
Lovinger, 2003). For example, rats provided with intermittent access to sucrose
display signs of dopamine sensitization through altered dopamine receptor
function(Sharpe et al., 2016).
Behavioural sensitization or hyper-locomotion is a motor response that increases
incrementally with repeated exposures to a drug. It is reflective of a hyposensitized
(or attenuated) reward pathway and is said to contribute to the craving or compulsive
seeking which characterizes addictive behaviour (Nona & Nobrega, 2018; T. E.
Robinson & Berridge, 2001). In both human and rodent studies, behavioural
sensitization has been observed as dose-dependent(Huber et al., 2018; Jing, Liu,
Zhang, & Liang, 2018) and shows considerable individual variation(T. E. Robinson,
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1988). Brain-derived neurotrophic factor (BDNF) is believed to modulate this
behaviour through overexpression of the dopamine D3 receptor(Bordet et al., 1997)
and has been implicated in drug addiction, schizophrenia (Guillin et al., 2001;
Gurevich et al., 1997; Staley & Mash, 1996) and novelty seeking behaviour in
obese rats(Savage et al., 2014).
Animals sensitized to one drug are often cross-sensitized to other substances of
abuse as well as non-drug substances (Avena et al., 2008). This has been
demonstrated with rats exposed to amphetamines becoming sensitized to
cocaine(Pierce & Kalivas, 1995), cocaine cross-sensitizing to alcohol (Itzhak &
Martin, 1999) and cocaine cross-sensitizing with stress(Covington & Miczek, 2001).
Similar to these, short term sucrose consumption increases binding affinity to opiates
(Avena et al., 2008), resulting in cross tolerance to other opioids such as morphine,
while chronic sucrose consumption reduces the analgesic properties of morphine
(D'Anci, Kanarek, & Marks-Kaufman, 1996; Fidler, Kalman, Ziemer, & Green,
1993; Kanarek, White, Biegen, & Marks-Kaufman, 1991; Steensland et al., 2010). A
number of studies have also shown opioid agonists such as morphine to increase the
impulsivity to feed when injected systemically or directly into the NAc,
paraventricular nucleus, AMG, hypothalamus and tegmentum (B. Gosnell, 1987;
Stanley, Lanthier, & Leibowitz, 1988; Woods & Leibowitz, 1985).
Further investigations have demonstrated how sugar access can lead to cross-
sensitization of dopamine-altering drugs. Intermittent access to sugar has been found
to cross-sensitize with amphetamines up to 8 days post sugar ingestion (Avena &
Hoebel, 2003) Female rats provided with either a 10% sucrose solution or a rotation
of sucrose solution and withdrawal, displayed behavioural cross-sensitization to a
small dose of amphetamine (Avena & Hoebel, 2003; Avena et al., 2008). Similar
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results for sucrose and cocaine (B. A. Gosnell, 2005) and sucrose and quinpirole (a
dopamine agonist) have also been found(Foley, Fudge, Kavaliers, & Ossenkopp,
2006). The rats maintained on the cyclic sucrose solution responded hyperactively to
amphetamine in comparison to controls, suggesting the binge-like sugar consumption
leads to increased amphetamine sensitivity, which may be due to neuroadaptations of
the dopamine system (Avena & Hoebel, 2003). Compelling evidence suggests
chronic stress leads to similar cross-sensitization (G. F. Koob, 2009b; Leão et al.,
2012). As globally the consumption of sugar is on the rise an interesting avenue of
investigation may be to examine the effect of sucrose consumption on stress derived
cross sensitization.
Dopamine and impulsivity
Dopamine is released in response to food cues, making it essential for the
motivational prompt to consume food (Flagel et al., 2011; Salamone & Correa,
2012). As dopamine neurons project from the VTA to the NAc, caudate, putamen,
PFC, hippocampus and AMG, they modulate a wide variety of behaviours and
emotions(de Jong, 2015) including impulsivity(Wade, de Wit, & Richards, 2000). In
relation to eating disorders such as obesity, there appears to be an impulsive
tendency to overconsume sweet foods in response to cravings for carbohydrates and
sugars. When a subject is withdrawn from an addictive substance, impulsivity
increases and can be quantified using a behavioural test called differential-
reinforcement-of-low-rate-schedule performance (DRL) (Mangabeira, Garcia-
Mijares, & Silva, 2015). The test requires the subject to withhold a response for a set
period of time before they are permitted to respond and thereby earn a
reward(Kirshenbaum, Brown, Hughes, & Doughty, 2008). Deprivation after long
term sugar consumption in rats resulted in impairment of DRL performance,
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confirming its resemblance with drug addiction and suggesting an increase in
impulsive behaviour following sugar deprivation (Mangabeira et al., 2015). The
suggested mechanism of action for this behaviour is the dopaminergic system as
dopamine receptor antagonists (pimozide(Wise, 2006), raclopride (Maldonado,
Valverde, & Berrendero, 2006), SCH 23390 (Verty, McGregor, & Mallet, 2004)) can
attenuate the desirability of highly palatable foods in rats (Mangabeira et al., 2015;
Wise, 2006).
The progression towards obesity is linked with a decline in neural responses
to reward similarly induced by cocaine and heroin, which moderate the transition
from casual user to the impulsive actions of a compulsive drug-taker(P. M. Johnson
& Kenny, 2010). Impulsive actions such as over eating are modulated by the
excitation of D1 dopaminergic neurons and inhibition of D2 dopaminergic neurons in
the dorsal striatum and the NAc (P. M. Johnson & Kenny, 2010). Preclinical and
clinical studies have been conducted to investigate the role of dopamine in obesity
with findings showing obese subjects have an inverse relationship between the
abundance of dopamine D2Rs and body mass index, suggesting a dopaminergic role
in compulsive eating(P. M. Johnson & Kenny, 2010; Pijl, 2003; Volkow & Wise,
2005; Wang et al., 2001). Male rats self-administering amphetamine showed a
reduction in the ability of D2Rs to inhibit dopamine release in the NAc supporting
the theory that desensitization due to short term substance abuse modulates
addiction-related behaviours such as impulsivity (E. P. Bello et al., 2011; Calipari et
al., 2014; Cools, Barker, Sahakian, & Robbins, 2003). In a human study, twelve
patients with Parkinson’s disease, a disorder of the mesolimbic dopamine system,
underwent several cognitive tests designed to provide a measure of impulsivity.
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Increased impulsive behaviour was found to correlate with greater dopamine
levels(Cools et al., 2003).
The neural mechanisms modulating motivation and drive are implicated in
both the loss of self-control exhibited by over eating and addiction to drugs of abuse
(Avena et al., 2008; Berridge, 1996). Neurofunctional imaging has documented
neuroadaptations in the PFC (Volkow & Fowler, 2000), hippocampus(Wang et al.,
2006), AMG and NAc (Volkow & Wise, 2005). A functional MRI study was
conducted showing female adolescents an image of either highly palatable food
(dessert) or a vegetable (Batterink, Yokum, & Stice, 2010). In comparison to leaner
adolescents, overweight adolescents had increased behavioural impulsivity and
decreased neural activation in frontal inhibitory regions of the brain (e.g. medial
prefrontal cortex, and orbitofrontal cortex) (Batterink et al., 2010). In addition,
activation of the reward region was positively correlated with body mass index when
shown the picture of dessert (Batterink et al., 2010). These findings suggest the
reduced functioning of inhibitory controls combined with increased responses in
food reward regions are relevant to weight gain (Batterink et al., 2010). The Stroop
test requires an individual to suppress their automatic response to certain stimuli.
This was used to assess the inhibitory control of overweight / obese 10 y old children
in comparison to normal weight children(Reyes, Peirano, Peigneux, Lozoff, &
Algarin, 2015). Results indicated changes to inhibitory control functions in
overweight children suggesting less emotional self-regulation, which in conjunction
with dysfunctional impulsivity control may contribute to over eating behaviour in the
overweight/ obese children(Reyes et al., 2015).
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10.6 Common anatomical structures and neural substrates of stress driven, emotional behaviour
The insular cortex and cingulate cortex (part of the limbic system) are
responsible for the processing of emotions in conjunction with processes involving
higher cognition (Rajmohan & Mohandas, 2007). The hippocampus provides
negative feedback for the HPA axis with both neuroadaptations to its volume and
capacity for neurogenesis implicated in emotional disorders(Jacobson & Sapolsky,
1991). The AMG processes, stores and retrieves fear memories, and initiates
appropriate behavioural responses(Martin, Ressler, Binder, & Nemeroff, 2009). It is
responsible for the manner in which we express fear, aggression and defensive
behaviour(Martin et al., 2009). The PFC regulates executive functions and reward
processing while impulse control and mood are modulated by the orbitofrontal
cortex(Martin et al., 2009). Emotional disorders involve a complex interconnected
number of neuroendocrine, neuropeptide, neurotransmitter and neuroanatomical
adaptations (Gulpinar & Yegen, 2004). These alterations may occur due to genetic
predisposition or as a result of environmental influence(Martin et al., 2009). We
assert that abundant evidence exists regarding the role overconsumption of sugar
plays in altering these brain regions and contributing to emotional disorders such as
depression, anxiety and fear.
10.6.1 Anxiety
Generalized anxiety disorder is defined by uncontrolled, exaggerated concern
over numerous endeavours (Association, 2013). Treatment often consists of
anxiolytic drugs that act primarily on the monoamines serotonin, noradrenalin and
dopamine(Martin et al., 2009). In the central nervous system these neurotransmitters
are released in conjunction with neuropeptides that have strong links to anxiety, such
as neuropeptide Y and cholecystokinin expressed in the limbic cortex where they
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influence emotions and stress levels(Martin et al., 2009). Other molecular substrates
involved in regulating the stress response include corticotropin-releasing factor and
adrenocorticotropic hormone in the HPA axis (Patriquin & Mathew, 2017). In cases
of anxiety, those carrying the short-allele of the serotonin transporter gene (5-
HTTLPR) demonstrate over activation of the AMGs in response to viewing fearful
and angry faces as compared to those carrying the long-allele(Hariri et al., 2005).
Two specific brain circuits are notable in anxiety disorders, the first involves the
dorsolateral PFC, anterior cingulate cortex, dorsal parietal cortex and precentral
gyrus, all associated with cognitive control, executive function, flexible cognition,
working memory and attention (Coplan, Webler, Gopinath, Abdallah, & Mathew,
2018). The second circuit is known as the negative affect circuit and as the name
suggests is activated by negative stimuli (Coplan et al., 2018). Anatomically the
hippocampus, AMG, medial PFC, and the dorsal, subgenual and pregenual regions of
the anterior cingulate cortex may undergo neural adaptation to skew negative bias
and threat by reducing attenuation through the cognitive control circuit (Coplan et
al., 2018).
Human studies show links between high caloric food consumption and anxiety.
Recent evidence from epidemiological studies found a suggestive link between
greater consumption of processed foods and widespread presence of anxiety
disorders (Jacka, Mykletun, Berk, Bjelland, & Tell, 2011; S. E. Quirk et al., 2013;
Westover & Marangell, 2002). There are also numerous studies linking adverse
childhood experiences (early life stress) with an increased risk of body weight gain
and obesity during adolescence and adulthood (D'Argenio et al., 2009; Gunstad et al.,
2006; Lumeng et al., 2013; Noll, Zeller, Trickett, & Putnam, 2007;
Ramasubramanian, Lane, & Rahman, 2013). More specific behavioural studies
333
observing acute and chronic withdrawal from sugar in rodents show anxiety is
induced when withdrawal follows extensive periods of sugar consumption (Avena et
al., 2008). Long exposures (1 month of 12 h daily access) to palatable foods did
result in rats showing increased anxiety-like behaviours on the elevated plus maze
(EPM) when tested 24 h after withdrawal (Avena et al., 2008). Rats exposed to 12 h
intermittent access to 10% sucrose solution for 28 days also displayed anxious
behaviour in the EPM after 36 h withdrawal (Avena et al., 2008). These rats showed
reduced conditioned suppression after 1 and 28 days of abstinence, measured by a
failure to significantly reduce the number of lever-presses for sucrose during the
presence of a tone stimulus paired with shock (Avena et al., 2008). Similar results
have been reported using longer cycling periods whereby rats given intermittent
access to a sucrose diet showed increased anxiety in the EPM and defensive
withdrawal tests after 8 and 9 weeks on the diet cycle followed by 48 h withdrawal
(Parylak, Cottone, Sabino, Rice, & Zorrilla, 2012). Alternate investigations have
shown rats fed a carbohydrate rich diet to have increased protein oxidation in the
frontal cortex which correlated with anxiogenic behaviour (Souza et al., 2007). These
studies suggest a high probability of long term sugar consumption may contribute to
symptoms of anxiety.
A long term study has shown an opposite effect in terms of anxiety and
depressive like behaviours(Cao, Lu, Lewis, & Li, 2007). Findings show that sugar
consumption had no significant effects on motor activity in an open field (test for
locomotor and anxious like behaviour), on exploration in a T-maze, or on anxiety in
an EMP(Cao et al., 2007). Another study looking at the anxiety effects of sugar after
a 1 year intervention of sucrose and honey found that anxiety decreased after 3
months of consumption, as tested with the EPM and open field test (Chepulis et al.,
334
2009). Importantly, this reduction was maintained until the end of the experiment at
12 months (Chepulis et al., 2009). Despite this, honey-fed rats showed significantly
less anxiety throughout the study as compared with those fed sucrose (Chepulis et al.,
2009). Together this data suggests that chronic (i.e., greater than 1 month)
intermittent access to high energy foods may increase anxiety-like behaviour
followed by a plateau observed after 3months (Chepulis et al., 2009).
10.6.2 Depression
Depression is characterized by feelings of sadness, hopelessness and reduced
pleasure in daily activities (Association, 2013). Functional, structural and
neurochemical factors associated with the pathophysiology of depression include
dysregulation of the HPA axis resulting in reductions in hippocampal, PFC and
striatal volumes (Koolschijn, van Haren, Lensvelt‐Mulders, Hulshoff Pol, & Kahn,
2009). In areas of emotional processing such as the AMG and PFC abnormal
metabolism of glucose and changes to cerebral blood flow have also been
demonstrated (Drevets, 1998). Although the association between obesity and
depression is well documented, less is known about the physiological and
corresponding psychological influence long-term sugar consumption has on
depression (Luppino et al., 2010). Recent research suggests a possible contributor to
the incidence rate of depression is sugar overconsumption (Sánchez-Villegas et al.,
2012; Westover & Marangell, 2002). We suggest the neuroadaptations that occur to
depression-related brain regions, namely the hippocampus, PFC and AMG,
following sugar intake (reviewed above) contributes to the incidence of depression
(Hsu et al., 2015; Moorman & Aston-Jones, 2014; M. J. Robinson et al., 2014).
The first study reporting a possible link between sugar and depression was
conducted in 2002 by Westover and Marangell (Westover & Marangell, 2002). Data
335
collected data from 6 countries showed a correlation between the consumption of
sugar (calories/capita/day) with the yearly rate of major depression disorder
(Westover & Marangell, 2002). Fast food further increases the potential of
developing depression, and commercial baked goods are also positively correlated
with depressive disorders (Sánchez-Villegas et al., 2012). Not only highly palatable
food, but sweetened beverages (either artificially or otherwise) also contribute to
depression (Sánchez-Villegas et al., 2012; Westover & Marangell, 2002). Regular
consumption of sweetened beverages indeed increases the occurrence of depression
and suicidal tendencies (Guo et al., 2014; Lien, Lien, Heyerdahl, Thoresen, &
Bjertness, 2006; Pan, Zhang, & Shi, 2011; Shi, Taylor, Wittert, Goldney, & Gill,
2010).
In rats given the same high sugar/chow diet cycle for 7 weeks, highly
palatable foods induce depressive-like behaviour as evidenced greater immobility in
the Forced Swim Test (test for depression) and decreased preference for 0.8%
sucrose (test for anhedonia) (Iemolo et al., 2012). Correlations with symptoms of
depression have been observed following long-term exposure to sucrose, with long
term overstimulation of the dopaminergic system during adolescence, which may
occur through sucrose binging (Rada et al., 2005) resulting in deficits in later life that
affect motivation, memory and happiness (Avena et al., 2008; Kendig, 2014; Naneix
et al., 2018). When a model designed to generate a depressed-like state (known as
social defeat-induced persistent stress) was used, rats showed increased motivation to
acquire a sucrose reward and reinstated sucrose-seeking induced by a cue (Riga,
Theijs, De Vries, Smit, & Spijker, 2015). Importantly, these studies showed the long-
term effects of stress exposure induced deficits in the ability to evaluate natural
rewards (Riga et al., 2015).
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Congenitally helpless rats, a genetic model of predisposition to major
depression, were used to respond less to the reward of sucrose solution (Vollmayr et
al., 2004). This lack of motivation to partake in sweet rewards is conversely shown
after long term sucrose consumption by adolescent rats, which results in depressive
like behaviour in adulthood including symptoms of anhedonia (inability to feel
pleasure) and increased anxiety-like behaviour (Gueye et al., 2018). Adult rats
consuming sucrose showed a similar depressive-like behaviour, but to a lesser
degree, suggesting the critical period of brain development that occurs during
adolescence can be moderated by sucrose consumption and may increase the instance
of disorders related to rewards, such as depression (Gueye et al., 2018).
As depression correlates with altered glucose metabolism, it is not surprising
that comorbidity of diabetes mellitus (DM) and depression occurs quite frequently,
greatly increasing the mortality risk (van Dooren et al., 2013). A study highlighting
the link between glucose and depression in patients whose histories included DM and
cardiac disease found there was a 34% increased chance of depression in subjects
with elevated blood glucose levels (Azimova et al., 2015). A study including 70,000
postmenopausal women found a higher risk of developing depression in women
consuming a high-sugar diet than those consuming high naturally occurring sugars
(Gangwisch et al., 2015). However, research suggests sugar may contribute to
depression more in the male population as was demonstrated by the Whitehall Study
II which tracked dietary regimes and the corresponding medical health of 8,000
participants over a 22 year period (Knüppel, Shipley, Llewellyn, & Brunner, 2017).
Observing a five year duration they found a 23% increase in likelihood of men being
diagnosed with depression if they consumed ≥ 67 g of sugar per day compared to ≤
40 g (Knüppel et al., 2017). Cumulatively, these studies, while not able to confirm
337
that sugar causes depression, appear to produce enough evidence to show sugar
overconsumption contributes to an increased risk of developing depression.
10.6.3 Neurogenesis
Neurogenesis is the term given to new neurons generated from neural stem cells
(Ming & Song, 2011). The molecular substrates of stress, although not well
understood are implicated in the regulation of adult neurogenesis, through molecular
pathways modulated by glucocorticoids, inflammatory mediators and neurotrophic
factors (Egeland, Zunszain, & Pariante, 2015). Studies on antipsychotic
pharmacology support these findings by linking the blockade of dopamine D2
receptors with reductions in parkinsonian symptoms, anhedonia and increased
neurogenesis (Aringhieri et al., 2018; Chikama et al., 2017). The effect of diet on
hippocampal neurogenesis has been relatively unexplored.
Rats given 1 month access to fructose showed almost a 40% reduction in the
number of BrdU / NeuN-immunopositive cells (mature neurons) in the dentate gyrus
of the hippocampus(Van der Borght et al., 2011). Alternatively, rats consuming
glucose had a similar number of BrdU / NeuN-immunopositive cells as the water
controls(Van der Borght et al., 2011). Density of immature neurons labelled with
PSA-NCAM was decreased following sucrose, fructose and glucose(Van der Borght
et al., 2011). Sugar consumption increased the number of proliferating cells positive
for Ki67 (a marker of cell proliferation (Scholzen & Gerdes, 2000)) in all cases and
rats offered sucrose or fructose showed more cell death in the dentate gyrus of the
hippocampus (Van der Borght et al., 2011).
Rats given a high fructose corn syrup solution throughout adolescence were
tested for hippocampal-dependent contextual memory. Impairments in memory
function were found in later in life, suggesting that sugar consumption early in life
338
may have long-term negative effects on memory function (Noble et al., 2017). High
fructose consumption is also linked to insulin resistance (Stranahan et al., 2008) with
reduced hippocampal neurogenesis (Van der Borght et al., 2011). Hence it appears
convincing that a high-sugar diet negatively affects adult neurogenesis which may
contribute to the anxiety and depressive like behaviour demonstrated by animals after
withdrawal. Decreased neurogenesis in the hippocampus has further been implicated
in memory dysfunction and cognitive impairment disorders such as Alzheimer’s
disease(Price et al., 2001) and the learning and memory processes involved in
appetitive control(Davidson et al., 2009; Lathe, 2001). The hippocampus may hold
further insight into the cognitive processes by which food becomes entwined with
motivation and new rewards.
10.6.4 Fear
The functional neuroanatomy of fear encompasses the AMG, which stimulates
the HPA axis and the hippocampus, which suppresses activity of the axis (Martin et
al., 2009). Hyper activation of the AMG was recorded in response to viewing faces
construed as fearful (R. Bryant et al., 2008) and has been implicated in post-
traumatic stress disorder (PTSD) (Cortese & Phan, 2005). The PFC is required for
the extinction of fear memories (Giustino & Maren, 2015), and in line with these
findings changes in volume of the anterior cingulate cortex have been implicated in a
reduced ability to extinguish fearful memories (R. A. Bryant et al., 2008). When fear
memories become dysfunctional it is thought that the intrusive, recurring thoughts
result from an inability of the cognitive control circuit to repress the negative effect
circuit (Morey, Petty, Cooper, LaBar, & McCarthy, 2008). In patients with PTSD
their information processing may be overpowered by the hyper activation of the
AMG when exposed to threat related stimuli (Falconer et al., 2008).
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At the molecular level corticotrophin-releasing factor from the paraventricular
nucleus of the hypothalamus initially activates the HPA axis in response to
threatening stimuli(Martin et al., 2009). Low levels of corticosterone, which acts
through binding to mineralocorticoid and glucocorticoid receptors, as well as
enhanced negative feedback of the HPA axis are associated with pathological fear
(Han, Ding, & Shi, 2014). Rats that underwent a single prolonged stress paradigm (a
reliable animal model of PTSD) have shown a down-regulation of both these
receptors in the hippocampus, enhanced fear and altered neuronal morphology of
AMG neurons(Han et al., 2014). Glucocorticoid receptors and dopamine receptors
have been implicated in processes within the PFC that drive extinction memory
learning. Infusions of corticosterone or a glucocorticoid receptor antagonist
(RU38486) into the infralimbic cortex and pretreatment with sulpiride (a dopamine
D2 receptor antagonist) attenuated fear expression suggesting enhanced fear
extinction (Dadkhah, Abdullahi, Rashidy-Pour, Sameni, & Vafaei, 2018). As
glucocorticoid receptors are also required for glucose homeostasis and play a role in
the development of hyperinsulinemia and obesity (Majer-Lobodzinska & Adamiec-
Mroczek, 2017) they are suspected as key players in neuroadaptations to the AMG,
hippocampus and PFC that occur after long-term sucrose consumption.
Juxtaposing the combined psychological and physical effects high sugar diets
have on pathological fear has, until recently, been an untouched topic of research.
Although no studies have been conducted on sugar consumption alone with regards
to pathological fear, some research has begun to explore the combined effect of high-
fat/sugar or high sugar and high carbohydrate diets (Baker & Reichelt, 2016;
Reichelt et al., 2015; Santos et al., 2018; Williams-Spooner, Richardson, & Baker,
2017). These studies are beginning to delineate the impact high caloric foods can
340
have on the intensity and duration of pathological fear, however research into the
effect of sugar alone is required to delineate the mechanisms involved. More recent
research has examined contextual fear following a standard contextual fear
conditioning protocol which was contradictory to this finding (Santos et al., 2018).
Specifically, high-sugar/carbohydrate diets significantly enhance fear-related
freezing to context (Santos et al., 2018). Whilst both examine contextual fear
memory formation, different fear conditioning protocols were used (trace fear
conditioning versus contextual fear conditioning). Indeed hippocampal function in
contextual fear memory has been noted as disparate, depending on the fear
conditioning protocol (see recent review by (Nicholas Chaaya, Battle, & Johnson,
2018, In Press)). Nevertheless, these studies highlight how diet can influence
hippocampal-dependent memory, as well as hippocampal function (Reichelt et al.,
2015; Santos et al., 2018).
Fear extinction is a protocol whereby, following excessive exposure to the
previously neutral fear conditioning CS (e.g. the context in contextual fear
conditioning, or a tone in auditory fear conditioning), a reduction of fear-related
freezing is seen (Maren, 2011). This reduction in fear is similar to basic theories
surrounding exposure therapy, whereby excessive exposure to fear-inducing stimuli
(via mental imagery, for example) results in a reduction in fear-related symptoms
(Maren, 2011; Milad & Quirk, 2012).
Investigation into the effect of PTSD symptoms on the consumption of highly
palatable ‘fast’ food and sweetened beverages was conducted to see if there was a
correlation with emotional eating behaviours and body mass index (Hirth, Rahman,
& Berenson, 2011). The answers to questions regarding frequency of consuming fast
food were collected from 3154 females and analyzed using regression analyses
341
(Hirth et al., 2011). To determine unhealthy eating habits, participants were asked if
they used diet pills, laxative, diuretics, skipped meals or vomited after eating.
Findings suggested that PTSD symptoms increased the frequency of consumption of
high caloric food and sodas, as well as contributing to unhealthy eating habits but did
not increase overall body mass index (Hirth et al., 2011). Cumulatively, data from
these various studies show a strong link between PTSD, fear memory and caloric
foods suggesting a possible correlation with high sugar consumption that requires
further investigation.
10.7 Sucrose Consumption Investigated
Negative states of emotion driven by substances of abuse are often shown by
examining anxiety-like behaviours, decreased pain tolerance, or an increase in the
point at which reward becomes sufficient to produce a stimulating effect and
memory deficits(G. Koob, 2018). The complexities of the systems involved, the
interconnectedness of the systems and the myriad of protocols used to study sucrose
dependence could potentially lead to a wide variety of outcomes and yet overall
investigations into high-sugar consumption in rats, mice and humans result in similar
outcomes with regards to memory, stress and emotion (see Table 3.)
Table 7 Published reports on the effect of sucrose and sweetener consumption on cognition, emotion and stress. Sucrose or sweetener consumption in rats
Authors and year
Subjects
Tasks Brain region involved
Findings
2h/day 10% sucrose for 28 days
Xu, T.J. and Reichelt, A.C., 2017 (T. J. Xu & Reichelt, 2018)
3 week-old male Sprague-Dawley rats (n = 8)
EPM, open-field, NPR and short- and long-term NOR1
hippocampus, basolateral amygdala
increased anxiety-like behaviours
Rat chow supplement
Reichelt, A., et al.,
6 week-old male
trace fear conditioning
hippocampus strengthened visual fears
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ed with meat pies, cakes and biscuits for 10 weeks
2015 (Reichelt et al., 2015)
Sprague–Dawley rats (n = 16)
and attenuated contextual fears
Powdered diet: 7.9% sucrose for 52 weeks
Chepulis, L.M., et al., 2009 (Chepulis et al., 2009)
2-month old Sprague Dawley rats (n = 10-14)
1 EPM, NOR Y maze, C
NA reduced spatial memory and increased anxiety
35% sucrose solution for 9 weeks
Lemos, C., et al., 2016 (Lemos et al., 2016)
12-weeks old, male Wistar rats
open field, object displacement, NOR, forced swimming test
Hippocampus
decreased memory performance and increased helpless behaviour
32% sucrose solution for eight weeks
Jurdak, N. and Kanarek, R.B., 2008 (Jurdak & Kanarek, 2009)
6 week old male Long-Evans rats (n = 10)
1 NOR NA decreased cognitive performance on object recognition
10% fructose in drinking water for 7 months
Sangüesa, G., et al., 2017 (Sangüesa et al., 2018)
female Sprague- Dawley rats
Morris water maze, 1 NOR
frontal cortex and hippocampus
reduced performance in the NOR test
30% sucrose solution, 30m, twice daily, 2-4wk Or 0.1% saccharin
Ulrich-Lai, Y.M., et al., 2010 (Ulrich-Lai et al., 2010)
Adult male Long-Evans rats
Restraint stress, social interaction test, open field, EPM
hypothalamic paraventricular nucleus, BLA
- reduced ACTH by sucrose and saccharin - reduced corticosterone only after sucrose sucrose - reduced restraint-induced tachycardia and behavioural anxiety
10-34% sucrose
Wilmouth, C.E., and
Adolescent male
Taste reactivity
NA greater positive taste
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solution Spear, L.P., 2009. (Wilmouth & Spear, 2009)
Sprague–Dawley rats
and voluntary consumption
reactivity and reduced negative responding
Free access to 5% sucrose or water for 16d
Vendruscolo, L.F., et al., (Vendruscolo et al., 2010)
Male Wistar rats
fixed- and progressive-ratio self administration of: - saccharin - maltodex-trin - cocaine
Sugar overconsumption during adolescence, reduced motivation for saccharin and maltodextrin
1% sugar solution (high fructose corn syrup) throughout the adolescent phase of development (post-natal day 26-56).
Noble, E.E., et al., 2019 (Noble et al., 2017)
Male rats novel object in context
- impairments in hippocampal-dependent memory function later in life - NOIC performance impaired at PN 175
Sucrose and sweetener consumption in mice
Authors and year
Subjects Tasks Brain region involved
Findings
High-carbohydrate diet (45% condensed milk, 10% sugar and 45% chow) for 8 weeks
Santos, C.J., et al., 2018 (Santos et al., 2018)
5 – 7 week old, male BALB/c mice
restraint stress, 1
EPM, contextual fear conditioning, tail suspension test
NA increased anxiety-like and depressive-like behaviour and aversive memory
10% sucrose, 3
I2BC for four weeks, withdrawal animals received only water for one week after
Kim, S., et al., 2017 (S. Kim et al., 2018)
C57BL/6 mice
tail suspension test,1 EPM, sucrose preference test
nucleus accumbens
withdrawal after sucrose overeating induces depression and anxiety-like behaviour.
344
the four weeks Acesulfame potassium and low carbohydrate diet for 4 weeks
Ibi, D., 2018 (Ibi, Suzuki, & Hiramatsu, 2018)
Male ddY strain mice (7–9 weeks old)
Y-maze and NOR, glucose levels
frontal cortex
decrease in short-term and object cognitive memories decreased glucose levels
Sucrose and sweetener consumption in humans
Authors and year
Subjects Tasks Brain region involved
Findings
Sugar, sweetened beverages, and fruit intake was assessed on a servings per day basis
Cohen, J.F.W., et al., 2018 (Cohen, Rifas-Shiman, Young, & Oken, 2018)
mother and child pairings, during pregnancy and childhood mean ages 3.3 yrs, 7.7 yrs (n = 1,234)
2 PPVT-III KBIT-II KBIT-II WRAVMA WRAML and HOME-SF
NA adverse impact on child memory and learning
High-sugar content milkshakes
Shearrer, G.E., et al., 2018 (Grace E Shearrer, Eric Stice, & Kyle S Burger, 2018)
133 adolescents
food picture exposure during fMRI
temporal gyrus, operculu, juxtapositional lobule, thalamus, caudate
increased signal in the reward learning, processing and motivation regions of the brain
Sucrose- or aspartame-sweetened beverage consumption three times per day for 2 weeks
Tyron, M.S., et al., 2015 (Tryon et al., 2015)
Nineteen women (age 18–40 y)
Salivary cortisol, Montreal Imaging Stress Task
hippocampus sucrose consumption resulted in: - higher activity in the left hippocampus - reduced stress-induced cortisol - lower reactivity to naltrexone,
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lower nausea, and a trend toward lower cortisol
1 EPM: elevated plus maze; NPR: novel place recognition; NOR: novel object recognition. 2 PPVT-III: Peabody Picture Vocabulary Test, third edition for maternal testing; WRAVMA: Wide Range Assessment of Visual Motor Abilities, KBIT-II: Kaufman Brief Intelligence Test, second edition, WRAML: Wide Range Assessment of Memory and Learning for childhood testing; HOME-SF: Environment short form test to evaluate home environment for cognitive stimulation and emotional support. 3 I2BC: intermittent 2 bottle choice. 4 LTP: long-term potentiation.
10.8 Therapeutics for obesity, derived from studies of sucrose consumption
10.8.1 Pharmacological approaches
Sucrose is shown to be addictive in rodents(Avena et al., 2008; Carlo Colantuoni
et al., 2002; Rada et al., 2005) and the studies listed in Table 3 show correlations
between sucrose consumption and emotional disturbances. The NK1- (Neurokinin-1)
receptor system is involved in the reinforcement mechanism that motivates the desire
to have stimuli that no longer create pleasure, a characteristic of addiction(Sandweiss
& Vanderah, 2015), and is implicated in both anxiety and depression(Mantyh, 2002).
Using the intermittent access model with a 5% sucrose solution, our laboratory
showed the NK1-receptor antagonist ezlopitant (which possesses anxiolytic effects)
was able to attenuate and inhibit sucrose intake in Long Evans rats(Steensland et al.,
2010). This finding suggests the NK1- receptor system to be a potential target for
sugar-related obesity therapeutics(Steensland et al., 2010). Other potential options
include antidiabetic drugs such as sodium-glucose cotransporter 2 (SGLT2)
inhibitors, which impede renal glucose reabsorption and are generally considered
effective, though lose efficacy when taken long term(Gillies et al., 2007). Indeed, a 4
week oral administration of ipragliflozin, (a SGLT2 inhibitor) with antidiabetic
effects on type 2 diabetes, decreases the caloric balance and improves symptoms of
diabetes, including obesity in mice fed on 20% glucose or sucrose solution(Chao &
Henry, 2010; Tahara, Takasu, Yokono, Imamura, & Kurosaki, 2018)
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A recent review detailing the current management for type 2 diabetes suggested
a step wise approach; including diet and exercise the patient would take metformin
(which lowers high blood glucose levels by decreasing the production of hepatic
glucose, increasing insulin sensitivity and lowering intestinal glucose
absorption(Rena, Hardie, & Pearson, 2017)), a glucagon-like peptide 1 receptor
agonist (to increase insulin output from the pancreas(Drucker & Nauck, 2006)) or a
SCLT2 inhibitor in addition to one of the approved weight-loss drugs (to control
appetite or the absorption of calories consumed(Padwal & Majumdar,
2007))(Burguera, Ali, & Brito, 2017). Table 4 lists doses and mechanisms of action
of therapeutics used in trials to alter sucrose consumption in an effort to discover
alternate treatments for diabetes and obesity.
Table 8 Therapeutics used in sugar consumption trials.
Author, year Drug / Dose Mechanism of action
Subjects Findings
Richard, D., et
al., 2000
(Richard,
Ferland,
Lalonde,
Samson, &
Deshaies,
2000)
Topiramate (30 mg / kg)
Blocks voltage gated sodium and calcium channels, glutamate, GABA (Anticonvulsant)
Rats Weight sucrose intake
Beczkowska,
I.W., 1992
(Beczkowska
et al., 1992)
Naltrexone Naloxone
Kappa and Mu2 (opioid) receptor antagonists (Treat drug and alcohol dependence
Rats Sucrose intake
Steensland, P.,
et al., 2010
(Steensland et
Ezlopitant (2,5,10 mg / kg)
NK1 receptor antagonist (Anxiolytic and
Rats obesity
347
al., 2010) antiemetic)
Shariff, M.,
2016 (Shariff
et al., 2016)
Varenicline (0.3,1,2 mg / kg)
nAChR partial agonist (Smoking cessation)
Rats sucrose intake
Tahara, A., et
al., 2018
(Tahara et al.,
2018)
Ipragliflozin (0.1 – 3 mg / kg)
SGLT2 inhibitor (Antidiabetic)
Mice sucrose intake
Muscat, R. and
Willner, P.,
1989 (Muscat
& Willner,
1989)
Sulpiride (20,40 mg / kg)
D2 and D3 receptor elective antagonist (Antipsychotic)
Rats desire for sucrose
Lof, E., et al.,
2010 (Löf et
al., 2010)
Methyllycaconitine(0.3,1 mg/kg)
α7 nAChRs selective antagonist
Rats sucrose
preference
Patkar, O., et
al., 2017
(Patkar et al.,
2017)
Buspirone (1,2.5,5 mg / kg)
5-HT1A/1B partial agonist (Anxiolytic)
Mice sucrose consumption
Lin, Z., et al.,
2013 (Z. Lin et
al., 2013)
Curcumin
(40 mg / kg)
Upregulation of
PPAR-γ activation
(Herbal
supplement)
Rats sucrose intake
Kurhe, Y., et al., 2014 (Kurhe, Radhakrishnan, & Gupta, 2014)
Ondansetron (1mg / kg)
Serotonin receptor (5-HT3) antagonist (Antiemetic)
Mice Sucrose
consumption
Badia‐Elder,
N.E., et al.,
2003 (Badia‐Elder et al.,
NPY (5µg / 5µl) (10µg / 10µl)
Inhibits GAD67 expression (Vasoconstrictor)
Rats sucrose intake
348
2003)
Pandit, R., et
al., 2015
(Pandit et al.,
2015)
AgRP (0.66 nmol) (1 nmol)
MC 3/4 receptor inverse agonist (Decreases metabolism)
Rats motivation for sucrose
10.8.2 Lifestyle Interventions
Diet and exercise are highly recommended approaches to treatment for obesity
(Bray & Bouchard, 2014). A 2007 systematic review and meta-analysis that
evaluated lifestyle interventions for patients with impaired glucose tolerance looked
at 21 randomized controlled trials designed to delay or prevent the onset of type 2
diabetes (Gillies et al., 2007). Data revealed evidence in support of interventions
such as diet and exercise being at least as effective as oral diabetes drugs, a Chinese
herbal remedy (jiangtang bushen) and the anti-obesity drug orlistat (Gillies et al.,
2007). The dilemma with lifestyle interventions is that regain of the weight lost is
common after a period of time and often results in weight gain greater than that
originally lost (Barte et al., 2010). Strategies that proved effective in maintaining
weight loss included a consistent regular meal pattern that included breakfast, a high
expectation to succeed, a good support system and behavioural self-monitoring
(Barte et al., 2010). Mindfulness meditation is another intervention studied as a way
to regulate emotional eating. A self-reported survey was conducted to examine
whether mindfulness practice could alter emotional eating (Levoy, Lazaridou,
Brewer, & Fulwiler, 2017). Surveys conducted before and after the intervention
revealed lower emotional eating scores after the meditation, suggesting a possible
role for mindfulness as a treatment for emotional eating (Levoy et al., 2017).
Mindfulness studies do not show a direct effect on weight loss itself, but appear to be
quite successful in lessening the addictive-like behaviours pursuant to relapse of
349
weight gain (Goldbacher, La Grotte, Komaroff, Vander Veur, & Foster, 2016;
Keesman, Aarts, Häfner, & Papies, 2017; Moor, Scott, & McIntosh, 2013).
10.8.3 Digital technology
Decreasing emotional eating has been proposed as a potential mechanism for the
long-term maintenance of weight loss (Barte et al., 2010). Digital technology may be
our best hope of achieving this goal. Interaction with online environments provide
the social support often lacking during maintenance of weight loss(K. O. Hwang et
al., 2010). Apps for exercise regimes, food tracking, meditation and positive thinking
all provide a support system to the user and promote networking with others in
similar situations. Support networks are useful tools as increases in body weight
decrease an individual’s ability to make informed decisions regarding highly
palatable food, resist temptation and regulate their emotions (Yeomans, 2017). These
challenges were supported through a study of 17 obese women and their
physiological reactions to verbal food cues which found that food and beverage
preferences affected physiologic responses as well as cognition and attention
(Fioravanti et al., 2004).
Wearable technology such as Fitbits (activity trackers), that interact directly with
the individual, provide information about the individual and promote long term
maintenance and support models of healthcare where the patient takes an active role
in their own wellness(Handel, 2011; Sama, Eapen, Weinfurt, Shah, & Schulman,
2014). Wearable technologies appear to be extremely effective in assisting weight
loss in patients with serious mental illness (Aschbrenner, Naslund, Shevenell,
Mueser, & Bartels, 2016). Thirteen obese individuals diagnosed with a serious
mental illness (e.g. schizophrenia and major depressive disorder) participated in 24
weeks of behavioural weight loss intervention encompassing group sessions for
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weight management and exercise, mobile health technology and social media for
motivation, peer support and a self-monitoring tool (Aschbrenner et al., 2016). At the
end of 6 months of interventions, 45 % of participants had reduced their weight
(below their baseline) and increased fitness (as measured through walking distance)
(Aschbrenner et al., 2016).
10.9 Discussion
The data summarized in this article suggests overconsumption of sugar can
lead to brain adaptations involving many different neural systems, molecular
substrates and subsequent changes in behaviour. The ease of availability and cost
effectiveness of high caloric, sweetened food and beverages appear to be a
contributing factor in the world wide increase in obesity(He et al., 2018; Sinha,
2018). Paradigms designed to investigate emotional eating show increases in weight
gain due to higher caloric intake, nevertheless examination of the correlation
between environmental and social inputs, individual thought processes and behaviour
that maintains emotional eating is yet to be defined. It seems feasible to suggest the
pleasurable sensations brought about through the consumption of sugar may provide
a self-medicating method to deal with daily stresses (Brewerton, 2011; Fortuna,
2010). Common hedonistic mechanisms play a role in both obesity and addiction to
drugs of abuse (P. M. Johnson & Kenny, 2010). While the root cause of obesity
remains elusive, the estimated global annual healthcare cost of treating illnesses
related to obesity may reach US $1.2 trillion per year by 2025.
Every day new digital applications are being developed to assist our pursuit of
health and happiness. In the future it may be possible to collect personal information
about the consumption of high-sugar foods and beverages in conjunction with
emotions felt on a daily basis. The possibilities of personal wellness monitoring,
351
implantable in vivo monitoring and drug delivery devices will also require further
robust study before they prove to be effective treatments for obesity and food
addiction. It would be interesting to investigate a possible correlation between sugar
consumption, emotions and body mass index (BMI > 30 denotes obesity). It would
also be significant to know if such applications combining game rewards might assist
with the childhood obesity epidemic as current predictions state that globally, 2.7
billion adults will be overweight and/or obese by 2025 (Haby, Markwick, Peeters,
Shaw, & Vos, 2012).
Functional brain imaging studies substantiate the higher preference and
increased emotional activation that occurs in response to images and verbal cues
related to high sugar content foods and beverages, making it more difficult for
overweight individuals to resist eating unhealthy food (Batterink et al., 2010; Pursey
et al., 2014). Optogenetic and chemogenetic studies may assist in defining the
combination of neural pathways and substrates involved. Other tools designed to
unravel the complexities between neuronal organisation and behaviour include
methods to quantify cellular populations that are functional for particular behaviours
(Hadley C Bergstrom et al., 2011). Using analytic methods (including micro-binning
and density mapping) to accurately compare functional neural network activity it
may be possible to produce microanatomical topography of molecular activity
resultant from the influence of long term sugar consumption on a variety of stress
and anxiety related behaviours (A Jacques et al., 2018). This would assist in
demonstrating pathophysiological neuroadaptive changes and perhaps lead to the
development of enhanced pharmacotherapeutic and technological strategies to assist
the reduction of excess sugar consumption.
352
In conclusion, the ease of access to sugar rich diets today is an environmental
contributor to obesity, but it may be sugars ability to generate a superior neurological
reward signal which overrides self-control mechanisms and leads to obesity(Lenoir
et al., 2007; Yeomans, 2017). Obesity and long term sugar consumption both result
in low basal levels of dopamine, particularly in the NAc, which may be the
mechanism which induces the desire to overeat in the hope to restore homeostatic
dopamine levels and avoid mild depression. Opioids, which induce feeding through
an abundance of brain regions, may be responsible for cue-induced relapse into
overeating behaviours, and binging on sugar postpones the release of acetylcholine
required to signal satiety. Each of these neuroadaptations implicates sucrose
consumptions ability to alter the way we perceive and process our emotions and
consequential behaviour. Perhaps a greater understanding of the neural mechanisms
of impulsivity and overeating are required to assist in the development of improved
obesity treatments.
It has been estimated that by 2020, 1.5 million people will die each year by
suicide, with 15 to 30 million attempting it(Weissman et al., 1999). Children
suffering anxiety disorders are twice as likely to attempt suicide, while those
suffering major depressive illnesses show a 3 fold chance at attempt(Weissman et al.,
1999).In support of these statistics, over 300 million people were reported as
suffering depression in 2016/17, with 264 million people reported to be suffering
from anxiety disorders(Organization, 2017a). This review has examined how
negative emotion can exacerbate sugar overconsumption, and vice versa. If negative
emotions are so prevalent in our children, and sugar intake so common, its
consumption may be considered a threat to the emotional stability of our race (see
reviews on mental health in children and adolescents(Beesdo, Knappe, & Pine, 2009;
353
Pine, Cohen, Johnson, & Brook, 2002; Wehry, Beesdo-Baum, Hennelly, Connolly,
& Strawn, 2015)). More importantly, reduction of sugar overconsumption may be
capable of significantly reducing the prevalence of negative emotion in a vast
number of individuals around the world.
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Appendix C:
Glucocorticoid Receptor (GR)
This appendix comprises the following published book chapter:
Jacques, A., Battle, A.R., Johnson, L.R., (2017). The Glucocorticoid Receptor
(GR). In Choi, Sangdun (Ed.) Encyclopedia of Signaling Molecules [2nd
edition]. Springer Reference, New York.
Published: 3 January, 2107.
https://link.springer.com/referenceworkentry/10.1007/978-3-319-67199-
4_101536
355
Synonyms GCCR; GCR; GCRST; Glucocorticoid nuclear receptor variant 1; GR; GRL; Nuclear receptor subfamily 3 group C member 1 (glucocorticoid receptor)
Background
The gene NR3C1 encoding the glucocorticoid receptor (GR) is located on
chromosome 5q31.3 in humans, chromosome 18 in rats and mice, and chromosome
13 in chickens (Flaherty et al. 2012). It counts 2201 bp and contains 15 exons. The
five prime untranslated region consists of exon 1 while the protein-encoding region
involves exons 2–9. Homologs are conserved in chimpanzees, dogs, rats, zebra fish,
and frogs. The structural organization of the GR arises from exons 2 to 9 and
includes a DNA-binding domain and hinge region between the N and C termini.
Exon 2 codes the N-terminal domain. Exons 3 and 4 encode the DNA-binding
domain consisting of two zinc fingers. The GR consists of 777 amino acids and is
expressed throughout the body. Subcellular locations include the cytoplasm,
mitochondrion, cell nucleus, and plasma membrane. It is presently unknown if
different structural forms of the GR are associated with the cytoplasm and
membrane.
The name glucocorticoids derive from original descriptions of GR function in
the regulation of gluconeogenesis, however the diverse functions of the GR and now
beginning to be understood. GRs bind the glucocorticoid hormones cortisol (humans)
and corticosterone (rodents) and regulate genes facilitating processes such as energy
metabolism, immune responses, growth and development, and brain and body
responses to stress and challenge.
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Glucocorticoids are released into the circulatory system throughout the day at
varying concentrations in a circadian-dependent manner. In addition, during times of
stress, additional volumes of glucocorticoids are released. Glucocorticoid release is
driven by corticotrophin-releasing hormone by the hypothalamus which is
transported to the anterior pituitary. Here adrenocorticotropic hormone (ACTH) is
released from the pituitary gland to the circulatory system which stimulates the
adrenal glands to release cortisol from the adrenal glands into the bloodstream.
Release is pulsatile which contributes to the fast “ultradian” rhythm of release.
Cortisol released from the adrenal gland targets organs throughout the body,
including the brain (Fig. 1).
Glucocorticoids bind to the GR to regulate gene transcription, this regulation
can result in either gene translation or transrepression (Prager and Johnson 2009). In
addition, GR may also act as fast-acting membrane-associated receptors regulating
cell structure and function (Prager and Johnson 2009; de Kloet 2014). Disorders
potentially associated with mutations in the gene or dysregulation of the GR function
include glucocorticoid resistance, Cushing’s syndrome lymphosarcoma, major
depressive disorder, posttraumatic stress disorder, and other diverse disorders
(Kadmiel and Cidlowski 2013).
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Figure 11-1 Glucocorticoid Receptor (GR) The hypothalamic–pituitary–adrenal axis. During times of stress, corticotrophin-releasing hormone and vasopressin are released from the hypothalamus and transported to the anterior pituitary. Adrenocorticotropic hormone (ACTH) is released from the pituitary gland which stimulates the adrenal glands to release cortisol from the adrenal glands into the bloodstream. Cortisol released from the adrenal gland targets organs throughout the body, including the brain.
Structure of GR
The GR protein has domains arising from exons 2 to 9. Exon 2 codes for the N-
terminus, containing the main transcriptional domain. The central region of the
protein which consists of two zinc fingers involved in DNA binding and homo-
dimerization (see below under structural studies) are encoded by exons 3 and 4. The
DNA binding domain is coded with the composition “zinc subdomain-helix-8-
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strand”. The two helixes are perpendicular to each other with the hydrophobic side
chains forming a protein core. The zinc sites are located at equidistance from the
outside of the core. The first subdomain is folded onto the core and connects with the
two helices and the C-terminal. The second protrudes out from the protein core,
forms a loop, an a-strand and a short a-helix (Haerd and Gustafsson 1993). The C
terminus includes the domains required for transcription and ligand binding.
The glucocorticoid receptor in humans has two splice variants labelled GRα
and GRβ. The two isoforms are structured identically from amino acid 1 to 727 and
then deviate. The GRα functions as a transcription factor, but GRβ does not bind
glucocorticoid and lacks transcriptional functionality. It has been implicated in
asthma-related glucocorticoid resistance due to its dominant- negative inhibition of
GRα.
Transcription Factor
Both GR and the mineralocorticoid receptor (MR) have similar mechanisms of
action and therefore similar functional architecture. The same receptor domains are
responsible for ligand binding interactions with a variety of heat shock proteins,
translocation to the nucleus, DNA binding, and other transcriptional regulatory
protein interactions (Haerd and Gustafsson 1993). The DNA-binding domain is a
highly conserved region of the GR, abundant in lysine, arginine, and cysteine. The 15
base pair glucocorticoid response element (GRE) core sequence
(GGTACANNNTGTTCT) contains two partially palindromic hexamer sequences
with three intervening nucleotides. This allows a recombinant DNA-binding domain
molecule to associate with one half-site of the GRE while a second one binds
cooperatively to the adjacent half-site. This action strongly depends on the three
nucleotides of the intervening sequence. A section of five amino acid residues has
359
been found to be essential for dimerization and the binding of GR to GRE (Haerd
and Gustafsson 1993).
The first reported crystallographic study of the rat GR was published in 1991
(Luisi et al. 1991), where it was found to dimerize when bound to DNA. Subsequent
studies have revealed that the mode of binding of GR to GREs plays a pivotal role in
regulating transcription through adopting different binding conformations. When in
positive mode (activating transcription) the GR binds to the GRE as a homodimer
(Meijsing et al. 2009; Hudson et al. 2016) (see Fig. 2), while repressing transcription
(nGRE), the GR binds at two different sides as monomers (Hudson et al. 2013) (Fig.
2).
Figure 11-2 Glucocorticoid Receptor (GR)
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The glucocorticoid receptor (GR) binds as a dimer on +GRE sites on DNA (Meijsing et al. 2009; Hudson et al. 2016) (a) to activate transcription or binds as two monomers to repress transcription (Hudson et al. 2013). (b) Blue and red colors indicate individual GR molecules bound to white +GRE or nGRE DNA strands.
Function of GR
Influential early work from de Kloet and colleagues identified differences in
receptor binding affinity between MR and GR (for review see Prager and Johnson
(2009) and de Kloet (2014)). This finding leads to the important concept that at
resting levels of adrenal corticosterone release, corticosterone bounds predominately
to MR, while at periods to elevate corticosterone release including as a result of
stress, corticosterone also bound to the lower affinity GR. Within the brain, GRs are
found in dense concentrations in neurons of the hippocampus, amygdala, and the
prefrontal cortex. Its abundant expression throughout the limbic system suggests an
important role in stress and defense reactions (Wolf et al. 2016).
During times of stress, GR is recruited. Stress triggers the activation of the
hypothalamic-pituitary–adrenal (HPA) axis. Corticotrophin-releasing hormone is
secreted from the anterior hypothalamus stimulating the pituitary gland to release
adrenocorticotropic hormone (ACTH) into the blood stream. ACTH stimulates the
adrenal glands to release corticosteroids (cortisol in humans, corticosterone in
rodents; CORT) into the blood stream (Reul and Kloet 1985). Once CORT enters the
brain via the blood stream the elevated levels act on the HPA-axis to inhibit the
release of more corticosteroids, reducing the initial stress reaction (transrepression).
GR returns target cells back to baseline after an initial stress reaction and enhances
recovery by increasing energy metabolism. Stress-related pathology may result from
dysregulation of this CORT/HPA-axis interaction (for detailed review see Millan et
al. (2012)). Depending on the amount of CORT expo- sure, the slow genomic action
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whereby CORT binding facilitates gene transcription may take up to 30 minutes
from receptor activation and can last for days (see Joels et al. 2012).
Pharmacology of GR
The endogenous ligands of GR include aldosterone, corticosterone, cortisol,
and deoxycortisone. Agonists include clobetasol, therapeutically used for skin
disorders such as psoriasis; fluticasone propionate – asthma medication;
desoximetasone – a metabolite; and methylprednisolone commonly used to treat
immunodeficiency syndromes. Mifepristone is a GR antagonist marketed as RU-486;
and a selective antagonist is onapristone, used to treat breast cancer and implicated as
a possible treatment for hormone-dependent tumors (Vilasco et al. 2011). Since
glucocorticoid therapy was instituted over 60 years ago it has become the pillar of
anti-inflammatory modulators (Kadmiel and Cidlowski 2013). Synthetic
glucocorticoids are now prescribed for conditions such as asthma and chronic
obstructive pulmonary disease. Dexamethasone is used in psychiatry to test for
functioning of the HPA axis and its feedback mechanisms (dexamethasone
suppression test). Dexamethasone, a synthetic CORT, acts centrally to suppress the
ongoing HPA activity and reduces endogenous CORT levels.
Membrane GR
Glucocorticoids can affect neuronal activity within seconds of exposure to
cells. When genomic regulation was found to be incompatible with rapid effects,
studies suggested these actions were mediated by the activation of membrane-
associated receptors (see Prager and Johnson (2009) and Wolf et al. (2016) for
review). Reports to date of these actions involve the limbic system and brainstem,
areas involving stress, learning, emotional memory, reproductive behavior, and
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movement. Studies involving the effects of glucocorticoids on learning, memory, and
stress show both inhibitory and excitatory processes.
In a classic study, the reproductive behavior of male rough-skinned newts was
shown to be suppressed by rapid corticosterone action that inhibited neural circuits of
the brain stem (Orchinik et al. 1991). In 1996, Sandi et al. reported systemic
glucocorticoid increased loco- motor activity of rats in a novel environment, finding
the effect was nitric oxide dependent (Sandi et al. 1996). Electrophysiological studies
by Joels et al. (2012) and Tasker and Herman(2011) have identified fast-acting GR
responses.
Direct anatomical localization of GR at neuron membranes including synapses
was shown by Johnson et al. (2005) who identified GR receptors localized in the
postsynaptic membranes of the lateral amygdala (Johnson et al. 2005; Prager et al.
2010). They established these receptors in the presynaptic terminals, the postsynaptic
density, dendrites, dendrites spines, and soma of neurons. Emerging data suggest that
these membrane GRs may rapidly regulate neuron dendrite spine structure (for
review see Russo et al. (2016)). Synaptic GR may play a role in the modulation of
synaptic plasticity related to memory (Prager and Johnson 2009; Wolf et al. 2016).
Evidence for mGRs in other cells have also been demonstrated. For example,
Gametchu and coworkers identified human mGR in leukemic cells membranes using
peptide antibody labelling (Gametchu et al. 1993).
Summary
Cortisol (or corticosterone) activates the glucocorticoid receptor which
functions both as a transcription factor itself, a regulator of other transcription
factors, and also as a fast-acting membrane receptor. The genomic GR is located in
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the cytoplasm and it is transported to the cell nucleus when ligand bound, where it
plays a role in transcription processes. Its functions include regulation of cell
proliferation, tissue differentiation, inflammatory processes, and neuronal plasticity.
GR gene mutations are linked with Cushing’s disease and glucocorticoid resistance
and other disorders. As GRs are located in almost every tissue of the body and act in
both genomic and nongenomic capacities, a comprehensive understanding of their
mechanisms of action will ensure their role as therapeutic targets.
References
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Appendix D:
Mineralocorticoid Receptor
Jacques, A., Johnson, L. R., Battle, A. R., (2017). The Mineralocorticoid
Receptor (MR). In Choi, Sangdun (Ed.) Encyclopedia of Signaling Molecules
[2nd edition]. Springer Reference, New York.
Published: 3 January, 2017.
https://link.springer.com/referenceworkentry/10.1007/978-3-319-67199-
4_101537
366
Synonyms MCR; MLR; MR; NR3C2; NR3C2VIT; Nuclear receptor subfamily 3 group C member 2
Historical Background
Located on chromosome 4q31.1 in humans, the gene NR3C2 encodes the
mineralocorticoid receptor (Fan et al. 1989; Morrison et al. 1990). It has 5201 bp, an
exon count of 12, and is located on chromosome 8 in mice, 19 in rats, 1 in zebra fish,
and 4 in chickens. Mineralocorticoids (MRs) belong to the nuclear receptor
subfamily 3 and are distributed throughout the epithelia of the kidneys, sweat glands,
and colon and the nonepithelial tis- sues of the heart and brain. The human MR was
first cloned in 1987, consists of 984 amino acids, and is similar in structure to the
glucocorticoid receptor, sharing 94% identity in the DNA-binding domain (Funder
1997).
It is unique among steroid receptors in that it plays signaling roles in both
mineralocorticoids (e.g., aldosterone and deoxycorticosterone) and the
glucocorticoids (cortisol in humans, corticosterone in rats). In particular, both
cortisol and corticosterone bind to MR with similar affinity as aldosterone, but
aldosterone will only bind to glucocorticoid (GR) at very high concentrations. This
was determined through structural studies that showed that despite both MR and GR
showing high sequence homology in their ligand-binding domains (Sturm et al.
2005), nonspecific amino acid interactions between sequences 804–844 were
identified as essential for aldosterone specificity (Rogerson et al. 1999). NR3C2
defects may result in autosomal dominant pseudo- hypoaldosteronism type I. This
disorder characteristically entails a high flow rate of very dilute urine. Other gene
mutations may result in early-onset hypertension severely exacerbated in pregnancy.
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MR is expressed in many cells in the body, where it modulates ion and fluid
balance, response to injury, and early responses to stress. Given the ubiquitous nature
of MR, this encyclopedia chapter focuses on the role of MR as a signaling molecule
in the brain. For recent general information on MR, the reader is directed to a 2014
review by Gomez- Sanchez (Gomez-Sanchez and Gomez-Sanchez 2014).
MR Signaling in the Brain
The body’s ability to adapt to stress requires the facilitation of neuronal
plasticity mediated by cortisol (in humans) and corticosterone (in rodents)
(Sarabdjitsingh and Joels 2014). Cortisol binds to genomic receptors such as the
mineralocorticoid (MR) and glucocorticoid (GR) receptors which function as
transcription factors and permit stress-related information to be stored for subsequent
use (Prager and Johnson 2009). The receptors regulate a variety of gene
transcriptional processes including the synthesis of new proteins which facilitate
synaptic plasticity (Prager and Johnson 2009). These processes are modulated by
binding of steroids, including cortisol (corticosterone in rodents) to both MRs and
GRs. Cortisol binds with higher affinity to MR than to GR (Prager and Johnson
2009; Joels et al. 2012; de Kloet 2014).
Localization of MR in the Brain and Body
MRs are distributed throughout the tissues of the brain, heart, kidney, colon,
hippocampus, hypo- thalamus, and adrenal fasciculata. Subcellular locations
include the cytoplasm, nucleus, endoplasmic reticulum membrane, and plasma mem-
brane. Epithelial locations include parts of the nephron (distally), the colon (distally),
and sweat and salivary glands. Nonepithelial loci include the neurons of the central
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nervous system, cardiac myocytes, and smooth muscle cells of large vessels such as
the aorta.
MRs are predominantly expressed in the learning and memory centers of the
brain such as the hippocampus and amygdala (Reul and Kloet 1985). Localized in
the membrane, they facilitate second messenger (e.g., G protein) cascades to directly
affect membrane proteins, including the regulation of membrane potential through
gated ion channels (Prager and Johnson 2009; Prager et al. 2010; Joels et al. 2012).
Electron microscopy studies have shown MRs expressed at nuclear locations within
the lateral amygdala on glutamatergic (excitatory) and GABAergic (inhibitory)
neurons and at the extranuclear loci of presynaptic terminals, dendrites, and their
spines (Prager et al. 2010). Electron microscopy has allowed identification of MRs
localized to Golgi apparatus and mitochondrial membranes (Johnson et al. 2005;
Prager et al. 2010).
Function of MR in the Brain and Body
Conditioned fear can coexist with or trigger the stress response, elevating
adrenal hormones in the brain. Amygdala-dependent activation of the hypothalamic-
pituitary-adrenal (HPA) axis facilitates expression of conditioned fear. The HPA axis
increases corticosterone blood concentrations which facilitates binding to MRs and
GRs within the limbic system. In the brain genomic MRs (gMRs) have high-affinity
binding with glucocorticoids (e.g., cortisol, corticosterone), mineralocorticoids (e.g.,
aldosterone), and progesterone (Krozowski and Funder 1983). The diverse
distribution of MRs implicates mineralocorticoid effects on neuronal function in
specific subregions of the brain. The lateral nucleus of the amygdala, which both
acquires and stores fear memories, is the primary site for the resultant synaptic
plasticity (Prager et al. 2010). Studies of chronic stress models have shown
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hypertrophy of dendrites in amygdala principal neurons (Johnson et al. 2005). In
epithelial tissues aldosterone activates MRs, by converting cortisol to NAD and
NADH. This facilitates protein expression which regulates the epithelial sodium
channel, sodium potassium pump, and serum- and glucocorticoid-induced kinase.
Sodium and water are reabsorbed resulting in increased extracellular volume,
increased blood pressure, and reduced potassium levels due to potassium excretion
(to maintain homeostasis).
Cortisol Signaling to Intracellular Genomic MR (gMR)
During stress corticosterone enters the brain rap- idly, binding to MRs in the
limbic brain regions such as the septum, hippocampus, and amygdala. In the
hippocampus, the receptors bind to a complex of heat shock proteins (e.g., HSP90,
70, 56, etc.) and, once activated, dissociate from these proteins to homodimerize with
other receptors (Rupprecht et al. 1993). The dimerized genomic MRs translocate to
the nucleus of the cell and bind to mineralocorticoid response element (MRE), which
modulates gene transcription into mRNA of the activated genes. MRE is a short
DNA dimer, denoted by a pair of inverted repeats that are partitioned by three
nucleotides. Located within the promoter of a gene, it specifically binds a steroid
hormone receptor complex in order to regulate transcription. MRs at postsynaptic
membrane densities of excitatory synapses have been shown to regulate synaptic
transmission (Prager et al. 2010).
Structure of MR
The MR adopts a quaternary structure and is com- prised of three domains: the
N-terminal domain, a DNA-binding domain, and a C-terminal ligand-binding domain
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(Pawlak et al. 2012) In the absence of a ligand, MR will form a hetero- multimeric
cytoplasmic complex with heat shock proteins HSP90, HSP70, and FKBP4 (Bruner
et al. 1996) In the presence of a ligand, after binding, it translocates to the nucleus to
bind to DNA as a homodimer and as a heterodimer with NR3C1.
The crystal structure of the MR in complex with the GRE was published in
2014 by Ortlund and coworkers (Hudson et al. 2014), revealing that during positive
transcription processes, it forms a dimeric complex with GRE (Fig. 1). Interestingly,
despite both the MR and GR exhibiting high sequence homology in the DNA-
binding domain (DBD), only the GR is able to bind both GRE and nGREs, which is
described in the recent work by (Hudson et al. 2016). In this report, they have shown
ancestral DBD was able to bind both GRE and nGREs; however amino acid
substitution in duplicated daughter genes resulted in this ability being lost in MRs
(Hudson et al. 2016).
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Figure 12-1 Mineralocorticoid Receptor The mineralocorticoid receptor (MR) binds as a dimer (Hudson et al. 2014) on GRE sites. Blue and red colors indicate MR molecules, white indicates GRE DNA strands
Cortisol Signaling to Membrane- Localized MR (mMR)
Like the closely related GR, MR has also been identified as a functional
membrane receptor (mMR). In neurons mMR are fast-acting receptors that have been
documented to regulate synaptic transmission (for review see Prager and Johnson
2009; Joels et al. 2012; Russo et al. 2016). Anatomical evidence for their existence
was recently documented using electron microscopy where possible mMR where
found in postsynaptic densities as well as presynaptic structures (Prager et al. 2010).
Functional evidence for a rapid signaling membrane-based MR has been documented
by extensive electrophysiological studies by Joels and colleagues; see Joels et al. for
review (Joels et al. 2012). In a classic study of fast-signaling actions, MR agonists
were found to facilitate presynaptic glutamate release (Karst et al. 2005).
Pharmacology of MR
The endogenous ligands of MR in order of potency are corticosterone, cortisol,
aldosterone, and progesterone. Nuclear MR binds with high affinity to corticosterone
and cortisol (Reul and Kloet 1985). Due to GR lower-binding affinity, its genomic
response occurs only after exposure to a stressful event (Joels 2008). The rate of
occupancy and activation of MR is comprehensive even when circulating hormone
levels are low, pointing to a role for MRs in variations of ultradian rhythm (Russo et
al. 2016). mMRs were found to regulate ion channels to moderate the speed of
neuronal depolarization and synaptic transmission (Prager and Johnson 2009).
Presynaptic calcium levels increased glutamate release in the hippocampus, while the
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postsynaptic efflux of potassium was inhibited, reducing hyperpolarization (Prager
and Johnson 2009).
Apart from cortisol, agonists of MR include aldosterone, produced in the
adrenal glands, that acts on the renin-angiotensin system; prednisolone and
dexamethasone, both anti-inflammatories; and progesterone, a sex hormone and
fludrocortisone, used to treat cerebral salt- wasting syndrome. MR antagonists
include spironolactone, a diuretic to prevent salt absorption and potassium excretion,
eplerenone an anti- hypertensive, finerenone used in the treatment of chronic heart
failure, and onapristone for the treatment of breast cancer.
Summary
When presented with stressful situations, corticosteroids are released effecting
the brain and, as a consequence, behavior. Both MR and GR mediate these actions
through their expression on neurons within the limbic system, but MR has a much
higher binding affinity to the naturally occurring cortisol and therefore does not
require the introduction of stress to become active. MR plays a major role in the
regulation of ion and water transport via the renin-angiotensin system. Gene
mutations are linked with autosomal dominant pseudohypoaldosteronism type I and
pregnancy-exacerbated hypertension. Alternate splicing can result in multiple
transcript variants.
MR moderates ligand-dependent transcription and binds to MRE to
transactivate specific target genes. The high-affinity binding of cortisol to MRs lends
credence to the nongenomic effects of mMRs to induce fast responses in neuronal
second messenger systems to regulate synaptic transmission (Prager et al. 2010). The
mineralocorticoid receptor-binding properties, interactions with other genes, and
373
extensive tissue expression afford its great functional diversity and multifarious
physiological regulation.
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