Targeting MicroRNAs for Modulation of the Anti-TumorTargeting MicroRNAs for Modulation of the...
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UNIVERSIDADE DE LISBOA
FACULDADE DE FARMÁCIA
Targeting MicroRNAs for Modulation of the Anti-Tumor
Human γδ T Cell Differentiation
Master Thesis Dissertation
Gisela Silva Gordino
Master in Biopharmaceutical Sciences - FFUL
Supervisor
Dra. Julie C. Ribot
Molecular Medicine Institute
2016
FFULisboa
Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 2
UNIVERSIDADE DE LISBOA
FACULDADE DE FARMÁCIA
Targeting MicroRNAs for Modulation of the Anti-Tumor
Human γδ T Cell Differentiation
Master Thesis Dissertation
Gisela Silva Gordino
Master in Biopharmaceutical Sciences - FFUL
Supervisor
Dra. Julie C. Ribot
Molecular Medicine Institute
2016
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 3
Abstract
Recent developments in the immunology field have validated immunotherapy as being
capable of extending cancer patients survival, highlighting its role as a promising anti-tumor
treatment. However, a wider and more successful application of adoptive cell therapy requires
a better understanding of the molecular determinants, in effector lymphocytes, of functional
differentiation, recognition and elimination of tumor cells.
γδ T cells have been proposed as the first line of immune defense, acting in response to a
variety of stress-inducible or pathogen-associated metabolites. Their activation includes a
potent cytolytic and inflammatory activity against a wide range of malignant cells as one of
its most important features. Although clinical trials have aimed at modulating their activity,
they were only able to achieve objective responses in the range of 10-33%. Therefore, it has
become evident that understanding the mechanisms involved in regulating γδ T cell activation
and functional differentiation is critical for their manipulation in clinical settings.
Importantly, data from the host laboratory has recently shown that the anti-tumor effector
properties of γδ T cells were selectively acquired upon stimulation with interleukin (IL)-2, but
not with IL-7. The effects caused by IL-2 depended on the Mitogen-Activated Protein
Kinase/Extracellular Signal-Related Kinase (MAPK/ERK) signaling and induced de novo
expression of the transcription factors (TFs) T-bet and eomesodermin, as well as the cytolytic
enzyme perforin. Based on this background, this project proposes to explore an additional
layer in the regulation of γδ T cell differentiation, by characterizing the post-transcriptional
mechanisms mediated by microRNAs (miRs).
In mammals, more than 700 miRs have already been identified and shown to regulate many
developmental and differentiation processes. For instance, with regard to the immune system,
overexpressing miR-491 decreases the interferon (IFN)-γ production in cluster of
differentiation (CD)8+ T cells, miR-583 inhibits natural killer (NK) cells differentiation
process, and miR-181a expression regulates activation of memory T helper (Th)17 cells
through modulation of ERK phosphorylation. This notwithstanding, no miRs have yet been
implicated in human γδ T cell differentiation into anti-tumor effectors.
By performing a ribonucleic acid-sequencing (RNA-seq) analysis, we have been able to
identify a discrete repertoire of miRs possibly implied in regulating γδ T cell type 1 functional
differentiation. Selected miRs candidates have been validated by reverse transcriptase-
polymerase chain reaction (RT-PCR): miR-135b, 10a and 20b were upregulated in mature γδ
T cells while; miR-181a and 196b were downregulated in mature γδ T cells.
We next manipulated the expression of these miRs following two different gain-of-function
strategies (electroporation with mimics and retroviral transduction). We observed that miR-
181a and 196b overexpression decreases γδ T cell differentiation into type 1 effectors, while
enhancing their proliferation. On the other hand, our preliminary results indicate that miR-
135b, 10a and 20b impair γδ T cell proliferation without affecting their anti-tumor functions.
If confirmed, these findings could have major implications for the manipulation of γδ T cells
in cancer immunotherapy.
Keywords: Anti-tumor; Differentiation; γδ T cells; Immunotherapy; microRNA.
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Resumo
Desenvolvimentos recentes na área da imunologia validaram a imunoterapia como sendo
capaz de aumentar a sobrevivência de pacientes com cancro, evidenciando o seu papel
promissor no tratamento desta doença. No entanto, uma aplicação mais abrangente e bem-
sucedida da terapia celular adoptiva requer um conhecimento profundo dos determinantes
moleculares, em linfócitos T efectores, de diferenciação funcional, reconhecimento e
eliminação de células tumorais.
Os linfócitos T γδ têm sido propostos como a primeira linha de defesa imunológica, em
resposta a uma variedade de metabolitos induzidos pelo stress ou associados a organismos
patogénicos. A activação destas células inclui uma actividade citolítica e inflamatória potente,
contra uma vasta gama de células malignas, como uma das suas características principais.
Embora os ensaios clínicos tenham tentado modular a actividade destes linfócitos, estes só
alcançaram respostas objectivas na ordem dos 10-33%. Tornou-se assim evidente que a
compreensão dos mecanismos envolvidos na regulação da activação e da diferenciação
funcional dos linfócitos T γδ é crítica para a sua manipulação em contextos clínicos.
De forma importante, resultados obtidos pelo laboratório de acolhimento demonstraram que
as propriedades anti-tumor efectoras dos linfócitos T γδ eram adquiridas, selectivamente,
mediante estimulação com IL-2, mas não quando estimulados com IL-7. Os efeitos mediados
por IL-2 eram dependentes da via de sinalização de MAPK/ERK e induziam a expressão de
novo dos factores de transcrição T-bet e eomesodermina, assim como da enzima citolítica
perforina. Com base nestes dados, este projecto propõe explorar um outro nível de regulação
na diferenciação de linfócitos T γδ, caracterizando os mecanismos pós-transcrição mediados
por miRs.
Em mamíferos, mais de 700 miRs foram já identificados e demonstrados como sendo capazes
de regular vários processos de desenvolvimento e diferenciação. Em relação ao sistema
imunitário, os padrões de expressão destas pequenas espécies de RNA variam consoante o
tipo de linfócitos e também de acordo com o estadio de desenvolvimento. Curiosamente,
alguns estudos demonstraram que esses perfis de expressão são únicos em cada etapa do
desenvolvimento das células T tímicas. De acordo com esta informação, a remoção dos miRs
maduros na fase inicial do desenvolvimento de timócitos resultou num bloqueio do seu
desenvolvimento e na consequente redução de linfócitos T αβ periféricos assim como reduziu
a pool de células iNKT. Adicionalmente, vários miRs têm sido também correlacionados com
a desregulação das capacidades efectoras de diversos tipos de linfócitos. Alguns exemplos
incluem: a sobreexpressão do miR-491 em linfócitos T CD8+ que demonstrou ser capaz de
diminuir a produção de IFN-γ; a expressão do miR-583 foi correlacionada com a inibição da
diferenciação de células NK e; a expressão do miR-181a demonstrou regular a activação de
linfócitos de memória Th17 ao modular a fosforilação da ERK. Apesar disso, não surgiram
ainda miRs implicados na diferenciação de linfócitos T γδ humanos em efectores anti-
tumorais.
Neste trabalho fomos capazes de identificar um repertório discreto de miRs possivelmente
implicados na regulação da diferenciação funcional do tipo 1 em linfócitos T γδ. Para tal,
recorremos à análise dos dados obtidos em ensaios de sequenciação de RNA em linfócitos T
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γδ imaturos versus maduros, isolados a partir de amostras de timo humano. Utilizando a
metodologia de RT-PCR validámos esses dados, reduzindo a lista inicial de treze para cinco
potenciais candidatos: miR-135b, 10a e 20b que se encontravam sobreexpressos em linfócitos
T γδ maduros e; miR-181a e 196b que tinham expressão reduzida em linfócitos T γδ maduros.
Surpreendentemente, quando analisada a presença destes miRs por RT-PCR em linfócitos T
γδ isolados da periferia (i.e. amostras de sangue de dadores saudáveis), o padrão de expressão
para os cinco candidatos era mais semelhante à sua expressão em timócitos γδ recém-isolados
– ou seja, imaturos – do que à expressão observada em timócitos γδ em cultura com IL-2 – ou
seja, maduros/diferenciados. Tais resultados parecem sugerir que o padrão de expressão
destes miRs candidatos poderá estar associado a um processo transiente, capaz de restaurar os
seus níveis basais assim que as células se encontrem completamente diferenciadas e entrem
num processo de repouso. De acordo com esta hipótese, quando os linfócitos T γδ periféricos
foram colocados em cultura com as citosinas IL-7 ou IL-2 – que atribui às células um estado
imaturo ou maduro, respectivamente – o estímulo induzido pelas citosinas demonstrou ser
capaz de romper com o estado de repouso das células, conforme comprovado pela alteração
nos padrões de expressão destes miRs. Mais ainda, em linfócitos T γδ periféricos cultivados
com IL-7, os miRs-135b, 10a e 20b aparentaram ter um comportamento diferente do
observado em timócitos γδ em cultura também com IL-7, visto que a sua expressão surge
aumentada nestas amostras periféricas.
Importantemente, estas experiências demonstraram também que as subpopulações Vδ1 and
Vδ2 isoladas a partir do sangue de controlos saudáveis obtiveram valores semelhantes para a
expressão dos miRs. Estes resultados estão então de acordo com o facto de se terem registado
padrões semelhantes na expressão dos miRs candidatos, quando comparados os resultados
entre as amostras de linfócitos T γδ recém-isoladas a partir do timo com as amostras recém-
isoladas a partir de sangue humano.
De seguida procedeu-se à manipulação da expressão destes miRs utilizando duas estratégias
de ganho-de-função diferentes: electroporação com mimetizadores ou transdução retroviral.
Ao sobreexpressar os miRs candidatos, usando o sistema Neon, fomos capazes de observar
que nos linfócitos T γδ periféricos em cultura com IL-7+IL-2 a proliferação aparenta estar
comprometida nas amostras que sobreexpressaram os miRs-135b, 10a e 20b, sobretudo
quando delimitados à subpopulação Vδ2. Tal resultado é consistente com os dados obtidos
durante as experiências de RT-PCR, que sugerem um papel para estes três miRs na regulação
das respostas funcionais em linfócitos T γδ que já se encontrem completamente diferenciados.
De forma importante, apesar de só se ter conseguido explorar a manipulação destes linfócitos
numa amostra de timo, quando utilizando o sistema Neon, os resultados obtidos foram
também indicativos de um papel na regulação do desenvolvimento e diferenciação de
linfócitos T γδ por parte dos miRs-181a e 196b, visto que estes foram capazes de reduzir a
expressão de TNF-α em cerca de 10% dentro da subpopulação Vδ1, ao mesmo tempo que
parecem induzir a sua proliferação.
De acordo com estes dados, a transdução retroviral do miR-181a em linfócitos T γδ
periféricos demonstrou ser capaz de aumentar a proliferação destas células enquanto, ao
mesmo tempo, diminuiu a expressão de IFN-γ e TNF-α. No que concerne os resultados do
processo de diferenciação, embora tenham ocorrido pequenas diferenças nas duas
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subpopulações, tais alterações na subpopulação Vδ2 foram demasiado reduzidas para serem
consideradas. Pelo contrário, a expressão de IFN-γ e TNF-α em células Vδ1+ foi
significativamente comprometida pela presença do miR-181a, reduzindo a produção destas
citosinas em cerca de 12%. Interessa salientar que esta não é a primeira vez que a
sobreexpressão do miR-181a é correlacionada com a redução da produção de IFN-γ e TNF-α,
visto que tal foi já observado recentemente em linfócitos humanos CD4+ ou na linha celular
HEK293K, respectivamente.
Estes resultados, em conjunto com outros relatórios semelhantes, sustentam um papel
regulatório do miR-181a na diferenciação funcional de células T, que deve ser aprofundado
no que diz respeito aos linfócitos T γδ.
No seu conjunto, estas experiências de ganho-de-função permitiram-nos observar que a
sobreexpressão dos miRs-181a e 196b diminui a diferenciação dos linfócitos T γδ em
efectores do tipo 1, ao mesmo tempo que aumenta a sua proliferação. Por outro lado, estes
resultados preliminares indicam que o miR-135b, 10a e 20b comprometem a proliferação
destes linfócitos sem afectar as suas propriedades anti-tumorais. Interessantemente, em ambas
as experiências de ganho-de-função, as células Vδ1+ surgiram com sendo a subpopulação que
registou as alterações mais significativas. Deste modo, seria importante sobreexpressar estes
miRs em mais amostras de timo, uma vez que os timócitos γδ são enriquecidos em células
Vδ1. Se validados, estes resultados poderão ter grandes implicações na manipulação dos
linfócitos T γδ para a imunoterapia em cancro.
Como seguimento lógico deste projecto, pretende-se identificar as redes de mRNAs
controladas pelos nossos miRs candidatos, recorrendo a uma combinação de ferramentas
bioinformáticas e bioquímicas. Esta visão integrada dos efeitos de um determinado miR no
transcriptoma humano será fundamental para compreender o seu papel na diferenciação
funcional dos linfócitos T γδ.
Adicionalmente, pretende-se tentar definir um perfil patogénico para estes miRs na
diferenciação funcional dos linfócitos T γδ, tendo como base amostras de pacientes com
cancro. Interessantemente, a presença e as capacidades efectoras dos linfócitos T γδ
periféricos foram anteriormente relatadas como estando comprometidas em amostras de
pacientes com melanoma, glioblastoma e cancro do estômago. Como tal, cremos ser possível
observar uma expressão anormal dos miRs envolvidos no processo de diferenciação funcional
dos linfócitos T γδ em amostras obtidas a partir de sangue periférico deste género de
pacientes. Para verificar essa hipótese, contamos com a parceria estabelecida com o
Departamento de Oncologia do Hospital de Santa Maria, através do Dr. Luís Costa, que nos
permitirá ter acesso a amostras de sangue obtidas a partir de um grupo definido de pacientes,
aquando a altura do diagnóstico.
Palavras-Chave: Anti-tumor; Diferenciação, Linfócitos T γδ; Imunoterapia; microRNAs.
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Acknowledgements
I wish to take this opportunity to thank to everyone who has been involved in this journey,
which allowed me to grow, not only professionally but also personally.
To begin, I would like to thank Professor Dr. Bruno Silva-Santos for giving me the
opportunity to integrate this already large “family”, known as the BSS lab. Thanks for always
believing in me and in this project, and to always try to push me further. Your constructive
criticism not only allowed me to grow scientifically, but thanks to you I have always tried to
improve my work every step of the way.
I also wish to give thanks to all of my colleagues in the BSS lab, who made me feel welcomed
since the first day, but in particular to those who have also endowed me with some of their
skills. In special, I want to thank Nina for the enormous sympathy and the amazing help she
gave me by teaching me how to work with the Neon system, and also with the retrovirus. I
could always come to you with a million questions that you always answered with kindness
and a smile on your face. Thank you for that! To Tiago, my main “retrovirus teacher”, who
taught me almost everything I know about culturing bacteria and producing virus. Thank you
for the many hours you have spent unveiling the mysteries of this “small world” to me and for
making these experiments look more “user friendly”. To Anita, thanks for always trying to
help me with my microRNA doubts and for teaching me how to do the sequencing step. To
Natacha, thanks for teaching me how to do an RT-PCR with those amazing 384 well plates,
and for helping me to do it three times!
There are other several colleagues, who have somehow made my life in the lab easier, and to
whom I wish to thank: to Karine for always trying to help when I was having some doubts in
my experiments and for listening to my first oral presentation when I was so nervous; to
Sérgio, for showing me where (and how) to pick up the thymus and blood samples, and for
processing the thymus for me sometimes, when I was too busy; to André, Biagio, Cláudia,
Paula and Pedro, for their selfless help, their sympathy and their kind word during rough
times; to Helena, our more recent teammate, in whom I have found an unexpected friend,
thank you for that special hug when I needed the most; and to Sofia, your constant help is
priceless, you have literally saved the final step of my retrovirus experiment, so if I have
results to show here it is thanks to you!
I also wish to give thanks to some of my friends and family. To my dad and my grandparents,
for supporting me in this step of my education, for never judging and for taking care of me the
best you knew and you were able to. Thank you for your enormous heart!
To my closest friends Rita and Andreia, for listening and for giving me support, and for never
complaining about me not having the time to visit you, even if just to drink a coffee. Thank
you for being there for me, always!
Finally, there are two special persons to whom I owe and to whom I want to thank the most.
To Julie: my mentor, my teacher and my friend. You have always trusted in me and in my
skills from the beginning and you always believed I would thrive not only in the lab, but also
in other many aspects of my life. Thank you for being my role model: an excellent
hardworking scientist and also an excellent person, as I have only met few during my whole
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life. I could not have asked for a better supervisor. In you I could always find a word of
encouragement, a sparkle of optimism and a solution for every problem. Even when I saw no
light at the end of the tunnel, you did, and you always made me believe that we were able to
pull this through, as we did! So thank you for this marvelous journey, for teaching me, for
letting me try to do things my own way, for having my back every time and for making me go
beyond my limits, but most of all thank you for choosing me.
To my boyfriend: I could write a whole page just to thank you and still there would be a lot
more to say! Thanks for being there for me every step of the way, for helping picking me up
when I was down, for believing in me and for listening to me talking about my work, even
when I had the same conversation over and over again, hopping that you were able to provide
me with the answers. Thank you for waking up earlier than you had to just so you could make
me company in those hard days when I had to deal with my “ghosts”, I would never forget
that in a million years! Thank you for the sleepy breakfasts, the long conversations at late
(and earlier) hours, and for your unselfish and unconditional love. You always said that
everything was going to be just fine, and you were right, it really did!
I cannot finish without thanking to my guardian angel, my mom, who raised me to be the
women I am today, and to whom I turn to in my darkest hours. I know you are always looking
out for me and that somehow you had always given the strength to achieve my goals.
Wherever you are, I hope I am making you proud.
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Table of Contents
Abstract ................................................................................................................................................... 3
Resumo .................................................................................................................................................... 4
Acknowledgements ................................................................................................................................. 7
List of Figures ....................................................................................................................................... 11
List of Abbreviations ............................................................................................................................. 13
1. Introduction ....................................................................................................................................... 17
1.1 γδ T Cells as Key-Players in Immunity ....................................................................................... 17
1.1.1 Adaptive vs Innate Immune System ..................................................................................... 17
1.1.2 γδ T Cell Development and Differentiation ......................................................................... 18
1.1.3 γδ T Cell Target Recognition and Response ........................................................................ 21
1.2 Cancer and γδ T Cells .................................................................................................................. 23
1.2.1 The Immune System and Cancer .......................................................................................... 23
1.2.2 γδ T Cells in Cancer ............................................................................................................. 24
1.3 MiR Role in Gene Regulation ..................................................................................................... 27
1.3.1 MiR Biogenesis and Maturation ........................................................................................... 27
1.3.2 MiR Silencing Mechanisms ................................................................................................. 28
1.3.3 MiR-Mediated Regulation of T Cell Biology ...................................................................... 29
2. Aims of the Thesis ............................................................................................................................. 33
3. Materials and Methods ...................................................................................................................... 35
3.1 Ethics ........................................................................................................................................... 35
3.2 Lymphocyte Preparations ............................................................................................................ 35
3.3 Cell Culture ................................................................................................................................. 35
3.4. Quantitative RT-PCR ................................................................................................................. 36
3.5 γδ T cell Transfection using Neon System for Mimics Delivery ................................................ 36
3.6 MiR-Overexpressing Recombinant Retrovirus Production ......................................................... 37
3.7 γδ T cell Transduction with the Retroviral Constructs ................................................................ 38
3.8 FACS Analysis ............................................................................................................................ 39
3.9 Statistical Analysis ...................................................................................................................... 40
4. Results ............................................................................................................................................... 42
4.1 MiR Signature Associated with γδ T Cell Type 1 Differentiation .............................................. 42
4.2 RT-PCR Validation of the miR Candidates ................................................................................ 43
4.2.1 RT-PCR in γδ Thymocytes Validates Top Five miR Candidates ........................................ 43
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4.2.2 MiR Candidates Expression Profile in Peripheral γδ T Cells is more Similar to Freshly-
isolated than to IL-2 Cultured γδ Thymocytes .............................................................................. 44
4.2.3 Comparison of miR Candidates Expression on Vδ1 vs Vδ2 Subpopulations Shows No
Significant Differences in their Expression Levels ....................................................................... 46
4.3 Setup of the Conditions for an Efficient miR Transfection using the Neon System ................... 47
4.4 Analysis of the Impact of Mimics Delivery on γδ T Cell Differentiation and Proliferation ....... 50
4.4.1 Peripheral Vδ2 γδ T Cells Transfected with miR-135b, 10a and 20b Mimics tend to
Proliferate Less when Cultured with IL-7 plus IL-2 ..................................................................... 51
4.4.2 Peripheral γδ T Cells Transfected with the miR Mimics show No Significant Differences in
IFN-γ and TNF-α Production ........................................................................................................ 52
4.4.3 MiR-181a and miR-196b Overexpression Decreases TNF-α Production in Vδ1
Electroporated γδ Thymocytes ...................................................................................................... 53
4.5 Overexpression of Candidate miRs using Retroviral Constructs ................................................ 54
4.5.1 MiR Overexpression was Not Efficient for All of the miR Constructs ................................ 54
4.5.2 MiR-181a Overexpression in Peripheral γδ T Cells Increases Cell Proliferation ................ 55
4.5.3 MiR-181a Overexpression in Peripheral γδ T Cells Increases Vδ1 T Cell Activation and
Programmed Cell Death ................................................................................................................ 56
4.5.4 Overexpression of miR-181a in Peripheral γδ T Cells did not Influence their
Effector/Memory Phenotype ......................................................................................................... 58
4.5.5 Overexpression of miR-181a in Peripheral γδ T Cells Reduces IFN-γ and TNF-α Production
in the Vδ1 Subpopulation .............................................................................................................. 60
5. Discussion ......................................................................................................................................... 63
6. Future Plans ....................................................................................................................................... 72
7. Bibliography ...................................................................................................................................... 74
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List of Figures
Figure 1 - γδ T cell recognition and response. ...................................................................................... 22
Figure 2 – Inferred leukocyte frequencies and prognostic associations in 25 human cancers. ............. 24
Figure 3 – Anti-tumor functions of γδ T cells. ...................................................................................... 26
Figure 4 – The “linear” canonical pathway of miR processing. ............................................................ 29
Figure 5 – Schematic representation of the procedure to develop a retroviral construct with the DNA
of interest. .............................................................................................................................................. 38
Figure 6 – Schematic representation of the transfection process. ......................................................... 39
Figure 7 – FACS gating strategy. .......................................................................................................... 40
Figure 8 – Human γδ thymocytes are devoid of IFN-γ production and cytotoxic functions but IL-2
signal differentiates them into cytotoxic type 1 effector T cells. .......................................................... 42
Figure 9 – MiR signature associated with γδ T cell type 1 differentiation. ........................................... 43
Figure 10 - RT-PCR in γδ thymocytes validates top five miR candidates. ........................................... 44
Figure 11 - MiR candidates expression profile in peripheral γδ T cells is more similar to freshly-
isolated than to IL-2 cultured γδ thymocytes. ....................................................................................... 45
Figure 12 – MiR-135b, 10a and 20b expression is upregulated in IL-7 or IL-2 cultured peripheral γδ T
cells indicating a rupture in their resting status. .................................................................................... 46
Figure 13 - Comparison of miR candidates expression on Vδ1 vs Vδ2 subpopulations shows no
significant differences in their expression levels. .................................................................................. 47
Figure 14 – Transfection of the siRNACD45 using the Neon system proves to be efficient on silencing
CD45 in γδ T cells. ................................................................................................................................ 48
Figure 15 – Transfection of the siRNACD45 using the Neon system does not compromise γδ T cell
survival. ................................................................................................................................................. 49
Figure 16 – Neon protocol timeline for mimics delivery. ..................................................................... 50
Figure 17 – Mimics delivery to PBMC-isolated γδ T cells using the Neon system occurred efficiently.
............................................................................................................................................................... 50
Figure 18 – Peripheral Vδ2 γδ T cells transfected with miR-135b, 10a and 20b mimics tend to
proliferate less when cultured with IL-7 plus IL-2. ............................................................................... 51
Figure 19 – Peripheral γδ T cells transfected with the miR mimics show no significant differences in
IFN-γ and TNF-α production. ............................................................................................................... 52
Figure 20 – MiR-181a and miR-196b overexpression decreases TNF-α production in Vδ1
electroporated γδ thymocytes. ............................................................................................................... 53
Figure 21 –MiR overexpression was not efficient for all of the miR constructs: 3T3 cells. ................. 54
Figure 22 – MiR overexpression was not efficient for all of the miR constructs: γδ T cells. ............... 55
Figure 23 – MiR-181a overexpression in peripheral γδ T cells increases cell proliferation. ................ 56
Figure 24 – MiR-181a overexpression in peripheral γδ T cells increases Vδ1 T cell activation. ......... 57
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Figure 25 – MiR-181a overexpression in peripheral γδ T cells increases Vδ1 T cell programmed cell
death. ..................................................................................................................................................... 58
Figure 26 – Overexpression of miR-181a in peripheral γδ T cells did not influence their
effector/memory phenotype. ................................................................................................................. 59
Figure 27 – Overexpression of miR-181a in peripheral γδ T cells reduces IFN-γ and TNF-α production
in the Vδ1 subpopulation....................................................................................................................... 60
Figure 28 – Summary of the results obtained upon overexpression of miR-181a in peripheral γδ T
cells........................................................................................................................................................ 61
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List of Abbreviations
Ab: antibody
ADCC: Ab-dependent cell cytotoxicity
Ag: antigen
AGO: Argonaute
AICD: activation-induced cell death
APC: antigen-presenting cells
ATPase: F1-Adenylpyrophosphatase
B: bone marrow-derived
BFA: brefeldin
BTN: butyrophilin family
C. elegans: Caenorhabditis elegans
CD: cluster of differentiation
cDNA: complementary deoxyribonucleic acid
CLL: chronic lymphocytic leukemia
CM: central memory
CMV: cytomegalovirus
D: diversity
DC: dendritic cell
DETC: dendritic epidermal γδ T cells
DGCR8: DiGeorge syndrome critical region 8
DN: double-negative
DOT: delta one T
DP: double-positive
EA-1: early activation antigen
EM: effector memory
ERK: extracellular signal-related kinase
Exp5: exportin-5
FACS: fluorescence-activated cell sorting
FISH: fluorescent in situ hybridization
γδ NKT cells: NK1.1+ γδ cells
γδ17 cells: IL-17A-producing γδ cells
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 14
GFP: green fluorescent protein
GLUT1: glucose transporter 1
HEK293T TAT: human embryonic kidney 293 cells with large T antigen and a transcription
transactivator
HITS-CLIP: high-throughput sequencing of RNA isolated by cross-linking
immunoprecipitation
HIV: human immunodeficiency virus
HMBPP: (E)-4-hydroxy-3-methyl-but-2-enylpyrophosphate
IFN-γ: interferon-γ
Ig: immunoglobulin
IL: interleukin
iNKT: invariant natural killer T
IPP: isopentenyl pyrophosphate
J: junctional
LNA: locked nucleic acid
mAb: monoclonal Ab
MACS: magnetic cell sorting
MAPK: mitogen-activated protein kinase
MDSCs: myeloid derived suppressor cells
MFI: mean-fluorescence intensity
MHC: major histocompatibility complex
MICA: MHC Class-I chain-related A
MICB: MHC Class-I chain-related B
miR: microRNA
mRNA: messenger RNA
NEAA: minimum essential amino acids
NKG2D: natural killer group 2 member D
NK: natural killer
NKR: NK cell receptor
NKT: natural killer T
P-Ag: phosphoantigen
PAMP: pathogen-associated molecular patterns
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PBL: peripheral blood lymphocyte
PBMC: peripheral blood monocyte cells
PCR: polymerase chain reaction
Pen/Strep: penicillin and streptomycin
pMIG-PGW: MSCV-IRES-GFP-PGW plasmid
pre-miR: precursor miR
pri-miR: primary miR
PRKC: protein kinase C epsilon
PRR: pattern recognition receptor
qPCR: quantitative PCR
RISC: RNA-induced silencing complex
RNA: ribonucleic acid
RNase: ribonuclease
RNA-seq: RNA sequencing
RT: reverse transcriptase
siRNA: small interfering RNA
SNORD44: small nucleolar RNA C/D Box 44
SP: single-positive
T: thymic-derived
T-ALL: T cell acute lymphoblastic leukemia
TAL: tumor-associated leukocytes
TCR: T-cell receptor
TF: transcription factor
Th: T helper
TIL: tumor infiltrating lymphocytes
TLR: toll-like receptor
TME: tumor microenvironment
TNF-α: tumor necrosis factor α
UTR: untranslated region
V: variable
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Introduction
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1. Introduction
1.1 γδ T Cells as Key-Players in Immunity
1.1.1 Adaptive vs Innate Immune System
Defense against microbial assaults is an essential necessity for all living organisms.
Consequently, all life forms have evolved strategies that are designed to limit the invasion of
the host by microorganisms (Schenten and Medzhitov, 2011). Immunity, as it is known, refers
to the global ability of the host to resist the predation of microbes that would otherwise
destroy it. It has many facets, but the greatest dichotomy separates adaptive immunity –
usually known as “acquired immunity” – from innate immunity – also termed as “natural
immunity” or “innate resistance” (Hoebe et al., 2004).
Traditionally, innate immunity is assumed to be rapid, “non-specific”, and identical
qualitatively and quantitatively each time the same pathogen is encountered. Many of the
innate immune cells are considered to be short-lived, making “memory” a moot concept. On
the other hand, adaptive immunity features are considered to include the generation of long-
lived, antigen (Ag)-specific cells after initial exposure to an Ag or pathogen. Thus, these cells
respond faster and more robustly to subsequent encounters with the same Ag or pathogen,
which consists the basis of vaccination (Clem, 2011; Lanier and Sun, 2009).
The adaptive immunity is usually attributed to two classes of specialized cells: thymic-derived
(T) lymphocytes and bone marrow-derived (B) lymphocytes, which further differentiate into
plasma cells in order to secrete antibodies (Ab) (Lanier and Sun, 2009; Medzhitov and
Janeway, 2000).
Deletion of self-reactive T and B cell clones during lymphocyte development forms the basis
for the discrimination between self and non-self by the adaptive immune system (Schenten
and Medzhitov, 2011). Lymphocytes able to overcome this clonal deletion will display a
single kind of structurally unique receptor, creating a very large and diverse repertoire of
antigen receptors in the entire lymphocyte population, in a process called clonal selection,
thus increasing the probability of an individual lymphocyte to encounter an Ag that binds to
its receptor. Upon infection these lymphocytes are activated and start to proliferate, by a
process known as clonal expansion, a mechanism which is essential for the generation of an
efficient immune response (Medzhitov and Janeway, 2000; Mogensen et al., 2009).
Adaptive immunity comprises many of the same effector mechanisms that are used in the
innate immune system, but is able to target them with greater precision. However, it takes
three to five days for sufficient numbers of clones to be produced and to differentiate into
effector cells, creating a time gap that would allow for most pathogens to damage the host. In
contrast, the effector mechanisms of innate immunity are activated immediately after
infection and rapidly control infecting pathogen replication. For this reason, containing the
infection until the lymphocytes can begin to deal with it is considered the main function of the
innate immunity (Janeway et al., 2001; Medzhitov and Janeway, 2000).
Granulocytes, monocytes, macrophages, dendritic cells (DCs), and NK cells have been
delegated to the innate immune system, which also comprises epithelial cell barriers, proteins
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of the complement system and anti-microbial peptides, and also other soluble factors (Hoebe
et al., 2004; Lanier and Sun, 2009).
The innate immune response relies on recognition of evolutionarily conserved structures on
pathogens, termed pathogen-associated molecular patterns (PAMPs), through a limited
number of germ line-encoded pattern recognition receptors (PRRs), of which the family of
Toll-like receptors (TLRs) is the most well-known (Akira et al., 2006; Medzhitov and
Janeway, 2000; Mogensen, 2009). In specific cases, PRRs also recognize host factors as
“danger” signals, as for instance when they are present in aberrant locations or abnormal
molecular complexes as a consequence of inflammation, infection or in other types of cellular
stress (Beg, 2002; Mogensen, 2009).
PAMPs are characterized by being invariant among entire classes of pathogens, essential for
pathogen survival, and distinguishable from “self” (Janeway, 1989). Detection of PAMPs by
PRRs leads to the induction of inflammatory responses and innate host defenses. PRR-
induced signal transduction pathways ultimately result in the activation of gene expression
and synthesis of a broad range of molecules, which include: cytokines, chemokines, cell
adhesion molecules, and immunoreceptors (Akira et al., 2006; Iwasaki and Medzhitov, 2015;
Mogensen, 2009). In addition, the sensing of microbes by PRRs expressed on antigen-
presenting cells (APCs), particularly DCs, will lead to the activation of adaptive immune
responses (Iwasaki and Medzhitov, 2015).
It is now universally recognized that innate instruction of adaptive immunity is a critical step
that controls the activation, types, and duration of the adaptive immune response. Innate
instruction occurs initially during the interaction between APCs and T cells. While this
interaction is critical for the generation of an adaptive immune response, it is clear that innate
control of the adaptive immunity is a process that occurs at multiple stages throughout the
immune response and involves all cell types contributing to a particular response (Hoebe et
al., 2004; Schenten and Medzhitov, 2011). Importantly, the distinctions between innate and
adaptive immunity have recently become blurred: certain subsets of B and T cells, such as B1
cells, γδ T cells, and invariant natural killer T (iNKT) cells, are often referred to as innate-like
lymphocytes (Murphy et al., 2008); while some innate immune cells, as for instance the NK
cells, seem not to fit the conventions, by showing features normally attributed exclusively to
cells of the adaptive immune system (Lanier and Sun, 2009; O’Leary et al., 2006; Sun et al.,
2009). In this project we will be focusing our attention to the less well-known subset of T
lymphocytes, which seems to comprise features from both the adaptive and the innate
immune system: the γδ T cells.
1.1.2 γδ T Cell Development and Differentiation
T lymphocytes develop from a common lymphoid progenitor in the bone marrow that also
gives rise to B lymphocytes, but those progeny destined to give rise to T cells leave the bone
marrow and migrate to the thymus (Janeway et al., 2001). T cell differentiation in the thymus
can be divided into discrete stages based on CD4 and CD8 expression: CD4 and CD8 double-
negative (DN) early thymic progenitors; more differentiated CD4 and CD8 double-positive
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(DP) thymocytes and; differentiated CD4 or CD8 single-positive (SP) thymocytes. The DN
stage is heterogeneous and can be subdivided into four distinct subsets in mice, based on the
expression of CD44 and CD25 (Germain, 2002). In humans, three distinct DN stages can be
recognized: a CD34+CD38
-CD1a
- stage that represents the most immature thymic subset and
the consecutive CD34+CD38
+CD1a
- and CD34
+CD38
+CD1a
+ stages, with CD1a expression
correlating with T lineage commitment (Dik et al., 2005; Joachims et al., 2006).
T-cell development has to accommodate the production of two distinct lineages of T cells
with different types of T-cell receptor (TCR): αβ and γδ (Janeway et al., 2001). Although
most T cells express αβ TCR and either of the TCR-associated molecules CD4 or CD8 on
their surface, a small minority (1–10%) demonstrates a γδ TCR, predominantly CD4–CD8
–
(i.e. DN) phenotype, an observation that can be explained by the fact that γδ TCR directly
binds to an Ag superstructure in an manner that is independent of the Major
Histocompatibility Complex (MHC)/peptide complexes (Deniger et al., 2014; Girardi, 2006;
Kabelitz et al., 2014; Lafont et al., 2014). Expression of a γδ TCR heterodimer at the surface
of a T cell in the thymus inhibits recombination of βTCR-chain locus during the CD4–CD8
–
stage, leading to commitment of the T cell to the γδ lineage (Deniger et al., 2014; Xiong and
Raulet, 2007). When exiting the thymus, this DN status is typically maintained probably
because co-receptors are dispensable for functional γδ TCR binding to Ags (Hayday, 2009;
Prinz et al., 2013). Importantly, commitment to the γδ T cell lineage has consistently been
shown to be dependent on the Notch signaling pathway, with the Notch-activators NOTCH1
and NOTCH3 genes instructing the earliest human intrathymic precursors to adopt a γδ T-cell
fate (Ciofani et al., 2006; García-Peydró et al., 2003; Van de Walle et al., 2013).
γδ T cells are defined by the expression of γ and δ heterodimer of TCR chains (γTCR/δTCR)
(Deniger et al., 2014; Xiang et al., 2014). For these two TCR loci, recombination of variable
(V), diversity (D, for δ chain), and junctional (J) region sequence elements generates a TCR
diversity, similar to the generation of B-cell Ab diversity via recombination of heavy and light
chains (Girardi, 2006).
The human γδ TCR complex is composed by the γδ TCR itself and various CD3 chains
following the stoichiometry: TCRγδCD32γδζ2 (Siegers et al., 2007). The assembly of a γδ
TCR complex in thymic progenitors has immediate consequences for γδ T-cell development
as the “strong” signals stemming from this kind of TCR – when compared to the “weaker”
signals from the pre-TCR – drive γδ/αβ common precursors into the γδ lineage (Haks et al.,
2005; Hayes et al., 2005; Zarin et al., 2015).
Functional responses by γδ T cells can be stratified by the V region of the TCRδ chain, also
usually termed as Vδ (Deniger et al., 2014; Kabelitz et al., 2014). In humans, γδ T cells are
limited to a small repertoire of V gene segments when undergoing chain rearrangement in
comparison with those available for Vα, Vβ, Immunoglobulin (Ig) light or Ig heavy chain
rearrangements. Three main Vδ gene segments – Vδ1, Vδ2 and Vδ3 – are most frequently
used in the rearrangement of this chain, although there are five less used Vδ segments that
have both a Vδ and a Vα designation, due to the location of the δ locus within the α locus on
the 14th
human chromosome (Adams et al., 2015; Boneville et al., 2010; Thedrez et al.,
2007). Seven functional Vγ gene segments - Vγ2, Vγ3, Vγ4, Vγ5, Vγ8, Vγ9 and Vγ11 -
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located within the γ locus on chromosome 7 in humans are used for rearrangement of the γ
chain, but several Vγ pseudogenes who are also found in this locus are not used in
productively arranged γδ TCRs (Adams et al., 2015). This restricted repertoire of Vδ and Vγ
gene segments available for rearrangement has led to speculate that these TCRs recognized
only conserved self-proteins of low variability. However, even though there are much more V
elements that may be used for α and β gene rearrangements, γδ TCRs have a greater potential
diversity than αβ T cells because of their ability to incorporate multiple tandem copies of their
D elements (Adams et al., 2015; Elliott et al., 1988; Girardi, 2006).
Productive recombination and pairing of matching the γ and δ chains facilitates the transition
of a “γδ quality control checkpoint”, selecting γδ T-cell precursors for their competence to
transduce signals via their TCR (Livak et al., 1995; Passoni et al., 1997; Prinz et al., 2006;
Prinz et al., 2013). In mice, naïve γδ T cells – as defined by a CD44lo
CD27+CD62L
+
phenotype – can leave the thymus at this point and populate secondary lymphoid organs and
blood (Prinz et al., 2013; Turchinovich and Pennington, 2011). However, γδ T cells that do
not exit the thymus at this stage can undergo further intrathymic differentiation before they
are exported to the periphery. This differentiation process will result in the development of
multiple murine γδ T-cell subsets, starting with the dendritic epidermal γδ cells (DETCs),
followed by the IL-17A-producing γδ cells (γδ17 cells), and ultimately the NK1.1+ γδ cells
(γδ NKT cells) (Azuara et al., 1997; Haas et al., 2009; Prinz et al., 2013; Vicari et al., 1996;
Zarin et al., 2015). Also, γδ T cells can be further differentiated into IFN-γ producers
(Schmolka et al., 2013; Shibata et al., 2012; Shibata et al., 2014; Zarin et al., 2015).
Mouse and human γδ T cells share many developmental and functional properties (Bonneville
et al., 2010; Vantourout and Hayday, 2013), such as the fact that γδ T cells are highly
effective at killing tumor cells and providing IFN-γ-mediated protective responses against
cancer. Moreover, the main determinants of tumor cell recognition — namely the γδ TCR and
NK cell receptors (NKRs), such as the natural killer group 2 member D (NKG2D) — are
shared by both species (Correia et al., 2013; Silva-Santos et al., 2015).
However, in contrast to what has been observed in mice, where functional properties of γδ T
cells can be acquired during their development in the thymus by a process known as
“developmental pre-programming” (Ribot et al., 2009), human γδ thymocytes are immature
(Ribot et al., 2014). In fact, their Th1/cytotoxic functions are selectively acquired only upon
ex vivo stimulation with IL-2 or IL-15 – but not when using IL-4 or IL-7 – through a process
dependent on the MAPK/ERK signaling (Ribot et al., 2014). This stimulus induced the de
novo expression of the TFs T-bet and eomesodermin, as well as the cytolytic enzyme perforin,
required for the cytotoxic type 1 program (Ribot et al., 2014).
In humans, γδ T cells can usually be found in the human mucosa, tongue, vagina, intestine,
lung, liver, and skin and can comprise up to 50% of the T cell population in intestinal
epithelial lymphocytes. Interestingly, in adult thymus, Vγ3, Vγ4 and Vγ1 rearrangements are
suppressed by the programmed rearrangement process, favoring production of Vγ2+ and other
adult-type γδ T cells. This indicates that a maturation process might confer these cells with
their specific homing properties which enables them to exit the thymus and home to
secondary lymphoid organs (Xiong and Raulet, 2007).
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Additionally, circulating γδ T cells can be found in the blood and lymphoid organs and
represent up to 0,5–16% (on average: 4%) of all CD3+ cells found in adult peripheral blood
(Carding and Egan, 2002; Deniger et al., 2014; Lafont et al., 2014). Specifically, the Vδ2
isotype is expressed by 50–95% of γδ T cells from human peripheral blood, whereas TCRs
including the Vδ1 and/or Vδ3 isotypes are predominantly found in γδ T cells from tissues
(skin, intestine, thymus) (Bonneville et al., 2010; Deniger et al., 2014; Lafont et al., 2014,
Ribot et al., 2014).
1.1.3 γδ T Cell Target Recognition and Response
Usually considered innate-like T cells (deBarros et al., 2011; Lafont et al., 2014), γδ T cells
possess a combination of innate and adaptive immune cell qualities. In fact, they can
rearrange TCR genes to produce J diversity and develop a memory phenotype, which is a
feature of the adaptive immune system, but they can also use a restricted TCR as a pattern
recognition receptor, which is a feature of the innate immune system (Girardi, 2006).
Some of their main features include being involved in the stress response to injured epithelia
and in tissue homeostasis by limiting the dissemination of malignant or infected cells and by
regulating the nature of the subsequent adaptive immune response. Additionally, γδ T cells
have potent MHC-unrestricted cytotoxicity, a high potential for cytokine release and a broad-
spectrum recognition of cancer cells (Hannani et al., 2012). Furthermore, ligands that interact
with γδ TCRs can be MHC molecules, MHC-like molecules or MHC-unrelated molecules
(figure 1), and receptor recognition varies accordingly with the γδ T cell subtype (Bonneville
et al., 2010; Hayday, 2009).
Most of the known human γδ T cell ligands are specific for the Vδ1 or the Vδ2 isotype
(Deniger et al., 2014). Vδ1+ γδ T cells are capable of recognizing several members of the
MHC superfamily family, all of which deemed MHC-like ligands, including members of the
CD1 family presenting lipids (Adams et al., 2015; Bonneville et al., 2010; Cardigan and
Egan, 2002). For instance, both Vγ1Vδ1 and Vγ2Vδ1 recognize the non-polymorphic MHC
molecule CD1c; Vγ5Vδ1 is a receptor for a galactosylceramide-CD1d complexes commonly
described in the activation of natural killer T (NKT) cells (Deniger et al., 2014; Spada et al.,
2000) and; recently CD1d has been shown to be a ligand for at least a subset of both Vδ1+ and
Vδ3+ γδ T cells (Adams et al., 2015). Moreover, Vδ1
+ cells have specificity for MHC Class-I
chain-related A and B (MICA and MICB, respectively), molecules that participate in evasion
of immune surveillance following viral infection and are usually present on tumor cells in
response to cellular stress (Bonneville et al., 2010; Gomes et al., 2010; Spada et al., 2010; Xu
et al., 2011).
Vδ2+ γδ T cells have preferred pairing with Vγ9, originating Vγ9Vδ2 cells, and this is the
most extensively studied sub-group of human γδ T cells. Several of its ligands have already
been identified, which include: the phosphoantigen (P-Ag) isopentenyl pyrophosphate (IPP)
and its isomer (E)-4-hydroxy-3-methyl-but-2-enylpyrophosphate (HMBPP) (Alexander et al.,
2008); members of the butyrophilin family (BTN), such as BTN3A1 (Harly et al., 2012); the
F1-Adenylpyrophosphatase (ATPase) (Scotet et al., 2005); the apolipoprotein A-I (Scotet et
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al., 2005) and; Mycobacterium tuberculosis and Mycobacterium leprae (Constant et al., 1994;
Lafont et al., 2001).
In contrast to the Vδ2 subpopulation, much less is known about the Vδ1 subset and even less
is known about the cells expressing the remainder Vγ chains. However, similarly to the
observed in Vγ9Vδ2 T cells, there is indirect evidence of a Vγ3 role in immunity against
cytomegalovirus (CMV) (Déchanet et al., 1999; Vermijlen et al., 2010) and human
immunodeficiency virus (HIV) (Wesch et al., 1998).
Figure 1 - γδ T cell recognition and response. For γδ T cells to be suitable for stress surveillance they recognize a spectrum
of molecules signifying dysregulation. These molecules may be self-encoded TCR and NKG2D ligands (T10 and T22, P-
Ags, MICA, etc.), or non-self-encoded, e.g., common products of multiple pathogens (HMBPP), unique products of very
common pathogens (e.g., putative CMV or Herpes ligands), or TLR ligands. The cells can then deploy several types of
function appropriate to different types of stress, directed against non-self or self-targets. The responses are negatively
regulated via inhibitory receptors for MHC-I, UPA, and probably other ligands. [Hayday, 2009]
Besides activation through the TCR- ligand mechanisms, γδ T cells can also be indirectly
activated by pro-inflammatory cytokines released by TLR-induced DCs and NK cells
(Bonneville et al., 2010; Hannani et al., 2012; Hao et al., 2010). Human γδ T cells can also
recognize and be activated by Ab-opsonized cells or microorganisms through binding of
IgGs, which mediates Ab-dependent cell cytotoxicity (ADCC) (Chen and Freedman, 2008;
Seidel et al., 2014).
Upon activation, these cells become capable of producing inflammatory cytokines,
chemokines, directly lyse infected or malignant cells, and establish a memory response to
attack pathogens upon re-exposure (Deniger et al., 2014; Hayday, 2009; Wesch et al., 2014),
as described below.
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1.2 Cancer and γδ T Cells
1.2.1 The Immune System and Cancer
Bearing several differences when compared to normal cells, cancer cells have two unique
main characteristics: uncontrolled growth and metastasis (Zhou, 2014). Additionally, recent
studies have shown that cancer cells have eight hallmarks, which include sustained
proliferative signaling, evading growth suppressors, cell death resistance, replicative
immortality, angiogenesis induction, metastasis and invasion activity, energy metabolism
reprogramming, and evading immune destruction (Hanahan and Weinberg, 2011; Zhou,
2014). Currently, cancer can be treated by using several methodologies, which include
surgery (Recht and Houlihan, 1995), radiotherapy (Bijker et al., 2006), chemotherapy (Goffin
et al., 2010), biological therapy (Manganoni et al., 2000), hormone therapy (Prezioso et al.,
2004), and targeted therapies (Urruticoechea et al., 2010; Vanneman and Dranoff, 2012).
However, since an occurrence of 20-30 million new cases of cancer is predicted to occur all
over the world by 2030 and, of those, it is expected that 13-17 million people will die from
the disease, no cancer therapy presently available seems to be entirely satisfactory
(Katikireddi and Setty, 2013; Siegel et al., 2012). Thus, cancer treatment remains a
challenging issue for both scientists and clinicians.
Recent developments in immunology, as for instance the successes of sipuleucel‑T and
ipilimumab in Phase III clinical trials, have validated the principle that immunotherapy can
also extend cancer patient survival, stressing out this type of therapy as a new anti-tumor
promising candidate (Vanneman and Dranoff, 2012; Zhou, 2014). Interestingly, research
published over the past decade in tumor immunology has validated the concept of cancer
immune surveillance which predicts that the immune system can recognize precursors of
cancer and, in most cases, destroy these precursors before they become clinically apparent
(Dunn et al., 2002; Galon et al., 2013; Hamaï et al., 2010). In fact, several studies have
identified specific patterns of immune activation associated with patient survival, proving that
the immune system can recognize and eliminate aberrant cancer cells arising within the
human body (Zhou, 2014). Additionally, twenty two types of leukocytes have been recently
associated with twenty five different types of cancer, whose presence or absence can indicate
a favorable or an adverse prognostic depending on the tumor type (figure 2) (Gentles et al.,
2015), with a special emphasis for the favorable outcome attributed to γδ T cell presence.
Thus, it has become clear that the immune system not only protects the host against tumor
development but also sculpts the immunogenic phenotype of a developing tumor and can
favor the emergence of resistant tumor cell variants (Galon et al., 2013; Hamaï et al., 2010;
Zhou, 2014).
In cancer patients, the immune system is apparently not proficient in eliminating cancer cells,
suggesting a suppression of its anti-tumor function. Indeed, it has been shown that several
factors may contribute to this immunosuppression such as a low frequency of high-avidity
anti-tumor T cells or the presence of CD4+CD25
+ regulatory T cells (Dugué et al., 2013; Frey
and Monu, 2006; Zhou, 2014). Therefore, using immunological methods capable of removing
this anti-tumor immunosuppression and/or able to increase the anti-tumor immunity can be
very useful for cancer treatment (Zhou, 2014).
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Figure 2 – Inferred leukocyte frequencies and prognostic associations in 25 human cancers. A) Global prognostic
associations for 22 leukocyte types across 25 cancers (n = 5,782 tumors; left) and 14 solid non-brain tumors (n = 3,238
tumors; right), ranked by unweighted meta-z score, with a false discovery rate (FDR) threshold of 25% indicated for each
plot. B) Concordance and differences in tumor-associated leukocytes (TAL) prognostic associations between breast cancers
and lung adenocarcinoma. Resting and activated subsets in are indicated by “–” and “+”, respectively. Red and blue bars in
indicate adverse and favorable prognostic associations, respectively. [Adapted from Gentles et al., 2015]
It is by now well established that the immune context of the tumor microenvironment (TME)
can influence cancer progression and outcome. Within the TME, several sub-populations of
effector cells can participate in cancer cell control and removal. All subsets of immune cells
can be found within tumors, but their presence differs accordingly with the tumor type and
stage of the disease and also between individuals (Hanahan and Weinberg, 2011; Lafont et
al., 2014).
In jawed vertebrates, B cells, αβ T cells and γδ T cells use genetically recombined receptors to
survey the environment and mediate the host defenses against disease. Among these
populations, the best studied T cell lineage is the one expressing αβ TCRs: within this group
the T cells often described as ‘‘conventional’’ are the most fully understood; less well
understood are the αβ T cell specialized populations that recognize non-peptide presenting
MHC molecules, who are often present at high frequencies in particular tissues or organs
(Adams et al., 2015; Deniger et al., 2014). Even more enigmatic are cells that express a γδ
TCR since this cell lineage remains the most poorly understood in terms of Ag recognition
and also to what concerns differentiation into effector cell subsets (Adams et al., 2015;
Hayday, 2009; Ribot et al., 2014). Albeit this fact, γδ T cells are already being targeted for
cancer immunotherapy due to multiple promising preclinical studies (Gomes et al., 2010; Liu
et al., 2008; Silva-Santos et al., 2015).
1.2.2 γδ T Cells in Cancer
γδ T cells have been proposed as the first line of immune defense that responds to a variety of
stress-inducible or pathogen-associated proteins or metabolites (Hayday, 2009). However,
A B
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contrary to mouse γδ thymocytes, human γδ thymocytes are functionally immature, but
nonetheless are highly poised to become type 1 cytotoxic effector cells. In fact, exogenous IL
‑2 or IL‑15 signals alone, in the absence of TCR stimulation, can upregulate type 1 TFs and
endow γδ thymocytes with IFN-γ-producing and tumor-killing functions (Ribot et al., 2014;
Silva-Santos et al., 2015).
Therefore, it is not surprising that γδ T cells actively contribute to the immune response
against many tumors, which include lymphoma, myeloma, melanoma, breast, colon, lung,
ovary, and prostate cancer (Bouet-Toussaint et al., 2008; Cordova et al., 2012; Dieli et al.,
2007; Kang et al., 2009; Lafont et al., 2014; Meraviglia et al., 2010). Of note, when expanded
in vitro in the presence of IL‑2, γδ T cells isolated from patients with melanoma,
glioblastoma, neuroblastoma or renal, breast, lung, ovarian, colon, pancreatic or blood cancers
efficiently killed tumor cell lines and/or primary cancer samples (Lo Presti et al., 2014; Silva-
Santos et al., 2015). This anti-tumor role can be accomplished directly through their cytotoxic
activity against tumors (Lafont et al., 2014). Moreover, γδ T cells can indirectly regulate the
biological functions of other cells, such as DCs, NK cells, NKT cells, CD4+CD8
+ T cells and
IFN-γ-producing CD8+ T cells by producing the pro-inflammatory cytokines IFN-γ and tumor
necrosis factor (TNF)-α, by Ag presenting or by producing signaling agents, such as IL-17,
IL-10 and IL-4 (figure 3) (Bonneville et al., 2010; Hao et al., 2010; Lafont et al., 2014; Rei et
al., 2015; Thedrez et al., 2007).
Owing to these potent effector functions, γδ T cells are currently attractive mediators for
immunotherapy, namely against cancer. However, clinical trials completed to date have
shown objective responses of only 10-33% (Gomes et al., 2010). This lack of response to
treatment could be explained by a deficient expansion and/or functions of effector γδ T cells.
Of note, Vδ1+ T cell lines have generally outperformed their Vδ2
+ counterparts (Correia et al.,
2011; Lo Presti et al., 2014) which makes it somewhat paradoxical that almost all of the
clinical applications of γδ T cells have thus far concentrated on Vγ9Vδ2+ T cells (Silva-
Santos et al., 2015).
Research on γδ T cell activation molecular mechanisms has demonstrated that TCR co-
stimulation plays a central role in this process, thus creating a possibility for positive (in case
of infection or cancer) or negative (in chronic inflammation or autoimmunity) modulation of
γδ T cell responses in the clinic (Ribot and Silva-Santos, 2013). Namely, CD27 expression
endows Vγ9Vδ2 peripheral blood lymphocytes (PBLs) with enhanced proliferative capacity,
expanding the γδ T cell group capable of producing IFN-γ, which clearly could be useful for
cancer immunotherapy (deBarros et al., 2011).
Earlier this year, recent advances in γδ T cell-based immunotherapy gave rise to the first
clinical application based on the Vδ1 subpopulation reported thus far: Delta One T (DOT)
cells, described as highly reproducible cells for selective, large scale expansion and
differentiation of cytotoxic Vδ1+ T cells, have showed a high potential in pre-clinical models
of chronic lymphocytic leukemia (CLL). Importantly, development of these cells did not
require any genetic manipulation and was able to specifically targeted leukemic, but not
healthy cells, in vitro. Their application prevented wide-scale tumor dissemination to
peripheral organs in vivo, without any signs of healthy tissue damage, providing proof-of-
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 26
principle for clinical application of DOT cells in adoptive immunotherapy of CLL (Almeida
et al., 2016).
Figure 3 – Anti-tumor functions of γδ T cells. A) γδ T cells can recognize tumor cells through interaction with (i) TCR
ligands, such as P-Ags, F1-ATPase, BTN3A1, EPCR,…, and (ii) innate receptor ligands, such as ULBP, MICA/B, and
nectin-like 5. Following sensing of tumor antigens or stress signals, γδ T cells are activated and can kill tumor cells through
cytotoxic mechanisms that rely on the perforin/granzyme pathway, the death receptor pathway in response to TRAIL or Fas-
L expression, and ADCC in the presence of tumor-specific antibodies. B) γδ T cell activation leads to TNF-α and IFN-γ
production and CD40-L expression that promote DC maturation and T cell differentiation into Th1 cells. IL-17-producing γδ
Th17 cells favor Th17 effector cell development. Th1 and Th17 effector T cells display anti-tumor functions to control tumor
development. C) Through a trogocytosis mechanism, activated γδ T cells can capture and express CD1d molecules and then
promote iNKT cell activation. Activated γδ T cells can also display antigen-presenting cell functions (MHCI and II, CD40,
CD83, and CD86 expression) and activate both naïve and effector T cells with cytotoxic activity against tumor cells. D)
Activated γδ T cells can provide a co-stimulatory signal to NK cells through CD137L expression to promote their anti-tumor
activity. E) In the presence of specific signals, activated γδ T cells can display a Tfh profile (i.e., IL-4, IL-10, and CXCL13
production and CD40-L expression) to help B cell antibody production. Although not yet demonstrated, production of
antibodies against specific tumor antigens could be involved in the humoral anti-tumor response. [Lafont et al., 2014]
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 27
However, some reports have emerged that suggest a pro-tumor role of γδ T cells in cancer.
Despite the well-established concept of γδ T cells as potent anti-tumor tumor infiltrating
lymphocytes (TIL), a study in 2007 on human breast cancer surprisingly revealed a potential
pro-tumor function of these cells (Peng et al., 2007; Rei et al., 2015), with the γδ TILs being
the most significant predictor of relapse and poor survival in these cancer patients (Ma et al.,
2012; Silva-Santos et al., 2015). Moreover, recent reports have revealed an unexpected series
of pro-tumor functions of γδ T cells in mouse models and human patients (Rei et al., 2014,
Rei et al., 2015). In particular, IL-17-producing (Vδ1+) γδ T cells have been reported, for the
first time in human, to promote the chronic inflammation associated with colorectal cancer,
through recruitment of myeloid derived suppressor cells (MDSCs) (Wu et al., 2014).
Additionally, γδ T cells have been shown to promote cancer progression by enhancing
angiogenesis (Wakita et al., 2010). Once more, the common mediator for all of these
functions appears to be the cytokine IL-17, whose pathogenic effects seem to be able to
override the anti-tumor immune response orchestrated by IFN-γ production (Rei et al., 2015).
Given this apparent dual role of γδ T cells in the context of cancer, depending on their
cytokine profile, it becomes fundamental to understand the molecular mechanisms associated
with the regulation of their functional differentiation.
1.3 MiR Role in Gene Regulation
1.3.1 MiR Biogenesis and Maturation
MiRs are evolutionarily conserved, short (20–23-nucleotide), single-stranded, non-coding
RNAs that regulate target genes at the post-transcriptional level by antisense binding to their
target 3’-untranslated regions (UTRs) (Sheppard et al., 2014; Winter et al., 2009; Zhou et al.,
2011), which results in translational repression and/or degradation of the targeted transcript
(Carthew and Sontheimer, 2009). MiRs were first discovered in Caenorhabditis elegans (C.
elegans) in 1993 (Lee et al., 1993; Zhou et al., 2011), and are estimated to regulate about 90%
of human protein-coding genes, which clearly indicates the powerful role of miRs in
regulating human genes (Friedman et al., 2008; Mogilyansky and Rigoutsos, 2013). In
mammalian cells, more than 700 miRs have been already identified and shown to regulate
many developmental and differentiation processes (Friedman et al., 2008).
Most of the human miRs reside in intergenic regions and use their own gene promoter for
expression (Lau et al., 2001; Lee et al., 2004; Zhou et al., 2011). The remainders are located
mostly in the introns of coding genes and are generally transcribed coincidentally with their
host genes (Saini et al., 2007).
MiR genes are very similar to protein coding genes since the majority of them are transcribed
by RNA Polymerase II, resulting in primary miR (pri-miR) transcripts that are then capped
and polyadenylated (Cai et al., 2004; Lee et al., 2004). Moreover, miR promoters are
regulated by the same epigenetic marks as those used in protein coding genes (Barski et al.,
2009). In the nucleus, the double-stranded RNA hairpin structure in a pri-miR transcript is
processed by a type III ribonuclease (RNase III) known as Drosha and a non-catalytic protein
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named DiGeorge syndrome critical region 8 (DGCR8). By forming a complex with the RNase
III enzyme, DGCR8 orients the catalytic domain of Drosha that releases hairpins from pri-
miRs, resulting into 60–80 nucleotide stem-loop structures, called precursor miRs (pre-miRs)
(Gregory et al., 2006; Zhou et al., 2011). However, not all miRs are processed in this way. A
special subset, known as mirtrons are not cleaved by Drosha, as the spliced intron already
corresponds to a specific processed miR precursor (Okamura et al., 2007).
After being formed, pre-miRs fold into small helical structures, allowing for their recognition
by Exportin-5 (Exp5). Then, Exp5 in complex with Ran-GTP exports the pre-miR from the
nucleus to the cytoplasm, where a second type III RNase, Dicer, cleaves the pre-miR into 18–
24 base pair duplexes, generating mature miRs (Winter et al., 2009). These mature species
contain a guide strand, which targets specific messenger RNAs (mRNAs) through the seed
sequence, and an antisense strand, also known as passenger strand or miR* strand (Meijer et
al., 2014). During miR maturation in the cytoplasm, the Argonaute protein (AGO) – a critical
component of RNA-induced silencing complexes (RISC) – is thought to stabilize the guide
strand, which is crucial for miR function (Kai and Pasquinelli, 2010; Zhou et al., 2011).
1.3.2 MiR Silencing Mechanisms
In order to elicit their silencing mechanisms, mature miRs are incorporated into the RISC by
loading their guide strand into the AGO protein. This protein normally uses the seed sequence
of the 5’ terminus – which is the thermodynamically less-stable end of the miR duplex – to
recognize complementary mRNA transcripts and then proceed with their degradation or
translational silencing (figure 4) (Bartel, 2004; Hutvágner and Zamore, 2002; Podshivalova
and Salomon, 2013; Winter et al., 2009).
There are four AGO proteins in human cells (AGO1-AGO4). AGO2 possesses catalytic
activity and can cleave bound mRNAs. The function of AGO1 is less well-defined but it can
affect miR-mediated inhibition of translation, splicing, and transcription (Matsui et al., 2015).
Although all AGO proteins have the ability to interact with small RNAs, it has been shown
that passenger strand cleavage and RNA chaperone activities that are intrinsic to both AGO1
and AGO2 are sufficient to load these small RNAs into the RISC complex (Wang et al., 2009;
Wang et al., 2012), highlighting the importance of these two proteins in the miR silencing
mechanisms.
Assembly of the miR into RISC is regulated by thermodynamic properties but it can also be
subject to additional regulation as the ratio miR:miR* varies dramatically depending on the
miR duplex properties, on the tissue where they are being processed and on the
developmental stages (Ro et al., 2007). Importantly, although direct cleavage of the targeted
mRNA will cause a reduction of the mRNA levels, inhibition of protein translation will not
affect the mRNA levels of the targeted protein (Zhou et al., 2011).
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 29
Figure 4 – The “linear” canonical pathway of miR processing. Canonical maturation includes production of the pri-miR
by RNA polymerase II (or III) and cleavage of the pri-miR by the Drosha–DGCR8 complex in the nucleus. The resulting pre-
miR, is exported by Exp5–Ran-GTP. Once in the cytoplasm, Dicer cleaves the pre-miR hairpin to its mature length. The
functional (guide) strand of the mature miR is loaded together with AGO proteins into the RISC, where it guides RISC to
silence target mRNAs, whereas the passenger strand (black) is degraded [Winter et al., 2009]
1.3.3 MiR-Mediated Regulation of T Cell Biology
MiR expression patterns vary among lymphocyte subsets and stages of development,
indicating that these small RNAs may contribute to lymphocyte identity or functional state
(Jeker and Bluestone, 2013). By instance, miR expression profiles at each thymic T cell
developmental stage have been shown to be unique, with some miRs undergoing expression
changes up to three orders of magnitude during maturation (Kirigin et al., 2012).
Accordingly, removal of all mature miRs at early stages of thymocyte development via Dicer
or Drosha knockouts has resulted in developmental blockage and consequent reduction of the
peripheral mature αβ T and iNKT cell pool (Cobb et al., 2005; Podshivalova and Salomon,
2013; Seo et al., 2010; Zhou et al., 2011).
Furthermore, it has been shown that expression of miR-181 promotes NK cell development,
through the suppression of NLK – a Notch inhibitor – providing an important link between
miRs and this signaling pathway (Cichocki et al., 2011). In agreement with this, deletion of
miR-181a-1/b-1 inhibited the development of Notch1 oncogene-induced T cell acute
lymphoblastic leukemia (T-ALL). Importantly, Notch oncogenes use normal thymic
progenitor cell genetic programs for tumor transformation. However, comparative analyses of
miR-181a-1/b-1 function in normal thymocyte and tumor development demonstrated that
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these miRs could be specifically targeted to inhibit tumor development with little toxicity to
normal development (Fragoso et al., 2012).
MiRs have also been implicated in T cell survival and proliferation. For instance,
overexpression of miR-491 in CD8+ and CD4
+ T cells inhibited cell proliferation and
promoted apoptosis. This inhibition was probably achieved by targeting the pro-survival
protein Bcl-xL, the CDK4 and the TF-1 proteins, involved in cell proliferation (Yu et al.,
2016). Also, in tumor-reactive CD8+ T cells, miR-29b and miR-198 overexpression reduced
anti-apoptotic and proliferation-associated gene products by down-modulating JAK3 and
MCL-1, leading to immune dysfunction (Gigante et al., 2016).
Several other miRs have also been shown to impact on T cell functional differentiation. As an
example, overexpressing miR-491 decreased the IFN-γ production in CD8+ T cells (Yu et al.,
2016). Analogously, miR-146a expression in these cells was correlated with a memory
phenotype (Sheppard et al., 2014). Furthermore, this miR controlled Th1-cell differentiation
of human CD4+ T lymphocytes by targeting protein kinase C epsilon (PRKC) (Möhnle et al.,
2015). Similarly, when overexpressed, miR-20a inhibited TCR-mediated signaling and
cytokine production in human naïve CD4+ T cells (Reddycherla et al., 2015), while miR-583
impaired NK cells differentiation process (Yun et al., 2014). Moreover, miR-181a expression
was shown to regulate the activation of human memory Th17 cells through modulation of
ERK phosphorylation (Mele et al., 2015), highlighting once more the importance of this miR
in regulating T cell biology.
In mice, CD8+ T cells failed to undergo robust expansion and differentiation into short-lived
terminal effector cells in response to primary infection with Listeria monocytogenes or
Vaccinia virus in the absence of miR-150 (Smith et al., 2015). Importantly, this miR has been
identified as a positive regulator of IL-10 cytokine secretion in Th1 cells (King et al., 2016)
and is also able to regulate the Notch signaling by reducing NOTCH3 levels in T-cell lines
(Ghisi et al., 2011).
MiR functions have also been implied in regulating oncogenesis, some of them by impairing
and others by enhancing T cell functions. For instance, miR-152 overexpression enhanced NK
cytolysis against hepatoma cells, while miR-155 overexpression increased CD8+ T-cell anti-
tumor activity by enhancing responsiveness to homeostatic γc cytokines (Bian et al., 2015; Ji
et al., 2015). In contrast, miR-23a expression worked as a strong functional repressor of the
TF BLIMP-1, impairing T lymphocyte cytotoxicity and effector differentiation. Blockage of
miR-23a in CD8+ cytotoxic T lymphocytes prevented tumor-dependent immunosuppression
by restoring these cells functional properties (Lin et al., 2014). Consistently, overexpressing
the miR-23a cluster (which includes miR-23a, 27a, and 24) reduced IFN-γ levels and Ag-
specific cytotoxicity in human CD8+ T cells (Chandran et al., 2014).
Interestingly, some miRs have also been implied in regulating the T cell metabolism. For
instance, downregulation of miR-150 in human activated CD4+
T cells increased the SCL2A1
gene expression. The latter encodes for the glucose transporter 1 (GLUT1) protein, which has
great importance in the cellular metabolism (King et al., 2016). Additionally, in cases of
ovarian cancer a glucose restriction was shown to be imposed on T cells, thus dampening
their functions by maintaining high levels of miR-101 and miR-26a. Such restriction
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constrained the methyltransferase EZH2 expression, which is an important epigenetic
modifier (Zhao et al., 2016). Importantly, EZH2 was able to activate the Notch pathway by
suppressing Notch repressors. Consequently, this suppression stimulated T cell polyfunctional
cytokine expression and promoted their survival via Bcl-2 signaling (Zhao et al., 2016).
Altogether these results establish an important link between cellular metabolism,
differentiation and proliferation processes in T cells, mediated by miRs expression.
This notwithstanding, no miRs have yet been shown to be implicated in the differentiation of
human γδ T cells. Assessing the functional roles of these RNA species that regulate the
differentiation of anti-tumor γδ T cells could open avenues for the manipulation of these cells
in cancer immunotherapy.
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Aims of the Thesis
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2. Aims of the Thesis
Recent data from the host laboratory have reported that the anti-tumor effector properties of
γδ T cells were selectively acquired upon stimulation with IL-2 or IL-15, but not IL-7 (Ribot
et al., 2014). The effects of IL-2/IL-15 depended on MAPK/ERK signaling and induced the
de novo expression of the TFs T-bet and eomesodermin, as well as the cytolytic enzyme
perforin, required for the cytotoxic type 1 program. With this project, we propose to explore
an additional layer in the regulation of γδ T cell differentiation, by characterizing the post-
transcriptional mechanisms mediated by miRs.
The study of gene expression regulation has been largely dominated by transcriptional
mechanisms. However, the recent identification of small non-coding miRs has dramatically
modified our perception of biological processes. MiRs exert a strong inhibitory function on
the expression of mRNAs, either promoting their degradation, or preventing their translation.
Since this key post-transcriptional process controls the expression of most mammalian genes
(Berezikov et al., 2011), it becomes particularly interesting to analyze its role in cellular
differentiation processes.
Building on these considerations, we proposed to investigate the miR-dependent mechanisms
controlling the functional differentiation of human γδ T cells. In particular, we were interested
in identifying novel targets to be considered for the manipulation of γδ T cells in cancer
immunotherapy.
Thus, our work plan consisted of two main goals:
1) Characterize the miR repertoire associated with γδ T cell functional differentiation by
RNA-seq. The bioinformatic analysis of these results allowed us to identify discrete
repertoires of miRs associated with functional differentiation in human γδ T cells. We then
screened and validated, by RT-PCR, candidate miRs carefully selected from the library
obtained by the RNA-seq assay.
2) Characterize the role of selected miRs by performing gain-of-function experiments.
For this, we developed two different gain-of-function approaches: electroporation with miR
mimics using the Neon system or retroviral transduction of miR vectors on γδ T cells isolated
from healthy donors peripheral blood monocyte cells (PBMCs) or thymic biopsies.
This project constitutes the first comprehensive study of the miR-based post-transcriptional
events that control human γδ T cell functional differentiation. The recent molecular biology
tools associated with miR analyses and manipulation will serve the purpose of understanding
this process and identifying novel molecular targets valuable for the design of new clinical
protocols for cancer immunotherapy.
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Materials and Methods
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 35
3. Materials and Methods
3.1 Ethics
Thymic specimens were routinely obtained during pediatric corrective cardiac surgery thanks
to an ongoing collaboration with Hospital de Santa Cruz (Carnaxide) and Dr. Ana E. Sousa
(IMM). Peripheral blood γδ T cells from healthy volunteers were obtained through the
Instituto Português do Sangue (Lisboa).
3.2 Lymphocyte Preparations
Thymic samples (from newborn to eight-year-old children) were processed by tissue
dispersion and Histopaque-1077 (Sigma-Aldrich) density gradient. PBMCs were collected
from anonymous healthy volunteers, diluted with 1 volume of PBS (Invitrogen Life
Technologies), and separated on a Histopaque-1077 density gradient.
γδ T cells were isolated (to >90% purity) through magnetic cell sorting (MACS) by positive
selection for the RT-PCR and Neon experiments or by negative selection for the retrovirus
experiments (both from Miltenyi Biotec). Alternatively, Vδ1 and Vδ2 T cells were
electronically sorted on a fluorescence-activated cell sorting (FACS) Aria cell sorter (BD
Biosciences).
3.3 Cell Culture
MACS isolated γδ T cells were cultured at a density of 5*105 cells/ml in round bottom 96-
well plates with RPMI 1640 and 2 mM L-glutamine supplemented with 10% FBS, 1% of
sodium pyruvate, 1% of HEPES, 1% of minimum essential amino acids (NEAA) , and 1% of
penicillin and streptomycin (Pen/Strep) (all from Invitrogen Life Technologies). Indicated
cytokines were added when mentioned (all from PreproTech, 10ng/ml).
Alternatively, for retroviral transduction experiments, γδ T cells were cultured at a 2*106
cell/ml density, in flat bottom 48-well plates in a DMEM medium (Invitrogen Life
Technologies) supplemented with 15% FBS, 1% of HEPES, 1% NEAA, and 1% of Pen/Strep.
Indicated cytokines were added when mentioned (all from PreproTech, 10ng/ml).
The human embryonic kidney 293 cells with large T antigen and a transcription transactivator
(HEK293T TAT) cell line (ATCC® CRL-11268™) and the NIH/3T3 cells (ATCC® CRL-
1658™) were grown in T 175 flasks in DMEM supplemented with 10% FBS, 1% of HEPES,
1% of NEAA, and 1% of Pen/Strep, during four to five days. After reaching the required cell
number for the experiment, the HEK293T TAT cell line was cultured in 10cm tissue culture
plates (Sigma-Aldrich TPP®) at a density of 5*10
5 cells/ml, whilst the NIH/3T3 cells were
cultured in flat bottom 6-well plates (Corning Incorporated) at a density of 7*104 cells/ml.
All incubations were performed at 37°C in a 5% CO2 environment.
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 36
3.4. Quantitative RT-PCR
For miR expression analysis total RNA was extracted from MACS isolated γδ T cells by
using the miRNeasy Mini Kit (Qiagen) according to the manufacturer’s protocol. RNA
concentration and purity were determined using Qubit® RNA HS (High Sensitivity) Assay
Kit (Thermo Fisher Scientific). For complementary deoxyribonucleic acid (cDNA) synthesis
and qPCR amplification the miRCURY locked nucleic acid (LNA)TM
Universal RT miR PCR
protocol (Exiqon, 2014) was performed. Total RNA was reverse-transcribed into cDNA using
the T100® Thermal Cycler (BioRad) and all quantitative PCRs (qPCRs) were performed in
MicroAmp® Optical 384-Well Reaction Plate (Applied Biosystems) using the RT-PCR
ViiA7TM
system (Applied Biosystems). LNATM
PCR primer sets (Exiqon) were used on the
cDNA previously obtained and relative quantification of specific miRs to small nucleolar
RNA C/D Box 44 (SNORD44) reference was carried out using SYBR on ABI ViiA7TM
cycler
(Applied Biosystems). Data was analyzed using ViiA7TM
software v1.2.1.
3.5 γδ T cell Transfection using Neon System for Mimics Delivery
When indicated, MACS isolated γδ T cells were activated with anti-CD3 (HIT3a) and anti-
CD28 (CD28.2) (both from eBioscience) and cultured in round bottom 96-well plates with the
indicated cytokines. Then, they were transfected by the Neon electroporation transfection
system (Invitrogen) with an optimized version of the manufacturers recommended protocol
(Invitrogen, 2010). Briefly, the small interfering-RNA (siRNA)CD45 or the selected
miRCURY LNA™ miR Mimics (both from Exiqon) were transfected using indicated
concentrations. After testing different parameters with the siRNACD45, which was used only
for the setup, the settings were optimized at 1600V with three 10 ms pulses, using 25.000 γδ
T cells per transfection. As a negative control a miR mimic that has the same unique design
with two LNA™-enhanced passenger strands as the miRCURY LNA™ miR Mimics, and
whose guide strands have no homology to any known miR or mRNA sequences in mouse, rat
or human was used. The target sequence for each miR mimic was the following:
- hsa-miR-135b-5p: UAUGGCUUUUCAUUCCUAUGUGA
- hsa-miR-10a-5p: UACCCUGUAGAUCCGAAUUUGUG
- hsa-miR-20b-5p: CAAAGUGCUCAUAGUGCAGGUAG
- hsa-miR-181a-2-3p: ACCACUGACCGUUGACUGUACC
- hsa-miR-196b-5p: UAGGUAGUUUCCUGUUGUUGGG
- cel-miR-39-3p (negative control): UCACCGGGUGUAAAUCAGCUUG
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 37
3.6 MiR-Overexpressing Recombinant Retrovirus Production
The native precursor stem-loop of the miRs candidates was cloned into the retroviral vector
MSCV-IRES-GFP-PGW (pMIG-PGW) using genomic DNA as a template and the following
primers (table 1):
Table 1 - Gene-specific primer design with 15bp extensions homologous to vector ends (in green for the forward primer, or
red for the reverse primer) for cloning of each one of the miR candidates into the pMig-PGW plasmid.
Primer Design for pMIG-PGW cloning Fwr_pMIG-PGW_miR-135b: CGCCGGAATTAGATCGAAGCCACCCCTCCTCATAC
Rev_pMIG-PGW_miR-135b: TAACCTCGAGAGATCACCGACTCAGAAAGCTCAGC
Fwr_pMIG-PGW_miR-10a: CGCCGGAATTAGATCAGGTTCTCGTCCCTTTGCG
Rev_pMIG-PGW_miR-10a: TAACCTCGAGAGATCTGCCTTCTTCGCACACTGA
Fwr_pMIG-PGW_miR-20b: CGCCGGAATTAGATCTGCAGTTAGTGAAGCAGCTTAGA
Rev_pMIG-PGW_miR-20b: TAACCTCGAGAGATCCCCGACAACAGCAAAACAGAT
Fwr_pMIG-PGW_miR-181a: CGCCGGAATTAGATCTCCTAGTATATAGCAGATCCCCAAT
Rev_pMIG-PGW_miR-181a: TAACCTCGAGAGATCGACTGCTCCTTACCTTGTTGAA
Fwr_pMIG-PGW_miR-196b: CGCCGGAATTAGATCTCCTCTCTCCCTGCCTTTCC
Rev_pMIG-PGW_miR-196b: TAACCTCGAGAGATCGGGACTGGTGTGTGTGTGT
After having the PCR product in which our miR-loop of interest was already amplified, the
Protocol I: In-Fusion Cloning Procedure w/Spin-Column Purification (Clontech, 2012) was
used to purify the PCR fragments (figure 5). For the first transformation process the StellarTM
Competent Cells were used according to the manufacturer’s protocol PT5055-2 (Clontech,
2011).
The plasmid pMIG-PGW either empty (pMIG-PGW-Empty) or containing the miR candidate
stem-loop fragment (pMIG-PGW-miR-X) was extracted from the bacteria using the GeneJET
Plasmid Miniprep Kit (Thermo Fisher Scientific). To confirm the presence of the miR insert
on the plasmid a DNA digestion using proper restriction enzymes was performed by
following the protocol for Fast Digestion of DNA (Thermo Fisher Scientific, 2012).
Besides the confirmation made by analyzing the samples fragment lengths in an agarose gel,
the presence of the miR candidates in each plasmid was accomplished by sending the samples
to sequence (STAB VIDA). Validation of the results was performed by analyzing the
sequences, expected and obtained, with the SnapGene® Viewer Software, Version 3.1.4.
After confirming each miR stem-loop presence and its integrity in the pMIG-PGW vector a
second transformation step was performed using a more stable strain of bacteria: this
transformation procedure followed the protocol One ShotTM Stbl3TM Chemically
Competent E. coli (Thermo Fisher Scientific, 2015). After a new plasmid purification and
another DNA digestion to confirm the presence of the clones, the viral supernatant was
produced by transfecting the plasmids pMIG-PGW (either empty or with miR-X), pCMV-
VSV-G and pCL-Eco with the Opti-MEM® (Life Technologies) and the X-tremeGENE9
DNA Transfection Reagent (Roche) into the HEK293T TAT cells cultured in 10cm culture
plates, as previously described (x-TremeGene, 2013). The efficiency of the viral particles was
tested by transducing 100.000 NIH/3T3 cells per well, in a Nunc™ 6-well plate, with 8μg/ml
of polybrene during a 60 min centrifugation at 37ºC, 2200 rpm. The cells rested in the
incubator at 37º C for 72h, washed with FACS buffer (1500 rpm 5 min), and resuspended in
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 38
FACS buffer for FACS acquisition. As readout the percentage of green fluorescent protein
(GFP)+ cells was measured to assess the transduction efficiency.
Figure 5 – Schematic representation of the procedure to develop a retroviral construct with the DNA of interest.
[Adapted from Clontech, 2012]
3.7 γδ T cell Transduction with the Retroviral Constructs
For viral transduction of the γδ T cells, the latter were cultured in 48-well plate as previously
described. The cells were activated overnight with plate-bound monoclonal Abs (mAbs) anti-
CD3 (1μg/ml; HIT3a; eBiosciences) and anti-CD28 (1μg/ml; CD28.2; eBiosciences) in
presence of IL-7 or IL-7+IL-2 (10 ng/m PeproTech). 5*105 γδ T cells per well were
transduced with pMIG-PGW-miR-20b, pMIG-PGW-miR-181a or pMIG-PGW-Empty vector
using 160μl of concentrated viral supernatant per well (figure 6). The transduction was
performed with the addition of 100uL of polybrene (8μg/ml) followed by a 45 min
centrifugation at 37ºC, 2200 rpm. After 48h the transduced γδ T cells were restimulated and
stained with mAbs for FACS analysis of their functional properties, as explained in the next
section.
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 39
Figure 6 – Schematic representation of the transfection process. Three different plasmids are required in order to produce
the virus - pMIG-PGW, pCMV-VSV-G and pCL-Eco. Virus production occurs in the HEK293T TAT cells; viral particles
are tested in the 3T3 cells for the GFP presence. Only then are the γδ T cells infected with the virus.
3.8 FACS Analysis
To assess γδ T cell proliferation, cytokine secretion and phenotypic features γδ T cells were
restimulated with PMA (50 ng/ml, Sigma-Aldrich; P-8139) and Ionomycin (1 μg/ml, Sigma-
Aldrich; I-0634) for 3h-4h at 37°C, with the addition of Brefeldin A (BFA) (10 μg/ml, Sigma-
Aldrich; B-7651). Cells were then transferred into 96-well V bottom plates (Nunc® Thermo
Fisher Scientific) and washed once in FACS buffer (1x PBS plus 2% FBS) supplemented with
BFA for 5 min, 1500 rpm at room temperature. For surface staining, cells were resuspended
in 50μl FACS buffer supplemented with BFA containing the indicated antibodies and
incubated for 30 min at 4ºC. Cells were then washed with FACS buffer supplemented with
BFA, resuspended in 150uL Fixation/Permeabilization buffer (eBiosciences) supplemented
with BFA and left to incubate for 30 min at 4ºC. Alternatively, to ensure the maintenance of
GFP expression, retrovirally transduced γδ T cells were fixed with 4% Paraformaldehyde
(Sigma-Aldrich) (Grupillo et al., 2011). Cells were then washed in Permeabilization buffer
(eBiosciences) and resuspended in Fc block (eBiosciences) for 10 min at 4ºC. For
intracellular cell staining the cells were incubated for 30 min at 4°C in 50μl of
FFULisboa
Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 40
Permeabilization buffer containing the indicated antibodies. Cells were washed twice, first in
Permeabilization buffer and then in FACS buffer, and resuspended in 200μl of FACS buffer
for FACS analysis (figure 7).
For the Neon experiments, cells were labeled with the following fluorescent mAbs: anti–Vδ1-
FITC (TS8.2) (Thermo Fisher Scientific); anti–Vδ2-PerCP (B6), anti–TNF-α-PE-Cy7
(MAb11), anti–IFN-γ-PE (4S.B3), anti-Ki67-BV421 (Ki-67) (all from BioLegend);
LiveDead-APC-Cy7 (Life Technologies); and anti-CD45-PerCP for the Neon setup (2D1)
(eBiosciences). For the retroviral experiments cells were labelled with the following
fluorescent mAbs: anti-Vδ1-PE (REA173) (Miltenyibiotec); anti-Annexin V-APC (VAA-33),
anti–CD27-PE-Cy7 (LG.7F9), anti- IFN-γ-APC (4S.B3) (all from eBiosciences); anti-CD69-
BV421 (FN50), anti-CD45RA-PB (HI100), anti-Ki67-PB (Ki-67), anti–TNF-α-PE-Cy7
(MAb11) and anti-Vδ2-PerCP (B6) (all from Biolegend).
All incubations were performed in the dark. FACS acquisition was performed on FACS
Fortessa (BD Biosciences) cell analyzer.
All the data were analyzed using FlowJo v.9.8.1 software.
Figure 7 – FACS gating strategy. LiveDead negative cells were identified after selection of Live Cells (from previously
MACS isolated γδ T cells) and doublets exclusion. The Vδ1 and Vδ2 subpopulations were then identified within the
LiveDead negative subset.
3.9 Statistical Analysis
Differences between populations were assessed using the Student t test and are indicated in
the figures when significant. *p < 0,05; **p < 0,005; ***p < 0,001.
SS
C-A
FSC-A
FS
C-W
FSC-A LiveDead
Vδ
1
0 103
104
105
0
103
104
105
160613 gd BC21 mimics IL7+IL2_miR C elegans.fcs…live´
<PerCP-A>: Vd2
<F
ITC
-A>
: V
d1
40.1
19.8
Vδ2
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 41
Results
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 42
4. Results
4.1 MiR Signature Associated with γδ T Cell Type 1 Differentiation
As illustrated below (figure 8), human γδ thymocytes are functionally immature and
differentiate into IFN-γ+ TNF-α
+ type 1 effector T cells upon IL-2 (but not IL-7) stimulation
(Ribot et al., 2014).
Figure 8 – Human γδ thymocytes are devoid of IFN-γ production and cytotoxic functions but IL-2 signal differentiates
them into cytotoxic type 1 effector T cells. MACS-purified γδ thymocytes either freshly isolated or cultured for 7 days in
the presence of the indicated cytokines, stained for IFN-γ and TNF-α following 4h of stimulation with PMA and ionomycin.
[Adapted from Ribot et al., 2014]
The effects caused by IL-2 depended on the MAPK/ERK signaling and induced de novo
expression of the TFs T-bet and eomesodermin, as well as the cytolytic enzyme perforin
(Ribot et al., 2014).
MiR discovery has brought another degree of complexity to γδ T cell regulation and clearly
needs to be considered when deciphering the molecular mechanisms that govern γδ T cell
differentiation. Thus, we decided to analyze by RNA-seq the miRs signature associated with
IL-2 (mature) versus IL-7 (immature) signaling in human γδ thymocytes. The unbiased
analysis of these results provided for the identification of discrete repertoires of miRs
associated with regulation of the transcriptional signatures for γδ T cell functional
differentiation.
We first proposed to select from this library 20 of the most abundant (in term of copy
numbers) and top differentially (all above 1.5 log fold-change) expressed miRs. These criteria
allowed us to build a heatmap highlighting the top candidate miRs for further screening
(figure 9). Whereas some miRs, such as miR-642a/10a/34c/20b/221 display a higher level of
expression within functional γδ T cells, others, namely miR-181a/196b/150/574/135a are
instead downregulated in those samples in comparison with immature γδ T cell controls.
Fresh
IL-7 IL-2
TNF-α
IFN
-γ
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 43
Figure 9 – MiR signature associated with γδ T cell type 1 differentiation. Heatmap representation of feature expression,
limiting to the top miRs differentially expressed (log Fold Change > 1.5) in γδ thymocytes cultured with IL-7 versus IL-2.
Differentially expressed features were identified with discovery rate = 0.001. The heatmap was generated with the Genesis
software package. Two independent biological samples are shown for each condition (n=2).
4.2 RT-PCR Validation of the miR Candidates
4.2.1 RT-PCR in γδ Thymocytes Validates Top Five miR Candidates
As a first step for the selection of the most promising candidates, the most abundant and more
differentially expressed miRs – highlighted in red in figure 9 – were identified. The
expression of these miR candidates (thirteen in total) was measured by RT-PCR in other
samples of γδ thymocytes, cultured with IL-7 vs IL-2. The analysis of those results allowed
for the validation of part of the miR repertoire previously characterized by RNA-seq. This
initial phase has enabled to set the top five priority miRs that were to be used as a foundation
to drive this investigation (figure 10).
#1 #2 #1 #2
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 44
Figure 10 - RT-PCR in γδ thymocytes validates top five miR candidates. Downregulated (A) versus upregulated (B)
miRs in differentiated γδ thymocytes, as identified by RNA-seq. miR expression was normalized to the average obtained
from freshly isolated γδ thymocyte samples. 5-8 independent thymi are depicted. The average results are represented by the
bold dash. γδ thymocytes cultured for 10-14 days with the indicated cytokines. Data obtained from 3 independent RT-PCR
experiments. *p < 0.05, **p < 0.005, ***p < 0.001.
4.2.2 MiR Candidates Expression Profile in Peripheral γδ T Cells is more Similar to
Freshly-isolated than to IL-2 Cultured γδ Thymocytes
Previous results from the lab have shown that γδ thymocytes are immature ex vivo but can
acquire a type 1 differentiation profile when cultured with IL-2 (Ribot et al., 2014). On the
other hand, given each individual history of infections, circulating γδ T cells are mostly
functionally mature cells, producing IFN-γ and TNF-α ex vivo (DeBarros et al., 2011).
Thus, to confirm that the selected miRs are involved in IL-2-induced differentiation of γδ T
cells, we assessed the expression of our miR candidates in γδ T cells freshly isolated from the
periphery. We were expecting to mimic the results obtained with IL-2 differentiated γδ
thymocytes. This was partially true with regard to the expression of miR-181a and 196b.
However, we observed that the expression of miR-135b, 10a and 20b displayed by peripheral
A
B
1,00E-06
1,00E-05
1,00E-04
1,00E-03
1,00E-02
1,00E-01
1,00E+00
Thy IL-7 Thy IL-2
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-181a
* 1,00E-05
1,00E-04
1,00E-03
1,00E-02
1,00E-01
1,00E+00
Thy IL-7 Thy IL-2
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-196b
*
0
10
20
30
40
50
Thy IL-7 Thy IL-2
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-135b
***
0
5
10
15
20
25
30
Thy IL-7 Thy IL-2
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-10a
*
0
0,5
1
1,5
2
2,5
3
Thy IL-7 Thy IL-2
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-20b
**
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 45
γδ T cells was more similar to what we had observed in freshly isolated (immature) γδ
thymocytes than to the observed in IL-2 cultured γδ thymocytes (figure 11).
Figure 11 - MiR candidates expression profile in peripheral γδ T cells is more similar to freshly-isolated than to IL-2
cultured γδ thymocytes. Downregulated (A) versus upregulated (B) miRs in differentiated γδ thymocytes, as identified by
RNA-seq. miR expression was normalized to the average obtained from freshly isolated γδ thymocyte samples. 5-8
independent thymi (T) and 6-12 independent buffy-coats (B). The average results are represented by the bold dash. γδ
thymocytes either freshly isolated or cultured for 10-14 days with IL-2; γδ T cells from buffy-coats freshly isolated. Data
obtained from 3 independent RT-PCR experiments. *p < 0.05, ***p < 0.001.
Given these results, we hypothesized that these candidates expression pattern could be a
transient process capable of restoring the basal levels once the terminally differentiated cells
return to a resting state. Thus, in order to break this resting status, we next analyzed the
expression of the miR candidates in peripheral γδ T cells isolated from buffy-coats, either
fresh or when using similar culture conditions (figure 12).
In agreement with this, the results show that when peripheral γδ T cells are stimulated with
IL-7, and even more with IL-2, miR-135b, 10a and 20b expression is upregulated, indicating a
probable role of those miRs in both the proliferation and differentiation processes.
A
B
1,00E-06
1,00E-05
1,00E-04
1,00E-03
1,00E-02
1,00E-01
1,00E+00
Fresh (T) IL-2 (T) Fresh (B)
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-181a
***
***
1,00E-05
1,00E-04
1,00E-03
1,00E-02
1,00E-01
1,00E+00
Fresh (T) IL-2 (T) Fresh (B)
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-196b
***
***
0
10
20
30
40
50
Fresh (T) IL-2 (T) Fresh (B)
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-135b
***
*
0
5
10
15
20
25
30
Fresh (T) IL-2 (T) Fresh (B)
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-10a
***
0
0,5
1
1,5
2
2,5
3
Fresh (T) IL-2 (T) Fresh (B)
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-20b
***
***
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 46
Figure 12 – MiR-135b, 10a and 20b expression is upregulated in IL-7 or IL-2 cultured peripheral γδ T cells indicating
a rupture in their resting status. Downregulated (A) versus upregulated (B) miRs in differentiated γδ thymocytes, as
identified by RNA-seq. miR expression was normalized to the average obtained from freshly isolated γδ T cell samples. 6-12
independent buffy-coats are depicted. The average results are represented by the bold dash. γδ T cells from buffy-coats were
either freshly isolated or cultured for 6-8 days with the indicated cytokines. Data obtained from 3 independent RT-PCR
experiments. *p < 0.05, **p < 0.005, ***p < 0.001.
4.2.3 Comparison of miR Candidates Expression on Vδ1 vs Vδ2 Subpopulations
Shows No Significant Differences in their Expression Levels
Human γδ T cells comprise two major subsets: Vδ1+ cells (5–30% of γδ PBLs, but more
abundant in tissues) and Vδ2+ cells (60–95% of γδ PBLs), both strongly biased toward
cytotoxic type 1 functions (Behr et al., 2009; Gomes et al., 2010; Ribot et al., 2014).
Interestingly, previous results from the lab have reported that those two subpopulations
followed similar rules of differentiation (Ribot et al., 2014).
We thus wondered whether the miRs profile associated with functional differentiation was
also shared between those two subsets. For this, we sorted Vδ1 and Vδ2 cells from healthy
donors PBMCs and compared their expression for the selected miR candidates.
A
B
0,01
0,1
1
BC Fresh BC IL-7 BC IL-2fo
ld-c
han
ge in
miR
exp
ress
ion
miR-181a
***
***
0,01
0,1
1
BC Fresh BC IL-7 BC IL-2
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-196b
***
**
0
1
2
3
4
5
BC Fresh BC IL-7 BC IL-2
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-135b
** **
0
1
2
3
4
5
6
7
8
BC Fresh BC IL-7 BC IL-2
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-10a
***
**
0
10
20
30
40
50
BC Fresh BC IL-7 BC IL-2
fold
-ch
ange
in m
iR e
xpre
ssio
n
miR-20b
*** ***
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 47
We observed no significant differences in the miRs expression between the two isotypes
(figure 13), in agreement with the similar miRs expression profile observed in peripheral and
thymic γδ T cells (figures 10 and 12).
Figure 13 - Comparison of miR candidates expression on Vδ1 vs Vδ2 subpopulations shows no significant differences
in their expression levels. Downregulated (A) versus upregulated (B) miRs in differentiated γδ thymocytes, as identified by
RNA-seq. 6 independent buffy-coats are depicted. The average results are represented by the bold dash. Data obtained from 3
independent RT-PCR experiments.
4.3 Setup of the Conditions for an Efficient miR Transfection using the Neon
System
To further validate the potential role of the selected miR candidates in human γδ T cell
differentiation, we conducted a set of experiments to test their impact on γδ T cell functions.
To do so, we adopted a gain-of-function strategy, by delivering designed mimics to cultured
γδ T cells using the Neon transfection system.
We first needed to optimize the electroporation settings by using a siRNA – as previously
shown on CD4+ cells (Simpson et al., 2014) – which in our case was the siRNACD45, seen
that CD45 is usually expressed in activated γδ T cells (Braakman et al., 1991). Several
A
B
0,1
1
10
100
Vδ1 Vδ2
Ab
solu
te V
alu
es
miR-181a
1
10
100
1000
Vδ1 Vδ2
Ab
solu
te V
alu
es
miR-196b
0,01
0,1
1
Vδ1 Vδ2
Ab
solu
te V
alu
es
miR-135b
0,01
0,1
1
Vδ1 Vδ2
Ab
solu
te V
alu
es
miR-10a
0,01
0,1
1
Vδ1 Vδ2 A
bso
lute
Val
ue
s miR-20b
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 48
parameters were tested in this experiment, namely: cytokine stimulation (IL-7 vs IL-2);
activation status (anti-CD3/CD28 platebound cultures); cell number per transfection;
electroporation voltage and; analysis timepoints. Based on the manufacturer’s manual
(Invitrogen, 2010), a mimic concentration of 200nM has been chosen for electroporation of
the γδ T cells with the top five miR candidates mimics.
As readout of the electroporation efficiency, we measured the CD45 mean-fluorescence
intensity (MFI) (figure 14). We also checked that the chosen electroporation conditions were
not compromising γδ T cell survival by measuring the percentage of live cells with a
LiveDead staining (figure 15).
Figure 14 – Transfection of the siRNACD45 using the Neon system proves to be efficient on silencing CD45 in γδ T
cells. Neon setup readout for anti-CD45 staining on 25.000 γδ thymocytes, electroporated with 1600V: A) γδ from thymus;
B) γδ from PMBC. The following parameters were tested: cytokine stimulation with either IL-7 or IL-2; anti-CD3/CD28
platebound cell activation (Act) or without activation (NA) and electroporation voltage with either 1500V (data not shown)
or 1600V. Time until readout: 120hours for thymus and 48hours for the PBMCs samples after the last electroporation. The
siRNACD45 concentration used was 500nM.
A
B
IL-7 IL-2
% o
f M
ax
CD45
Not Activated
0
3000
6000
9000
12000
15000
MFI
CD
45
(Th
ymu
s)
IL-7 IL-2 IL-7 IL-2
NA ACT
IL-7 IL-2
Activated
% o
f M
ax
CD45 0
3000
6000
9000
12000
MFI
CD
45
(P
BM
C)
IL-7 IL-2 IL-7 IL-2
NA ACT
siRNACD45
Control
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 49
Figure 15 – Transfection of the siRNACD45 using the Neon system does not compromise γδ T cell survival. Neon setup
readout for LiveDead staining on 25.000 γδ thymocytes, electroporated with 1600V: A) γδ from thymus; B) γδ from PMBC.
The following parameters were tested: cytokine stimulation with either IL-7 or IL-2; anti-CD3/CD28 platebound cell
activation (Act) or without activation (NA) and electroporation voltage with either 1500V (data not shown) or 1600V. Time
until readout: 96h after the last electroporation. The siRNACD45 concentration used was 500nM.
Based on these results, we described the following conditions for electroporation of γδ T cells
using the Neon system: 25.000 γδ T cells per transfection at 1600V with three pulses of 10ms
each.
Interestingly, although the silencing on γδ thymocytes worked without of TCR stimulation,
for the peripheral γδ T cells anti-CD3/CD28 platebound activation appeared to be crucial for
the transfection efficiency. The time between the last electroporation and the functional
readout by FACS was also determined to be different: 48 hours for γδ T cells from PBMCs
versus 120hours for γδ thymocytes. Additionally, cell fitness was improved in the presence of
IL-7 but not with IL-2, so after the setup the cell-culture medium used had IL-7 only, or IL-
7+IL-2.
Based on these set-up experiments, a protocol timeline was established (figure 16). Since ex
vivo isolated γδ thymocytes are immature, we needed an extended timeline when compared to
the already differentiated peripheral γδ T cells. Thus, the protocol for the transfection in γδ
thymocytes comprises four electroporations with the Neon system, while peripheral γδ T cells
will undergo only two electroporation steps.
A B
0
10
20
30
40
50
60
70
80
90
100
% L
ive
Ce
lls (
Thym
us)
IL-7 IL-2 IL-7 IL-2
NA ACT
0
10
20
30
40
50
60
70
80
90
100
% L
ive
Ce
lls (
PB
MC
)
IL-7 IL-2 IL-7 IL-2
NA ACT
siRNACD45
Control
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 50
Figure 16 – Neon protocol timeline for mimics delivery. Schematic representation of the timeline stablished for the Neon
experiment using mimics in γδ T cells isolated either from thymus or from PBMC. Three parameters were established as
readouts by using antibodies to perform cell staining: A) Cell proliferation (Ki67); B) Functional differentiation (IFN-γ vs
TNF-α); C) Phenotypical features (CD27 vs CD45RA); and D) Vδ1 and Vδ2 subpopulations.
4.4 Analysis of the Impact of Mimics Delivery on γδ T Cell Differentiation and
Proliferation
To verify the efficiency of the mimics delivery using the Neon system, we measured by RT-
PCR the expression of corresponding miR in electroporated peripheral γδ T cells. As
expected, we observed an upregulation of miR-181a, 196b, 135b and 10a expression when
cells have been electroporated with the corresponding mimics (figure 17). Data for the miR-
20b mimic is not available (N/A) because there were not enough cells to perform the
transfection for all the miR candidates.
Figure 17 – Mimics delivery to PBMC-isolated γδ T cells using the Neon system occurred efficiently. RT-PCR
experiment performed on γδ T cells which have been electroporated with the Neon system for miR mimics delivery, using
the previously stablished protocol timeline. Control value was calculated by normalizing the expression of unrelated miRs to
the expression of miR C. elegans. miR-20b data not available (N/A). The samples were frozen 48hours after the last
electroporation. Cells were cultured with IL-7+IL-2 at a 1/1000 ratio.
N/A
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 51
4.4.1 Peripheral Vδ2 γδ T Cells Transfected with miR-135b, 10a and 20b Mimics tend
to Proliferate Less when Cultured with IL-7 plus IL-2
Based on the timeline previously established (figure 16), cell proliferation in response to IL-7
plus IL-2 stimulation has been assessed by staining electroporated peripheral Vδ1+ versus
Vδ2+ γδ T cells with anti-Ki67 (figure 18).
Although the Vδ1 subpopulation did not show any clear differences, it appears that the Vδ2
subpopulation tends to proliferate less upon electroporation of γδ T cells with the miRs-135b,
10a and 20b mimics. However, such results would benefit of having more samples to support
this apparent pattern.
Figure 18 – Peripheral Vδ2 γδ T cells transfected with miR-135b, 10a and 20b mimics tend to proliferate less when
cultured with IL-7 plus IL-2. Proliferation results for the transfection with the top five candidate miR mimics on PBMC-
isolated γδ T cells using the Neon system. A) Vδ1 subpopulation; B) Vδ2 subpopulation. Populations electronically gated by
FACS analysis. Five independent buffy-coats are depicted (n=5) using dots with different colors for each sample. The
average results are represented by the bold dash. Cells were cultured with IL-7+IL-2 at a 1/1000 ratio.
A
B
0
10
20
30
40
50
60
70
80
90
100
% K
i67
+ C
ells
/ V
δ1
Ki67
Control miR-135b
0
10
20
30
40
50
60
70
80
90
100
% K
i67
+ C
ells
/ V
δ2
Control miR-135b
Ki67
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 52
4.4.2 Peripheral γδ T Cells Transfected with the miR Mimics show No Significant
Differences in IFN-γ and TNF-α Production
We next analyzed the differentiation status of peripheral γδ T cells upon mimics transfection.
Electronically gated Vδ1 and Vδ2 subsets have been analyzed by FACS for their IFN-γ and
TNF-α expression. The absence of differences among the tested conditions impaired us to
draw any particular conclusions at this point (figure 19).
Figure 19 – Peripheral γδ T cells transfected with the miR mimics show no significant differences in IFN-γ and TNF-α
production. IFN-γ and TNF-α expression results for the transfection with the top five candidate miR mimics on PBMC-
isolated γδ T cells using the Neon system. A) Vδ1 subpopulation; B) Vδ2 subpopulation. Populations electronically gated by
FACS analysis. Five independent buffy-coats are depicted (n=5) using dots with different colors for each sample. The
average results are represented by the bold dash. Cells were cultured with IL-7+IL-2 at a 1/1000 ratio.
A
B
0
5
10
15
20
25
30
35
% IF
N-γ
+ T
NF-α
+ C
ells
/ V
δ1
TNF-α
IFN
-γ
Control miR-135b
0
5
10
15
20
25
30
35
40
45
% IF
N-γ
+ T
NF-α
+ C
ells
/ V
δ2
TNF-α
IFN
-γ
Control miR-135b
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 53
4.4.3 MiR-181a and miR-196b Overexpression Decreases TNF-α Production in Vδ1
Electroporated γδ Thymocytes
Since γδ T cells at earlier stages of development have more plasticity than their mature
counterparts (Bonneville et al., 2010; Caccamo et al., 2013), we anticipated that thymic γδ T
cells would be more prone to be manipulated. Thus, we have isolated γδ thymocytes from
thymic samples extracted during pediatric corrective cardiac surgery and we have
electroporated those γδ thymocytes with the mimics of interest, based on the timeline
previously established (figure 16).
Interestingly, γδ thymocytes electroporation with miR-181a and 196b mimics seems to reduce
TNF-α production in the Vδ1 subset from 50,2% to 39,8% and 33,9%, respectively, a
decrease that is not verified for the other tested mimics. A similar behavior is observed for
miR-196b in the Vδ2 subpopulation, with the TNF-α production decreasing from 67,5% to
58,1%. Interestingly, this reduction in TNF-α production is accompanied by a discrete
increase in cell proliferation on both the Vδ1 and the Vδ2 samples from this miR, as attested
by the Ki67 staining. Such increase, although smaller, was also registered in the other mimics
transfected samples (figure 20). However, more samples would allow us to verify such a
pattern in γδ thymocytes proliferation, upon overexpression of these miR candidates.
Figure 20 – MiR-181a and miR-196b overexpression decreases TNF-α production in Vδ1 electroporated γδ
thymocytes. TNF-α and Ki67 expression for the transfection with the top five candidate miR mimics, plus the control, using
the Neon system on thymus-isolated γδ T cells. A) Vδ1 subpopulation; B) Vδ2 subpopulation. Populations electronically
gated by FACS analysis. One sample is depicted. Mimics concentration: 50nM. Cells were cultured with IL-7+IL-2 at a
1/1000 ratio.
Control miR-181a miR-196b miR-135b miR-10a miR-20b
TN
F-α
Ki67
A
B
Control miR-181a miR-196b miR-135b miR-10a miR-20b
TN
F-α
Ki67
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 54
4.5 Overexpression of Candidate miRs using Retroviral Constructs
4.5.1 MiR Overexpression was Not Efficient for All of the miR Constructs
In the past decade several tools have been developed to deliver genes into human cells.
Among them, genetically engineered retroviruses are currently the most popular tool for gene
delivery (Hu and Pathak, 2000; Liu and Berkhout, 2011). Thus, we also decided to use this
methodology as an alternative to our gain-of-function experiments.
The retroviral vectors encoding the precursor stem-loops for our top five miR candidates were
produced in the mammalian adherent cell line, HEK293T TAT and tested in the 3T3 cell line,
in order to verify the transduction efficiency. This was assessed through the measurement of
the GFP reporter in each sample (figure 21). Furthermore with this transduction test we
evaluated the produced viral titers.
FACS analysis shows that the transduction was efficiently achieved for the empty, the miR-
181a and the miR-20b vectors. However, we failed to detect any GFP expression in cells
transduced with the remaining miR vectors, thus restricting the list of miR candidates
available to be used in γδ T cells.
Figure 21 –MiR overexpression was not efficient for all of the miR constructs: 3T3 cells. Viral particle production in the
3T3 cell lineage using the top five miR candidates constructs, plus the empty vector, was assessed by measuring the GFP
expression levels: cells efficiently transduced verify higher percentage levels of GFP – empty, miR-181a and miR-20b –
while untransduced cells have lower or no GFP production at all, similar to the control cells profile (represented in grey).
Cells obtained from the 1st harvest, using a 5uL titer.
GFP
% o
f M
FI
Empty miR-181a miR-196b
84,9 61,7 0,9
miR-135b miR-10a miR-20b
0,53 0,75 14,1
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 55
We then infected primary peripheral γδ T cells with the viral particles produced by the
HEK293T TAT cell line. We observed that the transduction was efficient for the empty and
the miR-181a vector, while the miR-20b infected cells expressed a non-significant amount of
GFP (figure 22). Based on these results, we decided to focus our analysis on the impact of
miR-181a overexpression.
Figure 22 – MiR overexpression was not efficient for all of the miR constructs: γδ T cells. Viral particle production in
the PBMCs-isolated γδ T cells using either the empty, the miR-181a or the miR-20b vector was assessed by measuring the
GFP expression levels: cells efficiently transduced verify higher levels of GFP – empty and miR-181a – while untransduced
cells – namely miR-20b – have lower or no GFP production at all, similar to the control cells profile (represented in grey).
Cells were cultured with IL-7 or IL-7+IL-2 at a 1/1000 ratio.
4.5.2 MiR-181a Overexpression in Peripheral γδ T Cells Increases Cell Proliferation
Several parameters have been assessed on peripheral γδ T cells following miR-181a retroviral
transduction, and compared to the results obtained from the samples transduced with the
empty vector.
We first analyzed cell proliferation of retrovirally transduced γδ T cells based on their Ki67
expression (figure 23).
The results show that both the Vδ1 and the Vδ2 subsets proliferate more when transduced
with the miR-181a vector. However, GFP+ (i.e. transduced) Vδ1
+ T cells displayed a higher
cell proliferation (15%-20% increase). Of note, GFP-
(i.e. untransduced) cells registered no
significant changes when comparing the empty vector with the miR-181a cell samples, and
could be used as an internal negative control, as expected.
GFP
% o
f M
FI
Empty miR-181a miR-20b
70,3 38,3 3,31
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 56
Figure 23 – MiR-181a overexpression in peripheral γδ T cells increases cell proliferation. Proliferation has been
assessed by measuring Ki67 expression in GFP+ cells after transducing peripheral γδ T cells with either the empty vector or
with the miR-181a vector. A) Vδ1 subpopulation; B) Vδ2 subpopulation. Populations electronically gated by FACS analysis.
Three independent buffy-coats are depicted (n=3) using dots with different colors for each sample. The average results are
represented by the bold dash. The left panels represent the IL-7 cultured γδ T cells. Cells were cultured with IL-7 or IL-7+IL-
2 at a 1/1000 ratio. *p < 0.05.
4.5.3 MiR-181a Overexpression in Peripheral γδ T Cells Increases Vδ1 T Cell
Activation and Programmed Cell Death
We next analyzed the activation status of γδ T cells upon miR-181a overexpression, thus we
performed a CD69 staining on peripheral γδ T cells transduced with the miR181a vs empty
vector (figure 24). CD69, also known as early activation antigen (EA-1), is a phosphorylated
cell surface protein transiently expressed early following T-cell activation by
phytohemagglutinin and activators of protein kinase C (Rutella et al., 2009).
Gate
d G
FP
+V
δ1
+ T
cel
ls
Ki67
Empty miR-181a
18,2 41,6
A
B
0
10
20
30
40
50
% K
i67
+ C
ells
0
10
20
30
40
50
60
% K
i67
+ C
ells
*
IL-7 IL-7+IL-2
GFP+ GFP- GFP+ GFP-
Gate
d G
FP
+V
δ2
+ T
cel
ls
Ki67
5,78 10,4
Empty miR-181a
0
2
4
6
8
10
12
14
% K
i67
+ C
ells
0
5
10
15
20
25
30
% K
i67
+ C
ells
GFP+ GFP- GFP+ GFP-
IL-7 IL-7+IL-2
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 57
We showed that γδ T cell activation is increased in samples overexpressing miR-181a, when
compared with the control samples, especially when gated on the Vδ1 subpopulation. Of note,
such alteration in cell activation between miR-181a and the control sample (i.e. empty vector)
seems to be higher when cells are cultured with IL-7, than when they are cultured with IL-
7+IL-2.
Figure 24 – MiR-181a overexpression in peripheral γδ T cells increases Vδ1 T cell activation. Activation has been
assessed by measuring CD69 expression in GFP+ cells after transducing peripheral γδ T cells with either the empty vector or
with the miR-181a vector. A) Vδ1 subpopulation; B) Vδ2 subpopulation. Populations electronically gated by FACS analysis.
Three independent buffy-coats are depicted (n=3) using dots with different colors for each sample. The average results are
represented by the bold dash. The left panels represent the IL-7 cultured γδ T cells. Cells were cultured with IL-7 or IL-7+IL-
2 at a 1/1000 ratio. *p < 0.05.
A parameter intrinsically linked with cell activation is the programmed cell death, also known
as apoptosis. Thus, we stained peripheral γδ T cells with the apoptotic marker Annexin V to
analyze potential changes in the electronically gated Vδ1 and Vδ2 subpopulations. Although
not significant, our results show a higher percentage of Annexin V+ cells in the γδ T cells
overexpressing miR-181a than in the control cells. Importantly, this was true in all individual
transduced samples (figure 25).
A
B
Gate
d G
FP
+V
δ1
+ T
cel
ls
CD69
Empty miR-181a
20,9 35,7
0
10
20
30
40
% C
D6
9+
Ce
lls
*
0
10
20
30
40
50
60
% C
D6
9+
Ce
lls
IL-7 IL-7+IL-2
GFP+ GFP- GFP+ GFP-
Gate
d G
FP
+V
δ2
+ T
cel
ls
54,1 65,4
Empty miR-181a
CD69
0102030405060708090
100
% C
D6
9+
Ce
lls
0102030405060708090
100
% C
D6
9+
Ce
lls
IL-7 IL-7+IL-2
GFP+ GFP- GFP+ GFP-
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 58
Figure 25 – MiR-181a overexpression in peripheral γδ T cells increases Vδ1 T cell programmed cell death. Apoptosis
has been assessed by measuring Annexin V expression in GFP+ cells after transducing peripheral γδ T cells with either the
empty vector or with the miR-181a vector. A) Vδ1 subpopulation; B) Vδ2 subpopulation. Populations electronically gated by
FACS analysis. Three independent buffy-coats are depicted (n=3) using dots with different colors for each sample. The
average results are represented by the bold dash. The left panels represent the IL-7 cultured γδ T cells. Cells were cultured
with IL-7 or IL-7+IL-2 at a 1/1000 ratio.
4.5.4 Overexpression of miR-181a in Peripheral γδ T Cells did not Influence their
Effector/Memory Phenotype
It is commonly accepted that the expression of CD27 and CD45RA can help the
discrimination between effector/memory T cell subsets (Dieli et al., 2003). Thus, based on
these surface markers, we assessed whether miR-181a (over)expression could impact on
peripheral γδ T cells effector/memory phenotype (figure 26).
A
B
Gate
d G
FP
+V
δ1
+ T
cel
ls
Annexin V
20,9 35,7
Empty miR-181a
05
101520253035404550
% A
nn
exi
n V
+ C
ells
0
5
10
15
20
25
30
35
% A
nn
exi
n V
+ C
ells
IL-7 IL-7+IL-2
GFP+ GFP- GFP+ GFP-
0
10
20
30
40
50
60
70
80
% A
nn
exi
n V
+ C
ells
0
10
20
30
40
50
60
% A
nn
exi
n V
+ C
ells
IL-7 IL-7+IL-2
GFP+ GFP- GFP+ GFP-
56,4 71,4
Empty miR-181a
Annexin V
Gate
d G
FP
+V
δ2
+ T
cel
ls
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 59
Figure 26 – Overexpression of miR-181a in peripheral γδ T cells did not influence their effector/memory phenotype.
Phenotypic features have been assessed through a CD27 vs CD45RA staining on GFP+ cells after transducing peripheral γδ T
cells with either the empty vector or with the miR-181a vector. Only the central memory (CM) population is represented in
the graphics. A) Vδ1 subpopulation; B) Vδ2 subpopulation. Populations electronically gated by FACS analysis. Three
independent buffy-coats are depicted (n=3) using dots with different colors for each sample. The average results are
represented by the bold dash. The left panels represent the IL-7 cultured γδ T cells. EM= Effector Memory. Cells were
cultured with IL-7 or IL-7+IL-2 at a 1/1000 ratio.
A detailed analysis of the results for each one of the four phenotypic populations (data not
shown) did not allow us to highlight any significant changes between the transduced miR-
181a and the control samples, as illustrated above when focusing on the CD27+CD45
- (CM)
population (figure 26). This led us to the conclusion that miR-181a overexpression did not
cause particular alterations in peripheral γδ T cell phenotype based on those effector/memory
markers.
A
B
CM
M
Naïve
EM
M
Effector
M
Gate
d G
FP
+V
δ2
+ T
cel
ls
Empty miR-181a
CD45RA
CD
27
0
10
20
30
40
50
60
70
80
90
% C
M C
ells
0
10
20
30
40
50
60
70
80%
CM
Ce
lls
IL-7 IL-7+IL-2
GFP+ GFP- GFP+ GFP-
Gate
d G
FP
+V
δ1
+ T
cel
ls
CD45RA
CD
27
Empty miR-181a
0
2
4
6
8
10
12
14
% C
M C
ells
02468
101214161820
% C
M C
ells
IL-7 IL-7+IL-2
GFP+ GFP- GFP+ GFP-
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 60
4.5.5 Overexpression of miR-181a in Peripheral γδ T Cells Reduces IFN-γ and TNF-α
Production in the Vδ1 Subpopulation
We have finally analyzed the differentiation status of the Vδ1 and the Vδ2 subpopulations
upon miR-181a overexpression, by evaluating their IFN-γ and TNF-α expression.
Interestingly, we observed that overexpressing miR-181a on peripheral γδ T cells caused
alterations in both the IFN-γ and the TNF-α expression (figure 27). This was particularly
significant in the Vδ1+ subset and in the absence of IL-2.
Importantly, this is not the first time that miR-181a overexpression has been correlated with a
reduction in IFN-γ and TNF-α production, as it has been observed recently in human CD4+ T
lymphocytes or in the HEK293K cell line, respectively (Gao et al., 2016; Sang et al., 2015).
Figure 27 – Overexpression of miR-181a in peripheral γδ T cells reduces IFN-γ and TNF-α production in the Vδ1
subpopulation. Differentiation status was assessed by analyzing IFN-γ and TNF-α expression in GFP+ cells after transducing
peripheral γδ T cells with either the empty vector or with the miR-181a vector. A) Vδ1 subpopulation; B) Vδ2 subpopulation.
Populations electronically gated by FACS analysis. Three independent buffy-coats are depicted (n=3) using dots with
different colors for each sample. The average results are represented by the bold dash. The left panels represent the IL-7
cultured γδ T cells. Cells were cultured with IL-7 or IL-7+IL-2 at a 1/1000 ratio. *p < 0.05.
A
B
Gate
d G
FP
+V
δ1
+ T
cel
ls
TNF-α
IFN
-γ
Empty miR-181a
0
10
20
30
40
50
60
70
% IF
N-γ
+ TN
F-α
+ C
ells
*
0
10
20
30
40
50
60
% IF
N-γ
+ T
NF-α
+ C
ells
IL-7 IL-7+IL-2
GFP+ GFP- GFP+ GFP-
Gate
d G
FP
+V
δ2
+ T
cel
ls
Empty miR-181a
TNF-α
IFN
-γ
0
20
40
60
80
100
120
% IF
N-γ
+ TN
F-α
+ C
ells
0
20
40
60
80
100
120
% IF
N-γ
+ TN
F-α
+ C
ells
IL-7 IL-7+IL-2
GFP+ GFP- GFP+ GFP-
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 61
We finally sum up our data related to miR-181a transduction of peripheral γδ T cells (figure
28). We have gathered the different parameters assessed in the Vδ1 and Vδ2 subpopulations
and normalized those results with the data obtained for the control.
Importantly, although both the Vδ1 and the Vδ2 subpopulations have registered changes in
the γδ T cell features, it is worth noticing that the main changes following miR-181a
overexpression were verified in the Vδ1 subset, rather than in the Vδ2 subset.
Figure 28 – Summary of the results obtained upon overexpression of miR-181a in peripheral γδ T cells. A) Vδ1
subpopulation; B) Vδ2 subpopulation. Populations electronically gated by FACS analysis. Data obtained from the average
results of three independent buffy-coats and normalized to the average results obtained for the control samples (i.e. empty
vector data). The empty vector is represented by the grey bar; miR-181a vector represented by the black bar. Cells were
cultured with IL-7 or IL-7+IL-2 at a 1/1000 ratio. *p < 0.05.
Gate
d G
FP
+V
δ1
+ T
cel
ls
0
0,5
1
1,5
2
2,5
3
fold
-ch
ange
/ e
mp
ty (
Vδ
1) *
* *
*
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
5
fold
-ch
ange
/ e
mp
ty (
Vδ
1)
*
*
IL-7 IL-7+IL-2
A
B
Gate
d G
FP
+V
δ2
+ T
cel
ls
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1,8
2
fold
-ch
ange
/ e
mp
ty (
Vδ
2)
*
*
0
0,5
1
1,5
2
2,5
3
fold
-ch
ange
/ e
mp
ty (
Vδ
2) *
IL-7 IL-7+IL-2
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 62
Discussion
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 63
5. Discussion
It has been shown that miR expression patterns vary among lymphocyte subsets and stages of
development, which may contribute to their identity or functional state (Jeker and Bluestone,
2013). By instance, miR expression profiles among each thymic T cell developmental stage
have been shown to be unique, with some miRs undergoing expression changes up to three
orders of magnitude during maturation (Kirigin et al., 2012). Accordingly, removal of all
mature miRs at early stages of thymocyte development via Dicer or Drosha knockouts has
resulted in developmental blockage and consequent reduction of the peripheral mature αβ T
and iNKT cell pool (Cobb et al., 2005; Podshivalova and Salomon, 2013; Seo et al., 2010;
Zhou et al., 2011).
Recently, several reports have demonstrated that various miRs, namely miR-181/583 and
miR-146a/150, are respectively expressed in human NK and CD8 T cell subsets and regulate
several aspects of their development and function (Cichocki et al., 2011; Ghisi et al., 2011;
Sheppard et al., 2014; Yun et al., 2014). Furthermore, miR-146a has been linked to the
control of the Th1-type differentiation in human CD4+ T cells by targeting the protein PRKCε
(Möhnle et al., 2015), whereas miR-20a overexpression has been proved to inhibit their TCR-
mediated signaling (Reddycherla et al., 2015).
Owing to their potent effector functions, γδ T cells have been proposed as the first line of
immune defense that responds to a variety of stress-inducible or pathogen-associated proteins
or metabolites (Hayday, 2009). Upon activation, these cells become capable of producing
inflammatory cytokines and directly lyse infected or malignant cells (Deniger et al., 2014;
Wesch et al., 2014). Namely, γδ T cells actively contribute to the anti-tumor immune response
in many tumors, including lymphoma, myeloma, melanoma, breast, colon, lung, ovary, and
prostate cancer (Bouet-Toussaint et al., 2008; Cordova et al., 2012; Dieli et al., 2007; Kang et
al., 2009; Lafont et al., 2014; Meraviglia et al., 2010). However, to date, no miRs have yet
been shown to be implicated in the differentiation of these cells into anti-tumor effectors.
With this idea in mind, we set ourselves to assess the functional roles of individual miR
possibly involved in the regulation of γδ T cell functions. To do so, we identified putative
candidates by RNA-seq, a powerful sequence-based methodology that allows for an in-depth
analysis of transcriptional signatures that define cell functions in physiologic or disease states
(Porichis et al., 2015; Szeto et al., 2014). A group of five candidate miRs were then validated
by RT-PCR. A 50-100 fold downregulation of the expression of miR-181a and 196b and a 2-
40 fold upregulation of the expression of miR-135b, 10a and 20b was confirmed to be
associated with type 1 γδ T cell functional differentiation (figure 10). Importantly, miR-181a
expression has been recently reported to be involved in human CD4+ T lymphocytes
functional regulation by directly targeting IFN-γ production (Sang et al., 2015), thus
supporting an analogous correlation for the reduced expression of miR-181a in our
differentiated γδ T cells.
Given each individual history of infections, circulating γδ T cells are mostly functionally
mature cells, producing IFN-γ and TNF-α, and displaying a dominant effector/memory
phenotype (Ribot et al., 2014). We were thus expecting to phenocopy the results obtained
with IL-2 differentiated γδ thymocytes on freshly isolated peripheral γδ T cells. Whereas this
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 64
was partially true regarding the expression of miR-181a and 196b, we observed that the
expression of miR-135b, 10a and 20b displayed by peripheral γδ T cells was rather similar to
what we detected in freshly isolated (immature) γδ thymocytes (figure 11). Thus, we
postulated that these candidates expression pattern associated with functional differentiation is
a transient process that can restore basal levels once the terminally differentiated cells return
to a resting state.
To test this hypothesis, we next compared the expression of our miRs candidates by
peripheral γδ T cells before and after being stimulated with IL-7 and IL-2, in order to break
this resting status. Given the already established relationship between IL-7/IL-2 and γδ T cell
proliferation (Ribeiro et al., 2015), we could also envisage that the changes in miRs
expression displayed by cultured γδ T cells to be also related to this process. Indeed, miR-
135b, 10a and 20b have previously been reported to impact on the proliferation of lymphoma
cells, lung cancer cells and breast cancer cells, respectively (Matsuyama et al., 2011; Yu et
al., 2015; Zhou et al., 2014).
Knowing that the thymus and the blood are populated by different γδ T cell subsets (Vδ1+ and
Vδ2+, respectively) (Behr et al., 2009; Gomes et al., 2010), that follow the same rules of
functional differentiation upon IL-2 signaling (Ribot et al., 2014), we next wondered whether
our miR candidates expression profile would also follow the same miR expression pattern. As
anticipated, Vδ1 and Vδ2 PBMCs-sorted subpopulations displayed similar miR expression
levels (figure 13).
Having selected our top five miR candidates, we manipulated their expression in order to
potentially impact on γδ T cell effector functions. To this aim, we set two different strategies
for the gain-of-function experiments: 1) we overexpressed the miR candidates in γδ T cells by
mimics transfection using the Neon system and; 2) we transduced γδ T cells with retroviral
constructs overexpressing the selected miRs.
For the first gain-of-function methodology, we first optimized the parameters allowing for an
efficient mimics delivery into the γδ T cells. For this, we chose to work with siRNACD45, as
it is well-established that CD45 – a transmembrane tyrosine phosphatase found in all
leukocytes – is essential for the activation of T cells via the TCR (Altin and Sloan, 1997;
Janeway et al., 2001). Thus, we expected that an efficient transfection of this siRNA into our
γδ T cells would register a silencing effect on CD45, reasoned by a decrease on its MFI when
compared with the control samples. We also wanted to find a reasonable compromise between
the selected conditions and the γδ T cell survival rate that allowed us to electroporate these
lymphocytes without the risk of ending up without enough cells for the functional readout.
Such compromise was achieved by electroporating the γδ T cells with a voltage of 1600V,
during three 10ms pulses and using 25.000 cells per transfection.
Interestingly, whereas TCR activation seemed dispensable to achieve efficient silencing on γδ
thymocytes, it was crucial for peripheral γδ T cells, as previously reported in primary T cell
cultures (Jacobs et al., 2008; Rao et al., 2005; Tomkowicz et al., 2015). Once a naïve cell is
fully activated, the T cell activation threshold necessary to activate new gene transcription is
lowered in order to push the cell to proliferate and differentiate (Grossman et al., 2004; Mak
et al., 2014). Thus, we can expect that ex vivo isolated γδ thymocytes are constitutively TCR-
activated, whereas the peripheral mature γδ T cells are most probably in a resting state.
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 65
Interestingly, cell fitness was improved in the presence of IL-7 but not when cells were
cultured with IL-2 only. Such result is consistent with the fact that IL-7 has been shown to be
involved in the survival of human γδ T cells (Michel et al., 2012).
The mimics concentration that has been previously described varies a lot depending on the
nature of the electroporated cells and the experimentalists: for instance primary CD4+ T cells
can be transfected with a concentration that ranges between 75uM and 500nM (Palin et al.,
2013; Simpson et al. 2014); whereas lymphoblastoid cells and the BJAB cell line only require
15nM and 50pM of mimics, respectively (Kalabus et al., 2012; Manzano et al., 2013). Based
on these reports we started performing our experiments in γδ T cells using two different
mimics concentration: 500nM and 50nM. However, if the former concentration seemed to be
too toxic for our cells, the latter concentration showed no significant differences in almost all
of the miR mimics samples (data no shown). Thus, based on this knowledge and also based
on the manufacturer’s manual recommendations, we chose to use a concentration of 200nM
of mimics to electroporate γδ T cells. To verify if the transfection process was occurring
efficiently we assessed by RT-PCR the expression of the top five miRs in electroporated
PBMC-isolated γδ T cells. As control, we normalized the expression of unrelated miRs to the
expression of the recommended miR-39-3p specific to C. elegans. As expected, we observed
a 2-15 fold increase of the miR-181a, 196b, 135b and 10a expression (figure 17). However,
due to a lack of cells, we were not able to assess the potential increase of miR-20b expression.
Of note, this assay has been performed only once and needs to be reproduced. Ideally, for
each electroporation experiment, one should perform also an RT-PCR validation, in order to
draw definitive conclusions about the efficiency of the transfection process. Such analysis
was unfortunately not possible, since the amount of isolated γδ T cells did not provide us with
enough material. Nevertheless, our preliminary data indicates that this electroporation method
is efficient and confirms previous literature reporting up to five-fold increase expression of
the targeted miR (Cheng et al., 2015; Simpson et al., 2014).
Additionally, although the RT-PCR is a simple and commonly used method to measure the
level of a miR, it does not distinguish between miRs in functional or non-functional pools
(Thomson et al., 2013). Consequently, although we can measure the efficiency of the mimics
delivery with this transfection system by RT-PCR, we cannot know the amount of mimics
being loaded into the RISC complex. As an alternative, we could use the fluorescent in situ
hybridization (FISH), a methodology that uses fluorescent probes to detect specific nucleic
acid sequences at the single cell level. This technique can be multiplexed and combined with
fluorescent Ab protein staining (Porichis et al., 2015), which could be an advantage given that
we are using FACS to assess the γδ T cells functional properties. Thus combining miR
detection and functional read out would be less expensive and time consuming.
Taking advantage of the optimized protocol for mimics transfection, we first investigated the
impact of our selected miRs on γδ T cell proliferation. For this, we stained electroporated γδ T
cells for Ki67, a nuclear antigen present in all phases of the cell growth cycle but is absent in
resting cells (Bryant et al., 2006). Interestingly, we observed that the Vδ2+ subset
proliferation was specifically affected upon electroporation with miR-135b, 10a and 20b but
not with miR-181a and 196b. This experiment would deserve to be reproduced with more
samples since, so far, due to an important individual variation, it was not statistically
FFULisboa
Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 66
significant. However, this result is supported by our previous RT-PCR data, reporting an
increase of miR-135b, 10a and 20b in proliferating peripheral γδ T cells (figure 12).
We next analyzed the type 1 differentiation status of peripheral γδ T cells upon mimics
transfection by assessing the expression of IFN-γ and TNF-α. However, no significant
differences were observed. This could be explained by the following hypothesis: 1) although
the transfection occurred efficiently – as supposed by the results obtained in RT-PCR
experiment using the Neon transfected cells – the increase in miRs expression is not sufficient
to detect potential changes in the functional readout or; 2) the mimics transfection was not
efficient in all the PBMCs-isolated γδ T cell samples, which would explain at least part of the
heterogeneity observed in the results. Also, as discussed above, peripheral γδ T cells are
probably less easy to manipulate because they are already differentiated ex vivo, contrary to
immature γδ thymocytes that allow for the control of their differentiation program.
Consistent with this idea, we could indeed partially impair γδ T cell differentiation into TNF-
α producers when overexpressing miR-181a and 196b in γδ thymocytes, especially in the Vδ1
subpopulation. At the same time, electroporation of the γδ thymocytes with the miR mimics
seemed to modulate their proliferation, a result which was true not only for miR-181a and
miR-196b, but also for the other three miR candidates.
Analogously, overexpression of miR-181a in Hela cells suppressed the protein level of TNF-
α, while using an antagonist had the reverse effect, indicating that miR-181a can target TNF-α
and regulate its endogenous expression in human cells (Li et al., 2013). Importantly, although
miR-196b expression has been shown to increase cell proliferation and survival, and also
partially block differentiation of normal bone marrow hematopoietic progenitor cells in mice
(Popovic et al., 2009), this is the first time that this miR is implied in regulating TNF-α
production in humans. However, these results need to be confirmed with more thymic
samples.
As a footnote for the use of mimics in this experiment, although transient transfection of
chemically synthesized miRs is being widely used to study the functional properties of
endogenous miRs, it is still unclear whether transfected miR mimics behave similarly to
endogenous miRs. Importantly, Jin and colleagues have shown that transient electroporation
with miR mimics into HeLa cells led to the accumulation of high molecular weight RNA
species and that the use of high miR mimics concentrations induced non-specific alterations
in gene expression. In contrast, expression of the same miRs through lentiviral infection or
plasmid transfection of HeLa cells did not registered such anomalies (Jin et al., 2015), thus
emerging as a more reliable tool.
Based on this knowledge, we have decided to resort to an alternative gain-of-function
strategy, by overexpressing the miRs of interest using retrovirus. Most of the retroviral
technology used is based on the molony murine leukemia virus, which has a simple genome
with the gag, pol, and env genes encoding for the major structural proteins and essential
enzymes, flanked by long terminal repeats (Hu and Pathak, 2000; Pagès and Bru, 2004). Once
inside the cell, the inserted RNA is copied by the enzyme reverse transcriptase into double-
stranded DNA, which then becomes integrated into the host chromosome, allowing for the
long-term expression of the inserted gene (Liu and Berkhout, 2011). Importantly, using the
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original stem-loop of our miR contributes to a more physiological result avoiding false
positive results introduced by supra-physiological increase in miR levels generally achieved
after transient transfection (Thomson et al., 2011). Thus, we designed retroviral constructs
based on the original stem-loop of our top five miRs and cloned them into the plasmid pMIG-
PGW. Using two other plasmids – pCMV-VSV-G and pCL-Eco – the viral particles were
produced in the mammalian adherent cell line, HEK293T TAT.
Although we had assembled viral constructs for all our top five miR candidates – plus the
empty vector – we failed to produce high viral titers for miR-135b, 10a and 196b. To discard
the possibility of an error while transducing the 3T3 samples, we have also analyzed the
plates for the HEK293T TAT transduced cells using fluorescence microscopy. This allowed
us to see that, although in all plates a green fluorescence was emitted, the density of viral
particles was much higher for the empty, miR-20b and miR-181a vectors than for the other
three samples, in which the green fluorescent particles were only seen sparsely (data not
shown). Similarly, although we had successfully produced viral particles for this miR in the
HEK293T TAT cell line, we failed to transduce miR-20b in peripheral γδ T cells, This result
could be due to the fact that primary cells are usually more difficult to transduce than the cell
lines, probably because the latter are usually actively dividing, a feature due to their
immortalization which is not present in primary cells, but that is crucial for the transduction
efficiency.
The production of the retroviral constructs is a crucial step for an effective transduction and
several factors, such as the construction of the vector backbone and the transgene to be
transferred, can influence the viral vector production (Liu and Berkhout, 2011; Wightman et
al., 1991). In general, a bigger vector size leads to a lower efficiency of the viral transduction
(Hu and Pathak, 2000). However, this does not seem to be the problem in our case since the
viral vector sizes used varied between 6,806 Kb and 7,540 Kb. Another limiting factor for the
transduction efficiency is that the expression of the transgene in question can be toxic for the
host cells or can significantly reduce vector production (Hu and Pathak, 2000), meaning that
miR expression can impair the viral vector production by itself or that, in some cases, the
expression of the GFP can be compromised. This could explain why we were not able to
produce viral vectors for miR-135b, 10a, 20b and 196b. Moreover, the HEK293T TAT cell
line produces extracellular matrix proteins enriched with proteoglycans, and these
macromolecules are known to act as inhibitors during cell transduction, which might
eventually have reduced significantly the transduction efficiency (Le Doux et al., 1996; Le
Doux et al., 1998). A solution for this problem could be to use a suspension cell line to
produce our virus, as for instance the Jurkat cell line (Eaton et al., 2002). Additionally, the
TAT protein present in our cell line is essential in transcription and replication of viral genes
and has been proved to upregulate and downregulate several genes not only implicated in the
infection process but also implicated in cell proliferation and fitness (Park et al., 2007; Lee
and Park, 2009). Thus, we may envisage that miR-135b, 10a or 196b could be involved in the
regulation of one or more of those genes and that such a regulatory role could have impaired
the production of the viral particles in the HEK293T TAT cell line.
As a footnote, a more reliable assay to draw definite conclusions about the efficiency of the
virus production for our miR constructs would be to analyze the transduced cells by RT-PCR
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regarding these miRs expression, to assess if each specific miR had indeed an enhanced
expression or not. Unfortunately, due to material limitations, such analysis has not possible
for this experiment.
Similarly to the gain-of-function experiments using the Neon system, we have next analyzed
γδ T cell main features upon miR-181a overexpression by retroviral transduction.
Interestingly, we observed an increase in proliferation within miR-181a transduced (i.e.
GFP+) Vδ1 and Vδ2 subsets, when compared to the respective control samples (i.e. cell
transduced with the empty vector). As an additional internal control, we also verify that the
proliferation of untransduced cells (i.e. GFP-) within the same sample remained unaffected.
However, GFP+ Vδ1 T cells was the subpopulation that showed a higher increment in cell
proliferation, by increasing its Ki67 expression in 15% and 20% on average, when cultured
with IL-7 or with IL-7+IL-2, respectively.
Accordingly, miR-181a has already been implied in regulating cell proliferation. By instance,
human myeloid leukemia cell proliferation has been enhanced upon overexpression of miR-
181a due to a suppression effect elicited by this miR in the cell cycle inhibitor p27 (Wang et
al., 2009). Additionally, ectopic expression of miR-181a in HEK293T cells was shown to
significantly enhance cell proliferation by activating AKT, thus conferring cell resistance to
doxorubicin, a chemotherapeutic agent (Yan et al., 2015).
Consistently, in a NOTCH-induced T-ALL model, antagomir inhibition of miR-181a
expression in the human T-ALL cell line DND41 decreased cell proliferation and increased
apoptosis, through the downregulation of Notch targets (Fragoso et al., 2012). Notch
signaling plays a pivotal role in cell fate decision and lineage commitment of lymphocytes
(Gogoi et al., 2014), with major contributions for γδ T cell development (Ciofani et al., 2006;
García-Peydró et al., 2003; Van de Walle et al., 2013). Importantly, Notch signaling has also
been shown to control T cell proliferation as well as Th1/Th2 differentiation in the periphery
(Tanikagi et al., 2004). Furthermore, inhibition of Notch signaling by a γ-secretase inhibitor
was proved to decrease not only γδ T cell proliferation, but also to reduce CD69 and CD25
(IL-2R) expression, as well as impairing the CD107a expression, which is a degranulation
marker associated with cell cytotoxic potential (Gogoi et al., 2014).
In agreement with this, we observed an increase of peripheral γδ T cell activation upon miR-
181a overexpression. However, such alteration was more striking in the Vδ1 subpopulation
than in the Vδ2 counterpart, which might be explained by the fact that the Vδ2+ cells were
already much more activated (between 79-92% of the cells on average) than the Vδ1+
cells
(between 32-39% of the cells on average), probably not leaving much room for an increase in
their activation. Importantly, transduced cells verified higher activation levels than the
untransduced cells, consistent with the fact that the activation step in retrovirus-mediated gene
transfer plays a crucial role in the transduction efficiency when using retroviral vectors
(Costello et al., 2000).
We also observed that miR-181a overexpression induced an increase in cell apoptosis, as
measured by Annexin V staining, in agreement with previous reports showing that activated
CD69+ T cells were proven to engage apoptosis (Jiao et al., 2008; Meier et al., 2002; Rutella
et al., 2009). These results also highlight the fact that the phenomenon of activation-induced
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cell death (AICD) in T lymphocytes is regulated at every stage of their existence, playing a
crucial role in maintaining the T cell homeostasis during periods of intense cell activation by
balancing the life vs apoptosis switch (Green et al., 2003).
Regarding their phenotype based on the CD27 and CD45RA markers, the vast majority of γδ
thymocytes have previously showed an immature and naïve profile, contrary to the dominant
effector/memory phenotype of γδ PBLs. Importantly, peripheral γδ T cells were mainly
CD27-CD45
+, illustrating their dominant CM phenotype (50%), followed by an EM
phenotype (30%), an effector phenotype (15%) and finally a naïve phenotype (5%) (Dieli et
al., 2003; Qin et al., 2012; Ribot et al., 2014) Accordingly, in our results, the Vδ2 subset –
which constitutes the main subpopulation of circulating γδ T cells – registered a predominant
CM phenotype (between 50-80% of the cells) in both IL-7 and IL-7+IL-2 cultured γδ T cells.
However, it was interesting to observe that we have consistently registered a higher
percentage of naïve T cells in the Vδ1 subset – on average 40-80% were naïve cells – which
contrasted with the reduced pool of naïve T cells observed in the Vδ2 subset (on average 10-
30%). Such results are in agreement with the literature (deBarros et al., 2011; Qin et al.,
2012; Ribot et al., 2009). Also, this higher naïve phenotype in Vδ1+ cells is in agreement with
the previous observations that the Vδ1 subpopulation seems to be more prone to be
manipulated than the Vδ2 counterpart (Correia et al., 2011; Silva-Santos et al., 2015).
Albeit this fact, no significant alterations have been detected between the samples transduced
with the miR-181a vector and the samples transduced with the empty vector. Thus, it seems
that this miR does not have a significant impact in modulating γδ T cell phenotypic features,
which could be due to a lack of plasticity in these already mature cells. Importantly, plasticity
has been demonstrated to play a crucial role in the γδ T cell differentiation program: at earlier
stages, during γδ thymocyte development, these cells initially maintain a flexible lineage
differentiation, while at later stages they may become irreversibly committed to one lineage
(Bonneville et al., 2010; Caccamo et al., 2013).
Accordingly, it has been shown that with age the human cell pool of naïve T cell shrinks and
that old naïve T cells exhibit proliferation and effector-differentiation defects (Allman and
Miller, 2005; Linton and Dorshkind, 2004; Wertheimer et al., 2014). This could explain the
difficulties in modulating the phenotype of these already fully differentiated cells.
Furthermore, a specific memory subset of CD8+ T cells, called “memory T cells with a naive
phenotype”, has recently been discovered and proved to emerge with ageing. These cells
seemed to work as substitutes of the naïve cells, as they were the cell subset responding to the
presence of new infections (Pulko et al., 2016). Thus, we could envisage that a similar
behavior might be adopted in our γδ T cells, which would explain the fact of having many
more effector and memory T cells, than naïve T cells.
Last but not least, we have assessed if peripheral γδ T cells suffered alterations in their
functional differentiation upon miR-181a retroviral transduction. We observed a significant
reduction of IFN-γ and TNF-α expression within the Vδ1 subset differentiation when cells
were cultured with IL-7 or with IL-7+IL-2.
Consistently with our results, miR-181a overexpression has been previously correlated with a
reduction in IFN-γ production in human CD4+ T lymphocytes (Sang et al., 2015). Conversely,
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 70
downregulation of the miR-181 family paralleled the upregulation of IFN-γ secreted by
activated human CD4+ lymphocytes (Fayyad-Kazan et al., 2014). Moreover, miR-181a
overexpression in Hela cells suppressed TNF-α expression, while a miR-181a antagonist had
the reverse effect (Li et al., 2013). Finally, miR-181a mimic transfection of HEK293T cells
inhibited their TNF-α expression by directly binding to its 3’-UTR section (Gao et al., 2016),
in a way similar to the observed during the IFN-γ inhibition (Fayyad-Kazan et al., 2014).
These results, together with other similar reports, support for a regulatory role of miR-181a in
cell functional differentiation and also in inflammatory processes.
Lastly, it is important to stress out that most of the changes induced by these gain-of-function
experiments – in retroviral vector transduction as well as in mimics transfection – happened
substantially in the Vδ1 subpopulation rather than in Vδ2 subpopulation, in agreement with
the fact that thymic (i.e. immature and Vδ1 enriched) γδ T cells have more plasticity, and thus
are more prone to be manipulated. Also, these results are consistent with previous reports
showing that Vδ1+ T cells generally outperform their Vδ2
+ counterparts regarding the
manipulation of their functional properties (Correia et al., 2011; Silva-Santos et al., 2015).
This information is highly relevant and should be considered when addressing further
experiments concerning the regulation of γδ T cell functional differentiation.
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 71
Future Plans
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 72
6. Future Plans
In this work, we have been able to manipulate miRs expression on peripheral and thymic γδ T
cells by overexpressing candidates potentially involved in the regulation of γδ T cell
functional differentiation. Reproducing the presented results using more samples would be
crucial to support our conclusions. Given that our work results have demonstrated that the
retroviral approach seems to be a more reliable tool for the gain-of-function experiments, it
would be logical that the future thymic and PBMCs-isolated γδ T cells would be manipulated
using the retroviral methodology to overexpress these miR candidates.
As a logical follow-up of this project, we plan to identify the mRNA networks controlled by
our candidate miRs using a combination of bioinformatics and biochemical assays, and
couple the effects of miR and mRNA manipulation on γδ T cell differentiation. To achieve
that, we intend to resource to three different methodologies: high-throughput sequencing of
RNA isolated by cross-linking immunoprecipitation (HITS-CLIP); in silico analysis that
predict miR targets based on the seed sequence and; luciferase assays to verify if the predicted
mRNAs are indeed direct targets of these candidate miRs. This holistic view of the
transcriptome-wide effects of a given miR will be critical to understand its role in γδ T cell
differentiation.
Finally, we aim at defining a miR pathogenic signature of γδ T cell differentiation in cancer
patient samples. Interestingly, a reduced number and impaired pro-inflammatory cytokine
production in circulating γδ T cells from patients with melanoma (Argentati et al., 2003),
glioblastoma (Bryant et al., 2009) and gastric cancer (Kuroda et al., 2012) has been reported.
We thus hypothesize that our candidate miRs will be abnormally expressed in peripheral γδ T
cells isolated from those patients in comparison with healthy donors. By assessing the
expression of these miRs in circulating γδ T cells from these patients, we expect to validate
their potential as immunotherapy targets. Peripheral blood will be collected from a defined
cohort of patients at the time of diagnostic, to avoid any experimental artefacts that could be
introduced by anti-tumor treatment. For this, we already have established a connection with
the Oncology Department of the Hospital de Santa Maria, directed by Dr. Luís Costa.
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Targeting MicroRNAs for Modulation of the Anti-Tumor Human γδ T Cell Differentiation 74
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