© 2016 Richard Coffey - University of...
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MECHANISMS OF NON-TRANSFERRIN-BOUND IRON UPTAKE BY HUMAN β CELLS AND THE ROLE OF IRON IN DIABETIC PATHOGENESIS By RICHARD COFFEY A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2016
Transcript of © 2016 Richard Coffey - University of...
MECHANISMS OF NON-TRANSFERRIN-BOUND IRON UPTAKE BY HUMAN β CELLS AND THE ROLE OF IRON IN DIABETIC PATHOGENESIS
By
RICHARD COFFEY
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2016
© 2016 Richard Coffey
To my parents
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ACKNOWLEDGMENTS
I would first and foremost like to thank my major advisor Dr. Mitchell Knutson
who took a chance and gave me the opportunity to do research all those years ago. I
would not be where I am right now if I did not end up in your undergraduate lab
techniques class.
Also, I would like to thank my past and current labmates Chia-Yu Wang, Supak
Jenkitkasemwong, Wei Zhang, Hyeyoung Nam, Lin Zhang, Katie Sullivan, Lizzie
Paulus, Laura Diez-Ricote, Nike Akinyode, and Emily Mejia who have either taught me
most of what I know about lab work today or have helped me in innumerable other
ways. I wish you the best wherever you find yourself in life and know that you will be
successful.
I would also like to thank my committee members Dr. Clayton Mathews, who has
been instrumental in the design and execution of several experiments discussed in this
dissertation, Dr. James Collins, and Dr. Michelle Gumz who have all taken time out of
their busy lives to help me during this process.
Finally I would like to thank my parents who have always given me the
opportunity to pursue my interests and have been unconditionally supportive of my
decisions.
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TABLE OF CONTENTS
page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ABBREVIATIONS ........................................................................................... 10
ABSTRACT ................................................................................................................... 13
CHAPTER
1 INTRODUCTION .................................................................................................... 15
2 LITERATURE REVIEW .......................................................................................... 17
Basics of Iron Metabolism ....................................................................................... 17 General Information .......................................................................................... 17
Dietary Iron Absorption ..................................................................................... 18 Disorders of Iron Metabolism ............................................................................ 21
Non-Transferrin-Bound Iron Import Proteins .................................................... 25 The Iron and Diabetes Connection ......................................................................... 34
Elevated Iron Stores and Diabetic Pathology ................................................... 34 Pancreatic Iron Accumulation ........................................................................... 39
Mechanistic Evidence from Human Studies ..................................................... 43 Mechanistic Evidence from Animal Studies ...................................................... 47
Iron and Autoimmune Diabetes ........................................................................ 49
3 MATERIALS AND METHODS ................................................................................ 53
Animals and Diets ............................................................................................ 53 Iron Status Parameters and Blood Glucose Concentrations ............................ 54
Pancreatic Mineral Concentrations ................................................................... 55 Histological Analysis ......................................................................................... 55
RNA Isolation and Assessment of RNA Integrity .............................................. 55 Microarray Analysis .......................................................................................... 56
Relative mRNA Quantification .......................................................................... 56 Western Blotting ............................................................................................... 57
Cell Culture and Treatments ............................................................................. 58 In Vitro Glucose Stimulated Insulin Secretion ................................................... 59
Mouse Islet Isolation ......................................................................................... 60 Determination of DMT1, ZIP8, and ZIP14 mRNA Copy Numbers .................... 60
siRNA Knockdown of DMT1, ZIP8, and ZIP14 ................................................. 61
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Overexpression of DMT1, ZIP8, and ZIP14 ...................................................... 61 Immunofluorescencse ...................................................................................... 62
Cellular NTBI Uptake ........................................................................................ 63 Generation of Transgenic MIP-Zip14-HA Mice ................................................. 64
Statistical Analysis ............................................................................................ 65
4 TRANSCRIPTIONAL PROFILING OF PANCREATIC GENE EXPRESSION IN RESPONSE TO DIETARY IRON LOADING OR DEFICIENCY ............................. 66
Results .................................................................................................................... 67
Body Weight, Iron Status, and Blood Glucose Concentrations ......................... 67 Pancreatic Mineral Concentrations ................................................................... 67
Identification and Classification of Differentially Expressed Genes by Microarray Analysis ....................................................................................... 68
Confirmation of Up-Regulation of Alox15 Expression by QRT-PCR and Western Blotting ............................................................................................ 69
Confirmation of Reg Family mRNA Levels by QRT-PCR ................................. 70 Discrepancies Between Microarray and QRT-PCR Analysis Results ............... 70
Discussion .............................................................................................................. 71
5 MECHANISMS OF NTBI UPTAKE BY HUMAN β CELLS ...................................... 86
Results .................................................................................................................... 87 Overexpression of NTBI Transporters in Human β Cells .................................. 87
siRNA Knockdown of NTBI Transporters in Human β Cells ............................. 88 Cellular Localization of NTBI Transporters in Human Islets ............................. 89
Modulation of ZIP14 Expression by Iron in Human β Cells .............................. 89 Modulation of ZIP14 Expression By IL-1β in Human β Cells ............................ 90
Discussion .............................................................................................................. 90
6 THE INFLUENCE OF IRON STATUS ON DIABETIC PATHOLOGY AND β-CELL FUNCTION ................................................................................................. 103
Results .................................................................................................................. 105
Effect of Iron Status on Spontaneous Autoimmune Diabetes in NOD Mice.... 105 Effect of Dietary Iron on Rate of Growth and Systemic Iron Status ................ 105
Pancreatic Mineral Concentrations ................................................................. 107 Testing of β cell function During the Prediabetic Period ................................. 107
Effect of Iron Status on Human Islet GSIS ..................................................... 108 Generation of Mice Selectively Overexpressing Zip14 in β Cells ................... 108
Discussion ............................................................................................................ 109
7 CONCLUSIONS, LIMITATIONS, AND FUTURE DIRECTIONS ........................... 124
LIST OF REFERENCES ............................................................................................. 131
BIOGRAPHICAL SKETCH .......................................................................................... 155
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LIST OF TABLES
Table page 4-1 Body weight, iron status, and blood glucose concentration of rats ..................... 75
4-2 Pancreatic mineral concentrations ...................................................................... 76
4-3 Top 10 most up-regulated and down-regulated genes in FeD pancreata ........... 77
4-4 Top 10 most up-regulated and down-regulated genes in FeO pancreata ........... 78
4-5 Primers for qRT-PCR ......................................................................................... 79
4-6 Functional categories of pancreatic genes differentially expressed in response to iron deficiency ................................................................................. 80
4-7 Functional categories of pancreatic genes differentially expressed in response to iron overload ................................................................................... 81
6-1 Iron parameters of type 1 diabetes-prone NOD mice ....................................... 115
6-2 Pancreatic mineral concentrations in NOD mice .............................................. 116
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LIST OF FIGURES
Figure page 4-1 Functional classification of pancreatic genes up- or down-regulated in
response to iron deficiency and iron overload.. .................................................. 83
4-2 Effect of iron deficiency and overload on rat pancreatic Alox15 expression.. ..... 84
4-3 Effect of iron deficiency and overload on the expression of pancreatic Reg family genes. ...................................................................................................... 85
5-1 ZIP14 and ZIP8, but not DMT1, overexpression increases iron uptake by βlox5 cells ........................................................................................................... 95
5-2 When overexpressed in βlox5 cells, ZIP14 localizes to the plasma membrane whereas DMT1 mainly localizes intracellularly. .................................................. 96
5-3 Endogenous iron uptake by βlox5 cells is decreased by siRNA knockdown of ZIP14, but not ZIP8. ........................................................................................... 97
5-4 siRNA knockdown of ZIP14 decreases NTBI uptake by primary human islets ... 98
5-5 ZIP14 is detected in human pancreatic β cells by immunofluorescent analysis. ............................................................................................................. 99
5-6 Cellular iron levels and treatment with IL-1β increase ZIP14 levels in βlox5 cells but not primary human islets.. .................................................................. 100
5-7 mRNA copy numbers of NTBI transporters in primary human islets. qRT-PCR measurement of DMT1, ZIP14, and ZIP8 mRNA copy numbers in total RNA isolated from nondiabetic human islets. ................................................... 101
5-8 DMT1, but not ZIP8, is detected in human β cells by immunoflourescence staining.. ........................................................................................................... 102
6-1 Dietary iron deficiency, but not iron overload, results in a trend towards an increased incidence of spontaneous diabetes in female NOD mice.. ............... 117
6-2 NOD mice fed an iron-loaded diet initially experience diminished growth. ....... 118
6-3 Iron stores of mice fed an iron-deficient diet increase with age.. ...................... 119
6-4 Glucose tolerance and insulin secretory capacity is not affected by iron status in prediabetic NOD mice.. ................................................................................. 120
6-5 Iron-deficient prediabetic NOD mice show no differences in β cell iron status or insulitis compared with iron-adequate mice.. ................................................ 121
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6-6 Iron status does not affect glucose-stimulated insulin secretion by human islets in vitro.. .................................................................................................... 122
6-7 Generation of mice selectively overexpressing Zip14 in β cells.. ...................... 123
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LIST OF ABBREVIATIONS
Alox15 Arachidonate 15-lipoxygenase
AMPK 5’ adenosine monophosphate-activated protein kinase
Apoa1 Apolipoprotein A-1
BMI Body mass index
CCS Copper chaperone for superoxide dismutase
CRP C-reactive protein
CS Cell surface
DAVID Database for annotations, visualization, and integrated discovery
DCT1 Divalent cation transporter 1
DcytB Duodenal cytochrome B
DFO Deferoximine mesylate
DMT1 Divalent metal-ion transporter 1
ELISA Enzyme-linked immunosorbent assay
EV Empty vector
Fabp1 Fatty acid binding protein 1
Fabp2 Fatty acid binding protein 2
FAC Ferric ammonium citrate
FeA Iron adequate
FeD Iron deficient
Fe-NTA Ferric nitrilotriacetate
FeO Iron loaded
GFP Green fluorescent protein
GSIS Glucose-stimulated insulin secretion
HA Hemagglutinin antigen tag
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HAMP Hepcidin antimicrobial peptide
HETE Hydroxyeicosatetraenoic acid
hGH Human growth hormone intron region
HJV Hemojuvelin
ICP-MS Inductively coupled plasma mass spectrometry
IgG Immunoglobulin G
IL-1β Interleukin 1β
IP-GTT Intraperitoneal glucose tolerance testing
IRE Iron response element
KRB Kreb’s-Ringer Buffer
LPS Lipopolysaccharide
LTCC L-type calcium channel
MIP Mouse insulin 1 promoter
Mn-SOD Manganese superoxide dismutase
Na+/K+ ATPase Sodium-potassium adenosine triphosphatase
NOD Non-obese diabetic
NRAMP2 Natural-resistance associated macrophage protein 2
NTBI Non-transferrin bound iron
PPM Parts per million
qRT-PCR Quantitative reverse transcriptase polymerase chain reaction
Reg Rengenerating islet-derived gene
ROS Reactive oxygen speices
siRNA Small interfering ribonucleic acid
SLC11a2 Solute carrier family 11 member 2
SLC39a14 Solute transporter family 39 member 14
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SLC39a8 Solute transporter family 39 member 8
STEAP3 Six-Transmembrane epithelial antigen of prostate family member 3
sTFR Soluble transferrin receptor
TBI Transferrin bound iron
TCL Total cell lysate
TF Transferrin
TF sat Transferrin saturation
TFR Transferrin receptor
TFR1 Transferrin receptor 1
TFR2 Transferrin receptor 2
Tg Transgenic
TIBC Total iron binding capacity
TTCC T-type calcium channel
UTR Untranslated region
Wt Wild type
ZIP14 ZRT/IRT-Like protein 14
ZIP8 ZRT/IRT-Like protein 8
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
MECHANISMS OF NON-TRANSFERRIN-BOUND IRON UPTAKE BY HUMAN β
CELLS AND THE ROLE OF IRON IN DIABETIC PATHOGENESIS
By
Richard Coffey
May 2016
Chair: Mitchell D. Knutson Major: Nutritional Sciences
The relationship between iron and diabetes has long been recognized as
individuals with iron overload display an increased prevalence of diabetes and iron
depletion is thought to protect against the development of diabetes. This link is
attributed to the accumulation of iron in beta cells, which may impair cellular function.
However, the mechanisms by which beta cells take up iron, as well as the specifics of
how iron status affects diabetic pathogenesis, are undetermined. During iron overload
plasma iron levels exceed the carrying capacity of transferrin resulting in non-transferrin
bound iron (NTBI), which is rapidly taken up by tissues. Currently 3 mammalian proteins
which transport NTBI have been identified: Divalent metal-ion transporter 1 (DMT1),
ZRT/IRT-Like transporter 14 (ZIP14), and ZRT/IRT-Like transporter 8 (ZIP8). The aims
of this project were to determine the contribution of DMT1, ZIP14, and ZIP8 to iron
uptake by human beta cells and to investigate the influence of iron status on various
aspects of diabetic pathology. I found that overexpression of ZIP14 and ZIP8, but not
DMT1, resulted in increased iron uptake by Betalox5 cells, a human beta-cell line.
siRNA-mediated knockdown of ZIP14, but not ZIP8, resulted in 50% lower iron uptake
in Betalox5 cells. In primary human islets, knockdown of ZIP14 also reduced iron
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uptake by 50%. Immunofluorescence analysis of human pancreatic sections localized
ZIP14 and DMT1, but not ZIP8, to beta cells. To determine how iron status may affect
diabetic pathology I examined pancreatic gene expression in iron-deficient, iron-
adequate, and iron-loaded rats. Iron overload and deficiency were associated with
increased pancreatic expression of genes associated with pancreatic stress and linked
to the development of autoimmune diabetes. Additionally, non-obese diabetic mice fed
an iron-deficient, but not iron-loaded, diet trended towards an increased incidence of
diabetes compared with iron-adequate mice. However, this trend was not associated
with a reduction in beta-cell function during the prediabetic period. Iron depletion or
loading of isolated human islets also had no effect on beta-cell function. Overall, results
from these studies indicate that ZIP14 contributes to beta-cell NTBI uptake and suggest
that iron deficiency may not protect against the development of diabetes.
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CHAPTER 1 INTRODUCTION
Iron is an essential trace mineral that is necessary for many biological functions
including, but not limited to, mitochondrial respiration (1), hemoglobin production (2),
drug metabolism (3), and DNA synthesis (4). Disruptions of normal iron homeostasis,
due to either iron deficiency or iron overload, are some of the most common nutrition-
related issues worldwide. One complication of iron overload is an increased prevalence
of diabetes, as evidenced by humans with hemochromatosis or β-thalassemia major,
which result in systemic iron accumulation (5, 6).
Despite the well-documented association between excess iron and an increased
incidence of diabetes several questions remain unanswered. For example, little is
known regarding how pancreatic β cells take up iron, which is found in the plasma
bound to transferrin, under normal conditions, and as non-transferrin bound iron (NTBI)
during disorders of iron overload, when the carrying capacity of transferrin becomes
exceeded. NTBI is known to be a major contributor to iron loading of hepatocytes and
pancreatic acinar cells (7), but its contribution to iron loading of pancreatic β cells is not
well understood. Additionally, the details of how iron deficiency and overload may affect
the pathogenesis of diabetes have yet to be fully elucidated. The experiments described
in this dissertation sought to answer questions concerning the link between iron
metabolism and the pathogenesis of diabetes. Specifically, by determining the
mechanisms by which NTBI is taken up by pancreatic β cells and by evaluating the
impact of iron status on pancreatic gene expression and on aspects of diabetic
pathogenesis.
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To determine the molecular mechanisms of NTBI uptake by β cells I performed in
vitro experiments testing how altering the expression of established mammalian NTBI
transport proteins affects iron uptake by human β cells. Additionally I determined the
cellular localization of these transporters within the human pancreas to identify the cell
populations in which they may function. To evaluate the role of iron in the pathogenesis
of diabetes I investigated potential mechanisms by which iron status could affect the
development of diabetes by using microarray analysis to identify the altered expression
of genes previously linked to various aspects of diabetic pathogenesis or β cell function,
as described by other reports, in the pancreata of rats fed iron-deficient or -loaded diets.
To test the proposed relationship between iron status and the risk of developing type 1
diabetes I also determined the effect of systemic iron status on the development of
autoimmune diabetes in type 1-diabetes-prone NOD mice and measured the effect of
cellular iron status on insulin secretion by human islets in vitro. Lastly, I document the
generation of a novel transgenic mouse that overexpresses the iron transporter ZIP14,
which may provide a model to study β cell iron loading in vivo.
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CHAPTER 2 LITERATURE REVIEW
Basics of Iron Metabolism
General Information
Iron in biological systems has two main oxidation states, ferrous (Fe2+) or ferric
(Fe3+). These two oxidation states allow iron to readily exchange electrons and to
participate in oxidation-reduction reactions within biological systems. Iron is generally
categorized as either heme iron, in which iron is incorporated into a protoporphyrin ring
as found in hemoglobin and myoglobin, or non-heme iron, a broad term used to
describe all iron not incorporated into heme. The majority of dietary iron consumed is
non-heme iron (8), which is found in both plant and animal foods whereas heme iron is
only present in animal sources. Despite the greater bioavailablity of heme iron, the
majority of iron absorbed by the body is obtained from non-heme sources (8, 9), due to
the abundance of dietary non-heme iron. On average the human body contains 2.5 to 4
g of iron, for females and males respectively. The majority of body iron exists as
hemoglobin or myoglobin, which function in oxygen transport, or as stored iron within
ferritin, the iron-storage protein. The daily requirement for iron to support biological
functions, such as erythropoiesis and the production of other iron-containing proteins, is
approximately 24 mg/day. However, the majority of iron used daily is obtained from the
catabolism of senescent erythrocytes by splenic and hepatic macrophages, which can
then recycle the iron contained within erythrocytes for the production of new red blood
cells and iron-containing proteins by the bone marrow and systemic tissues. While this
process is efficient and provides approximately 90% of the daily iron requirement, minor
quantities of iron are lost through various routes including sweating and the sloughing of
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skin cells. Therefore, 1-2 mg of dietary iron is absorbed from the diet to offset daily
losses and maintain systemic iron homeostasis.
Dietary Iron Absorption
Iron metabolism in humans differs from that of other minerals, such as copper or
zinc, in that there is no major route of excretion by which substantial amounts of iron
can be eliminated from the body. Rodents are able to remove some excess iron through
biliary excretion, although this is insufficient to prevent iron overload (10). Therefore, the
absorption of iron from food sources is tightly regulated. The majority of dietary iron is
non-heme iron, which is solubilized at the acidic pH of gastric secretions and absorbed
in the proximal small intestine via Divalent Metal-ion Transporter 1 (DMT1), a
transmembrane protein that couples the transport of ferrous iron to a proton gradient
(11). DMT1 is indispensable for intestinal NTBI uptake as mice lacking DMT1 in the
intestine develop severe iron deficiency and display ablated iron absorption (12, 13). To
ensure that dietary iron exists as ferrous iron, the form which can be transported by
DMT1, ferric iron is thought to be reduced at the enterocyte brush border by the
reductase duodenal cytochrome B (Dcytb). However mice lacking Dcytb show no
difference in the uptake of radiolabeled ferric iron compared wild-type mice suggesting
that Dcytb is dispensable for the reduction of ferric iron in vivo (14). It is possible that
the absence of Dcytb can be compensated by the presence of unidentified brush-border
reductases or reducing agents, such as ascorbate either consumed in the diet or
secreted within digestive juices (15), to reduce iron within the gut lumen. After the
uptake of ferrous iron by the enterocyte iron can either be stored within the enterocyte
within ferritin, and eventually lost when the enterocyte is sloughed off into the gut lumen,
or exported into the portal circulation. If systemic iron stores are adequate or elevated,
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much of the iron taken up by enterocytes will be incorporated into the iron storage
protein ferritin, a multisubunit spherical protein with ferroxidase activity containing a core
composed of ferric iron. If systemic iron stores are low, or in response to stimuli
including anemia (16, 17) or hypoxia (18), enterocyte iron is exported into the portal
circulation by ferroportin, the only identified mammalian iron export protein. Ferroportin
is located on the enterocyte basolateral membrane and is essential for iron export as
mice selectively lacking intestinal ferroportin accumulate iron within enterocytes and
develop severe anemia (19). After export from the enterocyte, iron in the plasma binds
to the plasma iron transport protein transferrin (TF). However, ferrous iron exported
from enterocytes must be oxidized to the ferric state before binding to transferrin. The
oxidation of iron exported via ferroportin may be accomplished by the action of the
ferroxidase hephaestin, located at the basolateral membrane of the enterocyte. In mice
the loss of hephaestin function, due to genetic mutation (20) or genetic deletion (21),
results in decreased dietary iron absorption and iron accumulation in enterocytes
despite elevated levels of ferroportin (21, 22). Impaired iron transport by ferroportin in
response to the loss of functional hephaestin may be due to a necessary interaction
between ferroportin and hephaestin during iron export from the enterocyte. Hephaestin
and ferroportin have been reported to physically interact in rat enterocytes (23)
supporting this hypothesis. However, ferroportin is able to transport iron when
overexpressed in xenopus oocytes, independent of hephaestin overexpression,
suggesting that the interaction of ferroportin with hephaestin is not necessary for iron
export activity (24). Ferric iron circulating as TBI in the plasma is taken up by cells
through the binding of TF to transferrin receptor (TFR) on the cell surface, forming a
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complex which is then endocytosed. Iron is released from TF within the endosome by
endosomal acidification after which ferric iron is reduced to ferrous iron by STEAP3
(25), allowing for the export into the cytosol of ferrous iron via DMT1 and, potentially,
ZRT/IRT-like Protein 14 (ZIP14) and ZRT/IRT-like Protein 8 (ZIP8) (26-28). In the bone
marrow, the major site of transferrin-TFR iron acquisition, the export of endosomal iron
is accomplished by the action of DMT1, as evidenced by loss-of-function mutations in
DMT1 leading to impaired reticulocyte iron delivery by the endocytosed TF-TFR
complex (29) and microcytic anemia (26, 30).
In humans the majority of iron released into the circulation and delivered to
tissues by TF is provided by the release of iron by the macrophages of the
reticuloendothelial system and hepatocytes, with newly absorbed dietary iron being a
minor contributor to plasma iron under normal conditions (31). To ensure that
appropriate amounts of iron are provided for bodily functions, such as erythropoiesis,
the release of stored iron from macrophages and hepatocytes is tightly regulated. The
export of tissue iron stores into circulation is mediated by ferroportin (19, 32), similar to
the release of iron from enterocytes, allowing iron mobilization from body stores and
enterocytes to be regulated by a similar mechanism. The control of systemic iron
homeostasis centers on the regulation of ferroportin levels through the action of
hepcidin, a peptide hormone primarily produced by hepatocytes (33). Hepcidin binds to
ferroportin leading to endocytosis and subsequent degradation (34), thus preventing the
release of iron from cells. The expression of hepcidin is normally linked to systemic iron
status and decreases during iron deficiency (35) to increase dietary iron absorption and
the release of stored iron from macrophages and hepatocytes, while increasing during
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iron loading (36), to prevent excess dietary iron uptake and the release of stored iron.
The expression of hepcidin can also be regulated by factors including anemia, hypoxia,
and inflammatory stimuli (37).
Disorders of Iron Metabolism
While under normal conditions iron homeostasis is tightly regulated by hepcidin
to ensure adequate iron for cellular functions while preventing excess iron
accumulation, conditions can arise during which iron homeostasis becomes perturbed.
Dietary iron deficiency is the most common nutrient deficiency worldwide, with over 2
billion people estimated to be iron deficient (38). The greatest estimated incidence of
dietary iron deficiency is seen in infants, children (39), and pregnant women in
developing countries (38), but women of child-bearing age still suffer from iron
deficiency in industrialized countries. In the United States 10-15% of women of child-
bearing age are iron deficient (40). Iron deficiency in men, as well as post-menopausal
women, is less common in the United States (40). The negative effects of iron
deficiency include fatigue (41), impaired cognitive function (42), and pica (43).
On the opposite end of the spectrum from iron deficiency are disorders of iron
overload, characterized by iron accumulation in various organs resulting in iron-
mediated tissue damage. Iron overload disorders can most often be linked to genetic
mutations. One such disorder is hemochromatosis, a disease characterized by
excessive iron loading in the liver, pancreas, and heart, resulting in hepatic
fibrosis/cirrhosis (44), diabetes (45), and cardiomyopathy (46). The majority of
hemochromatosis cases result from a single point mutation in the hemochromatosis
gene, HFE (47) (48), and are referred to as type 1 hemochromatosis. HFE is involved in
plasma-iron sensing by hepatocytes and interacts with transferrin receptor 2 (TFR2) to
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regulate the hepatic production of hepcidin (48, 49). Homozygosity for this mutation
occurs at a frequency of 1/200 in individuals of northern European descent but has
incomplete penetrance (50). More severe and rapid iron accumulation is observed in
humans with juvenile hemochromatosis, or type 2 hemochromatosis, resulting from
mutations in the hemochromatosis type 2 gene (HFE2) (51), encoding Hemojuvelin
(HJV) involved in hepatocyte iron sensing (type 2A) (52), or in the hepcidin antimicrobial
peptide gene (HAMP) (53), encoding hepcidin itself (type 2B). Symptoms of iron
overload in juvenile hemochromatosis occur rapidly and are often detected in patients
with juvenile hemochromatosis before 30 years of age (54), earlier than type 1
hemochromatosis, which is usually diagnosed in middle-aged patients (55). The
majority of juvenile hemochromatosis cases are due to mutations in HFE2, with HAMP
mutations being less commonly documented (56). Type-3 hemochromatosis due to
mutations in the gene transferrin receptor 2 (TFR2) (57), another protein involved in
hepatocyte iron sensing (48), has also been documented to produce iron overload of an
intermediate degree, with iron symptoms of iron overload appearing before those seen
in with type 1 but later than type 2 hemochromatosis (58, 59).While these mutations
occur in various genes, including HFE, HFE2, TFR2, and HAMP, the end result is a
deficiency of hepcidin production by hepatocytes, resulting in the impaired down-
regulation of ferroportin and increased dietary iron absorption.
A unique form of iron overload linked to mutations in ferroportin, rather than a
dysregulation of hepcidin, is referred to as ferroportin disease or type-4
hemochromatosis. Mutations that inhibit the ability of hepcidin to bind ferroportin result
in a phenotype similar to other forms of hemochromatosis (60, 61), in which ferroportin
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expression is elevated, characterized by elevated dietary iron absorption, macrophage
iron export, transferrin saturation, and liver iron accumulation. While gain-of-function
mutations in ferroportin lead to iron overload, paradoxically, loss-of-function mutations in
ferroportin have also been reported to result in iron accumulation. Mutations that impair
the targeting of ferroportin to the plasma membrane (62) have been reported in humans
with ferroportin disease, characterized by iron accumulation in macrophages, elevated
serum ferritin prior to elevated transferrin saturation, and liver iron deposition (63).
Currently it remains to be clarified how a loss-of-function mutation in ferroportin can
simultaneously result in the loss of ferroportin activity in iron export by macrophages
while allowing for dietary iron uptake and the establishment of elevated liver iron levels.
Studies of intestinal iron absorption in humans or animals with loss-of-function
ferroportin mutations are needed.
Another genetic disorder that results in iron overload is β-thalassemia major,
caused by mutations in the β globin gene (64) leading to impaired hemoglobin
production. Treatment for β-thalassemia major involves regular blood transfusions that
produce transfusional iron overload. Additionally, the failure to produce adequate
hemoglobin results in persistent anemia that can suppress hepcidin production in
response to iron overload, (65, 66), resulting in elevated intestinal iron absorption
exacerbating transfusional iron loading (67). While patients with β-thalassemia major
are usually treated with iron chelators, iron overload still occurs characterized by severe
iron accumulation in peripheral tissues, often associated with the development of
endocrine complications and heart failure (68).
24
Iron overload resulting from high dietary iron is far less common than
hemochromatosis or β-thalassemia major and is best documented in individuals living in
rural Sub-Saharan Africa. In this region, the practice of brewing beer in metal containers
results in iron leaching into the beverage, which is often heavily consumed (69). Iron
loading in response to this highly bioavailable iron affects some individuals to a greater
degree than others indicating that there may be a genetic predisposition to iron
accumulation (70). Mutations in ferroportin, similar to that observed in ferroportin
disease, have been associated with African iron overload suggesting that this may
explain the susceptibility of some individuals to dietary iron loading (71). However, other
reports indicate that the presence of this mutation is not associated with markers of iron
overload in African families with previously identified iron overload (72). The interplay
between dietary iron consumption and genetic susceptibility to iron overload has yet to
be elucidated regarding African iron overload.
While many conditions lead to iron overload, a common phenotype of excess iron
deposition in peripheral tissues, to varying degrees, is observed in response to systemic
iron accumulation. During severe iron overload the amount of plasma iron exceeds the
binding capacity of TF leading to the appearance of non-transferrin bound iron (NTBI).
NTBI is cleared rapidly from the circulation by tissues, leading to iron deposition in
organs such as the liver, pancreas, and heart (73, 74), potentially leading to tissue
damage and dysfunction (60). While the deposition of iron in these tissues during iron
overload is well established, the mechanisms by which NTBI is initially taken up remain
to be elucidated for many cell types.
25
Non-Transferrin-Bound Iron Import Proteins
The study of cellular iron transport in mammals has in large part centered on the
action of transmembrane proteins that demonstrate the ability to transport free iron. The
first discovered mammalian iron transport protein identified was DMT1, formerly referred
to as divalent cation transporter 1 (DCT1) or natural resistance-associated macrophage
protein 2 (NRAMP2), a transmembrane protein encoded by the solute carrier family 11,
member 2 gene (SLC11A2) (11). DMT1 was originally identified by screening a cDNA
library from iron-deficient rat duodenum for iron transport activity (11). DMT1 expression
was also found to be strongly induced in the duodenum of rats fed a low-iron diet, a
treatment which greatly induces intestinal iron uptake. Intestinal expression of DMT1 is
most abundant in the proximal small intestine, which has an acidic microenvironment
near the brush border due to the presence of gastric secretions. In line with this
localization, DMT1 functions optimally at an acidic pH with iron transport ability
substantially decreasing, displaying only residual activity, at physiologic pH (75). The
pH-dependent nature of DMT1-mediated NTBI transport is due to the coupling of iron
transport with protons, necessitating a low pH for efficient activity. Since the initial
discovery and characterization of DMT1, further studies have reported multiple isoforms
of human DMT1 that differ at both the N and C-terminal regions, allowing for 4 isoforms.
At the N terminus DMT1 isoforms can be identified as either 1A or 1B, differentiated by
an additional amino acid sequence present on the 1A isoform proceeding the shared
sequence by both 1A and 1B isoforms (76). 1A/1B isoforms of DMT1 display differential
expression patterns, with 1B isoforms being expressed to some degree in all tissues
examined whereas 1A-DMT1 expression is restricted to the duodenum and kidney (77).
Isoforms of DMT1 differing in the C-terminal region can be classified as those translated
26
from mRNA sequences with or without an iron response element (IRE), identified as
+IRE or –IRE isoforms. 3’ iron response elements allow for posttranscriptional
regulation of mRNA levels, increasing mRNA stability during iron deficiency (78). While
the IREs are located in the 3’UTR, DMT1 translated from –IRE transcripts differs from
that translated from +IRE due to the substitution of the terminal 18 amino acids with a
different 25 amino acid sequence (79). Tissue characterization of DMT1 expression
between +IRE and –IRE isoforms indicate that most tissues examined express both
isoforms, with the exception of the liver, testis, and duodenum in which +IRE isoforms
were more abundant (77). Differences in the C-terminal region between DMT1 isoforms
have been documented to affect the intracellular targeting of DMT1 in a cell-type
specific manner (76, 80). However, intracellular targeting attributed to differences
between isoforms at the N-terminal region have yet to be reported. The isoform of
DMT1 that functions in the duodenum to upregulate iron uptake in response to iron
deficiency is likely DMT1+IRE, as the induction of DMT1+IRE, but not –IRE, has been
reported in the iron deficient mouse intestine (81), and 1A but not 1B isoforms, of DMT1
are regulated by iron status in Caco2 cells, an intestinal epithelial cell line (77).
While the role of DMT1 in intestinal iron uptake and the export of iron from
endosomes into the cytosol within developing erythrocytes is well established and
previously discussed, the contribution of DMT1 to NTBI uptake by other cell types
during iron overload is unclear. The initial characterization of DMT1 in rats reported low
level mRNA expression of Dmt1 in the liver, pancreas, and heart, relative to the level of
Dmt1 expressed in the kidney or intestine (11), suggesting that NTBI uptake via DMT1
may be limited in these tissues. DMT1 has been detected in rat liver at the protein level;
27
however, hepatic DMT1 protein levels are strongly reduced in response to liver iron
loading (82), suggesting that this pathway of NTBI uptake is unlikely to promote hepatic
iron accumulation. A study of mice selectively lacking DMT1 in hepatocytes also argues
against a role for DMT1 in NTBI uptake by the liver, as DMT1 was found to be
dispensable for hepatic NTBI uptake and hepatocyte iron accumulation in a mouse
model of hemochromatosis (83). Cardiac NTBI uptake through the action of DMT1 also
appears unlikely as Dmt1 expression in the heart is observed to decrease in response
to cardiac iron loading, similar to the trend observed in the liver (82). However,
mechanistic studies regarding the role that DMT1 plays in cardiac NTBI uptake have yet
to be carried out.
Unlike DMT1 expression in the liver or heart, DMT1 expression is reportedly
unchanged in response to pancreatic iron accumulation in rats (82). Acinar cells
comprise the majority of the pancreas and therefore the unaltered DMT1 expression
during iron loading likely reflects the state of this cell population. However, pancreatic
iron loading is often viewed in the context of the pathogenesis of diabetes, requiring the
study of pancreatic islets which constitute 1-2% of the pancreas. Due to the small
contribution of islets to overall pancreatic mass, techniques that measure whole-tissue
expression will be unable to draw accurate conclusions about islet gene expression as
mRNA or protein from islets will be diluted by that of acinar cells. Techniques that are
able to discern cell-type-specific changes in gene expression have reported that the
expression of Dmt1 within pancreatic islets in mice injected with iron decreases (84).
Additionally, the pattern of DMT1 expression within the cell types of the pancreas is
disputed in the literature. In humans pancreatic DMT1 is reported to be primarily
28
restricted to islets (85), whereas in mice contradictory reports exist demonstrating Dmt1
expression restricted to islets (60, 84) or to a similar level between islets and acinar
cells (86). Mice selectively lacking Dmt1 in the β cell have been generated but no
measure of NTBI uptake has been made in islets or β cells from this model (86).
The second identified mammalian NTBI transporter is ZIP14, encoded by the
gene SLC39A14. ZIP14 stands for ZRT/IRT-like Protein 14, named after the similarity
between this protein and both zinc-regulated transporters (ZRT) and iron-regulated
transporters (IRT). Members of the ZRT gene family transport zinc in Saccharomyces
cerevisiae (87, 88), and IRT1 was identified as a route of iron transport in the roots of
iron-deficient Arabidopsis thaliana (89). ZIP14 was originally identified as a zinc
transporter and the iron transport capability of ZIP14 was not assessed until later, when
ZIP14 expression was reported to affect NTBI uptake and iron accumulation in
mammalian cells (90). Unlike DMT1, iron transport by ZIP14 is electrically neutral (75)
indicating iron uptake is accompanied by either the co-transport of anionic or the export
of a cationic species but the specifics of this have not yet been elucidated. ZIP14 has
been demonstrated to transport iron optimally at a neutral pH, with a loss of iron
transport as pH decreases (27, 75). The ability of Zip14 to transport iron at a physiologic
pH, similar to that of plasma, is consistent with a role for ZIP14 in the clearance of NTBI
at the plasma membrane. ZIP14 in mouse hepatocytes has been localized to the
plasma membrane (82, 91), as well as intracellular locations (27). The expression of
ZIP14 at the cell surface and intracellularly has also been reported in cell lines
overexpressing ZIP14 (90-92) and those expressing ZIP14 at endogenous levels (27).
Despite decreased iron transport ability at an acidic pH, ZIP14 has been demonstrated
29
to colocalize with transferrin within endosomes and promote the assimilation of iron
from transferrin (27). More than 50% of TBI has been reported to dissociate from TF at
pH 6.5 (93), at which ZIP14 still demonstrates iron transport activity (75), indicating that
ZIP14 may contribute to the export of iron from the endosome into the cytosol. Four
mRNA transcript variants predicted to encode three unique protein isoforms of human
ZIP14 have been recorded within the UniGene database. Transcript variants 1, 2, and 3
encode isoforms of human ZIP14 comprised of 492 amino acids. However, the
predicted protein product of mRNA variant 2 differs mid-sequence from the protein
products of mRNA variants 1 and 3, which are identical in amino-acid sequence.
Transcript variant 4 encodes a 481-amino-acid peptide with a sequence identical to that
of variants 1 and 3 with the exception of the C-terminal region. Currently,
characterization of the differences in iron transport capabilities or intracellular targeting
between isoforms of human ZIP14 has not been performed but investigation into the
iron transport kinetics of mouse ZIP14 isoforms has been carried out in Xenopus
oocytes. Three transcript variants of mouse Zip14 encoding 2 protein isoforms, both
containing 489 amino acids but differing mid-sequence, have been reported and
identified as isoforms A and B. Isoform B is reported to demonstrate a greater affinity for
iron as well as a greater maximal rate of transport compared with isoform A (94).
However, this study did not control for differences in the expression of individual ZIP14
isoforms complicating the interpretation of these results as similar sequences in the
same expression vector may demonstrate differences in expression (76).
Early characterization of ZIP14 identified this protein as a potential contributor to
tissue NTBI uptake. Human ZIP14 mRNA expression was reportedly highest in the liver,
30
pancreas, and heart, tissues known to accumulate iron during iron overload (92). The
cellular/subcellular localization of ZIP14 in human tissues has not yet been performed.
However, in rats Zip14 is detected at the hepatocyte sinusoidal membrane and
throughout pancreatic acinar cells, at the plasma membrane as well as intracellular
locations, but not β cells (82). Recent determination of the role ZIP14 plays in tissue
NTBI uptake using mice lacking Zip14 has indicated that Zip14 is required for iron
loading in hepatocytes and pancreatic acinar cells in response to both genetic and
dietary iron overload (7). In the absence of Zip14, iron deposits are only observed in the
non-parenchymal cells of the liver and within pancreatic connective tissue indicating that
ZIP14 is likely the sole route of NTBI uptake within hepatocytes and acinar cells. ZIP14
is unlikely to contribute to cardiac NTBI accumulation as hearts from ZIP14 knockout
mice display no difference in NTBI uptake compared with mice with intact ZIP14. In
addition to providing a route of NTBI uptake within the liver and pancreas, ZIP14
expression has also been reported to increase in response to iron loading within these
tissues (82). Mechanistic studies of the regulation of ZIP14 by cellular iron status have
shown that iron regulates ZIP14 posttranscriptionally, by preventing the proteosomal
degradation of ZIP14 (95). The upregulation of hepatic and pancreatic ZIP14 in
response to iron loading suggests that iron accumulation in the liver and pancreas may
increase the future rate of iron uptake in these tissues. In agreement with this
hypothesis, iron loaded HepG2 cells (96) and rodent hepatocytes (97, 98) demonstrate
increased NTBI uptake. However, while iron loading increases total-cell ZIP14
expression the abundance of ZIP14 on the plasma membrane is not increased, relative
to non-iron treated HepG2 cells (95), arguing against increased plasma NTBI clearance
31
by ZIP14 during iron overload (although NTBI uptake in response to iron loading was
not measured in this study). Discrepancies in the subcellular distribution of ZIP14
between studies may be attributed to differences in the degree of iron loading achieved
(95, 96). Future studies will be required to determine if the increased rate of NTBI
uptake associated with cellular iron loading is attributed to ZIP14 upregulation.
The most recently described mammalian NTBI transporter is ZIP8, ZRT/IRT-Like
Protein 8, another member of the ZIP protein family encoded by the gene SLC39A8.
Within the ZIP protein family ZIP8 is the most similar to ZIP14 in amino-acid sequence,
with mouse ZIP8 and ZIP14 having approximately 50% shared identity (99). ZIP8 was
originally referred to as BIGM103 and identified as a protein induced in monocytes in
response to LPS or TNFα (100). Similar to ZIP14, ZIP8 was originally found to transport
zinc, as overexpression of ZIP8 increased zinc accumulation by CHO cells (100). In
light of the ability of ZIP14 to transport iron and the similarity between ZIP14 and ZIP8,
the iron transport activity of ZIP8 was measured revealing that ZIP8 overexpression and
suppression increase and decrease NTBI uptake, respectively, in mammalian cells (28).
ZIP8 is reported to transport iron optimally at pH 7.5, with a loss of transport activity with
decreasing pH, and is localized to the plasma membrane supporting the role of this
protein in iron uptake from the plasma (28). ZIP8 is also detected in endosomes
suggesting that ZIP8 may be able to contribute to iron export from endosomes into the
cytosol as ZIP8 demonstrates iron transport within mammalian cells at pH 6.5 (28, 101),
at which iron will dissociate from transferrin within endosomes (93). However, iron
transport in Xenopus oocytes is abrogated at pH 6.5, complicating the interpretation of
the role ZIP8 plays in endosomal iron transport (28). ZIP8 reportedly functions as an
32
electrically neutral symporter (102), coupling the transport of cations with the transport
of bicarbonate, as cells overexpressing ZIP8 demonstrate increased ion uptake in the
presence of added bicarbonate and decreased metal uptake when bicarbonate
transport is inhibited (101). However, the possibility that the effect of bicarbonate on
metal transport was mediated through the action of a transporter other than ZIP8 was
not accounted for in this experiment.
ZIP8 mRNA is detected in many tissues but concentrated in the pancreas, lung,
placenta, liver, and thymus (100). Detection of ZIP8 at the protein level within these
tissues, from either human or animal sources, has been carried out to a very limited
degree, both in regards to tissue protein expression and the cellular/subcellular
localization of ZIP8 in vivo. The lack of data regarding the expression profile of ZIP8
currently limits the ability to make conclusions as to the role of ZIP8 in tissue NTBI
uptake during iron overload. ZIP8 is reportedly expressed at the plasma membrane of
rat β cells (103) but experiments to determine the contribution of ZIP8 to β cell iron
uptake have not been performed. Study of the role ZIP8 plays in tissue NTBI uptake has
also proved difficult due to the embryonic lethality of Slc39a8 disruption in mice. Mice
with Slc39a8 alleles disrupted by the neomycin-resistance cassette display
hypomorphic ZIP8 expression and fail to survive past post-natal day 3 (104). The
embryonic lethality observed in response to the disruption of ZIP8 is thought to be due
to impaired hematopoiesis during embryonic development (104). Mice with selective
deletion of Zip8 in tissues that accumulate iron during iron-overload disorders have yet
to be investigated. However, it is unlikely that Zip8 plays a role in NTBI uptake by
hepatocytes or pancreatic acinar cells as these cell populations display no iron loading
33
in the absence of ZIP14 (7). ZIP8 may play a role in NTBI uptake by other hepatic or
pancreatic cell types as well as in other organs (e.g. heart) that are unaffected by the
loss of ZIP14. Similar to ZIP14, ZIP8 expression is upregulated by cellular iron loading.
However, unlike ZIP14 that demonstrates an increase in intracellular rather than cell
surface expression in iron-treated HepG2 cells (95), ZIP8 levels were observed to
increase at the cell surface in response to iron loading in H4IIE cells, a rat hepatoma
cell line (28). However, an increase in the protein level of ZIP8 in response to iron
loading in vivo has yet to be confirmed. More research is necessary to determine the
pattern of ZIP8 expression in human tissues and the contribution of ZIP8 to iron
transport.
Some reports indicate that NTBI can be taken up into cells through both L-type
Ca2+ channels (LTCC) and T-type Ca2+ channels (TTCC). Currently, the study of NTBI
transport by calcium channels has been restricted to cardiomyoctes, exploring
mechanisms by which iron may accumulate in cardiac tissue during iron overload.
LTCC agonists increase iron uptake by the mouse heart and LTCC blockers decrease
iron accumulation in mouse heart tissue perfused with ferrous sulfate (105).
Additionally, treatment with LTCC or TTCC blockers in vivo reduces cardiac iron
accumulation in mice injected with iron dextran and in a mouse model of β thalassemia
(106, 107). While Ca2+ channels show promising evidence for a role in cardiac NTBI
uptake, the contribution of Ca2+ channels to NTBI uptake by other cell populations has
not been investigated. However, other cell types, such as pancreatic β cells, express
both LTCC (108) and TTCC (109) and therefore may take up via calcium channels. The
34
contribution of both LTCC and TTCC in various cell populations should be a topic of
future study.
The Iron and Diabetes Connection
Elevated Iron Stores and Diabetic Pathology
A link between iron overload and the development of diabetes dates back to the
initial case report of the disorder which would later become known as hemochromatosis.
Autopsy of an individual who died due to diabetic complications in 1865 noted the
bronze coloration of skin and organs referring to the condition as “bronze diabetes.”
Many years later it was established that the bronze discoloration observed was due to
excess iron deposition in tissues. The initial link between excess iron accumulation and
diabetes has been strengthened since the initial discovery by studying the prevalence of
diabetes in patients with pathological iron overload disorders.
In patients with hemochromatosis the incidence of diabetes varies considerably
among reports but is substantially greater than the incidence reported in the general
population of middle-aged Americans and Northern Europeans, in which
hemochromatosis is prevalent (110, 111). A general trend is that the reported incidence
of diabetes in hemochromatotic patients declines with the passage of time, with the
highest prevalence being reported in earlier manuscripts and a lower prevalence in
more recent reports. Initial reports indicate that diabetes is observed in approximately
80% of patients with hemochromatosis (112), although the diagnostic methods used
were not discussed. A later report based on data collected by physicians from an
unspecified time until 1972 reported that 63% of patients with hemochromatosis, as
defined by serum iron indices and liver biopsy, were diagnosed with diabetes (113). A
study of hospital records from 1977 to 1997 found that 40% of patients admitted to the
35
hospital with hemochromatosis were diabetic (114). However, a substantially lower
incidence of diabetes was reported in a study of French and Canadian
hemochromatosis patients between 1970 and 1997 which only identified 15.9% of men
compared with 7.4% of women to have diabetes (115). However this study may have
under reported the prevalence of diabetes as patients were not tested for undiagnosed
diabetes in this study. Glucose tolerance testing in hemochromatosis patients between
2000 and 2006 determined that 23% of patients were diabetic and 30% had impaired
glucose tolerance or elevated fasting glucose (45). The same study corroborated these
findings by examination of the medical records from the study center from 1975 to 2006
which reported that 26% of hemochromatosis patients were diabetic, as defined by a
measurement of elevated fasting blood glucose.
The general decrease in the reported prevalence of diabetes in patients with
hemochromatosis over time may reflect improvements in diagnosis, such as genetic
testing, and improved implementation of treatments. In hemochromatosis patients
diagnosed after the advent of genetic testing, post 1995, the prevalence of diabetes was
17.7% compared with 35.6% in patients diagnosed with hemochromatosis by elevated
iron indices between 1983-1995, prior to genetic testing (116). However, it should be
noted that there was no difference in the age of diabetic diagnosis between the groups
in this study allowing the older patients, diagnosed with hemochromatosis by iron
parameters rather than by genetic testing, a longer time period to develop diabetes. In
line with this concept the prevalence of impaired glucose tolerance is greater in patients
diagnosed with hemochromatosis in recent years, 13%, compared with those diagnosed
pre-1995 (6.7%) (116). It is difficult to interpret from this study whether impaired glucose
36
tolerance will progress to diabetes, resulting in a similar prevalence of diabetes as was
observed in patients diagnosed with hemochromatosis by traditional metrics of iron
overload, rather than by genetic testing, if given the same amount of time. The study
carried out by Bussychard et al. (114) is in agreement with the concept that diabetes
incidence likely is inversely related to the management of hemochromatosis and
lifestyle choices as diabetes was prevalent in 53% of patients with cirrhosis but only
25% of those without cirrhosis. Ferritin levels were significantly higher in patients with
cirrhosis suggesting poor disease management relative to those with no cirrhosis and a
lower incidence of diabetes. The addition of other factors associated with diabetes risk,
such as obesity, may also affect the development of diabetes in hemochromatosis
patients. One report has documented that all patients with hemochromatosis who were
identified as diabetic by glucose tolerance testing were overweight or obese (45),
suggesting that the influence of iron overload on diabetic pathology may increase when
coupled with additional risk factors for diabetes, such as obesity. However, another
study reported no difference in BMI between patients with hemochromatosis alone, 24.2
kg/m2, or with hemochromatosis accompanied by diabetes, 25 kg/m2, (117). The
development of diabetes in non-obese hemochromatosis patients indicates that obesity
is not required for the development of diabetes in patients with iron overload.
Evidence linking pathological iron overload to the development of diabetes is also
provided by studies measuring the prevalence of diabetes in patients with thalassemia
major. Thalassemic patients frequently develop diabetes, often at a young age when
diabetes is rare in the general population. The prevalence of diabetes varies among
studies likely reflecting differences in demographic factors including frequency of
37
transfusion, age of patients, and improvements in disease management or treatment
options. One study reported an initial diabetic prevalence of 26%, increasing to 30%
after a 2-year follow up (118). Another study documented a 14% prevalence of diabetes
out of all cases seen over the course of a 30-year study (119). However, diabetes was
only documented in patients over the age of 23, yielding a prevalence of 30% in this
demographic within the study. The observed increase in the prevalence of diabetes with
age supports the concept of longer-duration iron accumulation increasing diabetes risk
in patients with thalassemia as well as novel treatment options potentially improving
disease prognosis, as evidenced by improved overall survival with increased chelator
treatment (119). A lower prevalence of diabetes, 6.5%, has been reported in patients
from various centers in Italy which followed patients diagnosed with thalassemia
between 1970 and 1983. The lower prevalence in this study may be partially by more
stringent diagnostic criteria for diabetes, fasting glucose higher than 140 mg/dL for
several consecutive days or overt symptoms of diabetes, such as glycosuria. It is
possible that asymptomatic or mild diabetes may have been overlooked in this study
(120).
While an increased incidence of diabetes in pathological disorders resulting in
iron overload is well established, elevated iron stores remaining within the normal range
may also contribute to the pathogenesis of diabetes. Many prospective epidemiological
studies have investigated the link between markers of iron status, most often
determined by serum ferritin, and the risk of developing diabetes in the future (121-125).
Nearly all studies report increased baseline serum ferritin levels in subjects that develop
diabetes at a later time point in the study supporting the link between elevated normal
38
iron stores and glucose dyshomeostasis. However, the use of serum ferritin as an
indicator of iron stores is problematic due to the nature of ferritin as an acute-phase
reactant induced by inflammatory signaling pathways (126). Chronic systemic
inflammation is hypothesized to contribute to diabetic pathology (127) and therefore it is
difficult to determine whether ferritin is elevated in subjects who develop diabetes due to
increased systemic iron or simply in response to low-grade inflammation preceding the
development of diabetes. In line with the concern that serum ferritin is a marker of
inflammation rather than a barometer of iron stores, markers of inflammation, such as
C-reactive protein (CRP), are often higher in the diabetic cohort at baseline relative to
the control patients which did not develop diabetes during follow up (121, 123, 125).
However, when relative risk is adjusted to account for differences in CRP, serum ferritin
levels remain an independent risk factor for developing diabetes (123).
Fewer studies make use of soluble transferrin receptor (sTFR) levels in serum to
measure iron status, either alone or in combination with serum ferritin (123-125). sTFR
is reported to inversely correlate with iron stores independently of inflammatory stimuli
(128, 129). Unlike serum ferritin, sTFR as an indicator of iron status does not suggest
elevated baseline iron stores in patients who developed diabetes arguing against the
modulation of diabetes risk by iron stores within normal levels (123). Some studies do
not directly report the value for sTFR, instead referencing the ratio of sTFR to serum
ferritin (130). However, the data for ferritin listed individually in these studies suggest
that sTFR is largely similar at baseline between normal patients and future diabetics.
Elevated sTFR, suggesting lower iron stores, has even been reported in baseline
measurements from patients who developed diabetes compared with controls (125). In
39
light of the confounding influence of inflammation on ferritin levels and the lack of a
clear trend observed with sTFR levels, the link between variation in non-pathological
iron stores and the development of diabetes appears unsubstantiated, despite being
often mentioned in the literature.
Pathological iron accumulation leads to transferrin saturation and the appearance
of NTBI in circulation. Additionally, plasma NTBI has been reported in type 2 diabetics
despite normal levels of transferrin saturation and without pathological iron
accumulation (131). Plasma NTBI present in diabetics or in patients with severe iron
loading can be taken up by tissues such as the pancreas.
Pancreatic Iron Accumulation
Due to the role of the pancreas, specifically the pancreatic β cells, in glucose
homeostasis and the pathogenesis of diabetes, pancreatic iron accumulation is thought
to potentially account for the increased prevalence of diabetes in humans with
pathological iron overload.
Reports of iron loading in the human pancreas from patients with either
hemochromatosis or thalassemia indicate that iron deposits heavily within the acinar
cells of the exocrine pancreas and to a somewhat lesser degree within islets of the
endocrine pancreas as determined by various iron-staining techniques (132-134).
Within the islet, iron staining has been determined to be primarily restricted to β cells,
with α cells remaining relatively free of iron deposits (132, 134). No studies have
reported iron accumulation in the other cell populations of the pancreatic islet (e.g. δ
cells, ε cells). Due to the invasive nature of pancreatic biopsy, all data published on
human pancreatic iron loading at this point are derived from autopsy cases. Therefore,
the current reports indicate the pattern of pancreatic iron loading at end of life usually
40
after long periods of iron overload. Magnetic resonance imaging has been used to
detect pancreatic iron accumulation and could be used to measure the progression of
pancreatic iron overload over long periods in patients with iron overload (135). However,
magnetic resonance imaging is not currently able to distinguish between cell
populations within the pancreas precluding the use of this method to discern between
iron loading in the exocrine versus endocrine pancreas. New non-invasive methods of
measuring pancreatic iron in vivo will need to be developed before the nuanced
characterization of pancreatic iron loading over time in humans is feasible.
While the cell type-specific examination of pancreatic iron loading is not possible
in humans, extensive studies in rodents have been carried out with both genetic and
dietary models of iron overload. However, rodent models fail to appropriately model
human pancreatic iron loading as both mouse and rat islets remain largely free of iron
deposition even during severe iron overload (60, 134, 136, 137). Studies using Hjv
(138) and Hamp (136) knockout mice, resulting in severe pancreatic iron accumulation,
clearly show that β cells/islets fail to accumulate iron despite iron building up in the
surrounding exocrine pancreas. In accord with the lack of islet iron deposition in these
models no diabetic phenotype, characterized by impaired glucose tolerance resulting
from insufficient insulin secretion, is detected even up to a year of age in HAMP
knockout mice (136). The resistance of mice islets to iron loading and subsequent
diabetic pathology during severe iron overload is best evidenced by a novel mouse
model with a mutation in ferroportin resulting in impaired regulation by hepcidin leading
to severe tissue iron loading (60). Massive iron accumulation in this mouse model
results in death due to exocrine pancreas failure, a phenotype not reported in other
41
mouse models of iron overload, suggesting that this model potentially represents the
most severe model of pancreatic iron loading. Yet, even in this mouse model islets are
spared from iron deposition and glucose homeostasis is unchanged compared with
mice that have the wild-type ferroportin allele.
Rats also fail to recapitulate the human condition with regard to substantial islet
iron loading and the development of a diabetic phenotype. Several feeding studies with
high-iron diets at various concentrations and durations have been performed using rats
(137, 139-142). However, none report a diabetic phenotype even when pancreatic iron
loading was observed. Studies that have achieved pancreatic iron loading in rats
through prolonged feeding with high-iron diets report that, similar to mice, iron
deposition occurs within the exocrine pancreas, in both acinar cells and interstitial
areas, but islets were spared from iron loading (140). High dietary iron in rats has also
been reported to lead to pancreatic atrophy, of both the exocrine and endocrine
pancreas, during which pancreatic tissue is replaced by adipose tissue (141, 142).
However, iron staining or the measurement of pancreatic iron was not reported in these
studies to demonstrate that iron deposits were localized to atrophic cells. Currently it
remains unclear whether islet atrophy in response to high dietary iron is due to islet iron
accumulation or simply the loss of normal surrounding pancreatic morphology, as islets
are reported to resist atrophy longer than acinar cells (142). The most extreme
incidence of pancreatic iron loading through diet combined the surgical bypass of the
liver by the portal circulation in conjunction with feeding a high-iron diet for an extended
period of time resulted in severe pancreatic iron overload (137). However, within the
islet very little iron deposition was observed relative to the surrounding acinar cells. No
42
determination of glucose homeostasis was carried out in this study, but the authors
reported no overt diabetic phenotype despite an extreme treatment to induce pancreatic
iron loading. Additionally pancreatic atrophy was only reported in one rat that received
this treatment. It is possible that genetic differences between rat strains account for
differences in susceptibility to pancreatic atrophy in response to iron loading.
Iron loading of the pancreas has also been reported in rats injected with high
doses of iron. Rats administered iron dextran demonstrate detectable iron staining
within the exocrine pancreas, with the most prominent staining reportedly detected
within macrophages, while islets remained free of iron deposits. Injection of ferric
nitrilotriacetate (Fe-NTA) daily for 120 days in rats resulted in heavy iron deposits visible
by Perl’s staining in acinar cells, macrophages, and pancreatic connective tissue but
iron loading was sparse within islets (143). Similar experiments repeated by the same
group produced similar results reporting iron deposits in the exocrine pancreas, while
the islet remained free of detectable iron accumulation until 6 months after the start of
injections, when faint staining was detected within islets (144). Experiments that use Fe-
NTA injections to produce iron overload are unique in that they report a diabetic
phenotype, characterized by hyperglycemia and reduced insulin secretion (143, 144).
However, these studies report the development of diabetes within 1-2 (143, 144)
months of iron injections while iron deposits are not detectable in islets until 6 months
after the start of injections (144) making it difficult to conclude that it is β cell iron loading
that directly leads to impaired insulin production in this model. Additionally Fe-NTA
administered via injection represents both a non-physiologic source and method of iron
loading.
43
Mechanistic Evidence from Human Studies
Currently there is a lack of an established physiologically relevant rodent model
that accurately models the pancreas of humans with iron overload, characterized by
significant iron deposition in β cells in response to genetic, transfusional, or dietary iron
overload. Due to the limitation of in vivo models, mechanistic investigation into the
pathogenesis of iron-induced diabetes has been difficult; however, studies using
humans with iron-overload disorders provide some insight into the pathology of iron-
induced diabetes.
Diabetes in humans with iron overload can result from either impaired insulin
production by β cells, decreased peripheral insulin sensitivity, or a combination of these
factors (145). The study of humans with hemochromatosis suggests that impaired
glucose homeostasis is due, at least in part, to impaired insulin secretory capacity.
Hemochromatosis is associated with a trend toward decreased first and second phase
insulin secretion during glucose tolerance testing and individuals with hemochromatosis
and diabetes show drastically reduced first phase insulin secretion (117). This finding
has been corroborated in the literature as middle-aged, non-obese patients with
hemochromatosis demonstrate lower glucose-stimulated insulin secretion compared
with relatives without hemochromatosis (45). Somewhat conflicting evidence exists for
the role of insulin sensitivity in glucose homeostasis during hemochromatosis. One
report indicates that insulin sensitivity is not different between control subjects and
patients with hemochromatosis without cirrhosis or diabetes (117). However, in patients
with hemochromatosis and either cirrhosis or diabetes, insulin sensitivity is significantly
impaired, potentially attributable to greater iron loading, as indicated by higher serum
ferritin levels, compared with non-symptomatic hemochromatosis. Another study
44
reported that hemochromatosis patients with normal glucose tolerance maintain greater
insulin sensitivity, despite similar age and body mass indices, compared with normal
patients, although again this was lost in patients with diabetic complications (45).
Studies from animal models of hemochromatosis support the position of unchanged
insulin sensitivity in human hemochromatosis as insulin sensitivity in both Hamp-/- and
Hfe-/- mice is similar to that in wild-type mice (136, 146). Conflicting evidence also exists
regarding the effect of iron depletion by phlebotomy on β cell function and insulin
sensitivity in hemochromatosis. The removal of systemic iron stores, as indicated by
diminished plasma ferritin levels, by phlebotomy is reported to improve insulin secretory
capacity to varying degrees (147, 148), although this is interestingly associated with
unchanged glucose tolerance attributed to either decreased insulin sensitivity or
increased hepatic insulin clearance. The extent of improvement in insulin secretion, post
phlebotomy, may be due to the amount of iron removed and the severity of initial iron
accumulation, as a more prominent improvement in insulin secretory capacity was seen
in individuals with greater initial iron loading (147) compared with more mild iron
accumulation (148). However, normalization of iron stores by phlebotomy has also been
reported to have no effect on either first-phase insulin secretion, although longer time
points were not examined and insulin sensitivity was not measured (117). In diabetic
patients without hemochromatosis but with elevated ferritin levels, phlebotomy improved
both insulin secretory capacity and insulin sensitivity, supporting the idea that elevated
iron levels negatively affect glucose homeostasis by modulating both β cell function and
peripheral tissue insulin sensitivity (149).
45
In patients with β thalassemia major glucose dyshomeostasis may result from
either decreased insulin sensitivity, impaired β cell function, or some degree of both of
these factors. In thalassemics over the age of 18, insulin secretion in response to
glucose was elevated but glucose tolerance was unchanged relative to control subjects,
indicating diminished insulin sensitivity but increased β cell function (150). Decreased
insulin sensitivity compensated by increased insulin production in patients with
thalassemia has been reported elsewhere and it was documented that the phenotype
became more pronounced with the extent of transfusional iron overload (151). With the
onset of adulthood, glucose tolerance worsened and a loss of first-phase insulin
secretion was observed, indicating impaired β cell function after initial insulin resistance
(151). The finding of eventual β cell failure corroborates other reports of decreased
insulin sensitivity and a lack of compensatory insulin secretion leading to impaired
glucose tolerance, attributed to β cell failure (152). Impaired glucose tolerance in
patients with thalassemia has also been attributed to impaired insulin secretion by other
studies, however insulin sensitivity was not directly measured in these reports (153,
154). In patients with thalassemia improved treatment with iron chelators is reported to
marginally improve glucose tolerance, attributed to improved insulin sensitivity rather
than increased insulin secretion (155). Currently, the specifics of the relative
contribution between systemic insulin resistance and declining β cell function to the
development of diabetes in patients with thalassemia are still undefined. Variation in
these reports likely reflects differences between the subjects studied possibly including
lifestyle factors such as effectiveness of chelation therapy, extent of iron overload, or
age of participants.
46
While the clinical characteristics of hemochromatosis and thalassemia differ with
regard to insulin sensitivity, both disorders are linked to a diminished insulin secretory
capacity suggesting impaired β cell function. Several reports indicate that islets from
humans, as well as rodents, express low levels of or display low activity of antioxidant
proteins, relative to other tissues such as the liver, suggesting that islets may be easily
damaged by oxidative stress (156-158). During iron overload it has been hypothesized
that iron accumulation catalyzes the formation of reactive oxygen species (ROS), which
damage islets and leads to a loss of insulin secretory capacity. However, the effect of
iron loading on β cell function, evidenced by markers of oxidative stress and indicators
of β cell function such as glucose-stimulated insulin secretion, has not been examined
in human islets.
In addition to iron accumulating directly in β cells leading to impaired function it
has also been suggested that iron loading of the exocrine pancreas can result in
pancreatic atrophy, compromising β cell function due to the loss of normal pancreatic
morphology. Diminished pancreatic mass and an increased proportion of fatty tissue in
the pancreas has been reported in patients with hemochromatosis that have developed
diabetes (132). However, it is possible that in patients with both iron overload and
diabetes diminished pancreatic mass is a result, rather than the cause, of decreased β
cell function as insulin is thought to promote acinar cell growth (159) and a lack of
insulin is associated with decreased pancreatic mass (160). Compelling evidence
against the “degeneration-dysfunction” hypothesis is provided by a mouse model of iron
overload that demonstrates severe degeneration of the exocrine pancreas and the
failure to produce digestive enzymes resulting in death. However, glucose tolerance is
47
intact in this model despite pancreatic degradation (60) suggesting that iron-mediated
damage of the exocrine pancreas has little effect on β cell function.
Mechanistic Evidence from Animal Studies
Studies carried out using mice are complicated by the lack of substantial β cell
iron accumulation and the development of a diabetic phenotype. However, several
studies have still been performed in mouse models of iron overload which have reported
differences in β cell function and/or mass. Islets from Hfe knockout (Hfe-/-) mice on a
129/SvEvTac genetic background are reported to have marginally elevated iron levels
compared with Hfe+/+ mice and this is associated with diminished islet mass, pancreatic
insulin content, and in vitro GSIS by isolated islets (146) attributed to increased β cell
oxidative damage and apoptosis. However, in vivo glucose tolerance was improved in
Hfe-/- mice compared with Hfe+/+ controls (146, 161), despite slightly reduced insulin
secretory capacity during the first 30 min following intraperitoneal glucose injection
(146). In aged Hfe-/- mice, on a C57BL6 background, glucose tolerance was reportedly
reduced attributed to a failure to increase insulin secretion compared with Hfe+/+ mice,
suggesting the burden of added iron results in increased β cell dysfunction with time.
Follow up experiments with the Hfe-/- mice have suggested that the phenotype observed
is due to a cellular manganese deficiency in response to elevated iron levels leading to
insufficient Mn-SOD activity and impaired mitochondrial function (162). Supplementation
with manganese improved insulin secretion and glucose tolerance in Hfe-/- mice. The
Hfe-/- mouse represents a relatively mild model of iron overload, accumulating little
additional iron in the pancreas (7), and it would be expected that the non-significant
trend towards reduced insulin secretion in this model, on both 129/SvEvTac and
C57BL6, backgrounds would manifest as an overt phenotype in a more drastic model of
48
iron overload, such as Hamp knockout mice. However, aged mice lacking hepcidin,
resulting in massive iron overload, report no difference in either glucose tolerance or
GSIS (136), suggesting the slight differences observed in Hfe-/- mice may be attributable
to a factor other than iron overload. Additionally the phenotype of improved glucose
tolerance, attributed to increased glucose disposal as a result of increased AMPK
activity in Hfe-/- mice, was not reported in Hamp-/- mice, again suggesting other signaling
pathways besides iron sensing may be altered in Hfe-/- mice.
Leptin-deficient, Ob/Ob mice on a C57BL/6J background have also been used as
a model to reveal the possible contribution of iron status to dysglycemia during
conditions when β cell function is stressed. It has been reported that feeding of a diet
containing 500 ppm iron vs 35 ppm results in increased insulin resistance and a loss of
insulin secretory capacity in response to obesity (163). Similar findings were also
reported in response to iron chelation in the same study. However, in this study no iron
measurements were performed for any tissue making it difficult to conclude whether the
observed differences were due to the impact of iron on the β cell or rather systemic
differences.
Dietary iron deficiency and phlebotomy have also been reported to increase
pancreatic insulin content in obese rats compared with those fed a control diet, in which
pancreatic insulin levels were unaffected by iron deficiency, (164) attributed to lower
levels of islet fibrosis. However, it unlikely that the fibrosis was due to islet iron
accumulation as iron staining did not reveal any iron deposits in the islets, with iron only
accumulating in macrophages, and islet iron was not otherwise quantified in any
manner. Rats in this study were not fed an iron-loaded diet but plasma ferritin levels
49
were elevated suggesting systemic inflammation, potentially accounting for the
observed islet fibrosis and loss of function.
Rodents are poor models of β cell iron loading, as previously discussed,
complicating the interpretation that changes in β cell function in response to iron loading
in vivo are due to β cell iron accumulation rather than other systemic factors as a result
of systemic iron accumulation. Currently, investigation into the influence of islet iron
status on function in vitro, without the confounding factors potentially introduced by
changes in systemic iron status in vivo, has not been carried out. However, treatment of
rat islets with iron has been demonstrated to modestly decrease islet viability, attributed
to β cell death, and increase markers of oxidative stress (165). Future studies will be
needed to determine if increased oxidative stress due to iron loading results in impaired
islet function.
Iron and Autoimmune Diabetes
The vast majority of research examining the relationship between iron status and
diabetic pathology has focused on the impact of iron on exacerbating β cell stress or
worsening insulin resistance. The failure to produce adequate insulin to regulate
glucose levels due to either β cell exhaustion, insulin resistance, or a combination of
these factors is associated with type 2 diabetes; however, less is known about the
impact of iron status on the development of autoimmune type 1 diabetes.
Epidemiological evidence is less prevalent and provides a less compelling link between
iron and autoimmune diabetes compared with type 2 diabetes. The consumption of
greater dietary iron within the first 4 months of life has been associated with an
increased risk of developing type 1 diabetes before the age of 6 (166). However, the
collection of data for this study relied on 1 retrospective survey, administered 6 to 10
50
years after the feeding period of interest, in which the parents were asked about the
child’s food consumption. Therefore, the data obtained in this study, and the
conclusions drawn, may be questionable. Elevated transferrin saturation, indicating
higher iron status, was also associated with an increased risk of type1 diabetes in adults
(167). However, it is also a possibility that in this study elevated transferrin saturation
was a result of diabetes, rather than a causative factor, as hepatic insulin signaling has
been demonstrated to regulate hepcidin production and iron absorption (168). One
caveat in these epidemiological studies is that type 1 diabetes is defined as “insulin-
dependent” diabetes rather than directly indicated as type 1A diabetes, indicating
autoimmune involvement, or type 1B diabetes indicating an idiopathic origin. No
indicators of the mechanistic origin of type 1 diabetes are measured in the current
studies and therefore it is unclear whether insulin deficiency is attributable to the
autoimmune destruction of β cells. Additionally it has been suggested that some cases
of late-onset type 1 diabetes may be the result of undiagnosed hemochromatosis
leading to the loss of β cell function. The prevalence of HFE C282Y homozygosity was
greater in type1 diabetics who were diagnosed after the age of 30, allowing time for
systemic iron loading, compared with the general population and that follow up
investigation of these patients indicated iron overload (169). However this study did not
determine the mechanism of diabetic development in patients with type 1 diabetes and
iron overload and therefore it cannot be established if hemochromatosis was directly
responsible for the loss of β cell function or if iron had an effect on autoimmunity in
these patients. Currently, the epidemiological evidence linking iron status with
autoimmune diabetes is tentative and very limited; a more compelling case can be
51
made from studies that treated rodent models of autoimmune diabetes with factors
capable of modulating aspects of iron metabolism.
Several reports provide indirect evidence that iron restriction may limit critical
aspects of β cell death during autoimmune diabetes. Injection of the iron chelator
desferrioxamine prolongs the survival of islets grafts in NOD mice, suggesting that iron
restriction may limit T cell mediated β cell destruction (170). However, no measure of
either systemic or cellular iron status or further mechanistic investigation was carried out
in this study, thus complicating the interpretation of these results. Treatment with anti-
TFR antibodies has also been demonstrated to impair T cell proliferation and
cytotoxicity in vitro (171). The mechanism by which TFR interference affects T cell
activation is unclear but could potentially be linked to impaired iron acquisition limiting
activity or proliferation. However, the antibody used to bind TFR is reported to not
interfere with TF-TFR interaction, arguing against the blockade of iron uptake as an
explanation for impaired the impaired T cell response. It should be noted that in this
study the actual measure of cellular iron uptake was not carried out and it was only
assumed that if binding between TFR and TF was able to occur then successful iron
acquisition was also unhindered (172). In line with TFR signaling playing a role in
immune activation, apotransferrin has demonstrated a protective effect towards islets
during diabetic pathogenesis. Treatment of cultured mouse islets with apotransferrin
reduces the loss of viability observed in response to incubation with proinflammatory
cytokines and administration of apotransferrin to rodent models of spontaneous
autoimmune diabetes decreases the incidence and delays the onset of diabetes (173).
The protective benefits of apotransferrin in these models have been attributed to
52
decreased production of proinflammatory cytokines and reduced insulitis, characterized
by the infiltration of pancreatic islets by immune cells. The mechanism through which
apotransferrin elicits this shift in the immune response has not been elucidated but
could potentially affect cellular iron acquisition by interfering with cellular iron uptake via
TF. However the binding affinity for apotransferrin is considerably lower compared with
holotransferrin arguing against this theoretical mechanism (174, 175). While current
findings indirectly suggest that iron depletion may be protective against the
development of autoimmune diabetes no investigation into the direct influence of
systemic or cellular iron status on diabetic pathology has been performed at this time.
Future studies will need to be carried out to determine the impact of iron status on the
development of autoimmune diabetes.
53
CHAPTER 3 MATERIALS AND METHODS
Animals and Diets
Weanling male Sprague-Dawley rats (Charles River Laboratories), used in
experiments detailed within chapter 4, were randomized (n=6/group) to receive either
iron-deficient (FeD), iron-adequate (FeA), or iron-overloaded (FeO) diets. Purified diets
were prepared according to the AIN-93G formulation, but with no added iron (FeD), 35
mg/kg ferric citrate (FeA), or 2% carbonyl iron (Sigma-Aldrich) (FeO). Iron contents of
the diets, as determined by inductively coupled plasma mass spectroscopy (ICP-MS),
were 5 ppm (FeD), 36 ppm (FeA), and 20,275 ppm (FeO). Diets were also modified to
contain Avicel® microcrystalline cellulose instead of cellulose and 20% sucrose instead
of 10% sucrose (while reducing the amount of cornstarch accordingly) (176). After 3
weeks of feeding, overnight-fasted rats were sacrificed by exsanguination from the
descending aorta. Blood was collected into heparinized syringes and then centrifuged
to obtain plasma. Pancreata were quickly harvested, immediately frozen in liquid
nitrogen, and maintained at -80 °C for subsequent analyses. Weanling female
NOD/ShiLtJ mice (The Jackson Laboratory) ,used in experiments detailed within
chapter 6, were randomized to receive either iron-deficient (FeD), iron-adequate (FeA),
or iron-overloaded (FeO) diets. Purified diets were prepared according to the AIN-76A
formulation, but with no added iron (FeD), 270 mg/kg ferric citrate, or 10000 mg/kg
carbonyl iron. Diets were modified to contain 5% wheat, to increase the diabetic
potential of the diet, and avicel microcrystalline cellulose, replacing cellulose, to reduce
contaminate iron. Additionally diets were heated in a convection oven at 125°C for 30
m, as this treatment has previously been reported to increase the incidence of diabetes
54
in mice fed a purified diet, and subsequently irradiated. Iron contents of the finalized
diets, as determined inductively coupled plasma mass spectroscopy (ICP-MS) analysis
were 14 mg/kg (FeD), 359 mg/kg (FeA), and 6629 (FeO) mg/kg dry matter. To account
for potential heat degradation of vitamins during diet preparation an additional 10 g/kg
vitamin mix was added to all diets. Animals were fed their respective diets until removal
from the study, due to either assignment to prediabetic analysis at 10 wk of age, study
termination at 30 wk of age, or detection of diabetes during the study. Testing for
diabetes was carried out biweekly starting at 8 wk of age by using a handheld
glucometer (Accu-Chek). Animals were considered to be diabetic if glycosuria was
detected on 2 consecutive days followed by 2 consecutive blood glucose readings of
>250 mg/dL. Upon removal from the study, animals were fasted overnight and sacrificed
by isofluorane inhalation.
Iron Status Parameters and Blood Glucose Concentrations
Hemoglobin levels in heparinized blood were measured by using a HemoCue Hb
201+ Hemoglobin Analyzer (HemoCue). Liver non-heme iron levels were determined by
colorimetric analysis of acid-digested tissues, as described previously (177). Briefly,
tissues were weighed and digested in a solution of HCl, trichloroacetic acid. After
incubation at 65ᵒ C for 20 h, the iron content of dissolved samples was measured by the
addition of the dissolved sample to a chromogen reagent containing
bathophenanthroline disulphonate, thioglycolic acid, and saturated sodium acetate.
Color change, indicating non-heme iron concentration, was measured by using a
spectrophotometer and compared with a dilution series made from an iron-reference
solution (Fisher). Plasma iron and total iron-binding capacity (TIBC) were measured
colorimetrically by the methods described by Cook (1985). Briefly, for plasma iron
55
determination a solution of trichloroacetic acid, HCl, and thioglycolic acid was added to
plasma followed by centrifugation to precipitate proteins and reduce plasma iron.
Plasma supernatant was then added to a chromogen solution composed of sodium
acetate and ferrozine and iron concentration of unknown samples and a reference iron
dilution series (Fisher) measured by using a spectrophotometer. TIBC was calculated
by saturating plasma transferrin with iron followed by addition of magnesium carbonate
to remove excess iron prior to the measurement of plasma iron. Blood glucose
concentrations were determined in freshly collected heparinized blood by using a
handheld glucometer (Accu-Chek).
Pancreatic Mineral Concentrations
Concentrations of iron, zinc, manganese, copper, and cobalt in pancreas
samples were determined by using ICP-MS. Analyses were performed by the Michigan
State University Diagnostic Center for Population and Animal Health.
Histological Analysis
10% buffered formalin phosphate-fixed and paraffin embedded pancreases from
10 wk NOD were stained with hematoxylin and eosin and scored for insulitis. Insulitis
was scored based on the scale 0, no insulitis; 1, peripheral-insulitis, 2, mild-insulitis
(<50% of islet infiltrated); 3, severe insulitis (≥ 50% islet infiltrated). An average of 35
islets from at least 3 unique pancreatic sections per animal were scored and the
average insulitis score reported.
RNA Isolation and Assessment of RNA Integrity
Frozen pancreas samples were submerged in liquid nitrogen and finely ground
by using a mortar and pestle. After grinding, total RNA was isolated by using the
RNeasy Mini Kit (Qiagen) following the manufacturer’s protocol. Integrity of isolated
56
RNA was confirmed by denaturing agarose gel electrophoresis followed by visualization
of 18S and 28S ribosomal RNA bands. Prior to microarray analysis, RNA integrity was
additionally assessed by using the Agilent 2100 Bioanalyzer and RNA 6000 Nano Kit
(Agilent).
Microarray Analysis
RNA samples (n=6) from each dietary group were pooled and analyzed by using
a Rat GE 4x44K v3 Microarray (Agilent). Both FeD and FeO pooled cDNA samples
were individually compared with FeA in duplicate measurements. The assignment of
fluorescent Cy3 or Cy5 dye to each comparison group was alternated between the
duplicates. To identify differential gene expression, values of signal intensity were log2
transformed and normalized before the Student’s t-test was performed for probe-
specific comparisons. Genes showing a statistically significant (P<0.05) log2-
transformed fold change of at least ± 2 were analyzed to identify functional biological
categories by using the Database for Annotation, Visualization and Integrated Discovery
(DAVID) (178). Microarray analysis was conducted at the Interdisciplinary Center for
Biotechnology at the University of Florida. The microarray data discussed herein have
been deposited in NCBI's Gene Expression Omnibus (179) and are accessible through
GEO Series accession number GSE44699
Relative mRNA Quantification
cDNA was synthesized from total RNA by using the High Capacity cDNA
Reverse Transcription Kit (Applied Biosystems). Specific primers for genes of interest
were generated (Table 4-5) and confirmed for specificity by using the NCBI Basic Local
Alignment and Search Tool (BLAST) (180). Quantitative reverse transcriptase
polymerase chain reaction (qRT-PCR) was performed by using Power SYBR Green
57
PCR Mastermix (Applied Biosystems) and an Applied Biosystems 7300 Real-Time PCR
System. Dissociation curve analysis of PCR products revealed single products and all
PCR amplification efficiencies were 100 ± 10%. Quantitation of mRNA was determined
by comparison to standard curves generated by four 10-fold serial dilutions of standard
cDNA. Transcript levels were normalized to those of cyclophilin B (peptidylprolyl
isomerase B, PPIB).
Western Blotting
Pancreas samples were homogenized in ice-cold buffer containing 0.05 M Tris-
HCl (pH 7.4), 0.05 M NaCl, 0.001 M EDTA, 0.25% Tween-20, and Complete, Mini
Protease Inhibitor Cocktail (Roche). Tissue homogenates were clarified by
centrifugation at 10,000 x g for 10 minutes at 4 ºC, followed by sonication of the
supernatant. Protein concentrations were determined by using the RC DC Protein
Assay Kit (Bio-Rad). Proteins were mixed with Laemmli buffer, incubated at 70 °C for
10 minutes, and then electrophoretically separated by sodium dodecyl sulfate-
polyacrylamide gel electrophoresis (SDS-PAGE) on a 7.5% gel. Separated proteins
were transferred to a polyvinyl difluoride (PVDF) membrane (Bio-Rad), and incubated in
blocking buffer (5% nonfat dry milk in Tris-buffered saline (TBS)-Tween 20 (TBS-T)) for
1 hour. The blot was then incubated with rabbit anti-rat Alox15 antibody (kindly provided
by Dr. James F. Collins, University of Florida), 1:8,000 dilution for 2 hour. After washing
with TBS-T, the blot was incubated with horseradish peroxidase (HRP)-conjugated
donkey anti-rabbit IgG secondary antibody (Amersham Biosciences), 1:10,000 dilution
for 45 minutes. After washing with TBS-T and TBS, antibody binding was observed by
using enhanced chemiluminescence (SuperSignal West Pico, Pierce) and the
Fluorchem E imaging system (ProteinSimple). To indicate lane loading, the blot was
58
stripped and reprobed with a mouse anti-α-tubulin antibody (Sigma) at a 1:5,000
dilution, followed by an HRP-conjugated goat anti-mouse IgG secondary antibody
(Santa Cruz) at a 1:10,000 dilution. Densitometry was performed by using AlphaView
software (ProteinSimple). Cell samples were lysed and sonicated in RIPA buffer
containing 150 mM sodium chloride, 1% IGEPAL, .5% sodium deoxycholate, .1%
sodium dodecyl sulfate, and 50 mM tris-base, and Complete, Mini Protease Inhibitor
Cocktail (Roche). The RC DC Protein Assay Kit (Bio-Rad) was used to determine lysate
protein concentrations and samples were mixed with Laemmli buffer and incubated at
37 °C for 20 minutes prior to western blotting analysis of ZIP14, ZIP8, and DMT1 or
incubated at 95 °C for 10 min for other proteins. The procedure of western blotting was
performed as previously discussed with the exception of nitrocellulose replacing PVDF
membranes (139). Primary antibodies used were rabbit anti-mDMT1 antibody at a
concentration of 1:1,000 (generously contributed by Dr. Francois Cannone-Hergaux),
rabbit anti-hZIP8 at 1:5,000 concentration (Prestige Antibodies,Sigma Aldrich), rabbit
anti-hZIP14 at 1:5,000 concentration (Prestige Antibodies, Sigma Aldrich), rabbit anti-
CCS at 1:200 (Santa Cruz), mouse anti-Na+/K+-ATPase at 1:200 (Santa Cruz), Goat
anti-ferritin light chain 1:4,000 (Novus Biologicals), mouse anti-α tubulin 1:5,000 (Sigma
Aldrich).
Cell Culture and Treatments
βlox5 cells were cultured in low glucose (1g/L) Dulbecco’s Modified Eagle’s
Medium (Cellgro) containing 100 units/ml penicillin, 100 ug/ml streptomycin, 10% fetal
bovine serum (FBS) (Atlanta Biologicals), 1% Minimum Eagle’s Medium Non-Essential
Amino Acids (Corning), and 15 mM HEPES (Cellgro). βlox5 cells were treated with 100
µM ferric ammonium citrate (MP Biomedicals) and 1 mM ascorbate (EMD Millipore) for
59
24 h to promote iron loading. Recombinant interleukin 1β (IL-1β) (Peprotech) was
added to media at a concentration of 100 U/ml for specified times. Primary human islets
of at least 90% purity and viability were obtained from the Integrated Islet Distribution
Program (IIDP) and were cultured in Ultra-Low attachment plates (Corning) with
CMRL1066 media containing 10% FBS, 100 units/ml penicillin, and 100 ug/ml
streptomycin. Depletion of cellular iron or iron loading was performed by treating islets
with 50 µM Deferoxamine (Hospira), or 100 µM ferric ammonium citrate (MP
Biomedicals) and 1 mM ascorbate (EMD Millipore), respectively, for 48 h. Prior to
transfection islets were dissociated by incubating in Accutase (Life Technologies) for 15
min at 37 °C followed by pipetting to ensure a single cell suspension. Dissociated islets
were transfected for 48 h. All cells were maintained in 5% CO2 at 37 °C.
In Vitro Glucose Stimulated Insulin Secretion
Triplicate groups of approximately 10 human islets were selected for glucose–
stimulated insulin secretion (GSIS) testing and incubated in low glucose 2.8 mM Kreb’s-
ringer buffer (KRB) containing 25 mM HEPES, 115 mM NaCl, 24 mM NaHCO3, 5 mM
KCl, 1 mM MgCl26H20, .1% BSA, 2.5 mM CaCl2, adjusted to pH 7.4 for 30 m. After
incubation islets were transferred to fresh 2.8 mM glucose KRB and incubated for 1 hr
after which KRB was sampled for determination of basal insulin secretion and replaced
with high-glucose KRB containing 22 mM glucose. Islets were again incubated for 1 hr
before high-glucose KRB was sampled and removed. Insulin concentrations in sampled
KRB were measured by using the ALPCO Ultra-sensitive ELISA KIT (Alpco) and used
to determine total insulin secretion during each time period. Results were normalized to
levels of islet DNA measured by using the Quant-IT PicoGreen dsDNA assay kit (Life
Technologies).
60
Mouse Islet Isolation
Islets from 10 wk mice used in glucose-stimulated insulin secretion testing were
isolated by using a collagenase infiltration method with Liberase TL (Roche) as
previously detailed (181). In brief, pancreases were incubated at 37°C for 15 m and
islets released by manually agitating the tissues with ice-cold HBSS followed by
centrifugation at 300 g at 4°C to pellet the tissue. Manual agitation and centrifugation
was repeated 5 times. After dissolution of the pancreas, islets were handpicked and
transferred to RPMI1640 medium for culture prior to analysis. Islets from 2-3 mice from
each dietary group were pooled and lysed in RIPA buffer (50 mM Tris-HCl, 1% NP-40,
0.25% Na-deoxycholate, 150 mM NaCl, and 1 mM EDTA, adjusted to pH 7.4) for
western blot analysis. Islet iron status was determined by western blotting for transferrin
receptor by using mouse anti-transferrin receptor (Life Technologies) at a concentration
of 1:4000 ug/ml.
Determination of DMT1, ZIP8, and ZIP14 mRNA Copy Numbers
Total RNA was isolated from primary human islets by using RNAzol (Molecular
Research center) following the manufacturer’s protocol. cDNA synthesis from isolated
RNA was carried out by using the High Capacity cDNA Archive Kit (Life Technologies).
Quantitative RT-PCR was performed by using SYBR Select Master Mix (Life
Technologies) and a CFX96 Real Time PCR Detection System (Bio-Rad). Copy
numbers of DMT1, ZIP8, and ZIP14 were calculated by comparing the Ct values from
human islet cDNA samples to standard curves constructed from known quantities of the
plasmids pBluescriptR-hDMT1 (BC100014; Addgene), pCMV-Sport6-hZIP8 (BC012125;
Open Biosystems), and pCMV-XL4-hZIP14 (BC015770; Open Biosystems). The
primers used to detect DMT1 (F, 5’- TGCATCTTGCTGAAGTATGTCACC-3’ and R, 5’-
61
CTCCACCATCAGCCACAGGAT-3’), ZIP14 (F, 5’-CAAGTCTGCAGTGGTGTTTG-3’
and R 5’-GTGTCCATGATGATGCTCATTT-3’), and ZIP8 (F, 5’-
CAGTGTGGTATCTCTACAGGATGGA-3’ and R, 5’-CAGTTTGGGCCCCTTCAAA-3’)
were designed to detect all known mRNA transcripts.
siRNA Knockdown of DMT1, ZIP8, and ZIP14
SMARTpool siRNA targeting either human DMT1 or ZIP14 (Thermo Scientific)
and Flexitube siRNA targeting ZIP8 (Qiagen) were used to suppress mRNA levels.
Transfection was performed by using Lipofectamine siRNAiMAX (Life Technologies)
and Opti-MEM Media (Life Technologies) for siRNA and reagent suspension following
the manufacturer’s protocol to yield a final concentration of 12 nM siRNA after addition
to the complex to plated cells. In brief Opti-MEM media was added to separate vials of
either siRNA or Lipofectamine siRNAiMAX after which the contents of each vial were
combined and allowed an incubation period of 15 min. After incubation 500 µL of the
transfection mixture was then added to each well of a 6 well plate containing 2 ml of cell
culture media and cultured for 48 h prior to collection. Successful knockdown was
confirmed by immunoblotting.
Overexpression of DMT1, ZIP8, and ZIP14
Cultured βlox5 cells were transiently transfected with either pcDNA3.1hDMT1-
(1A/+IRE) flag, generously contributed by Dr. Natascha Wolff, pCMV-Sport6-hZip14
(BC015770), pCMV-Sport6-hZIP8 (BC012125), or pCMV-Sport6-empty vector, by using
Effectene Transfection Reagent (Qiagen) according to the manufacture’s protocol. After
24 h cells were harvested for confirmation of overexpression or used in iron uptake
experiments.
62
Immunofluorescencse
Paraffin-embedded tail sections of human pancreas were obtained through the
Network for Pancreatic Organ Donors with Diabetes (nPOD). Paraffin was cleared with
xylene and tissues rehydrated in stages. After hydration slides underwent heat-induced
epitope retrieval in sodium citrate buffer containing 10 mM sodium citrate, .05% tween
20, and adjusted to pH 6.0 with HCl. Slides were then briefly cooled in distilled H20 and
washed with TBS to remove residual sodium citrate buffer. Washed slides were then
incubated in blocking buffer containing 2% goat serum for 30 min to prevent nonspecific
binding of secondary antibody. During the primary antibody incubation human sections
were triple stained for insulin, glucagon, and either DMT1, ZIP8, or ZIP14 by using
guinea pig anti-insulin (1:200, Abcam), mouse anti-glucagon (1:1,000, Abcam), and
either rabbit anti-DMT1 (1:1,000, Prestige Antibodies Sigma Aldrich), rabbit anti-Zip8
(1:250 Peprotech), and rabbit anti-ZIP14 (1:1,000, Prestige antibodies Sigma Aldrich)
antibodies. During staining for ZIP8 slides were also permeabilized with 5% triton x-100
for 15 min after antigen retrieval. To control for nonspecific primary antibody binding
serial sections were also triple stained with nonimmune rabbit IgG replacing the primary
antibody for DMT1, ZIP8, or ZIP14 at the same concentration. The incubation with
primary antibodies was carried out at 4 degrees C overnight in a humidified chamber.
After the primary incubation slides were washed with TBS and incubated for 2 h at room
temperature with the secondary antibodies goat anti-guinea pig Alexa Flour 594 (1:250,
Life Technologies) for insulin, goat anti-mouse Pacific Blue (1:250, Life Technologies)
for glucagon, and goat anti-rabbit Alexa Flour 488 (1:250, Life Technologies) for either
DMT1, ZIP8, or ZIP14 to obtain a fluorescent signal. After this incubation period slides
were washed with TBS and cover slips carefully mounted. Confocal microscopy was
63
performed and images obtained by using an Olympus IX2-DSU spinning disk confocal
fluorescent microscope equipped with a Hamamatsu ORCA-AG camera and 3i
SlideBook v4.2 software.
Cellular NTBI Uptake
Cultured βlox5 cells or isolated human islets were washed twice with serum–free
media (SFM) and incubated for 1 h in SFM containing 2% bovine serum albumin to bind
residual transferrin and prevent iron uptake via transferrin-bound iron endocytosis.
Following this incubation period cells were again washed with SFM before the addition
of media containing 2 μM ferric ammonium citrate (MP Biomedicals) radiolabeled with
59Fe and 1 mM ascorbate. Cells were incubated with radiolabeled media for 2 h during
siRNA experiments and for 1 h during overexpression experiments. After the incubation
period media was aspirated and cells were washed 3 times with an iron chelator
solution containing 1 mM diethylenetriaminepentaaetic acid and 1mM
bathophenanthroline disulfonate to remove residual radiolabeled iron. Cells were then
lysed in SDS lysis buffer and the lysate collected measured for radioactivity by using a
WIZARD2 gamma counter (PerkinElmer). Counts for each sample were then
normalized to cellular protein concentrations. To account for variation in 59Fe uptake
between independent experiments, NTBI uptake relative to cellular protein was
measured in separate groups of βlox5 cells and dissociated human islets to establish a
reference level of 59Fe uptake in each independent experiment. Reference levels from
separate independent experiments were compared and used to generate adjustment
ratios by which experimental groups would be multiplied by to produce a control groups
with a relative 59Fe uptake value of 1 while preserving variance in the experimental
control group for statistical analysis.
64
Generation of Transgenic MIP-Zip14-HA Mice
The transgenic construct previously used to generate mice selectively
overexpressing green fluorescent protein (GFP) in β cells under control of the mouse
insulin 1 promoter, MIP-GFP was generously contributed by Drs. Graeme Bell and
Manami Hara (University of Chicago, Chicago, IL) and has been detailed previously
(182). In brief the construct consisted of a fragment of the mouse insulin 1 promoter,
the GFP coding region, and an intron region of the human growth hormone gene to
enhance expression. The region of this vector encoding GFP was excised by restriction
digestion with Xho1 and hemagglutinin antigen (HA) tagged Zip14 was subcloned into
the MIP vector, resulting in the generation of a MIP-ZIP14-HA construct. The MIP-
Zip14-HA-hGH fragment was isolated from the vector backbone by restriction digestion
with HindIII and SfiI followed by agarose gel separation, extraction, and purification.
DNA was introduced into fertilized embryos from C57BL/6J mice by pronuclear
microinjection carried out at the Mouse Models Core at the University of Florida.
Founders were screened based on the presence of HA tag using the primers 5’
TACCCTTACGACGTGCCT 3’ and 5’ AGGAGAGAGGCCAGGTTAAT 3’ to differentiate
between endogenous Zip14 and successful transgene insertion. Transgene copy
numbers were determined by the comparison of genomic DNA to a standard curve
constructed from known amounts of MIP-Zip14-HA plasmid DNA. The primer set used
was 5’ CCTTACGACGTGCCTGATTA 3’ and 5’ TTCAGCTGTGTCAGGGTAAG 3’,
targeting HA tag. Founders were bred with wild-type C57BL/6J mice to establish
transgenic lines. Overexpression of Zip14-HA in β cells was confirmed by
immunofluorescence detection of HA tag in mouse pancreatic sections (Mouse anti-HA
65
1:100, Roche) following the protocol previously detailed for IF in human pancreatic
sections.
Statistical Analysis
Data were analyzed for statistical significance by using one-way ANOVA and
Tukey’s multiple comparison post-hoc test or Student’s T-test (GraphPad Prism) where
indicated. Unequal variance between groups was accounted for by log transformation,
where applicable, to normalize variance before statistical analysis. Survival curves were
analyzed for significance by using the Gehan-Breslow-Wilcoxon Test (Graphpad Prism).
66
CHAPTER 4 TRANSCRIPTIONAL PROFILING OF PANCREATIC GENE EXPRESSION IN
RESPONSE TO DIETARY IRON LOADING OR DEFICIENCY1
The association between excess iron and pancreatic dysfunction has long been
observed in the iron overload disorder hereditary hemochromatosis (183). Patients with
hemochromatosis have a higher prevalence of diabetes, decreased insulin secretory
capacity, and impaired glucose tolerance relative to the normal population (45). The Hfe
knockout mouse, the animal model of hemochromatosis, also displays alterations in
pancreatic function, including decreased insulin secretory capacity (146). In humans,
insulin secretory capacity and glucose tolerance improves after iron stores are
normalized by phlebotomy, suggesting that tissue iron levels are an important
determinant of insulin action (184). Consistent with this idea are animal studies
showing that a decrease in iron stores (in response to phlebotomy or a low-iron diet)
can increase insulin secretion and pancreatic insulin levels (163, 164). However, iron
depletion to the point of iron deficiency and anemia has been shown to negatively affect
glucose homeostasis by increasing blood glucose concentrations (185).
The effects of iron overload and deficiency on glucose homeostasis are likely
mediated, at least in part, by iron-related changes in the expression of genes involved in
glucose metabolism. For example, iron deficiency has been reported to be associated
with higher levels of rate-limiting gluconeogenic enzymes in rat liver (186) and iron-
loaded Hfe knockout mice display increased glucose uptake by isolated soleus muscle
1Reprinted with permission from Coffey R, Nam H, Knutson MD. Microarray analysis of rat pancreas
reveals altered expression of Alox15 and regenerating islet-derived genes in response to iron deficiency and overload. PLoS One. 2014;9:e86019.
67
and decreased glucose oxidation by isolated hepatic mitochondria (161, 187). Little
information, however, exists regarding iron-related gene expression in the pancreas.
Given that the pancreas hormonally controls whole-body glucose homeostasis, the aim
of the present study was to examine global changes in pancreatic gene expression in
response to iron deficiency and overload. Identification of pancreatic genes that are
regulated by iron status may offer insight not only into how iron status perturbs glucose
homeostasis, but also how iron overload may contribute to β cell destruction and
diabetes.
Results
Body Weight, Iron Status, and Blood Glucose Concentrations
After 3 weeks of feeding the experimental diets, body weights were significantly
lower in the FeD and FeO groups relative to FeA controls, but did not differ between
FeD and FeO animals (Table 4-1). Liver non-heme iron concentrations, an indicator of
body iron stores, confirmed that rats fed the FeD diet became iron deficient whereas
rats fed the FeO diet became iron overloaded. In FeO animals, liver non-heme iron
concentrations were nearly 40 times higher than controls. FeD rats also became
anemic with hemoglobin levels that were 41% lower than normal (Table 4-1). Blood
glucose concentrations were elevated in FeD rats compared with FeA controls, whereas
those in FeO animals did not differ from controls (Table 4-1).
Pancreatic Mineral Concentrations
In FeO rats, pancreatic iron concentrations were 155% higher than those in FeA
animals, whereas in FeD rats, iron concentrations were 40% lower than controls (Table
4-2). Given that iron deficiency and overload can affect tissue concentrations of other
trace minerals (176), I measured pancreatic concentrations of zinc, manganese, copper,
68
and cobalt (Table 4-2). Pancreatic zinc concentrations were found to be 26% higher in
FeD rats, and copper concentrations were 74% lower in FeO rats, when compared with
FeA controls. By contrast, pancreatic manganese and cobalt concentrations did not
differ among groups. It should be noted that the concentrations of zinc, manganese,
copper, and cobalt did not differ among the experimental diets (data not shown).
Identification and Classification of Differentially Expressed Genes by Microarray Analysis
Microarray analysis was used to identify candidate genes that are differentially
expressed in FeD and FeO pancreas, especially those that may influence the risk for
diabetes. Using a log2 fold change of ± 2 and P < 0.05 as a cutoff, I identified a total of
230 genes as differentially expressed in FeD and FeO pancreas relative to FeA
pancreas. In FeD pancreas 66 genes were differentially expressed (56 down-regulated
and 10 up-regulated) (Figure 4-1A). In FeO pancreas 164 genes were differentially
expressed (82 down-regulated and 82 up-regulated) (Figure 4-1B). The differentially
expressed genes were analyzed by using DAVID bioinformatics resources to identify
gene ontology categories. In FeD pancreas, the category with the highest number of
genes was “lipid transport” (7 genes), followed by “antimicrobial” (4 genes),
“neuropeptide” (4 genes), and “pancreatitis-associated protein” (3 genes) (Figure 4-1A).
All but two of the genes in these categories were down-regulated in FeD pancreas. By
contrast, in FeO pancreas, most gene ontology categories were enriched with up-
regulated genes (Figure 4-1B). For example, 6 of 8 genes were up-regulated in the
“pattern binding” category in FeO pancreas. Of note, the gene ontology category
“pancreatitis-associated protein” was identified in both FeD and FeO pancreas. Lists of
the genes in each category along with fold change are provided in Table 4-6 and Table
69
4-7. The top 10 most up-regulated and down-regulated genes in FeD and FeO
pancreas, ordered by mean magnitude change (P< 0.05), are shown in Table 4-3 and
Table 4-4. The genes listed in Table 4-6, Table 4-7, Table 4-3, and Table 4-4 were
surveyed in the literature to identify those with reported associations with diabetes
and/or glucose homeostasis, and several of these were subsequently selected for
validation by qRT-PCR of individual rat samples (n=6 group).
Confirmation of Up-Regulation of Alox15 Expression by QRT-PCR and Western Blotting
According to the microarray analysis, the most up-regulated gene in FeD
pancreas was Alox15 (arachidonate 15-lipoxygenase) (Table 4-3). Alox15 catalyzes the
oxidation of polyunsaturated fatty acids, such as arachidonic acid, during the formation
of inflammatory mediators and has been linked to the development of type 1 diabetes
(188, 189). QRT-PCR analysis confirmed the up-regulation of Alox15 mRNA levels in
FeD pancreas (Figure 4-2A), and western blot analysis revealed higher Alox15 protein
levels in FeD pancreas (Figure 4-2B). Alox15 protein levels were also found to be
higher in FeO pancreas compared with FeA controls despite no increase in Alox15
mRNA levels. As western blotting controls for rat Alox15, jejunum samples from iron-
adequate (JA) and iron-deficient (JD) rats were analyzed in parallel with the rat
pancreas samples. Consistent with a previous study by Collins et al. (190), Alox15 was
detected at approximately 70 kDa and was markedly up-regulated in iron-deficient
jejunum (JD) (Figure 4-2B). Densitometric analysis of the western blots indicated that
Alox15 protein levels were approximately 8- and 9-fold higher (P<0.001) in FeD and
FeO pancreas, respectively, compared with FeA controls (n=6/group; data not shown).
70
Confirmation of Reg Family mRNA Levels by QRT-PCR
Of the genes showing positive regulation during FeO, the most elevated
belonged to the Reg family of regenerating islet-derived genes (Table 4-4). The genes
of the Reg family, most notably Reg1a, have been linked to pancreatic regeneration as
well as cellular growth and survival during oxidative stress (191-194). Consistent with
the microarray data, qRT-PCR analysis revealed that mRNA levels of these genes were
up-regulated in FeO pancreas. Mean mRNA levels of Reg1a, Reg3a, and Reg3b were
found to be 21, 37, and 18 times higher, respectively, in FeO pancreas than FeA
controls (Figure 4-3). Also consistent with the microarray, qRT-PCR analysis found that
Reg1a mRNA levels were up-regulated in FeD pancreas. Reg mRNA levels varied
considerably among rats, particularly in the FeO and FeD groups in which two or three
values were notably higher than the others. Repeated analyses confirmed that the high
values do not represent analytical artifacts. In the FeD, FeA, and FeO groups, the high
values for Reg1a and Reg3a (but not Reg3b) mRNA are from the same animals,
suggesting that Reg1a and Reg3a are up-regulated in parallel.
Discrepancies Between Microarray and QRT-PCR Analysis Results
According to the microarray, both FeD and FeO pancreas showed large down-
regulations in the expression of Fabp1, Fabp2, and Apoa1 (Tables 3 and 4), which
clustered in the gene ontology category of “lipid transport” (Table S3). As all of these
genes have been associated with diabetes in the surveyed literature (195-197) they
were selected for follow-up study. However, the expression levels of these genes in
pancreas were found to be below the detection limit of qRT-PCR, similar to previous
studies that have failed to detect the expression of these genes in pancreas (198, 199).
71
Discussion
Because iron status can affect glucose homeostasis, I sought to identify glucose
metabolism-related genes in rat pancreas whose expression might be affected by iron
deficiency or overload. Unexpectedly, our microarray data, and subsequent functional
enrichment analysis by DAVID, did not identify any changes in the expression of genes
known to be involved in glucose metabolism. A limitation to our study is that many of the
glucose-responsive genes are found in islet cells (200), which constitute only 1-2% of
the mass of the pancreas, and therefore changes in islet-cell gene expression may not
be readily detectable in whole pancreas tissue. The most notable finding from our
microarray analyses was the identification of differentially expressed genes that are
associated with diabetes and/or pancreatic stress. More specifically, Alox15 was
identified as the most up-regulated mRNA in iron deficiency, and Reg family transcripts
Reg1a, Reg3a, and Reg3b, were found to be markedly up-regulated in iron overload.
Alox15 encodes arachidonate 15-lipoxygenase, a non-heme iron-containing
enzyme that catalyzes the oxygenation of polyunsaturated fatty acids to form
inflammatory mediators (201). Despite the name suggesting 15-lipoxygenase activity,
Alox15 in rodents has been demonstrated to function primarily as a 12-lipoxygenase
with secondary 15-lipoxygenase function (202). Therefore the term leukocyte 12-
lipoxgenase, as well as the hybrid term 12/15-lipoxygenase, is often used in reference
towards Alox15. A link between iron deficiency and Alox15 was first reported in a
microarray study by Collins et al. (190), who identified Alox15 as the most strongly
induced gene in the intestine of iron-deficient rats. Alox15 has also been identified as
the most highly induced gene in microarray studies of iron-deficient rat liver (186) and
brain (203). Similar to Collins et al. (190), I found that elevated Alox15 mRNA levels
72
were associated with higher Alox15 protein levels. Protein levels of Alox15 were also
found to be elevated in iron-loaded rat pancreas, despite no up-regulation of Alox15
mRNA levels, suggesting post-transcriptional regulation under iron-overload conditions.
In the pancreas, Alox15 is present in β cells (204) where it appears to play a role
in the pathogenesis of diabetes. Genetic deletion of a locus containing Alox15 has
been shown to protect nonobese diabetic (NOD) mice from developing autoimmune
diabetes, with knockout mice exhibiting superior islet mass and glucose tolerance (188).
Recent experiments using siRNA against Alox15 provide evidence that diminished
Alox15 levels are responsible for the protective phenotype (189). Resistance to the
development of a diabetic phenotype induced via streptozotocin was also observed in
mice lacking Alox15 (205). It has been proposed that Alox15 contributes to the
development of diabetes via its ability to catalyze the formation of inflammatory
mediators such as 12-HETE (hydroxyeicosatetraenoic acid), which causes β cell
dysfunction and death (204, 206, 207), recently linked to excessive production of
reactive oxygen species (208). Our observation that iron deficiency causes a marked
elevation in Alox15 mRNA and protein levels in the pancreas raises the possibility that
iron deficiency—in addition to iron overload—may increase the risk of developing
diabetes through up-regulation of Alox15. Such a possibility appears opposite to recent
studies showing a protective effect of iron restriction on diabetes risk. For example,
Cooksey et al. (163) observed that an iron-restricted diet enhanced β cell function and
insulin sensitivity in the ob/ob mouse model of type 2 diabetes. Similarly, Minamiyama
et al. (164) found that feeding an iron-restricted diet to type 2 diabetic rats normalized
73
plasma insulin levels. It should be noted, however, that in the study by Cooksey et al.
(163), iron-restriction did not result in iron deficiency or anemia in contrast to our study.
Although it is well known that individuals with iron overload are susceptible to
developing diabetes (183), the molecular mechanisms involved remain poorly
understood. Our observation that iron-overloaded rats have highly elevated Alox15
protein levels in the pancreas suggests that Alox15 may contribute to β cell loss and β
cell dysfunction in iron overload. Indeed, the pancreases of iron-loaded rats appear to
be under stress as indicated by the elevated expression of the regenerating islet-
derived gene family members Reg1a, Reg3a, and Reg3b. As indicated by their name,
Reg genes were first identified by their strong induction in regenerating pancreatic islets
in response to stress/damage (209). Reg1a is a 165-a.a secreted protein that has been
shown to play an important role in β cell function in vivo (191). Disruption of murine
Reg1 (the ortholog of rat Reg1a) resulted in decreased proliferative capacity of
pancreatic β cells (210), whereas administration of recombinant rat Reg1a resulted in β
cell regeneration and reversal of diabetes in rats after surgical resection of 90% of the
pancreas (191). Similar to Reg1, Reg3a and Reg3b have been associated with islet
regeneration and protection against diabetes (193, 211). Reg3 proteins are also known
as pancreatitis-associated proteins (PAP) that become highly expressed in acinar cells
in response to injury (212). Our observation of elevated Reg3 expression in iron-loaded
rat pancreas is consistent with a previous report of hypotransferrinemic mice, which
displayed pancreatic iron loading and markedly elevated expression of Reg3 mRNA
(213). However, in that study, a time course analysis of pancreatic iron loading
indicated that Reg3/PAP mRNA became detectable only when pancreatic non-heme
74
iron concentrations had reached levels that were ~50 times normal. In our study of iron-
loaded rats, I found that even modest elevations in pancreatic iron concentrations (2.5
times normal) are associated with enhanced expression of Reg3 mRNA, suggesting
that Reg mRNA levels could serve as an early biomarker of iron-related pancreatic
stress/damage in rats. The apparent discrepancy in pancreatic iron load required to
elicit increased Reg3 expression between mice and rats is likely attributable to
interspecies variability. Mice are largely resistant to the degenerative effects of
pancreatic iron loading whereas rats exhibit acinar cell degradation, indicative of
pancreatic damage, following dietary iron overload (136, 142). One caveat is that the
elevated pancreatic Reg expression in iron-loaded rats could be confounded by the
abnormally low (i.e., ~25% of normal) copper concentrations in these animals. Copper
deficiency in rats has been shown to result in pronounced atrophy of the exocrine
pancreas (214). Pancreatic atrophy is observed during pancreatitis, a state which
promotes extensive expression of Reg family genes (215). Also, during copper
deficiency islet hyperplasia and β cell neogenesis have been documented (216) in line
with the islet-regenerating properties of Reg proteins. More research is needed to
determine if low copper levels induce the expression of these genes.
In conclusion, microarray analysis of rat pancreas has revealed that iron
deficiency and overload increase the expression of one or more genes strongly
associated with diabetes and pancreatic stress, thus highlighting the importance of iron
status in the pancreas.
75
Table 4-1. Body weight, iron status, and blood glucose concentration of rats
Group Body weight
(g) Liver non-heme iron
(μg/g) Hemoglobin
(g/dL) Glucose (mg/dL)
FeD 193.3 ± 20.2a 3.5 ± 3.6a 7.5 ± 2.2a 154.5 ± 39.0b
FeA 224.3 ± 11.1b 25.4 ±17.7b 12.8 ± 0.4b 99.0 ± 16.0a
FeO 170.3 ± 23.6a 980.6 ± 310.2c 13.6 ± 0.6b 117.7 ± 18.5a
FeD, iron deficient; FeA, iron adequate; FeO, iron overloaded. Values represent means ± SD, n = 6. Means without a common superscript are significantly different P< 0.05
76
Table 4-2. Pancreatic mineral concentrations
Mineral concentrations (μg/g dry weight) were measured by using ICP-MS. Values represent means ± SD, n = 6 Means without a common superscript are significantly different P<0.05
Group Iron Zinc Manganese Copper Cobalt
FeD 38.2 ± 5.7a 107.5 ± 18.0b 8.7 ± 1.9 4.7 ± 1.0b 0.05 ± 0.02
FeA 63.7 ± 14.3b 85.0 ± 16.0a 6.2 ± 1.9 3.8 ± 0.7b 0.03 ± 0.01
FeO 162.7 ± 59.5c 75.2 ± 8.5a 7.0 ± 1.2 1.0 ± 0.0a 0.04 ± 0.01
77
Table 4-3. Top 10 most up-regulated and down-regulated genes in FeD pancreata
Gene Name Symbol Accession Fold changea
arachidonate 15-lipoxygenase Alox15 NM_031010 4.1
L-threonine dehydrogenase Tdh NM_001106044 3.3
RT1 class I, locus CE5 RT1-CE5 NM_001008843 3.3
S100 calcium binding protein A9* S100a9 NM_053587 2.7 transient receptor potential cation channel, subfamily C, member 3
Trpc3 NM_021771 2.4
vascular endothelial growth factor B Vegfb NM_053549 2.4
regenerating islet-derived 1 alpha* Reg1a NM_012641 2.2
secretoglobin, family 2A, member 1 Scgb2a1 NM_080770 2.2
potassium intermediate/small conductance Ca-activated channel, subfamily N, member 1
Kcnn1 NM_019313 2.0
alanine-glyoxylate aminotransferase 2 Agxt2 NM_031835 1.9
fatty acid binding protein 1, liver* Fabp1 NM_012556 -6.1
fatty acid binding protein 2, intestinal* Fabp2 NM_013068 -6.0
LOC494499 protein LOC494499 NM_001010921 -5.0
proline-rich acidic protein 1 Prap1 NM_031669 -5.0
s100 calcium binding protein G S100g NM_012521 -4.9
monoacylglycerol O-acyltransferase 2*
Mogat2 NM_001109436 -4.9
apolipoprotein A-I* Apoa1 NM_012738 -4.6
cAMP responsive element binding protein 3-like 3
Creb3l3 NM_001012115 -4.5
similar to carboxylesterase 5 LOC679368 XM_001056053 -4.3
carboxylesterase 5-like LOC688542 XR_086144 -4.2
Fold change log2 relative to iron-adequate rat pancreas.* In gene ontology category in Figure 2-1 and supplemental Table S2.
78
Table 4-4. Top 10 most up-regulated and down-regulated genes in FeO pancreata
Gene name Symbol Accession Fold changea
regenerating islet-derived 3 alpha* Reg3a NM_172077 4.8
regenerating islet-derived 3 beta* Reg3b NM_053289 4.3
extracellular proteinase inhibitor Expi NM_133537 4.3
regenerating islet-derived 1 alpha* Reg1a NM_012641 4.2
prepronociceptin Pnoc NM_013007 3.8
beta-galactosidase-like protein Bin2a NM_001009524 3.6
calmodulin-like 3 Calml3 NM_001012054 3.5
vascular endothelial growth factor B* Vegfb NM_053549 3.5
phospholipase A2, group IIA* Pla2g2a NM_031598 3.5
upper zone of growth plate and cartilage matrix associated
Ucma NM_001106121 3.2
similar to Robo-1 LOC691352 NM_001109638 -8.7
fatty acid binding protein 2, intestinal * Fabp2 NM_013068 -7.0
fatty acid binding protein 1, liver* Fabp1 NM_012556 -7.0
proline-rich acidic protein 1 Prap1 NM_031669 -6.6
lectin, galactoside-binding, soluble, 4 Lgals4 NM_012975 -6.0
apolipoprotein A-I * Apoa1 NM_012738 -5.8
hydroxysteroid (17-beta) dehydrogenase 2
Hsd17b2 NM_024391 -5.6
S100 calcium binding protein G S100g NM_012521 -5.6
LOC494499 protein* LOC494499 NM_001010921 -5.4
hypothetical protein LOC691259 LOC691259 NM_001109632 -5.2
Fold change log2 relative to iron-adequate rat pancreas. *In gene ontology category in Figure 1 and supplemental Table S3.
79
Table 4-5. Primers for qRT-PCR
Symbol Name GenBank Accession No.
Forward primer (5'-3')
Reverse primer (5'-3')
Alox15 arachidonate 15-lipoxygenase
NM_031010 CCCTGTCGGGACTCGGAAGC
CCAGTGCCCTCAGGGAGGCT
Reg1a regenerating islet-derived 1 alpha
NM_012641 TTGTCTCAGCCTGCAGAGATTG
CATGATGAGCAGCAGACTGTCTT
Reg3a regenerating islet-derived 3 alpha
NM_172077 CCGTGGTAACTGTGGCAGTCT
GTGATGGTCTCCCCACTTCAG
Reg3b regenerating islet-derived 3 beta
NM_053289 AAAGATGATGAGAGTTAAGATGTTGCA
AGCAGCATCCAGGACATGACT
Fabp1 fatty acid binding protein 1, liver
NM_012556 AGGTCAAGGCAGTGGTTAAGAT
TGTCATGGTATTGGTGATTGTGT
Fabp2 fatty acid binding protein 2, intestinal
NM_013068 TCACTGGGACCTGGACCATG
CATATGTGTAGGTCTGGATTAGT
Apoa1 apolipoprotein A-I
NM_012738 TCCACTTTGGGCAAACAGCTGAAC
TCCTGTAGGCGACCAACAGTTGAA
80
Table 4-6. Functional categories of pancreatic genes differentially expressed in response to iron deficiency
Functional category Gene symbol Description GenBank number Fold change
Lipid transport Npc1l1 NPC1 (Niemann-Pick disease, type C1, gene)-like 1
NM_001002025 -2.27
Mttp microsomal triglyceride transfer protein
NM_001107727 -2.97
Apoc3 apolipoprotein C-III NM_012501 -3.19
Apoa1 apolipoprotein A-I NM_012738 -4.57
Mogat2 monoacylglycerol O-acyltransferase 2
NM_001109436 -4.88
Fabp2 fatty acid binding protein 2, intestinal
NM_013068 -5.96
Fabp1 fatty acid binding protein 1, liver
NM_012556 -6.10
Antimicrobial S100a9 S100 calcium binding protein A9
NM_053587 2.69
Defa6 defensin alpha 6 NM_001033076 -2.71
Defa-rs1 defensin alpha-related sequence 1
NM_001033073 -2.82
Defa8 defensin alpha 8 NM_001033077 -3.11
Neuropeptide Calca calcitonin-related polypeptide alpha
NM_017338 -2.08
Cartpt CART prepropeptide NM_017110 -2.48
Gal galanin prepropeptide
NM_033237 -3.08
Vip vasoactive intestinal peptide
NM_053991 -3.51
Pancreatitis-associated protein
Reg1a regenerating islet-derived 1 alpha
NM_012641 2.24
Reg3g regenerating islet-derived 3 gamma
NM_173097 -2.48
Reg4 regenerating islet-derived family, member 4
NM_001004096 -3.08
Fold change log2 relative to iron-adequate rat pancreas
81
Table 4-7. Functional categories of pancreatic genes differentially expressed in
response to iron overload
Functional category Gene symbol Description Genbank number Fold change
Pattern binding Vegfb vascular endothelial growth factor B
NM_053549 3.45
pla2g5 phospholipase A2, group V NM_017174 3.11
Itgam integrin, alpha M NM_012711 2.65
Ccl7 chemokine (C-C motif) ligand 7
NM_001007612 2.65
Prg4 proteoglycan 4, (megakaryocyte stimulating factor, articular superficial zone protein, camptodactyly, arthropathy, coxa vara, pericarditis syndrome)
NM_001105962 2.14
Tpsb2 tryptase beta 2 NM_019180 2.07
Abp1 amiloride binding protein 1 (amine oxidase, copper-containing)
NM_022935 -2.29
Colq collagen-like tail subunit (single strand of homotrimer) of asymmetric acetylcholinesterase
NM_019274 -2.42
Glutathione and drug metabolism
Cyp2e1 cytochrome P450, family 2, subfamily e, polypeptide 1
NM_031543 3.16
Aox1 aldehyde oxidase 1 NM_019363 3.10
Fmo1 flavin containing monooxygenase 1
NM_012792 2.72
Gpx2 glutathione peroxidase 2 NM_183403 -2.61
Gsta5 glutathione S-transferase Yc2 subunit
NM_001159739 -3.57
Gsta2 glutathione S-transferase A2 NM_017013 -4.55
Loc494499 LOC494499 protein NM_001010921 -5.44
Pancreatitis-associated protein
Reg3a regenerating islet-derived 3 alpha
NM_172077 4.83
Reg3b regenerating islet-derived 3 beta
NM_053289 4.29
Reg1a regenerating islet-derived 1 alpha
NM_012641 4.18
Reg3g regenerating islet-derived 3 gamma
NM_173097 -2.26
Reg4 regenerating islet-derived family, member 4
NM_001004096 -4.38
82
Table 4-7. Continued
Functional category Gene symbol Description Genbank number Fold change
Digestive system process
Tff1 trefoil factor 1 NM_057129 -3.86
Mogat2 monoacylglycerol O-acyltransferase 2
NM_001109436 -3.99
Fabp1 fatty acid binding protein 1, liver
NM_012556 -7.00
Fabp2 fatty acid binding protein 2, intestinal
NM_013068 -7.01
Defensin propeptide
Defa24 defensin, alpha, 24 NM_001013053 -2.57
Defa-rs1 defensin alpha-related sequence 1
NM_001033073 -3.14
Defa8 defensin alpha 8 NM_001033077 -3.54
Regulation of lipid transport
Adipoq adiponectin, C1Q and collagen domain containing
NM_144744 2.22
Apoc3 apolipoprotein C-III NM_012501 -2.19
Apoa1 apolipoprotein A-I NM_012738 -5.77
Protease activity Cma1 chymase 1, mast cell NM_013092 2.58
Mcpt1l3 mast cell protease 1-like 4
ENSRNOT00000043182 2.32
Tmprss8 transmembrane protease, serine 8 (intestinal)
NM_199371 -2.02
Spink4 serine peptidase inhibitor, Kazal type 4
NM_001008871 -2.48
Mep1b meprin 1 beta NM_013183 -3.81
Mmp7 matrix metallopeptidase 7
NM_012864 -5.10
Complement activation
Cfd complement factor D (adipsin)
NM_001077642 2.37
C4bpa complement component 4 binding protein, alpha
NM_012516 2.42
C6 complement component 6
NM_176074 3.05
Fold change Log2 relative to iron-adequate rat pancreas
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Figure 4-1. Functional classification of pancreatic genes up- or down-regulated in
response to iron deficiency and iron overload. Microarray analysis identified a total of 66 differentially expressed genes in response to iron deficiency (Panel A) and 164 genes in response to iron overload (Panel B). Genes were then subjected to DAVID analysis to identify functional categories. A) Functional gene categories identified in iron-deficient pancreas and the number of genes in each category. B) Functional gene categories identified in iron-overloaded pancreas and the number of genes in each category.
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Figure 4-2. Effect of iron deficiency and overload on rat pancreatic Alox15 expression.
A) Total RNA was isolated from rat pancreas and the relative transcript abundance of Alox15 was measured by using qRT-PCR. Transcript abundances were normalized to the housekeeping transcript cyclophilin B and are expressed relative to the FeA group mean (set to 1). B) Immunoblot analysis of Alox15 from a representative sample of FeD, FeA, and FeO rats. Jejunum from iron-adequate (JA) and iron-deficient (JD) rats were analyzed in parallel to serve as negative and positive controls respectively for immunodetection of Alox15. The blot was stripped and reprobed for tubulin to indicate protein loading among lanes. Values are expressed as the mean ± SEM, n=6. Asterisks indicate a significant difference relative to FeA controls, **P<0.01.
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Figure 4-3. Effect of iron deficiency and overload on the expression of pancreatic Reg family genes.Total RNA was isolated from rat pancreas and the relative transcript abundances of Reg family genes were determined by qRT-PCR. Transcript abundances were normalized to levels of cyclophilin B and are expressed relative to the FeA group mean (set to 1). Statistical significance was determined by one-way ANOVA. Asterisks indicate a significant difference relative to FeA controls *P<0.05, **P<0.01, ***P<0.001.
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CHAPTER 5 MECHANISMS OF NTBI UPTAKE BY HUMAN β CELLS
Iron is an essential trace mineral necessary for numerous biological functions,
including oxidation-reduction reactions, due, in part, to the ability of iron to exist in
multiple oxidation states. While these reactions are required for normal physiologic
processes, iron can also catalyze the generation of hydroxyl radicals, which can
damage lipids, protein, and DNA (217). Due to the duality of iron redox chemistry, iron
transport and homeostasis are tightly regulated in vivo to prevent the production of
reactive oxygen species by free iron. However in genetic disorders such as
hemochromatosis, in which excessive amounts of dietary iron are absorbed, or β-
thalassemia major, which requires blood transfusions, excess iron overwhelms the
normal mechanisms of iron transport. One such consequence is the appearance of
plasma non-transferrin-bound iron (NTBI), a form of iron that appears when the carrying
capacity of transferrin, the circulating iron transport protein, becomes exceeded. The
exact chemical nature of NTBI in the plasma is not known, but is thought to consist
mainly of ferric citrate and other low-molecular-weight iron species (218, 219). Although
it is generally believed that NTBI is a pathologic species that appears only when
transferrin saturation exceeds 75% (220), plasma NTBI has been reported to be
commonly present in diabetics with transferrin saturations below 60% (131).
Studies in mice have shown that plasma NTBI is rapidly cleared mostly by the liver, and
to a lesser extent, the pancreas, kidney, and heart (73, 74, 83). Accordingly, NTBI is a
major contributor to iron loading of the liver and other tissues in iron overload disorders.
In the liver and pancreas, NTBI is taken up mainly by hepatocytes and acinar cells via
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ZRT/IRT-Like Protein 14, ZIP14 (SLC39A14) (7). How NTBI is taken up by the kidney,
heart, and other organs/cell types remains to be established.
Studies of iron-loaded human pancreas have revealed that iron not only accumulates in
acinar cells, but also in β cells of the islets (132-134). Iron loading of the β cell has
been proposed to contribute to the well-known β cell dysfunction and diabetes in
individuals with clinical iron overload (85, 133). Given the known role of NTBI uptake to
iron loading of various organs and cells, we hypothesize that human β cells are able to
take up NTBI. The aim of the present study was to examine the potential roles of the
transmembrane transporters DMT1 (divalent metal-ion transporter 1), ZIP14, and ZIP8
in NTBI uptake by human β cells. We focused on these three transporters because of
their well-documented roles in NTBI uptake/iron metabolism (7, 11, 13, 28, 90), and in
the case of DMT1 and ZIP8, also because DMT1 has been reported to be expressed in
human islets and ZIP8 has been reported in rat β cells (85, 103).
Results
Overexpression of NTBI Transporters in Human β Cells
To determine whether the expression of established NTBI transporters could
promote iron uptake in β cells, ZIP14, ZIP8, and DMT1 were overexpressed in βlox5
cells, a human β cell line (221), and NTBI uptake was measured. NTBI uptake was
assessed at pH 7.4, the pH of blood plasma. I found that overexpression of ZIP14 or
ZIP8, but not DMT1, increased the ability of βlox5 cells to take up NTBI when compared
with cells transfected with empty vector control (Figure 5-1). To explore the possibility
that the lack of DMT1-mediated NTBI transport in βlox5 cells results from poor DMT1
expression at the cell surface, I isolated cell-surface proteins from cells overexpressing
DMT1. Western blotting analysis of total-cell lysate and isolated cell-surface proteins
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revealed that the majority of DMT1 was indeed intracellular with little expression at the
cell surface, thus potentially accounting for the lack of additional NTBI uptake during
DMT1 overexpression (Figure 5-2A). In contrast to DMT1, overexpressed ZIP14 was
enriched at the cell surface (Figure 5-2B). The proteins copper chaperone for
superoxide dismutase (CCS) and Na+/K+ ATPase were measured to indicate
intracellular and cell-surface protein fractions, respectively.
siRNA Knockdown of NTBI Transporters in Human β Cells
To define the contribution of endogenous NTBI transporter expression to iron
uptake by human β cells, siRNA was used to suppress the expression of ZIP14, ZIP8,
and DMT1 in βlox5 cells. siRNA-mediated suppression of ZIP14 expression decreased
cellular iron uptake by approximately 50% (Figure 5-3A). By contrast, siRNA knockdown
of ZIP8 did not affect iron uptake (Figure 5-3B), suggesting that endogenous ZIP8-
mediated NTBI uptake is negligible in βlox5 cells. I was unable to achieve successful
knockdown of DMT1 in this cell line because the cells died shortly after transfection.
Interestingly, cell death could be prevented by supplementing the cell culture medium
with 50 µM ferric ammonium citrate (FAC), suggesting that decreased cellular viability
was related to cellular iron deficiency (data not shown).
Similar to βlox5 cells (Figure 5-3A), knockdown of ZIP14 in primary human islets
decreased NTBI uptake by approximately 50% (Figure 5-4), suggesting that ZIP14 is a
major route of NTBI uptake in human β cells. Analysis of mRNA copy numbers in
human primary islets (Figure 5-7) indicates that the number of mRNA transcripts
encoding ZIP14 is approximately 2 and 4 times the number of ZIP8 and DMT1
transcripts, respectively (Figure 5-7).
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Cellular Localization of NTBI Transporters in Human Islets
As pancreatic islets represent a non-homogenous population of cells, consisting
primarily of β and α cells, my methods using whole islets are unable to discern the
contribution of individual cell types to mRNA expression and iron uptake. Therefore,
immunofluorescence analysis was used to determine if ZIP14, ZIP8, and DMT1 are
expressed at the protein level in β cells or in other cells comprising pancreatic islets. In
the case of ZIP14 I found that protein expression is largely restricted to β cells with
negligible expression in α cells (Figure 5-5). ZIP14 staining in β cells displayed a diffuse
speckled pattern throughout the cytosol (Figure 5-5B).
Staining for DMT1 in the human pancreas indicated that its expression was
restricted to β cells with no signal detected from α cells (Figure 5-8A). DMT1 displayed
a punctate, granular staining pattern suggesting an intracellular localization, consistent
with the known role of DMT1 in endosomal iron transport in some cell types (26).
Staining for ZIP8 in the human pancreas revealed only low-level diffuse staining in
pancreatic acinar cells. No islet staining was observed beyond non-specific levels
detected with non-immuned IgG substituted for anti-ZIP8 primary antibody (Figure 5-
8B).
Modulation of ZIP14 Expression by Iron in Human β Cells
Previous reports have indicated that ZIP14 protein levels are modulated by
cellular iron status. For example, in human hepatoma HepG2 cells, ZIP14 protein levels
are induced by iron loading with ferric ammonium citrate (FAC) (82, 95). ZIP14 protein
levels are also elevated in iron-loaded rat liver and pancreas (82). To determine if ZIP14
levels are induced by iron loading in human β cells, I treated βlox5 cells and primary
human islets with FAC and measured ZIP14 protein expression. I found that cellular iron
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loading, confirmed by elevated ferritin protein levels, increased ZIP14 protein
expression in βlox5 cells (Figure 5-6A) but not primary islets (Figure 5-6B). Depletion of
cellular iron levels by the iron chelator desferrioxamine (DFO) has been documented to
decrease ZIP14 protein levels in HepG2 cells (95). However in primary human islets
treated with DFO, I detected no difference in ZIP14 protein levels after DFO-induced
iron depletion, as confirmed by elevated TFR1 protein levels (Figure 5-6B). I was unable
to test the effect of iron deficiency on ZIP14 expression in βlox5 cells as DFO treatment
did not successfully alter TFR1 levels in this cell line (data not shown).
Modulation of ZIP14 Expression By IL-1β in Human β Cells
IL-1β levels are elevated in primary islets isolated from type 2 diabetics.
Additionally, ZIP14 mRNA levels have been observed to increase in response to IL-1β
in isolated mouse hepatocytes (222). To determine if IL-1β induces ZIP14 expression in
human β cells, βlox5 cells were treated with IL-1β for either 8 or 24 h. Both of these
treatment times increased ZIP14 protein levels to a similar degree (Figure 5-6C).
However, treatment of human islets with IL-1β (for 24 h) resulted in no induction of
ZIP14 protein (Figure 5-6D).
Discussion
Disorders of iron overload in humans are associated with β cell iron accumulation
(132-134), which is currently thought to impair β cell function (45). While β cell iron
loading is has been documented during these disorders, little is known regarding the
mechanisms by which β cells take up iron. In the present study I examined the
contribution of the established NTBI transport proteins DMT1, ZIP14, and ZIP8 to β cell
NTBI uptake. The observation that suppression of ZIP14 expression decreased NTBI
uptake by approximately 50% in the human pancreatic β cell line βlox5 suggests that
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ZIP14 is a major route of NTBI uptake by human β cells. A similar reduction in NTBI
uptake was observed after suppression of ZIP14 expression in isolated primary human
islets, which I found express ZIP14 in β but not cells. Iron loading in human islets is
reported to be restricted to β cells, in line with the pattern of ZIP14 expression in human
islets, suggesting that the lack of iron accumulation in α cells may be due to a lack of
ZIP14 expression (132, 134). Although ZIP14 in the human pancreas is detected in β
cells, more robust ZIP14 staining was observed in surrounding acinar cells, similar to
our previous studies of ZIP14 expression in rat pancreas (82). Indeed, the more robust
expression of ZIP14 in acinar cells likely explains why iron loads in the exocrine
pancreas during iron overload (7). However, in contrast to the pattern of ZIP14
expression in human pancreas, ZIP14 in rat pancreas was not detectable in β cells (82).
Based on these observations, I speculate that the lack of β cell ZIP14 in rodents
accounts for the fact that rodent β cells do not load iron, even in the context of massive
iron overload (60, 136, 138, 223, 224). I am aware of only 2 studies that have
demonstrated iron loading by Perls’ staining in rodent β cells, both of which have utilized
non-physiologic models of iron loading (e.g, portacaval shunting and iron dextran
injections) (137, 144).
Recently it has been reported that plasma NTBI levels are elevated in type 2
diabetics, even in the absence of systemic iron overload in which plasma iron levels
exceed the binding capacity of transferrin (131). Due to the ability of cellular iron loading
to increase ZIP14 expression in other cell populations (82, 95), plasma NTBI could
initially be taken up by β cells, leading to an upregulation of ZIP14 and therefore an
increased capacity for subsequent β cell NTBI uptake. Excess β cell iron is proposed to
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decrease insulin secretory capacity (45). Thus, the mechanism by which iron uptake
and accumulation increases subsequent iron loading may be relevant in the context of
diabetic pathology. While I found that iron loading increased ZIP14 levels in βlox5 cells
this trend was not observed in primary human islets arguing against a cyclic mechanism
of iron uptake and ZIP14 upregulation. The upregulation of ZIP14 observed in βlox5
cells but not in primary human islets may be due to the iron status of these cell
populations under normal culture conditions. Isolated islets are reported to be quiescent
in vitro (225) whereas βlox5 cells rapidly proliferate resulting in a basal state of iron
deficiency, evidenced by a lack of induction in TFR1 levels after treatment with DFO
(data not shown). Therefore, it is possible that the upregulation of ZIP14 in βlox5 cells
after treatment with iron is not due to cellular iron loading but rather the restoration of
adequate iron status, a change which does not occur in cultured primary islets as iron
status may be adequate to support cellular function, even after temporary iron chelation
with DFO, due to a lack of proliferation.
β cells from individuals with type 2 diabetes display increased levels of the
cytokine IL-1β, attributed to exposure to elevated levels of glucose (226). Given that IL-
1β was previously demonstrated to increase Zip14 expression in isolated mouse
hepatocytes (222), I hypothesized that IL-1β may increase β cell ZIP14 levels which, in
diabetics with plasma NTBI, could increase β cell NTBI uptake. While ZIP14 levels
increased in βlox5 cells following IL-1β treatment no effect was observed in primary
human islets suggesting that IL-1β is unlikely to upregulate ZIP14 in islets of patients
with diabetes. Differences in gene expression between βlox5 cells and primary human β
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cells have previously been reported (221), potentially accounting for the differential
effect of IL-1β treatment observed in primary islets and βlox5 cells.
The expression of DMT1 in human islets has been reported previously and it has
been hypothesized that DMT1 may be responsible for β cell iron loading (85). While I
observed that DMT1 is expressed in β cells, and is also absent from α cells reflecting
the pattern of islet iron loading in humans, other results from the present study argue
against a role of DMT1 in the process of NTBI uptake by β cells. For example, I found
that overexpression of DMT1 fails to increase NTBI uptake in βlox5 cells, likely due to
the intracellular localization of DMT1 precluding iron uptake at the cell surface.
Immunofluorescense analysis of DMT1 in human islets also suggests that DMT1 is
localized intracellularly, due to the punctate, granular staining pattern observed. In
addition to the intracellular localization of DMT1, the functional properties of DMT1,
specifically the coupling of efficient iron transport to a proton gradient, argue against
DMT1 contributing substantially to the uptake of plasma NTBI by β cells. DMT1
functions optimally at pH 5.5 (11), in line with the established function of DMT1 in
intestinal (13) and endosomal NTBI transport (26), and transports iron relatively poorly
at the physiologic pH of 7.4 for plasma.
ZIP8 is reported to be expressed at the plasma membrane of rat β cells (103)
and ZIP8 mRNA is abundantly expressed in the human pancreas, relative to other
tissues (100), suggesting that ZIP8 may contribute to β cell iron uptake. In the present
study I found that the overexpression of ZIP8 in βlox5 cells increased NTBI uptake but
that suppression of endogenous ZIP8 expression had no effect on NTBI uptake,
suggesting that ZIP8 levels are not abundant in human β cells. Additionally, I detected
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modest amounts of ZIP8 protein in acinar cells but not in β cells strengthening the
finding that ZIP8 protein expression is negligible, and therefore is unlikely to contribute
to NTBI uptake in human β cells.
In conclusion I have identified ZIP14 as a major contributor to NTBI uptake by
human β cells. The identification of ZIP14 as a route of β cell NTBI uptake provides a
target for inhibitors that could be used to prevent β cell iron accumulation during iron
overload. Future study of the role ZIP14 plays in in-vivo NTBI uptake by β cells, and β
cell function, will need to be carried out using rodent models which successfully reflect
the human phenotype characterized by β cell ZIP14 expression and β cell iron
accumulation.
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Figure 5-1. ZIP14 and ZIP8, but not DMT1, overexpression increases iron uptake by
βlox5 cells. A) Western blot analysis of cell lysates from blox5 cells transfected with pCMV-Sport6-empty vector (EV), DMT1, ZIP14, or ZIP8. Tubulin is shown to indicate lane loading. B) Effect of ZIP14, ZIP8, or DMT1 overexpression on the uptake of iron by βlox5 cells. To measure iron uptake,
cells were incubated for 1 h in serum-free medium containing 2 μM [59
Fe]
ferric citrate and 1 mM ascorbate and the cellular uptake of 59
Fe was measured by gamma counting. Data represent the mean ± S.E. of 3 independent experiments performed in triplicate. Group means were compared by unpaired Student’s t-test. Asterisks indicate differences relative to cells transfected with EV (*P < 0.05).
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Figure 5-2. When overexpressed in βlox5 cells, ZIP14 localizes to the plasma membrane whereas DMT1 mainly localizes intracellularly. Western blot
analysis of ZIP14, DMT1, Na+/K
+-ATPase, and copper chaperone for
superoxide dismutase (CCS) in total-cell lysate (TCL) or cell-surface (CS) proteins isolated from βlox5 cells transfected with either empty vector (EV), A) ZIP14, or B) DMT1. Plasma membrane proteins were labeled with Sulfo-NHS-SS-Biotin and affinity purified by using streptavidin-agarose columns
prior to western blotting. Na+/K
+-ATPase and CCS serve as markers for
plasma membrane and cytosolic proteins, respectively.
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Figure 5-3. Endogenous iron uptake by βlox5 cells is decreased by siRNA knockdown of ZIP14, but not ZIP8. A) Western blot analysis of lysates from βlox5 cells transfected with negative control siRNA (siNC) or siRNA targeting either ZIP14 (siZIP14, left panel) or ZIP8 (siZIP8, right panel). B) To measure NTBI uptake, cells were incubated for 2 h in serum-free medium containing 2 μM
[59
Fe] ferric citrate and 1 mM ascorbate and the cellular uptake of 59
Fe was measured by gamma counting. Data represent the mean ± S.E. of 3 independent experiments performed in triplicate. Group means were compared by unpaired Student’s t-test. Asterisks indicate differences relative to cells transfected with siNC (*P < 0.05).
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Figure 5-4. siRNA knockdown of ZIP14 decreases NTBI uptake by primary human islets. A) Western blot analysis of cell lysates from isolated human islets transfected with either negative control siRNA (siNC) or siRNA targeting ZIP14 (siZIP14). B) To measure iron uptake, cells were incubated for 2 h in
serum-free medium containing 2 μM [59
Fe] ferric citrate and 1 mM ascorbate
and the cellular uptake of 59
Fe was measured by gamma counting. Data represent the mean ± S.E. of 3 independent experiments performed in triplicate. Group means were compared by unpaired Student’s t-test. Asterisks indicate differences relative to cells transfected with siNC (**P < 0.01).
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Figure 5-5. ZIP14 is detected in human pancreatic β cells by immunofluorescent analysis. Immunofluorescent images taken at either A) 20x or B) 60x of human pancreatic tail sections co-stained for ZIP14 (green), insulin (β cell marker, red), and glucagon (α cell marker, blue). Panels show the same tissue region as stained for ZIP14 only (I), ZIP14 with insulin (II), or ZIP14 with insulin and glucagon (III). Serial sections co-stained in parallel, but with non-immune IgG replacing the ZIP14 primary antibody are shown to indicate background staining (IV).
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Figure 5-6. Cellular iron levels and treatment with IL-1β increase ZIP14 levels in βlox5 cells but not primary human islets. A) Western blot analysis of βlox5 cell lysates for ZIP14 and ferritin after 24 h incubation in control (CON) medium or medium supplemented with 100 μM ferric ammonium citrate +1 mM ascorbate (FAC). B) Western blot analysis of ZIP14, TFR1, and ferritin in human-islet lysates 48 h after treatment with CON medium or medium containing 50 µM deferoxamine (DFO) or 100 µM ferric ammonium citrate + 1mM ascorbate (FAC). Lysates from βlox5 cells transfected with either siNC or siZIP14 siRNA are shown to confirm the band size of ZIP14 protein. C) Western blot analysis of ZIP14 in βlox5 lysates after incubation in CON medium or medium supplemented with 100 U/ml recombinant human IL-1β for either 8 or 24 h. D) Western blot analysis for ZIP14 in human-islet lysates after incubation in CON medium or medium containing 100 U/ml recombinant human IL-1β for 24 h. Lysates from βlox5 cells transfected with either siNC or siZIP14 siRNA are shown to confirm the band size. Tubulin is shown to indicate lane loading.
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Figure 5-7. mRNA copy numbers of NTBI transporters in primary human islets. qRT-PCR measurement of DMT1, ZIP14, and ZIP8 mRNA copy numbers in total RNA isolated from nondiabetic human islets. Copy numbers were calculated based on standard curves constructed from known concentrations of plasmid DNA encoding either DMT1, ZIP14, or ZIP8. Data represent the mean mRNA copy numbers ± S.E. obtained from 4 independent donors.
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Figure 5-8. DMT1, but not ZIP8, is detected in human β cells by immunoflourescence
staining. A) Immunoflourescence images (60x) of human pancreatic tail sections co-stained for DMT1 (green), insulin (red), and glucagon (blue). Panels show the same tissue region as DMT1 signal only (I),DMT1 with insulin signal (II), or DMT1 with insulin and glucagon signal (III). Serial sections co-stained in parallel, but with non-immune IgG replacing the DMT1 primary antibody are shown to indicate background DMT1 signal (IV). B) Immunoflourescence images (20x) of human pancreatic tail sections co-stained for ZIP8 (green), insulin (red), and glucagon (blue). Panels show the same tissue region as ZIP8 signal only (I), ZIP8 with insulin signal (II), or ZIP8 with insulin and glucagon signal (III). Serial sections co-stained in parallel, but with non-immune IgG replacing the ZIP8 primary antibody are shown to indicate background ZIP8 signal (IV).
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CHAPTER 6 THE INFLUENCE OF IRON STATUS ON DIABETIC PATHOLOGY AND β-CELL
FUNCTION
The link between iron status and diabetes has been extensively documented in
patients with iron overload disorders, in which the prevalence of diabetes is elevated
compared with the general population (45, 113, 114, 118). Patients with the iron
overload disorder hemochromatosis are reported to display diminished insulin secretion
in response to glucose (45), suggesting that excess iron accumulation impairs the ability
of β cells to regulate glucose homeostasis. Diabetes has historically been categorized
as either type 2 diabetes, resulting from systemic insulin resistance, or type 1 diabetes,
resulting from a loss of insulin secretory capacity. Type 1 diabetes most often results
from the autoimmune-mediated destruction of pancreatic β cells. While the link between
systemic iron status and diabetes has been extensively documented, little is currently
known about the influence of iron in the pathogenesis of autoimmune diabetes.
The evidence linking iron status to autoimmune diabetes in humans is currently
limited to retrospective epidemiology demonstrating a potential link between iron and
diabetes risk. Increased iron consumption during infancy is reported to be associated
with a greater risk for developing diabetes during childhood (166) and elevated
transferrin saturation, an indicator of iron status, is associated with an increased
prevalence of type 1 diabetes in adults (167). However, direct mechanistic evidence for
a causative role of iron in autoimmune diabetes is currently lacking. Studies carried out
in animal models have indirectly suggested that iron depletion may be protective against
autoimmune diabetes. Treatment with the iron chelator desferroxiamine protects islet
grafts from autoimmune destruction in NOD mice (170), a mouse model of autoimmune
diabetes. Also, the administration of apotransferrin to NOD mice reduces the incidence
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of spontaneous diabetes (173), an effect hypothesized to be attributed to the binding of
plasma NTBI, which may be elevated in diabetics (131). T-cell proliferation and
cytotoxicity are also reduced by treatment with anti-transferrin receptor antibodies (171),
although the ability of T cells to acquire iron in response to antibody treatment was not
determined. Currently no controlled trials have determined the influence of systemic iron
status on the development of autoimmune diabetes.
Evidence for β cell iron status directly affecting β cell function is also lacking as
studies evaluating glucose homeostasis during iron deficiency or overload have done so
in the context of systemic changes in iron status (45, 146, 147, 163). Changes in whole-
body iron levels may affect β cell function through the modulation of known or unknown
systemic factors, such as erythropoietin, which promotes β cell proliferation and
prevents apoptosis (227). Current in vitro studies investigating the direct impact of iron
status on islets in isolation have been limited to rat islets (165) and have not determined
the effect of iron status on islet function, as evidenced by insulin secretion, directly. To
date no studies have determined the effect of iron depletion or loading on the insulin
secretory capacity of human islets. Additionally, the study of β cell iron loading in vivo
has been complicated by the lack of a mouse model which accumulates substantial iron
within β cells, similar to the pattern observed in humans with iron overload (132, 133). β
cells in mouse models of iron overload demonstrate a remarkable resistance to iron
loading (60, 136, 138) making it difficult to determine the effect of progressive β cell iron
accumulation on β cell function.
In the current study I aimed to examine the role of iron in diabetic pathology by
determining the influence of systemic iron status on the development of autoimmune
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diabetes in NOD mice and the effect of iron status on glucose-stimulated insulin
secretion (GSIS) by isolated human islets. I hypothesized that iron deficiency would be
protective against the development of diabetes and increase GSIS by human islets
while iron loading would result in an increased incidence of autoimmune diabetes in
NOD mice and impair GSIS by human islets. Additionally I produced a transgenic
mouse expressing the mammalian iron transporter Zip14 under control of the mouse
insulin 1 promoter with the aim of generating a mouse model of β cell iron loading.
Results
Effect of Iron Status on Spontaneous Autoimmune Diabetes in NOD Mice
To determine whether differences in systemic iron status could affect the
development of autoimmune diabetes female NOD mice were fed either iron-deficient
(FeD), iron-adequate (FeA), or iron-loaded (FeO) diets from weaning until 30 wk of age.
FeD mice demonstrated a trend towards a greater incidence of diabetes relative to FeA
mice, 80% diabetic at 30 wk of age compared with 60%, although this difference did not
reach statistical significance (P=0.06) (Figure 6-1). The initial onset of diabetes was
also earlier in FeD mice, first detected at 11 wk of age, compared with FeA mice in
which diabetes was first detected at 14 wk of age. No differences were observed
between the incidences of diabetes in FeO compared with FeA mice and the
development of diabetes was initially detected at similar ages in these groups.
Effect of Dietary Iron on Rate of Growth and Systemic Iron Status
Iron deficient and loaded diets have previously been demonstrated to affect
growth in rodents (176, 228, 229) and higher body weights are associated with
increased susceptibility to autoimmune diabetes in BioBreeding rats (230). However, no
differences were observed in the rate of growth between FeD and FeA mice at any point
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in the study. Mice in the FeO group gained weight at a reduced rate initially but the
difference was corrected during the prediabetic period, before 11 wk of age, and body
weights between groups did not differ after this point (Figure 6-2). To confirm that
systemic iron status was successfully altered by dietary treatments prior to the
development of diabetes I measured indices of iron status in 10-wk-old mice that had
consumed FeD, FeA, or FeO diets since weaning (Table 6-1). FeD mice had lower 12%
lower hemoglobin levels compared with FeA mice but plasma iron levels were not
diminished in response to dietary iron deficiency. FeO mice had elevated transferrin
saturation, attributed primarily to diminished total iron binding capacity (TIBC), whereas
no difference was detected between FeD and FeA mice, in line with the similar plasma
iron concentrations between these groups. Iron stores, indicated by liver non-heme iron
concentrations, were depleted in FeD mice and 10 times greater in FeO animals
compared with FeA mice, indicating that iron status was successfully altered by dietary
intervention during the period preceding the development of diabetes.
Plasma iron was found to be greater in 30 wk-old-FeO mice compared with other
groups, although transferrin saturation was similar between age groups. Iron status in
FeD mice normalized with age, as evidenced by the recovery of hemoglobin values in
30-wk-old FeD mice (Table 6-1). Additionally liver iron stores increased in 30-wk-old
FeD mice relative to those measured in FeD mice at 10 wk of age. However, iron stores
were found to still be significantly greater in FeA mice compared with FeD at 30 wk of
age. Unlike at 10 wk of age, plasma iron was significantly greater in 30-wk-old FeO
mice compared with the other groups. Measurement of liver non-heme iron levels from
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FeD mice at various ages between 10 and 30 wk of age indicate that iron stores
gradually increase in a linear fashion after 10 wk of age (Figure 6-3).
Pancreatic Mineral Concentrations
Our lab has demonstrated that alterations in dietary iron can alter pancreatic
mineral concentrations, which may contribute to pancreatic dysfunction (139). To
explore this possibility I measured trace minerals in the pancreases from prediabetic
FeD, FeA, and FeO mice by using ICP-MS analysis. In 10-wk-old mice pancreatic iron
levels in FeD and FeO mice were 52% and 172% of FeA levels, respectively (Table 6-
2). Modest differences were also detected in pancreatic zinc and copper, which were
elevated in FeD mice compared with the FeA and FeO animals, and in selenium which
was lower in FeO mice. Pancreatic Iron levels in 30-wk-old mice were not different
between FeD and FeA animals but were 3.5 times greater in FeO mice. Differences in
pancreatic zinc levels were not detected between groups at 30 wk of age but FeD mice
had slightly elevated copper levels.
Testing of β cell function During the Prediabetic Period
To investigate whether systemic iron status has an effect on β cell function in
NOD mice I performed glucose tolerance testing using 10 wk prediabetic mice. No
differences in glucose tolerance or fasting glucose levels between groups were detected
(Figure 6-4A). However, all groups reported poor glucose tolerance, maintaining blood
glucose levels >300 even 2 h post injection. GSIS capacity was also measured in
conjunction with glucose tolerance and no significant differences in plasma insulin levels
were detected between groups at any time point (Figure 6-4B).
As β cell iron status has been hypothesized to affect β cell function, islets from
mice used in glucose tolerance testing were isolated and iron status was determined by
108
measuring transferrin receptor 1 (TFR1) expression, which is well documented to
inversely reflect cellular iron levels (78). TFR1 levels in FeO mice were lower compared
with FeD and FeA mice, indicating increased levels of islet iron (Figure 6-5A). However,
no difference was observed between FeD and FeA mice indicating that islet iron status
was not different between FeD and FeA mice at 10 wk. Also, histological analysis of
pancreas sections from 10 wk mice revealed no difference in the degree of insulitis
between groups, with all groups reporting only mild insulitis (Figure 6-5B).
Effect of Iron Status on Human Islet GSIS
The feeding of NOD mice with an iron-deficient did not result in diminished β cell
iron status in vivo. To determine the effect of iron status on β cell function I altered the
iron status of isolated primary human islets in vitro prior to GSIS testing to indicate islet
function. Islets were treated for 48 h with either control medium (CON), 50 μM
deferoxamine mesylate (DFO), an iron chelator to deplete islets of iron, or 100 μM ferric
ammonium citrate and 1 mM ascorbate (FAC), to load islets with iron. The successful
alteration of islet iron status was confirmed by western blot analysis for both TFR1 and
ferritin, an indicator of cellular iron stores. TFR1 expression was elevated in islets
treated with DFO relative to CON islets, indicating a reduction in cellular iron levels, and
decreased in islets treated with FAC, indicating cellular iron loading (Figure 6-6A).
However, no differences in insulin secretion between groups, during either basal or
glucose stimulated conditions, were measured during GSIS testing indicating that
altered iron status does not affect GSIS (Figure 6-6B).
Generation of Mice Selectively Overexpressing Zip14 in β Cells
Previous studies using mouse models of severe iron overload have
demonstrated that mouse islets are resistant to substantial iron loading in vivo (60, 136,
109
138). Recent findings by our lab have indicated that ZIP14 is required for iron loading of
the exocrine pancreas (7) and that ZIP14 contributes to β cell iron uptake by human
islets (unpublished results). In light of these findings I aimed to generate transgenic
mice overexpressing ZIP14 in pancreatic β cells to create a novel mouse model
predisposed to β cell iron loading. I generated a vector construct containing mZip14
tagged with human influenza hemagglutinin antigen (HA) under control of the mouse
insulin 1 promoter (MIP) and containing a downstream intronic region of human growth
hormone (Figure 6-7A). Four founder animals were obtained and bred to establish 4
individual transgenic mouse lines. Analysis of the number of transgene copies per
genome by using qRT-PCR revealed that 2 founders possessed approximately 5
copies, referred to as low-copy lines, while other founders had approximately either 13
or 500 copies, referred to as moderate- or high-copy lines. Comparison between
transgene copy numbers in founder animals and subsequent generations indicate that
the transgenes are completely inherited in the lines carrying low- and moderate-copy
lines. However, incomplete inheritance of the transgene was detected within the high-
copy line with some offspring inheriting a low number of copies. Successful expression
of the transgene in β cells was confirmed by immunostaining for HA in pancreatic
sections (Figure 6-7B). Animals from 3 out of 4 transgenic lines demonstrated
transgene expression to various degrees in line with the number of transgene copies
detected.
Discussion
Excess iron accumulation is associated with an increased prevalence of diabetes
and is believed to influence aspects of diabetic pathology (45, 113, 114, 118). Previous
experiments have suggested that systemic iron levels may be a risk factor for the
110
development of autoimmune diabetes (167, 169) and that β cell iron accumulation
results in diminished insulin secretory capacity (45, 146). However, direct evidence for
these claims is currently lacking. The present study using NOD mice produced the
unexpected results that iron overload did not increase the incidence of diabetes and that
dietary iron restriction was not protective, potentially even promoting to the development
of autoimmune diabetes. Diabetes was detected earlier in FeD mice and the overall
incidence of diabetes trended strongly towards being increased by dietary iron
restriction, suggesting that iron deficiency may increase susceptibility to the
development of autoimmune diabetes. Furthermore, dietary iron overload failed to
increase the incidence of diabetes compared with mice fed FeA diets, arguing against
the hypothesis that elevated iron stores increase the risk of developing type 1 diabetes.
While iron deficiency may negatively impact the development of diabetes in NOD
mice, experiments in prediabetic mice failed to explain the trend toward an increased
incidence of diabetes in in iron-restricted mice. Glucose tolerance testing in prediabetic
NOD mice did not reveal any differences between groups regarding either glucose
tolerance or GSIS, indicating that systemic iron status did not affect glucose
homeostasis or β cell function during the prediabetic period. All groups investigated
reported poor glucose tolerance, potentially attributed to the high sucrose diet used in
this study (231). Other studies have reported that feeding an iron-deficient or loaded
diet can affect insulin sensitivity in rodents (232-234). In this study I did not detect any
differences in insulin sensitivity as both glucose tolerance and insulin secretion were
similar for all groups measured. Discrepancies between the current study and previous
studies regarding the influence of iron loading or depletion on insulin sensitivity in
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rodents may result from differences in either dietary iron content or feeding duration. In
general the iron content of the diets and the duration of feeding used in the present
study were less extreme compared with diets and timelines used in previous studies
reporting differences in insulin sensitivity in response to dietary iron deficiency or
overload (232-234).
The lack of difference in GSIS between groups during glucose tolerance testing
indicates that β cell function was not affected by altering systemic iron stores during the
prediabetic period. While iron stores were lower in 10-wk-old FeD mice relative to FeA
mice, as evidenced by diminished liver non-heme iron and mild anemia in FeD animals,
islet TFR1 levels were not different between these groups suggesting that the trend
toward increased diabetic development was not attributable to β cell iron deficiency.
Serum iron was also similar between FeD and FeA mice suggesting that circulating iron
levels were adequate to supply islets with iron, thus preventing islets from becoming
iron deficient. Additionally, dietary iron overload increased islet iron status but did not
result in altered insulin secretory capacity or glucose tolerance, suggesting that islet iron
loading, at least to the degree observed in this study, does not have an effect on β cell
function.
The finding that islet iron status does not affect GSIS is supported by the testing
of iron-loaded and iron-depleted human islets in vitro, also carried out in this study. I
determined that there was no effect of iron status on GSIS, during either basal or high
glucose conditions, arguing against the hypothesis that β cell iron status impacts insulin
secretory capacity. To my knowledge this is the first report of the effect of iron depletion
or iron loading on human β cell function in an isolated cell-culture system, eliminating
112
the potential influence of systemic factors, on β cell function. The lack of effect on GSIS
by both iron loading and iron depletion was unexpected based on previous reports
demonstrating a role for ROS in the process of glucose stimulated insulin secretion
(235) and the hypothesized role of cellular iron in the generation of intracellular ROS
(86). It is possible that in the current study the degree of iron loading of human islets did
not exceed the ability of the β cell to sequester iron within ferritin, preventing the buildup
of free intracellular iron which would be capable of catalyzing ROS formation. While iron
status was clearly different between control, iron-depleted, and iron-loaded islets in the
current study, as indicated by TFR1 and ferritin levels, future investigation of the
influence β cell iron status has on insulin secretory capacity may benefit from longer
term or higher-dose iron loading. Additionally it is possible that the alteration of β cell
iron status affects first-phase insulin secretion, an early indicator of impaired β cell
function (236, 237), which would not be detected by the methods used in this study.
Future studies may benefit from the use of more nuanced methods of GSIS testing,
such as islet perifusion, which is capable of discerning differences in GSIS at individual
time points.
The study of long-term, progressive β cell iron accumulation would benefit from
the availability of a mouse model that accumulates iron in β cells, similar to what has
been observed in humans with iron overload disorders (132, 133). In this study I also
detailed the generation of transgenic mice that overexpress mZIP14 in pancreatic β
cells under the control of the mouse insulin 1 promoter. Previous reports by our lab
have indicated that ZIP14 is required for iron loading of pancreatic acinar cells (7) and
that ZIP14 contributes to non-transferrin bound iron uptake by human islets (publication
113
under review), suggesting that overexpressing ZIP14 in mouse β cells may result in
increased iron uptake and accumulation. In this study I reported the successful
overexpression of mZIP14 in β cells, localized to the plasma membrane and intracellular
locations within β cells of transgenic mice. Future testing is needed to confirm that this
novel model demonstrates β cell iron loading during systemic iron overload.
A limitation of the current study is that iron deficiency resolved in the FeD mice
with age, potentially attributable to the plateau of growth after mice reached
approximately 10 wk of age. Due to the recovery of iron status it is possible that early
trends observed regarding the incidence of diabetes in FeD mice were somewhat
ablated in older mice and could potentially be more dramatic under conditions of more
severe iron deficiency. In the current study the iron-deficient diet contained 14 ppm iron,
a greater concentration than traditional iron-deficient diets (163, 238), due to the
inclusion of 5% wheat in the diet. Wheat was added to the diets as additional wheat has
been reported to increase the diabetic potential of purified diets (239), which usually
result in a low incidence of diabetes in NOD mice (240, 241). Future studies
investigating the role of iron deficiency in autoimmune diabetes may benefit from the
use of BioBreeding rats, which also develop spontaneous autoimmune diabetes and in
which iron deficiency may be easier to induce due to the greater increase in rat body
weight relative to that of mice.
In conclusion I report that iron deficiency, but not iron overload, may increase the
development of autoimmune diabetes in NOD mice. Additionally β cell iron status did
not affect β cell function, calling into question the long-hypothesized mechanism thought
to account for the link between iron loading and diabetes. Future studies will be required
114
to determine the influence of both systemic and β cell iron content in the pathogenesis
of diabetes.
115
Table 6-1. Iron parameters of type 1 diabetes-prone NOD mice
Age Group Hemoglobin
(g/dL)
Liver non-heme iron (μg Fe/g)
Plasma Iron (μg/dL)
TIBC (μg/dL)
TF Sat (%)
10 wk FeD 12.7 ± 1.3a 16.7 ± 3.8a 158.1 ± 61.7a 383.3 ± 43.7a 37.4 ± 15.5a
10 wk FeA 14.5 ± 1.1b 247.2 ± 41.8b 198.9 ± 46.7a 402.2 ± 22.8a 49.6 ± 12.1ab
10 wk FeO 14.5 ± 0.6b 2490.4 ± 410.1c 231.4 ± 51.2a 313.9 ± 38.9b 74.4 ± 18.3b
30 wk FeD 14.1 ± 0.6a 118.4 ± 27.5a# 133.3 ± 16.7a 392.8 ± 22.5a 33.9 ± 2.4a
30 wk FeA 14.1 ± 0.9a 424.2 ± 23.3b# 136.1 ± 32.9a 366.2 ± 24.1a 37.1 ± 8.4a
30 wk FeO 15.1 ± 0.4a 3124.2 ± 1027.3c 281.5 ± 97.0b 385.6 ± 74.2a 71.6 ± 13.1b
Liver non-heme iron levels are reported as μg Fe/g wet tissue. Liver non-heme iron, plasma iron, and total iron-binding capacity (TIBC) were determined colorimetrically. Transferrin saturation (TF Sat) was calculated as plasma iron as a percentage of TIBC. Values are presented as means ± SD, n=3-8. Statistical significance was determined by one-way ANOVA. Means without a common superscript are significantly different compared with other groups at the same age. Values at 30 wk of age that are significantly different from those of the same group at 10 wk of age are indicated by # (P<0.05).
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Table 6-2. Pancreatic mineral concentrations in NOD mice
Age Group Iron Zinc Manganese Copper Cobalt Selenium Molybdenum
10 wk FeD 77.7 ± 13.4a 259.0 ± 57.6a 7.6 ± 1.1a 7.6 ± 1.3a 0.16 ± 0.01a 1.4 ± 0.20a 0.5 ± 0.04a
10 wk FeA 148.8 ± 26.6b 194.8 ± 24.8b 7.4 ± 1.3a 6.3 ± 0.5b 0.16 ± 0.02a 1.3 ± 0.08a 0.5 ± 0.08a
10 wk FeO 256.7 ± 67.3c 164.4 ± 25.0b 7.9 ± 1.1a 5.5 ± 0.5b 0.14 ± 0.01a 1.0 ± 0.20b 0.5 ± 0.05a
30 wk FeD 251.5 ± 58.0a# 183.0 ± 40.6a# 10.4 ± 0.5a 6.9 ± 0.4a 0.18 ± 0.03a 1.3 ± 0.1a 0.5 ± 0.03a
30 wk FeA 260.7 ± 25.5a 161.5 ± 40.6a 8.5 ± 0.7a 5.4 ± 0.5b 0.14 ± 0.01a 1.3 ± 0.2a 0.4 ± 0.04a
30 wk FeO 931.69.7 ± 654.5b# 153.7 ± 17.4a 17.7 ± 11.8a# 5.1 ± 0.5b 0.13 ± 0.03a 1.3 ± 0.3a 0.5 ± 0.07a
Mineral concentrations (mg/g dry weight) were measured by using ICP-MS. Values represent means ± SD, n = 4-6. Means without a common superscript are significantly different P<0.05. Statistical significance was determined by one-way ANOVA. Means without a common superscript are significantly different compared with other groups at the same age. Values at 30 wk of age that are significantly different from those of the same group at 10 wk of age are indicated by # (P<0.05).
117
0 5 10 15 20 25 300
20
40
60
80
100FeD (n=20)
FeA (n=20)
FeO (n=20)
P=0.06
Age (weeks)
% W
ith
ou
t D
iab
ete
s
Figure 6-1. Dietary iron deficiency, but not iron overload, results in a trend towards an
increased incidence of spontaneous diabetes in female NOD mice. Cumulative diabetes incidence in female NOD mice fed either iron deficient (FeD), iron adequate (FeA), or iron loaded (FeO) diets starting at 3 wk of age. Spontaneous development of diabetes was monitored by glycosuria starting at 8 wk of age. Survival curves were compared to FeA by using the Gehan-Breslow-Wilcoxon test.
118
3 6 9 12 15 18 21 24 27 300
5
10
15
20
25
30
35
FeD (n=20)
FeA (n=20)
FeO (n=20)** ***
****
Age (Weeks)
Bo
dy
We
igh
t (g
)
Figure 6-2. NOD mice fed an iron-loaded diet initially experience diminished growth.
Body weights of female NOD mice fed either iron-deficient (FeD), iron-adequate (FeA) or Iron-loaded (FeO) diets from 3 to 30 wk of age. Animals were weighed every 3 days until 12 wk of age, at which point body weights were recorded weekly. Body weights at individual time points were compared by one-way ANOVA. Asterisks indicate significant differences in bodyweights of FeO animals compared with FeA animals (*P<0.05, **P<0.01, ***P<0.001)
119
10 15 20 25 300
50
100
150
200
r=0.92
Age (Weeks)
Liv
er
No
nh
em
e Iro
n (
g/g
tis
su
e)
Figure 6-3. Iron stores of mice fed an iron-deficient diet increase with age. Liver non-
heme iron concentrations of FeD mice were measured colorimetrcally in 10-wk-old prediabetic mice, diabetic mice of various ages, and 30 wk nondiabetic mice fed an iron-deficient diet. The correlation coefficient (r) was calculated by using a linear model.
120
Figure 6-4. Glucose tolerance and insulin secretory capacity is not affected by iron
status in prediabetic NOD mice. A) Results of intraperitoneal glucose tolerance testing in fasted 10-wk-old prediabetic female NOD mice. Values reported as mean ± SEM, n=5 per group. B) Plasma insulin levels in mice used in glucose tolerance testing. Values reported as mean ± SEM, n=3-5 per group. Blood glucose and plasma insulin levels between groups at individual time points were compared by one-way ANOVA.
121
Figure 6-5. Iron-deficient prediabetic NOD mice show no differences in β cell iron status
or insulitis compared with iron-adequate mice. A) Western blot analysis of mouse islet total-cell lysate from 10-wk-old prediabetic iron-deficient (FeD), iron-adequate (FeA), and iron-loaded (FeO) NOD mice for transferrin receptor (TFR1). Islets were pooled from 2-3 mice per group. Tubulin is shown to indicate lane loading. B) Average insulitis score from FeD, FeA, and FeO 10-wk-old prediabetic female NOD mice. Values are expressed as the mean ± SEM, n=6. Statistical significance was determined by one-way ANOVA.
122
Figure 6-6. Iron status does not affect glucose-stimulated insulin secretion by human
islets in vitro. A) Western blotting of human islet total cell lysate for transferrin receptor (TFR1) and ferritin. Islets were treated for 48 h with either control medium (CON), 50 μM deferoxamine mesylate (DFO) or 100 μM ferric ammounium citrate and 1 mM ascorbate (FAC) prior to analysis. Tubulin is shown to indicate lane loading. B) Ability of islets to secrete insulin after DFO or FAC treatment. Total insulin secreted by islets during a 1-h incubation in media containing 2.8 mM D-Glucose followed by a 1-h incubation in media containing 22 mM D-glucose was measured and normalized to islet DNA. Data represent the mean ± SEM of 4 independent experiments performed in triplicate. Treatment group means were compared by one-way ANOVA.
123
Figure 6-7. Generation of mice selectively overexpressing Zip14 in β cells. A) Vector
map of the construct used to generate MIP-Zip14-HA transgenic mice consisting of a region of the mouse insulin 1 promoter (MIP), HA-tagged ZIP14 (ZIP14-HA), and an intronic region of human growth hormone (hGH Intron). DNA was digested with HindIII and SfiI, to remove the vector backbone, purified, and introduced to fertilized embryos by pronuclear injection. B) Confirmation of transgene expression by Immunofluorescense analysis. Mouse pancreas sections were co-stained for HA (green), insulin (red), and DAPI (blue). Panels show the same tissue section as HA with insulin and DAPI (panel I), HA with DAPI (panel II), insulin with DAPI (panel III). A pancreatic section from a wild-type mouse co-stained in parallel for HA, insulin, and DAPI is shown to indicate non-specific background HA signal (panel IV).
124
CHAPTER 7 CONCLUSIONS, LIMITATIONS, AND FUTURE DIRECTIONS
The experiments detailed in this dissertation explore the connection between iron
status and diabetes and can be categorized into 2 general topics: the study of NTBI
uptake by pancreatic β cells and the effect of iron status on diabetic pathology.
Experiments investigating the process of β cell NTBI uptake evaluated the evidence for
the involvement of the 3 identified mammalian NTBI transport proteins, DMT1, ZIP14,
and ZIP8 in β cell iron uptake. As iron loading is more extensively documented in
human than in rodent islets, experiments were carried out using human β cell lines,
isolated primary human islets, and human pancreas samples. Experiments examining
both the cellular and subcellular localization, as well as effect of expression on iron
transport, provided evidence for the contribution of ZIP14, but not DMT1 or ZIP8 to the
process of NTBI uptake by human β cells. Previous investigation into iron uptake, either
as TBI or NTBI, by β cells has not been carried out making this finding a novel
contribution to the literature. However, certain limitations in the experiments performed
leave unanswered questions and provide direction for future studies.
Investigation of iron uptake by β cells in this study focused on mechanisms of
NTBI uptake. Although NTBI is thought to be the primary form of iron taken up by the
pancreas during iron overload (7, 242), studies have not determined the contribution of
TBI uptake in iron accumulation by pancreatic β cells. A prominent role for TBI uptake in
the process of β cell iron loading seems unlikely, due to the negative regulation of TFR
by cellular iron status. However, human β cells express TFR (243) and the possibility
cannot be ruled out as the contribution of TBI to β cell iron uptake was not directly
determined by experiments in this dissertation. Another unexplored aspect of β cell iron
125
loading is the discrepancy between human β cells, which accumulate iron deposits
(132, 133), and mouse β cells, which resist iron accumulation during iron overload (60,
136). The findings in this report indicating that ZIP14 contributes to NTBI uptake in
human β cells, as well as the recently published finding by our lab that ZIP14 is required
for iron loading in mouse pancreatic acinar cells (7), pose the obvious question of
whether the resistance of mouse β cells to iron loading is due to the absence or reduced
abundance of ZIP14 in mouse β cells. However, despite repeated efforts, I have been
unable to successfully detect ZIP14 in the mouse pancreas by immunofluorescence
analysis or immunohistochemistry. Future experiments confirming that ZIP14 is either
not expressed, or far less abundantly expressed, in mouse β cells compared with
human β cells will be necessary to conclude that the discrepancy in β cell iron loading
between species is due to differential ZIP14 expression.
Investigation into potential mechanisms by which iron status may influence
diabetic pathology was initiated by the analysis of pancreatic gene expression in rats
fed either iron-deficient or iron-loaded diets. The primary finding in this study was that
both iron overload and deficiency increased the expression of Reg family genes,
associated with pancreatic stress (244) and β cell regeneration (209), as well as Alox15,
a gene linked to the development of autoimmune diabetes in NOD mice (188). The idea
that iron deficiency, as well as iron overload, can elicit changes in gene expression
associated with diabetic pathology is novel and at odds with what has been previously
reported suggesting that iron deficiency is protective against the development of
diabetes ((163, 164). However, this study was observational in nature and did not
provide further mechanistic evidence for a role of either changes in the expression of
126
Reg family genes Alox15 in pancreatic function in response to altered iron status.
Additionally attributing the observed changes in gene expression directly to pancreatic
iron status is problematic due to differences in pancreatic mineral concentrations
between groups. Iron-loaded animals displayed markedly reduced pancreatic copper
levels and copper deficiency has been reported to result in pancreatic degradation
(214), potentially contributing to pancreatic stress that may have induced the expression
of Reg family genes. Unpublished observations indicate that iron-loaded rats in this
study demonstrate lower copper levels in the liver as well, possibly resulting from
impaired dietary copper absorption when placed on a high iron diet, as lower tissue
copper levels were not observed in genetic models of iron overload fed normal diets.
Future studies are needed to determine the effect of altered copper status on pancreatic
stress and to determine if the differences observed in response to dietary iron overload
are due to elevated iron or rather depleted pancreatic copper levels. Another caveat is
that due to the nature of microarray analysis this study was only designed to detect
differences in gene expression at the mRNA level. Differences in the posttranscriptional
regulation of pancreatic gene expression, such as the upregulation of ALOX15 during
iron overload which was fortunately measured by western blotting, will usually go
undetected by using this methodology. Finally, due to the low level of pancreatic iron,
even in response to a high iron diet, I was unable to successfully stain pancreatic
sections from these rats to determine the location of iron deposition as the level of
pancreatic iron was below the threshold for enhanced Perls’ staining in my hands.
Therefore, it is difficult to conclude which cell populations are being affected by
differential iron status and are thus likely to be responsible for the alterations in gene
127
expression. Future studies may benefit from the isolation of islets or acinar cells prior to
gene expression analysis to eliminate the issue of dealing with heterogeneous cell
populations.
In line with the concept of determining the effect of iron status on isolated
populations of cells, I performed experiments testing the ability of isolated human islets
to secrete insulin in response to glucose after either iron depletion or loading. Previous
studies suggesting that iron status affects β cell function have been carried out in vivo
(146, 147, 163), where other factors besides β cell iron status could potentially alter
insulin secretion. In contrast to what has been suggested by other studies I found that
the alteration of islet iron status had no effect on GSIS. This finding was unexpected
and in disagreement with the existing literature reporting differences in insulin secretory
capacity in response to changes in whole body iron status. However, there are obvious
limitations with this experiment as I only measured total insulin secretion after 1 h
exposure to either low or high glucose. It is possible that differences in first-phase
insulin secretion, indicative of reduced β cell function (236, 237), manifest in response
to altered iron status but could be corrected by subsequent increased insulin secretion
at later time points. Future experiments would benefit from the use a perifusion system
to measure the rate of insulin secretion continuously throughout testing.
Another limitation of the in vitro islet function testing I carried out is that the
treatments may have been insufficient, in either duration or severity, to induce the
physiologic changes necessary to alter insulin secretory capacity. Time points and
doses of supplemental iron or deferoxamine in the present study were selected as they
successfully altered the expression of TFR1, a well-established indicator of cellular iron
128
status (78). It is possible that treatment with increased concentrations of iron chelators
or additional iron for longer time periods may affect insulin secretion but due to limited
resources these possibilities were not investigated. Additionally, it is possible that
alterations in iron status alone are not sufficient to perturb β cell function but may
exacerbate a loss of function when combined with metabolic stress due to chronically
elevated glucose and/or free-fatty acids, as observed during diabetic conditions (245).
Future experiments should investigate these possibilities before concluding that iron
status does not affect GSIS. It would also be beneficial to measure other indicators of
cellular damage, similar to what has been published using isolated rat islets (165).
Generation of MIP-Zip14-HA transgenic mice overexpressing ZIP14 in β cells
should allow for the future study of β cell iron loading on β cell function in an in vivo
model of systemic iron overload. However, it has not yet been confirmed that these
mice successfully load iron in β cells during iron overload conditions. Future
experiments confirming this either by crossing MIP-Zip14-HA mice with models of
genetic iron overload or by injecting MIP-Zip14-HA mice with iron dextran will be
needed to determine if the overexpression of ZIP14 in mouse β cells results in mouse β
cell iron accumulation.
The investigation of the effect of iron status on the development of autoimmune
diabetes in NOD mice was carried out as a pilot study to determine whether the
hypothesized connection between iron status and type 1 diabetes would manifest as a
clear trend in a controlled trial using an animal model. Contrary to what is suggested by
epidemiological studies (166, 167) and preliminary cell culture or animal experiments
(170, 171, 173), iron overload did not increase the incidence of diabetes in NOD mice.
129
Additionally mice fed an iron-deficient diet, which was hypothesized to be protective
against diabetes, trended towards an increased incidence of diabetes. This novel
finding suggests that the relationship between iron status and autoimmune diabetes
may be different than what has been been previously proposed. However, as a
preliminary study there are many limitations which will need to be addressed by future
experiments.
Preliminary testing in this study did not reveal any mechanisms by which iron
deficiency may affect the development of diabetes in NOD mice. In prediabetic NOD
mice I detected no difference between groups in either glucose tolerance, GSIS, or the
degree of insulitis leaving the trend towards an increased incidence of diabetes in iron-
deficient NOD mice unexplained. Future studies will need to further investigate the
influence of iron status on aspects of diabetic pathology in NOD mice to elucidate
potential mechanisms by which iron deficiency may be detrimental. Another limitation of
this study is fact that iron-deficient mice slowly recovered their iron status over the
duration of the study. Due to the gradual buildup of iron stores it cannot be said that
FeD mice were in fact iron deficient after 10 wk of age, although their iron stores were
still depleted compared with other groups. In line with this concern plasma iron levels
were not different between FeD and FeA mice, even at 10 wk of age. The relative
ineffectiveness of the iron-deficient diet at inducing iron deficiency over a prolonged
period is likely due to the extra iron provided by the addition of wheat to the diet,
necessary to increase the prevalence of diabetes in NOD mice fed a purified diet (239).
The iron content of the diet is unlikely to be further reduced mandating the use of other
methods to increase the severity of iron depletion. Future studies may benefit from the
130
use of BioBreeding rats in place of NOD mice, as rats are more susceptible to iron
deficiency. Additionally animals could be housed in wire-bottom cages long term to
prevent coprophagy, although this assumes that adequate iron could not be obtained
from the diet alone. Periodic bleeding to reduce iron stores is another option but may be
complicated by altered glucose metabolism due to erythropoietin production in response
to bleeding (227, 246).
In summary, the results of these experiments provide new insight into both
mechanisms of NTBI uptake by human β cells and the role of iron in diabetic pathology.
The results of the experiments previously discussed question many aspects of the
proposed link between iron status, β cell function, and the development of autoimmune
diabetes. Future studies will hopefully clarify these issues.
131
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BIOGRAPHICAL SKETCH
Richard Coffey was born in New York, New York in 1988. He completed his B.S.
in nutritional sciences at the University of Florida in the spring of 2011. After graduation
he joined Dr. Mitchell Knutson’s laboratory at the University of Florida focusing on
mechanisms of iron transport and the influence of iron on aspects of diabetic pathology.
He received his Ph.D. in nutritional sciences from the University of Florida in the spring
of 2016.
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