Arsenic-Associated Diabetes: Mechanisms and the Role of Arsenic Metabolism

ARSENIC-ASSOCIATED DIABETES: MECHANISMS AND THE ROLE OF ARSENIC METABOLISM

Madelyn C. Huang

A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum of Toxicology within the School of Medicine.

Chapel Hill 2018

Approved by:

Miroslav Stýblo

Rebecca Fry

Ilona Jaspers

Eric Klett

Zuzana Drobná

ABSTRACT

Madelyn C. Huang: Arsenic-associated diabetes: mechanisms and the role of arsenic metabolism (Under the direction of Miroslav Stýblo)

Inorganic arsenic (iAs) is a ubiquitous drinking water and food contaminant. Chronic exposure to iAs has been linked to diabetes mellitus in many populations. Experimental studies support these associations and suggest mechanisms, but the precise pathogenesis of iAs- associated diabetes is unclear. Efficient metabolism of iAs through methylation, catalyzed by arsenic (+3) methyltransferase (AS3MT), influences the risk of iAs-associated disease.

Additionally, higher intake of methyl donor nutrients such as folate and methylcobalamin (B12) has been linked to increased efficiency of iAs metabolism and lowered disease risk. However, while metabolism of iAs facilitates its excretion from the body, iAs metabolites are more toxic than iAs itself. The role of iAs metabolism in iAs toxicity is understudied, especially in cardiometabolic disease. Understanding how iAs metabolism affects iAs-associated diabetes will help design appropriate intervention strategies and improve risk assessment.

This project investigated how iAs metabolism influences iAs-associated diabetes. First, we compared metabolite profiles of C57BL6 (WT) with As3mt-KO (KO) mice, which cannot methylate iAs, with and without exposure to iAs. We found that while iAs exposure was linked to very few metabolites, knock-out of As3mt was associated with metabolites in a sex-specific manner. Second, we investigated the effect of iAs exposure at environmentally-relevant doses on glucose metabolism in mice and the role of methyl donor nutrient supplementation. Prenatal

iii exposure to iAs elicited hyperglycemia and insulin resistance, particularly in males. Chronic iAs exposure in adult mice increased insulin resistance only with a low-folate, high-fat diet. Males were more insulin resistant than females; KO mice more than WT mice. In both studies, supplementation with B12 and/or folate protected against iAs-associated impairments in glucose metabolism, despite having a minimal effect on iAs metabolism. Third, we investigated how iAs and its methylated metabolites inhibit insulin secretion in pancreatic β-cells. These arsenicals may be impairing calcium influx by affecting ion channels. Interestingly, methylated metabolites appear to target different mechanisms than iAs. Taken together, this research adds novel knowledge about the effects of environmentally-relevant iAs exposures on glucose metabolism in mice, the underlying mechanisms, and the role of iAs metabolism and methyl donor nutrients in metabolic health.

To the One who created it all for us to discover and enjoy

ACKNOWLEDGMENTS

I would like to thank Mirek Stýblo for being a fantastic mentor. He has taught me much about being a scientist and has always been very supportive in all of my scientific, career, and life endeavors. I would like to thank my committee members for their thoughtful advice and for sitting through too many slides with too many bar charts. It has been an honor to learn from and work with such excellent scientists.

This work could not have been completed, or as enjoyable, without other members of the

Stýblo lab, both past and present. In particular, I would like to thank Dr. Christelle Douillet for being a great coworker and for teaching me everything I know about benchwork, mice, and running marathons. I would also like to thank the Nutritional Biochemistry Department for letting me in on their holiday parties and connecting me with valuable colleagues and friends.

Many thanks to all the friends that I have made along the way in this adventure called graduate school. I have learned so much from every one of you, about science, about faith, about living life. To the other members of the best Tox cohort ever, for the all the study sessions, the comisery, and laughs. To my family for their unending love and continuous support throughout my life. And to my husband Shawn for his unconditional love, support, and everlasting smile.

This work was supported by NIH grant R01ES022697 to M.S., the Curriculum in

Toxicology training grant 5T32ES007126-32, and NIEHS F31 Fellowship Award F31ES027743 to M.H.

PREFACE

Many people have contributed to the research presented in this dissertation and their contributions are detailed below:

Chapter 1: I wrote this chapter which was reviewed by Mirek Stýblo.

Chapter 2: This work was published previously in Archives of Toxicology and reproduced with

permission. Individuals involved include: myself, Christelle Douillet, Mingming Su,

Kejun Zhou, Tao Wu, Wenlian Chen, Joseph A. Galanko, Zuzana Drobná, R. Jesse

Saunders, Elizabeth Martin, Rebecca C. Fry, Wei Jia, and Miroslav Stýblo. Lipid

analyses were conducted at the University of Colorado Lipidomics Core, by Dr. Robert

Murphy and Karin Zemski Berry. I would like to thank Dr. David Thomas (US EPA) and

Dr. Rosalind Coleman (Department of Nutrition, UNC Chapel Hill) for reviewing the

data generated by metabolomic analyses and for their helpful suggestions regarding the

data interpretation. I wrote this chapter with edits by Mirek Stýblo.

Chapter 3: The prenatal exposure study involved the contribution of myself, Christelle Douillet,

Ellen Nicole Dover, and Mirek Stýblo. Dr. Zuzana Drobná (North Carolina State

University, Raleigh, NC) provided advice regarding prenatal folate and B12

supplementation. Henry Gong and Jin Chen helped with animal care and data entry. I

wrote this chapter with edits by Mirek Stýblo.

Chapter 4: The adult exposure study involved the contribution of myself, Christelle Douillet,

Ellen Nicole Dover, Chongben Zhang, Rowan Beck, and Mirek Stýblo. Sergey Krupenko

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and Eric Klett provided valuable comments on the discussion. Ahmad Tejan-Sie and

Alexis Dunlap helped with animal care and data entry. I wrote this chapter with edits by

Mirek Stýblo.

Chapter 5: This work was completed by myself with help from Will Ostrom conducting the MTT

assay. Pablo Ariel and the UNC Microscopy Services Laboratoy provided technical

assistance for monitoring calcium influx. I wrote this chapter with edits by Mirek Stýblo.

Chapter 6: I wrote this chapter myself.

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TABLE OF CONTENTS

ABSTRACT ...... iii

ACKNOWLEDGMENTS ...... vi

PREFACE ...... vii

TABLE OF CONTENTS ...... ix

LIST OF TABLES ...... xiii

LIST OF FIGURES ...... xiv

LIST OF ABBREVIATIONS ...... xvii

1. CHAPTER 1- Introduction ...... 1

1.1 Human Exposure to iAs ...... 2

1.1.1 As in nature ...... 2

1.1.2 Uses of As ...... 3

1.1.3 Routes of exposure to As ...... 5

1.2 iAs Metabolism ...... 10

1.2.1 iAs methylation ...... 10

1.2.2 Genetic modifiers of iAs methylation ...... 13

1.2.3 Dietary modifiers of iAs methylation ...... 15

1.2.4 iAs metabolism and toxicity/disease risk ...... 17

1.3 iAs-associated diabetes ...... 18

1.3.1 Diabetes: Diagnosis, Pathogenesis, and Classification ...... 19

1.3.2 iAs as an environmental diabetogen...... 22

1.3.3 Mechanisms of iAs-associated diabetes ...... 24

1.3.4 Metabolism of iAs and diabetes risk ...... 26

1.4 Summary and Approach ...... 27

REFERENCES ...... 29

2. CHAPTER 2- Metabolomic profiles of arsenic (+3 oxidation state) methyltransferase knockout mice: Effect of sex and arsenic exposure ...... 44

2.1 Plasma and Urinary Metabolite Profiles ...... 44

2.1.2 Overview ...... 44

2.1.2 Introduction ...... 45

2.1.3 Methods ...... 48

2.1.4 Results ...... 51

2.1.5 Discussion ...... 64

2.1.6 Conclusions ...... 69

2.2 Hepatic phosphatidylcholines, ...... 71

2.2.1 Introduction ...... 71

2.2.2 Materials and Methods ...... 72

2.2.3 Results and Discussion ...... 73

2.2.4 Conclusion ...... 76

REFERENCES ...... 77

3. CHAPTER 3- Prenatal arsenic exposure and dietary folate and methylcobalamin supplementation alter the metabolic phenotype of C57BL6 mice in a sex-specific manner ...... 84

3.1 Overview ...... 84

3.2 Introduction ...... 85

3.3 Methods ...... 89

3.4 Results ...... 93

Maternal measures ...... 93

Phenotypes of the offspring ...... 96

3.5 Discussion ...... 103

REFERENCES ...... 109

4. CHAPTER 4- Metabolic phenotype of folate-deficient and folate-supplemented male and female mice exposed to inorganic arsenic ...... 116

4.1 Overview ...... 116

4.2 Introduction ...... 117

4.3 Methods ...... 119

4.4 Results ...... 122

4.5 Discussion ...... 131

4.6 Conclusions ...... 136

REFERENCES ...... 137

5. CHAPTER 5- Arsenite and its trivalent methylated metabolites inhibit glucose- stimulated calcium influx in murine pancreatic islets...... 143

5.1 Overview ...... 143

5.2 Introduction ...... 144

5.3 Methods ...... 146

5.4 Results ...... 149

5.5 Discussion ...... 155

5.6 Conclusion ...... 159

REFERENCES ...... 161

6. CHAPTER 6- Synthesis ...... 165

6.1 Metabolomic assessment of iAs exposure and the As3mt-KO genotype ...... 166

6.2 In vivo studies of iAs-associated diabetes and B-vitamin nutritional interventions ...... 168

6.3 Mechanisms underlying the inhibition of glucose-stimulated insulin secretion by trivalent arsenicals ...... 171

6.4 Sex as an important biological variable ...... 172

6.5 Implications ...... 175

REFERENCES ...... 178

A. Appendix A: Supplemental Material for Chapter 2 ...... 182

B. Appendix B: Supplemental Material for Chapter 3 ...... 204

C. Appendix C: Supplemental Material for Chapter 4 ...... 207

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LIST OF TABLES

Table 1.1 Criteria for the diagnosis of prediabetes and diabetes (ADA 2010)...... 22

Table 2.1. Plasma and urinary metabolites found significantly changed (q<0.05) in comparisons assessing effect of iAs treatment in male and female wild-type (WT) and As3mt-knockout (KO) mice...... 53

Table 2.2. Plasma and urinary metabolites found significantly changed (q<0.05) in comparisons of As3mt–KO (KO) and wild-type (WT) male and female mice...... 56

Table 2.3. Phosphatidylcholine (PC) species significantly changed in comparisons of As3mt-knockout (KO) and wild-type (WT) mice in both plasma and liver...... 75

Table 3.1. p-values from three-way ANOVA tests assessing the significance of sex, dam diet, or prenatal iAs exposure and the significance of interactions of these variables in measures of metabolic phenotype...... 102

Table A.1. Metabolites identified by LC-MS and GC-MS in urine and plasma of mice in all experimental groups...... 186

Table A.2. Plasma and urinary metabolites significantly changed (p<0.05) by iAs treatment in male and female wild-type (WT) and As3mt-KO (KO) mice ...... 191

Table A.3. Plasma and urinary metabolites significantly changed (p<0.05) in comparisons of male and female As3mt–KO (KO) and wild-type (WT) mice...... 194

Table A.4. Pathways enriched for plasma (P) and urine (U) metabolites differentially changed in response to As3mt knockout or iAs treatment...... 199

Table A.5. Significantly changed (P<0.05) hepatic PCs in comparisons of untreated (0) and iAs-treated (1) animals, separated by sex (M and F) and genotype (As3mt- knockout (KO) and wild-type (WT) mice)...... 202

Table A.6. Significantly changed (P<0.05) hepatic PCs in comparisons of As3mt-knockout (KO) and wild-type (WT) mice, separated by sex (M and F) and iAs-treatment (0 and 1 ppm)...... 203

Table B.1. Total numbers of offspring in each treatment group...... 206

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LIST OF FIGURES

Figure 1.1. iAs contamination of major aquifers around the world...... 7

Figure 1.2. Concentration of iAs in 18,850 potable water samples from public water supply systems and other water sources in the US...... 8

Figure 1.3. Oxidative (A) and reductive (B) models of iAs methylation (Drobná et al. 2009). .. 12

Figure 1.4 One-carbon metabolism (OCM), iAs metabolism, and nutritional contributions...... 16

Figure 1.5. Mechanism of glucose-stimulated insulin secretion in pancreatic β-cells...... 20

Figure 1.6. Insulin-stimulated glucose uptake in adipocytes and myotubes...... 20

Figure 2.1. Urinary and plasma metabolites identified by metabolomic analyses ...... 52

Figure 2.2. Number of urinary and plasma metabolites significantly changed (q<0.05) in As3mt-knockout (KO) male and female mice in comparison with wild-type (WT) counterparts...... 54

Figure 2.3. Relative concentrations of selected metabolites determined by GC-MS or LC-MS (C, F) in urine of wild-type (WT) and As3mt-knockout (KO), male (M) and female (F) mice, control (0 ppm) or iAs-treated (1 ppm)...... 58

Figure 2.4. Diagram of metabolic pathways involving metabolites significantly altered (q<0.05) in plasma or urine of control and/or iAs-treated male and female As3mt-knockout mice when compared to corresponding wild-type males and females...... 63

Figure 2.5. Concentrations of lipids in plasma of wild-type (WT) and As3mt-knockout (KO), male (M) and female (F), control (0 ppm) and iAs-treated (1 ppm) mice ...... 62

Figure 2.6. Number of hepatic (A) and plasma (B) PC species significantly changed (P<0.05) in comparisons of As3mt-knockout (KO) and wild-type (WT) control and iAs-treated mice...... 74

Figure 2.7. Relative abundance of the phosphatidylcholine (PC) species (C38:3 in liver (A) and plasma (B) of wild-type (WT) and As3mt-knockout (KO), untreated (0 ppm) and iAs-treated (1 ppm), male (M) and female (F) mice...... 76

Figure 3.1. Plasma folate concentrations of dams fed folate/B12 adequate and supplemented diets and exposed to iAs...... 94

Figure 3.2. Arsenic metabolites in urine of dams fed a folate/B12-adequate or supplemented diet and exposed to 0, 100, or 1000 ppb iAs...... 95

Figure 3.3. Body weight and composition of male and female offspring prenatally exposed to inorganic arsenic ...... 97 xiv

Figure 3.4. Fasting blood glucose of offspring prenatally exposed to iAs...... 98

Figure 3.5. Glucose tolerance of offspring prenatally exposed to iAs...... 99

Figure 3.6. Fasting plasma insulin and HOMA-IR measures of offspring prenatally exposed to iAs ...... 101

Figure 3.7. Percent global DNA methylation in livers of male offspring exposed prenatally to iAs...... 103

Figure 4.1. Plasma folate levels...... 123

Figure 4.5. Body composition of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water, before and after a high-fat diet...... 129

Figure 5.1. Glucose-stimulated insulin secretion in arsenic-exposed pancreatic islets...... 149

Figure 5.2. Glucose-stimulated calcium influx in arsenic-exposed pancreatic islets...... 150

Figure 5.3. KCl-stimulated calcium influx in arsenic-exposed pancreatic islets...... 152

Figure 5.4. Bay K8644-stimulated insulin secretion and Cav1.2 channel expression in arsenic-exposed pancreatic islets...... 153

Figure 5.5. Expression of the alpha subunit of L-type calcium channels in arsenical-exposed islets...... 154

Figure 5.6. Tolbutamide-stimulated insulin secretion in arsenic-exposed pancreatic islets...... 155

Figure 6.1. Summary of the knowledge about the relationship of arsenic and arsenic metabolism with diabetes at the start of this dissertation work...... 166

Figure 6.2. Contribution of this work to knowledge of the relationship of arsenic and arsenic metabolism with diabetes...... 172

Figure A.1. Arsenic species in urine of control and iAs-treated male (M) and female (F) wild-type (WT) and As3mt-knockout (KO) mice...... 182

Figure A.2. Mouse body weights at start of the experiment (A), at sacrifice four weeks later (B), and change in body weight (weight at sacrifice – weight at start) (C) of male (M) and female (F) wild-type (WT) and As3mt-KO (KO) mice exposed to 1 ppm As (1) and the corresponding untreated controls (0)...... 183

Figure A.3. Food and water consumption...... 184

Figure A.4. Number of significantly changed urinary and plasma metabolites (q<0.05) in As3mt-KO (KO) control and iAs-treated (1 ppm) mice as compared to wild-type (WT) counterparts ...... 184

Figure A.5. Hierarchical clustering of hepatic phosphatidylcholines (PCs)...... 185

Figure B.1. Study design...... 204

Figure B.2. Maternal body weight...... 204

Figure B.3. Proportions of arsenic metabolites in urine of dams fed a folate/B12-adequate or supplemented diet and exposed to 0, 100, or 1000 ppb iAs...... 205

Figure B.4. Growth curves of offspring prenatally exposed to iAs, with and without maternal folate and B12 supplementation...... 205

Figure C.1. Study design...... 207

Figure C.2. Food consumption of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water...... 207

Figure C.3. Water consumption of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water...... 208

Figure C.4. Body weight over time of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water, before and after high-fat diet...... 208

Figure C.5. Weight gain of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water, before and after high- fat diet...... 209

Figure C.6. Glucose tolerance of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water for 5 weeks...... 210

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LIST OF ABBREVIATIONS

2HPG 2-hour plasma glucose ADA American Diabetes Association ADP adenosine diphosphate AKT protein kinase B As arsenic AS3MT arsenic (+3) methyltransferase ATP adenosine triphosphate AUC area under the curve B12 methylcobalamin B9 Folate Ca2+ calcium ion

CaV1.2 L-type calcium channels DIW deionized water DMAs di-methylarsenic DMAsIII dimethylarsinous acid DMAsV dimethylarsinic acid EPA Environmental Protection Agency FBG fasting blood glucose FDA Food and Drug Administration FPG fasting plasma glucose FPI fasting plasma insulin GC-MS gas chromatography-mass spectrometry GSIS glucose-stimulated insulin secretion HFD high-fat diet HG-CT-AAS hydride generation-cryotrap-atomic absorption spectrometry HOMA-IR homeostatic model assessment for insulin resistance iAs inorganic arsenic iAsIII arsenite iAsV arsenate IPGTT intraperitoneal glucose tolerance test K+ potassium ion

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KATP ATP-sensitive potassium channel KCl potassium chloride KO knockout LC-MS liquid chromatography-mass spectrometry LFD low-fat diet MAs mono-methylarsenic MAsIII methylarsonous acid MAsV methylarsonate OCM one-carbon metabolism OGTT oral glucose tolerance test PC phosphatidylcholine ppb part per billion ppm part per million SAM S-adenosylmethionine SNP single nucleotide polymorphisms SUR sulfonylurea receptor T1D type 1 diabetes T2D type 2 diabetes THF 5-methyl tetrahydrofolate TMAsIII trimethylarsine TMAsV trimethylarsinous acid µM micromolar US United States WHO World Health Organization WT wild-type

xviii

1. CHAPTER 1- Introduction

Inorganic arsenic (iAs) is a metalloid that is found ubiquitously in the Earth’s crust and natural and anthropogenic processes release iAs into the air or water. Exposure to iAs in humans occurs primarily through ingestion of water and food. Chronic exposure to iAs has been associated with numerous cancers, such as lung, urothelial, and liver, skin lesions, cardiovascular disease, and neurological dysfunction (Abdul 2015). In the last two decades, iAs exposure has been linked to type 2 diabetes mellitus in many populations around the world (Maull et al. 2012;

Kuo 2017). Experimental studies in vitro and in vivo support these associations. Several mechanisms of how iAs affects glucose metabolism have been suggested but how exactly iAs- associated diabetes develops in humans remains to be elucidated.

Diabetes mellitus is a complex, systemic metabolic disease that is characterized by improper insulin secretion and/or insulin action, leading to chronic hyperglycemia. It is the 8th leading cause of death in the world and, if uncontrolled, can lead to renal failure, peripheral neuropathy, and retinopathy (WHO 2016). The majority of people with diabetes have type 2 diabetes (T2D) which generally results from an ineffective use of insulin in the body. Individuals with T2D are at increased risk of cardiovascular disease and some cancers (CDC 2017;

Giovannucci 2010). Around 9% of the global population is diagnosed with diabetes, which is double the prevalence 20 years ago, and is expected to continue increasing. As such, diabetes is one of the four diseases the World Health Organization focuses on in their Global Action Plan

for the Prevention of Noncommunicable Disease 2013-2020 (WHO 2013). The high mortality rate and growing morbidity of diabetes and its comorbidities highlights the necessity for understanding the influence of iAs, a widespread environmental diabetogen.

Studies suggest that the ability to efficiently metabolize iAs influences the risk of iAs- associated disease, including T2D (Antonelli 2012; Kuo 2017; Hall and Gamble 2012). In most organisms and in humans, metabolism of iAs occurs through a series of methylation steps, forming monomethyl-arsenic (MAs) and dimethyl-arsenic (DMAs). DMAs is easily cleared by the body, thus metabolism is important for the excretion of iAs from the body. However, experimental studies show that the intermediates in iAs metabolism can be more toxic than iAs.

The role of iAs metabolism in iAs toxicity is understudied, especially in the context of cardiometabolic disease.

This project was designed to determine how iAs metabolism influences iAs-associated diabetes. Understanding how different efficiencies of iAs metabolism can either improve or worsen iAs-associated diabetes will help design appropriate intervention strategies to reduce risk of diabetes in iAs-exposed populations. This research will also improve risk assessment and the identification of susceptible populations. Principles identified here can be expanded to understand the role of iAs metabolism in other iAs-associated diseases.

1.1 Human Exposure to iAs

1.1.1 As in nature Arsenic (As) is found ubiquitously in the natural environment. Chemically classified as metalloid, As is 20th in abundance in the Earth’s crust, 14th in seawater, and 12th in the human body (Mandel and Suzuki 2002). The terrestrial abundance of As is 1.5-3 mg/kg (Mandel and

Suzuki 2002). As is often found combined with oxygen, chlorine, or sulfur in ores of silver, gold, cobalt, and antimony. As has been found in over 150 minerals (EPA doc; Mandel and Suzuki

2002). Arsenopyrite, containing 46% As, is the most common As-containing mineral (Kirk

Encyclopedia). The primary oxidation states of As are +3 and +5. As in the environment is typically found in its inorganic form but numerous organic forms of As, where As has formed bonds with carbon and hydrogen, also exist. Organic As compounds include arsenobetaine, arsenocholine, arsenosugars, arsenolipids, MAs, and DMAs. Many of these compounds are found primarily in marine animals (Borak and Hosgood 2007). Inorganic and organic As compounds are white to colorless powders, odorless and tasteless while pure elemental As is a metallic grey.

1.1.2 Uses of As In Agriculture

As-containing pesticides were widely used in the late 1800s to mid 1900s. Copper acetoarsenite (i.e. Paris Green) was used to control the Colorado potato beetle from 1987-1990

(Peryea 1998). Lead arsenate became widely used from the late 1800s until 1960 when it was phased out due to concerns of toxicity. Chromated copper arsenate (CCA) is a wood preservative that is still used in the US as an insecticide and antimicrobial agent. Registered in 1940, CCA was discontinued from residential use in 2003 but is still permitted for nonresidential purposes

(i.e. in marine facilities, utility poles, and sand highway structures) (EPA website). Organic arsenical pesticides came into use in the 1950s and were favored due to decreased toxicity compared to the inorganic arsenic pesticides. Dimethylarsinic acid (DMAsV) was discontinued in 2009 but monosodium methanearsonate (MSMA) is still in use as a herbicide, although with

restrictions (EPA MSMA). Reregistration of MSMA for continued use began in 2013 and is scheduled to be completed in 2019.

Organic arsenic compounds were used extensively in the cattle, swine, and poultry industry. Roxarsone, nitarsone, arsanilic acid, and carbarsone were put in animal feed as an anticoccidial and growth stimulator as early as 1944 when roxarsone was approved for use by the

FDA (Silbergeld and Nachman 2008). Due to reports of iAs in meat of treated animals, manufacturers began suspending sales of roxarsone and similar products in the 2010s. By 2015, the last organic As compound in use, nitarsone, was withdrawn from the market and ceased to be used in the growing season of 2016 (FDA).

In semiconductors

Elemental As is an alloying additive for metals, batteries, and lead ammunition (EPA

1998). High-purity elemental As is needed for the production of semiconductors and electronics, such as LEDs, infrared detectors, and lasers (EPA 1998). Specifically, gallium arsenide is used in high-speed semiconductor component, high-power microwave, detectors, and fiber optics due to its high electron mobility and light-emitting, photovoltaic, and electromagnetic properties (IARC

2006).

In medicine

Medicinal uses of As date back to before 2000 BCE. Famous early physicians such as

Hippocrates, Aristotle, and Paracelsus have been reported to treat various illnesses with As

(Waxman and Anderson 2001; Jolliffe 1993). While pure metallic As was rarely used, arsenides and arsenic salts were prescribed to prevent maladies, reduce fevers, stimulate bile production, remove hair, treat cervical dysplasia, and as antiseptics, antispasmodics, sedatives, and tonics

(Waxman and Anderson 2001; Jolliffe 1993). Additionally, As was used to treat bleeding gastric

ulcers, hypertension, heartburn, chronic rheumatism, and cancers of the skin and breast (Antman

2001). As-containing treatments were administered as inhaled vapors, ingested solutions and solids, intravenously, and dermally as pastes.

Records from the 18th to 20th century record prevalent use of a potassium bicarbonate arsenic trioxide solution (1% w/v) developed by Thomas Fowler. In 1878, Fowler's solution was reported to reduce white blood cell counts and became a standard remedy to treat leukemias

(Waxman and Anderson 2001) until the introduction of radiation and other anti-cancer agents. In the late 20th century, arsenic trioxide (ATO), or Trisenox ®, reemerged as a potential therapy for acute promyelocytic leukemia (APL). Even with combination therapy and all-trans retionic acid

(ATRA) treatment regimens, approximately 20-30% of patients relapse and die (Antman 2001).

Studies in China starting in 1990 observed that ATO monotherapy had remission rates of 80-

90% and was effective in relapsed APL patients (Waxman and Anderson 2001). Long-term treatment with ATO caused complete (molecular) remission in some patients, which is not observed with ATRA. ATO was approved by the FDA in 2000 to treat APL patients that are unresponsive to ATRA. Ongoing studies are investigating the use of ATO-ATRA combination therapies. An organic arsenical, Darinaparsin (S-dimethylarsino-glutathione), is also undergoing clinical trials as a chemotherapeutic agent.

1.1.3 Routes of exposure to As Ingestion of contaminated water or food is the primary route of exposure to As. Organic forms of As, typically found in seafoods, are understood to be less toxic than iAs. There is some concern that some organic arsenicals convert into more toxic intermediates through the process of degradation to DMAs (Molin et al. 2015). Studies in the past decade suggest that some organic As-compounds may be as toxic as iAs (Taylor et al. 2017). However, the health risk of

organic arsenicals has been hard to estimate due to the lack of experimental and human data on acute and chronic exposures and effects. The discussion hereafter will focus on exposure to iAs.

In the general US population, estimated average intake of iAs from drinking water and food, specifically rice, was 4.2 μg/day and 1.4 μg/day, respectively (Mantha et al. 2017).

Individuals of other countries with higher rice intake may consume up to 2.8 ug/day (Mantha et al. 2017). Excluding smokers and individuals with occupational exposures to As via inhalation, exposure via air is not a significant contributor to total As exposure. The European Commission estimates that air accounts for less than 1% of the total exposure to As, even when models use high estimates of As concentrations in air (EU Commission 2000). The following sections discuss in more detail each source of exposure to As.

1.1.3.1 Water Arsenic can be found in both surface and groundwater due to natural weathering and dissolution processes which occur based on pH, redox conditions, temperature, and solution composition (Smedley and Kinniburgh 2002). Arsenic in water is in the inorganic form, in either trivalent or pentavalent oxidation states (Cullen and Reimer 1989). Geothermal waters can contain high levels of iAs (Webster and Nordstrom 2003) and can be introduced into drinking water sources through natural hot springs in rivers or geothermal energy production (Bundschuh and Maity 2015). Runoff from mines, industrial plants, animal production facilities, and soil loaded with As-based agricultural products also contribute to As in water (Miller 2000).

Figure 1.1. iAs contamination of major aquifers around the world. Based off the 2002 British Geological Survey (Smedley 2002).

The current WHO and US EPA maximum contaminant limit (MCL) for iAs in drinking water is 10 µg/L (part per billion, ppb). Areas with iAs in water that exceeds this limit have been reported in numerous countries around the world, including Taiwan, Bangladesh, India, Vietnam,

Thailand, Argentina, northern Chile, US, Australia, Canada, Cambodia, and Mexico

(summarized in Smedley and Kinniburgh 2002; Figure 1.1). Concentrations of iAs in the more highly-exposed populations can exceed 2000 ppb (ATSDR 2007). In the US, the average concentration of iAs in groundwater is 1 ppb with higher concentrations of iAs in the western states (Focazio et al. 2000; Figure 1.2). Levels as high as 1000 ppb have been found in western states of the US (Smedley and Kinniburgh 2002). It is estimated the 8% of major public potable water supply systems in the US use water that exceeds the EPA MCL (Focazio et al. 2000).

Private wells, which are not regulated by the EPA, supply water for around 14% of the US population (Maupin et al. 2014). Areas with the highest private well water usage are also likely

to be in parts of the US with high As in well water (Ayotte et al. 2017). North Carolina has the fourth largest population of individuals using private well water >10 ppb (Ayotte et al. 2017).

Figure 1.2. Concentration of iAs in 18,850 potable water samples from public water supply systems and other water sources in the US. Data obtained from the United States Geological Survey National Water Information System. (Focacio et al. 2000).

1.1.3.2 Food Food is another major contributor to As exposure. In areas of the US where iAs in water is low, dietary intake of iAs is estimated to contribute 50-85% of total iAs exposure (Kurzius-

Spencer et al. 2014, Xue et al. 2010). Organic forms of As are found in marine organisms like seaweed, crustaceans, and fish in the form of arsenobetaine, arsenosugars, and arsenolipids

(Molin et al. 2015). Inorganic forms of As has been found in foods, including wine, rice,

seaweed, and fruit juices (Wilson et al. 2015). iAs in food results from the use of iAs- contaminated water for irrigation, use of iAs-based pesticides, or presence of iAs in soil, which plants readily take up (Li et al. 2016). Poultry and eggs can also contain iAs, due to the use of

As-based compounds in the animal production industry. The FDA has a general limit of 0.5-2 ppm iAs in foods. In 2013, a limit of 10 ppb iAs was proposed for apple juice due to its consumption among children (FDA 2013).

Levels of iAs in rice are of great concern because of the high consumption of rice in many countries with high iAs-contamination. Additionally, many newborn formula and infant foods are rice-based and, thus, can be significant sources of iAs exposure in infants who are more susceptible to environmental insults. In fact, the dietary exposure of infants and young children per kilogram of body weight is 2-3 times higher than in adults. (European Food Safety

Authority 2009). Total As in rice can be up to 100-400 ng/g and exists primarily in the form of iAs and DMAs (Williams et al. 2005). In the FDA’s most recent survey of As in rice and rice products, they found that on average white rice had more iAs than brown rice (92 vs. 154 ppb).

Infant dry rice cereals contained on average 103 ppb iAs (104 ppb in white-rice cereal, 119 ppb in brown-rice cereal) (FDA 2016). Currently, there are no regulations on As in rice or rice-based products. In 2015, the European Commission adopted a 100 ppb iAs limit for “rice destined for the production of infant and children’s foods”. In 2016, the FDA proposed a 100 ppb iAs limit in infant rice cereals.

1.1.3.3 Inhalation Release of As from geological formations into the atmosphere, water, or soil occurs through natural and anthropogenic processes. Volcanic eruption and forest fires are natural processes that release As into the atmosphere (Walsh et al. 1979; EPA TEACH 2007). Industrial

processes such as mining and smelting or burning of As-rich coal emit As into the air, where it attaches to particulate matter (typically <2 µM) (Mandel and Suzuki 2002). Burning of CCA- treated wood or application of As-based pesticides are other methods of volatilizing As. While mean total arsenic concentrations in air range from 0.02–4 ng/m3 in remote and rural areas, and from 3–200 ng/m3 in urban areas, higher concentrations (> 1000 ng/m3) have been measured in the vicinity of industrial plants (WHO 2001). Due to the use of As-based pesticides on tobacco plants, cigarettes can contain 0-1.4 µg As/cigarette (Smith et al. 1997; Lazarevic et al. 2012;

Afridi et al. 2015) and is a source of exposure to As and other metals for smokers.

1.2 iAs Metabolism

Combinations of natural and anthropogenic processes cause widespread release of iAs into the environment. In response, organisms have developed mechanisms to clear iAs from the body. Biotransformation of iAs through enzymatic methylation facilitates the clearance of iAs from the body as monomethyl-, dimethyl-, and sometimes trimethyl-arsenic species. The following sections discuss the process of iAs methylation, modifiers of iAs metabolism, and the importance of methylation in toxicity and iAs-associated disease.

1.2.1 iAs methylation Metabolism of iAs in humans and other mammalian species occurs through a series of methylation steps catalyzed primarily by arsenic (+3 oxidation state) methyltransferase

(AS3MT). Homologs of AS3MT have been found in many organisms, ranging from bacteria to humans. Other methyltransferases have been reported to methylate iAs, such as N-6 adenine- specific DNA methyltransferase 1 (Ren et al. 2011). However, AS3MT is the primary iAs- methylating enzyme. Transfection of UROtsa urothelial cells which lack AS3MT, confers iAs methylation capacity (Drobná et al. 2005). Conversely, silencing of AS3MT in human HepG2

hepatocytes via RNA interference reduces iAs methylation (Drobná et al. 2006). Furthermore,

As3mt-knockout mice produce virtually no methylated As species as measured in the urine and in tissues (Drobná et al. 2009; Currier et al. 2016).

AS3MT is highly expressed in the liver and has been found in testis, adrenal glands, and in all regions of the brain (Healy et al. 1988; Human Protein Atlas; Sánchez-Peña et al. 2014).

Initial observations in liver lysates determined that iAs methylation used S-adenosylmethionine

(SAM) as the methyl group donor (Buchet and Lawreys 1985) and that physiological reductants like glutathione (GSH), thioredoxin, lipoic acid, and other non-physiological reductants like dithiolthreitol increased methylation (Buchet and Lauwerys 1987; Buchet and Lauwerys 1988;

Stýblo et al. 1996, Waters et al. 2004). The As3mt enzyme was first purified from the cytosolic fraction of livers from adult male Fisher 344 rats (Lin et al. 2000), facilitating study of the actual enzyme in vitro.

The methylation of iAs by AS3MT is outlined in two conceptual models. The first model was developed by Frederick Challenger and colleagues (Challenger 1945; Challenger 1951) and revised by Cullen in 1984 (Cullen et al. 1984). In this model, AS3MT catalyzes a series of alternating reductions and oxidative methylations (Figure 1.3A). Products include methylarsonate (MAsV), dimethylarsinic acid (DMAsV), and, more minorly, trimethylarsine oxide (TMAsV). Trivalent As species, methylarsonous acid (MAsIII), dimethylarsinous acid

(DMAsIII), trimethylarsine (TMAsIII) are intermediates.

Figure 1.3. Oxidative (A) and reductive (B) models of iAs methylation (Drobná et al. 2009).

The second model was proposed by Hayakawa and colleagues in 2005, and further developed by Rosen et al. in 2014 (Figure 1.3B). Here, GSH-bound arsenic, As(GSH)3 and

As(GSH)2, are the preferred substrates of AS3MT and As remains in the trivalent form throughout the entire methylation process. Dissociation with GSH yields unstable MAsIII and

DMAsIII which rapidly oxidize to MAsV and DMAsV. AsIII was found to be bound to proteins during methylation, suggesting that the reaction could also occur with As bound to intracellular thiols (Naranmandura et al. 2006). A study of a recombinant human AS3MT enzyme supports the second model of methylation (Dheeman et al. 2014). As(GSH)3 bound preferentially to

AS3MT over iAsIII and MAsIII was the major product formed in the first minutes of catalysis, which remained enzyme-bound until remethylation. Through site mutagenesis of four conserved cysteine residues in AS3MT, they deduced that Cys156 and Cys206 form the binding site for iAsIII and MAsIII and are required for all steps in methylation. Cys32 and Cys61 are not essential for methylation but form a disulfide bond in the process, a bond that is reduced by thioredoxin to regenerate active AS3MT.

In humans, iAs and its metabolites are excreted primarily in the urine, although some excretion through feces is possible. Thus, urinary As is a common biomarker of iAs exposure.

Efficient iAs metabolism is generally understood as a decreased proportion of urinary iAs and an increased proportion of urinary DMAs. Primary and secondary methylation indices, ratio of

MAs/iAs and DMAs/MAs respectively, are used to describe the efficiency of the individual methylation steps. There are benefits and limitations to using urine samples to assess iAs metabolism. Urine is an easy and cost-effective sample to collect. However, urine As only reflects recent iAs exposures, can be influenced by hydration status, and may not represent actual tissue levels of iAs exposure. As in hair, nails, and blood have also been used to measure iAs exposure.

1.2.2 Genetic modifiers of iAs methylation Single nucleotide polymorphisms (SNPs) in AS3MT have been identified in both exonic and intronic regions and have been associated with differences in iAs methylation. SNPs in exonic regions may influence the activity of AS3MT by modifying the protein structure and/or function. For example, the rs11191439 polymorphism is an exonic SNP that results in a single allele mutation at position 14458 leading to a Met287Thr substitution. This SNP was linked to higher urinary MAs in more than two human studies (Agusa et al. 2011; Antonelli et al. 2012).

Indeed, the Met287Thr substitution in AS3MT of heterozygotic donor of primary human hepatocytes significantly increased production of MAs (Drobná et al. 2004) and COS-1 cells transfected with the 287Thr allozyme had higher in vitro protein levels than the wild type protein in untransfected cells (Wood et al. 2006). Assessment of recombinant human AS3MT expressing eight different exonic SNPs showed low methylation activity in all allozymes (Li et al. 2017), thus connecting AS3MT polymorphisms directly with iAs methylation efficiency. Intronic SNPs

may influence iAs metabolism by regulating gene and/or protein expression. Two intronic polymorphisms (e.g. rs3740390 and rs11191453) were associated with significant changes in urinary MAs in at least two different human studies (Antonelli et al. 2012; Agusa et al. 2011).

Other intronic SNPs (e.g. rs3740400 in the 5’ region and rs1046778 in the 3’ untranslated region) were significantly associated with AS3MT protein expression (Engström et al. 2011).

Genetic variation in other enzymes related to iAs metabolism have also been evaluated.

GSH-S-transferase omegas (GSTO) and purine nucleotide phosphorylase (PNP) are hypothesized to catalyze the reduction of iAsV (Radabaugh et al., 2002; Zakharyan et al., 2001).

However, the current literature does not indicate that polymorphisms in GSTO, other gluthation-

S-transferase enzymes, or PNP significantly influence iAs metabolism (Antonelli et al. 2012; unpublished data from Stýblo lab). The methyl donor for iAs methylation, SAM, is synthesized through the one-carbon metabolism pathway. Polymorphisms in genes coding enzymes involved in this pathway, such as methylenetetrahydrofolate reductase and cystathionine-beta-synthase, have been linked to iAs metabolism in population studies (Porter et al. 2010; Engström et al.

2007; Engström et al. 2009; Beebe-Dimmer et al. 2012; Kile and Ronnenberg 2008). It was proposed that SNPs in other SAM-utilizing methyltransferases could influence iAs metabolism.

However, data supporting this association is limited and inconsistent (Yang et al. 2016;

Engström et al. 2011).

Proportions of arsenic species in urine is also determined in part by mechanisms that transport As out of cells. Membrane transporters such as phosphate transporters, aquaglyceroporins, ATP-binding cassette transporter multidrug resistance proteins (MRPs), organic anion transporters (OATPs), and some glucose transporters have been shown to export

As species in vitro (Roggenbeck et al. 2016). Hepatocytes expressing MRP4 variants have

altered capacity to export DMAs (Banerjee et al. 2014) and SNPs in SLCO1B1, a member of

OATPs, have been associated with iAs metabolism in a small cohort study (Gribble et al. 2013).

1.2.3 Dietary modifiers of iAs methylation S-adenosylmethionine (SAM), the methyl group donor for iAs methylation, is synthesized through the one-carbon metabolism (OCM) pathway, a set of biochemical reactions that mediate the transfer of methyl groups (Selhub 2002) (Figure 1.4). Methionine is an essential amino acid that is the precursor to SAM. Transfer of the methyl group in SAM results in homocysteine (Hcys), which can be remethylated to form methionine. 5-methyl tetrahydrofolate

(THF), formed from the chemical conversion of folic acid or obtained through dietary folate, or betaine can provide the methyl group for Hcys methylation. In addition to producing SAM for methylation of iAs, DNA, and proteins, OCM also contributes to the production of thymidine and purines for DNA/RNA synthesis (Ducker and Rabinowitz 2017).

Some B vitamins, choline, and essential amino acids obtained from the diet are integral in

OCM (Figure 1.4). B vitamins are water-soluble and found in meats, dairy products, eggs, green leafy vegetables, whole grains, legumes, fruits, and nutritional supplements. Folic acid (B9), the primary form of folate in enriched foods and supplements, is a precursor to tetrahydrofolate

(THF). Methylcobalamin (B12) is an essential cofactor for methionine synthase. Riboflavin (B2) is needed for the formation of 5-methyl THF. Activated pyridoxine (B6), pyrodoxal phosphate

(PLP), is used in the conversion of THF to 5,10-methylene THF as well as in the transsulfuration pathway to form cysteine from homocysteine. Additionally, cysteine is needed for synthesis of

GSH, which is an important reductant in iAs methylation (Thomas et al. 2007). Betaine and its precursor, choline, can contribute to the SAM donor pool by regenerating methionine via betaine

homocysteine methyltransferase. Adequate protein intake is also important as methionine is needed for SAM synthesis and other amino acids are utilized in OCM reactions.

Figure 1.4 One-carbon metabolism (OCM), iAs metabolism, and nutritional contributions. From Spratlen et al. 2017. (1) Folic acid is reduced to dihydrofolate (DHF) and tetrahydrofolate (THF) by dihydrofolate reductase. (2) Serine hydroxymethyl-transferase transfers one carbon unit from serine to THF, forming 5,10-methylene-THF and glycine. (3) 5,10-methyl THF reductase can reduce 5,10-methylene-THF to 5-methyl-THF. Dietary folates absorbed from the GI tract can also enter the one-carbon metabolic pathway as 5 methyl THF. (4) Methionine synthetase utilizes vitamin B12 as a cofactor to transfer the methyl group of 5-methyl-THF to homocysteine (Hcys), generating methionine and THF. Additionally, betaine can donate a methyl group for the remethylation of homocysteine to methionine in a reaction catalyzed by betaine homocysteine methyltransferase (BHMT). (5) Methionine adenosyltransferase activates methionine to form S-adenosylmethionine (SAM). (6) SAM is a methyl donor for a variety of acceptors, including guanidinoacetate (GAA—precursor to creatine), DNA, and As, in reactions catalyzed by methyltransferases. (7) Hydrolysis of the byproduct of these methylation reactions, S-adenosylhomocysteine (SAH), generates Hcys. (8) Hcys is either used to regenerate methionine or undergoes transsulfuration to be converted to cysteine.

Many of these nutrients have been studied in the context of iAs metabolism. Overall, the data indicate that some B vitamins are associated with iAs metabolism. However, the strength or

direction of the relationship tends to vary by population, possibly due to age, iAs exposure, or sufficiency/insufficiency of other nutrients. Folate has been the most studied vitamin and was linked to iAs metabolism both experimentally and in human populations (Kile and Ronnenberg

2008; Hall and Gamble 2012; Kurzius-Spencer et al. 2017). Notably, supplementation of a highly-iAs exposed Bangladesh population decreased urinary %iAs and increased urinary

%DMAs, showing that supplementation may be a viable method to improve efficiency of iAs metabolism and possibly reduce iAs toxicity (Gamble et al. 2006). Intake of vitamin B2, B6, and

B12 and choline have been significantly associated with increased efficiency of iAs metabolism

(Spratlen et al. 2017, Heck et al. 2007, Hall et al. 2009, Argos et al. 2010; López-Carrillo 2016).

Individuals with low protein or amino acids levels tend to have a decreased ability to methylate iAs due to methionine deficiency, as demonstrated in US cohorts (Steinmaus et al. 2005;

Kurzius-Spencer et al. 2017) and in Bangladesh (Heck et al. 2007). These dietary nutrients have some association with iAs metabolism but further research is needed to clarify the nature of the relationship and if modifying intake of these nutrients can alter iAs metabolism and disease risk.

1.2.4 iAs metabolism and toxicity/disease risk Although metabolism of iAs produces DMAsV which is more easily cleared by the body, the methylation process generates As species that are more toxic than iAs. Methylated trivalent forms of arsenic are more toxic than unmethylated or methylated pentavalent forms (reviewed in

Thomas et al. 2001). Due to the toxicity of many intermediates of iAs methylation, iAs metabolism and its modifiers may be important factors determining risks of various iAs- associated diseases. Higher urinary %MAs is associated with increased risk of skin lesions, lung cancer, hypertension, and urothelial cancer (Antonelli et al. 2014). SNPs in AS3MT that are associated with increases in urinary %MAs have been linked to increased risk of coronary heart

disease, hyperlipidemia, skin lesions, and bladder and lung cancer (Gong and O’Bryant 2012;

Das et al. 2016; De la Rosa et al. 2017). Polymorphisms in GSTO and PNP have also been correlated with disease (Antonelli et al. 2014). Nutritional modifiers of iAs metabolism, primarily folate and other B vitamins, and their influence on disease risk has also been investigated in the context of skin lesions, urothelial cancer, and cardiovascular disease (Kile and

Ronnenberg 2008; Hall and Gamble 2012; Howe et al. 2017). Many of these studies were done in human populations. Fewer data on how iAs metabolism influences disease development and mechanisms are available in experimental models.

In summary, iAs metabolism via methylation facilitates clearance of iAs from the body.

However, it is also an activation process, generating more toxic metabolites that may contribute to iAs toxicity. Variations in its efficiency, either through genetic polymorphisms or nutritional influences, may influence disease development and risk.

1.3 iAs-associated diabetes

Diabetes, a condition of chronic hyperglycemia, is a serious, growing public health problem. Global prevalence of diabetes in adults has increased substantially, from 4.7% in 1980 to 8.5% in 2014, and in all major geographical regions (WHO 2016). Unfortunately, prevalence and incidence of diabetes is also growing in children (Dabelea et al. 2014; Mayer-Davis et al.

2017). While the concurrent rise of obesity is a main cause, it does not fully explain the concerning increase in diabetes observed in the last few decades. Evidence for endocrine- disrupting chemicals and their role as obesogens and diabetogens has been growing (Janesick and Blumberg 2016; Heindel et al. 2017; Thayer et al. 2011). Thus, environmental exposures may also be important contributors to the development of diabetes. Exposure to iAs has been

associated with higher incidence and prevalance of diabetes, specifically T2D, in multiple populations around the world.

1.3.1 Diabetes: Diagnosis, Pathogenesis, and Classification In 2012, diabetes mellitus was the 8th leading cause of death overall, the 5th leading cause of death in women specifically (WHO 2016). In addition, diabetes mellitus is often comorbid with cardiovascular disease, cancer, and renal disease, which also have high mortality rates. The global cost of diabetes in 2014 was estimated to be $612 billion, around 11% of the world’s healthcare costs (da Rocha Fernandes et al. 2016).

Insulin is the primary glucose-lowering hormone responsible for maintaining glucose homeostasis. It is secreted by the pancreatic β-cells which are found in the islets of Langerhans in the pancreas. In healthy individuals, high levels of blood glucose trigger insulin secretion by the β-cells (Figure 1.5). Insulin then stimulates glucose uptake in adipose tissue and skeletal muscle and utilization of glucose in the liver and other tissues, thus lowering blood glucose levels to normoglycemia (Figure 1.6). In adipose tissue, insulin also works to inhibit lipolysis and stimulate synthesis of triacylglycerides (Dimitriadis et al. 2011). In skeletal muscle, insulin stimulates glycogen synthesis and protein synthesis. In the liver, insulin activates synthesis of glycogen and triglycerides and inhibits gluconeogenesis. Overall, insulin is an anabolic hormone, stimulating the usage of glucose for synthesis of fatty acids, glycogen, and protein.

Figure 1.5. Mechanism of glucose-stimulated insulin secretion in pancreatic β-cells. Glucose enters the cell passively through glucose transporter 2, GLUT2 (1). Glucose is metabolized through glycolysis (2), generating ATP and increasing the ATP:ADP ratio (3). ATP blocks the + + flow of K into the cell through ATP-sensitive potassium channels (KATP) (4). The blockage of K flow into the cell stimulates depolarization of the plasma membrane (5), leading to the opening of voltage-gated calcium channels (VDCC) (6). Increases in intracellular calcium (Ca2+) (7) stimulates Ca2+-dependent mechanisms that control fusion of insulin granules and release of insulin (8).

Figure 1.6. Insulin-stimulated glucose uptake in adipocytes and myotubes. Binding of insulin to the insulin receptor stimulates phosphorylation of IRS-1/2 (1). Recruitment of PI3K by phospho- IRS-1/2 enables the conversion of PIP2 to PIP3 (2), which recruits PDK1 and AKT to the cell surface (3). PDK1 stimulates the phosphorylation of AKT (4). Phospho-AKT facilitates the activation of Rab proteins, leading to translocation of GLUT4 receptors to the plasma membrane (5) and uptake of extracellular glucose (6). Abbreviations: IRS-1/2: Insulin receptor substrate protein 1/2; PI3K: phosphatidylinositol 3-kinase; PIP2: phosphatidylinositol 4,5-bisphosphate; PIP3: phosphatidylinositol 3,4,5-phosphate; PDK1: protein kinase 1; AKT: protein kinase B; GLUT4: glucose transporter 4.

Defects in insulin secretion and/or insulin action lead to hyperglycemia. In type 1 diabetes (T1D), there is insufficient or a lack of insulin due to the destruction of β-cells via targeted autoimmunity. T1D constitutes 5-10% of diabetes cases. Causes of T1D tend to be genetic but some environmental exposures have been suggested (Knip and Simell 2012). The majority of individuals with diabetes have type 2 diabetes (T2D), which is characterized by the development of insulin resistance where tissues become less responsive to insulin (ADA 2015).

Insulin resistance results in a chronic overproduction of insulin by β-cells, which can then lead to

β-cell dysfunction and failure. Obesity is a major risk factor for T2D; other risk factors include age, diet, physical inactivity, ethnicity, and sex. The development of diabetes during pregnancy, gestational diabetes, increases risk of T2D in women later in life by at least seven-fold (Bellamy et al. 2009).

Diabetes is diagnosed in humans using three measurements: fasting plasma glucose

(FPG), 2-hour plasma glucose (2HPG), and glycosylated hemoglobin (A1C) (Table 1). Meeting the “diabetes” threshold in at least one test is sufficient for diagnosis of diabetes. FPG is the measurement of blood glucose levels after an 8-hour fast on two separate occasions. 2HPG is measured through an oral glucose tolerance test (OGTT) which measures the ability of the body to reduce blood glucose after a meal. Here, plasma glucose is measured two hours after an oral glucose load (75 grams for adults). Spontaneous adduction of glucose to hemoglobin in red blood cells occurs in the presence of glucose, forming glycosylated hemoglobin that persists through the lifespan of the red blood cell. Thus, A1C reflects blood glucose levels over 2-3 months and can be an indicator of chronic hyperglycemia (ADA 2015). The American Diabetes

Association (ADA) has also defined prediabetic ranges of these indicators (Table 1). Individuals with prediabetes are at risk for diabetes, cardiovascular disease, and strokes (Bansal 2015). It is

estimated that 7.8% of the global population has prediabetes (IDF 2013). In 2015, 33.9% of US adults were prediabetic.

Table 1.1 Criteria for the diagnosis of prediabetes and diabetes (ADA 2010).

Test Normal Prediabetes Diabetes

FPG <100 mg/dl ≥100 to < 126 mg/dl ≥ 126 mg/dl

2HPG <140 mg/dl ≥140 to < 200 mg/dl ≥ 200 mg/dl

A1C <5.7% ≥5.7% to < 6.5% ≥ 6.5%

1.3.2 iAs as an environmental diabetogen Chronic exposure to iAs has been associated with increased prevalence or incidence of

T2D in many populations around the world. In the 2011 National Toxicology Program workshop assessing iAs as a diabetogen, it was concluded that there was sufficient evidence of this association at high levels of exposure (>150 ppb) but insufficient at low to moderate levels (<150 ppb) (Maull et al. 2012). Since then, more epidemiological studies have been published on confirming this association in populations with low to moderate exposures. Two meta-analyses of these studies report pooled relative risks of developing T2D given iAs exposure of 1.7 and

1.23 (Sung et al. 2015; Wang et al. 2014). The association of iAs exposure has largely been observed in the context of T2D. There may be an association of iAs with T1D but very few studies have investigated this relationship (Chafe et al 2018; Grau-Perez et al. 2017). Studies in rodents report hyperglycemia, impaired glucose tolerance and changes in fasting insulin after exposure to ppm levels of iAs (Maull et al. 2012). The diabetogenic phenotype associated with iAs differs by study, possibly due to differences in exposure level and/or ethnicity/strain.

The period of exposure could also contribute to the variability in phenotype observed in studies of iAs-associated diabetes. Epidemiological studies of populations in iAs-endemic areas

study individuals that have likely been exposed to iAs their entire life. Furthermore, these individuals could also have been exposed to iAs in utero or even preconception. In these populations, the effects of iAs during particular exposure windows, i.e. adulthood or during prenatal periods, are inseparable. The role of early life iAs exposures on adult disease outcomes, also known as the developmental origins of disease paradigm (Heindel et al. 2015), has only begun to be addressed.

How iAs is involved diabetes pathogenesis is still unclear, making it difficult to classify iAs-associated diabetes as type 1, type 2, or another type of diabetes. In a Mexican cohort, fasting plasma insulin and HOMA-IR, measures of insulin resistance, were negatively associated with iAs exposure (Del Razo et al. 2011). These findings suggest a defect in insulin secretion in iAs-associated diabetes. In a different Mexican cohort, analysis of urinary and plasma metabolites found a subset of metabolites that were specific to iAs-exposed, diabetic individuals

(Martin et al. 2015). Many of these metabolites were not metabolites associated with T2D patients in other studies, suggesting that iAs-associated diabetes may have a different pathology.

While iAs have been found to impair both insulin secretion and insulin action, further investigation is needed to elucidate the progression of iAs-associated diabetes, identifying how defects in insulin secretion and/or insulin action develop and the importance of these impairments in disease pathogenesis. A better understanding of these mechanisms, and their temporality in diabetes development, will help determine the appropriate typology classification for iAs-associated diabetes.

1.3.3 Mechanisms of iAs-associated diabetes Experimental studies have found that iAs affects both insulin action (resistance) and insulin secretion. Other proposed mechanisms include epigenetic modifications such as changes in DNA methylation or microRNAs, leading to differential gene expression and disease development. Altering DNA methylation is particularly relevant for prenatal iAs exposures since

DNA methylation is important for proper growth and development of the fetus (Chmurzynska

2010). The sections below summarize the evidence for these potential mechanisms of iAs.

1.3.3.1 Effects on insulin action Insulin resistance arises when insulin action is inhibited. Some iAs-exposed rodents develop insulin resistance (Huang et al. 2015; Palacios et al. 2012) but some do not (Kirkley et al. 2017; Paul et al. 2011; Liu et al. 2014; Douillet et al. 2017). iAs reduced insulin-stimulated glucose uptake (ISGU) in both adipocytes and skeletal myotubes by reducing expression of glucose transporter GLUT4 (Paul et al. 2007; Padmaja Divya et al. 2015). This decreased translocation of GLUT4 to the outer membrane has been proposed to be due to impairments in

Akt or Sirt3 pathways. Exposure to iAsIII and MAsIII suppressed PDK-1 activity, leading to decreased phosphorylation of Akt and, thus, decreased GLUT4 transport to the membrane (Paul et al. 2007). In adipocytes and myotubes, the reduction in ISGU was attributed to increased oxidative stress, due to decreased Sirt3 signaling, which normally upregulates antioxidant defense (Padmaja Divya et al. 2015). In primary hepatocytes, exposure to iAsIII and MAsIII decreased glycogen content, a phenomenon also observed in vivo (Zhang et al. 2017; Huang et al. 2015). This decrease was concurrent with inhibition of insulin-dependent phosphorylation of

Akt that regulates glycogen metabolizing enzymes in the liver (Zhang et al. 2017). Thus, iAs and

its metabolites interfere with insulin-dependent signaling pathways in adipose tissue, skeletal muscle, and the liver.

Obesity is strongly associated with insulin resistance due to elevated levels of fatty acids, adipokines, and cytokines which interfere with insulin-dependent signaling pathways (Jung et al.

2014). iAs may promote insulin resistance by influencing adiposity. Though iAs inhibited differentiation of pre-adipocytes, it increased adipocyte size, lipogenesis, and lipolysis (Maull et al. 2012; Ceja-Galicia et al. 2017). There is also some data suggesting that iAs modulates adipokines to promote obesity. For example, iAs decreased mRNA expression of adiponectin, an adipokine that improves insulin sensitivity in the liver and muscle (Ceja-Galicia et al. 2017). In addition to direct effects on insulin signaling mechanisms, iAs may promote an obesogenic environment that would increase an individual’s risk of diabetes.

1.3.3.2 Effects on pancreatic β-cells and insulin secretion Animals exposed to high levels of iAs have decreased fasting plasma insulin

(Bonaventura et al. 2017; Paul et al. 2011), without decreases in β-cell mass (Kirkley et al.

2017). Exposure to iAs caused inflammation of pancreatic tissues (Liu et al. 2014) and islet cell apoptosis (Lu et al. 2011), attributes observed in diabetic pancreata (Donath et al. 2008; Johnson and Luciani 2010). In vitro, iAs and its metabolites inhibit glucose-stimulated insulin secretion

(GSIS) in β-cells and pancreatic islets at non-cytotoxic levels (Fu et al. 2010; Lu et al. 2011;

Douillet et al. 2013; Dover et al. 2018). This inhibition may be due to increased reactive oxygen species, elevated endoplasmic reticulum stress, mitochondrial dysfunction, changes in calcium influx, and/or impairments in secretion mechanisms (Fu et al. 2010; Wu et al. 2018; Lu et al.

2011; Dover et al. 2018; Díaz-Villaseñor et al. 2008; Bonini and Sargis 2018). Reactive oxygen species were also implicated in iAs-induced apoptosis of β-cells (Zhu et al. 2014; Lu et al. 2011;

Yao et al. 2015; Pan et al. 2016). iAs has direct effects on β-cell dysfunction or destruction which could lead to decreased insulin secretion.

1.3.3.3 Effects via epigenetics and microRNAs iAs exposure has been linked to alterations in DNA methylation and in microRNA profiles, epigenetic modifications that regulate gene expression. In an adult population in

Mexico, iAs exposure was associated with differential DNA methylation of genes related to T1D and T2D (Bailey et al. 2013). Mother-child cohort studies found that prenatal iAs exposure, as measured by maternal urinary As, was linked to changes in DNA methylation in cord blood

(Rojas et al. 2015; Broberg et al. 2014). Specifically, iAs exposure was associated with differential methylation of KCNQ1, an imprinted gene that encodes a potassium channel protein involved in GSIS (Rojas et al. 2015). iAs exposure is associated with expression of microRNAs that are associated with diabetes. These microRNAs control insulin signaling pathways in the liver (miR-29), β-cell proliferation (miR-24), and GSIS (miR-375) (Beck et al. 2017). Thus, iAs may contribute to the development of diabetes by modifying expression of important genes involved in glucose homeostasis through differential DNA methylation or microRNA expression.

1.3.4 Metabolism of iAs and diabetes risk Efficiency of iAs metabolism has been associated with altered risk of diabetes. However, unlike cancer and cardiovascular disease where higher urinary MAs was linked to increased risk, higher urinary DMAs and lower urinary MAs were associated with diabetes in at least five different population studies (Kuo et al. 2017). The association of elevated urinary MAs with adverse health effects fits well with the hypothesis of iAs metabolism as a detoxifying process, where incomplete methylation contributes to iAs toxicity. However, the association of DMAs

with diabetes and related conditions, such as BMI and obesity (Del Razo et al. 2011; Gomez-

Rubio et al. 2011; Gribble et al. 2013), suggest that the process of complete methylation to

DMAs may contribute to toxicity.

In most mechanistic studies, methylated trivalent As species are more toxic than iAsIII.

Exposure to 2 µM MAsIII decreased ISGU and inhibited Akt phosphorylation to a similar extent as 50 µM iAsIII (Paul et al. 2007). Hepatic glycogen and Akt phosphorylation was significantly decreased at 0.5 µM iAsIII and 0.2 µM MAsIII (Zhang et al. 2017). In pancreatic islets, MAsIII and DMAsIII significantly inhibited GSIS at lower concentrations than iAsIII (0.1 µM vs. 2 µM)

(Douillet et al. 2013). These findings suggest that the methylated trivalent metabolites may be important contributors to iAs-associated diabetes. Interestingly, mice lacking the As3mt gene develop obesity and insulin resistance. These animals were not exposed to iAs in drinking water and had some iAs intake through diet (Douillet et al. 2017). Since As3mt-KO mice accumulate more iAs in tissues than WT mice (Currier et al. 2016), the toxicity of accumulated iAs may explain the differences in metabolism in As3mt-KO mice. Alternatively, knockout of As3mt may have impacted regulation of other genes that would lead to the adverse metabolic states observed in KO mice. In summary, iAs metabolism appears to be important in iAs-associated diabetes but the specifics of how and to what extent methylated trivalent arsenicals contribute to disease development remain unknown.

1.4 Summary and Approach

Chronic exposure to iAs has been associated with diabetes in many populations around the world. Though metabolism of iAs through methylation facilitates clearance, it also generates toxic metabolites that can impair insulin secretion and insulin resistance. Thus, the role of iAs

metabolism in diabetes development remains unclear. This project seeks to understand the role of iAs metabolism in the development of iAs-associated diabetes through three distinct approaches.

First, we used metabolomics to characterize the As3mt-KO mouse, analyzing metabolites to identify disruptions in biochemical pathways associated with the As3mt-KO genotype (i.e. inability to methylate iAs) and with iAs exposure (Chapter 2). Second, we used nutritional interventions to modify iAs metabolism and metabolic phenotype in mice, in prenatal and adult exposure studies (Chapter 3 and 4). Third, we conducted in vitro experiments in isolated pancreatic islets to determine the mechanism of how iAs and its methylated metabolites inhibit

GSIS (Chapter 5). Synthesis of the research presented is in Chapter 6.

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2. CHAPTER 2- Metabolomic profiles of arsenic (+3 oxidation state) methyltransferase

knockout mice: Effect of sex and arsenic exposure

The first half of this chapter outlines a metabolomics study assessing the effect of iAs exposure and As3mt-KO on urinary and plasma metabolite profiles in mice. Based off findings from shifts in plasma and urinary metabolites, we conducted a follow-up study on liver phosphatidylcholines in these animals and is described in the latter half of the chapter.

2.1 Plasma and Urinary Metabolite Profiles1

2.1.2 Overview Arsenic (+3 oxidation state) methyltransferase (AS3MT) is the key enzyme in the pathway for methylation of inorganic arsenic (iAs). Altered As3mt expression and AS3MT polymorphism have been linked to changes in iAs metabolism and in susceptibility to iAs toxicity in laboratory models and in humans. As3mt-knockout mice have been used to study the association between iAs metabolism and adverse effects of iAs exposure. However, little is known about systemic changes in metabolism of these mice and how these changes lead to their increased susceptibility to iAs toxicity. Here, we compared plasma and urinary metabolomes of male and female wild-type (WT) and As3mt-KO (KO) C57BL6 mice and examined metabolomic

1 Madelyn C. Huang, Christelle Douillet, Mingming Su, Kejun Zhou, Tao Wu, Wenlian Chen, Joseph A. Galanko, Zuzana Drobná, R. Jesse Saunders, Elizabeth Martin, Rebecca C. Fry, Wei Jia, Miroslav Stýblo. (2017). Archives of Toxicology. 91:189-202. Reproduced with permission.

shifts associated with iAs exposure in drinking water. Surprisingly, exposure to 1 ppm As elicited only small changes in the metabolite profiles of either WT or KO mice. In contrast, comparisons of KO mice with WT mice revealed significant differences in plasma and urinary metabolites associated with lipid (phosphatidylcholines, cytidine, acyl-carnitine), amino acid

(hippuric acid, acetylglycine, urea), and carbohydrate (L-sorbose, galactonic acid, gluconic acid) metabolism. Notably, most of these differences were sex-specific. Sex-specific differences were also found between WT and KO mice in plasma triglyceride and lipoprotein cholesterol levels.

Some of the differentially changed metabolites (phosphatidylcholines, carnosine, and sarcosine) are substrates or products of reactions catalyzed by other methyltransferases. These results suggest that As3mt KO alters major metabolic pathways in a sex-specific manner, independent of iAs treatment, and that As3mt may be involved in other cellular processes beyond iAs methylation.

2.1.2 Introduction Chronic exposure to inorganic arsenic (iAs) via drinking water affects millions of people worldwide and is associated with an increased risk of cancer (NRC, 1999) as well as non-cancer diseases such as neurological (Tyler and Allen, 2014) and cardiometabolic disorders, including cardiovascular disease (Tsuji et al., 2014) and diabetes (Maull et al., 2012). In spite of the abundant evidence linking iAs exposure to a variety of adverse health effects, questions remain about the underlying biological mechanisms. Studies using powerful omics technologies, including metabolomics, may help to answer these questions.

Several recent studies have used metabolomics to characterize metabolic profiles associated with iAs exposure. A metabolomic study carried out in the general Chinese population identified several potential biomarkers of iAs exposure in urine (Zhang et al., 2014).

Similarly, our recently published metabolomic analyses of urine and plasma from residents of

Chihuahua, Mexico found 132 metabolites that were altered in iAs-exposed individuals (Martin et al., 2015). Metabolomic responses to iAs exposure have also been studied in laboratory animals, although most studies have focused on the effect of exposure to high levels of iAs

(Garcia-Sevillano et al., 2014a,b; Wang et al., 2014), which may not be relevant to human environmental exposures. The only other study that examined effects of low, chronic iAs exposure was carried out in rats (Wang et al., 2015). However, rats are not considered a suitable animal model because avid binding of iAs metabolites to rat hemoglobin generates unique kinetics and pattern of iAs metabolism that differ from those in humans and other mammals (Lu et al., 2004). Overall, the effects of chronic iAs exposure on the metabolome of humans and of rodent species commonly used in laboratory studies of iAs toxicity, specifically mice, remain understudied.

Results of both population and laboratory studies suggest that severity of adverse effects of iAs exposure is determined, in part, by efficiency of iAs metabolism (Antonelli et al., 2014;

Tseng et al., 2009). In humans and many other species, metabolism of iAs occurs by subsequent enzymatic methylation of iAs to form monomethyl-As (MAs) and dimethyl-As (DMAs), and in some species also trimethyl-As (TMAs) metabolites (Thomas et al., 2007). Arsenic (+3 oxidation state) methyltransferase (AS3MT) is the primary enzyme that catalyzes the methylation of iAs (Thomas et al., 2007). Experimental expression of rat As3mt in human urothelial (UROtsa) cells that do not express the AS3MT gene enables iAs methylation, and silencing of AS3MT in human hepatocellular carcinoma (HepG2) cells reduces the cell’s capacity for iAs methylation (Drobná et al., 2005; Drobná et al., 2006). Furthermore, knockout of As3mt in mice results in a significant decrease in the rate of whole-body As clearance and in the

accumulation of iAs in tissues (Drobná et al., 2009). For this reason, As3mt-catalyzed methylation of iAs was thought to be a detoxification process because it increases clearance of iAs from the body. However, we and others have shown that the methylated metabolites containing trivalent As (AsIII) contribute to the toxicities associated with iAs exposure (McCoy et al., 2015; Rehman et al., 2014; Stýblo et al., 2000; Tseng, 2009; Watanabe et al., 2012). Thus, while iAs methylation

Since As3mt is the enzyme responsible for clearance of iAs from the body and for generation of trivalent methylated metabolites, As3mt-knockout (KO) cells or mice are useful models for studying the role of iAs metabolism in adverse effects of iAs exposure. These models can also be used to explore other potential functions of AS3MT. AS3MT orthologs are found in many living organisms, ranging from simple invertebrates to modern-day humans

(Thomas et al., 2007). The conservation of AS3MT across species and during evolution suggests that, in addition to detoxifying iAs, this enzyme may act on other substrates (Thomas et al.,

2007) and could play a role in metabolic pathways essential for survival (Uthus, 1992). While

As3mt-KO mice are viable and do not show signs of dysfunction, a metabolomic analysis may reveal clues about the role of AS3MT beyond iAs methylation.

The present study examined plasma and urine metabolomes in As3mt-KO C57B6 mice and C57B6 wild-type (WT) mice exposed to iAs in drinking water and in unexposed control mice. The main goals were: (i) to characterize the metabolic response to iAs treatment, thus providing additional clues about the mechanisms underlying effects of iAs exposure, (ii) to compare the metabolic responses of WT mice that can efficiently methylate iAs with those of

As3mt-KO mice that have severely impaired iAs methylation capacity, and (iii) to probe for altered pathways that may indicate novel roles of AS3MT.

2.1.3 Methods Animals and treatment. All procedures involving mice were approved by the University of North

Carolina (UNC) Institutional Animal Care and Use Committee. Male and female C57BL6 WT mice were obtained from Jackson Laboratories (Bar Harbor, ME) at 17 weeks (±3 days) of age.

Male and female As3mt-KO mice on a C57BL6 background (Drobná 2009) were bred at the

UNC Animal Facilities and were 17-20 weeks old at the beginning of the study. All mice were housed under controlled conditions (12-h light/dark cycle at 22+-1oC and 50±10% relative humidity) with ad libitum access to pelleted rodent chow (Prolab Isopro RMH 3000, LabDiet, St.

Louis, MO). Mice drank either deionized water or deionized water containing 1 ppm As as arsenite (iAsIII) ad libitum for four weeks (N=15-21 per experimental group). Water with iAsIII was freshly prepared every week to minimize oxidation of iAsIII to iAsV. Food and water consumption and body weight were monitored in all treatment groups every week and two weeks, respectively.

Urine and plasma collection. At the end of iAs-treatment, urine was collected from mice housed in metabolic cages (1 mouse per cage) for 24 hours without access to food to avoid food contamination in urine samples. Urine was aliquoted and stored at -80ºC until analysis. Blood from non-fasted mice was collected prior to sacrifice via submandibular bleeding using heparinized Caraway micro-capillaries (Fisher Sc, Pittsburgh, PA). Plasma was isolated by centrifugation at 2000g for 10 minutes (at 4oC) and stored at -80ºC until further analysis.

Analysis of iAs metabolites in urine. Hydride generation-atomic absorption spectrometry coupled with a cryotrap (HG-CT-AAS) was used to determine the concentration of As species, including total iAs (i.e., iAsIII + iAsV), total MAs (MAsIII + MAsV) and total DMAs (DMAsIII + DMAsV)

(Hernandez-Zavala et al., 2008). As previously reported, limits of detection for this method are:

0.05 ng As/mL for MAs or DMAs and 0.1 ng As/mL for iAs (Martin et al., 2015). The concentration of total speciated As in urine was calculated as sum of iAs, MAs, and DMAs. For quality control, a certified standard reference material Arsenic Species in Frozen Human Urine

(SRM 2669; National Institute of Standards and Technology) was analyzed with the mouse urine samples. The recoveries of As from this SRM ranged from 87% for DMAs to 91% for iAs,

Metabolomic analysis. Numbers of plasma and urine samples used for the metabolomic analysis ranged from 13 to 21 per experimental group (volumes collected from some mice were not sufficient for the analyses). Samples were prepared as previously described with some modifications (Ni et al., 2014; Qiu et al., 2014; Qiu et al., 2013). Briefly, the samples were extracted with cold organic solvents (for plasma - methanol:chloroform, 3:1; for urine - acetonitrile). The supernatant was split for targeted metabolic profiling of 180 amino acids and lipids with an Acquity ultra performance liquid chromatography coupled to a Xevo TQ-S mass spectrometer (LC-MS, Waters Corp., Milford, MA), and for untargeted metabolic profiling with an Agilent 7890A gas chromatograph coupled to a Leco Pegasus time of flight mass spectrometer (GC-MS, Leco Corp., St Joseph, MI). The raw data files generated from LC-MS and GC-MS were processed, respectively, with TargetLynx Application Manager (Waters Corp.,

Milford, MA) and ChromaTOF software (Leco Corp., St Joseph, MI), in order to extract peak signal (normalized by dividing metabolite peak area by total chromatogram area), mass spectral data, and retention times for each metabolite. The detected metabolites from both analytical platforms were annotated and combined using an automated mass spectral data processing

(AMSDP) software package (Ni et al., 2012).

Metabolomic data analysis. ANOVA and hierarchical clustering analysis were conducted using

Partek Genomics SuiteTM Software (St. Louis, MO). Data were analyzed using a three-way multi-variate ANCOVA model including sex, genotype, and iAs treatment as covariates. To identify significantly changed metabolites between groups (i.e., differentially changed metabolites), significance was set with a False Discovery Rate (FDR) corrected q-value of q<0.05. Unsupervised two-way hierarchical clustering was performed on standardized data from

LC-MS and GC-MS analysis with unknown metabolites excluded. Metabolites or animals were clustered using a Euclidean distance metric and average linkage. Results were displayed as a heatmap representing fold-difference from the overall mean of each metabolite. To maximize the probability of capturing relevant metabolites for pathway analysis, a less stringent cutoff value of p<0.05 was used to select metabolites for pathway analysis. Pathways with p<0.05 using the online tool, MetaboAnalyst (Xia, 2010), were determined to be significant. The KEGG Pathway database was used to create a pathway map specific to this investigation (Kanehisa, 2000).

Plasma lipid analysis. Plasma triglycerides and cholesterol associated with high density lipoproteins (HDL) fraction and with a combined low density/very low density lipoprotein

(LDL/VLDL) fraction were analyzed using commercial enzymatic assays according to manufacturer’s instructions (Wako Diagnostics, Mountainview, CA and Abcam, Cambridge,

MA, respectively). Data was analyzed with a full factorial three-way ANOVA analysis considering sex, treatment, and genotype with post hoc Student’s t tests assessing relevant comparisons using JMP 10.1 (SAS Institute Inc, Cary, NC).

2.1.4 Results iAs methylation phenotype and body weight

The speciation analysis of As by HG-CT-AAS was conducted in the 24-hour urine samples collected from mice in all experimental groups. The average concentrations of total speciated As in the urine of WT and As3mt-KO mice treated with 1 ppm As in drinking water were about 10-fold higher than in the urine of the corresponding controls (466 ng/ml vs. 47 ng/ml in WT, 545 ng/ml vs. 57 ng/ml in KO mice) (Figure A.1). Knockout of As3mt had no statistically significant effects on total urinary As levels within the control groups. However, in iAs-treated animals, KO mice had significantly higher levels of total speciated As levels in urine as compared to iAs-treated WT mice. No significant differences were found between male and female mice in any groups. The majority of total speciated As in the urine of As3mt-KO mice was represented by iAs: on average 58% for controls and 91% for iAs-treated mice. In contrast, iAs accounted for only 8 and 10% of total speciated As in urine of WT controls and iAs-treated mice, respectively. DMAs was the major As metabolite, representing 40% of total urinary As in control KO mice and 89% in control WT mice. In iAs-treated animals, DMAs constituted 9% of total urinary As in KO mice and 92% in WT mice. Notably, treatment with 1 ppm As had no significant effects on body weights of either WT or As3mt-KO mice (Figure A.2). There were no differences in the weights of female KO and WT mice, regardless of treatment. However, male

KO mice treated with iAs were heavier at sacrifice than the iAs-treated, WT males. There were no differences in food consumption. Male WT mice drank more water than female WT mice and male KO mice (Figure A.3).

Plasma and urine metabolites identified by metabolomics analyses

The targeted analysis using LC-MS identified 172 metabolites in plasma and 135 metabolites in urine. Global GC-MS profiling detected an additional 76 and 98 metabolites in

plasma and urine, respectively, and 184 plasma and 320 urinary unidentified metabolites which were excluded from subsequent analyses. Thus, a total of 248 plasma and 233 urinary metabolites were identified (Table A.1) and used in the statistical analyses. Unsupervised two-way clustering analysis showed that animals with similar metabolite profiles clustered primarily by sex and, to a much lesser extent, by genotype or iAs treatment (Figure 2.1). Further comparisons characterized differences in the plasma and urine metabolomes between experimental groups differentiated by sex, genotype and treatment.

Figure 2.1. Urinary and plasma metabolites identified by metabolomic analyses: Heat maps generated by unsupervised two-way clustering analysis show relative levels of metabolites (columns) in plasma (A) and urine (B) of control (0 ppm) and iAs-treated (1 ppm) male (M) and female (F) wild-type (WT) and As3mt-KO (KO) mice (rows). The metabolite levels are mean centered with high relative levels indicated in red and low relative levels indicated in blue.

Changes in plasma and urine metabolomes associated with iAs treatment

Direct comparisons of plasma and urinary metabolite concentrations of control mice and iAs-treated mice revealed only minor shifts associated with iAs treatment, and only in plasma of female As3mt-KO mice (Table 2.1). All three significantly changed urine metabolites (two acyl- carnitine species and a sphingomyelin species) were associated with lipid metabolism. No metabolites were found significantly changed due to iAs treatment in other experimental groups.

However, the effect of iAs treatment can be inferred indirectly by comparing the number of KO- associated metabolites in iAs-treated and control mice. For example, 42 metabolites were changed in plasma and urine of control female KO mice when compared with control female WT mice; however, only 15 metabolites were found significantly changed in comparison of KO and

WT female mice treated with 1 ppm iAs (Table 2.1; Figure A.4). Similar differences in numbers and types of KO-associated metabolites can be seen in male control mice when compared to male iAs-treated mice (Table 2.1; Figure A.4). These observations imply that the effect of iAs- treatment on the plasma and urine metabolite profiles may be greater than that indicated by direct comparison.

Table 2.1. Plasma and urinary metabolites found significantly changed (q<0.05) in comparisons assessing effect of iAs treatment in male and female wild-type (WT) and As3mt-knockout (KO) mice. Fold change indicates difference from the second group in the comparison.

Urine Plasma

Comparison Metabolite q- Fold Metabolite q-value Fold value Change change KO, female C18:1 0.01 -1.50 none 1 ppm vs 0 ppm C14 0.02 -1.36 SM (OH) C14:1 0.01 1.79 KO, male none none 1 ppm vs 0 ppm WT, female none none 1 ppm vs 0 ppm WT, male none none 1 ppm vs 0 ppm *CX:Z, acyl-carnitine; SM(OH)CX:Z, sphingomyolipid (X indicates number of carbons of the fatty acid tail and Z indicates total number of double bonds).

Changes in plasma and urine metabolomes associated with As3mt KO

To characterize the effect of As3mt knockout on plasma and urine metabolomes, we identified all plasma and urinary metabolites that were shifted in control and iAs-treated male and female KO mice when compared to their respective wild-type counterparts (Table 2.2). From these comparisons, we selected the plasma and urinary metabolites that changed in both KO male and female mice, thus isolating metabolites associated with As3mt KO, regardless of sex.

Control female KO mice had more than double the number of the KO-associated metabolites found in male KO mice (Figure 2.2). In the iAs-treated groups, female KO mice showed half as many KO-associated metabolites as compared to males (Figure 2.2). The KO-associated metabolites common to control male and female KO mice were two phosphatidylcholine (PC) species (PC aa C36:0, PC aa C40:4) and cytidine, all increased in KO mice. Three KO- associated metabolites, all PC species, were common to iAs-treated male and female KO mice

(PC aa C26:0, PC aa C36:5, PC ae C34:1).

Figure 2.2. Number of urinary and plasma metabolites significantly changed (q<0.05) in As3mt-knockout (KO) male and female mice in comparison with wild-type (WT) counterparts. Venn diagrams show overlapping and sex-specific plasma and urinary metabolites of control (left) and iAs-treated (right) mice. The bolded numbers represent the total number of changed metabolites in each section of the diagram; the arrows indicate metabolites that increased or decreased in response to knockout of As3mt.

KO-associated changes in urine metabolome

In comparisons of control animals, female KO mice had more differentially changed urinary metabolites than KO males when compared to respective WT mice. A total of 27 metabolites were changed in KO female controls but only seven metabolites had a fold change greater than 2: 2-hydroxybutyric acid, L-sorbose, gluconic acid, galactonic acid, cytidine, urea, and sarcosine (Table 2.2, Figure 2.3A-C). Thirteen metabolites were changed in control KO males, including five with fold changes greater than 2: cytidine, acetylglycine, hippuric acid, carnosine, and one acyl-carnitine species (C14:2-OH) (Table 2.2, Figure 2.3A, D-F). The highest fold changes were found for acetylglycine and hippuric acid, but acetylglycine decreased by 8- fold while hippuric acid increased by 16-fold. Similar, though not significant, changes in hippuric acid and acetylglycine were found in comparisons of female KO mice vs. WT mice.

Cytidine was increased in urine of both male and female KO mice as compared to respective WT mice (Table 2.2, Figure 2.3A). In comparisons of iAs-treated mice, male KO mice had more significantly changed metabolites than KO females (15 vs. 11) when compared to the respective

WT mice. Of the 11 changed metabolites in female KO mice, six metabolites had fold changes greater than 2: citrulline, aminoadipic acid, 2-hydroxybutyric acid, glutamate, and two PCs (PC aa C26:0 and PC ae C30:2) (Table 2.2). The urinary concentration of 2-hydroxybutyric acid remained significantly decreased in female KO mice after treatment with iAs (Table 2.2). In male KO mice, seven metabolites had fold changes greater than 2: 4-hydroxybenzoic acid, carnosine, gluconolactone, cytidine, hippuric acid, and two acylcarnitine species ( C14:2-OH and

C14:2) (Table 2.2). Cytidine and hippuric acid were still significantly increased in iAs-treated male KO mice as compared to iAs-treated male WT mice but acetylglycine levels were no longer significantly different (Figure 2.3A, B). Hippuric acid and acetylglycine concentrations were

lower in iAs-treated as compared to control mice, but these differences were not statistically significant (Figure 2.3A,B). The urinary levels of these two metabolites followed similar trends in female mice but changes due to knockout of As3mt were not statistically significant.

Table 2.2. Plasma and urinary metabolites found significantly changed (q<0.05) in comparisons of As3mt–KO (KO) and wild-type (WT) male and female mice. Fold change indicates difference from the second group in the comparison.

Urine Plasma

Comparison Metabolite q-value Fold Metabolite q- Fold Chang value chang e e Control, female Urea 0.03 -5.97 PC aa C38:3 <0.01 1.34 KO vs WT (42)* 2-Hydroxybutyric acid <0.01 -2.48 PC aa C38:0 <0.01 1.29 Suberic acid 0.02 -1.81 PC aa C36:3 0.04 1.17 Adipic acid 0.01 -1.57 PC aa C42:1 0.04 1.19 Pelargonic acid 0.03 -1.45 PC ae C40:2 0.02 1.20 Glycolic acid 0.03 -1.39 PC aa C36:1 0.02 1.20 3-Methyl-2-oxovaleric acid 0.04 -1.22 PC ae C42:2 0.04 1.21 Creatinine 0.04 -1.04 PC ae C36:5 0.01 1.22 PC ae C36:5 0.04 1.21 PC aa C42:2 0.02 1.23 PC aa C36:0 0.01 1.25 PC aa C36:2 0.04 1.23 PC ae C36:3 <0.01 1.26 PC aa C40:1 0.02 1.24 PC ae C36:4 <0.01 1.28 PC aa C40:4 0.03 1.26 C18:1-OH 0.04 1.38 PC aa C40:6 0.02 1.27 C18 0.02 1.45 PC aa C38:4 0.03 1.28 D-Threitol 0.03 1.50 Valine 0.04 1.28 Ribonolactone 0.04 1.52 Myoinositol 0.02 1.60 C18:1 <0.01 1.62 L-Arabitol 0.01 1.63 1,5-Anhydrosorbitol 0.03 1.77 Adenosine 0.03 1.78 Dopamine 0.04 1.80 Cytidine 0.04 2.21 Sarcosine 0.04 2.21 Galactonic acid <0.01 3.36 Gluconic acid <0.01 3.36 L-Sorbose 0.05 4.00 Control, male Acetylglycine <0.01 -16.82 Carnosine 0.01 -1.49 KO vs WT (17)* Carnosine <0.01 -2.44 PC aa C26:0 <0.01 -1.16

C14:2-OH 0.02 -2.31 PC aa C32:1 <0.01 1.47 Aspartate <0.01 -1.96 Oxoglutaric acid 0.04 1.59 C7-DC 0.02 -1.85 Uracil 0.04 -1.64 PC aa C36:0 <0.01 1.29 PC aa C40:4 <0.01 1.58 2-Hydroxy-3- <0.01 1.69 methylbutyric acid C6 (C4:1-DC) 0.01 1.69 PC aa C40:5 <0.01 1.82 Cytidine <0.01 2.74 Hippuric acid <0.01 8.51 iAs-treated, female Citrulline 0.01 -3.58 PC aa C26:0 <0.01 -1.19 KO vs WT (15)* PC aa C26:0 0.02 -2.50 PC aa C38:3 0.01 1.29 Aminoadipic acid 0.01 -2.41 PC aa C36:5 0.01 1.27 2-Hydroxybutyric acid <0.01 -2.32 PC aa C36:1 <0.01 1.26 PC ae C30:2 <0.01 -2.30 Glutamate 0.02 -2.15 Ketoleucine <0.01 -1.64 PC ae C34:1 <0.01 -1.39 PC aa C38:6 <0.01 -1.33 PC ae C40:6 <0.01 -1.28 PC ae C36:4 <0.01 1.24 iAs-treated, male C14:2-OH <0.01 -2.91 C18:1 <0.01 -1.99 KO vs WT (36)* 4-Hydroxybenzoic acid 0.03 -2.81 C18:2 <0.01 -1.97 Carnosine <0.01 -2.25 C7-DC 0.01 -1.86 Gluconolactone 0.01 -2.01 C18 <0.01 -1.86 C14:2 0.02 -1.98 Carnosine <0.01 -1.82 Uridine 0.02 -1.90 C16 <0.01 -1.75 D-Glucuronic acid 0.04 -1.31 Spermidine <0.01 -1.70 2-Hydroxy-3- <0.01 1.45 C16:2-OH <0.01 -1.61 methylbutyric acid Adenine 0.03 1.52 C18:1-OH 0.03 -1.59 Succinic acid 0.05 1.79 Kynurenine 0.02 -1.51 O-Phosphoethanolamine 0.01 1.86 C14:2 0.01 -1.50 Glycerol 3-phosphate 0.01 1.94 C16:1-OH 0.03 -1.45 PC aa C40:5 <0.01 2.18 t4-OH-Pro <0.01 -1.42 Cytidine <0.01 2.49 Phenylalanine 0.03 -1.37 Hippuric acid 0.01 9.42 Tryptophan 0.04 -1.33 PC ae C34:1 0.02 -1.24 PC aa C26:0 <0.01 -1.14 PC aa C36:5 0.02 1.26 PC aa C40:4 0.01 1.34

PC aa C40:3 0.01 1.51 PC aa C32:1 <0.01 1.59 *Numbers in parentheses indicate total number of changed metabolites in that comparison. CX; acyl- carnitine where X indicates number of carbons of the fatty acid tail. SM; sphingomyolipid. Phosphatidylcholine notation: PC; phosphatidylcholine, aa; two fatty acid ester linkages, ae; one ester and one ether linkage, CY:Z is a description of the fatty acid composition of two PC tails where Y indicates total number of carbons in both fatty acid tails and Z indicates total number of double bonds.

Figure 2.3. Relative concentrations of selected metabolites determined by GC-MS (A,B,D,E) or LC-MS (C, F) in urine of wild-type (WT) and As3mt-knockout (KO), male (M) and female (F) mice, control (0 ppm) or iAs-treated (1 ppm). Mean and SE are shown with N=13-21 per group. *Indicates statistically significant differences between experimental groups (q<0.05)

KO-associated changes in plasma metabolome

Fewer metabolites were found to be significantly changed due to knockout of As3mt in plasma than in urine. Notably, most of the KO-associated metabolites in plasma were PC species. The greatest number of changed PCs was found in the comparison of control KO and

WT females; all these PCs were increased in plasma of KO females, although the fold-change did not exceed 1.34 (Table 2.2). When male control KO mice were compared with control WT males, two PCs were changed (one increased and one decreased), along with carnosine

(decreased) and oxoglutaric acid (increased). In comparisons of iAs-treated KO and WT mice,

PCs were changed (generally increased) in plasma of both female and male KO mice (Table 2.2).

All 4 changed metabolites in plasma of females were PCs (3 increased, 1 decreased). The iAs- treated male KO mice had more significantly changed PCs in the plasma than iAs-treated females (6 vs. 4), as well as more significantly changed metabolites overall (21 vs. 4), when compared to the respective WT mice. The KO-associated metabolite changes in iAs-treated male mice included decreased levels of carnosine, spermidine, kynurenine, and various acyl-carnitine species, though fold-changes did not exceed 2. Plasma acyl-carnitines were changed only in comparison of iAs-treated male KO and WT mice.

Metabolic pathways affected by iAs treatment and As3mt knockout

Because very few metabolites were found to be changed by iAs treatment using the q<0.05 filter (Table 2.1), a less stringent cutoff value of p<0.05 was used to identify significantly changed plasma and urinary metabolites for the analysis of pathways affected by iAs-treatment, as well as by As3mt KO (Table A.2 and A.3). Using Metaboanalyst, many pathways were identified as enriched for the differentially changed metabolites; however, these pathways

contained only one to three changed metabolites (hits) and often the same few metabolites were responsible for hits in multiple pathways (Table A.4). Furthermore, the Metaboanalyst program considered all PC species as one metabolite, thus underestimating effects on PC metabolism.

Even with a less stringent cutoff, a relatively small number of metabolites were identified as significantly changed due to iAs treatment (Table A.2). The pathways enriched as a result of iAs-treatment were genotype- and sex-specific. Pathways of amino acid metabolism were enriched only in iAs-treated male and female WT mice when compared to the respective controls; however, the enriched pathways in WT females were associated with D-glutamate, histidine, and valine/leucine/isoleucine metabolism, while pathways of L-glutamate, alanine/aspartate/glutamate, arginine/proline, and beta-alanine metabolism were enriched in male

WT mice (Table A.4). In KO mice, the pathways enriched by iAs treatment were associated mainly with metabolism of lipids (in males) and carbohydrates (in females).

Pathways of amino acid metabolism were also enriched in comparisons of As3mt-KO and

WT mice, but again the numbers and types of these pathways were both sex- and treatment- specific (Table A.4). Enrichment of pathways associated with lipid (including PC) metabolism was found in control and iAs-treated KO males, but not in KO female mice. Although most changes in individual PC metabolites were detected in the comparison of control female KO mice to control WT females (Table 2.2), no significantly enriched pathways of PC metabolism, or lipid metabolism in general, were found by the pathway analysis, likely because the program treated the numerous PC species as one metabolite. In iAs-treated mice, As3mt KO was also linked to the enrichment of pathways of carbohydrate metabolism, but the enriched carbohydrate pathways differed between males and females. Additional enriched pathways associated with

As3mt KO in either male or female mice included the pathways of glutathione, propanoate, and panthothenate and coenzyme A metabolism (Table A.4).

To generate a holistic picture of the pathways affected by As3mt KO without the restrictions inherent to the pathway analysis program, a pathway map of metabolites differentially changed (q<0.05) due to As3mt KO was constructed using KEGG’s mus musculus- specific pathway map database (Figure 2.4). The resulting map includes the enriched pathways described in Table 2, but highlights the sex-specific differences, the direction of change, and depicts the metabolites that are reported to form connections between the affected pathways.

Effects of iAs-treatment and As3mt KO on plasma lipid concentrations

To further probe changes in plasma lipids, particularly the PC metabolites that were observed across all comparisons, we measured concentrations of plasma triglycerides and of lipoprotein cholesterol as indirect measures of plasma lipoprotein concentrations. Average triglyceride concentrations were generally higher in KO mice as compared to WT mice, but this difference was statistically significant only in iAs-treated males (Figure 2.5A). The plasma of

KO male mice contained significantly more triglycerides than plasma of KO females, regardless of iAs treatment, and control WT male mice had higher levels of triglycerides than control WT females. Treatment with iAs had no significant effects of triglyceride levels in either WT or KO mice. Very few statistically significant differences were found in the VLDL/LDL-cholesterol levels (Figure 2.5B). WT females had lower VLDL/LDL cholesterol than KO females but this difference was only significant in iAs-treated animals. In contrast, WT males had higher

VLDL/LDL-cholesterol levels than KO males, but only statistically significant in control males.

Two-way ANOVA analysis of VLDL/LDL-cholesterol levels found the sex and genotype

interaction to be statistically significant (p=0.005), thus confirming that the effect of genotype on

VLDL/LDL cholesterol is different in males than in females. Treatment with iAs had no effect on VLDL/LDL- or HDL-cholesterol in any group. HDL-cholesterol levels were higher in plasma of male as compared to female mice, in both WT and KO animals (Figure 2.5C).

Figure 2.4. Concentrations of lipids in plasma of wild-type (WT) and As3mt-knockout (KO), male (M) and female (F), control (0 ppm) and iAs-treated (1 ppm) mice: A, triglycerides (N=9- 19); B, LDL/VLDL-cholesterol (N= 8-15); C, HDL-cholesterol (N= 8-15). Data shown as mean ± SE. * Indicates statistically significant differences between experimental groups (p<0.05)

Figure 2.5. Diagram of metabolic pathways involving metabolites significantly altered (q<0.05) in plasma or urine of control and/or iAs-treated male and female As3mt-knockout mice when compared to corresponding wild-type males and females. Arrows indicate a relationship based off KEGG pathways specific to mus musculus and may represent more than one enzymatic step.

2.1.5 Discussion

Effects of iAs treatment We identified very few metabolites in KO and WT mice that were significantly changed due to iAs treatment using a direct ANOVA comparison. This result is consistent with data reported by Wang and associates (2015) who conducted a metabolomic study in male Sprague-

Dawley rats exposed to 0.5, 2, or 10 ppm iAs in drinking water for 3 months. They found no or very few changes in the plasma metabolome at 0.5 and 2 ppm, but observed major changes in lipid, amino acid, and nucleotide metabolism at 10 ppm As. Thus, treatment with 1 ppm As for only 4 weeks may not be sufficient to result in metabolic disturbances in rats or mice. It is also possible that mice in our study adapted to iAs, due to background exposure to iAs in the diet.

Adaptation to iAs has been reported in mice exposed to iAs via inhalation (Aranyi et al., 1984).

Though we did not analyze As in the mouse diet, the results of As analysis in urine of control mice (Figure A.1) indicate a low-level background As exposure. Thus, background exposure to iAs from diet used in this study could result in adaptation leading to only minor metabolic changes after additional exposure to 1 ppm As.

The lack of major metabolic shifts in mice exposed to 1 ppm As also raises questions about differences between rodents and humans in metabolism of and susceptibility to iAs. Mice are thought to metabolize iAs more efficiently than humans (Vahter, 1994), and thus may be more resistant to iAs exposure. Our recent study in Chihuahua, Mexico showed that exposures to only ppb levels of As in drinking water (up to ~200 ppb) result in extensive metabolite shifts in human plasma and urine (Martin et al., 2015). This result may indicate higher susceptibility of humans to iAs toxicity and may justify application of higher doses of iAs in laboratory studies using mice to model iAs exposure in humans.

Effects due to As3mt KO: The role of sex Unsupervised hierarchical clustering analysis showed that mice with similar metabolite profiles were clustered primarily by sex, and less by genotype or treatment (Figure 2.1).

Significantly changed metabolites were different in males and females, further indicating that sex modifies the effect of the KO genotype on the metabolite profiles. Pathway analysis suggests that changes associated with knockout of As3mt were related to lipid, amino acid, and carbohydrate metabolism. Lipid metabolism was changed in both KO males and females but alterations in other pathways appeared to be largely sex specific.

Effects on lipid metabolism

Decreased levels of plasma PCs, increased triglycerides, increased acyl-carnitines and lyso-PCs have been previously reported in mice administered 3 mg As/kg/day by gavage

(Garcia-Sevillano et al., 2014a). Increased levels of acyl-carnitines and lyso-PCs were also found in plasma of rats exposed to 10 ppm As in drinking water (Wang et al., 2015). Changes in the same types of lipid metabolites were also found in the present study, but these changes were primarily associated with As3mt KO and to a much lesser extent with iAs treatment.

Knockout of As3mt was associated with extensive changes in PC species and a related metabolite, cytidine, across all comparisons, with the most prominent changes in the comparison of female KO vs. female WT mice (Table 2.2). Biosynthesis of PCs occurs via two pathways: the cytidine-diphosphate (CDP) pathway and the pathway catalyzed by phosphatidyethanolamine N- methyltransferase (PEMT). In the CDP-pathway, CDP-choline is combined with diacylglycerol to form PC. The PEMT pathway involves three subsequent methylations of phosphatidylethanolamine using S-adenosylmethionine (SAM) as a methyl group donor. The

PEMT pathway occurs only in the liver and synthesizes ~30% of hepatic PCs (Sundler and

Akesson, 1975). Notably, knockout of PEMT in mice has been shown to alter plasma lipoproteins levels in a sex-specific manner (Noga and Vance, 2003) and PEMT activity appears to be regulated by estrogen (Resseguie et al., 2007). Both CDP- and PEMT-pathways are necessary for normal production of plasma lipoproteins (Yao and Vance, 1988; Noga et al.,

2002; Jacobs et al., 2004), specifically VLDL.

Results of the plasma triglyceride and lipoprotein cholesterol analyses performed in this study did not mirror the changes we observed in PC metabolites, although the higher levels of

VLDL/LDL cholesterol in iAs-treated female KO vs. WT mice were consistent with the overall higher levels of plasma PCs (Table 2.2, Figure 2.5). Triglycerides in plasma of iAs-treated male

KO mice were significantly higher than in plasma of WT counterparts; this difference may be associated with higher body weight of the KO mice (Figure A.2). Nevertheless, we found differences due to sex in all lipid measures. Future studies should examine mechanisms by which knockout of As3mt affects pathways of PC synthesis and degradation, and lipid metabolism in general, in a sex-specific manner.

Acyl-carnitines were also among lipid metabolites changed in response to As3mt KO, particularly in male mice. Acyl-carnitines facilitate transfer of fatty acids into the mitochondria for fatty acid oxidation (Peng et al., 2013; Reuter and Evans, 2012). The changes in acyl- carnitines observed in KO mice in this study could reflect changes in rate of fatty acid oxidation, and consequently, in energy production.

Effects on amino acid and nitrogen metabolism

A number of metabolites related to amino acid metabolism were changed in As3mt-KO mice, particularly in male mice (Table 2.2). Two metabolites - hippuric acid and acetylglycine -

had the greatest fold changes in the urine of male KO mice as compared to WT mice. However, while the hippuric acid level increased, the level of acetylglycine decreased in As3mt-KO mice

(Figure 2.3D,E). The same trends, while not statistically significant, were found in female mice.

Hippuric acid and acetylglycine are products of the conjugation of glycine with benzoyl-CoA and acetyl-CoA, respectively, which is catalyzed by glycine N-acyltransferase (Dempsey et al.,

2014; Schachter and Taggart, 1954). Degradation of dietary polyphenols by gut microorganisms is thought to be a major source of benzoic acid and other glycine conjugation substrates (Knights and Miners, 2012; Lees et al., 2013). Elimination of the gut bacteria using antibiotics or germ- free mice was shown to eliminate hippuric acid excretion (Lees et al., 2013; Nicholls et al.,

2003; Yap et al., 2008). As such, hippuric acid is considered a urinary microbial-mammalian cometabolite. Excretion of acetylglycine in urine was increased in mice after ingestion of alcohol, presumably due to increased formation of acetic acid (Manna et al., 2011). Colonic bacteria can ferment dietary fiber to form acetate, possibly explaining the association of acetylglycine with dietary fiber intake in humans (Cummings et al. 1983; Lustgarten et al.,

2014). Thus, the changes in urinary levels of hippuric acid and acetylglycine found in this study could be associated with influences on the microbiome, possibly due to As3mt KO or due to different diets used at the UNC animal facility where the KO mice were bred and at the Jackson lab where the WT mice were purchased.

An almost 6-fold decrease in urea levels was found in urine of female KO, but not in urine of male KO mice, in comparisons with corresponding WT mice (Figure 2.3B). The production of urea in the urea cycle is the major mechanism for disposal of nitrogen during catabolism of amino acids. The decrease in urea production seen here in female KO mice may indicate a potential disruption of this mechanism in a sex-dependent manner.

Effects on energy/carbohydrate metabolism

Growing evidence suggests that iAs is an environmental diabetogen that impairs glucose homeostasis (Maull et al., 2012). Previous metabolomics studies have shown changes in carbohydrate metabolites in humans (Martin et al., 2015) and laboratory animals (Garcia-

Sevillano et al., 2014 a,b; Wang et al., 2015) exposed to iAs. However, our metabolomics analysis found only minor effects of iAs treatment on pathways of carbohydrate metabolism, and only in KO female mice (Table A.4). Most of the changes in carbohydrate metabolites and metabolic pathways were associated with As3mt KO, and these changes were sex-specific (Table

2.2; Table A.4).

Effects on metabolites associated with methylation reactions

The inability of As3mt-KO mice to efficiently methylate iAs may have resulted in altered availability of SAM for other methylation reactions, thus influencing plasma or urinary levels of substrates and/or products of these reactions. The KO-associated increases in plasma PC levels may reflect higher activity of the PEMT pathway due to increased SAM availability. Currently, iAs is the only known substrate of As3mt but our metabolomic analyses in As3mt-KO mice could indicate other, so far unidentified, substrates. Here, we found that knockout of As3mt in mice affected levels of metabolites in two other methylation pathways: the metabolism of carnosine and sarcosine.

The concentration of carnosine was significantly decreased in the plasma and urine of both control and iAs-treated KO male animals as compared to corresponding WT males (Figure

2.3F). Carnosine is a dipeptide of beta-alanine and L-histidine and is found at high concentrations in skeletal muscle and the brain (Boldyrev et al., 2013). Methylation of carnosine by carnosine N-methyltransferase generates anserine, a storage form of carnosine in tissues

(Boldyrev et al., 2013; McManus, 1962; Winnick and Winnick, 1959). A decrease in plasma carnosine in KO males could reflect an increased formation of anserine due to increased SAM availability. The sex differences in carnosine levels in plasma of WT mice found in this study

(Figure 2.3F) are consistent with previous studies showing that males have more carnosine in skeletal muscle than females (Baguet et al., 2012; Penafiel et al., 2004). Sarcosine levels were significantly higher in urine of control KO females as compared to control WT females, and also in urine of control KO females as compared to KO males (Figure 2.3C). Sarcosine is formed by methylation of glycine, catalyzed by glycine N-methyltransferase (GNMT), and is also a byproduct of hydrolysis of dimethylglycine in the betaine homocysteine methyltransferase pathway (Luka et al., 2009). The formation of sarcosine by GNMT is thought to play a role in regulation of cellular methylation capacity (Cook et al., 1989; Horne et al., 1989; Luka et al.,

2009; Mudd et al., 1980). Thus, an increase in sarcosine could indicate an increased availability of SAM in control KO mice that are not able to efficiently methylate iAs in the diet. The sex- dependence and the effects of iAs treatment on these changes, however, require further investigation.

2.1.6 Conclusions This study was the first to examine metabolomic profiles associated with knockout of

As3mt in mice treated with iAs and in control mice. Surprisingly, we found that treatment with 1 ppm As for 4 weeks elicited little or no change in the metabolite profile of both WT and As3mt-

KO mice, males and females. This finding is consistent with results of previous metabolomics studies in rodents exposed to low levels of iAs, suggesting that higher exposure levels may be required to reproduce in mice the metabolomic changes observed in people exposed to iAs in the ppb range. In contrast, knockout of As3mt was associated with significant changes in both

plasma and urine metabolomes and these changes were often sex specific, implying that sex hormones may play an important role in regulation of the As3mt-catalyzed reactions or in systemic responses to the knockout of As3mt. Many of the altered metabolites were associated with lipid (specifically PC) and lipoprotein metabolism, carbohydrate metabolism, and amino acid metabolism. Some of the altered metabolites were substrates or products of SAM-dependent methylation reactions, suggesting that knockout of As3mt altered SAM utilization in these reactions or that As3mt may methylate other, As-unrelated substrates. It is unclear, why WT male mice drank more water than mice in any other group and whether this difference could explain some of the sex- or KO-dependent differences in the urine or plasma metabolomes. However, these data suggest that studies comparing WT and As3mt-KO mice should monitor water consumption and account for possible differences in data evaluation. Future studies using targeted, tissue-specific metabolomics or metabolite/enzyme-specific analyses are needed to explain the KO-associated changes in metabolite profiles observed in this study, as well as the sex-specific differences.

2.2 Hepatic phosphatidylcholines2,3

2.2.1 Introduction Inorganic arsenic (iAs) exposure through drinking water affects millions of people worldwide and is implicated in multiple cancers as well as non-cancer diseases. In humans, iAs is enzymatically methylated to mono- and dimethylated metabolites that are excreted mainly in urine (Thomas et al. 2007). Methylation of iAs is catalyzed by arsenic (+3 oxidation state) methyltransferase (AS3MT). Polymorphisms in AS3MT have been found to be associated with altered iAs metabolism and differing risks of cancer and metabolic disease (Antonelli et al.

2014). The As3mt-knockout mouse is a useful model for assessing the role of iAs metabolism in disease associated with iAs exposure. However, apart from the inability to methylate arsenic, the metabolism of the As3mt-KO mice has been poorly characterized.

Recently, we published a study comparing the urinary and plasma metabolomes of

C57BL/6 mice (WT) and As3mt-knockout (KO) mice (Huang et al. 2016), with and without exposure to iAs. While we found few metabolites to be changed due to iAs treatment in either mouse strain, we found more significantly changed metabolites in comparisons of WT and KO animals, particularly among phosphatidylcholines (PCs) and other metabolites related to lipid metabolism.

PCs are primary components of cellular membranes, are precursors to signaling molecules, and make up 60-80% of the phospholipid component of plasma lipoproteins (Cole et al. 2012). All nucleated cells synthesize PCs, but the liver is a major site for PC synthesis and

2 Madelyn C. Huang, Christelle Douillet, Miroslav Styblo. (2016) Archives of Toxicology. 90:3125-3128. Reproduced with permission.

3 Lipid analyses were conducted at the University of Colorado Lipidomics Core, by Dr. Robert Murphy and Karin Zemski Berry. 71

plasma lipoprotein assembly (Cole et al. 2012). Notably, methylation of iAs occurs primarily in the liver (Watanabe and Hirano 2013), making it a target organ for iAs toxicity. The liver of

As3mt-KO mice is particularly susceptible because it accumulates iAs (Currier et al. 2016;

Hughes et al. 2010). To further probe the effects of As3mt knockout on PC metabolism, we measured concentrations of molecular PC species in livers from mice in our recent metabolomics study and compared hepatic PCs profiles with those previously found in plasma.

2.2.2 Materials and Methods Animals and treatment. In the metabolomic study, adult male and female C57BL/6J WT and

As3mt-KO mice were exposed to 0 and 1 ppm As as arsenite in drinking water for 4 weeks

(N=15-21/group) (Huang et al. 2016). Right medial lobes of the livers were collected at sacrifice, snap-frozen in liquid nitrogen, and stored at -80oC until analyzed.

PC analysis. Liver tissue homogenate (150 µg protein) was extracted as previously described

(Bligh and Dyer 1959) except substituting dichloromethane for chloroform. An internal standard

1,2-dimyristoyl-sn-glycero-3-phosphocholine (1.0 µg, Avanti Polar Lipids, Alabaster, AL) was added to the organic layer and an aliquot (10%) was injected into a tandem quadrupole mass spectrometer for continuous electrospray ionization (positive ions). Precursor ions of m/z 184 were measured for analysis of PC molecular species (Han et al. 2012) and quantitated using

LipidView (Simons et al. 2012). Signals for molecular species of PC were ratioed to the signal from the internal standard.

Data Analysis. The relative concentrations of PC species were analyzed as described in our metabolomic study, using Partek for hierarchical clustering analysis and for ANCOVA analysis with sex, genotype, and iAs treatment as covariates (Huang et al. 2016). A p-value cutoff at p<0.05 and a false-discovery rate q-value cutoff of q<0.1 were used to determine PC species significantly changed due to genotype, sex or iAs treatment.

2.2.3 Results and Discussion Twenty nine PC species were identified in livers from all treatment groups. These species clustered primarily due to sex, followed by genotype, and not due to iAs treatment (Figure A.5).

There were only 1-3 significantly changed hepatic PCs in comparisons of 0 ppm (untreated) and

1 ppm groups (Table A.5). These results are consistent with the major effect of sex and only minor effects of iAs exposure on plasma and urine metabolomes found in our metabolomics study (Huang et al. 2016).

As3mt KO changed greater numbers of hepatic PCs than did iAs exposure, but the extent of these changes differed between male and female mice. In untreated mice, more hepatic PCs were changed due to KO in male as compared to female mice (12 vs. 4), and this trend was also seen in iAs-treated mice (9 vs. 8) (Figure 2.6A). Conversely, in the plasma, more PCs were changed due to As3mt KO in female as compared to male mice in both unexposed (48 vs. 14) and exposed groups (16 vs. 12) (Figure 2.6B). In addition, As3mt KO affected different PCs in males and females; few PCs were found changed in both sexes. Only 1 hepatic PC, PC (38:3), was increased due to KO in both male and female untreated mice; in iAs-treated mice, two PCs were changed—PC (38:3) increased and PC (32:0) decreased—in both males and females

(Figure 2.6A, Figure 2.7). In plasma, only 8 PCs were changed in both untreated males and females due to KO and only 5 PCs in iAs-treated mice (Figure 2.6B). The fold change of PC

concentrations in KO mice did not exceed 1.6; however, great numbers of PCs in both liver and plasma were affected (Figure 2.6; Table A.6). Taken together, these results suggest that As3mt

KO has a major effect on PC metabolism and that this effect is sex-dependent.

Figure 2.6. Number of hepatic (A) and plasma (B) PC species significantly changed (P<0.05) in comparisons of As3mt-knockout (KO) and wild-type (WT) control and iAs-treated mice. Bolded numbers indicate total number of PCs changed in each comparison, for the compartment; arrows indicate increase or decrease of PCs in KO mice.

The KO- or sex-associated changes in hepatic and plasma PC profiles may reflect alterations in hepatic PC synthesis (Vance 2008), export (Robins et al. 1991), or in PC remodeling (Hermansson et al. 2011), as well as in use of PCs as precursors to signaling

molecules (Cole et al. 2012). Any combination of these processes could underlie the observed changes in PC profiles. Notably, in all of the KO vs WT comparisons, some PC species were significantly changed in both plasma and liver (Table 2.3). In particular, PC (38:3) was found changed (increased) due to KO in both plasma and liver of both untreated and iAs-treated male and female mice (Figure 2.7). Future studies should examine the fatty acid tail composition and metabolism of PC (38:3) in iAs-exposed mice. With further characterization, this PC species may serve as a biomarker of iAs exposure or of iAs methylation capacity.

Table 2.3. Phosphatidylcholine (PC) species significantly changed in comparisons of As3mt- knockout (KO) and wild-type (WT) mice in both plasma and liver.

Comparison PC speciesa Fold changeb in Fold change in liver plasma Untreated females PC (C38:3) 1.34 1.23 KO vs WT PC (C38:4) 1.27 1.12 PC (C40:4) 1.26 1.50

Untreated males PC (C32:1) 1.47 1.21 KO vs WT PC (C38:3) 1.33 1.22 PC (O-C40:6) -1.25 -1.30 iAs-treated females PC (C38:3) 1.29 1.16 KO vs WT PC (C36:2) 1.17 1.21

iAs-treated males PC (C32:1) 1.51 1.35 KO vs WT PC (C36:5) 1.16 1.19 PC (C38:3) 1.29 1.42 PC (C40:4) 1.23 1.26 PC (O-C36:2) -1.18 -1.36 PC (O-C40:6) -1.49 -1.36 aPhosphatidylcholine notation: PC(X:Y) indicates a phosphatidylcholine X total carbons and Y double bonds. PC(O-X:Y) indicates a phosphatidylcholine with one acyl and one ester linkage to the fatty acid tails with X total carbons and Y double bonds. bFold change indicates an increase or decrease in KO animals

2.2.4 Conclusion In conclusion, we found differences in hepatic PCs of WT and As3mt-KO mice that paralleled our previous findings in urinary and plasma metabolomes. While only minor differences in hepatic PCs were linked with iAs-treatment, major changes PCs were associated with As3mt KO, in a sex-specific manner. Notably, some As3mt-KO-associated PCs were found in both plasma and liver, suggesting that the effects of As3mt KO on PCs are systemic. These results are a further step in understanding the role iAs metabolism plays in adverse effects of iAs exposure.

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3. CHAPTER 3- Prenatal arsenic exposure and dietary folate and methylcobalamin

supplementation alter the metabolic phenotype of C57BL6 mice in a sex-specific

manner4

3.1 Overview

Inorganic arsenic (iAs) is an established environmental diabetogen. The link between iAs exposure and diabetes is supported by evidence from adult human cohorts and adult laboratory animals. The contribution of prenatal iAs exposure to the development of diabetes and underlying mechanisms are understudied. The role of factors that modulate iAs metabolism and toxicity in adults and their potential to influence diabetogenic effects of prenatal iAs exposure are also unclear. The goal of this study was to determine if prenatal exposure to iAs impairs glucose metabolism in mice and if maternal supplementation with folate and methylcobalamin

(B12) can modify this outcome. C57BL/6J dams were exposed to iAs in drinking water (0, 100, and 1000 µg As/L) and fed a folate/B12 adequate or supplemented diet from before mating to birth of offspring. After birth, dams and offspring drank deionized water and were fed the folate/B12 adequate diet. The metabolic phenotype of offspring was assessed over the course of

14 weeks. Male offspring from iAs-exposed dams fed the folate/B12-adequate diet developed fasting hyperglycemia and insulin resistance. Maternal folate/B12 supplementation rescued this phenotype but had only marginal effects on iAs metabolism in dams. The diabetogenic effects of

4 The authors would like to thank Dr. Zuzana Drobna (North Carolina State University, Raleigh, NC) for her advice regarding prenatal folate and B12 supplementation. 84

prenatal iAs exposure in male offspring were not associated with changes in global DNA methylation in the liver. Only minimal effects of prenatal iAs exposure or maternal supplementation were observed in female offspring. These results suggest that prenatal iAs exposure impairs glucose metabolism in a sex-specific manner and that maternal folate/B12 supplementation may improve the metabolic phenotype in offspring. Further studies are needed to identify the mechanisms underlying these effects.

3.2 Introduction

Globally, 9% of the human population has diabetes mellitus, a condition of chronic hyperglycemia (WHO 2016), which is associated with high morbidity and for which there is no cure. Type 2 diabetes mellitus (T2D) represents 90-95% of diabetes cases and is characterized by insulin resistance followed by beta cell dysfunction and impaired insulin secretion. The prevalence of T2D has almost doubled since 1980 (WHO 2016) and is expected to continue increasing (Rowley et al. 2017). While the increase in obesity is the main driver for the rise of

T2D, particularly in children (Mayer-Davis et al. 2017), obesity alone does not explain the trends in T2D seen around the world. Exposures to environmental chemicals such as inorganic arsenic

(iAs), bisphenol A, or various persistent organic pollutants may contribute (Thayer et al. 2011;

Maull et al. 2012). Furthermore, a growing amount of data on environmentally-induced T2D supports the developmental origins of health and disease paradigm, in which adverse health and disease outcomes later in life are results of prenatal exposures to environmental chemicals

(Heindel et al. 2015). This paradigm is also applicable to T2D associated with chronic exposure to iAs.

iAs is a common drinking water and food contaminant. The World Health Organization and U.S. EPA limit for iAs concentration in drinking water is 10 parts per billion (ppb), or 10 µg

As/L (WHO 2017; US EPA 2001). In many areas around the world, iAs levels in drinking water exceed this limit. Chronic exposure to iAs has been shown to be associated with increased incidence or prevalence of T2D in many geographical regions (Maull et al. 2012; Sung et al.

2015). In some of these regions, residents have been exposed to iAs only recently. For example, in Bangladesh, chronic exposure to iAs in drinking water began with the introduction of new wells drilled in the 1970s (Smith et al. 2000). However, in other regions, water contaminated with iAs has been used for drinking and cooking for generations (e.g., in Taiwan or Mexico); many residents in these regions have been exposed to iAs from preconception to adulthood.

Thus, it is unclear how iAs exposure during different developmental windows (e.g., prenatal vs postnatal) contributes to the prevalence or incidence of T2D in these regions.

Data on the effects of prenatal iAs exposure on T2D development later in life is limited.

In humans, cord blood As levels, a marker of in utero exposure, have been associated with changes in DNA methylation of genes that are implicated in T2D and with altered expression of microRNAs that are involved in T2D-related dysfunctions (Martin et al. 2017a). In laboratory studies, rats and mice exposed to iAs prenatally or during both pre- and postnatal windows developed diabetic phenotypes (Dávila-Esqueda et al. 2011; Bonaventura et al. 2017; Ditzel et al.

2016; Sanchez-Soria et al. 2014; Rodriguez et al. 2016). However, different phenotypes were associated with different levels of iAs exposure and with different exposure windows. Taken together, human and animal data indicate that prenatal exposure alone, or combined with postnatal exposure, can result in T2D development later in life.

Mechanisms underlying the diabetogenic effects of iAs during the prenatal exposure window have never been systematically studied. The human data suggest that iAs exposure could disrupt DNA methylation during fetal development in utero, and that a competition between iAs and DNA methylation pathways for a methyl group donor could be an underlying mechanism

(Martin et al. 2017a). In humans and many animal species, iAs is metabolized in a series of methylation steps to form monomethylarsenic (MAs), dimethylarsenic (DMAs), and in some species also trimethylarsenic. Metabolism of iAs facilitates its excretion from the body. The methylation of iAs is catalyzed by arsenic (+3 oxidation state) methyltransferase which uses S- adenosylmethionine (SAM) as a methyl group donor (Thomas et al. 2007). SAM is also the methyl donor for DNA methylation by DNA methyltransferases (Anderson et al. 2012). It has been hypothesized that an increased demand for SAM during iAs exposures could disrupt DNA methylation of genes that regulate insulin production and/or glucose metabolism, thus, affecting the susceptibility of adult offspring to T2D.

Several micronutrients, including the B-vitamins folate (B9) and methylcobalamin (B12), are required for the synthesis of SAM (Selhub 1999). In adult cohort studies, high folate intake has been associated with greater efficiency of iAs metabolism, typically understood as a lower proportion of urinary iAs and MAs and higher proportion of urinary DMAs, and with lower risk of iAs-associated urothelial cancers and skin lesions (Argos et al. 2010; Gamble et al. 2005;

Gamble et al. 2006; Hall et al. 2007; Hall et al. 2012; Huang et al. 2008). Similar effects have been reported for B12, but data are limited and inconsistent (Hall et al. 2012; Heck et al. 2007).

Animal studies generally support the positive association between folate intake and efficiency of iAs metabolism (Wlodarczyk et al. 2012; Spiegelstein et al. 2003; Tsang et al. 2012; Spiegelstein et al. 2005), but few studies have investigated the role of B12. Combined folate and B12

supplementation has been found to alleviate iAs-induced apoptosis, oxidative stress, and hepatotoxicity (Acharyya et al. 2015; Mukherjee et al. 2006; Chattopadhyay et al. 2012), suggesting possible protective effects of these vitamins in iAs-associated diseases.

Increasing efficiency of iAs methylation, thus increasing clearance of iAs from the body, by increasing folate and/or B12 intake may also improve health outcomes associated with in utero iAs exposures, although few studies have addressed this hypothesis. In a Bangladesh cohort, higher maternal folate and B12 levels were associated with lower concentrations of cord blood As (Hall et al. 2007). On the other hand, the increased efficiency of iAs metabolism throughout pregnancy was not associated with folate or B12 status in a different cohort of

Bangladeshi women (Gardner et al. 2011; Li et al. 2008). Mice with impaired folate transport had higher rates of exencephaly than wild-type mice when exposed in utero to iAs. However, the deficiency in folate transport in these mice was not associated with altered iAs methylation efficiency (Wlodarczyk et al. 2001). In our studies, high dietary folate intake decreased iAs levels and increased ratios of DMAs/MAs in livers of pregnant CD-1 mice exposed to iAs

(Tsang et al. 2012). Exposure to 85 ppm iAs combined with folate supplementation had more profound effects on global DNA methylation in fetal livers than exposure to iAs or folate supplementation alone (Tsang et al. 2012), suggesting that epigenetic effects of prenatal iAs exposure in fetus can be modulated by maternal folate intake.

No studies to date have investigated the role of maternal dietary folate or B12 supplementation in modifying the risk of T2D in offspring exposed prenatally to iAs. The goal of the present study was to determine (i) if prenatal exposure to environmentally relevant levels of iAs results in an impaired metabolic phenotype (e.g. hyperglycemia, insulin resistance, glucose intolerance) in male and female offspring, and (ii) if dietary supplementation with folate and B12

affects the metabolism of iAs in dams and the metabolic phenotype of the offspring. Our results suggest that prenatal iAs exposure impairs glucose homeostasis in offspring in a sex-dependent manner. Notably, maternal folate and B12 supplementation had a minimal effect on iAs metabolism in dams but appeared to alleviate some adverse metabolic phenotypes in the offspring.

3.3 Methods

Mice and Diets. All procedures involving mice were approved by the University of North

Carolina Institutional Animal Care and Use Committee. Eight-week old male and female

C57BL/6J mice were obtained from Jackson Laboratories (Bar Harbor, ME, USA). Two types of purified AIN-93G diet (Dyets, Bethlehem, PA) were used: (1) diet that contained 2 mg/kg folate, the level needed to support pregnancy and fetal health (Reeves et al. 1993), and 10 µg/kg B12 the level that represents adequate intake for adult mice (American Institute of Nutrition 1977), and (2) diet containing 6 mg/kg folate and 50 µg/kg B12 (Dyets, Bethlehem, PA). These diets are hereafter referred to as folate/B12 adequate and folate/B12 supplemented, respectively. A large amount of each diet from the same lot was purchased for this study to avoid biases associated with lot-to-lot differences in diet composition. After delivery to UNC, both male and female mice were fed the folate/B12 adequate diet and drank deionized water (DIW) for one week prior to exposure. Mice were housed under controlled conditions with 12-h light/dark cycle at 22 ±

1°C and 50 ± 10% relative humidity. Food and water consumption was measured weekly.

Exposures and mating. After one week on the folate/B12 adequate diet and DIW, dams and sires were randomly divided into six treatment groups with different levels of iAs in drinking water

and different levels of folate and B12 in the diet: (1) 0 ppb As and folate/B12 adequate diet, (2)

100 ppb As and folate/B12 adequate diet, (3) 1000 ppb As and folate/B12 adequate diet, (4) 0 ppb As, and folate/B12 supplemented diet, (5) 100 ppb As and folate/B12 supplemented diet, and (6) 1000 ppb As and folate/B12 supplemented diet. There were 6 dams in each treatment group except for the 0 ppb/folate/B12 supplemented group which had 9 dams. Drinking water

(DIW) with iAs (sodium arsenite, ≥99% pure; Sigma-Aldrich, St. Louis, MO, USA) was prepared weekly to minimize oxidation of iAsIII to iAsV. After one week of treatment with iAs and feeding with either folate/B12 adequate or supplemented diet, dams and sires from the same treatment group were allowed to mate for one week (3 dams:1 sire). During mating, one dam in the 100 ppb/folate/B12 adequate group had to be euthanized because of significant loss of weight, leaving only 5 dams in this group. After mating, dams were housed 3 per cage and remained on the exposures and diets throughout gestation until parturition. After giving birth, the dams in all treatment groups were placed on the folate/B12 adequate diet and drank only pure

DIW. All offspring were weaned at three weeks of age and maintained on the folate/B12 adequate diet and DIW for the remainder of the study. Experimental design is summarized in

Figure 1 in Appendix B (Figure B.1).

Analysis of iAs and its metabolites in mouse diet. Arsenic species were measured in the adequate and folate/B12 supplemented diets using hydride-generation atomic absorption spectrometry coupled with a cryotrap (HG-CT-AAS) as previously described (Hernández-Zavala et al. 2008;

Currier et al. 2011). The diets were pulverized and microwave digested in ultra-pure phosphoric acid (J. T. Baker, Phillipsburg, NJ, USA) prior to analysis (Currier et al. 2016).

Analyses carried out in dams:

Analysis of iAs and its metabolites in urine. HG-CT-AAS was used to determine the concentrations of arsenic (As) species in spot urine samples collected from dams after the first week of exposure, just prior to mating. The HG-CT-AAS analysis determined concentrations of total iAs (iAsIII + iAsV), total MAs (MAsIII + MAsV) and total DMAs (DMAsIII + DMAsV); no

TMAs was detected. In this study, total As in urine is defined as the sum of all As species detected.

Plasma folate analysis. Plasma was isolated from blood collected from dams via tail bleed after one week of iAs exposure. Samples were diluted in Dulbecco’s phosphate-buffered saline

(Mediatech, Manassas, VA, USA) and analyzed using ELISA kits following manufacturer’s protocol (Monobind Inc, Lake Forest, CA).

Procedures involving offspring:

Phenotyping. Body weight of weaned offspring was monitored weekly. Body composition (lean and fat mass) was determined at 14 weeks of age by magnetic resonance imaging using

EchoMRI Three-in-One Composition Analyzer and Labmaster version 3.2.2 software (Echo

Medical Systems, Houston, TX, USA). Blood for measurement of fasting blood glucose (FBG) and fasting plasma insulin (FPI) was obtained via tail bleeds after a 6-hour fast. FBG was measured by a OneTouch Ultra 2 glucometer (LifeScan, Milpitas, CA, USA) at 7, 8, 13, 14, and

18 weeks of age. FPI was measured at week 8 and 14. Plasma insulin levels were determined by

ELISA (Millipore, Billerica, MA) following the manufacturer’s protocol. FBG (mg/dL) and FPI

(µU/mL) were used to calculate the homeostasis model assessment-insulin resistance (HOMA-

IR) value, an estimate of insulin sensitivity typically used to assess insulin resistance in humans:

퐹푃퐺 ∗ 퐹푃퐼 퐻푂푀퐴 − 퐼푅 = 405

Glucose tolerance tests were administered at 7 and 13 weeks of age. Fasted mice were injected intraperitoneally (i.p.) with 2 g/kg body weight of D-glucose (Sigma-Aldrich) dissolved in

Dulbecco’s phosphate-buffered saline (Mediatech, Manassas, VA, USA). Blood glucose levels were measured at 15, 30, 60 and 120 min post-injection. To quantify glucose tolerance, the area under the curve (AUC) was calculated for a plot of blood glucose (mg/dL) versus time (minutes) using Prism 5 (GraphPad, La Jolla, CA, USA).

Global DNA methylation in liver of offspring. Livers collected during sacrifice were flash-frozen in liquid nitrogen, and stored at -80C. DNA was extracted using Allprep DNA/RNA extraction kit (Qiagen, Venlo, Nederlands). Global DNA methylation was assessed using the MethylFlash

Methylated DNA Quantification kit (Epigentek, Farmingdale, NY). Percent 5-mC was calculated using the equation provided by the manufacturer:

(푆푎푚푝푙푒 퐴푏푠 − 푁퐶 퐴푏푠) ÷ 푛푔 푠푎푚푝푙푒 퐷푁퐴 %5 − 푚퐶 = × 100 (푃퐶 퐴푏푠 − 푁퐶 퐴푏푠) × 2 ÷ 푛푔 푃퐶 퐷푁퐴 where Abs= absorbance at 450 nm, NC= negative control (0% 5-mC), and PC= positive control

(50% 5-mC).

Statistical Analysis. Differences due to iAs exposure were assessed using ANOVA and Tukey’s post-hoc tests. Differences due to sex or diet were assessed using two-tailed Student’s T-tests between groups of interest (e.g., to determine significant effect of sex: male offspring from dams fed the folate/B12 adequate diet and exposed to 0 ppb iAs were compared to female offspring from dams fed the folate/B12 adequate diet and exposed to 0 ppb iAs animals). Three-way factorial ANOVAs were used to test for a significant effect of individual variables (sex, dam

diet, dam iAs exposure) in most metabolic measures. This analysis also identified significant two-way and three-way interactions between the three variables. All statistical analyses were conducted using JMP 12 (SAS Institute, Cary, NC).

3.4 Results

Maternal measures Dams were exposed to iAs in drinking water and fed the specified diets 1 week before and 1 week during the mating. This time period is sufficient to establish equilibrium of As

(Hughes et al, 2003) and folate (Schmitz et al. 1994) in blood and tissues of mice. With limited data on B12, we assumed that this time was also sufficient to reach B12 equilibrium. There were no differences in body weight at the start of exposure or at the beginning of mating (Figure B.2).

Folate levels in plasma of the folate/B12 supplemented dams were significantly higher than in dams fed the folate/B12 adequate diet (Figure 3.1). Notably, exposure to 100 ppb iAs, but not

1000 ppb iAs, significantly increased plasma folate in dams in both adequate and supplemented groups. Plasma B12 concentration could not be measured due to insufficient volume of blood collected from tail bleeds.

Figure 3.1. Plasma folate concentrations of dams fed folate/B12 adequate and supplemented diets and exposed to iAs. Plasma was isolated from blood collected via tail bleed from dams after exposure to iAs for one week, shortly before mating. Mean +SEM for N=3 are shown. * p<0.05 significantly different from controls (0 ppb iAs) within the same diet group; # p<0.05 significantly different from dams fed the adequate diet.

To determine effects of folate/B12 intake on iAs metabolism, we measured concentrations of iAs and its metabolites in spot urine samples collected from dams (Figure 3.2).

Total As concentrations were proportionally higher in urine of dams exposed to 100 ppb and

1000 ppb iAs as compared to untreated controls. DMAs was the major urinary metabolite representing 85-94% and 79-93% of total As in urine of iAs-treated dams fed adequate diet and supplemented diet, respectively. There were no significant differences in %DMAs, %MAs, or

%iAs in urine due to the exposure or supplementation (Figure B.3). Folate/B12 supplementation increased total As levels (i.e., sum of As species) in urine in both 100 and 1000 ppb exposure groups, but these effects were not statistically significant (Figure 3.2D). Marginally significant effects of folate/B12 supplementation were found in the urine of dams exposed to 100 ppb iAs

(Figure 3.2B-C); here, MAs and DMAs concentrations were higher in urine of the supplemented

dams (p=0.11 and 0.079, respectively). The content of As in the folate/B12 adequate and supplemented diets ranged from 40 to 43 ppb; only iAs was detected.

Figure 3.2. Arsenic metabolites in urine of dams fed a folate/B12-adequate or supplemented diet and exposed to 0, 100, or 1000 ppb iAs. (A) iAs, (B) MAs, (C) DMAs, and (D) sum of As. Spot urine samples were collected from dams exposed to iAs for 1 week, shortly before mating. Mean + SEM for N=5-9 is shown. *p<0.05 significantly different from respective control (0 ppb) within the same diet group; ǂ p<0.05 significantly different from 100 ppb iAs exposure.

Number of offspring. Since dams were housed 3 per cage, the number of pups per litter could not be determined and assessed statistically. Instead, the total numbers of offspring in each treatment group were recorded (Table B.1).

Phenotypes of the offspring Food and water consumption. There were no significant differences in food or water consumption due to prenatal iAs exposure or maternal supplementation throughout the course of the study (data not shown). Male offspring tended to eat and drink more than female offspring.

Body weight. Only minimal differences were observed in body weight between offspring of the same sex after weaning, regardless of treatment (Figure B.4). Males were heavier than females throughout the entire study. Few statistically significant differences were found between treatment groups and only at few time points. There were no significant differences in body weight at the end of the study, except that female offspring from supplemented dams exposed to

100 ppb iAs were heavier than controls at sacrifice.

Body composition. Body composition was assessed at 14 weeks of age. At this time point, male offspring had higher body weight and higher lean mass than female offspring, but had similar

%fat (Figure 3.3). Prenatal exposure to iAs had little or no effect on body composition of the offspring from either the folate/B12 adequate or supplemented dams. However, male offspring from dams fed the adequate diet and prenatally exposed to 1000 ppb iAs had higher %fat than the corresponding controls (p=0.09) and males exposed prenatally to 100 ppb iAs (p=0.018)

(Figure 3.3B). Notably, maternal supplementation significantly lowered %fat in male offspring exposed to 1000 ppb iAs to levels even lower than those found in controls (Figure 3.3B).

Maternal supplementation also increased body weight and lean mass in female controls, but had no statistically significant effect on %fat (Figure 3.3).

Fasting blood glucose (FBG). Blood glucose after a 6-hour fast was measured at week 7, 8, 13,

14, and 18 of age (Figure 3.4). In general, male offspring had higher FBG than female offspring, regardless of supplementation or exposure. Prenatal exposure to iAs had significant effects on

FBG, but almost exclusively in male offspring from dams fed the folate/B12 adequate diet.

Figure 3.3. Body weight and composition of male and female offspring prenatally exposed to inorganic arsenic: (A) Body weight, (B) fat mass (expressed as % of body weight), and (C) lean mass were determined in 14-week old offspring from dams exposed to 0, 100, or 1000 ppb iAs in drinking water and fed either a folate/B12-adequate. Mean + SEM for N=7-16 is shown. ǂ p<0.05 significantly different from 100 ppb iAs exposure; # p<0.05 significantly different from offspring from dams fed the adequate-diet.

Figure 3.4. Fasting blood glucose of offspring prenatally exposed to iAs. Fasting blood glucose was measured in (A) male and (B) female offspring from dams exposed to 0, 100, or 1000 ppb iAs in drinking water and fed either a folate/B12-adequate (closed circles) or supplemented (open squares) diet. Blood was collected from offspring at age 7, 8, 13, 14 and 18 weeks. Mean + SEM of N=5-16 is shown. * p<0.05 significantly different from controls (0 ppb iAs) within the same diet goup; ǂ p<0.05 significantly different from 100 ppb iAs exposure; # p<0.05 significantly different from offspring from dams fed the adequate-diet.

Specifically, males exposed prenatally to iAs had higher FBG than control males at multiple time points (Figure 3.4A). The trajectory of FBG levels during the 18 weeks of the study differed between males in different exposure groups. In males exposed prenatally to 100 ppb, FBG peaked between weeks 13 and 14 and then decreased to the control level at week 18; in males exposed to 1000 ppb iAs, FBG increased at week 14 and remained significantly higher than the control levels until the end of the study. In comparison, iAs exposure did not increase FBG in male offspring from folate/B12 supplemented dams, or the effects were not statistically

significant. In contrast, prenatal iAs exposure or folate/B12 supplementation had little or no effects on FBG of female offspring (Figure 3.4B).

FBG was higher in 7-week old females from adequate-diet dams exposed to 100 ppb iAs compared to respective controls, but no effects of iAs exposure were observed in other groups or time points. Maternal supplementation significantly decreased FBG in 7-week old females prenatally exposed to 100 ppb iAs, but no statistically significant effects of the supplementation were found in this or the 1000 ppb iAs exposure group at other time points. Additionally, control females from supplemented dams had higher FBG than control females from adequate diet dams, but only at week 7; the effects of maternal supplementation at later time points were not statistically significant.

Glucose tolerance. Intraperitoneal glucose tolerance tests were conducted at week 7 and 13

(Figure 3.5). Female offspring were generally more glucose tolerant than males, regardless of

Figure 3.5. Glucose tolerance of offspring prenatally exposed to iAs. Intraperitoneal glucose tolerance tests were administered to offspring of dams exposed to 0, 100, or 1000 ppb iAs in drinking water and fed either a folate/B12-adequate or supplemented diet. Area under the curve for (A) 7-week old and (B) 13-week old offspring is shown. Mean + SEM of N=7-16 is shown. *p<0.05 significantly different from controls (0 ppb iAs) within the same diet group; # p<0.05 significantly different from offspring from dams fed the adequate-diet.

prenatal iAs exposure or maternal supplementation. Surprisingly, 7-week old female offspring from supplemented dams in the control group were more glucose intolerant that females from control adequate dams (Figure 3.5A). At this time point, glucose tolerance was significantly lower in female offspring from adequate-diet dams exposed to 100 ppb iAs as compared to the respective controls (Figure 3.5A). There were no significant effects of prenatal iAs on glucose tolerance in 7-week old male offspring. At week 13, there were no differences in glucose tolerance in females or males due to prenatal iAs exposure or maternal supplementation (Figure

3.5B). In male offspring, supplementation improved glucose tolerance in animals prenatally exposed to 100 ppb iAs.

Fasting plasma insulin (FPI) and HOMA-IR. FBG and FPI values were used to calculate

HOMA-IR, a measure of insulin resistance, at weeks 8 and 14. FPI and HOMA-IR in all offspring were higher at week 14 compared to week 8 (Figure 3.6). At week 8, there were no significant differences in FPI and HOMA-IR due to prenatal iAs exposure in either female or male offspring (Figure 3.6A-B). Maternal supplementation lowered HOMA-IR in control male offspring and in female offspring exposed prenatally to 1000 ppb iAs, but these effects were only marginally significant. At week 14, there were no significant differences in FPI and HOMA-IR in female offspring related to iAs exposure or maternal diet. In contrast, in males from dams fed the adequate diet, prenatal iAs exposure increased HOMA-IR compared to the respective controls (Figure 3.6D). This effect was statistically significant for 100 ppb exposure but not for

1000 ppb exposure (p=0.21). However, no effects of iAs exposure on FPI were found. Notably, maternal supplementation decreased HOMA-IR in both 100 and 1000 ppb groups and decreased

FPI with marginal significance.

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Figure 3.6. Fasting plasma insulin and HOMA-IR measures of offspring prenatally exposed to iAs: Fasting plasma insulin (A, C) and HOMA-IR (B,D) of 8-week old offspring (A,B) (N=7-16) and 14-week old offspring (C, D) (N=5-15) from dams exposed to 0, 100, or 1000 ppb iAs in drinking water and fed either a folate/B12-adequate or supplemented diet. Mean + SEM is shown. *p<0.05 significantly different from controls (0 ppb iAs) within the same diet group; # p<0.05 significantly different from offspring from dams fed the adequate-diet.

Significant effects and interactions. Three-way factorial ANOVAs were conducted to assess overall significance of sex, maternal supplementation (dam diet), and prenatal iAs exposure as variables affecting the measurements of metabolic phenotype (e.g., to assess if sex is a significant variable in FBG measures, after averaging over all other variables). Sex of the offspring was a significant variable in most metabolic measures (Table 3.1). Dam diet and iAs exposure were significant variables only in some outcomes. The ANOVA analysis also identified significant interactive effects of these variables. The interaction of sex of the offspring with dam diet or iAs exposure were significant in some outcomes, indicating that the effect of dam diet or iAs exposure may be different in male compared to female offspring (Table 3.1).

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Effect

Outcomes Sex Dam Diet Prenatal iAs Sex*Diet Sex*iAs Diet*iAs Sex*iAs*Diet Week 7 Measures AUC <0.01 0.04 0.60 0.05 0.50 0.02 0.14 FPI 0.02 0.35 0.98 0.24 0.89 0.09 0.16 HOMA-IR <0.01 0.33 0.81 0.14 0.92 0.14 0.10 Week 14 measures Body Weight <.0001 0.29 0.72 0.43 0.03 0.46 0.48 Lean mass <.0001 0.39 0.11 0.32 0.02 1.00 0.88 %Fat 0.86 0.85 0.01 0.60 0.85 0.12 <0.01 AUC <.0001 0.78 0.47 0.81 0.46 0.08 0.66 FPI 0.01 0.42 0.83 0.41 0.96 0.19 0.12 HOMA-IR 0.01 0.15 0.69 0.33 0.69 0.01 0.03 Fasting Blood Glucose Week 7 <.0001 0.31 0.17 0.01 0.13 0.75 0.06 Week 8 <.0001 0.33 0.12 0.01 0.30 0.89 0.05 Week 13 0.01 0.13 0.05 0.35 0.03 0.04 0.09 Week 14 0.08 0.02 0.24 0.52 <0.01 <0.01 0.04 Week 18 0.20 0.90 0.16 0.05 0.34 0.10 0.09

Global DNA methylation. DNA methylation was assessed only in livers of male offspring from folate/B12 adequate and supplemented dams exposed to 0 or 1000 ppb iAs, i.e., the offspring with different phenotypes at the time of sacrifice. Specifically, the male offspring from the folate/B12 adequate dams exposed to 1000 ppb iAs had significantly higher FBG levels and

HOMA-IR than male offspring from the adequate control dams (Figures 3.4 and 3.6).

Supplementation of dams with folate/B12 prevented this increase, suggesting that epigenetic mechanisms may underlie the effects of iAs exposure and/or folate/B12 supplementation. We found that the fraction of methylated DNA (%5-mC) was significantly higher in livers of

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offspring from both control and iAs-exposed dams fed the folate/B12 supplemented diet as compared to offspring from dams fed the adequate diet (Figure 3.7). However, no significant effects of iAs exposure on DNA methylation were found.

Figure 3.7. Percent global DNA methylation in livers of male offspring exposed prenatally to iAs. DNA methylation was assessed in livers of male offspring of dams exposed to 0 and 1000 ppb iAs in drinking water and fed a folate/B12-adequate or supplemented diet. Mean + SEM for N=3-7 is shown. # p<0.05 significantly different from offspring of adequate-diet dams.

3.5 Discussion

Effect of prenatal iAs exposure Numerous studies in adult human cohorts and in adult laboratory animals support the association between iAs exposure and T2D (Maull et al. 2012; Kuo et al. 2017). Less is known about the role of prenatal iAs exposure in the development of T2D, or metabolic disease in general, later in life. Previously published laboratory studies used rats or mice with either prenatal exposure or combined prenatal and postnatal exposures. In rats, the combined pre- and post-natal exposure to iAs (3-50 ppm) produced hyperglycemia, impaired glucose tolerance, and insulin resistance (increased HOMA-IR). Effects on plasma insulin levels were inconsistent

(Bonaventura et al. 2017; Dávila-Esqueda et al. 2011). In a study using Sprague-Dawley rats,

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whole-life exposure to 50 ppm iAs increased HOMA-IR, but only in female offspring

(Bonaventura et al. 2017). In another study, female CD-1 offspring exposed to a very low level of iAs in drinking water (10 ppb) during gestation accumulated more fat than unexposed controls and exhibited impaired glucose tolerance and fasting hyperinsulinemia; male mice were not studied (Rodriguez et al. 2016). In a study by Ditzel et al., Swiss Webster mice were exposed to

100 ppb iAs either prenatally, postnatally, or both pre- and postnatally, and fed a Western-style diet (2016). Here, only male mice with combined prenatal and postnatal exposures developed an adverse metabolic phenotype characterized by insulin resistance and increased plasma triglyceride levels. These males were also heavier than unexposed male controls. Thus, the published studies on prenatal iAs exposures and its effects on glucose metabolism include a variety of exposure periods and iAs doses, and report a variety of metabolic phenotypes, some of which are sex-dependent. Notably, levels of iAs in rodent diets used in these studies are usually not measured or reported. Analyses of several types of rodent chows in our lab and in other laboratories have found total As levels to range from ~10 to over 400 ppb, with iAs being a major As species in some of these diets (Douillet et al. 2017; Murko et al. 2018). Thus, exposure to iAs from chow diet could be a significant confounder in laboratory studies, particularly in studies examining effects of low-level iAs exposure in drinking water.

In this study, we used the C57BL/6J mice, a strain commonly used for laboratory study of obesity-related T2D and metabolic disease. To limit background exposure to iAs from diet, we fed the mice a purified diet that contained only ~40-50 ppb iAs. We exposed these mice to 100 or 1000 ppb As in the form of iAs, levels that are consistent with human environmental exposures. We found that mice exposed to iAs during preconception and gestation periods developed an adverse metabolic phenotype characterized mainly by fasting hyperglycemia,

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insulin resistance, and fat accumulation. These phenotypic characteristics have also been reported in some of the previous prenatal exposure studies (Ditzel et al. 2016; Dávila-Esqueda et al. 2011; Bonaventura et al. 2017; Sanchez-Soria et al. 2012). We did not observe significant differences in glucose tolerance, as had been reported by Rodriguez et al. (2016) and

Bonaventura et al (2017). Impaired glucose tolerance indicates a failure of the pancreas to adequately respond to a high glucose dose, while high FBG usually reflects increased activity of gluconeogenesis or glycogenolysis during fasting (Petersen et al. 2017). The lack of differences in glucose tolerance between exposed and control offspring in this study suggests that the pancreas was able to produce enough insulin to handle the glucose challenge during the glucose tolerance tests. Increased FBG levels, which appear to drive the increase in HOMA-IR, may point to the mechanisms regulating hepatic gluconeogenesis and/or glycogenolysis as a target of iAs exposure. In general, the effects on glucose homeostasis found in our study were not as dramatic or persistent as those found in some of the other studies, in which animals were exposed to iAs both pre- and postnatally, and which used higher iAs doses or did not control for the exposure to iAs from animal diet. It is possible that higher iAs doses and/or continued postnatal iAs exposure, in addition to the prenatal exposure, are necessary to observe more robust or persistent effects on metabolic outcomes.

In the present study, manifestation of the adverse metabolic phenotype depended strongly on sex, an important biological variable that has not been systematically examined in previous studies. We found that the male offspring were more susceptible than the female offspring to the adverse effects of prenatal iAs exposure. These results differ from findings reported in rats after whole-life (pre- and postnatal) exposure to much higher levels of iAs (50 ppm) where females were more susceptible to developing insulin resistance than males (Bonaventura et al. 2017).

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Sex-dependent responses to prenatal iAs exposure, specifically smaller fetal size and altered

DNA methylation patterns in cord blood, have also been reported in human studies (Broberg et al. 2014; Pilsner et al. 2012). It has been suggested that these differences may be related to sexual dimorphisms in epigenetic pattering in the placenta, which could lead to differential transport of toxicants, nutrients, and signaling factors (Martin et al. 2017b). Inclusion of male and female offspring in future prenatal iAs exposure studies in both animals and humans should help characterize the extent and mechanisms of this differential susceptibility.

Effects of maternal folate and B12 supplementation Low folate and/or vitamin B12 intake have been linked to low iAs methylation capacity in human studies (Argos et al. 2010; Gamble et al. 2005; Gamble et al. 2006; Hall et al. 2007). It has been proposed that risk of iAs-associated disease could be lowered by stimulating iAs metabolism via increasing SAM availability through dietary supplementation with these B- vitamins. In the present study, plasma folate levels were higher in the folate/B12-supplemented dams as compared to dams fed the adequate diet. However, no significant stimulation of iAs metabolism was observed. The concentrations of MAs and DMAs were higher in urine of the supplemented dams compared to dams fed the adequate diet, but because of small numbers of mice per treatment group, these differences were only marginally significant. No differences were found in urinary total As levels or in %DMAs, the indicator of iAs methylation efficiency.

In comparison, folate supplementation in an iAs-exposed Bangladeshi population increased

%DMAs in urine from 72% to 79% (Gamble et al. 2006). However, this population was folate- deficient while the mice in our study had adequate folate intake. This may explain why we did not find significant effects on iAs metabolism.

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Notably, we found that exposure to 100 ppb iAs increased plasma folate levels in dams fed either the adequate or supplemented diet (Figure 3.1). This finding has never been reported in human or laboratory studies. Chi and coworkers have recently reported that iAs exposure results in upregulation of microbial genes that regulate synthesis and metabolism of folate in mouse colon (Chi et al. 2017). Thus, the increase in plasma folate in iAs-exposed dams in the present study may be due to stimulation of folate synthesis in gut microbiome.

Despite only small effects on iAs metabolites in urine, maternal folate and B12 supplementation rescued the metabolic defects observed in male offspring exposed prenatally to iAs. These protective effects could be due to changes in the methylation of fetal DNA. Maternal folate and/or B12 supplementation has previously been reported to affect DNA methylation in offspring (Cooney et al., 2002; Waterland et al., 2003; Yadav et al. 2018) and to alter body weight, glucose tolerance, insulin resistance, and expression of genes regulating food intake in rat offspring (Cho et al. 2015). Effects of gestational iAs exposure on DNA methylation and expression of genes in the fetus have also been reported. Specifically, gestational exposure to iAs was associated with altered DNA methylation and expression of genes in cord blood of newborns, including methylation of genes associated with T2D (Rojas et al. 2015). It is possible that maternal folate/B12 may prevent the reprograming of fetal T2D-associated genes by iAs exposure, conferring the protective effects observed in the present study. In this study, we did find that maternal supplementation with folate/B12 increased global DNA methylation in the livers of male offspring; however, we did not assess DNA methylation of specific genes. Our future studies will focus on DNA methylation of T2D-associated genes as a potential mechanism underlying the adverse phenotype in offspring exposed prenatally to iAs and on the role of sex.

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In conclusion, we found modest defects in the metabolic phenotype of C57B/6J mice after prenatal (combined preconception and in utero) exposure to environmentally relevant levels of iAs in drinking water. These defects included hyperglycemia and insulin resistance and were found almost exclusively in male offspring, indicating that sex plays a major role in T2D risk associated with this type of iAs exposure. Maternal folate and B12 supplementation alleviated the adverse effects of prenatal iAs exposure for some metabolic endpoints but did not increase urinary As excretion or significantly change the profiles of As species in urine. Tissue analyses of As metabolites may help to better understand the role these vitamins play in iAs metabolism in mice. Altered DNA methylation remains a potential mechanism of the adverse metabolic effects of prenatal iAs exposure and of the protective effects of maternal folate/B12 supplementation in the offspring. Further investigation into the methylation of specific T2D- related genes will be needed to probe this mechanism.

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4. CHAPTER 4- Metabolic phenotype of folate-deficient and folate-supplemented male

and female mice exposed to inorganic arsenic

4.1 Overview

Chronic exposure to inorganic arsenic (iAs) is associated with type 2 diabetes mellitus.

Increased efficiency of iAs metabolism, catalyzed by arsenic (+3 oxidation state) methyltransferase (AS3MT), is linked to lowered risk of iAs-associated diseases. High folate intake has been associated with increased iAs metabolism. The goal of this study was to assess how modifying iAs metabolism using nutritional intervention alters diabetogenic effects of iAs exposure, with and without high-fat diet (HFD). Male and female C57BL6 and As3mt-knockout mice drank 100 ppb iAs for 37 weeks. Mice ate a purified diet with low (0.2 mg/kg) or high (10 mg/kg) folate for 24 weeks, then HFD with the same folate levels for 13 weeks. Metabolic phenotype, body composition, and iAs metabolism were examined. We found that high folate intake had little effect on iAs metabolism. At 24 weeks, iAs exposure and folate intake had minimal effects on metabolic parameters. As3mt-KO mice gained more fat and had higher fasting plasma insulin than WT. KO males were more insulin resistant than KO females despite similar

%fat mass. HFD increased differences in insulin resistance between WT and KO, and between sexes. Notably, with HFD, iAs-exposed KO and WT males with low folate gained more fat and were more insulin resistant than respective unexposed controls. Our data suggest that folate intake and low-dose iAs exposure have little effect on metabolic parameters. However, iAs exposure and low folate intake may elicit adverse effects when combined with HFD, highlighting

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potential toxicant-nutrient interactions. Male mice, both WT and KO, were more prone to developing insulin resistance.

4.2 Introduction

Chronic exposure to inorganic arsenic (iAs) is a global public health problem. Over 200 million people worldwide are exposed to iAs in drinking water at levels higher than the 10 µg

As/L limit established by the World Health Organization and US Environmental Protection

Agency (Naujokas et al. 2013). Even more people consume foods with potentially unsafe iAs levels (Stanton et al. 2015). Chronic exposure to iAs has been linked to cancer (Abdul et al.

2015) and non-cancer diseases such as type 2 diabetes mellitus (T2D) (Maull et al. 2012, Sung et al. 2015, Wang et al. 2014). T2D is a condition of chronic hyperglycemia and affects 9% of the world population. The prevalence of T2D has increased rapidly over the past decade (CDC 2017,

WHO 2016). Though obesity is a major risk factor for T2D, it does not fully explain the increase in T2D prevalence. Growing evidence suggests that environmental diabetogens like iAs may play a role (Maull et al. 2012).

Efficiency of iAs metabolism has been associated with differing risks of iAs-associated diseases, including T2D (Antonelli et al. 2012). In humans and some animal species, iAs is metabolized in a series of methylation steps catalyzed by arsenic (+3 oxidation state) methyltransferase (AS3MT), forming monomethyl-arsenic (MAs) and dimethyl-arsenic (DMAs), which are excreted primarily in the urine. High proportions of DMA (%DMAs) and low proportions of iAs (%iAs) and MAs (%MAs) in the urine are generally considered indicators of an efficient iAs metabolism. While urinary high %MAs and low %DMAs have been positively associated with risk of cancer, hypertension, and skin lesions (Antonelli et al. 2012, Kuo et al.

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2017), T2D risk has been linked to high urinary %DMAs and/or low %MAs (Kuo 2017 et al.;

Mendez et al. 2016; Grau-Perez et al. 2017), indicating that full methylation of iAs to DMAs, possibly to its toxic trivalent form (DMAsIII) may enhance the diabetogenic effects of iAs exposure (Del Razo et al. 2011).

S-adenosylmethionine (SAM) is the methyl group donor for the AS3MT-catalyzed reactions (Thomas et al. 2007). Synthesis of SAM occurs through the one-carbon metabolism pathway and requires folate, an essential micronutrient and vitamin (Selhub 1999). In human studies carried out in arsenicosis-endemic areas of Bangladesh, high folate status has been associated with higher %DMAs and lower %iAs in urine (Gamble et al. 2005). Folate supplementation increased efficiency of iAs metabolism as manifested by increased %DMAs in urine and blood (Gamble et al. 2006; Peters et al. 2015). High folate intake was also inversely related to precancerous skin lesions in this population (Pilsner 2009). Thus, studies in high iAs exposure areas such as Bangladesh suggest that, by modifying iAs metabolism, folate intake could alter risk of some diseases associated with iAs exposure. Furthermore, a study in Taiwan found that higher plasma folate and urinary %DMAs were associated with decreased risk of urothelial cancers (Huang et al. 2008). Other results from human cohorts in other geographical regions, however, have been inconsistent (Spratlen et al. 2017, Steinmaus et al. 2005, Kurzius-

Spencer et al. 2017), possibly due to differences in iAs exposures (generally lower than in

Bangladesh), ethnicity, or nutritional status of the study populations. Notably, the associations between folate intake, iAs metabolism and iAs-associated T2D have never been examined in human populations. In addition, there have been no experimental studies of the role of dietary folate in iAs-associated T2D, or metabolic disease in general.

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The goal of our present study was to determine if varying folate intake modifies the metabolic phenotype of wild-type (WT) male and female C57BL/6J mice exposed to iAs (100 ppb in drinking water), with and without a high-fat diet as an additional metabolic stressor. To determine if folate affects T2D development by mechanisms independent of iAs methylation, we also used As3mt-knockout (KO) mice that cannot methylate iAs. Our results show that iAs exposure alone had only minimal effects on metabolic phenotypes of WT and KO mice while fed a low-fat diet with low or high folate content. However, iAs exposure increased insulin resistance in both male and female WT and KO mice on the high-fat diet with low folate content; high folate intake may have a protective effect against effects of iAs. We also found that KO mice, particularly males, accumulated more fat than WT mice and developed insulin resistance regardless of iAs exposure or folate intake. Folate-supplemented WT female mice had lower

%iAs in the urine and livers as compared to WT females with low folate intake. However, folate intake had little to no effect on measures of iAs metabolism in WT male or KO male and female mice. Thus, low iAs exposure did not produce a diabetic phenotype in C57BL/6J mice unless combined with high-fat diet and low folate intake. Folate supplementation may protect against the diabetogenic effects of the combined exposure to iAs and high-fat diet, but this effect does not seem to involve a modification of iAs metabolism.

4.3 Methods

Mice. All procedures involving mice were approved by the University of North Carolina

Institutional Animal Care and Use Committee. Male and female C57BL/6J WT mice were obtained from Jackson Laboratories (Bar Harbor, ME, USA) at 3 weeks of age. Male and female

As3mt-KO mice on a C57BL/6 background (Drobná, et al. 2009) were bred at the UNC Animal

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Facilities. Both WT and As3mt-KO mice were housed under controlled conditions with 12-h light/dark cycle at 22 ± 1 °C and 50 ± 10% relative humidity.

Study design and treatment. Three-week old male and female C57BL6 and As3mt-KO mice consumed a purified low-fat AIN-93G diet (LFD) (17% calories from fat, Envigo Teklad,

Madison, WI) with adequate folate content (2 mg/kg) and drank deionized water (DIW) for two weeks prior to the initial metabolic phenotyping. After two weeks on the purified, folate- adequate diet, a baseline intraperitoneal glucose tolerance test (IPGTT) was administered and fasting blood glucose (FBG) levels were measured in all mice. Male and female WT and KO mice were then separated into 4 groups with similar average baseline fasting blood glucose levels

(N=16-20 per group). Groups were randomly assigned to treatments: 1) 0 ppb iAs, low-folate diet, 2) 100 ppb iAs, low-folate diet, 3) 0 ppb iAs, high-folate diet, or 4) 100 ppb iAs, high-folate diet. Drinking water containing iAs (AsNaO2, ≥99% pure; Sigma-Aldrich, St. Louis, MO, USA) was prepared weekly to minimize oxidation of iAsIII to iAsV. Animals were fed the AIN-93G diet with either low- (0.2 mg/kg) or high (10 mg/kg) folate content. After 24 weeks, mice in all treatment groups were switched from LFD to a purified AIN-93G diet with high fat content

(HFD) (45% calories from fat; Envigo Teklad, Madison, WI) with the same low or high folate levels. Mice were maintained on this diet for additional 13 weeks. Water consumption was measured weekly throughout the study. Food consumption and body weight were monitored biweekly.

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Metabolic phenotyping. All mice were phenotyped at week 5 and 24 while on LFD, and after 8 weeks on HFD. Tissues were collected at sacrifice after 37 weeks of iAs exposure (13 weeks on

HFD). The study design is summarized in Figure B.1. The phenotyping included:

(1) Body composition assessment using EchoMRI three-in-one Composition Analyzer and Labmaster version 3.2.2 software (Echo Medical Systems, Houston, TX, USA).

(2) Measurements of fasting blood glucose (FBG) and fasting plasma insulin (FPI) in blood collected via tail cut from mice fasted for 6 hours. FBG levels were measured using the

OneTouch Ultra 2 glucometer (LifeScan, Milpitas, CA, USA) at weeks 5 and 24 on LFD and after 8 weeks on HFD. FPI was measured at week 24 on LFD and after 8 weeks on HFD using

ELISA (Crystal Chem, Elk Grove Village, IL). FBG (mg/dL) and FPI (µU/mL) were used to calculate the homeostasis model assessment-insulin resistance (HOMA-IR) value:

퐹푃퐺 ∗ 퐹푃퐼 퐻푂푀퐴 − 퐼푅 = 405

(3) IPGTT was administered to all mice at week 5 on LFD. Fasted mice were injected intraperitoneally with 2 g/kg b.w. of D-glucose (Sigma-Aldrich) dissolved in Dulbecco’s phosphate-buffered saline (Mediatech, Manassas, VA, USA) and glucose levels were measured in blood collected by tail bleeds 15, 30, 60 and 120 min post-injection. To quantify glucose tolerance, area under the curve (AUC) was determined for a plot of blood glucose (mg/dL) versus time (minutes) using Prism 5 (GraphPad, La Jolla, CA, USA).

Plasma folate analysis. Plasma for folate analysis was isolated from blood collected via tail bleed at week 6 on LFD and at sacrifice (after 13 weeks on HFD). Plasma was diluted in

Dulbecco’s phosphate-buffered saline (Mediatech, Manassas, VA, USA) prior to measurement using commercially available ELISA kits (Monobind Inc, Lake Forest, CA).

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Analysis of arsenic metabolites in urine, liver, and diet. A spot urine sample was collected from all mice after 5 weeks on LFD and at sacrifice. Liver was collected at sacrifice. Arsenic (As) speciation analysis was carried out using hydride-generation atomic absorption spectrometry coupled with a cryotrap (HG-CT-AAS), as previously described (Hernandez-Zavala et al. 2008;

Currier et al. 2011). The HG-CT-AAS analysis determined concentrations of total iAs (iAsIII + iAsV), total MAs (MAsIII + MAsV) and total DMAs (DMAsIII + DMAsV); no trimethylarsenic was detected. Total As content in urine and liver was calculated as the sum of all As species detected. Arsenic species in pulverized diets were measured via HG-CT-AAS after phosphoric acid digestion (Currier et al. 2016).

Statistical analysis. Four-way ANOVAs were done assessing the effect of arsenic, folate, sex, and genotype on all measured endpoints. Post-hoc Student’s T-tests between groups of interest were assessed (e.g. to determine a significant effect of folate: male, 0 ppb iAs, low folate WT mice were compared to male, 0 ppb iAs, high folate WT mice). Analyses were conducted in JMP

(SAS Institute Inc, Cary, NC).

4.4 Results

Food and water consumption. Exposure to iAs and folate intake had little to no effects on food or water consumption throughout the entire study (Figure C.2 and C.3). No significant differences were found in food consumption between WT and KO mice, although KO mice in several LFD and HFD groups drank less water than their WT counterparts. Females tended to consume less food than males regardless of treatment. Food consumption increased among both female and male mice after switching to HFD.

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Arsenic levels in diets. As was measured in each of the diets used in the study. Total As in the diet ranged from 36-49 ppb and consisted primarily of iAs (89-100%).

Plasma folate. As expected, mice fed the high-folate diet had significantly higher plasma folate levels than mice on the low-folate diet, in both LFD and HFD conditions (Figure 4.1). There were no statistically significant differences in plasma folate levels due to iAs exposure and very few due to As3mt KO. Significant differences were found between KO males and KO females between the LFD and HFD. Here, levels of plasma folate in males exposed to 0 and 100 ppb iAs were higher than plasma of females in respective treatment groups. In addition, folate levels in plasma of male and female WT mice on a LFD was higher at week 6 than at sacrifice, i.e. 13 weeks after switching to HFD.

Figure 4.1. Plasma folate levels. Folate was measured using commercial ELISA kits (Monobind) in plasma collected after 5 weeks on LFD and iAs (A: LFD) and at sacrifice (B: HFD). Data shown as mean ± SEM for N= 6-10. F= significant effect of folate, G= significant effect of genotype, S= significant difference due to sex.

Arsenic species analysis

Arsenic species in the urine. Concentrations of As species in urine were measured after 6 weeks on LFD and at sacrifice (Figure 4.2). At both time points, total As concentrations were higher in

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urine of WT and KO mice exposed to 100 ppb iAs as compared to controls. Total As concentrations were higher in WT than KO mice exposed to iAs. Female WT and KO mice exposed to iAs had higher concentrations of total As in urine than their male counterparts, but this difference was statistically significant only for WT mice at week 6 on LFD. Urine from WT mice contained primarily of DMAs (75-96% of total As), followed by iAs (8-40%). DMAs was not detected in urine of As3mt-KO mice; iAs was the primary As species representing 97-100% of total As. MAs was not detected or was present only in traces in urine of either WT or KO mice. Effects of folate intake on proportions of As species in urine were relatively minor and inconsistent across the treatments. In most treatment groups, folate intake had no statistically significant effects on total urinary As. High-folate intake increased total As levels in urine only in male KO mice fed LFD for 6 weeks and exposed to iAs (Figure 4.2A). This was also observed in the control WT female group at sacrifice (Figure 4.2D). High folate intake had some significant effects on %As species in urine. Specifically, %iAs was lower and %DMAs was higher in urine of both control and iAs-exposed female WT mice after 6 weeks on LFD with high folate content as compared to WT females with low folate intake (Figure 4.2B,C).

High folate intake also decreased %iAs in urine of control female WT mice at sacrifice

(Figure 4.2E), but had no effects on %DMAs (Figure 4.2F). There were little or no statistically significant effects of folate on proportions of As species in urine of KO female or WT and KO male mice in other treatment groups.

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Figure 4.2. Arsenic speciation in urine collected from male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water, before and after a high-fat diet. Total As, %iAs, and %DMAs in urine after 6 weeks of exposure and low-fat diet (A- C) and at sacrifice (37 weeks exposure, 13 weeks of high-fat diet; panels D-F). MAs were not detected in any sample. Mean ± SEM for N=8-17. P<0.05 for the following comparisons: S, males vs females of the same diet, exposure, and genotype; F, low vs high folate intake animals of the same genotype, exposure, and sex; G, KO vs WT mice of the same sex, diet, and exposure; E, 0 vs 100 ppb iAs-exposed mice of the same sex, genotype, and folate level.

Arsenic species in the liver. Concentrations of As species were measure in livers collected at sacrifice (Figure 4.3). Except for WT female mice, the hepatic concentrations of total As were higher in mice exposed to 100 ppb iAs as compared to unexposed controls. Total As concentrations were generally higher in livers of KO as compared to WT mice, and were higher in livers of female KO mice exposed to 100 ppb iAs than in livers of male KO mice in the same exposure group (Figure 4.3A). iAs and MAs were the major As species found in livers of control male and female WT mice representing 31-65% and 17-38% of total As, respectively (Figure

4.3B,C); only traces of DMA were detected in most liver samples. Exposure to 100 ppb iAs increased total As levels, lowered %iAs and increased %DMAs in the livers of both male and female WT mice (Figure 4.3D); %MAs was not affected (Figure 4.3C). Livers of KO mice 125

contained mainly iAs (82-97% of total As) regardless of the exposure. High folate intake lowered total liver As in female WT mice, with marginal significance (p=0.06) in control females. However, high folate intake lowered %iAs, increased %MAs, and increased %DMAs

(p=0.06) in female WT mice exposed to 100 ppb iAs (Figure 4.3). There were no differences in total As or proportions of As in liver of other treatment groups.

Figure 4.3. Arsenic speciation in liver collected from male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water, before and after a high-fat diet. Total As (sum As species) (A), %iAs (B), %MAs (C), and %DMAs (D) were determined in liver samples collected at sacrifice (37 weeks exposure, 13 weeks of high-fat diet). Mean ± SEM for N=8-14. P<0.05 for the following comparisons: S, males vs females of the same diet, exposure, and genotype; F, low vs high folate intake animals of the same genotype, exposure, and sex; G, KO vs WT mice of the same sex, diet, and exposure; E, 0 vs 100 ppb iAs- exposed mice of the same sex, genotype, and folate level.

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Metabolic phenotypes

Body weight. Over the first 24 weeks of exposure while fed LFD, growth rates of male and female mice were similar; males tended to be heavier throughout (Figure C.4). Males gained more weight than females (Fig C.5A, C). There were minor differences in body weight due to iAs exposure or folate intake. KO females gained more weight than WT females over the course of 24 weeks on the LFD; there were no differences in weight gain between WT and KO males

(Figure C.5A, C). On the HFD, weight gain increased in all groups. Male and female weight gain became more similar during HFD consumption, with %weight gain being higher in female groups (Figure C.5B, D). KO female mice gained more weight during HFD feeding than KO males and WT females (Figure C.5B, D). Still, there were only few differences in body weight or weigh gain associated with iAs exposure or folate intake.

Glucose metabolism. FBG was measured prior to exposure, at weeks 5 and 24 in iAs-exposed and control mice on LFD, and at week 8 on HFD. Glucose tolerance was assessed prior to exposure and at week 5. Prior to exposure, both WT and KO males had higher FBG and lower glucose tolerance than respective females. No differences in FBG or glucose tolerance between treatment groups were found (data not shown). Exposure to iAs and folate intake had little effects on FBG or glucose tolerance across all time points. However, differences were found between KO and WT mice. After 5 weeks of iAs exposure, male and female KO mice had higher

FBG than WT mice (Figure C.6A); no differences due to KO were found in glucose tolerance

(Figure C.6B). The differences in FBG between WT and KO mice persisted at week 24 on LFD

(Figure 4.4A). At week 8 after switching to HFD, FBG in mice in all treatment groups increased

(Figure 4.4B). In general, FBG was lower in female mice as compared to males, but these

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differences were independent of iAs exposure or folate intake, and were not always statistically significant. Feeding HFD eliminated many of the significant differences in FBG between WT and KO mice found at weeks 5 and 24.

Figure 4.4. Fasting blood glucose (FBG) of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water, before and after a high-fat diet. FBG was measured in mice after 24 weeks of exposure and low-fat diet (A) and after 8 weeks on a high-fat diet (B). Mean ± SEM for N= 14-20. P<0.05 for the following comparisons: S, males vs females of the same diet, exposure, and genotype; F, low vs high folate intake animals of the same genotype, exposure, and sex; G, KO vs WT mice of the same sex, diet, and exposure; E, 0 vs 100 ppb iAs-exposed mice of the same sex, genotype, and folate level.

Body composition. All males were significantly heavier than females at week 24 on LFD and after 8 weeks of HFD (Figure 4.5). Body composition was assessed using MRI. After 24 weeks of iAs exposure while on LFD, both male and female KO mice had higher %fat mass and lower

%lean mass compared to WT mice (Figure 4.5C,E). There were little or no significant differences in %fat or % lean mass between males and females in either WT or KO groups. No effects of iAs exposure and only random effects of folate on body composition were found. After

8 weeks on HFD, all mice gained weight and had higher %fat mass and lower %lean mass than at week 24 on LFD (Figure 4.5B,D,C). KO mice still had higher %fat mass and lower %lean mass than WT mice. The differences in %fat mass between female KO and WT mice on HFD

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were greater than the differences found on LFD. However, %fat mass and %lean mass were not significantly different between male and female mice in either KO and WT groups.

Figure 4.5. Body composition of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water, before and after a high-fat diet. Body weight, %fat, and % lean mass were analyzed after 24 weeks on a low-fat diet (A, C, E) and after 8 weeks on a high-fat diet (B, D, F). Mean ± SEM for N= 13-20. P<0.05 for the following comparisons: S, males vs females of the same diet, exposure, and genotype; F, low vs high folate intake animals of the same genotype, exposure, and sex; G, KO vs WT mice of the same sex, diet, and exposure; E, 0 vs 100 ppb iAs-exposed mice of the same sex, genotype, and folate level.

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Insulin resistance. Both WT and KO male mice had significantly higher FPI and HOMA-IR compared to respective female mice at week 24 on LFD and after 8 weeks on HFD (Figure 4.6).

After 24 weeks on LFD, KO mice had significantly higher FPI and HOMA-IR than WT mice in all treatment groups, more pronounced in males than in females (Figure 4.6A). There were no significant differences in FPI or HOMA-IR due to iAs exposure and only a minimal effect of folate intake was found in control female WT mice (Figure 4.6C). FPI and HOMA-IR increased in all animals after 8 weeks on HFD, with greater increases found in male KO mice (Figure

4.6B,D).

Figure 4.6. Fasting plasma insulin and HOMA-IR values of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water, before and after a high-fat diet. Fasting plasma insulin and HOMA-IR were analyzed after 24 weeks of iAs exposure on a low-fat diet (A,C) and after 8 weeks on a high-fat diet (B, D). Mean ± SEM for N= 13-20. P<0.05 for the following comparisons: S, males vs females of the same diet, exposure, and genotype; F, low vs high folate intake animals of the same genotype, exposure, and sex; G, KO vs WT mice of the same sex, diet, and exposure; E, 0 vs 100 ppb iAs-exposed mice of the same sex, genotype, and folate level. 130

Unlike during LFD, iAs exposure had significant effects during HFD. Here, exposure to iAs increased HOMA-IR values in WT females, WT males, and KO males consuming the low-folate diet (Figure 4.6D). In contrast, no significant effects of iAs exposure were found among groups of mice fed the high-folate diet. High folate intake decreased FPI and HOMA-IR in WT males exposed to 100 ppb iAs, but these effects were only marginally significant (FPI: p=0.07, HOMA-

IR: 0.07). In KO males, high folate intake did not change either FPI or HOMA-IR.

4.5 Discussion

Effect of iAs exposure In humans, chronic exposures to moderate to high levels of iAs in drinking water (≥150 ppb) have been associated with T2D (Maull et al. 2012, Kuo et al. 2013). Even lower iAs exposures have been linked to an increased prevalence or incidence of T2D in recent studies

(Kuo et al. 2017). In laboratory studies of iAs-associated diabetes, mice and rats developed impaired glucose tolerance and increased FBG after chronic exposure to high (ppm) levels of iAs in drinking water (Maull et al. 2012, Paul et al. 2007, Paul et al. 2011, Davila-Esqueda et al.

2012). However, at lower doses that are more consistent with human exposures, the results are inconsistent. For example, Huang et al. have reported that female ICR mice exposed to 50 ppb iAs develop hyperglycemia, impaired glucose tolerance, and decreased fasting plasma insulin

(2015). In contrast, our laboratory found only minor impairment of fasting glycemia and glucose tolerance in male and female C57BL/6 mice after exposure to 100 ppb iAs (Douillet et al. 2017).

The differences between these findings could be due to strain differences but also due to iAs exposure through diet, which most laboratories do not address. Grain-based chow diets can contain as much as 400 ppb As, with iAs often being the major species (Murko et al. 2018).

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Thus, the background dietary iAs exposure could be a confounding factor especially for studies examining effects of low (ppb) levels of iAs in drinking water. In the present study, we used a purified diet that contained 36-49 ppb iAs.

We did not observe any major effects of iAs exposure on glucose metabolism in mice after 24 weeks on LFD. However, after an additional 8 weeks on a HFD, there was a significant increase in FPI and HOMA-IR in exposed male KO mice and both male and female WT mice.

This finding suggests that the combined low-level iAs exposure and HFD may elicit a diabetogenic phenotype characterize by insulin resistance. In comparison, we have previously reported that mice fed a HFD and exposed to much higher levels of iAs in drinking water (25 and

50 ppm) were more glucose intolerant, but had lower FPI and HOMA-IR, and had less %fat mass than control unexposed mice (Paul et al. 2011). In that study, little to no effects of iAs exposure were found in mice fed a LFD. Thus, both the Paul et al. study and the present study strongly suggest that iAs exposure elicit more pronounced diabetogenic effects when combined with consumption of a HFD. However, the diabetogenic phenotypes associated with low vs high iAs exposure (%fat mass, fasting glycemia, FPI, glucose tolerance and insulin resistance) differ significantly, suggesting that different pathologies may underlie iAs-associated diabetes at low and high levels of exposure.

Effect of As3mt-KO We have recently reported that As3mt-KO mice that drank DIW but were exposed to iAs

(~100 ppb) from a rodent chow diet became obese and insulin resistant. (Douillet et al. 2017). In the present study, a similar phenotype was found in control As3mt-KO mice fed a purified diet with lower iAs content (36-49 ppb). These mice (particularly males) accumulated more fat and were more insulin resistant than WT mice (Figure 4.4 and 4.5). HFD further exacerbated the

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adverse phenotype in both female and male KO mice. Results of these two studies suggest that the predisposition of As3mt-KO mice to obesity and development of insulin resistance could be due to the background exposure to iAs from the diets. Indeed, KO mice that could not effectively methylate iAs retained more As in the liver (and probably in other tissues) and excreted less As in urine than WT mice (Figures 4.2 and 4.3). Dietary iAs that accumulated in the tissues could impair mechanisms that regulate glucose metabolism, specifically insulin signaling pathways, resulting in insulin resistance. iAs exposure has also been shown to alter epigenetic programing of genes regulating glucose metabolism (Martin et al. 2017; Martin et al. 2016). Thus, the As3mt-

KO mice that are bred on a regular rodent chow may be pre-programmed to become insulin resistant later in life.

The question is, however, what causes fat accumulation in these mice. There were no significant differences in food consumption (i.e., energy intake) between WT and KO mice in this study (Figure C.3). It is possible that there may be differences in energy expenditure that could lead to obesity. Obesity alone can cause, and clearly contributes to insulin resistance in KO mice. However, exposure to iAs has not been linked to obesity in human studies and iAsIII has been shown to inhibit adipocyte differentiation and adipogenesis in in vitro models (Wang et al.

2005; Trouba 2000). Hyperinsulinemia, which characterizes the metabolic phenotype of KO mice, has been shown to drive lipid synthesis even in the presence of insulin resistance

(Shimomura et al. 2000; Brown and Goldstein 2008). Thus, this mechanism may explain fat accumulation by KO mice. It is also possible that accumulation of iAs in tissues of KO mice in utero or in postnatal life affects other mechanisms involved in metabolic homeostasis, including methylation and/or transcription of genes regulating lipid metabolism.

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Effect of sex We found a significant difference in insulin resistance between male and female mice which did not seem to be related to fat accumulation. In KO and WT mice, males and females had similar %fat mass; however, males were much more insulin resistant than the females. Thus, fat accumulation does not explain the sex difference in insulin resistance. The sexual dimorphism in susceptibility to metabolic disease has been documented in humans and in mice (Kautzky-

Willer et al. 2016; Mauvais-Jarvis 2015; Jaworski et al. 2011; Bonaventura et al. 2017; Soares

2017). Differences in adipose tissue storage, energy metabolism, and sex hormone programming and activation are known to underlie this difference (Kautzky-Willer 2016). This difference was more pronounced in As3mt-KO mice as compare to WT mice, as was observed in our previous study (Douillet et al. 2017). iAs accumulation in tissues as a result of As3mt-KO may exaggerate these pathways, resulting in a greater difference between sexes than those in WT mice.

Effect of folate Folate is a dietary nutrient that is required for SAM synthesis (Selhub 1999).

Supplementation with folateor high intake of folate has been associated with increasing iAs methylation capacity and lowered disease risk in human studies (Hall and Gamble 2012).

However, the potentially protective effects of folate via iAs metabolism in either humans or laboratory animals have not been adequately studied. In mice that lack folate transporter proteins, iAs metabolism was found to be less efficient than in WT mice, but only when fed a folate-deficient diet (Spiegelstein 2005, Spiegelstein 2003). Dietary folate supplementation was shown to significantly decrease iAs concentration and increase DMAs/MAs ratio in the liver of in iAs-exposed CD-1 mice (Tsang et al. 2012). Thus, when designing the present study, we expected to find major differences in iAs metabolism efficiency between mice fed the low-folate diet and the supplemented mice that were fed the diet with 50 times more folate. We also

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expected that a high folate intake would reduce adverse effects of iAs exposure by stimulating iAs metabolism and excretion. Additionally, we predicted that altering folate levels in As3mt-KO mice would not alter diabetogenic effects of iAs because these mice cannot methylate iAs.

However, the results of the study did not fully confirm these expectations.

The consumption of a high-folate diet significantly alter proportions of As species in the urine of WT female mice at week 6, decreasing %iAs and increasing %DMAs (Figure 4.2B-C).

Similar shifts in %iAs (decreased by 4%) and %DMA (increased by 7%) have been reported in urine of Bangladeshi residents after supplementation with folate (Gamble et al. 2006), changes which were interpreted as stimulation of iAs metabolism by folate. However, in the present study the high folate intake did not increase total amount of As excreted in urine and did not decrease

As levels in the livers of WT female mice at the end of the study (Figure 4.3). In addition, high folate intake had no effect on iAs metabolism in male WT mice. In this study, we did not use antibiotics to inhibit folate production by the gut bacteria (Rossi et al. 2011), which may explain the relatively modest, two-fold difference in plasma folate levels between mice with low and high folate intake, despite a 50-fold difference in dietary folate levels. The minimal difference in iAs metabolism between mice on the low vs. high-folate diet could be also due to stimulation of folate production by gut microbiota. A recent study showed that iAs exposure upregulates microbial genes responsible for folate synthesis in mouse colon (Chi et al. 2017).

Despite the minor or no effects on iAs metabolism, the high folate intake seemed to rescue the adverse phenotype in female and male WT mice exposed to iAs, by preventing increases in FPI and HOMA-IR observed in mice with low folate intake. However, a similar trend was observed in male KO mice, suggesting that this effect of folate is independent of iAs metabolism. Thus, questions remain about to what extent folate intake affects metabolism

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(excretion and tissue retention) of As in WT mice and what the implications of our findings are for folate interventions in human populations exposed to iAs.

4.6 Conclusions

Our data indicate that chronic exposure to 100 ppb iAs in drinking water, in addition to the background exposure to 36-49 ppb iAs in the diet, had little effect on the metabolic phenotype of WT male and female mice fed LFD, regardless of folate intake. However, when combined with a HFD, iAs exposure and low folate intake elicit insulin resistance in both male and female WT mice, highlighting potential toxicant-nutrient interactions. In contrast with findings in human studies, high folate intake did not significantly increase the efficiency of iAs metabolism, but it did protect against the diabetogenic effects of iAs exposure, specifically iAs- induced insulin resistance. As previously observed, As3mt-KO mice are more prone to developing obesity and insulin resistance than WT mice, which could be due to the background exposure to iAs in the diet and/or the accumulation of iAs from the diet in tissues. The differences in the susceptibility of male and female mice to developing insulin resistance, despite having similar levels of obesity and iAs metabolism efficiency, point to sex as an important factor in the susceptibility to iAs exposure.

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5. CHAPTER 5- Arsenite and its trivalent methylated metabolites inhibit glucose-

stimulated calcium influx in murine pancreatic islets5

5.1 Overview

Chronic exposure to inorganic arsenic (iAs), a common drinking water and food contaminant, has been associated with an increased risk of type 2 diabetes mellitus in numerous populations around the world. The mechanisms underlying the diabetogenic effects of iAs exposure have been investigated in our laboratory using both animal and in vitro models. We have previously shown that in pancreatic islets, arsenite (iAsIII) and its methylated trivalent metabolites, methylarsonite (MAsIII) and dimethylarsinite (DMAsIII), inhibit glucose-stimulated insulin secretion (GSIS), without effects on insulin gene expression or insulin content. The goal of the present study was to determine if inhibition of GSIS is due to effects of arsenicals on calcium (Ca2+) influx, an essential regulatory step that activates insulin granule fusion and release of insulin from β-cells in response to glucose. Glucose metabolism in β-cells stimulates production of ATP, which results in closure of KATP channels and leads to membrane depolarization, followed by opening of L-type voltage-gated calcium (Cav1.2) channels. Influx of

Ca2+ stimulates insulin release. We found that in vitro exposure for 48 hours to non-cytotoxic concentrations of iAsIII, MAsIII, and DMAsIII inhibited Ca2+ influx in isolated murine pancreatic islets stimulated with glucose. MAsIII was a more potent inhibitor of Ca2+ influx than either iAsIII

5 The authors would like to thank Dr. Pablo Ariel and the UNC Microscopy Services Laboratory for their technical assistance in this research 143

or DMAsIII. The arsenicals also inhibited Ca2+ influx and insulin secretion in islets treated with depolarizing levels of potassium chloride in the absence of glucose. Treatment with Bay K8644, a Cav1.2 channel agonist, did not restore insulin secretion in arsenical-exposed islets. In contrast,

III III tolbutamide, a KATP channel blocker, restored insulin secretion in MAs and DMAs -exposed islets but not in islets exposed to iAsIII. Our findings show that iAsIII, MAsIII, and DMAsIII inhibit glucose-stimulated calcium influx in islets, possibly by interfering with KATP and/or Cav1.2 channel function. The mechanisms underlying inhibition of GSIS by iAsIII may differ from those of its trivalent methylated metabolites.

5.2 Introduction

Around 9% of the global population is diagnosed with diabetes, a condition of chronic hyperglycemia (WHO 2016). The majority of these individuals have type 2 diabetes (T2D), which involves insulin resistance, a result of impaired insulin signaling pathways in insulin- sensitive tissues, followed by insufficient insulin secretion by pancreatic β-cells. The prevalence of T2D is increasing and is expected to double in the next twenty years if current trends continue

(Chen et al. 2012). While obesity is a major factor in the rise of T2D, environmental chemicals, termed “diabetogens”, have also been implicated (Maull et al. 2012).

Numerous epidemiological studies from populations around the world have shown inorganic arsenic (iAs), a prevalent drinking water and food contaminant, to be a diabetogen

(Maull et al. 2012; Kuo et al. 2017). We have reported that human exposure to iAs is negatively associated with fasting plasma insulin and HOMA-IR, a measure of insulin resistance (Del Razo et al. 2011), suggesting that iAs may interfere with insulin secretion by the pancreas. However, results of laboratory studies using animal and in vitro models suggest that trivalent iAs, arsenite

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(iAsIII), can impair both insulin secretion in pancreatic β-cells and insulin action in glucose utilizing tissues (Douillet et al. 2013, Fu et al. 2010, Paul et al. 2007, Zhang et al. 2017). In addition, exposure to iAs has been shown to modify DNA methylation and/or expression of T2D associated genes in both human population and laboratory models. Hence, the contributions of these mechanism to the pathogenesis of iAs-associated diabetes in vivo remain unclear.

Our lab has shown that in vitro exposure of isolated murine pancreatic islets to iAsIII and its trivalent methylated metabolites, methylarsonite (MAsIII) and dimethylarsinite (DMAsIII), inhibited glucose-stimulated insulin secretion (GSIS) without altering cell viability, insulin content, or insulin gene expression (Douillet et al. 2013). These findings indicate that arsenicals impair the insulin secretion process rather than insulin production. Furthermore, methylated metabolites of iAsIII were more potent inhibitors of GSIS than iAsIII. These findings are consistent with results of several studies that have linked T2D risk in iAs-exposed populations to high proportions of DMAs or low proportions of MAs in urine (Del Razo et al. 2010; Kuo et al

2017). Thus, the production of methylated metabolites in the pathway of iAs metabolism is likely to play an important role in etiology of iAs-associated diabetes. Yet, the exact mechanisms by which these metabolites impair GSIS have never been systematically investigated.

GSIS begins with the entry of glucose into the β-cell followed by oxidation of glucose in glycolysis and downstream ATP-producing pathways, including oxidative phosphorylation. The generation of ATP leads to the closure of ATP-dependent potassium (KATP) channels, specifically KIR6.1 or KIR6.2 channels, causing depolarization of the cell membrane.

Depolarization initiates the opening of voltage-gated calcium channels, primarily L-type channels (Cav1.2) (Wiser et al. 1999, Barg et al. 2001), leading to an increase in intracellular

Ca2+. Ca2+ then activates the exocytotic machinery, enabling fusion of insulin granules with the

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plasma membrane and the release of insulin into the bloodstream. The fact that insulin secretion does not occur when β-cells are stimulated with glucose in Ca2+-free medium strongly suggests that Ca2+influx and insulin secretion are tightly linked. In addition, inhibition of calcium channels with pharmacological agents was shown to agents GSIS (Wollheim and Sharp 1981).

In 2008, Díaz-Villaseñor et al. reported that iAsIII decreased insulin secretion and glucose-stimulated Ca2+ influx in a Rin-m5F β-cell line (Díaz-Villaseñor et al. 2008). Our study builds on this finding demonstrating that (1) exposure to iAsIII or its methylated metabolites, inhibits glucose-stimulated Ca2+ influx in isolated murine pancreatic islets, and that (2) both

KATP and Cav1.2 channels are likely targets of these arsenicals in β-cells.

5.3 Methods

Cells and treatment. Pancreatic islets were isolated from adult C57BL6 male mice as previously described (Douillet et al. 2013). Briefly, the pancreas was perfused and digested with 1 mg/ml collagenase P (Roche Diagnostics Crop, Indianapolis, IN) and islets were purified using a Ficoll

PM-400 gradient (GE Healthcare, Chicago, IL). After isolation, islets were incubated overnight at 37 °C with 5% CO2 in RPMI 1640 medium with 10% fetal bovine serum, 10 mM HEPES, 1 mM sodium pyruvate, 100 U/ml penicillin, and 100 μg/ml streptomycin (all from Gibco,

Waltham, MA). Islets were exposed for 48 hours to either iAsIII (sodium arsenite, >99% pure,

Sigma-Aldrich, St. Louis, MO), MAsIII (methylarsine oxide, >98% pure), or DMAsIII

(iododimethylarsine, >98% pure). Media were changed every 24 hours to minimize oxidation of trivalent arsenicals to pentavalency.

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Measurement of intracellular calcium. Islets (40-50 per experiment) exposed to arsenicals and control (unexposed) islets were incubated with 2 µM fura-2AM (Thermo Fisher Scientific) and arsenicals for 30 minutes at 37º C in KRP buffer (114 mM NaCl, 4.7 mM KCl, 1.2 mM KH2PO4,

1.16 mM MgSO4, 10 mM HEPES, 2.5 mM CaCl2, 0.1% BSA, 25.5 mM NaHCO3, pH 7.2).

Imaging was carried out in a Delta T live-cell imaging flow-chamber (Bioptechs) at 37C, 5%

CO2, and a perfusion flow rate of 0.3 ml/min. Fura-2 fluorescence at 520 nm wavelength following excitation with 340 nm (F340) or 380 nm (F380) wavelengths (Semrock FURA2-C000 dichroic filter set) was captured every 6 seconds using an Olympus IX81 inverted microscope and a Hammamatsu ORCA R2 cooled CCD camera. Camera exposure times were kept consistent between experiments. Imaging was controlled by Velocity (PerkinElmer, Coventry,

England). The ratio (F340/F380), a proxy for calcium concentrations, was calculated for each measurement using ImageJ, assessing only changes in fluorescence in islets within the image.

All islets were visible in the image. Ratio values were normalized to (i.e. divided by) the average ratio value after 5 minutes in 2.5 mM glucose. After plotting ratio vs. time, the area under the curve for 8 minutes, starting when 16.7 mM glucose/KCl entered the perfusion system, was calculated in Excel and assessed for statistical significance.

Insulin secretion assay. Islets (15 islets/well) were transferred into 12-well plates containing a glucose-free buffer (114 mM NaCl, 4.7 mM KCl, 1.2 mM KH2PO4, 1.16 mM MgSO4, 20 mM

HEPES, 2.5 mM CaCl2, 0.2% bovine serum albumin, and 25.5 mM NaHCO3 (all from Sigma-

Aldrich, St. Louis, MO)) for 1 h at 37 °C and 5% CO2, followed by a 1-hour incubation with 2.5 mM glucose (Sigma-Aldrich, St. Louis, MO) and a 1-hour incubation with 16.7 mM glucose and/or insulin secretagogues (potassium chloride, tolbutamide, and Bay K8644) (Sigma Aldrich,

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St. Louis, MO). Medium from each incubation step was frozen and stored for insulin analysis.

Insulin concentrations were determined using Rat/Mouse Insulin ELISA kit (MilliporeSigma,

Burlington, MA).

3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay. After the 48-hour exposure, islets were incubated in phenol-free RPMI islet medium with 0.5 mg/ml MTT for 1 hour at 37C. The formed formazan was dissolved in DMSO and absorbance read at 570 nm with a background correction of 630 nm (Stýblo et al. 2002).

Quantitative PCR. Total RNA was collected from islets (100/treatment) after stimulation with

16.7 mM glucose using RNeasy kits (Qiagen, Valencia, CA) and analyzed by a Nanodrop 2000c spectrophotometer (Thermo Fisher Scientific, Waltham, MA). cDNA was prepared with the iScript cDNA synthesis kit (Biorad, Hercules, CA). Gene expression was quantified using

PowerUp SYBR green master mix (Thermo Fisher Scientific, Waltham, MA) and primers for the alpha subunit in Cav1.2 channels, Cacna1c, and 18S, purchased through Thermo Fisher

Scientific (Waltham, MA). Primer sequences for Cacna1c were

CAGCCCAGAAAAGAAACAGG (forward) and GGATTCTCCATCGGCTGTAA (reverse); primer sequences for 18S were CGCGGTTCTATTTTGTTGGT (forward) and

AGTCGGCATCGTTTATGGTC (reverse) (Santulli et al. 2015). Target gene expression was normalized to the 18S gene. PCR reactions were performed in a LightCycler 480 II (Roche,

Indianapolis, IN).

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Statistical Analysis. Data presented as mean ± SEM. Significance was determined using one-way

ANOVAs followed by post hoc Student’s two-tailed T-tests. Analyses were conducted in JMP

(SAS Institute, RTP, NC).

5.4 Results

Following the design of our published study (Douillet et al. 2013), we first re-examined effects of trivalent arsenicals on GSIS in isolated pancreatic islets. We confirmed that 48-hour exposures to 2 µM iAsIII, 0.5 µM MAsIII, and 0.5 µM DMAsIII significantly inhibit GSIS (Figure

5.1A), but are not cytotoxic for the islets (Figure 5.1B).

Figure 5.1. Glucose-stimulated insulin secretion in arsenic-exposed pancreatic islets. Isolated pancreatic islets were exposed to arsenicals for 48 hours and stimulated with 2.5 mM glucose for 1 hour followed by 16.7 mM glucose for 1 hour. Insulin secretion (A) and cell viability measured via MTT assay (B) are shown as mean + SEM for N=5, 15 islets/experiment. *p<0.05 compared to control.

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Effect of trivalent arsenicals on calcium influx Calcium influx was examined in islets exposed to a range of iAsIII, MAsIII, and DMAsIII concentrations and in control, unexposed islets. Here, the Fura-2 signal was measured in islets at

2.5 mM glucose followed by stimulation with 16.7 mM (Figure 5.2). In control islets, introduction of 16.7 mM glucose into the flow system was followed by a sharp increase in Ca2+ influx that plateaued. Ca2+ influx was inhibited in islets exposed to arsenicals as indicated by a smaller or delayed increase in the fluorescent signal and a smaller area under the curve (Figure

5.2A-C). The impairment of Ca2+ influx by arsenicals did not follow a typical dose-response pattern, but MAsIII was clearly the most potent inhibitor, significantly suppressing Ca2+ influx at concentrations as low as 0.05 µM (Figure 5.2D).

Figure 5.2. Glucose-stimulated calcium influx in arsenic-exposed pancreatic islets. Isolated pancreatic islets were exposed to arsenicals for 48 hours and stimulated with 16.7 mM glucose (indicated by arrow). Levels of intracellular calcium were measured over time and are shown in time course profiles of an average of all experiments (A-C). Area under the curve (D) was calculated from time of glucose injection to end of run and are shown as mean ± SEM for N=5- 10, 30-40 islets/experiment. *p<0.05 compared to control; §p<0.05 from 0.1 μM.

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There were no differences in basal intracellular calcium levels between control and exposed islets prior to stimulation with 16.7 mM glucose. For subsequent experiments, we used concentrations of arsenicals that caused significant decreases in intracellular calcium levels after stimulation with 16.7 mM glucose.

Effect of trivalent arsenicals on Cav1.2 channels

Inhibition of KATP channels by increased levels of ATP causes depolarization of the β- cell membrane, stimulating the opening of L-type voltage-gated calcium channels (Cav1.2)

(Wiser et al. 1999, Barg et al. 2001). To determine if the inhibition of calcium influx by arsenicals was due to inhibition of events after depolarization, we stimulated the islets with depolarizing levels of potassium chloride (35 mM KCl) in the absence of glucose. Treatment with KCl stimulated both Ca2+ influx and insulin secretion in control islets (Figure 5.3). KCl- stimulated Ca2+ influx was significantly decreased in MAsIII- and DMAsIII-exposed islets; the decrease was marginally significant in iAsIII-exposed islets (p=0.1) (Figure 5.3A-B). All three arsenicals significantly decreased KCl-stimulated insulin secretion (Figure 5.3C).

To test if arsenicals inhibit insulin secretion by affecting calcium influx through Cav1.2 channels, we measured GSIS in control and arsenical-exposed islets treated with Bay K8644, a

Cav1.2 channel-specific agonist that prolongs the time Cav1.2 channels are in the open state and potentiates insulin secretion (Panten et al. 1985). Unexposed islets stimulated with 16.7 mM glucose and 2 µM Bay K8644 secreted more insulin than those stimulated with 16.7 mM glucose alone (Figure 5.4). However, stimulation with glucose and Bay K8644 did not rescue the inhibition of insulin secretion in islets exposed to arsenicals. Though, arsenical-exposed islets stimulated with glucose and Bay K8644 secreted more insulin than the exposed islets stimulated with only glucose.

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Figure 5.3. KCl-stimulated calcium influx in arsenic-exposed pancreatic islets. Isolated pancreatic islets were exposed to arsenicals for 48 hours and stimulated with 35 mM potassium chloride (KCl), a potent insulin secretagogue. Levels of intracellular calcium (A) were measured over time and are shown in time course profiles of an average of 4 biological experiments, where the arrow indicates injection of KCl. Area under the curve (B) was calculated from time of KCl injection to end of run and shown as mean ± SEM of N=4, 30-40 islets/experiment. Insulin secretion (C) was measured in the absence of glucose (Basal) and after 1-hour stimulation with KCl. *p<0.05 compared to control.

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Figure 5.4. Bay K8644-stimulated insulin secretion and Cav1.2 channel expression in arsenic- exposed pancreatic islets. Isolated pancreatic islets were exposed to arsenicals for 48 hours and stimulated for 1 hour each with 2.5 mM glucose, 16.7 mM glucose, and 16.7 mM glucose with 2 µM Bay K8644. Mean ± SEM of N=3-4 biological replicates (each assay done in triplicate, 15 islets/condition) are shown. *p<0.05 for comparison of arsenical-treated islets to unexposed controls. ǂp<0.05 for the comparison of 16.7 mM glucose and 16.7 mM glucose + 2 µM Bay K8644.

Thus, arsenicals may be affecting the transcription or activity of Cav1.2 channels or downstream mechanisms that depend on the function of these channels. To assess Cav1.2 expression, we measured mRNA levels of Cav1.2α1, the pore-forming subunit of Cav1.2 channel, in control islets and in islets exposed to 2 µM iAsIII, 0.5 µM MAsIII or 0.5 µM DMAsIII for 48 hours. We found no statistically significant differences in Cav1.2α1 mRNA levels between control islets and islets exposed to arsenicals (Figure 5.5).

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Figure 5.5. Expression of the alpha subunit of L-type calcium channels in arsenical-exposed islets. Isolated pancreatic islets were exposed to arsenicals for 48 hours and stimulated for 1 hour each with 2.5 mM glucose and 16.7 mM glucose. Relative Quantitative Value (RQV) of the expression of the alpha subunit of Cav1.2 channels (Cacna1c) after GSIS is shown as mean ± SEM for N=4 biological replicates.

Effect of trivalent arsenicals on the KATP channel

Closure of KATP channels by ATP prevents influx of potassium ions and triggers membrane depolarization. To further test the effects of arsenicals on membrane depolarization, we measured insulin secretion from control and arsenical-exposed glucose-stimulated islets after treatment with tolbutamide, a KATP-channel blocker. Tolbutamide-stimulated insulin secretion was inhibited in islets exposed to iAsIII but restored insulin secretion in MAsIII- and DMAsIII- exposed islets, although these concentrations prevented glucose-stimulated Ca2+ influx (Figure

2). Insulin secretion in MAsIII- and DMAsIII- exposed islets was restored to the level of insulin secretion in unexposed islets (Figure 5.6).

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Figure 5.6. Tolbutamide-stimulated insulin secretion in arsenic-exposed pancreatic islets. Isolated pancreatic islets were exposed to arsenicals for 48 hours and stimulated for 1 hour each with 2.5 mM glucose, 16.7 mM glucose, and 16.7 mM glucose +200 µM tolbutamide. Mean ± SEM of N=3-4 biological replicates (each assay done in triplicate, 15 islets/condition) are shown. *p<0.05 for comparison of arsenical-treated islets to unexposed controls. ǂp<0.05 for the comparison of 16.7 mM glucose and 16.7 mM glucose +200 µM tolbutamide.

5.5 Discussion

Exposure to iAs has been associated with diabetes in many populations around the world

(Maull et al. 2012, Kuo et al. 2017). Experimental studies show that iAsIII and its methylated trivalent metabolites inhibit insulin secretion in pancreatic islets and β-cells (Douillet et al. 2013;

Fu et al. 2010; Díaz-Villaseñor et al. 2008; Dover et al. 2018) and some mechanisms for this inhibition have been proposed. For example, chronic exposure to iAsIII has been shown to upregulate Nrf2, an oxidative stress-sensitive transcription factor, and Nrf2-dependent transcription of antioxidant enzymes in INS-1 β cells and other cell types. (Lau et al. 2013). Fu et al. proposed that stimulation of antioxidant defenses after iAsIII exposure suppressed reactive

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oxygen species involved in GSIS signaling (Fu et al. 2010). Dover et al. found that exposure to iAsIII and MAsIII, but not DMAsIII, impaired mitochondrial respiration in glucose-stimulated

INS-1 cells (Dover et al. 2018), suggesting an impairment in ATP production, a critical step in

GSIS regulation. In 2008, Díaz-Villaseñor et al. showed that exposure of rat insulinoma

(RINm5F) cells to iAsIII inhibited glucose-stimulated Ca2+ influx and increased calpain-10 activity and SNAP-25 proteolysis, proposed mechanisms involved in granule exocytosis (Díaz-

Villaseñor et al. 2008).

In this study, we found that iAsIII and its methylated trivalent metabolites inhibited glucose-stimulated Ca2+ influx in isolated pancreatic islets at concentrations that also inhibit

GSIS, but do not impair islet viability. We also found that MAsIII and DMAsIII, and to a lesser extent iAsIII, inhibited Ca2+ influx and insulin secretion in islets stimulated with a membrane depolarizing concentration of KCl, suggesting that arsenicals affect insulin regulating mechanisms downstream from the membrane depolarization. Furthermore, stimulation with the

Cav1.2 channel agonist, Bay K8644, did not restore GSIS in islets exposed to arsenicals.

Notably, 48-hour exposure to arsenicals did not suppress the Cav1.2 mRNA expression, pointing to inhibition of Ca2+ channel trafficking or activity as a mechanism underlying the inhibition of

GSIS by trivalent arsenicals. Taken together, these data suggest that iAsIII, MAsIII and DMAsIII may target Cav1.2 channels, either interfering with the channel trafficking or inhibiting activity/conductance of the Cav1.2 channels within the plasma membrane of β-cells. It is also possible that these arsenicals target Ca2+ dependent mechanism(s) that stimulate insulin vesicle assembly and exocytosis downstream of Ca2+ channels.

Cav1.2 channel expression at the plasma membrane and activity are regulated by various proteins, including calcium channel auxiliary subunits, kinases, and exocytotic proteins.

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Auxiliary subunits of calcium channels regulate channel translocation to the membrane or channel activity (Dolphin 2012). In β-cells, the α2 subunit is bound to the δ subunit through a disulfide bond (Dolphin 2012). This α2- δ complex then binds to core α1 subunits and can increase plasma membrane expression of Cav1.2, leading to increased glucose-stimulated calcium influx (Dolphin 2012; Yang and Berggen 2006; Gao et al. 2000). Since trivalent arsenicals bind readily to thiols, they could be interfering with the formation of the α2- δ complex and inhibiting channel translocation.

Cav1.2 channel trafficking and activity are also regulated by various kinases (reviewed in

Yang and Berggren 2006). In islets and β-cells, inhibition of Ca2+/calmodulin-dependent kinase

II decreased insulin secretion, paralleled by a decrease in calcium influx (Dadi et al. 2014).

Protein kinase Akt upregulates Cav1.2 channels in excitable cells; phosphorylation by Akt promotes Cav1.2 stabilization and increases trafficking to the plasma membrane (Catalucci et al.

2009; Viard et al. 2004). Akt kinases are expressed in β-cells and has been reported to be a positive regulator of insulin secretion (Cui et al. 2012). We have previously shown that iAsIII and its trivalent methylated metabolites, MAsIII and DMAsIII, inhibit the phosphorylation/activation of Akt and the Akt-mediated signaling in adipocytes (Paul et al. 2007) and hepatocytes (Zhang et al. 2017). This inhibition of Akt phosphorylation could contribute to the inhibition of Ca2+ influx in the islets exposed to trivalent arsenicals.

Our experimental data suggest that arsenicals may interfere with Cav1.2 channel function directly, i.e. by reacting with the proteins/subunits that are responsible for regulation of Ca2+ flow through the channel. However, it is unlikely that the trivalent arsenical would be directly binding to these channels since Cav1.2 channels contain few cysteine residues and the primary amino acid that confers calcium specificity is glutamate, which does not have sulfhydryl groups.

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Alternatively, arsenicals could be binding to allosteric binding sites, leading to changes in calcium channel activity. Measurement of calcium flux across the Cav1.2 channel via the patch clamp technique would be needed to determine if arsenicals directly impair flow of Ca2+ through the membrane-bound channels.

Similar to KCl, tolbutamide stimulates membrane depolarization by blocking KATP channels at the SUR1 subunit, leading to insulin secretion. However, depolarization with tolbutamide is more physiological than artificially raising ion levels with KCl. Stimulation with tolbutamide restored GSIS in islets exposed to MAsIII and DMAsIII, suggesting that these arsenicals also affect KATP channels. The KATP channel has two subunits that regulate channel closure: ATP interacts with the Kir6.2 subunit and tolbutamide interacts with the sulfonylurea receptor protein 1 (SUR1) (Babenko 1999). MAsIII and DMAsIII may be interfering with ATP levels or ATP-KATP channel interactions at the Kir6.2 subunit, effects which would be overcome by tolbutamide stimulation. Real-time measurement of intracellular ATP would determine if this is the case. Suppression of ATP activation of KATP channels by arsenicals could also explain why

Bay K8644-stimulation did not restore insulin secretion. During glucose stimulation, the membrane potential of the β-cell membrane oscillates between depolarizing and non- depolarizing levels, thus Cav1.2 channels oscillate between open and closed states, leading to oscillations in intracellular Ca2+ levels during glucose stimulation (Henquin 2009). If arsenicals are inhibiting KATP closure, the membrane is spending less time at a depolarizing voltage, leading to less time Cav1.2 channels are open and lower calcium influx. Thus, although Bay

III K8644 maintains open Cav1.2 channels, there are fewer open Cav1.2 channels in MAs - and

DMAsIII -exposed islets than unexposed islets, leading to less insulin secretion despite Bay

K8644 treatment.

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The restoration of GSIS by tolbutamide in MAsIII and DMAsIII- exposed islets appears to contradict the data showing their effects on Ca2+ influx and/or downstream mechanisms.

However, tolbutamide has been shown to stimulate insulin secretion apart from its effects on the

KATP channel (Barg et al. 1999). SURs have been also found in insulin granules and are thought to contribute to the acidification of insulin granules, a priming step needed for exocytosis

(Eliasson et al. 2003). Thus, tolbutamide may be able to stimulate insulin secretion via this Ca2+- independent pathway in MAsIII and DMAsIII-, but not iAsIII- exposed islets.

Tolbutamide did not restore GSIS in iAsIII-exposed islets, suggesting that other actions of iAsIII in the β-cell may be more important in inhibiting insulin secretion. iAsIII may be interfering with remodeling of β-actin microfilaments that are needed for insulin granule transport and secretion, effects which tolbutamide stimulation would not be able to overcome (Izdebska et al.

2013; Izdebska et al. 2014; Qian et al. 2005). Since actin reorganization is important for insulin granule transport, priming, and release (Wang and Thurmond 2009), iAsIII may be inhibiting the availability of releasable insulin granules. Future studies should investigate this potential mechanism.

5.6 Conclusion

We found that iAsIII and its methylated trivalent metabolites (MAsIII and DMAsIII) impaired glucose-stimulated Ca2+ influx in isolated pancreatic islets, at concentrations that also inhibit GSIS but do not affect islet viability. The methylated metabolites have been previously shown to be more potent inhibitors of GSIS than iAsIII. Here, we show that one of these metabolites, MAsIII, is also more potent than iAsIII as an inhibitor of glucose-stimulated Ca2+ influx in islets. DMAsIII which was not as potent as MAs inhibiting Ca2+ influx, may be targeting

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other mechanisms that regulate GSIS in β-cells. Future studies should focus on identification of these targets to further characterize the role of iAs metabolism in the development of arsenic- associated diabetes.

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Díaz-Villaseñor, A., Burns, A.L., Hiriart, M., Cebrián, M.E., and Ostrosky-Wegman, P. (2007). Arsenic-induced alteration in the expression of genes related to type 2 diabetes mellitus. Toxicology and Applied Pharmacology 225, 123–133.

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Dover, E.N., Beck, R., Huang, M.C., Douillet, C., Wang, Z., Klett, E.L., and Stýblo, M. (2017). Arsenite and methylarsonite inhibit mitochondrial metabolism and glucose-stimulated insulin secretion in INS-1 832/13 β cells. Arch. Toxicol. 92(2), 693-70.

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6. CHAPTER 6- Synthesis

Support for iAs-associated diabetes, even in populations with low levels of chronic exposure (<150 ppb), is increasing. Despite these epidemiological associations, our understanding of how iAs induces or predisposes individuals to diabetes is limited. Since methylation of iAs facilitates the clearance of iAs from the body, it has been proposed that increasing the rate of iAs methylation by increasing dietary nutrients used to generate SAM could reduce risk of iAs-associated disease. However, methylated trivalent metabolites of iAs also interfere with insulin signaling and insulin secretion more potently than iAs itself. A summary of our understanding of the relationship between arsenic and arsenic metabolism with diabetes in human, rodent, and in vitro studies is shown in Figure 6.1. A clearer understanding of how iAs metabolism contributes to the development of diabetes is essential for determining if increasing efficiency of iAs metabolism through nutritional means can be a protective intervention strategy. Furthermore, risk assessors would be able to better predict iAs toxicity based on an individual’s capacity to metabolize iAs and to identify and protect susceptible populations.

This dissertation is a compilation of research that aimed to understand mechanisms of iAs-associated diabetes and the role of iAs metabolism through a diversity of approaches.

Previous chapters have described projects involving metabolomics, in vivo exposure studies, and in vitro mechanistic studies. This chapter summarizes the results, suggests future directions, and highlights the implications of these projects.

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Figure 6.1. Summary of the knowledge about the relationship of arsenic and arsenic metabolism with diabetes at the start of this dissertation work.

6.1 Metabolomic assessment of iAs exposure and the As3mt-KO genotype

Metabolomic studies have been conducted in iAs-exposed humans in efforts to understand mechanisms of iAs toxicity and identify biomarkers of exposure (Zhang et al. 2014;

Martin et al. 2015; Wu et al. 2018). In Chapter 2, we investigated metabolite changes in C57BL6 mice exposed to iAs in order to compare with iAs-associated metabolite changes in humans. By comparing these iAs-associated metabolites in mice and humans, we hoped to identify common metabolites that would indicate disrupted pathways that could be investigated further. We found that exposure to 1 ppm iAs for 4 weeks had little to no effect on urinary and plasma metabolites

(Table 2.1), unlike in humans exposed to 20-152 ppb iAs where 132 metabolites were associated with exposure (Martin et al. 2015). These results were surpising, considering that the mice were 166

exposed to a higher dose of iAs for a relatively short period of time. These findings indicated that mice are more tolerant of iAs exposure than humans and are capable of metabolizing As faster.

Since 1 ppm iAs exposure for four weeks did not change metabolite profiles, future metabolomic studies on iAs-associated biochemical changes in mice could modify the exposure dose either by concentration or length of exposure.

We also evaluated metabolites in mice with an impaired capacity to methylate iAs (i.e. knockout of As3mt) in efforts to characterize this mouse model in the context of metabolic disease. Many metabolites were found to be different in As3mt-KO compared to C57BL6 (WT) mice. Urinary and plasma metabolites associated with KO were related to lipid metabolism, amino acid metabolism, and other methylation reactions. Interestingly, metabolite changes associated with As3mt-KO were different depending on the sex. Analysis of liver phosphatidylcholines showed similar findings to analyses of urinary and plasma metabolites: few metabolites associated with exposure and many metabolites associated with As3mt-KO in a sex- dependent manner.

This metabolomics study was the first to suggest that there may be underlying metabolic differences in As3mt-KO mice. Subsequently, our lab assessed glucose metabolism in

As3mt-KO mice compared to WT mice (Douillet et al. 2017). In that study, we found that the KO mice gained more weight, accumulated more fat, and were more insulin resistant than WT mice even without iAs exposure in drinking water. Interestingly, male KO mice were more insulin resistant than female KO mice, paralleling the sex-specific differences in their metabolic profiles described in Chapter 2. Additional investigation could determine if As3mt-KO mice are different from WT due to heightened susceptibility to iAs exposure in the diet or due to other mechanisms. It is possible that the deletion of As3mt in these animals interfered with regulatory

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components of genes related to lipid and/or/ glucose metabolism, leading to differential gene expression and this adverse metabolic phenotype. Another possible mechanism is through the gut microbiome, particularly since hippuric acid, a metabolite linked to the microbiome, was the metabolite with the highest fold change in As3mt-KO mice.

Complete elimination of As3mt and iAs metabolism is not observed in humans; instead, human populations consist of individuals with different single nucleotide polymorphisms and haplotypes that are linked to modest changes in iAs methylation. Mice populations with high genetic diversity such as the Collaboratic Cross mice or the Jackson Lab’s Diversity Outbred mice emulate the genetic diversity in humans and would be useful populations in which to study the role of iAs metabolism and polymorphisms in As3mt in disease.

6.2 In vivo studies of iAs-associated diabetes and B-vitamin nutritional interventions

Mice exposed to 3- 50 ppm levels of iAs develop impaired glucose tolerance and hyperglycemia (Maull et al. 2012; Paul et al. 2007). Given that humans are exposed to ppb levels of iAs, there is much interest in understanding the effects of more environmentally-relevant doses in animal models. Additionally, people are often exposed to iAs throughout the entire life, from preconception to adulthood. The effects of iAs exposure during early developmental periods and its potential to result in diabetes later in life has not been thoroughly examined in human or animal studies. One of the advantages of experimental studies is that we can separate these two windows of exposure and investigate mechanisms specific to each period of exposure.

In Chapters 3 and 4, we discussed a prenatal exposure study and an adult exposure study in mice assessing the diabetogenic effects of low-level (100 ppb) iAs exposure on diabetes outcomes. B- vitamin, particularly folate, supplementation has been hypothesized as a potential intervention to

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protect against iAs-associated toxicities. However, there are few human studies and experimental studies to support this theory. We aimed to address this knowledge gap as well in these studies.

In Chapter 3, we studied the effects of prenatal iAs exposure in C57BL6 mice on glucose metabolism. Our results showed that prenatal exposure to 100 ppb iAs increased fasting blood glucose, insulin resistance, and %fat but only in male offspring. Interestingly, though maternal folate and B12 supplementation minimally stimulated iAs metabolism, it prevented the hyperglycemia, increased insulin resistance, and gains in fat mass in iAs-exposed male offspring, suggesting that maternal supplementation may protect against the diabetogenic effects of iAs.

In Chapter 4, we assessed how folate intake modifies iAs metabolism and iAs-associated diabetes in adult male and female mice. We included the As3mt-KO mice with the hypothesis that altered folate intake in KO animals would not modify iAs-associated impairments in glucose metabolism because KO animals do not methylate iAs. To emulate how humans can be exposed to multiple environmental metabolic stressors at a time, we placed these animals on a high-fat diet after 24 weeks on a low-fat diet and continued iAs exposure.

We found that high folate intake increased plasma folate by 3- to 4-fold but had minor effects on iAs metabolism, as measured by urinary As and liver As species. Neither exposure to

100 ppb iAs or high folate intake for 24 weeks elicited significant effects on glucose metabolism measures in KO or WT mice. However, after eight weeks on a high-fat diet, mice exposed to iAs developed insulin resistance and gained more fat than non-exposed mice. These changes tended to occur in mice fed the low-folate diet. While on a purified diet containing ~40 ppb iAs, As3mt-

KO were more insulin resistant than WT, confirming our previous study of KO animals consuming a chow diet that contained 100 ppb iAs (Douillet et al. 2017). Males were more susceptible to developing insulin resistance than females, in both WT and KO mice.

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Results from these in vivo studies provide four main conclusions. First, mice exposed chronically to doses of iAs that are consistent with exposures in human populations do not demonstrate clear indications of diabetes. These studies and others show that mice appear to be more efficient methylators of iAs and more resistant to the effects of iAs at the levels to which humans are exposed. Additional studies need to be done to determine the appropriate iAs doses for mice in drinking water to generate tissue level exposures that are equivalent to those in exposed humans. Second, mice that do not metabolize iAs develop an adverse metabolic phenotype. While this could be due to changes in gene expression associated with knockout of

As3mt, it is possible that dietary iAs exposure may be responsible for these effects. As3mt-KO mice accumulate more iAs in the tissue, suggesting that although there is no production of toxic methylated As metabolites, the increased concentration of iAs in the tissue is detrimental. Third, supplementation with the B-vitamins folate and B12 had very minimal effects on iAs metabolism at the doses used in these studies. Higher iAs exposure or higher supplementation may be needed to produce significant changes in iAs metabolism akin to changes observed in humans. Other vitamins or nutritional factors may also be important in modifying iAs metabolism and could be subjects of future studies. Lastly, male mice were more insulin resistant than female mice.

Further investigation into mechanisms underlying the sex differences, such as the role of estrogen (Huang et al. 2015), and how they modify iAs toxicity would significantly advance risk assessment.

Other avenues for future study include investigations into the interaction of high-fat diet, iAs exposure, and low folate that was observed in Chapter 4. The design of the presented study does not allow us to differentiate between effects of high-fat diet and age. The interactive effects of high-fat diet and chronic low-dose iAs exposure should be repeated with parallel low-fat and

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high-fat diet groups. The role of nutrition in toxicology is less recognized than it deserves. While the addition of more variables complicates animal studies, understanding toxicity in these complex models is important and very relevant to human exposures.

6.3 Mechanisms underlying the inhibition of glucose-stimulated insulin secretion by trivalent arsenicals

Studies from our lab and others have shown the iAs and its methylated trivalent metabolites inhibit glucose-stimulated insulin secretion in pancreatic islets and β-cells (Douillet et al. 2013, Fu et al. 2010, Dover et al. 2018). Since the mechanism underlying this inhibition is largely unknown, we investigated if impairments in insulin secretion were due to effects on glucose-stimulated calcium influx. As described in Chapter 5, we found that iAsIII, MAsIII, and

DMAsIII impaired glucose-stimulated calcium influx in pancreatic islets. This impairment could be due to interference with calcium channels or KATP channels. Notably, the methylated metabolites may target different steps in insulin secretion than iAs, increasing the complexity of iAs toxicity.

Further studies could be done to determine how arsenicals affect these ion channels in the

β-cell. Since trivalent arsenicals are highly reactive and can bind with thiol groups in proteins, broader assessments of impairments in β-cell function, such as transcriptomic, proteomic, or metabolomic assessments, would help clarify the mechanisms underlying the inhibition of insulin secretion by arsenicals.

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Figure 6.2. Contribution of this work to the knowledge of the relationship of arsenic and arsenic metabolism with diabetes.

6.4 Sex as an important biological variable

In all the animal studies, we repeatedly observed significant differences between male and female mice in response to iAs exposure. Male WT mice were more prone to developing insulin resistance after prenatal iAs exposure (Chapter 3) and male As3mt-KO mice, which have exposure to iAs through diet, were more insulin resistant than female KO mice (Chapter 4).

Furthermore, metabolites associated with As3mt-KO were different in KO males compared to females (Chapter 2). There were even differences in insulin resistance among WT males and females, regardless of iAs exposure in drinking water.

Differences in susceptibility to diabetes and metabolic disease have been reported in human populations. Women are more tolerant to weight gain and often have more adipose mass than men, while men and post-menopausal women are more prone to developing diabetes

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(Mauvais-Jarvis et al. 2015). Differences in estrogen and androgen levels is a major reason for sex differences in metabolic disease because both are heavily involved in the regulation of energy expenditure and tissue metabolism. Estrogen can influence neurological signaling in the brain to decrease food intake and regulate energy expenditure, has antilipogenic and antiadipogenic effects, is shown to protect β-cell survival and function (Tiano and Mauvais-

Jarvis 2012), may influence insulin sensitivity in muscle and liver, and mediates immunological responses (Mauvais-Jarvis et al. 2013). Androgen deficiency is linked to increased risk of cardiovascular disease, obesity, and T2D (Navarro et al. 2015). Since testosterone is converted into 17β-estradiol via aromatase, lower testosterone availability may lead to lower E2 production for influencing tissue metabolism. But testosterone can also influence metabolism through androgen receptors. Androgen receptors have been shown to regulate adiponectin production, stimulate differentiation away from adipocytes to myoctyes, improve insulin sensitivity, and also play a role in neural circuits regulating energy expenditure (Navarro et al. 2015). Additionally, sex differences in metabolic disease could be due to genetic and epigenetic differences: differences in fetal programming, in placental gene expression, and in expression of X- chromosome-linked genes (Kautzky-Willer et al. 2016).

The sex-specific effects of iAs exposure has been observed in other iAs-associated toxicities, such as in cancer and respiratory disease (Vahter et al. 2007; Ferrario et al. 2016), less so in diabetes (Huang et al. 2015; Palacios et al. 2012). These sex differences can be attributed to a variety of biological reasons. Women are more efficient methylators of iAs than men (Shen et al. 2016), possibly because women have higher levels of choline (Fischer et al. 2010) due to the induction of phosphatidylethanolamine-N-methyltransferase by estrogen (Resseguie et al. 2007).

In our studies, female WT mice tended to have higher levels of total As and %DMAs in the

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urine, as in humans (Chapter 2, 4), potentially explaining the increased susceptibility of males to iAs-associated insulin resistance. However, at the tissue level, females have more total As and lower %DMAs in the liver (Chapter 4; Douillet et al. 2017). This finding is perplexing and suggests that other actions of iAs could influence sex differences.

As discussed above, estrogen and androgen steroid hormone receptors are integral in the regulation of energy use and metabolism. Disruption of these receptors by iAs could explain sex- specific effects on metabolism. iAs has been shown to alter steroid hormone receptor-mediated gene regulation in all five steroid receptor types (estrogen, androgen, glucocorticoid, mineralcorticoid, and progesterone) (Bodwell et al. 2006; Davey et al 2007; Kaltreider et al.

2001) and gene regulation via retinoic acid and thyroid hormone receptors (Davey et al. 2008).

The mechanism underlying this disruption by iAs is unknown but appears to be unique from other endocrine disruptors which bind to the receptor as a hormone mimetic and/or compete as an antagonist. Since there is minimal sequence and structural similarity across these receptor types, it is thought that iAs affects common coregulatory proteins and/or pathways that regulate these receptors (Davey et al. 2007; Barr et al. 2009). Alternatively, iAs could be binding to sulfhydrl groups within these receptors, or related regulatory proteins, in a way that impairs function.

Due to the prevalence of iAs in chow diets, all animals received from suppliers could potentially have had prenatal iAs exposure, which could result in differential programming of the fetus or the placenta by sex. Sex-specific differences in DNA methylation changes associated with iAs exposure have been reported (Broberg et al. 2014; Pilsner et al. 2012; Niedzwiecki et al.

2015). Furthermore, iAs exposure has been linked to differential DNA methylation patterns in the placenta (Green et al. 2016). Although these data were not stratified by sex of the offspring,

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other studies suggest that differential placenta DNA methylation could drive sex differences due to differential regulation of transporters and signaling molecules in the placenta (Martin et al

2017). Interestingly, folate acid supplementation modified placenta DNA methylation in a manner that was dependent on sex of the fetus (Penailillo et al. 2015), potentially explaining the differences in the protective effects of maternal supplementation in prenatal iAs exposures

(Chapter 3). Further investigation of epigenetics as a mechanism underlying sex differences and iAs-associated diabetes is needed.

6.5 Implications

Our animal and in vitro studies show that the role of iAs metabolism in diabetes and metabolic disease is complex. We and others have found that methylated trivalent arsenicals are more toxic than iAsIII (Chapter 5) yet the inability to methylate iAs is associated with adverse metabolic health (Chapter 2, 4). There appears to be two forces involved in the effects of iAs on metabolic health: the production of methylated metabolites and the accumulation of iAs. Both processes lead to adverse effects on glucose metabolism. Since humans are never completely without iAs methylation, the toxicity of the methylated metabolites seems to be more relevant, but this theory needs further investigation. The effects of iAs metabolism on glucose metabolism may be better examined in models with a milder disruption of iAs metabolism.

The research presented in this dissertation has, at least, three major implications. Firstly, our in vivo studies call for a reconsideration of the appropriate iAs doses used for assessment of iAs-associated toxicity, in the context of metabolic disease and others. Additionally, background dietary iAs may be a confounding factor that needs to be considered in future study designs. In our studies, exposure of C57BL6 mice to levels of iAs that humans are exposed to engendered

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only mild differences in metabolites and in glucose metabolism. The clear species difference between man and mouse in susceptibility to iAs toxicity, likely due to differences in iAs metabolism, needs to be addressed going forward by developing more accurate biological models of iAs exposure.

Secondly, more research is needed to come to conclusions about proper strategies to protect against iAs-associated diabetes. Our data add insight on the mechanisms underlying the development of diabetes after iAs exposure, pointing to insulin resistance as an important characteristic, but more work needs to be done to detail the temporal progression of iAs- associated diabetes. Based on our current understanding, iAs-associated diabetes appears to be distinct from both type 1 and type 2 diabetes. It is not associated with β-cell destruction, but rather β-cell dysfunction, leading to impaired insulin secretion. On the contrary, iAs-exposed animals develop high fasting plasma insulin and insulin resistance. With a better understanding of the development of iAs-associated diabetes, we will be able to accurately classify it into the proper diabetes typology, or delineate a novel category, and recommend treatment strategies in the clinic.

Supplementation with B-vitamins is often discussed as a promising intervention to stimulate iAs metabolism, increase excretion of As, and, thus, diminish iAs toxicity. However, our findings indicate that more studies need to be done to determine if and how iAs metabolism can be modified via nutritional means and what changes in iAs metabolism mean in the context of iAs-associated diabetes and metabolic disease. The best strategy to suggest at this point is to reduce exposure to iAs through the dissemination of effective water purification technologies.

Additionally, healthy eating and exercise lifestyles are still likely the most effective methods to

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reduce risk of obesity and diabetes in society. We can seek to encourage healthy eating and exercise lifestyles through education and through improving access to healthy food for all.

Lastly, our data only strengthen the rationale for the NIH policy calling for the inclusion of both sexes in preclinical studies (Clayton and Collins 2014). Undoubtably, understanding how sex influences biology and the body’s response to toxicants will be important for the design, conduct, and interpretation of future toxicological studies in order to maximize protection of human health.

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A. Appendix A: Supplemental Material for Chapter 2

Figure A.1. Arsenic species in urine of control and iAs-treated male (M) and female (F) wild- type (WT) and As3mt-knockout (KO) mice: iAs, inorganic arsenic; MAs, methyl-arsenic; DMAs, dimethyl-arsenic. Mean and SE values are shown for N=15-21 per group. *Indicates statistically significant differences in total speciated arsenic (iAs+MAs+DMAs) between experimental groups (p<0.05).

182

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Figure A.3. Food and water consumption. Average food (A) and water (B) consumption over the 4 weeks of iAs exposure was monitored in male and female, WT and KO mice.

Figure A.4. Number of significantly changed urinary and plasma metabolites (q<0.05) in As3mt-KO (KO) control and iAs-treated (1 ppm) mice as compared to wild-type (WT) counterparts. Venn diagrams show overlapping and iAs-associated plasma and urinary metabolites in female (left) and male (right) animals. Bolded numbers indicate the total number of changed metabolites in each comparison/overlap; arrows indicate direction of change of metabolites.

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Figure A.5. Hierarchical clustering of hepatic phosphatidylcholines (PCs). Heat map generated by unsupervised two-way clustering analysis shows relative levels of PCs (columns) in untreated (0 ppm) and iAs-treated (1 ppm), male (M) and female (F), wild-type (WT) and As3mt-knockout (KO) mice (rows). The PC levels are mean centered with high relative levels indicated in red and low relative levels indicated in blue.

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Table A.1. Metabolites identified by LC-MS and GC-MS in urine and plasma of mice in all experimental groups.

Urine Plasma LC-MS GC-MS LC-MS GC-MS Alanine Dimethylglycine L-Alanine Dimethylglycine Arginine 2-Hydroxypyridine L-Arginine 2-Hydroxypyridine Asparagine Pyruvic acid L-Asparagine Pyruvic acid Aspartic acid L-Lactic acid L-Aspartic acid L-Lactic acid Citrulline Glycolic acid Citrulline Glycolic acid Glutamine Alpha-ketoisovaleric acid L-Glutamine Alpha-ketoisovaleric acid Glutamic acid 2-Hydroxybutyric acid L-Glutamic acid 2-Hydroxybutyric acid Glycine Oxalic acid Glycine Oxalic acid Histidine 2-Hydroxy-3-methylbutyric L-Histidine 2-Hydroxy-3-methylbutyric acid acid leucine/Isoleucine Ketoleucine L-Isoleucine L-Alpha-aminobutyric acid Methionine 3-Methyl-2-oxovaleric acid L-Lysine Ketoleucine Ornithine Malonic acid L-Methionine 3-Methyl-2-oxovaleric acid Phenylalanine Methylmalonic acid Ornithine Pyrophosphate Proline Glyceraldehyde L-Phenylalanine Benzoic acid Serine Urea L-Proline Glycerol Threonine Benzoic acid L-Serine L-Alloisoleucine Tryptophan Glycerol L-Threonine Succinic acid Tyrosine Succinic acid L-Tryptophan Glyceric acid Valine Glyceric acid L-Tyrosine Uracil Asymmetric Uracil L-Valine Fumaric acid dimethylarginine Symmetric Fumaric acid N-Acetylornithine 3-Oxoalanine dimethylarginine Carnosine Pelargonic acid Asymmetric Glutaric acid dimethylarginine Creatinine Acetylglycine Symmetric dimethylarginine Beta-Alanine DOPA Indoxyl sulfate Aminoadipic acid Erythrose Dopamine L-Pipecolic acid Carnosine Malic acid Histamine 3-Oxoalanine Creatinine 2-Phenylglycine Kynurenine Methylcysteine Dopa Pyroglutamic acid Methionine sulfoxide Beta-Alanine Dopamine L-Cysteine t4-Hydroxyproline L-Homoserine Histamine Creatine Putrescine Erythrose L-Kynurenine D-2-Hydroxyglutaric acid Serotonin 3-Aminoisobutanoic acid Methionine sulfoxide Oxoglutaric acid Spermidine Malic acid 4-Hydroxyproline 3-Hydroxybutyric acid Spermine Adipic acid 1-Phenylethylamine Dodecanoic acid Taurine D-Threitol Putrescine D-Xylose C0 Pyroglutamic acid Sarcosine L-Arabinose

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C10 Cytosine Serotonin Ribonolactone C10:1 Gamma-Aminobutyric acid Spermidine D-Ribose C10:2 L-Cysteine Spermine Rhamnose C12 Creatine Taurine L-Arabitol C12-DC Hydroxypropionic acid C10 Glycerol 3-phosphate C12:1 D-2-Hydroxyglutaric acid C10:1 O-Phosphoethanolamine C14 Oxoglutaric acid C10:2 Isocitric acid C14:1 Phenylpyruvic acid C12 Citric acid C14:1-OH 3-Hydroxybutyric acid C12:1 Myristic acid C14:2 Pimelic acid C12-DC 1,5-Anhydrosorbitol C14:2-OH 4-Hydroxybenzoic acid C14 L-Sorbose C16 N-Acetyl-L-aspartic acid C14:1 Gluconolactone C16-OH D-Xylose C14:1-OH D-Fructose C16:1 L-Arabinose C14:2 D-Mannose C16:1-OH Ribonolactone C14:2-OH D-Galactose C16:2 D-Ribose C16 D-Glucose C16:2-OH Suberic acid C16:1 Mannitol C18 Aminoadipic acid C16:1-OH D-Glucuronic acid C18:1 Rhamnose C16:2 Sorbitol C18:1-OH L-Arabitol C16:2-OH Pantothenic acid C18:2 Ribitol C16-OH Palmitoleic acid C2 Glycerol 3-phosphate C18 Galactonic acid C3 O-Phosphoethanolamine C18:1 Gluconic acid C5-OH (C3-DC-M) Hippuric acid C18:1-OH Palmitic acid C3-OH Isocitric acid C18:2 3-Indolepropionic acid C3:1 1-Octanoyl-rac-glycerol C2 Myoinositol C4 Citric acid C3 Heptadecanoic acid C3-DC (C4-OH) 1,5-Anhydrosorbitol C3:1 Linoleic acid C4:1 Adenine C3-DC (C4-OH) Petroselinic acid C5 Quinic acid C3-OH Oleic acid C5-DC (C6-OH) L-Sorbose C4 Elaidic acid C5-M-DC Allantoin C4:1 Stearic acid C5:1 Gluconolactone C5 Nonadecanoic acid C5:1-DC D-Fructose C5:1 Arachidonic acid C6 (C4:1-DC) Hydroxyphenyllactic acid C5:1-DC Arachidic acid C6:1 D-Mannose C5-DC (C6-OH) Docosahexaenoic acid C7-DC D-Galactose C5-M-DC MG(16:0/0:0/0:0) C8 D-Glucose C5-OH (C3-DC-M) Sucrose C9 4-Hydroxycinnamic acid C6 (C4:1-DC) MG(18:2(9Z,12Z)/0:0/0:0)[rac ] lysoPC a C14:0 Mannitol C6:1 MG(18:1(9Z)/0:0/0:0)

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lysoPC a C16:0 D-Glucuronic acid C7-DC Cholesterol lysoPC a C16:1 Sorbitol C8 184 unidentified metabolites lysoPC a C17:0 Pantothenic acid C9 lysoPC a C18:0 Galactonic acid PC aa C24:0 lysoPC a C18:1 Gluconic acid PC aa C26:0 lysoPC a C18:2 Guanidinosuccinic acid PC aa C28:1 lysoPC a C20:3 Palmitic acid PC aa C30:0 lysoPC a C20:4 Uric acid PC aa C32:0 lysoPC a C24:0 Myoinositol PC aa C32:3 lysoPC a C26:0 Normetanephrine PC aa C34:1 lysoPC a C26:1 Heptadecanoic acid PC aa C34:2 lysoPC a C28:0 Stearic acid PC aa C34:3 lysoPC a C28:1 Nonadecanoic acid PC aa C34:4

PC aa C24:0 Glucose 6-phosphate PC aa C36:0

PC aa C26:0 Uridine PC aa C36:1

PC aa C28:1 Inosine PC aa C36:2

PC aa C30:0 Adenosine PC aa C36:3

PC aa C32:0 Sucrose PC aa C36:4

PC aa C32:1 Xanthosine PC aa C36:5

PC aa C32:3 Cytidine PC aa C36:6

PC aa C34:1 Alpha-Lactose PC aa C38:0

PC aa C34:2 MG(18:2(9Z,12Z)/0:0/0:0)[rac PC aa C38:3 ] PC aa C34:3 D-Maltose PC aa C38:4

PC aa C34:4 320 unidentified metabolites PC aa C38:5

PC aa C36:0 PC aa C38:6

PC aa C36:1 PC aa C40:2

PC aa C36:2 PC aa C40:3

PC aa C36:3 PC aa C40:4

PC aa C36:4 PC aa C40:5

PC aa C36:5 PC aa C40:6

PC aa C36:6 PC aa C42:1

PC aa C38:0 PC aa C42:4

PC aa C38:3 PC aa C42:5

PC aa C38:4 PC aa C42:6

PC aa C38:5 PC ae C30:2

PC aa C38:6 PC ae C32:1

PC aa C40:1 PC ae C32:2

PC aa C40:2 PC ae C34:0

PC aa C40:3 PC ae C34:1

PC aa C40:4 PC ae C34:2

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PC aa C40:5 PC ae C36:0

PC aa C40:6 PC ae C36:1

PC aa C42:0 PC ae C36:2

PC aa C42:1 PC ae C36:3

PC aa C42:2 PC ae C36:4

PC aa C42:4 PC ae C36:5

PC aa C42:5 PC ae C38:0

PC aa C42:6 PC ae C38:1

PC ae C30:0 PC ae C38:3

PC ae C30:2 PC ae C38:4

PC ae C32:2 PC ae C38:5

PC ae C34:0 PC ae C38:6

PC ae C34:1 PC ae C40:3

PC ae C34:2 PC ae C40:4

PC ae C34:3 PC ae C40:5

PC ae C36:0 PC ae C40:6

PC ae C36:1 PC ae C42:5

PC ae C36:2 SM (OH) C14:1

PC ae C36:3 SM (OH) C22:1

PC ae C36:4 SM C24:0

PC ae C36:5

PC ae C38:0

PC ae C38:1

PC ae C38:3

PC ae C38:4

PC ae C38:5

PC ae C38:6

PC ae C40:1

PC ae C40:2

PC ae C40:3

PC ae C40:4

PC ae C40:5

PC ae C40:6

PC ae C42:0

PC ae C42:1

PC ae C42:2

PC ae C42:3

PC ae C42:4

PC ae C42:5

PC ae C44:3

PC ae C44:4

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PC ae C44:5

PC ae C44:6

SM (OH) C14:1

SM (OH) C16:1

SM (OH) C22:1

SM (OH) C22:2

SM (OH) C24:1

SM C16:0

SM C16:1

SM C18:0

SM C18:1

SM C20:2

SM C24:0

SM C24:1

SM C26:0

SM C26:1 CX:Y; acyl-carnitine, where X indicates number of carbons of the fatty acid tail and Y indicates number of double bonds. Phosphatidylcholine notation: PC; phosphatidylcholine, aa; two fatty acid ester linkages, ae; one ester and one ether linkage, CA:Z is a description of the fatty acid composition of two PC tails where A indicates total number of carbons in both fatty acid tails and Z indicates total number of double bonds.

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Table A.2. Plasma and urinary metabolites significantly changed (p<0.05) by iAs treatment in male and female wild-type (WT) and As3mt-KO (KO) mice. Fold change indicates difference from the second group in the comparison.

Urine Plasma

Comparison Metabolite p- Fold Metabolite p- Fold value Change value change KO, female Dopamine <0.01 -1.64 3-Indolepropionic 0.05 -1.35 1 ppm vs 0 ppm (55)* acid D-Fructose 0.03 -2.59 PC ae C40:6 <0.01 -1.24

Mannitol 0.02 -2.14 PC aa C38:6 <0.01 -1.23

D-Galactose 0.02 -1.52 PC aa C40:6 <0.01 -1.22 D-Glucose 0.02 -1.52 SM C18:0 0.01 -1.21 C18:1 <0.01 -1.50 PC ae C30:2 0.02 -1.20

Adenosine 0.04 -1.48 Citric acid <0.01 -1.20 PEA 0.01 -1.47 PC aa C38:4 0.01 -1.19 Ribitol 0.01 -1.44 SM C18:1 0.02 -1.19 L-Arabitol 0.01 -1.41 PC ae C42:0 0.04 -1.18 Myoinositol 0.02 -1.38 PC ae C38:4 0.01 -1.17 Ribonolactone 0.03 -1.38 PC aa C40:5 0.03 -1.16 C14 <0.01 -1.36 PC aa C36:4 0.01 -1.16 C14:2 0.03 -1.32 Histidine 0.04 -1.14 C18 0.01 -1.31 PC aa C40:4 0.01 -1.14 C5-DC (C6-OH) 0.04 -1.23 PC aa C42:4 0.04 -1.13 C18:1-OH 0.04 -1.22 PC aa C24:0 0.04 -1.13 PC ae C34:1 <0.01 -1.21 PC ae C40:4 0.05 -1.13 PC ae C36:3 0.02 -1.10 PC ae C40:2 0.01 -1.12 Creatinine <0.01 1.03 PC ae C38:6 0.04 -1.12 PC aa C36:1 0.03 1.19 PC aa C38:0 0.02 -1.12 C10:1 0.04 1.28 PC aa C38:3 0.03 -1.12 PC aa C34:2 0.01 1.35 PC ae C40:5 0.03 -1.11 PC aa C42:6 0.04 1.37 PC aa C38:5 0.03 -1.11 SM (OH) C22:1 0.02 1.44 PC aa C36:5 0.04 1.09

SM C24:0 0.01 1.44

PC ae C40:3 0.01 1.55

SM (OH) C14:1 <0.01 1.79

Normetanephrine <0.01 1.94 Alanine 0.03 2.22 KO, male Benzoic acid <0.01 5.07 lysoPC a C20:4 0.02 -1.66

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1 ppm vs 0 ppm (18)* PC aa C36:0 0.02 -1.12 lysoPC a C17:0 0.03 -1.55 Citric acid 0.02 -1.59 lysoPC a C16:1 0.03 -1.49 PC ae C36:4 0.04 -1.17 lysoPC a C18:1 0.03 -1.45 L-Arabitol 0.01 1.38 lysoPC a C20:3 0.03 -1.41 O- 0.01 1.40 C16:1-OH 0.01 -1.38 Phosphoethanolamine 1,5-Anhydrosorbitol 0.02 1.44 Glycerol 0.01 -1.30 Glycerol 3-phosphate 0.02 1.45 Nonadecanoic acid 0.01 -1.23

L-Cysteine 0.02 1.53 Adenine <0.01 1.56 Histidine 0.05 -1.85 Dopa 0.02 1.46 WT, female 1 ppm vs 0 ppm (22)* C10 0.03 -1.29

PC ae C36:3 0.04 1.13

PC ae C36:5 0.04 1.16

PC aa C38:6 0.02 1.16

PC ae C34:1 <0.01 1.22

Ketoleucine 0.04 1.24

PC ae C40:6 <0.01 1.26

PC aa C34:1 0.04 1.26

C12:1 0.04 1.26

PC aa C36:1 0.01 1.33

PC aa C36:2 0.02 1.36

C10:1 0.01 1.38

PC ae C38:3 0.03 1.47

PC ae C40:4 0.03 1.47

PC ae C34:2 0.01 1.55

D-Ribose 0.05 1.57

PC ae C36:0 0.04 1.72

PC ae C30:2 <0.01 2.11

Glutamate <0.01 2.43 Citric acid <0.01 3.19 WT, male Alpha-Lactose 0.05 -5.01 Glutamate 0.03 1.26 1 ppm vs 0 ppm (14)* Gluconolactone <0.01 1.78 2-Hydroxybutyric 0.05 1.34 acid Spermidine 0.02 1.35

C8 0.04 1.40

C7-DC 0.04 1.43

D-2-Hydroxy- 0.04 1.43 glutaric acid C18:1 0.03 1.47

C18:2 0.02 1.49

Fumaric acid 0.02 1.49

Malic acid 0.01 1.53

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Uracil 0.02 1.86 3-Oxoalanine 0.03 1.94 *Number in parentheses indicates total number of metabolites significantly different in the comparison. CX:Y; acyl-carnitine, where X indicates number of carbons of the fatty acid tail and Y indicates number of double bonds. Phosphatidylcholine notation: PC; phosphatidylcholine, aa; two fatty acid ester linkages, ae; one ester and one ether linkage, CA:Z is a description of the fatty acid composition of two PC tails where A indicates total number of carbons in both fatty acid tails and Z indicates total number of double bonds.

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Table A.3. Plasma and urinary metabolites significantly changed (p<0.05) in comparisons of male and female As3mt–KO (KO) and wild-type (WT) mice. Fold change indicates difference from the second group in the comparison.

Urine Plasma

Comparison Metabolite p- Fold Metabolite p- Fold value Change value change Control, female Serine 0.05 -2.64 Gluconic acid 0.01 -2.60 Urea 0.01 -5.97 Carnosine 0.01 -1.45 KO vs WT (102)* Acetylglycine 0.03 -3.68 Pyruvic acid 0.02 -1.29 2-Hydroxybutyric acid <0.01 -2.48 PC aa C26:0 0.03 -1.10 Ornithine 0.04 -2.38 PC aa C32:0 0.05 1.09 Suberic acid 0.01 -1.81 PC aa C32:3 0.05 1.10 Tryptophan 0.02 -1.69 PC aa C38:6 0.05 1.14 Proline 0.01 -1.68 PC ae C42:3 0.04 1.14 Adipic acid <0.01 -1.57 PC aa C36:5 0.01 1.14 Pelargonic acid 0.01 -1.45 PC ae C36:2 0.04 1.15 Glycolic acid 0.01 -1.39 PC aa C38:5 0.01 1.15 Beta-Alanine 0.05 -1.34 PC ae C40:5 0.01 1.15 C5-M-DC 0.01 -1.33 PC ae C42:4 0.01 1.16 3-Methyl-2-oxovaleric 0.01 -1.22 PC ae C38:5 0.01 1.16 acid Creatinine 0.01 -1.04 SM C16:0 0.02 1.16 PC ae C38:5 0.04 1.14 PC aa C42:5 0.02 1.17 PC ae C36:5 <0.01 1.21 PC ae C44:6 0.01 1.17 C14 0.03 1.22 PC ae C38:6 0.01 1.17 PC aa C36:0 <0.01 1.25 PC aa C42:6 0.04 1.17 PC ae C36:3 <0.01 1.26 PC aa C36:3 <0.01 1.17 PC ae C36:4 <0.01 1.28 PC ae C38:4 0.01 1.18 C3-OH 0.03 1.29 PC ae C44:5 0.03 1.18 PC aa C36:2 0.04 1.30 PC ae C32:2 0.01 1.19 PC aa C38:3 0.01 1.36 PC aa C40:5 0.02 1.19 C18:1-OH <0.01 1.38 PC aa C36:4 0.01 1.19 PC ae C38:3 0.05 1.40 PC aa C42:1 <0.01 1.19 C18 <0.01 1.45 PC ae C38:3 <0.01 1.19 D-Threitol 0.01 1.50 PC aa C34:2 0.03 1.19 Ribonolactone 0.02 1.52 PC ae C36:4 <0.01 1.20 Myoinositol <0.01 1.60 PC ae C40:2 <0.01 1.20 C18:1 <0.01 1.62 PC aa C36:6 <0.01 1.20 L-Arabitol <0.01 1.63 SM C16:1 0.01 1.20 O-Phosphoethanolamine 0.04 1.67 PC aa C36:1 <0.01 1.20 N-Acetylornithine 0.02 1.71 PC aa C40:3 <0.01 1.20 1,5-Anhydrosorbitol 0.01 1.77 PC ae C42:2 <0.01 1.21 Adenosine 0.01 1.78 PC aa C42:0 0.01 1.21 Dopamine <0.01 1.80 PC aa C40:2 0.01 1.22

194

Mannitol 0.04 1.95 PC ae C36:5 <0.01 1.22 Cytosine 0.05 1.96 PC aa C42:2 <0.01 1.23 Cytidine 0.02 2.21 PC aa C36:2 0.01 1.23 Sarcosine <0.01 2.21 PC aa C40:1 <0.01 1.24 Hydroxypropionic acid 0.03 2.22 PC ae C40:4 <0.01 1.25 Galactonic acid <0.01 3.36 PC aa C40:4 <0.01 1.26 Gluconic acid <0.01 3.36 PC aa C42:4 <0.01 1.26 D-Fructose 0.03 3.40 SM C24:0 0.01 1.27 Methionine sulfoxide 0.01 3.43 PC aa C40:6 <0.01 1.27 L-Sorbose 0.02 4.00 PC ae C40:3 <0.01 1.27

PC aa C38:4 <0.01 1.28

Valine <0.01 1.28

SM (OH) C22:1 0.01 1.28

PC aa C38:0 <0.01 1.29 PC aa C38:3 <0.01 1.34 Ornithine 0.01 1.35 C14:2-OH 0.04 1.36 3- <0.01 2.10 Indolepropionic acid

Control, male Carnosine <0.01 -2.44 Carnosine <0.01 -1.49 KO vs WT (52)* Acetylglycine <0.01 -16.82 SDMA <0.01 -1.41 Alpha-Lactose 0.02 -6.77 Creatinine <0.01 -1.36 Aminoadipic acid 0.02 -2.53 Putrescine 0.04 -1.32 C14:2-OH <0.01 -2.31 t4-OH-Pro 0.01 -1.26 Aspartate <0.01 -1.96 PC ae C40:6 0.01 -1.25 C7-DC <0.01 -1.85 PC ae C30:2 0.01 -1.19 Uracil <0.01 -1.64 PC ae C30:0 0.02 -1.16 Uridine 0.02 -1.61 PC aa C26:0 <0.01 -1.16 Beta-Alanine 0.04 -1.48 PC aa C40:2 0.03 1.16 PC aa C36:0 <0.01 1.29 Glutamine 0.03 1.18 Putrescine 0.03 1.35 PC ae C38:0 <0.01 1.19 PC aa C40:4 <0.01 1.58 PC aa C36:6 0.01 1.19 Malic acid 0.03 1.67 PC aa C36:5 <0.01 1.20 O-Phosphoethanolamine 0.02 1.67 Histidine 0.01 1.21 2-Hydroxy-3- <0.01 1.69 PC aa C40:3 <0.01 1.24 methylbutyric acid C6 (C4:1-DC) <0.01 1.69 PC aa C40:4 <0.01 1.25 Oxoglutaric acid 0.01 1.73 PC aa C34:4 <0.01 1.25 PC aa C40:5 <0.01 1.82 PC aa C36:3 0.01 1.26 Sarcosine 0.04 2.36 Glutamate <0.01 1.32 Cytidine <0.01 2.74 PC aa C38:3 0.01 1.33

195

Hippuric acid <0.01 8.51 Alanine 0.02 1.36

PC aa C32:1 <0.01 1.47

Myristic acid 0.03 1.53

Petroselinic acid 0.01 1.53

Oleic acid 0.01 1.53

Palmitic acid <0.01 1.54

Oxoglutaric acid <0.01 1.59

lysoPC a C20:3 <0.01 1.76 Palmitoleic acid <0.01 1.88 iAs-treated, female Citric acid <0.01 -3.58 Carnosine 0.01 -1.42 KO vs WT (59)* PC aa C26:0 <0.01 -2.50 Beta-Alanine 0.01 -1.86 Lysine 0.02 -2.47 Isocitric acid 0.01 -1.40 DOPA 0.02 -2.44 Citric acid 0.01 -1.40 Aminoadipic acid <0.01 -2.41 C12 0.01 -1.37 Arginine 0.02 -2.35 C9 0.04 -1.35 2-Hydroxybutyric acid <0.01 -2.32 PC ae C40:6 <0.01 -1.33 PC ae C30:2 <0.01 -2.30 Myoinositol 0.05 -1.25 Glutamate <0.01 -2.15 Glycolic acid 0.03 -1.22 Phenylalanine 0.03 -1.72 PC aa C26:0 <0.01 -1.19 Ketoleucine <0.01 -1.64 PC aa C28:1 0.02 -1.13 Adipic acid 0.01 -1.55 PC ae C34:0 0.04 1.12 PC ae C34:2 0.01 -1.51 PC aa C34:3 0.01 1.13 C5-M-DC 0.01 -1.44 PC aa C40:3 0.03 1.16 PC ae C34:1 <0.01 -1.39 PC aa C40:2 0.03 1.16 PC aa C38:6 <0.01 -1.33 PC aa C36:2 0.03 1.17 3-Methyl-2-oxovaleric <0.01 -1.31 PC ae C44:6 <0.01 1.18 acid C5-DC (C6-OH) 0.02 -1.30 PC aa C38:0 <0.01 1.18 PC ae C40:6 <0.01 -1.28 PC aa C36:6 <0.01 1.19 PC aa C42:4 <0.01 -1.27 PC aa C42:2 <0.01 1.20 PC ae C38:4 0.02 1.19 PC aa C36:1 <0.01 1.26 PC aa C36:0 0.01 1.20 SM C24:0 0.01 1.26 PC ae C36:4 <0.01 1.24 PC aa C36:5 <0.01 1.27 C16:1 0.03 1.28 PC aa C38:3 <0.01 1.29 PC ae C42:5 0.03 1.29 Histamine 0.01 1.71 PC aa C40:3 0.04 1.34 Serotonin 0.02 2.16

C3-OH 0.04 1.36

SM (OH) C14:1 0.03 1.40

Methylmalonic acid 0.01 1.47

Histamine 0.01 1.85

O-Phosphoethanolamine <0.01 1.92

Benzoic acid 0.01 2.49

196

L-Homoserine 0.03 2.50 iAs-treated, male C14:2-OH <0.01 -2.91 C18:1 <0.01 -1.89 KO vs WT (78)* 4-Hydroxybenzoic acid <0.01 -2.81 C18:2 <0.01 -1.88 Aminoadipic acid 0.02 -2.25 C7-DC <0.01 -1.80 Carnosine <0.01 -2.25 C18 <0.01 -1.77 Gluconolactone <0.01 -2.01 Carnosine <0.01 -1.73 C14:2 <0.01 -1.98 C16 <0.01 -1.67 Uridine <0.01 -1.90 Spermidine <0.01 -1.62 C16:1-OH 0.01 -1.82 Beta-Alanine 0.05 -1.56 Pelargonic acid 0.01 -1.80 C16:2-OH <0.01 -1.54 C5:1 0.03 -1.77 2-Hydroxy- <0.01 -1.53 butyric acid C14:1-OH 0.01 -1.70 C18:1-OH 0.01 -1.53 C18 0.03 -1.68 PC ae C40:6 <0.01 -1.49 C7-DC 0.02 -1.66 Kynurenine <0.01 -1.44 C14:1 0.02 -1.60 L-Cysteine 0.03 -1.44 3-Methyl-2-oxovaleric 0.02 -1.47 C14:2 <0.01 -1.44 acid PC ae C38:0 0.01 -1.41 C16:1-OH 0.01 -1.43 PC ae C40:5 0.01 -1.37 Malic acid 0.02 -1.42 Hydroxyphenyllactic 0.02 -1.33 C14 0.03 -1.38 acid PC aa C36:6 0.04 -1.33 t4-Hydroxy- <0.01 -1.35 proline PC aa C38:0 0.02 -1.32 PC ae C38:6 <0.01 -1.32 D-Glucuronic acid 0.01 -1.31 Phenylalanine 0.01 -1.30 PC ae C40:6 0.01 -1.29 Tryptophan 0.02 -1.26 PC aa C40:6 0.02 -1.27 PC ae C36:4 0.04 -1.22 PC aa C38:6 0.01 -1.26 PC ae C36:2 0.04 -1.18 Oxalic acid 0.04 1.33 PC ae C34:1 0.01 -1.18 L-Arabitol 0.02 1.34 PC ae C36:1 0.02 -1.17 C6 (C4:1-DC) 0.02 1.35 PC aa C26:0 <0.01 -1.14 PC aa C40:4 <0.01 1.39 PC aa C36:5 0.01 1.16 1,5-Anhydrosorbitol 0.04 1.40 PC aa C40:4 <0.01 1.23 Putrescine 0.01 1.44 PC aa C40:3 <0.01 1.28 2-Hydroxy-3- <0.01 1.45 PC aa C38:3 0.02 1.30 methylbutyric acid Myoinositol 0.03 1.49 Alanine 0.02 1.38 Adenine 0.00 1.52 PC aa C32:1 <0.01 1.51 Palmitic acid 0.03 1.53 3-Indole- 0.03 1.72 propionic acid D-Ribose 0.02 1.58 D-Xylose 0.02 1.94

D-Galactose 0.01 1.58

C4 0.02 1.65

Succinic acid 0.01 1.79

197

O-Phosphoethanolamine <0.01 1.86

Glycerol 3-phosphate <0.01 1.94

PC aa C40:5 <0.01 2.18

Cytidine <0.01 2.49 Hippuric acid <0.01 9.42 *Number in parentheses indicates total number of metabolites significantly different in the comparison. CX:Y; acyl-carnitine, where X indicates number of carbons of the fatty acid tail and Y indicates number of double bonds. SM; sphingomyolipid. Phosphatidylcholine notation: PC; phosphatidylcholine, aa; two fatty acid ester linkages, ae; one ester and one ether linkage, CA:Z is a description of the fatty acid composition of two PC tails where A indicates total number of carbons in both fatty acid tails and Z indicates total number of double bonds.

198

Table A.4. Pathways enriched for plasma (P) and urine (U) metabolites differentially changed in response to As3mt knockout or iAs treatment. Increase and decrease in metabolite levels are indicated with (+) and (-), respectively.

Knockout vs wild-type iAs-treated vs control Control, iAs- Control, iAs-treated, KO, WT, KO, WT, Female treated, Male, Male, Female, Female, Male, Male, KO vs Female KO vs WT KO vs WT 1 ppm vs 1 ppm vs 1 ppm vs 0 1 ppm vs 0 WT KO vs 0 0 WT Amino Acid Alanine, P=0.01 P=0.009 aspartate and Aspartate (-, U), L-glutamic acid glutamine (+, P), (+, P), fumaric glutamate oxoglutaric acid acid (+, P) metabolism (+, P) Arginine and P=0.008 P=0.002 proline Aspartate (-, U), Spermidine glutamine (+, P), (+, P), glutamic metabolism hydroxyl- acid (+, P), proline (-, P), fumaric acid

putresine (-, P) (+, P) 199 beta-Alanine P= 0.004 P=0.004

metabolism Aspartate (-, U), Spermidine uracil (-, U), (+, P), β-alanine (-, U) uracil (+, P) D-Glutamine P=6.03e-5 P=0.02 P=0.03 and D- Glutamine (+, P), D-glutamic acid L-glutamic acid glutamate (+, P), (+, U) (+, P) glutamate oxoglutaric acid metabolism (+, P) Glycine, P=0.04 serine and Sarcosine (+, U), threonine pyruvate (-, P), metabolism serine (-, U) Histidine P=0.03 P=0.0024 P=0.04 metabolism Carnosine Histidine (+, P), histidine (-, U) (-, P), histamine aspartate (-, U), (+, P/U) carnosine (-, U) Lysine P=0.002 biosynthesis Lysine (-, U), aminoadipic acid (-, U) Valine, P=0.03 P=0.05 leucine and Pyruvate (-, P), ketoleucine valine (+, P) (+, U)

isoleucine biosynthesis

Lipid alpha- P=0.03 Linolenic PC (+, U) acid metabolism Fatty acid P=0.05 biosynthesis oleic acid (+, P), myristic acid (+, P), palmitic acid (+, P) Glycerolipid P=0.008 metabolism Glycerol (-, P), glycerol-3- phosphate (+, U) Glycerophosp P=0.02 P=0.04 P=4.8e-5 holipid PC (+/-, U/P), PC (+/-, U/P), O-phospho- O-phospho- glycerol-3- ethanolamine (+, metabolism ethanolamine phosphate U), glycerol-3- (+, U), (+, U), phosphate (+, U), 200 lysoPC (+, P) O-phospho- PC (-, U),

ethanolamine lysoPC (-, P)

(+, U) Linoleic acid P=0.03 P=0.05 metabolism PC (+, U) PC (-, U) Carbohydrate Ascorbate P=0.02 and aldarate Myoinositol (+, U), metabolism D-glucuronic acid (-, U) Citrate cycle P=0.04 (TCA cycle) citric acid (-, P), isocitric acid (-, P) Galactose P=0.04 metabolism D-glucose (-, U), myoinositol (-, U) Glyoxylate P=0.003 and Citric acid (-, P), isocitric dicarboxylate acid (-, P), metabolism glycolic acid (-, P)

Pentose and P=0.007 glucuronate L-arabitol (+, U), D-glucuronic interconversi acid (-, U), ons D-xylose (+, P) Starch and P=0.02 sucrose D-glucose (-, U), D-fructose metabolism (-, U) Other Glutathione P=0.03 P=0.01 metabolism L-cysteine (-, P), Spermidine putrescine (+, U), (+, P), spermidine L-glutamic acid (-, P) (+, P) Nitrogen P=0.01 P=0.04 metabolism Glutamine (+, P), histidine (-, U) histidine (+, P) Pantothenate P=0.05 P=0.03 P=0.05 and CoA Valine (+, P), β-alanine (-, U), L-cysteine β-alanine (-, U) uracil (-, U) (-, P), biosynthesis β-alanine (-, P) Propanoate P=0.012 P=0.04 P=0.01 metabolism β-alanine (-, U), β-alanine (-, P), succinic acid (+,

2-hydroxy- 2-hydroxy- U), 201 butyric acid butyric acid β-alanine (-, P),

(-, U), (-, U) 2-hydroxy- hydroxyl- butyric acid propionic acid (-, P) (+, U) Pyrimidine P=8e-4 metabolism Glutamine (+, P), cytidine (+, U), uridine (-, U), uracil (-, U), β-alanine (-, U) Significantly changed metabolites with a p-value<0.05 were used for pathway analysis using MetaboAnalyst. Significantly enriched pathways (p<0.05) are shown.

Table A.5. Significantly changed (P<0.05) hepatic PCs in comparisons of untreated (0) and iAs- treated (1) animals, separated by sex (M and F) and genotype (As3mt-knockout (KO) and wild- type (WT) mice).

Comparison Metabolite p- Fold Change value WT 1 F vs WT 0 F (2) PC(34:1) 0.05 1.14 PC(38:2) 0.04 1.48

WT 1 M vs WT 0 M (1) PC(38:2) 0.02 1.33 KO 1 F vs KO 0 F (3) PC(36:2) 0.01 1.11 PC(34:1) 0.03 1.16 PC(36:1) 0.04 1.27 KO 1 M vs KO 0 M (3) PC(36:0) 0.02 -2.35 PC(38:3) 0.04 1.15 PC(32:1) 0.04 1.16 aPhosphatidylcholine notation: PC(X:Y) indicates a phosphatidylcholine X total carbons and Y double bonds. PC(O-X:Y) indicates a phosphatidylcholine with one acyl and one ester linkage to the fatty acid tails with X total carbons and Y double bonds. bFold change indicates an increase or decrease in iAs-treated animals

202

Table A.6. Significantly changed (P<0.05) hepatic PCs in comparisons of As3mt-knockout (KO) and wild-type (WT) mice, separated by sex (M and F) and iAs-treatment (0 and 1 ppm).

Comparison Metabolite p-value Fold Change KO 0 F vs WT 0 F (4) PC(34:1) 0.020822 -1.18006 PC(38:4) 0.036602 1.11768 PC(38:3) 0.008267 1.22672 PC(40:4) 0.026064 1.5 KO 0 M vs WT 0 M (12) PC(O-40:4) 0.000659 -1.47126 PC(34:2) 4.68E-10 -1.39544 PC(O-40:6) 0.000436 -1.3037 PC(40:6) 0.003391 -1.28049 PC(38:6) 0.000135 -1.24784 PC(O-36:2) 0.013842 -1.21739 PC(32:0) 0.004818 -1.16583 PC(36:2) 0.013628 -1.13956 PC(36:4) 0.018577 -1.13131 PC(32:1) 0.043028 1.21403 PC(38:3) 0.01923 1.22603 PC(38:2) 0.000518 1.45673 KO 1 F vs WT 1 F (8) PC(O-36:1) 0.010833 -1.35333 PC(34:1) 0.014616 -1.16294 PC(O-34:1) 0.032848 -1.16098 PC(32:0) 0.011641 -1.11679 PC(36:2) 2.12E-05 1.21239 PC(38:3) 0.029214 1.16103 PC(38:4) 0.003345 1.17083 SM(34:1) 0.02794 -1.11429 KO 1 M vs WT 1 M (11) PC(32:2) 0.001262 1.53947 PC(40:4) 0.035402 1.26563 PC(34:2) 1.71E-08 -1.32809 PC(32:1) 0.000558 1.35517 PC(O-36:2) 0.000443 -1.35519 PC(36:5) 0.042453 1.18617 PC(38:6) 0.003432 -1.17678 PC(O-40:6) 4.42E-05 -1.36364 PC(32:0) 0.003397 -1.16586 PC(O-38:4) 0.001113 -1.3037 PC(38:3) 2.16E-05 1.42396 aPhosphatidylcholine notation: PC(X:Y) indicates a phosphatidylcholine X total carbons and Y double bonds. PC(O-X:Y) indicates a phosphatidylcholine with one acyl and one ester linkage to the fatty acid tails with X total carbons and Y double bonds. bFold change indicates an increase or decrease in KO animals.

B. Appendix B: Supplemental Material for Chapter 3

Figure B.1. Study design. Dams were exposed to iAs in drinking (deionized) water (0, 100, and 1000 ug As/L) and fed either the folate/B12-adequate or supplemented diet, based on a purified AIN-93G diet. The exposure and diet feeding started one week before mating and ended at the time dams gave birth to offspring. Offspring were maintained on deionized water and the folate/B12 adequate diet for 18 weeks. Phenotyping measurements were conducted on all offspring through the study period, measuring fasting blood glucose (FBG), intraperitoneal glucose tolerance test (IPGTT), fasting plasma insulin (FPI), and body composition (MRI).

Figure B.2. Maternal body weight. Body weights of dams exposed to iAs and fed a folate/B12 adequate or supplemented diet were measured (A) prior to exposure and (B) at the start of mating. Mean + SEM for N=6-9 is shown.

Figure B.3. Proportions of arsenic metabolites in urine of dams fed a folate/B12-adequate or supplemented diet and exposed to 0, 100, or 1000 ppb iAs. (A) %iAs, (B) %MAs, and (C) %DMAs in spot urine samples collected from dams exposed to iAs for 1 week, shortly before mating. Mean + SEM for N=5-9 is shown.

Figure B.4. Growth curves of offspring prenatally exposed to iAs, with and without maternal folate and B12 supplementation. Body weight was measured weekly after weaning in (A) female and (B) male offspring whose mothers were exposed to 0, 100, or 1000 ppb iAs in drinking water and fed either folate/B12-adequate (closed circles) or supplemented (open squares) diet before and during gestation. Mean for N=7-21 is shown. * p<0.05 significantly different from controls (0 ppb iAs) from the same diet; ǂ p<0.05 significantly different from 100 ppb iAs exposure; # p<0.05 significantly different from offspring from adequate-diet dams.

Table B.1. Total numbers of offspring in each treatment group.

C. Appendix C: Supplemental Material for Chapter 4

Figure C.1. Study design. Male and female C57BL6 (WT) and As3mt-knockout (KO) mice were given a folate-adequate (2 mg/kg) diet and drank deionized water for 4 weeks after weaning. Baseline metabolic measures were assessed. Animals were then exposed to 0 or 100 ppb iAs in drinking water and consumed either a low (0.2 mg/kg) or high (10 mg/kg) folate purified diet for 24 weeks. After 24 weeks, all animals were fed a high-fat diet (HFD) with respective folate levels and continued iAs exposure until the end of the study. Metabolic phenotyping was conducted at week 5 and 24, and after 8 weeks on the HFD. Urine was collected for iAs speciation analysis at week 5 and at the end of the study. IPGTT, intraperitoneal glucose tolerance test; FBG, fasting blood glucose; FPI, fasting plasma insulin; MRI, magnetic resonance imaging to assess body composition.

Figure C.2. Food consumption of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water. Mean ± SEM for N=3-4 cages. P<0.05 for the following comparisons: S, males vs females of the same diet, exposure, and genotype; F, low vs high folate intake animals of the same genotype, exposure, and sex; G, KO vs WT mice of the same sex, diet, and exposure; E, 0 vs 100 ppb iAs-exposed mice of the same sex, genotype, and folate level.

Figure C.3. Water consumption of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water. Mean ± SEM for N=3-4. P<0.05 for the following comparisons: S, males vs females of the same diet, exposure, and genotype; F, low vs high folate intake animals of the same genotype, exposure, and sex; G, KO vs WT mice of the same sex, diet, and exposure; E, 0 vs 100 ppb iAs-exposed mice of the same sex, genotype, and folate level.

Figure C.4. Body weight over time of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water, before and after high-fat diet. Points represent mean body weight for a given time point for N=14-20.

Figure C.5. Weight gain of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water, before and after high-fat diet. Weight gain of animals during LFD and HFD is shown in grams (A, B) and as % of weight prior to exposure or prior to HFD as appropriate (C, D). Mean ± SEM for N=14-20. P<0.05 for the following comparisons: S, males vs females of the same diet, exposure, and genotype; F, low vs high folate intake animals of the same genotype, exposure, and sex; G, KO vs WT mice of the same sex, diet, and exposure; E, 0 vs 100 ppb iAs-exposed mice of the same sex, genotype, and folate level.

Figure C.6. Glucose tolerance of male and female WT and As3mt-KO mice on a low or high folate diet and exposed to 0 or 100 ppb iAs in drinking water for 5 weeks. Mean ± SEM for N=14-20. P<0.05 for the following comparisons: S, males vs females of the same diet, exposure, and genotype; E, 0 vs 100 ppb iAs-exposed mice of the same sex, genotype, and folate level.