Investigations Into the Role of Exogenous Estrogenic Endocrine Disrupting Chemicals on Immune Dysregulation in Autoimmune Disease

Investigations into the role of exogenous estrogenic endocrine disrupting chemicals on immune dysregulation in autoimmune disease

Michael R. Edwards

Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of

Doctor of Philosophy In Biomedical and Veterinary Sciences

S. Ansar Ahmed Thomas E. Cecere Liwu Li Xin M. Luo Lijuan Yuan

May 8, 2019 Blacksburg, VA

Keywords: Autoantibody, nephritis, immune complex, dietary

This work is licensed under the Creative Commons Attribution 4.0 International License. To

view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to

Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Investigations into the role of exogenous estrogenic endocrine disrupting chemicals on immune dysregulation in autoimmune disease

Michael Edwards

ABSTRACT

Estrogenic endocrine disrupting chemicals (EEDCs) are defined as chemicals that bind to estrogen receptors (ERs) and augment estrogenic functions, either through promoting or blocking estrogen receptor signaling. Recent reports highlight the growing concern surrounding environmental exposure to EEDCs and immune system modulation. A commonly prescribed EEDC, 17α-ethinyl estradiol, is a synthetic analog of 17β-estradiol (E2), and is also found in many environmental reservoirs of human and animal exposure. Little is known regarding the immunomodulatory effects of this EEDC. Autoimmune diseases, such as systemic lupus erythematosus (SLE), are characterized by a dysregulated immune system that has lost tolerance to self-antigens. The pathogenesis of SLE is still poorly understood. However, it is likely that genetics, epigenetics, hormones, and environmental factors, such as EEDC exposure, contribute to the pathogenesis and severity of SLE. The work presented in this dissertation focused on investigating the immunomodulatory effects of exogenous estrogens in mouse models of SLE. Chapter 1 describes an overview of environmental endocrine disruptors and autoimmune disease, with a particular emphasis on estrogens. Chapter 2 represents a review of the current and pertinent literature surrounding the contributions of sex differences, hormones, and EDCs to the induction of autoantibodies and development of autoimmunity, as well as the contributions of anti- microbial responses to SLE. We explored the contribution of dietary components to SLE disease severity. Mice fed a diet devoid of exogenous phytoestrogens developed significantly reduced glomerulonephritis and glomerular immune complex deposition compared to mice fed a diet containing soy isoflavones. Diet also influenced cytokine production and epigenetics of LPS-stimulated splenic leukocytes. We identified similar effects of E2 and EE implantation with regards to innate immunity, and distinct cellular subset, cytokine production profiles, gene expression, and epigenetic responses between E2 and EE treated NZB/WF1 mice. Oral exposure to a very low human relevant dose of EE promoted glomerulonephritis and augmented responses to viral and bacterial mimics in MRL/lpr mice. Overall, our findings suggest that chronic exposure to environmental EEDCs exacerbates lupus nephritis and alter an already dysregulated immune system in genetically susceptible individuals and have greatly expanded the current body of knowledge surrounding 17α-ethinyl estradiol.

Investigations into the role of exogenous estrogenic endocrine disrupting chemicals on immune dysregulation in autoimmune disease

Michael Edwards

GENERAL AUDIENCE ABSTRACT

Chemicals that can disrupt the normal effects of hormones are termed endocrine disrupting chemicals (EDCs). Estrogenic EDCs promote or suppress the ability of estrogen receptors to carry out normal functions within the body. Normal immune system functions require a fine balance of inflammatory and anti-inflammatory cellular responses. This delicate balance is a prime target for dysregulation by EDC exposure. Autoimmune diseases, such as systemic lupus erythematosus, are characterized by a loss of immune tolerance to ones’ own cells and tissues. There is a lack of knowledge surrounding the immunomodulatory effects of a commonly prescribed EDC, 17α-ethinyl estradiol, especially as it pertains to autoimmune disease patients. The aim of this dissertation work is to investigate the immunomodulatory effects of exogenous EDC exposure in mouse models of SLE. We found that MRL/lpr mice fed a diet devoid of phytoestrogens had reduced kidney disease and immune-complex deposition and had augmented cytokine response and epigenetics in LPS-stimulated splenic leukocytes compared to mice fed a diet high in isoflavones. We next compared the immunomodulatory effects of chronic pharmacologic dose exposure to 17β-estradiol or EE, and found that while both estrogens have similar effects on innate immune cellular responses, EE has distinct effects on T cell population subsets, cytokine production, gene response and epigenetic alterations in female NZB/WF1 mice. Finally, chronic low-dose oral exposure to EE exacerbated clinical signs of kidney disease and suppressed the normal response of toll-like receptor 9 in MRL/lpr mice. Overall, we have found that chronic exposure to environmental estrogenic EDCs exacerbates lupus nephritis and alter an already dysregulated immune system in genetically susceptible individuals.

iii

Acknowledgements

Many people deserve my gratitude and have been instrumental in guiding and supporting my success. I would like to acknowledge the many people who contributed to the following works and to my graduate career as a whole, including my graduate committee Dr. Ahmed, Dr. Dr. Thomas Cecere, Dr. Liwu Li, Dr. Xin Luo, and Dr. Lijuan Yuan. Thank you especially to my mentor Dr. S Ansar Ahmed who has facilitated my growth as a student and researcher. I also would like to acknowledge and thank Dr. Divaker Choubey. Dr. Choubey has agreed to travel the long distance to VA from the University of Cincinnati in order to serve as an external examiner. The many researchers who have either directly contributed to the following works, or indirectly through support and mentorship include Dr. Qinghui Mu, Dr. Xiaofeng Liao, and Melissa Makris.

Additionally, I would like to acknowledge and thank my lab mates, Dr. Rujuan Dai, Dr. Catharine Cowan, Zhuang Wang, and Bettina Heid. Without their input, tutelage, and continued support, none of this would have been possible. Thank you for the times you got me back on track, patiently taught me a technique, and discussed science in a positive, engaging manner. The TRACSS staff and BMVS administration were integral to helping maintain the animal colony and health.

Thank you to my parents, siblings, and in-laws for all the encouragement and support. Finally, I would certainly like to thank my beloved wife, Dr. Virginia Edwards, and my two boys Gibson and Noah, for all their support throughout this academic training journey. Without the emotional and intellectual support of my family, I would not be where I am today.

Table of Contents Page #

Abstract……………………………………………………………………………………………ii General Audience Abstract……………………………………………………………………….iii Acknowledgements……………………………………………………………………………….iv Table of Contents………………………………………………………………………………….v List of Figures…………………………………………………………………………………....vii List of Tables……………………………………………………………………………………..ix Attribution……………….……………………………………………………………………...... x

Chapter 1: Introduction: Autoimmune dysregulation and estrogenic EDC exposure .…………...1

Chapter 2: Literature Review: Our environment shapes us: The importance of environment and sex differences in regulation of autoantibody production …………………………………….....22

2.1 Abstract………………………………………………………………………………22 2.2 Introduction………………………………………………………………..….….…..24 2.3 Sex Differences in Genetics and autoimmunity ……………………………….….…26 2.4 Sex Hormones and Environmental Endocrine Disrupting Chemical Regulation of Immunity and Autoimmunity ………………………………………….………..27 2.5 Sex Differences in Stress Response of Autoimmunity …………………………...…35 2.6 Sex Differences in Epigenetic Regulation and Autoimmunity …………………..….37 2.7 Autoimmunity and Microbial Agents ……………………………………………….42 2.8 Conclusions and Future Directions ………………………………………………….48

Chapter 3: Commercial rodent diets differentially regulate autoimmune glomerulonephritis, epigenetics, and microbiota in MRL/lpr mice …………………………………………………. 91

3.1 Abstract………………………………………………………………………………91 3.2 Introduction…………………………………………………………………………..93 3.3 Materials and Methods…………………………………………………………….…94 3.4 Results………………………………………………………………………………102 3.5 Discussion…………………………………………………………………………..110

Chapter 4: 17-β estradiol and 17α-ethinyl estradiol exhibit immunologic and epigenetic regulatory effects in NZB/WF1 female mice ….…………………………………………….....147

4.1 Abstract……………………………………………………………………………..147 4.2 Introduction…………………………………………………………………………148 4.3 Materials and Methods……………………………………………………………...151 4.4 Results………………………………………………………………………………157 4.5 Discussion…………………………………………………………………………..165

Chapter 5: Environmental exposure to 17α-ethinyl estradiol augments kidney disease and TLR7/9 signaling in female autoimmune-prone MRL/lpr mice ...... ……………………...……224

5.1Abstract…………………………………………………………………………….. 224 5.2 Introduction…………………………………………………………………………226 5.3 Materials and Methods………………………………………………………...……229 5.4 Results…………………………………………………………………………...….235 5.5 Discussion………………………………………………………………………..…241

Chapter 6: Conclusions and Future Directions……………………………………………...….279

Appendix A: Figures Relevant to the dissertation…………………………………………...…295

Appendix B: A Complete list of published works……………………..…………………….....299

List of Figures

Chapter 2: Figure 1………………………………………………………………………………………..…83 Figure 2……………………………………………………………………………..……………85 Figure 3………………………………………………………………………………..…………86 Figure 4…………………………………………………………………………………..………87 Figure 5……………………………………………………………………………………..……88 Figure 6………………………………………………………………………………………..…89

Chapter 3 Figure 1…………………………………………………………………………………………123 Figure 2…………………………………………………………………………………………125 Figure 3…………………………………………………………………………………………127 Figure 4…………………………………………………………………………………………129 Figure 5…………………………………………………………………………………………131 Figure 6…………………………………………………………………………………………132 Figure 7…………………………………………………………………………………………133 Figure 8…………………………………………………………………………………………135 Supplementary Figure 1 ………………………………………………………………………..139 Supplementary Figure 2 ………………………………………………………………………..141 Supplementary Figure 3 ………………………………………………………………………..143 Supplementary Figure 4 ………………………………………………………………………..144 Supplementary Figure 5 ………………………………………………………………………..146

Chapter 4 Figure 1…………………………………………………………………………………………186 Figure 2…………………………………………………………………………………………188 Figure 3…………………………………………………………………………………………190 Figure 4…………………………………………………………………………………………192 Figure 5…………………………………………………………………………………………194 Figure 6…………………………………………………………………………………………195 Figure 7…………………………………………………………………………………………196 Figure 8…………………………………………………………………………………………197 Figure 9…………………………………………………………………………………………199 Figure 10……………………………………………………………………………………..…201 Supplementary Figure 1 ………………………………………………………………………..203 Supplementary Figure 2 ………………………………………………………………………..204

Chapter 5 Figure 1…………………………………………………………………………………………259 Figure 2…………………………………………………………………………………………261 Figure 3…………………………………………………………………………………………262 Figure 4…………………………………………………………………………………………264 Figure 5…………………………………………………………………………………………266 Figure 6…………………………………………………………………………………………267

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Figure 7…………………………………………………………………………………………269 Figure 8…………………………………………………………………………………………271 Supplementary Figure 1 ………………………………………………………………………..273 Supplementary Figure 2 ………………………………………………………………………..275 Supplementary Figure 3 ………………………………………………………………………..276

Appendix A Appendix A1……………………………………………………………………………………295 Appendix A2……………………………………………………………………………………297 Appendix A3……………………………………………………………………………………298

viii

List of Tables

Chapter 3 Table I …………………….……………………………………………………………………136 Table II …………………………………………………………………………………………137 Supplementary Table I ……....…………………………………………………………………138

Chapter 4 Supplementary Table I …………………………………………………………………………206 Supplementary Table II ………………………………………………………………………...219

Chapter 5 Supplementary Table I …………………………………………………………………………278

Attribution

Chapter 4 is a manuscript that was co-first authored with Rujuan Dai. In this manuscript,

I was involved with experimental planning, prepping mice for surgery and was responsible for mouse post-operative care. I also performed and contributed data for the mouse studies included in Figures 3-5, and 7-9. I was responsible for writing and revising the majority of the manuscript text. Both Dr. Dai and I interpreted the data and drew conclusions.

Chapter 1

Introduction: Autoimmune dysregulation and estrogenic EDC exposure

The immune system is a complex network of finely tuned, interconnected cells, organs, and communication mechanisms that exist in an equilibrium in a healthy individual. This system must also interact with, and help support, multiple “non-immune” systems, such as the endocrine and cardiovascular systems within the body, as well. Cells of the immune system must be able to sense and respond to pathogenic stimuli or damage associated molecular patterns. In general, the initial response of the immune system is to induce inflammation to fight off invading pathogens or foreign entities, which is then followed by a period of dampening of the initial response and repair, resulting in homeostasis. When all pieces work seamlessly together, a balance exists to maintain the health of the host organisms. When a dysregulation occurs, this results in a diseased organism. In autoimmunity, the ability to recognize and tolerate normal healthy self-antigens is lost. This leads to the immune system aggressively reacting to self-antigens, leading to aberrant damaging inflammatory responses.

Many of the currently recognized autoimmune disorders exhibit a sex-bias in disease prevalence. A vast majority of autoimmune diseases predominantly occur in females. Systemic lupus erythematosus (SLE) has a reported 9-13:1 ratio of female to male patients (1-4). Some autoimmune diseases have no sex bias, such as ulcerative colitis, or have a male preponderance, such as ankylosing spondylitis (5-13). Interestingly, the prevalence for multiple sclerosis, which historically has been reported to be about 1:1 female to male patients through the 1960’s, has been becoming a more predominantly female biased disease, currently estimated as a 3:1 female to male ratio (14-17). This rapid change in sex predisposition suggests that an environmental

1 factor is contributing to disease development in females, or suppressing disease development in males, as the time frame is much too short for human genetic changes to be the primary driving force.

Currently, the etiology of multiple autoimmune diseases is not well understood. When focusing specifically on the disease pathogenesis of SLE, it is recognized that multiple components, including genetic, epigenetic, hormonal, and environmental factors all contribute to disease pathobiology. People of African, Hispanic, and Asian descent are more likely to develop

SLE (18,19). Multiple genetic markers have been identified as susceptibility genes. However, the contribution of genetic abnormalities to disease development is complex and no clear inheritance pattern has emerged (20). Potentially independent of genetics, epigenetic changes have been associated with SLE as well. Human SLE patients were found to have hypomethylated CD4+ T cells, which correlated with disease severity on the SELENA-SLEDAI scale (21,22). Dr.

Ahmed’s laboratory has previously also identified SLE associated microRNA changes in 3 different mouse models of lupus (23). Genetically identical monozygotic twins have been reported to exhibit about 40% concurrence rate for autoimmune diseases (24-26). Increased interferon alpha (IFNα) activity in the serum clusters in families, and is also considered a heritable risk factor for developing SLE(27). Pet dogs of owners with SLE have a higher incidence of SLE diagnosis than dogs of non-SLE patients (28). These intriguing details suggest that environmental factors may play an important role in the development of autoimmune disease phenotypes in genetically susceptible individuals. It has been reported that environmental stimuli may alter epigenetic status (29-31), supporting the possibility of multiple, complex, interconnecting mechanisms for disease pathogenesis.

The three primary causes for mortality in SLE patients include lupus nephritis, infections, and cardiovascular disease (32,33). Lupus nephritis is characterized by an immune complex glomerular nephritis, which is more often identified in males than females, and occurs frequently in childhood onset SLE (34,35). Podocyte damage from subepithelial immune-complex deposition can lead to secondary membranous glomerulonephritis and nephrotic syndrome (36).

Recent advances in therapy for lupus nephritis are improving patient success rates and reducing mortality associated with nephritis.

Anti-viral immunity and various infections, such as Epstein-Barr virus, have been associated with SLE development as well (37-41). Pathogen sensing receptors TLR7 and TLR9 have been linked to SLE disease development and severity in mouse models of disease (42-46).

Autoimmune disease was abrogated when TLR7 was knocked out in B6.Nba2 mice, one mouse model of SLE, and disease severity was exacerbated in the same strain of mice when TLR9 was knocked out (44,47). TLR7 recognizes single-stranded RNA, while TLR9 recognizes unmethylated CpG DNA commonly found in bacteria, but less often in mammals. TLR7/9 receptors signal through various signaling cascade proteins, including MyD88, IRAK1, IRAK4,

TRAF3, TRAF6, and IRF7, as well as through the NF-κB pathway, promoting expression and production of type I interferons (IFN), primarily IFN-α from plasmacytoid dendritic cells

(pDCs), and pro-inflammatory cytokines such as IL-6 and tumor necrosis factor alpha (TNFα) respectively (48). Common infections in SLE patients also include fungal disease, most often of the Candida species (49). A major contributor to increased infection risk in SLE patients is treatment with immune-suppressive medications, such as glucocorticoids (50,51). Cardiovascular disease can manifest in myriad presentations, including hypertension, vasculitis, atherosclerosis, endocarditis, pericarditis, or myocarditis (52). Our work focused primarily on how EDCs

3 influence lupus nephritis and augment the ability of the immune system to respond to surrogates of viral and bacterial pathogens.

Estrogenic Endocrine Disruptors

Of the estrogens, 17β-estradiol (E2) is the most comprehensively studied estrogen to-date

(53-57). As reviewed in Chapter 2, estrogens have wide ranging effects on cellular processes, and especially with respect to immune cells (58). All currently identified immune cells express estrogen receptors(53). Estrogen’s primary mechanism of action is through acting as a ligand and binding to an estrogen receptor. In general, estrogen receptor alpha (ERα) is considered to be the classical ER and promote gene transcription, whereas ERβ contains a repressor domain (59). The

ER may dimerize or bind to other proteins, such as AP-1, and rapidly bind to the Estrogen

Response Element (ERE) on the DNA or non-ERE sites respectively, initiating transcription of various pro-inflammatory or anti-inflammatory genes. Some genes encoding immune cell products that are regulated by ER binding include iNOS, IFNγ, IL-6, IL-17, IL-10, and FoxP3

(53). ERs may also bind to EREs in the absence of soluble estrogen, through a ligand- independent pathway involving ER phosphorylation (60). Complete TLR7/9 responses require

ER binding to ERE, though a ligand is not necessarily required (61,62). Binding of estrogens to

GPR30 will lead to non-genomic responses(53). Estrogens have been associated with exacerbation of multiple autoimmune diseases and likely contribute to the sex bias of SLE.

Environmental endocrine disrupting chemicals are molecular compounds that are able to bind to hormone receptors and augment the normal function and/or production of endogenous hormones. Over 1,500 chemicals have been classified as endocrine disruptors to date, including multiple broad categories of chemical classifications (63,64). Recognition of endocrine system

4 dysregulation following ingestion of certain plants has occurred for centuries. Ancient civilizations recognized medicinal values of certain plants, and reproductive abnormalities within herds grazing on pastures harboring specific clovers containing coumestrol (65). These represent early observations of effects due to estrogenic EDC ingestion.

Estrogenic endocrine disruptors have direct or indirect action on ERα, ERβ, both, or

GPR30, a seven-transmembrane estrogen receptor. The first known synthetic estrogen

(diethylstilbestrol, DES) was produced and prescribed starting in 1938, and widely used under the misconception of DES being an effective, safe medication for pregnancy support. It took decades for physicians and investigators of the time to recognize the harmful effects that DES may cause, and the negative consequences continue to harm offspring of DES-treated individuals, multiple generations later (66). As synthetic estrogen production grew in breadth, toxicologists also began to investigate potential adverse effects. Environmental and wastewater contamination with estrogens was identified in 1965 (67), followed by synthetic estrogen identification in 1970 (Tabak and Bunch, 1970). Around the 1990’s, a resurgence in concern over the toxicity of EDCs occurred (68). EDC exposure was regarded as one of the numerous methods by which humans and wildlife were subjected to developmental and reproductive toxicity, resulting in adverse outcomes for individuals and offspring.

Chapter 2 reviews the current knowledge surrounding the interplay among sex differences, autoimmune disease and autoantibody development, and environmental exposures to

EDCs and infectious organisms. Many gaps in the literature still remain. The current literature includes immunomodulatory effects of limited specific EEDCs. The vast majority of work investigating EEDC exposure focuses primarily on reproductive changes, with limited information on immunomodulatory effects due to exposure, especially in autoimmune diseases.

Of studies available, bisphenol A (BPA) is one of the more commonly explored EEDCs(69-71).

BPA is commonly used to describe general effects of EEDCs, and estrogens are often lumped together as chemicals that act in a similar fashion due to ERα binding, though this may not be an accurate assumption. There exists a veritable paucity of literature exploring 17α-ethinyl estradiol’s specific immunomodulatory effects in autoimmune disease models.

It is understood that individual replicates in animal models of disease will have some underlying biological variation. However, environmental differences encountered during experimental timelines may promote variability of mouse models between institutions, or even between labs at the same institution. Reports exploring these variable phenotypes between various laboratories is difficult to find. Dietary components, both nutritional as well as contaminant chemical inclusions, may contribute to unintended variations in disease model phenotypes. We hypothesize that chronic exposure to an exogenous estrogenic endocrine disruptor will enhance renal disease, exacerbate autoimmune disease parameters, and alter the immune response to pathogenic stimuli in autoimmune-prone mice. To address some of these limitations, and to test this hypothesis, we performed the studies that will be described throughout this dissertation.

Due to the observation that disease phenotype in genetically-identical animal models can vary between laboratories (72), we investigated the contribution of dietary nutrient sources on autoimmune disease development. We chose to use a classical model of autoimmune associated nephritis, the MRL/lpr mouse model. This model develops multiple autoantibodies, lymphadenopathy, diffuse proliferative glomerulonephritis including mesangial cell proliferation and crescent formation, and dies on average at 17 weeks of age (73-77). Interstitial nephritis also

6 occurs, and even in the absence of autoantibodies, glomerular disease and progressive interstitial disease have been observed (34).

In Chapter 3 while controlling for all other environmental variables, we used genetically similar female autoimmune-prone MRL/lpr mice, bred in our vivarium and randomly assigned to treatment groups. Mice were weaned onto and fed one of three commercially available diets throughout the study. Mice fed on the phytoestrogen-free purified-ingredients diet D11112226 from Research Diets Inc. developed significantly reduced immune complex deposition in the glomeruli, along with reduced proteinuria compared to mice fed on one of the chow-based diets which contained the highest levels of one class of phytoestrogens named isoflavones. One of the primary differences among the diets used in this study was the content of phytoestrogens. The mice exposed to chow-based diets also had increased relative abundance of Lachnospiraceae species in their fecal microbiota, though Lactobacillus sp. were at extremely low abundance among all groups. This data is in contrast to the much higher relative abundance of Lactobacillus species in MRL/lpr mice reported by investigators whose mice were housed in the same vivarium in different rooms (78,79). We also observed epigenetic alterations, including miRNA and DNA methylation differences, based on diet group. The data gathered during this experiment suggests that diet selection is an important step in experiment planning, and many of the differences observed among experiment groups were likely due to differences in estrogenic compounds in each of the diets.

Over forty mouse models of SLE exist for studying various aspects of this complex and devastating disease process (34). This suggests that no single mouse model is ideal for studying all types of SLE presentation, or pathogenesis. It appears that at this point in time, it is important to confirm findings observed in one mouse model with multiple other mouse models of disease.

This can reduce the impact of a single mutation or genetic variation, and support an actual role in autoimmunity. A finding confirmed in multiple mouse models of disease does not, however, necessarily indicate that a finding will be translatable to human patients.

In my dissertation work, whenever possible, I have used well-established murine models of lupus, the MRL/lpr and NZB/WF1 mice. The classical NZB/WF1 mouse model develops a disease resembling human SLE characterized by autoantibodies, progressive immune complex glomerulonephritis, and exhibits a sex bias. The average age that female NZB/WF1 mice die is

245 days, and males die after about 406 days(75,80-83). Much work has been done exploring the effects of E2 on immune system responses as well as autoimmune disease (53,84-92). Even though EE has been one of the most extensively prescribed estrogens for various medical conditions, in hormone replacement therapy, and in oral contraceptive pills, few published studies have investigated the effects EE has on the immune response. E2 and EE have very similar chemical structures, though EE is more resistant to metabolism and has improved bioavailability compared to E2. EE has a 100-times greater potency for ERα than E2, and low potential for binding ERβ (93). Almost no studies directly compared E2 and EE effects on immune response and transcriptional regulation, especially in an autoimmune dysregulated immune system. The experiments described in Chapter 4 directly compared and contrasted immune specific effects of long-term E2 and EE exposure in autoimmune-prone female

NZB/WF1 mice. E2 and EE exhibit similar effects on innate cells, and distinct immune-regulatory effects in the adaptive branch of the immune system, regulation of gene expression, and distinct miRNA expression levels (94).

As previously stated, in Chapter 3 we developed a better understanding of how the purified-ingredients diet D11112226 influenced autoimmune disease parameters, and further

8 developed a model for investigating chronic oral exposure to EDCs. In the past, models of estrogen exposure have included surgical implantation (as we utilized in Chapter 4), or oral exposure by combining an estrogenic chemical with an oil, often corn oil. The oil and estrogen mixture could be gavaged, however this will bypass the oral microbiota and some chemical metabolism and enzymatic degradation. These methods can be time intensive, may lead to an inconsistent dose exposure of the chemical of interest, induce a stress response during handling, and may lead to damage caused during the procedure. We worked with a research diet company to incorporate a very low dose of EE into the mouse feed, allowing for chronic and consistent exposure over time. The work detailed in Chapter 5 describes the immune-regulatory effects of a human-relevant dose of EE exposure. Primarily, we observed an exacerbation of clinical markers of renal disease, including proteinuria, blood urea nitrogen, and BUN:Creatinine ratio, in mice exposed to EE. We also found increased IgG and IgG2a immune complex deposition in the glomeruli of EE exposed mice. EE exposed mice had impaired renal cytokine production following TLR7 and TLR9 in vivo stimulation, and TLR9 specific IL-6 production in splenic leukocytes was suppressed by EE exposure. Further analysis into the signaling cascade pathway revealed a reduction in basal MyD88 gene expression in mice exposed to EE and stimulated with imiquimod or ODN 2395, and MyD88 protein level in splenic leukocytes of mice exposed to EE.

Overall the studies described in the subsequent chapters begin to elucidate the role of the estrogenic endocrine disruptor, EE, in alteration of the dysregulated immune system of autoimmune-prone mice. Utilizing two common mouse models of autoimmunity, we were able to evaluate two different doses and routes of exposure for EE. The objective of this work is to identify the immune-regulatory effects of a commonly encountered estrogenic EDC and to

9 eventually extend this work into preventive and therapeutic strategies for autoimmune- susceptible individuals or populations.

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Chapter 2

Literature Review: Our environment shapes us: The importance of environment and sex differences in regulation of autoantibody production

Edwards, MR., Dai, R., S. Ansar Ahmed

Department of Biomedical Sciences and Pathobiology, Virginia Maryland College of Veterinary

Medicine, Virginia Tech, Blacksburg, Virginia, USA

This work was published in Frontiers in Immunology and reproduced with permission.

Edwards, MR, Dai, R, Ahmed, S Ansar. Our environment shapes us: The importance of environment and sex differences in regulation of autoantibody production. Frontiers in

Immunology. 2018 March 8; 9: 478. doi: 10.3389/fimmu.2018.00478. PMID: 29662485.

Abstract

Consequential differences exist between the male and female immune systems’ ability to respond to pathogens, environmental insults or self-antigens, and subsequent effects on immunoregulation. In general, females when compared to their male counterparts, respond to pathogenic stimuli and vaccines more robustly, with heightened production of antibodies, proinflammatory cytokines, and chemokines. While the precise reasons for sex differences in immune response to different stimuli are not yet well understood, females are more resistant to infectious diseases and much more likely to develop autoimmune diseases. Intrinsic (i.e. sex hormones, sex chromosomes, etc.) and extrinsic (microbiome composition, external triggers and immune modulators) factors appear to impact the overall outcome of immune responses between sexes. Evidence suggests that interactions between environmental contaminants (e.g. endocrine

22 disrupting chemicals, EDCs) and host leukocytes affect the ability of the immune system to mount a response to exogenous and endogenous insults, and/or return to normal activity following clearance of the threat. Inherently, males and females have differential immune response to external triggers. In this review, we describe how environmental chemicals, including EDCs, may have sex differential influence on the outcome of immune responses through alterations in epigenetic status (such as modulation of microRNA expression, gene methylation or histone modification status), direct and indirect activation of the estrogen receptors to drive hormonal effects, and differential modulation of microbial sensing and composition of host microbiota. Taken together, an intriguing question develops as to how an individual’s envirome directly and indirectly contributes to an altered immune response, dysregulation of autoantibody production, and influence autoimmune disease development. Few studies exist utilizing well-controlled cohorts of both sexes to explore the sex-differences in response to EDC exposure and the effects on autoimmune disease development. Translational studies incorporating multiple environmental factors in animal models of autoimmune disease are necessary to determine the interrelationships that occur between potential etiopathologic factors. The presence or absence of autoantibodies is not a reliable predictor of disease.

Therefore, future studies should incorporate all the susceptibility/influencing factors, coupled with individual genomics, epigenomics and proteomics, to develop a model that better predicts, diagnoses, and treats autoimmune diseases in a personalized-medicine fashion.

1. Introduction

The incidence of autoimmune and allergic diseases has been increasing for multiple decades

(95,96). Despite intensive studies in many laboratories, the etiology of autoimmune diseases is not well understood. It is nevertheless clear that there is no single genetic factor that solely determines the susceptibility to autoimmune diseases. Rather, susceptibility to autoimmune diseases appears to involve complex interactions of genetic, epigenetic, hormonal, and environmental factors, including potentially an increase in IFNα activity in serum. Many (but not all) autoimmune diseases preferentially demonstrate a female dominant susceptibility bias. The high female to male incidence ratios in autoimmune diseases such as autoimmune thyroiditis, systemic lupus erythematosus (SLE) and Sjögren’s syndrome in both humans and relevant animal models have been widely reported (53,89,92,97-99). Interestingly, even those diseases that did not show a strong female bias of susceptibility in the past, such as multiple sclerosis

(MS), now appear to tilt towards female predisposition. Patients diagnosed with MS were initially reported to have close to a 1:1 female to male ratio in the 1950’s (100). This ratio increased to 2:1 in the 1980’s (15), and further to 3:1 in recent reports (16). While the precise reasons for sex differences are not known, the potential contribution of changes in environmental factors remains an intriguing possibility. The implication of non-genetic factors (eg. epigenetic and environmental factors) is also evident in studies that reported the concordance rate of monozygotic twins manifesting autoimmune diseases is only between 20-35% (24-26,101).

Further evidence for an environmental component driving autoimmune pathology exists with the

Gullah population in South Carolina who are genetically very similar to members of their ancestral home of Sierra Leone. In a recent report, while the SLE disease prevalence (as measured by serum antinuclear antibodies) in the Gullah population is similar to their African

24 counterparts, notably, the African cohort had higher levels of circulating anti-Smith and anti- cardiolipin autoantibodies, as well as increased numbers of seropositive individuals to multiple viral infections (102). This suggests that in genetically very similar populations, environmental factors can promote autoantibody production. The potential contribution of differences in exposure to environmental chemicals between these two population groups cannot be discounted.

Interestingly, human SLE patients with pet dogs are more likely to have dogs that also suffer from SLE (28). This finding supports the claim that a transmissible or common environmental agent, or agents, may be present that increased the risk for SLE development within the human and canine populations. Even in genetically susceptible inbred mice that spontaneously develop autoimmune diseases, such as lupus, differences in the outcome or severity of the diseases has been noted among various laboratories (72,78,79,103-106). This supports non-genetic environmental factors influence on autoimmune disease.

It is now recognized that sex differences in the immune system cannot be solely attributable to differences in sex chromosomes and sex hormones (53,92,107). Direct comparisons among various studies exploring the specific mechanisms underlying the observed female bias in many autoimmune disorders are difficult due to differences in study methodology, population cohorts, and various extrinsic factors unable to be controlled for in human populations. Nevertheless, when the data is explored as a whole, the consequence of these variations can be mitigated and trends can be identified regarding sex-based differences in multiple systems.

In general, normal healthy males are thought to have immune systems that maintain tolerance, while the female immune system is susceptible to break in immune tolerance as evidenced by higher production of autoantibodies (108-110). Sex chromosomes contribute genetic differences, with multiple genes involved in immune system responses present on the X-chromosome,

25 including genes for FoxP3, androgen receptor (AR), and toll-like receptors 7 and 8. These genes can be differentially expressed, or activated, in males and females due to incomplete X- chromosome inactivation in the females, leading to potentially increased gene expression in females (111). Steroidal sex hormone levels vary between sexes, with female predominant estrogens promoting B cell survival and contributing to exacerbation of multiple autoimmune diseases, and androgens exerting immune regulatory effects to prevent, suppress, or delay autoimmunity (56,59). Endocrine disrupting chemicals act through multiple mechanisms, displaying both estrogenic and anti-estrogenic properties, reducing androgen production, and influencing epigenetic regulation (110,112). It is now prudent to incorporate environmental influences (ex. EDCs) in studying the development of autoimmune diseases, which will provide a more comprehensive understanding of mechanisms of autoimmune diseases (113). Assessed individually, these various factors may not be able to induce autoimmunity sufficiently.

However, when multiple internal and environmental factors interact, these may cause the loss of tolerance, the production of autoantibodies, and drive autoimmune disease pathogenesis (Fig. 1).

This review will focus on how sex differences identified in genetics, epigenetics, hormonal responses, and response to microbial stimuli influence immune tolerance dysregulation and autoantibody production, with an emphasis on the contributing effects of EDCs on immunologic functions.

2. Sex differences in genetics and autoimmunity

Female cells are genetically the same as male cells in all chromosomes except the sex specific X and Y chromosomes. To compensate for gene copy differences, female cells, other than egg cells, undergo X-chromosome inactivation, thereby permanently silencing one copy of the X chromosome. This process may be incomplete in some individuals, leading to overexpression of

26 genes present on the X-chromosome. Abnormalities in chromosome numbers may exist, such as in Klinefelter syndrome, where males have one or more extra X chromosomes. Notably, men with Klinefelter syndrome are predicted to have a similar risk of SLE to that of females, and a

14-fold increase in SLE risk compared to healthy males (114). It is conceivable that in the context of incomplete X-chromosome inactivation, females could have alterations in the expression of X-chromosome linked genes that promote inflammation and subsequent autoimmunity, such as TLR7/8.

Many autosomal genes are differentially expressed in males and females. The transcription factor vestigial-like family 3 (VGLL3) was recently found to be upregulated in female tissues, such as ovaries, the uterus, adipose tissue, and smooth muscle. VGLL3 is located on chromosome 3, and it is unknown at this time what contributes to this sexual differential expression pattern. This transcription factor contributes to the differential expression of hundreds of genes between sexes.

Genes of interest regulated by VGLL3 include BAFF, ITGAM, IL-7, ICAM-1, MMP9, and ETS1.

These female biased genes are associated with known autoimmunity susceptibility loci and inflammatory processes, and the increased expression of these genes appears independent of sex hormone regulation (115). Further, it is also possible that other newly identified and unknown transcription factors are contributing to the sex bias gene expression and autoimmune disease susceptibility.

3. Sex hormones and environmental endocrine disrupting chemical regulation of immunity and autoimmunity

Sex differences in sex steroid hormone levels and regulation on the immune system of normal and autoimmune individuals have been extensively studied (54,55,87,92,116,117). While the sex

27 differential effects on immunity and autoimmunity cannot be solely attributable to sex hormone profiles, sex steroid hormones do have a major impact on various aspects of the immune system, including their contribution to cell differentiation, cytokine profiles, epigenetic alterations, and autoimmune disease (54,55,86,116-120). The case for the role of sex hormones in autoimmune diseases can be further made by the fact that a majority of autoimmune diseases are manifested after sexual maturity, at a time when sex hormone levels are elevated and differential biological responses of sex-hormone regulated genes are evident (Fig. 2). Interestingly, it is not yet understood why women are at a higher risk of developing autoimmune diseases such as SLE, rheumatoid arthritis, Graves’ disease, and thyroiditis following menopause (121). EDCs are able to exert agonistic or antagonistic roles on normal physiologic sex hormone actions, enhancing or mitigating hormonal effects on immune cells (85,107,122,123). As with many endocrine components, sex hormones and EDCs exert differential effects, not only due to dosage, but also in temporally dependent and context specific manners.

Exposure to EDCs is nearly impossible to avoid in current societies. These compounds can be present in drinking water, cosmetic products, paper products, food and beverage containers, many forms of plastics, and the food we eat (112). The route and dosage of exposure are important considerations when determining the effect EDCs will have on various aspects of health and physiology. Many EDCs have been determined to be able to elicit bi-phasic dose responses, with evidence that very low EDC concentrations can exert a positive effect, while at higher concentrations they may have opposite effects, and vice versa. Currently, controversy exists regarding evaluation of internal concentrations, metabolites, and daily exposure levels of

EDCs (124).

Little consensus has been reached regarding when, where, and how EDCs disrupt endocrine homeostasis in exposed individuals. One vital issue that impairs our understanding of the mechanisms and overall influence of EDCs on health is the potential lag between exposure and development of clinical signs, such as reproductive disorders. In humans, the lag period may be years or decades before sexual maturity and fertility can be tested (125). Much of the current data on EDC functions and effects are targeting alterations in reproductive systems. Due to the wide variety of compounds and exposure routes, this review will only address how well-studied models of EDCs, such as bisphenol-A (BPA) and phytoestrogens, may affect the immune system.

3.1 Estrogen, natural and environmental

The effects of estrogen on immune cell populations and functions have been extensively studied and reviewed (53-55,87,116,117). We will highlight the important aspects of estrogen’s actions that promote or inhibit autoantibody production. Estrogens are able to exert effects on multiple immune cell phenotypes through activating either estrogen receptors (ERs)-mediated genomic signaling or G protein-coupled estrogen receptor 1 (GPR30/GPER1)-coupled non-genomic signaling pathways (59). Following ligand binding, ERα and/or ERβ binds to the estrogen response element (ERE), which drives transcriptional regulation, particularly Pax5, BSAP,

HOXC4/HoxC4, and AID genes in B cells, promoting B cell maturation and survival. Estrogen activated GPR30 signals through P38/ERK MAPK and PI3 kinase pathways, driving B cell activation and rearrangement of the Ig heavy and light chain, as well as activating NF-κB (59).

Further, sex hormones and hormone metabolites can also induce their effects on target cells

(such as cells of the immune system) in sex-hormone receptor independent mechanisms

(126,127). Activated estrogen receptors can bind to other transcription factors (such as NF-κB)

29 to mediate cell signaling for regulating gene expression. In addition, ERs can be activated independent of ligand binding (60,128). Therefore, it is conceivable that, in females, direct and indirect activation of estrogen receptors by external triggers, such as endocrine disruptors, can potentially have differential effects compared to males.

In most instances, estrogen enhances both cell-mediated and humoral immunity. Studies in peroxisome proliferator activated receptor (PPAR) knockout mice show that T follicular helper cell responses, important for antibody production, were upregulated in female but not male CD4-

PPARγKO mice, in part due to estrogen (129). In regards to B cells and antibody production, estrogen drives B cell maturation, immunoglobulin class switch recombination, and somatic hypermutation in germinal centers, promotes B cell survival, and enhances antibody production

(59,130,131). Directly, estrogen regulates gene transcription through ERs binding to ERE sites.

Indirectly, estrogen promotes B cell survival through increased B-cell activating factor (BAFF) production. In mouse models, BAFF gene expression is up-regulated by estrogens and interferon

(IFN) stimulation both at the mRNA and protein levels through a mechanism involving ERα,

IRF5, or STAT1. Treatment of the mouse macrophage cell line RAW264.7 with IFNα, IFNγ, or estrogen induced p202, which correlated with increased BAFF production, contributing to sex differences (132). In humans, under normal physiologic conditions, no detectable differences are found between male and female BAFF levels. However, following estradiol treatment, both sexes had an increase in BAFF production, with the increase in females being much more profound (133). Thus, in the presence of estrogen, B cell survival and maturation is enhanced through multiple mechanisms, potentially increasing the ability of autoreactive B cells to break tolerance and drive autoantibody production (Fig. 3).

BALB/c transgenic mice treated with estrogen had increased Bcl-2 production that allowed naïve

B cells to break tolerance induction and drive anti-dsDNA autoantibody production (134). Anti- cardiolipin autoantibody was shown to be enhanced in orchiectomized male and normal female

B6 mice treated with 17β-estradiol, but replacement of dihydrotestosterone (DHT) in castrated males had no effect, and intact males had lower levels of circulating autoantibodies than females

(86). Auto-reactive B cell pools are created predominantly in females, and ER signal-mediated activation of DCs was found to modulate T and B cell responses (135-137). Enhanced B cell survival through estrogen’s various actions promotes self-reactive B cell escape from negative selection in the bone marrow, and progression of autoantibody production (138,139). Therefore, it is conceivable that the lower levels of circulating E2 in males, in combination with higher levels of androgens, allows for better regulation and removal of autoreactive B cell populations prior to autoimmunity onset.

Evolutionarily, the female immune system is biologically equipped to robustly respond to infectious threats to protect the young dependent offspring and in the larger sense aiding in the survival of species. With the passage of time in the relatively recent era, introduction of new chemicals and emerging and re-emerging infections now pose unique challenges to the primed female immune system. It can be argued that initially the female immune system had biologically been exposed to natural estrogens. With the advent of, and exposure to, endocrine disrupting chemicals, the female immune system may be exposed to “surges or overloads” of endocrine compounds with competing endocrine effects, thus increasing the chance for deviation of immune modulation by hormones. Whether these new threats have contributed to increased autoimmune diseases is an open question that warrants investigation.

The ability of EDCs to influence the immune system subsets and alter disease susceptibility is poorly understood at this time. BPA has multiple estrogenic like functions that alter T cell subset, B cell function and dendritic cell activity, inducing abnormal immune signaling and disrupting ER and PPAR signaling, thus, altering target gene transcription. In mice, BPA induced splenocyte proliferation, and shifted the cytokine profile from Th2 to Th1 mediated cytokines, enhancing autoimmunity (112). For example, BPA has been associated with development of type 1 diabetes mellitus (T1DM) in Non-Obese Diabetic (NOD) mice, a mouse model of insulitis and leukocytic infiltration of pancreatic islets leading to type 1 diabetes mellitus (140-142). Human studies have also associated EDCs with development of organ- specific autoimmune diseases mediated by autoreactive T cells. For example, serum BPA levels correlated with increased antithyroperoxidase in human patients of Hashimoto’s Thyroiditis, and estrogens regulate miR-21, which may drive inflammation in polymyositis (143,144).

Exaggerated T cell activation and polar Th1/Th2 shifts are due in part to increased antigen specific IFNγ following BPA exposure (145). In this way, BPA shows an antagonistic effect to physiologic estrogen responses. BPA can affect the MAPK and STAT pathways, disrupting the normal prevention of autoreactive T cell proliferation and survival (146). IFN associated mechanisms modulated by BPA have been shown to influence SLE pathogenesis (147).

Interestingly, prenatally BPA exposed mice showed an increase in IL-4 and IFNγ. However, mice exposed after reaching adulthood showed increases in IL-4, IL-10 and IL-13, but not IFNγ.

In both cases, Tregs were reduced (148). This disparity shows that the effects of EDCs are strongly dependent on age at exposure.

BPA has been shown repeatedly to increase immunoglobulin production in B cells. B1 cells have been associated with Sjögren’s syndrome and rheumatoid arthritis patients (149-151), and in

32 mouse models of SLE have been shown to be more sensitive to EDC’s modulatory effects than

B2 cells (152). B1 cells increased production of anti-dsDNA autoantibody, enhanced IgG deposition and glomerulonephritis and overall worsened SLE signs following BPA implantation

(153). MRL/lpr mice fed diets that contained phytoestrogen compounds diadzin and genistin had higher levels of IgG and complement component C3 deposition in glomeruli, along with altered immune cell infiltration into glomeruli compared to mice fed a diet devoid of estrogenic components (106). The majority of evidence supports that exposure to EDCs enhances autoantibody production and autoimmunity in mouse models of disease.

The complex immunological effects of EDCs can also display immune-suppressive effects. In people under 18 years of age, circulating BPA levels were negatively associated with anti- cytomegalovirus antibody titers, suggesting that some EDCs may attenuate antiviral immunity

(154). Short term BPA exposure in NZB/WF1 mice suppressed autoimmunity, reduced albuminuria, and extended the disease-free period, through modulation of IFNγ (155). Thus, when determining the impact of EDC exposure on disease states, it is vital to view data in the context of dosage, exposure length, age, and infection status, due to the wide range of effects that may be altered by EDC exposure.

To date, less is known regarding the role EDC exposure has on androgens, and androgen receptors (ARs) compared to EDC effects on ERs. EDCs may act in a manner that primarily disrupts the balance between androgen and estrogenic signals, altering the endogenous ratio of testosterone, DHT, and 17β-estradiol synthesis (110). Urinary BPA concentration was inversely correlated to free androgen index in males (156) with evidence that BPA can potentially interfere with androgen production and function (157-159). EDCs did not affect functions of normal ARs, though it is possible that in certain disease states, such as prostate cancer, EDCs could influence

33 patient therapy through mutant ARs (160,161). Much work needs to be done to evaluate the differential effects that EDCs have on the various immune pathways important in disease management, tolerance, and autoantibody production in a sex dependent context. It is possible that the actions exerted by various EDCs on androgens may reduce the immunoregulatory efficacy and tip the delicate balance toward promotion of autoimmunity.

Nevertheless, much work needs to be done to definitively understand the effects of endocrine disruptors on autoimmunity. Distinct sex-based responses to EDC exposure may contribute to dysregulation of the immune system to varying degrees in a disease specific manner (Fig. 4).

Significant effort is still required to identify and molecularly characterize the impact of various environmental triggers, especially ubiquitous environmental contaminants, on the regulatory mechanisms of host immune systems.

3.2 Androgens

The effects of DHT and testosterone in mammalian species have been shown to be primarily immunosuppressive (56,162-165). ARs are expressed in lymphoid and non-lymphoid cells of the thymus and bone marrow. However, they have not been found in peripheral lymphocytes (166).

This suggests that while androgens may not have a direct effect on lymphocyte function, they are important in developmental stages of T and B lymphocytes. Thymic epithelial cells and bone marrow stromal cells also act as mediators of androgen’s effects on immature lymphocytes

(166). AR levels were not altered in the thymus following castration, and were present on

CD3+CD4+ and CD3+CD8+ thymic cells, with the highest level found on CD3loCD8+ immature lymphocytes. ARs were also present in both cortical and medullary regions of the thymus following castration (166). The effects of castration extend to B cell development, leading to

34 increased immature B cell populations in the bone marrow, as well as increased splenic B cells and enhanced antibody and autoantibody production in mice. Androgen replacement reversed the changes in the bone marrow, but did not affect splenic B cells (167).

In general, there is good evidence that androgens downregulate immune system responses in both normal and autoimmune individuals (Fig. 5) . Gonadectomized male mice, compared to intact females, had increased responses to, and reduced infection by, protozoans and fungi

(56,162-164). Androgens have been shown to suppress a variety of autoimmune disorders including lupus and autoimmune thyroiditis (168,169). Androgen deprivation led to increased T cell numbers (170). Inhibition of IL-12 induced STAT4 phosphorylation occurs through the AR binding to Ptpn1 conserved region, inhibiting IL-12 signaling in CD4+ T cells and suppressing

Th1 differentiation (171). Androgens reduce IFNγ production through decreased PPARγ (172).

Suppressive effects are also exerted on B cell antibody production by androgens (173). In psoriatic arthritis patients, testosterone appears to exert a protective effect. Higher serum BAFF concentrations are associated with increased disease activity, while serum BAFF concentrations negatively correlate with circulating levels of testosterone (174). Therefore, it is possible androgens can regulate the immune responses of a genetically autoimmune susceptible individual to favor the maintenance of homeostasis (Fig. 5).

4. Sex difference in stress response and autoimmunity

Associations have long been suspected between stressor events in a patient’s past and development of autoimmune diseases, such as SLE, MS, RA, and T1DM. Lack of evidence- based and prospective studies contribute to the skepticism that stressful life events are major etiopathologic factors to consider in autoimmune disease development. Nevertheless, these

35 events cannot be discounted, as stress responses can directly and indirectly influence immune responses. Sexual dimorphism exists in the Hypothalamus-Pituitary-Adrenal (HPA) axis, a major component of the physiologic stress response (175). Stress primarily acts upon the immune system through release of glucocorticoids, leading to alterations in cytokine production. In general, glucocorticoids inhibit the production of proinflammatory cytokines, such as IL-6,

TNFα, and IFNα, whereas IL-4 and IL-10 are unaffected (176). Glucocorticoids are also able to inhibit the activation, proliferation, and differentiation of many cell types (177-180).

Fetal exposure to glucocorticoids can potentially impact a person’s HPA axis, either directly or indirectly; an effect to which female offspring are particularly vulnerable. Females exposed to a

“prenatal stressor” had higher HPA reactivity than similarly exposed male offspring (181). Sex differences in cortisol response have been found in multiple life stages. Boys younger than 8 years of age had higher cortisol response than females of the same age. From 8 years to 18 years of age, females have higher cortisol reactivity than males, an effect that is reversed in adulthood

(181-186). Men have a more robust acute HPA response when compared to women, as determined by cortisol levels and sympathetic nervous system evaluation (187,188). Men had higher glucocorticoid sensitivity and reduction in LPS-stimulated cytokine production, whereas women had a decreased glucocorticoid sensitivity and increased LPS-stimulated cytokine production following a stress challenge (189). The type of stressor is also important when evaluating sexual dimorphism, as women had greater levels of cortisol in response to a social rejection challenge, while males had higher levels of cortisol in response to an achievement stimulus (190). Stress is able to alter plasma estradiol levels (191,192) and estrogens have been shown to dampen the HPA and sympathetic nervous system response in certain studies

(187,193). However, other studies report a higher female HPA response independent of

36 circulating gonadal hormone levels, suggesting either an innate difference in HPA mechanisms of action or an early developmental difference in response to sex hormone exposure (194).

A recent meta-analysis of 14 retrospective case-control studies supports major psychosocial stress as a risk factor for autoimmune disease development (195). This associated risk remained independent of the autoimmune disease reported. Appropriate controls in human studies exploring the role of stressors on autoimmune disease are difficult to determine, as the etiology of autoimmune diseases are still not well characterized. Most human retrospective studies rely on patient recall of stressful events that occur relatively close to disease diagnosis. It is possible that immune dysregulation and autoantibody production occur many years prior to the appearance of clinical signs. This would suggest that a stressor event that happens temporally close to the time of diagnosis would be a disease exacerbator, rather than an etiologic factor. Consideration must also be given to the potential that the recrudescence of a latent virus or alteration in microbiota composition induced by a stressful event may be a driving factor in autoimmune disease development. Due to experimental limitations on human subjects, and the species differences in

HPA axis response, these questions remain difficult to address, though the use of humanized rodent models may help to mitigate these limitations.

5. Sex differences in epigenetic regulation and autoimmunity

Recent studies highlight the importance of epigenetic regulation in biological systems development and function. Abnormal epigenetic regulation, such as miRNA dysregulation and

DNA hypomethylation, have been implicated in autoimmune diseases (21,22,196-200).

Epigenetic mechanisms are important contributors to the balance between functional gene expression and regulation. These pathways, such as those that drive specific gene DNA

37 methylation status, are dynamic processes, and may potentially be altered in response to environmental cues and contaminants.

5.1 Hormones influence epigenetic regulation

Recent studies have suggested that sex hormones regulates immunity and autoimmunity through epigenetic mechanisms (Fig. 3 and 5). microRNA (miRNAs) is a class of small non-coding

RNAs that has emerged as a key epigenetic regulator of immune system functions in the last two decades (201). We have reported that estrogen regulated a set of miRNA in the splenic cells of normal B6 mice, of which, miR-146a and miR-223 were further validated to contribute to enhanced inflammation in splenocytes from estrogen-treated mice (202). Many estrogen- regulated miRNAs such as miR-17-92, miR-125, miR-181a, miR-155, miR-150 , have been implicated in the regulation of B cell development and antibody production by targeting different genes such as Bim, C-Myc, Lin28, Pu.1 and activation-induced cytidine deaminase(AID)

(201,203,204). This suggests that estrogen may regulate B cell functions and antibody production via miRNA regulation. We recently reported that select lupus-associated miRNAs were differentially expressed in male and female NZB/WF1 mice and that estrogen promoted the expression of these lupus-associated miRNAs in orchidectomized male NZB/WF1 mice. Estrogen conferred a female expression pattern of miRNAs on the male NZB/WF1 mice, contributing to the female bias of lupus (116). Estrogen-regulation of miRNA expression and the underlying mechanism has been further reviewed in more detail in our previous publication (98).

Increasing evidence indicates that sex influences the DNA methylome, which contributes to the sex differences in organ development, function, and susceptibility to specific diseases. Estrogen regulation of DNA methylation is suggested by the finding of the positive correlation between

ER positive status and promoter hypermethylation in breast tumors (205,206). Estrogen has been reported to up-regulate DNA methyltransferase (DNMT)3b expression in Ishikawa endometrial adenocarcinoma cells to facilitate malignant transformation of endometrial cancer cells (207).

However, the inhibitory effect of estrogen on DNA methylation has also been observed in prostate cancer cell lines, which was mediated by the activation of ERβ, suggesting the importance of context on estrogen’s actions (208). There is limited data with regard to estrogen regulation of DNA methylation in immune cells. Autoimmune regulator (AIRE) is a negative regulator of autoimmunity, which is differentially expressed in the male and female thymus and contributes to the gender difference of autoimmune diseases (117). A recent study revealed that estrogen downregulated AIRE expression by inducing DNA methylation at the promoter, contributing to the female bias of autoimmune diseases (117). Nevertheless, the detailed mechanism of estrogen-mediated promotion of DNA methylation at the AIRE promoter remains to be clarified in future studies.

DNA methylation plays an essential role in regulation of sexual dimorphism of brain function during early development. It has been shown that females display higher DNMTs activity and hypermethylation in the highly sexually dimorphic preoptic area at postnatal day 1. Treatment with the testosterone metabolite estradiol significantly reduced global methylation at the preoptic area, leading to brain masculinization (209). Yolk testosterone was positively correlated with methylation levels of the ERα promoter in the diencephalon (210). The androgen receptor can both prevent DNA methylation through binding of the AR to the promoter of a gene of interest, and promote DNA methylation through interaction of the AR with a suppressor, silencing expression of the gene of interest and eventual DNA methylation. AR function is associated with distinct DNA-methylation patterns in genital tissues (211). Interestingly, the DNA methylation

39 analysis of human blood revealed that there was a tendency of higher methylation levels in healthy males when compared to healthy females (212). Although the mechanism was unknown, we observed a reduction of global DNA methylation in splenocytes from estrogen-treated B6 mice when compared to placebo-controls. Given that DNA hypomethylation plays an important role in autoimmune diseases, such as lupus, it is significant to understand whether the gender difference in DNA methylation in immune cells contributes to the female bias of autoimmune disease directly and whether estrogen plays a role in the sexual dimorphism of DNA methylation in immune cells. It is noteworthy that sex hormones may regulate DNA methylation differentially in the context of different tissues, developmental stages, and pathological conditions. It should also be considered that the effect of estrogen on the global methylation level and the methylation of specific gene loci in defined subsets of cells of the immune system may be different.

5.2 Endocrine disrupting chemicals influence epigenetic regulation

An individual’s ability to respond to an immunologic stimulus can be modified generations before that individual is even conceived, primarily through the trans-generational effect of EDC exposure on epigenetic regulation of immune system development (29-31). After conception, maternal exposure to EDCs can also lead to alterations in the fetal epigenome, potentially leading to aberrant development of multiple body systems in the developing fetus (124).

Following birth, that individual will continue to encounter EDCs through various sources and routes of exposure including, but not limited to, drinking water, cosmetics and personal hygiene products, handling of food containers and consumption of the stored contaminated food, medications, and pesticides (213-215). Exposure to these myriad EDCs can potentially alter an

40 individual’s epigenome throughout all stages of life, influencing the body’s development and overall response to stimuli.

EDCs have been shown to be involved in the three known forms of epigenetic regulation: miRNA production, DNA methylation and histone modification. BPA is commonly used as a model EDC to investigate mechanisms by which estrogenic EDCs are able to modulate cellular functions. To date, most studies have focused on BPA’s ability to alter non-lymphoid tissue epigenetics. Dose and sex-specific changes were noted in estrogen receptor gene expression,

DNMT1 and DNMT3a expression, and DNA methylation status of the ERα gene Esr1 in various areas of the brains of BALB/c mice exposed in utero to BPA. Male mice had increased Esr1,

Esr2, Esrrg, DNMT1 and DNMT3a expression in the hypothalamus at low and mid-range doses of BPA, but reduced expression at high doses, while the females showed the reverse effect.

Female mice showed hypomethylation on multiple exons of the Esr1 gene when exposed in utero (29). BPA exposure by pre-pubescent girls in Egypt led to evidence of hypomethylation of

CpG-islands on the X-chromosome and reduced methylation levels in multiple genes associated with immune function (216). Overall, exposure to estrogenic EDCs, such as BPA, is associated with hypomethylation. CD4+ T cells have been shown to be hypomethylated in human SLE patients compared to healthy control (217). Therefore, it is possible that the reduced methylation associated with exposure to EDCs contributes to the hypomethylation of CD4+ T cells seen in

SLE and systemic sclerosis patients, promoting aberrant gene expression in these cells, contributing to disease pathology.

Estrogenic environmental agent exposure can lead to aberrant miRNA expression profiles. BPA and DDT are able to alter the miRNA expression in a similar manner to estrogen. Increases have been seen in miR-21 and miR-146a. BPA was shown to decrease miR-134 (70,218-220). Lupus-

41 prone MRL/lpr mice fed a chow-based diet containing phytoestrogens had increased expression of multiple miRNA and higher levels of global DNA methylation following LPS stimulation in splenic leukocytes along with increased DNMT1 expression (106). While the precise mechanism for this paradoxical finding is not yet known, it is possible that in select immune cell subsets,

DNA methylation was reduced, or that the increased DNA methylation status suppressed immunoregulatory pathways, contributing to the enhanced disease phenotype seen in these mice.

We are further investigating these findings. Long-term BPA exposure enhanced the expression and function of histone deacetylase 2 (HDAC2) in adult mice, specifically in the hippocampus

(221). Currently, there are no known effects of epigenetic regulation by BPA specifically on immune cell subsets. Further investigation is warranted into mechanisms by which estrogenic

EDCs can alter the epigenome in immune cell subsets and promote tolerance dysregulation and antibody production.

6. Autoimmunity and microbial agents

Observational relationships between infections and autoimmune diseases have long been recognized. Infections have been reported in a number of autoimmune diseases that either preceded overt expression of autoimmune disease or noted concurrently. Associations have been made between human Cytomegalovirus (CMV) and Epstein-Barr Virus (EBV) and autoantibody production in SLE patients, EBV and mycoplasma arthritidis in RA patients, and multiple viruses, including Hepatitis E virus (HEV), in type I diabetes mellitus (38,222-225). In most associations, antigenic mimicry is thought to be the mechanism that drives autoantibody production. It is evident that for the majority of vaccinations and viral pathogens, females mount a much stronger antibody response, suggesting that if subsets of female B cells were to break central and peripheral tolerance, that these abnormal B cells would drive higher autoantibody

42 production than male autoreactive B cells. In the same manner as gene associations, it is very difficult to link a single infection, or multiple infections, with causation of autoimmune diseases.

Alterations in commensal gut microbiota composition have been found in multiple mouse models of SLE as well as human SLE patients (78,226). Breaks in the mucosal barrier during stressful events or during the female reproductive cycle may expose the immune system to both infectious and commensal microbes (227). As the role of infectious agents potentially contributing to autoimmunity has been well documented to date, this review will focus on recent evidence linking sex differences in response to microbial stimulation, commensal microbiota and environmental factors that may influence autoantibody production in susceptible individuals.

6.1 Sex differences and microbiota

The host microbiota, which has repeatedly been shown to influence immune phenotype, is dependent on multiple host factors, including age, diet, sex hormones, antibiotic usage, host genetics, obesity status, and various lifestyle choices. Early host-microbe interactions during childhood development can have long term and profound consequences on adult health through immune system “training” and induction of tolerance (228). Males and females have distinct microbial profiles, seen both in humans as well as mouse models of disease (229,230).

Gnotobiotic male and female C57BL/6 mice were administered the colonic contents of a human male. Upon analysis, the female mice had higher diversity as assessed by Shannon Diversity index, and a separate profile, whereas the male mice more closely resembled the donor profile.

Forty-six distinct operational taxonomic units (OTUs) were different between the sexes, with thirty-three OTUs being overrepresented in the female fecal microbiota (229). Sex differences in microbial profiles were observed in multiple strains of mice. Gonadectomy with or without hormone replacement revealed further evidence of hormone effects on sex-differences in mouse

43 gut microbiota (231). In humans, the microbiota of males had reduced representation of

Bacteroides at a BMI >33, and the level of these microbes was reduced with increasing BMI.

Post-menopausal females did not show an alteration in Bacteroides associated with BMI (232).

An early critical window for microbial alteration of disease was shown for T1DM development in NOD mice. Genetically similar mice housed in separate facilities eventually led to differing rates of T1DM development resulting in NODlow and NODhigh communities. Co-housing or oral gavage of fecal contents from the NODhigh mice to NODlow weanlings did not alter T1DM incidence. However, the offspring of the co-housed NODlow mice did have increased T1DM incidence, suggesting that a window exists either in utero or prior to weaning where alterations of the microbiota result in disease development later in life (72). The importance of differing environmental, housing, and laboratory conditions on animal models of disease phenotypes is evident in this study. It is plausible that male and female epithelial and immune cells respond in a differential manner to microbial recognition during the formative periods in infancy and early childhood, and this differential response contributes to the distinct differences found in microbial composition in adulthood. It follows that if male and female cells inherently respond differently to microbial recognition during development, then exposure to EDCs during this important time period could drastically alter the microbial composition, thereby exerting long-term consequences on adult health from childhood exposures. Further investigation is vital to determine the role EDCs play in the development of microbial composition and resultant functional alterations in childhood and adulthood.

Communication between the host and commensal microbes occurs through multiple pathways, which are not yet well understood. Recognition of microbial associated molecular patterns

(MAMPs), production of soluble mediators, and interactions within the microbiota-gut-brain axis

44 are thought to be the predominant methods of host-microbe communication. Resident immune cells at mucosal sites are able to recognize MAMPs and promote inflammation or induce regulation through Foxp3 positive T cell populations (233-235). Cytokines, metabolites, hormones, mucus, and anti-microbial peptides are all mediators produced by the host in response to the presence of microbes, while microbes release short chain fatty acids (SCFAs), polysaccharide A, formyl peptides, and D-glyceo-β-D-mannoheptose-1,7-bisphosphate (HBP), all of which can modulate the host response and microenvironment (236). It is currently understood that a two-way communication channel exists between the gut microbiota and the central nervous system (CNS). Microbes exert effects on the vagal afferents and enteric nervous system, and resultant stress responses act through the hypothalamus-pituitary-adrenal axis to drive or dampen cortisol secretion. Cortisol acts both locally and systemically on immune cells, promoting the secretion of various cytokines and chemokines, which in turn alters gut permeability and intestinal barrier function. These actions alter the microbial composition within the gut (237,238). Thus, complex mechanisms of communication between the host and microbiota, which is dependent upon specific microbial composition, may promote either tolerance or inflammation, both locally or systemically in a context dependent manner.

In the context of autoimmune disease, Markle et al investigated the sex differences in microbiota in a mouse model of autoimmune T1DM. Transfer of the male microbiota to female mice led to systemic alterations in sex hormone levels and protection of female mice against development of

T1DM (230). This protective effect conferred by the transfer of male microbiota to female mice was dependent upon androgen receptor activity (AR). Blockage of AR activity by the AR antagonist flutamide, attenuated the protection from insulitis, autoantibody production, metabolome changes, and the capacity of T cell transfer to confer autoimmune disease in NOD

SCID mice. Bacteria are able to metabolize sex hormones, thereby regulating the balance between active and inactive hormones, and potentially modulating hormone function (239).

Probiotics were shown to enhance antibody response to vaccines, potentially affecting the efficacy of oral vaccinations due to gut microbiota (240,241). We and colleagues showed that in a mouse model of SLE, Lactobacillus spp. was inversely associated with disease severity.

Supplementation with Lactobacillus spp. led to reduced IL-6 production, suppression of IgG2a production and glomerular deposition, and increased IL-10 in circulation along with increased

Treg populations and decreased Th17 subsets. Lactobacillus was also associated with reduced renal pathology. These protective effects were seen in female and castrated male MRL/lpr mice, but not in intact MRL/lpr mice, suggesting that some microbial effects act in a sex-hormone dependent manner (78). Sex-based differences in microbiota and effects on disease development or progression in the context of sex-biased autoimmune diseases are summarized in Table 1.

6.2 Sex differences, pathogen sensing and TLR7/8/9

Microbial signals are recognized through multiple pattern recognition receptors (PRR), including toll-like receptors (TLR), NOD-like receptors (NLRs), C-type lectin receptors (CLRs) and RIG-

I-like receptors (RLRs) that are present in varying levels between cell subsets. Males and females tend to be exposed to the same pathogens and inflammatory triggers. However, the responses and outcomes to these exposures can be vastly different between sexes. One key sex difference in pathogen and inflammatory trigger sensing comes from the different levels of receptor expression. There are multiple immune-related genes, including TLR7, TLR8, FOXP3,

CD40L and CD13 that are present on the X-chromosome (242). It is possible that incomplete X chromosome inactivation can lead to increased numbers of FoxP3+ cells. Interestingly, while the number of FoxP3+ cells increased, the mean fluorescent intensity of FoxP3 was decreased and

46 functional ability of these cells to regulate immune responses was suppressed (243). There is a potential for increased levels of toll-like receptor (TLR)7 and 8 expression due to incomplete inactivation of the X-chromosome in females. The balance among TLR7, 8, and 9 has been shown to be vital to disease development in mouse models of SLE. TLR7 and 8 sense single- stranded RNA, and TLR9 binds to specific unmethylated CpG DNA motifs. Mice with increased levels of TLR7, or loss of TLR9, had enhanced autoantibody production and diseases severity, with TLR9 suppressing the autoantibody production induced by TLR7 (42). Male mice had higher levels of TLR4, circulating lipopolysaccharide binding protein, and increased expression of CD14 on macrophages than female mice following LPS stimulation in vivo. These changes were seen at the protein level, while mRNA expression was unchanged between sexes (244,245).

Therefore, the potential differences in ability of male and female cells to recognize microbial or self stimuli may contribute to the observed differential responses.

Sex differences are also observed in how immune cells respond internally following ligand binding to receptors. Female Kuppfer cells produce IL-6 in a MyD88-dependent pathway, while male cells produce IL-6 in a MyD88-independent pathway (246). The ligation of CD200-

CD200R is important in the suppression of TLR7 response to pathogens and control of IFNα production (247). Release of this inhibition through the knock out of the CD200 gene in mice enhanced sex differences in TLR7 and outcomes of viral infection. In HIV-1 infection, female plasmacytoid dendritic cells (pDCs) produced higher levels of IFNα after TLR7 stimulation than males, and also had enhanced expression levels of all 13 IFNα subtypes and IFNβ following stimulation of TLR7 on peripheral blood mononuclear cells (PBMCs) (247). Caution must be taken when evaluating changes in expression levels and correlating that to immune responses, as

PBMCs from human SLE patients showed increased expression of TLR9 mRNA and proteins

47 compared to healthy controls. However the function of TLR9 was impaired in SLE PBMCs leading to reduced IFNα following stimulation (248). Therefore, it is plausible that female cells may be more sensitive to PAMPs, and following receptor binding, internal signaling differences may enhance inflammatory responses compared to male cells.

7. Conclusions and future directions

Biological differences exist in immunologic responses to stimuli between males and females, and this likely contributes to the sex difference in the loss of immunologic tolerance and production of autoantibodies. These differences manifest in a complex network of intrinsic differences in the ability of immune cells to recognize a stimulus, respond, and return to homeostasis. Female and male cells could have differential cell signaling and outcomes to environmental contaminants exposure, concurrent disease or pathogen exposure, commensal populations, age, sex hormone fluctuations, and other environmental influences. While this biologic end point generally protects females from infectious diseases, it predisposes genetically susceptible individuals to chronic inflammatory conditions and development of autoimmunity compared to their male counterparts.

Our understanding of environmental interactions with sex-specific characteristics remains incomplete, with evidence that sex-specific therapies or preventive measures may exist. Sex- based differences have been seen in response to treatment with methylprednisolone and rituximab (a monoclonal antibody against CD20) (249,250). Sex-disparities in clinical presentation, progression and outcome of autoimmune diseases exist, suggesting that separation of sex-based groups in the evaluation of treatment strategies may help to appropriately tailor multiple treatment modalities in the future (251,252). Environmental exposures may also influence an individual’s response to medication, highlighting the importance of considering a patient’s envirome when determining the best possible treatment regimen. Further investigation

48 into the precise mechanism of how environmental chemical exposure alters distinct ligand- receptor signaling cascades in specific immune cell subsets is vital for better understanding the extent to which these ubiquitous chemicals can modify human and animal physiology. The exploration into associations between commensal microbial dysregulation and host disease susceptibility, or severity, must also take into account, to the best extent possible, the envirome to which that particular individual has been exposed. Microbial metabolism of EDCs may exert protection or promote exacerbation of certain disease processes depending on resultant metabolites and bioavailability.

Given that multi-factors are likely required for the induction of autoimmune diseases, a different approach is needed to understand sex differences in susceptibility to these chronic conditions.

Biologically, male and female cells of the immune system are differentially exposed to sex hormones and sex hormone-regulated proteins (other unidentified internal regulators) and manifest inherent genetic differences in sex chromosomes. Thus, the cells of the innate and adaptive immune system could be molecularly primed differently between sexes. It is therefore conceivable that male and female cells will differently perceive the binding of their receptor to the same ligand. In females, exposure to these triggers may have adverse effects. Following external triggers, the female immune system may be more prone to dysregulation (e.g. break in immune tolerance), and have augmented induction of autoantibodies and cytokines/chemokines that have been associated with autoimmune diseases. We further postulate that in genetically susceptible individuals, sex differences, both intrinsic and in response to environmental contaminants, such as estrogenic endocrine disrupting chemicals, contribute to female bias of immune tolerance dysregulation and drive autoantibody production and subsequent pathology

(Fig. 6). Understanding the complex interactions among differences in sex hormones, a wide

49 range of chemicals, genetic variations, infections and environmental triggers and deriving conclusions about impacts on pathogenesis will likely require the utilization of complex computational models. With the progression of individualized medicine, these environmental exposures will likely prove unique to various lifestyles and geographical locations.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

All authors listed have made substantial, direct, and intellectual contribution to the work and approved it for publication.

Funding

Preparation of this publication was supported by the Virginia-Maryland College of Veterinary

Medicine (VMCVM) Intramural Research Competition (IRC) Grant (grant number 175185);

Interdepartmental funds to SAA, and by the National Institute of Health T32 training grant (grant number 5T32OD010430-09). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or VMCVM.

Abbreviations

AID- activation-induced cytidine deaminase, AIRE- Autoimmune regulator, AR- androgen receptor, BAFF- B-cell activating factor, BPA- bisphenol-A, CLR- C-type lectin receptors, 50

CMV- cytomegalovirus, DHT- dihydrotestosterone, DNMT- DNA methyltransferase, EBV-

Epstein-Barr virus, EDC- endocrine disrupting chemicals, ER- estrogen receptor, GPR- G protein-coupled estrogen receptor, HDAC- histone deacetylase, HEV-hepatitis E virus, IFN- interferon, LPS- lipopolysaccharide, MAMP- microbial associated molecular patterns, miRNA- microRNA, MRL/lpr- MRL/MpJ-Faslpr/J, MS- multiple sclerosis, NLR- NOD-like receptor,

NZB/WF1- New Zeland Black/White F1 progeny, NOD mice- Non-obese diabetic mice,

PBMCs- peripheral blood mononuclear cells, pDCs- plasmacytoid dendritic cells, PPAR- peroxisome proliferator-activated receptor, RLR- RIG-I-like receptors, SCFA- short chain fatty acid, SLE- systemic lupus erythematosus, T1DM-Type-1 diabetes mellitus, TLR- toll-like receptor

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Evaluation of TLR9 expression on PBMCs and CpG ODN-TLR9 ligation on IFN-alpha production in SLE patients. Immunopharmacol Immunotoxicol (2017) 39(1):11-8. Epub

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Figure 1

Figure 1: Interactions of multiple factors are required for the development of autoimmunity. Genetic susceptibility, sex chromosomes, sex hormones, infections and 83 microbial stimuli, and environmental factors are all thought to contribute to autoimmune pathogenesis and lead to autoantibody production. There is little evidence that any one particular factor is able to initiate autoimmunity without input from another factor. The specific relationships and interplay among each of the various factors, such as the relative importance of one factor compared to the others, age at, or duration of, exposure, is not yet understood.

Figure 2

Figure 2: Autoimmune disease prevalence in relation to life stage. Autoimmune diseases can develop during childhood, but most autoimmune diseases develop following the onset of puberty and in later life.

Figure 3

Figure 3: Possible mechanism for estrogens to influence immunity and autoimmune disease development. The exact mechanism for estrogenic influence on autoimmune disease development is likely disease- and context-dependent, and research is ongoing to identify distinct pathways in which estrogen is able to exert its effects. The figure illustrates potential molecular mechanisms of estrogen regulation. AIRE = autoimmune regulator.

Figure 4

Figure 4: Possible mechanism for sex differences in environmental EDC exposure on immune function. The exact mechanism for immune system alterations due to EDC exposure in each sex is not yet well understood. Here, we propose possible mechanisms in which EDCs may exert sex-specific influence on immune cell functions.

Figure 5

Figure 5: Possible mechanism for androgens to influence immunity and autoimmune disease development. The exact mechanism for androgenic influence on autoimmune disease development is likely disease- and context-dependent. Potential mechanisms of androgen regulation are depicted. Research is ongoing to identify distinct pathways in which androgens are able to exert effects on immune system regulation.

Figure 6

Figure 6: Overview of the responses by male and female B cells to external stimuli and subsequent production of autoantibodies. Prior to conception, the maternal systems are augmented in various ways, including exposure to endocrine disrupting chemicals (EDCs).

Following conception, the fetus is subjected to EDC exposure through continued maternal exposure. Fetal and maternal hormones contribute to immune system development. Following parturition, an infant becomes exposed to microbial organisms and the establishment of commensal microbial population begins, being influenced by both endogenous and exogenous factors prior to stabilization. The microbiota composition will influence the developing immune system and promote recognition of pathogens and support the development of tolerance mechanisms. Juveniles continue to be exposed to EDCs and pathogenic microbes will be encountered, further stimulating the immune system. At puberty, cells become exposed to higher levels of female or male sex hormones, altering immune cell function and signaling pathways.

During adulthood, immune cells continue to be exposed to EDCs and microbial stimuli, both commensal and pathogenic. Sex differential levels of cytokines, chemokines, hormones, and other soluble factors make up the microenvironment that the immune cells are exposed to, further promoting or inhibiting immune cell activation and response. The female cells, which may be primed due to sex differences and environmental contributing factors, generally respond more robustly to the same immunological stimulus compared to male cells. This more robust response likely contributes to the female biased dominance in many autoimmune diseases.

Chapter 3

Commercial rodent diets differentially regulate autoimmune glomerulonephritis, epigenetics, and microbiota in MRL/lpr mice.

Edwards, MR., Dai, R., Heid, B., Cecere, T., Khan, D., Mu, Q., Cowan, C., Luo, XM., S

Ansar Ahmed

Department of Biomedical Sciences and Pathobiology, Virginia Maryland College of Veterinary

Medicine, Virginia Tech, Blacksburg, Virginia, USA

This chapter was published in International Immunology and reproduced with permission.

Edwards, M., Dai, R., Heid, B., Cecere, T., Khan, D., Mu, Q., Cowan, C., Luo, XM., Ahmed, SA.

(2017). Commercial rodent diets differentially regulate autoimmune glomerulonephritis, epigenetics, and microbiota in MRL/lpr mice. International Immunology. June. 29(6):263-276.

Abstract

The course and severity of lupus in spontaneous murine lupus models varies among laboratories, which may be due to variations in diet, housing and/or local environmental conditions. In this study, we investigated the influence of common rodent diets while keeping other factors constant. Female MRL/lpr mice were subjected to the same housing and given one of the three diets: Teklad 7013 containing isoflavone rich soy and alfalfa, Harlan 2018 isoflavone rich soy- based diet, or Research Diets Inc. D11112226 (RD) purified ingredients diet containing casein and no phytoestrogens. While the total caloric intake was similar among all three treatment groups, mice fed on 2018 or 7013 diet developed proteinuria and glomerulonephritis.

Remarkably, mice fed the RD diet had markedly decreased proteinuria with diminished C3, total

IgG, IgG3, and IgG1 immune-complex deposition, along with reduced CD11b+ cellular infiltration into the glomeruli. The type of diet intake also influenced LPS-activated cytokine production, microbiota (increased Lachnospiraceae in mice fed on 2018), altered miRNAs

(higher levels of lupus-associated miR-148a and miR-183 in mice fed on 7013 and/or 2018) and altered DNA methylation. Our results suggest that while total caloric intake was similar among all groups, mice fed on 2018 or 7013 diets developed glomerulonephritis characterized by changes in immune-complex protein deposition, regulation of distinct cellular infiltration phenotypes, as well as altered miRNAs and DNA methylation in LPS-stimulated splenocytes.

This is the first study to comprehensively compare the cellular and molecular effects of these commercial diets in murine lupus.

Introduction

Autoimmune diseases are characterized by a loss of tolerance and subsequent recognition of self- antigens by dysregulated immune cells. Systemic lupus erythematosus (SLE) is an incurable, multi-systemic autoimmune disease that most often affects women of child-bearing age.

Prevalence for SLE throughout the world ranges from 20-150 cases per 100,000 people, with females being 9-13 times more likely than males to develop the disease symptoms (1,2,4,253).

Current 10-year survival rates are estimated to be between 70-90% with the majority of deaths occurring due to cardiovascular failure, infections, or renal failure (254,255). The full etiology of

SLE is unknown at this time, with contributions from genetic, epigenetic, hormonal, and environmental factors driving the breakdown of immune cell tolerance, immune attack on target tissues, and subsequent development of disease in susceptible individuals (4,253,256,257). In

SLE immune dysregulation is evident in all major immune cell types culminating in the development of autoantibodies against multiple self-antigens (including autoantibodies against nuclear components, phospholipids, and Sm), increased expression of multiple cytokines, abnormal epigenetics (DNA hypomethylation, altered microRNAs, histone modification, and nucleosome remodeling), and altered phenotype and function of innate immune cells, such as dendritic cells and neutrophils (23,116,258-263).

The prevalence of immune-mediated diseases such as SLE in industrialized countries has been increasing rapidly in the past six decades, which in part may be due to changes in dietary and environmental factors (253,257). Recently, there has been an increased focus in identifying potential environmental factors that may trigger disease onset in genetically susceptible individuals. The time of onset and severity of expression of lupus in genetically-susceptible lupus-prone MRL/lpr mice varies among different laboratories, which is likely attributable to

93 many variables. These include differences in mouse commercial diets, housing conditions (room temperature, dark/light cycle, humidity, bedding, type of endocrine-disrupting containing plastic cages, animal handing), sex, and minor alterations in genetics, among other local environmental conditions. To address whether commercial rodent diets have an influence on murine lupus, it is thus imperative to control for the above conditions to the extent possible. Therefore, in this study, we utilized female MRL/lpr mice (a classical mouse model to study immune-complex glomerulonephritis that resembles human lupus nephritis) and controlled for the above conditions with one variable- mouse diets. All female MRL/lpr mice were purchased from one vendor, and were fed on one of the three diets: i) phytoestrogenic isoflavone-rich soy-based chow diet (2018), ii) a chow diet containing soy and alfalfa (7013), or iii) a purified ingredients diet (RD) with a casein protein source that lacks phytoestrogens. All mice were housed in the same room and exposed to the same housing and handling conditions. Our studies clearly demonstrate that these diets have a differential regulation on the expression of lupus-associated cellular and molecular parameters, and the type of immune-complex glomerulonephritis.

2. Materials and Methods

2.1. Mice and Rodent Diets

Genetically lupus-prone MRL/MpJ-Faslpr/J (MRL/lpr, stock# 000485) breeders were purchased from a single vendor, Jackson Laboratory, ME, USA, and bred in house. All mice were housed in the AAALAC certified animal facility at the Virginia-Maryland College of Veterinary

Medicine (VMCVM), Virginia Tech. Only female MRL/lpr mice were used in this study and fed the following three different diets: (i) Open Standard diet D11112226 purified ingredients diet (Research Diets, Inc., New Brunswick, NJ, USA; RD), (ii) 7013 NIH-31 Modified 6%

Mouse/Rat Sterilizable Diet (Harlan Laboratory, Madison, WI, USA), (iii) commercial 2018

Teklad Global 18% Protein Rodent Diet. The RD diet protein source is derived entirely from casein, while diet 7013 is from fish meal, soybean meal and alfalfa meal, and diet 2018 is from soybean meal. These protein sources lead to altered phytoestrogen content between diets, with the RD diet having undetectable levels of isoflavones, 7013 having moderate isoflavone content, while the 2018 diet contained the highest level of isoflavones. Isoflavone content of each diet was measured both internally through the toxicology lab at VMCVM, as well as through the commercial company Covance (Princeton, NJ, USA). Protein and fat concentration were comparable between all three diets, with similar levels of vitamin D, choline, thiamine, folate, and other B vitamins. The vitamin content of the RD diet was slightly lower in vitamin A, vitamin K, Niacin, and pantothenic acid than either 2018 or 7013. Both RD and 7013 had lower concentrations of vitamin E and B vitamins riboflavin, biotin, and pyridoxine-HCl compared to diet 2018. Mice were started on each respective diet at weaning (3 weeks of age) until the end of the study at 16 weeks of age. Food consumption was carefully monitored throughout the study, and average caloric ingestion per mouse calculated weekly. We chose to use only females in this study to retain sex-uniformity among groups (and avoid the complication of gender effects). Care was taken to ensure that all three groups were subjected to the same housing, local environment and handling conditions. All animal procedures and experiments were performed in accordance with guidelines of the Institutional Animal Care and Use Committee (IACUC) at Virginia Tech.

2.2 Splenocyte preparation, and cellular culture

Whole splenocytes were isolated using standard lab procedures described in detail previously

(23,116,118). Briefly, the spleens were dissociated by gently scraping through a steel screen, and

95 the cell suspension was passed through a 70-μm cell strainer to remove undissociated tissue debris. The splenocytes were isolated by lysing red blood cells with ACK-Tris-NH4Cl buffer and then washing with complete RPMI-1640 medium (Mediatech, Inc., Manassas, VA, USA) that was supplemented with 10% charcoal-stripped fetal bovine serum (Atlanta Biologicals, Flowery

Branch, GA, USA), 2 mM L-glutamine (HyClone Labs Inc, Logan, UT, USA), 100 IU/ml penicillin and 100 g/ml streptomycin (HyClone), and 1% non-essential amino acids (HyClone) before seeding into cell culture plate for treatment.

2.3 Multiplex Cytokine Assay

Ciraplex® Chemiluminescent Assay kits (Aushon Biosystem, Billerica, MA, USA) were used to quantify the levels of IFN-γ, IL-1β, IL-2, IL-6, IL-10, IL-12p70, IL-17 and TNFα in cell culture supernatants per the manufacturer’s instructions (259). The image of chemiluminescent array plates were captured with Cirascan image system (Aushon) and the image data was processed with Cirasoft software.

Due to serum sample volume restrictions, a Cytometric Bead Array Th1/Th2/Th17 Cytokine kit

(BD Biosciences, San Jose, Ca, USA) was used to quantify levels of IL-2, IL-4, IL-6, IFN-γ, IL-

17A, and IL-10 in serum samples simultaneously per the manufacturer’s instructions. The assay was performed on a BD FACSAria platform.

2.4 Assay of serum anti-dsDNA autoantibodies

Serum anti-dsDNA autoantibodies

The female MRL/lpr mice were aged in our facility and bled submandibularly every 2 weeks after they reached 6 weeks of age. The serum anti-dsDNA antibody levels were

96 measured by ELISA per our previous reports (23,116,118). Briefly, the Costar 96-well plate was coated overnight with 100 μg/ml calf thymus dsDNA (Sigma-Aldrich, St. Louis, MO, USA).

After washing, the plate was blocked with PBS and 1%BSA, incubated with serum samples, followed by incubation with HRP conjugated goat-anti mouse IgG-gamma (Sigma), IgG1,

IgG2a, IgG2b, or IgG3 (ThermoFisher Scientific Waltham, MA), and lastly TMB substrate for signal development (KPL, Inc., Gaithersburg, MD, USA). The absorbance was measured by reading the plate at 380 nm with a SpectraMax M5 Microplate Reader (Molecular Devices,

Sunnyvale, CA, USA).

Serum anti-cardiolipin

End-point serum anti-cardiolipin levels were measured by ELISA. The Costar 96-well plate was coated overnight with 50 μg/ml cardiolipin from bovine hearts (Sigma-Aldrich, St. Louis, MO,

USA). The plate was blocked with Tris-buffered saline with 2% BSA, then washed with 1x Tris- buffered saline. The serum samples were diluted 1:100 in TBS with 1% BSA and incubated at

37 degrees Celsius in 0% CO2, washed, followed by incubation with AP conjugated goat-anti- mouse IgG (Southern Biotech, Birmingham, AL, USA). Lastly, the plate was incubated for 1 hour with 5 mg para-Nitrophenylphosphate (Sigma) dissolved in diethanolamine substrate buffer

(Thermo Scientific, Waltham, MA, USA), 50 μg/well. The absorbance was measured by reading the plate at 405 nm with a SpectraMax M5 Microplate Reader (Molecular Devices, Sunnyvale,

CA, USA).

Serum anti-SmD1

The level of anti-Sm protein autoantibodies in the serum of 16 week old MRL/lpr mice was measured using the Mouse Anti-SmD1 ELISA Kit (Signosis, Santa Clara, CA, USA). 96-well plates were pre-coated with SmD1 antigen, samples and controls were added at 1:100 dilution

97 and incubated at room temperature with shaking as per directions. Wells were washed with the provided Assay Buffer, then HRP-conjugated anti-mouse IgG was added to each well and allowed to incubate. The wells were washed and included kit Substrate was added, followed by

Stop solution. The absorbance was measured by reading the plate at 450nm with a SpectraMax

M5 Microplate Reader (Molecular Devices, Sunnyvale, CA, USA).

2.5 Measurement of proteinuria

Proteinuria was measured by dipstick analysis using Chemistrip-2GP (Roche Diagnostics

Corporation, Indianapolis, IN, USA). The semi-quantitative scale was demonstrated as follows: “−”, negative or trace; “+”, 30 mg/dl; “++”, 100 mg/dl; and “+++”, 500 mg/dl or over.

2.6 Renal histopathology

As previously described (116), the kidneys from the MRL/lpr mice were collected and fixed in 10% buffered formalin and embedded in paraffin. Five-micron sections were stained with hematoxylin and eosin (H&E) or periodic acid-Schiff (PAS) in the histopathology lab at

Virginia-Maryland College of Veterinary Medicine, Virginia Tech. The stained renal sections were assessed by Dr. Tom Cecere, a board certified pathologist, in a blinded fashion. A grade of

0 to 4 (0 = perfect, no change; 1 = minimal; 2 = moderate; 3 = marked; and 4 = severe) was given to reflect the glomerular, tubular, interstitial, and vessel inflammation and lesions, respectively. By adding the scores together, we derived an overall renal score for the microscopic changes in each sample.

2.7 Renal Immunofluorescence

Kidneys were embedded in Tissue-Tek O.C.T. Compound (Sakura Finetek, Torrance, CA, USA) and flash-frozen in a bath of dry ice and isopentane. Frozen OCT samples were cut to 5µm sections by the pathology department at Virginia-Maryland College of Veterinary Medicine and unstained slides were stored at -80°C. Frozen slides were warmed to room temperature and allowed to air dry for 30 minutes, followed by fixation in -20°C cold acetone at room temperature for 10 min. After washing in cold PBS three times, slides were blocked with PBS containing 1% BSA and anti-mouse CD16/32 for 20 min at room temperature. Slides were then incubated with fluorochrome-conjugated antibody mixture for 1 hour at room temperature in a dark humid box. Coverslips were mounted with Prolong Gold anti-fade reagent containing

DAPI (Invitrogen, Grand Island, NY, USA). The following goat anti-mouse antibodies were used in immunofluorescence analysis: complement C3-PE (Cedarlane, Burlington, NC, USA);

IgG-FITC (Sigma); IgG1-Alexa Fluor 568, IgG2a-Alexa Fluor 568, IgG2b-Alexa Fluor 488,

IgG3-Alexa Fluor 488 (ThermoFisher); CD11c-FITC, CD19-FITC, CD4-PE, CD8-FITC

(eBioscience, San Diego, CA, USA); and CD11b-PE, Ly6G-PE (BD Biosciences, San Jose, CA,

USA). Kidney Sections were examined by fluorescent microscopy. All image parameters including exposure length, magnification and light intensity were kept constant for each antibody tested. Bright-field images were used to identify the basement membrane of each glomeruli.

Using the Fiji/ImageJ image processing program (264-266), the basement membranes were traced and the fluorescent intensity of the selected area was measured. The background fluorescence adjacent to each glomerulus was also measured. Background intensity was then subtracted from the glomerular fluorescent intensity. Fifteen (15) glomeruli were evaluated per antibody, per sample. The corrected glomerular fluorescent intensity values are shown in Figures

1, 2, and 3 along with the representative images for each marker tested.

2.8 Quantification of miRNA expression

Total RNA, containing small RNA, was isolated from whole splenocytes that were either non- stimulated or after 24 hours of LPS stimulation, using a miRNeasy Mini Kit (Qiagen, Valencia,

CA, USA). On-column DNase digestion with RNase-free DNase (Qiagen) was performed to remove residual amounts of DNA contamination in the isolated RNA. The RNA concentration was quantified using a NanoDrop 2000 (ThermoFisher Scientific Inc., Wilmington, DE, USA).

As we described in detail previously (23,116), Taqman miRNA assay reagent (Applied

Biosystems, Grand Island, NY, USA) was used to quantify the miRNA expression per the manufacturer's instructions. The expression level of miRNA was normalized to small RNA housekeeping control snoRNA 202. The data was shown as relative expression level to an appropriate control by using the 2−ΔΔCt formula (Livak method).

To analyze circulating miRNA in the serum, aliquots (50 µL) of serum were shipped on dry ice to FireflyBioworks (Abcam, Cambridge, MA, USA) for miRNA analysis utilizing a custom

Multiplex Circulating miRNA Assay. Probes used targeted miR-18a-5p, miR-20a-5p, miR-21a-

5p, miR-31-5p, miR-125a-5p, miR-126a-3p, miR-127-3p, miR-146a-5p, miR-148a-3p, miR-150-

5p, miR154-5p, miR155-5p, miR-181a-5p, miR-182-5p, miR-200b-3p, miR-223-3p, miR-379-

5p, miR-451a, let-7d-5p, let-7g-5p, let-7i-5p. Analysis of results was performed using Firefly

Analysis Workbench software, and signal intensities for each miR- probe were normalized to the intensities of the let-7 group.

2.9 Microbiota sampling, DNA extraction, and PCR.

100

During the study, fecal microbiota samples were obtained by taking individual mice out of their cage and collecting fecal pellets. At the conclusion of the study, colonic microbiota samples were collected within 20 min after euthanasia. To avoid cross-contamination, each microbiota sample was collected using a new pair of sterile tweezers. All samples were stored at -80°C until being processed at the same time. DNA was extracted using the MoBio PowerSoil DNA Isolation kit

(MoBio, Carlsbad, CA, USA). Total bacterial DNA was amplified using primers F430 and R514, and Lachnospiraceae DNA was amplified using primers 338F and 491R, and Lactobacillaceae

DNA was amplified using primers LabF362 and LabR677. Primer sequences are listed in

Supplemental Table 1. Relative abundance was evaluated using the 2ΔCt method (267).

2.10 RT-qPCR Gene Expression Assays

RNA was extracted from splenic leukocyte cell pellets that were either untreated, or stimulated for 24 hours with LPS 500ng/ml, and stored at -80C. The kidney tissue extract was protected by

RNAlater (Thermo Fisher) then stored at -80C. RNA was extracted using miRNEasy Mini Kit

(Qiagen). The RNA concentration was quantified using a NanoDrop 2000 (ThermoFisher

Scientific). cDNA was created using the iScript Reverse Transcription Supermix (Bio-Rad).

Taqman IFNγ “Mm01168134_m1” IL-6 “Mm00446190_m1”, TNF “Mm00443258_m1”, and

Dnmt1 assay reagent “Mm01151063_m1” (ThermoFisher Scientific Inc) were used to quantify the expression of each gene per the manufacturer's instructions. The expression level of each mRNA was normalized to Actb2. The data was shown as relative expression level to an appropriate control by using the 2−ΔΔCt formula (Livak method).

2.11 CoBRA

101

Combined bisulfite restriction analysis (CoBRA) was used to analyze the promoter region for miR-148a. Bisulfite modification of extracted cellular DNA was carried out as described previously using Epitect Fast Bisulfite conversion kit (Qiagen). The bisulfite PCR primers were designed to amplify three specific regions within the CpG island region of the miR-148a promoter using MethPrimer (http://www.urogene.org/methprimer/). Digestion of PCR products with BstUI (CGCG) (New England Biolabs Inc., Ipswich, MA, USA) was carried out at 60°C for

60 minutes prior to visualization on a 2% agarose gel.

2.11 Statistical Analysis

All values in the graphs are given as means ± SEM, or as otherwise stated in the figure legend.

To assess statistical significance, unpaired student’s t-test or one-way ANOVA and the Tukey-

Kramer multiple comparisons tests were performed where appropriate using GraphPad Prism

(version 6.07 for Windows).

Results

MRL/lpr mice were fed one of the three experimental diets for 13 weeks, from the time of weaning until the date of sacrifice. Changes in the body weight of mice fed diets RD and 2018 were comparable, while mice fed the 7013 diet had significantly lower initial weight gain in the first week (Supplementary Figure 1A, available at International Immunology Online), and then had comparable weight gain for the remainder of the study (Supplementary Figure 1B, available at International Immunology Online). The mice fed the 7013 diet had consistently lower body weights than the mice fed either the 2018 or the RD diet throughout the study due to this initial

102 lower weight gain (Supplementary Figure 1A, available at International Immunology Online).

While weekly mass of food ingested per mouse was slightly different between groups

(Supplementary Figure 1C, available at International Immunology Online), total caloric ingestion remained similar between groups throughout the study (Supplementary Figure 1D, available at

International Immunology Online). The RD diet has undetectable levels of isoflavones, 7013 diet contains about half the isoflavone content of 2018, while diet 2018 had the highest levels of multiple isoflavones as glycosides, daidzin, genistin, and glycitin, (Table 1). Diet 7013 also contains alfalfa, which may contain other phytoestrogenic compunds.

Mice fed RD diet had reduced proteinuria and immune-complex deposition in the kidneys

MRL/lpr mice rapidly develop severe glomerulonephritis by 12 weeks of age that is characterized by immune-complex deposition, which causes inflammation and damage, leading to proteinuria as a measurable indicator of glomerular damage. Kinetics of proteinuria development revealed that mice fed the RD diet developed the lowest levels of proteinuria, which were significantly lower than the mice fed the 2018 diet during late stage kidney disease at

16 weeks of age (Fig. 1A). Remarkably, at the endpoint of the study (16 weeks), the mice fed the RD diet had minimal evidence of proteinuria compared to mice fed the 2018 diet (Fig. 1B, p<0.05). Histopathologic evaluation of the kidneys of MRL/lpr mice following PAS staining revealed not only glomerulonephritis but also renal pathological changes in the interstitium, tubules and renal vessels (Fig. 1C). While the glomeruli of mice fed the RD diet appear to have markedly reduced pathologic changes compared to mice fed the 2018 diet, blinded scoring by a board certified pathologist did not achieve significance (p=0.09) (Fig. 1C and Table 2).

103

In addition to PAS staining of kidneys, we also evaluated for the IgG immune complex and complement protein 3 (C3) deposition within the glomeruli, as the deposition of these proteins, or protein complexes, in the glomeruli leads to further cellular infiltration and damage.

Immunofluorescent microscopy revealed striking differences in IgG and C3 deposition among the mice fed on different diets. It is noteworthy that mice fed on the RD diet, consistent with reduced proteinuria, also had minimal C3 and IgG deposition (Fig. 1C). Mice fed the 7013 or

2018 diets had a much higher degree of glomerular IgG deposition compared to mice fed the RD diet (p<0.05) (Fig. 1C and D). Mice fed on the 2018 diet had a significantly higher level of C3 deposition within the glomeruli compared to mice fed the other 2 diets (p<0.05) (Fig. 1C and E).

It has been previously reported that while IgG3 is a small fraction of the total circulating IgG, it is the primary IgG subclass that is deposited within the glomeruli of MRL/lpr mice, contributing to glomerular damage and progression of glomerulonephritis (104,268). To further determine the cause of the different levels of IgG deposition among diet groups, we performed immunofluorescence imaging to identify specific IgG subclasses deposited within the glomeruli.

Mice fed on the 7013 and 2018 diets had higher levels of IgG3 deposition (Fig. 2A and C). In addition, mice fed on 2018 also had significantly increased IgG1 (Fig. 2A and B). In contrast, mice fed on the RD diet had noticeably decreased deposition of IgG1 and IgG3 compared to mice fed 2018 (Fig. 2A-C). Interestingly, deposition of IgG2a and IgG2b was similar among all diet groups (Supplementary Figure 2, available at International Immunology Online).

Infiltrating cellular phenotype in the glomeruli of MRL/lpr mice are influenced by diet

As shown in the PAS-stained histopathologic micrographs (Fig. 1C), the degree of cellular infiltration of glomeruli among diet groups was altered. This disparity led us to investigate

104 potential differences in infiltrating immune cell phenotypes that are commonly implicated in lupus pathogenesis. These include CD11b+ (monocytes, macrophages, cDCs), CD19+ (B cells),

CD4+ (T helper, CD4+DCs), CD8+ (T cytotoxic, CD8+DCs), CD11c+ (pDCs, cDCs, some macrophages), and Ly6G+ (neutrophils). Of the cells investigated, no diet group had identifiable glomerular infiltration of CD11c+, CD8+ T cells or CD19+ B cells (data not shown). The casein based RD diet fed mice had low, but identifiable, amounts of CD11b+ staining, along with minimal CD4+ and Ly6G+ stained cells (Fig. 3A-D). The group fed the soymeal and alfalfa meal based diet 7013 had no statistically different levels of cellular infiltration compared to the RD diet group, while CD4+ cells were slightly more abundant (Fig. 3A and C, p=0.085).

Consumption of soymeal-based diet 2018 led to the highest level of infiltration of CD11b+ cells with similar levels of CD4+ and Ly6G+ cells to the other two diet groups (Fig. 3B-D).

Dietary source has minimal effect on serologic lupus parameters in MRL/lpr mice

Given that MRL/lpr mice fed 2018 or 7013 manifested greater glomerular immune-complex deposition than mice fed the RD diet, we next assessed whether similar alterations in lupus- associated autoimmune antibodies (anti-dsDNA, anti-cardiolipin, and anti-Sm autoantibodies) are also evident. There was no measurable difference in the circulating levels of any of the three autoantibodies among diet groups, except for anti-dsDNA at week 11 of age (Fig. 4A and B,

Supplementary Figure 3, available at International Immunology Online).). It has been previously reported that IgG2a is the predominant circulating IgG subclass (104,269). To further evaluate the effects of the diets on circulating anti-dsDNA, we measured the levels of anti-dsDNA IgG subclasses IgG1, IgG2a, IgG2b, and IgG3. While our data confirms that IgG2a is the predominant circulating subclass, there were no differences found among diet groups, either in

105 early disease stage at 10 weeks of age, or in late stage disease at 14 weeks of age (Fig. 4C and

D).

Systemic cytokine production was not influenced by diet source without a secondary stimulus

SLE is associated with changes in pro-inflammatory cytokines (IFNγ, IL-6, and TNFα) in the secondary lymphoid organs, including the spleen and lymph nodes. mRNA levels of IFNγ, IL-6, and TNFα were analyzed in both unstimulated splenocytes and kidney tissues. No differences were found in cytokine mRNA expression among diet groups (Fig. 5A and B). In past experiences, we did not see detectable levels of cytokine proteins in non-stimulated splenocytes, so we evaluated the cytokine production from splenocytes and mesenteric lymphoid cells after

24 hours of stimulation with LPS.

There were no statistically significant changes found in cytokine levels of the mesenteric lymphocytes, or in circulation, among diet groups (data not shown). Changes to cytokine levels were only observed following splenic leukocyte in vitro stimulation with LPS. Mice fed diets

7013 or 2018, compared to the RD group, had a reduction in the levels of multiple cytokines from LPS-activated splenocytes. Compared to the RD diet fed mice, mice fed the 7013 diet had reduced IL-12p70, and mice fed the 2018 diet had reduced IFNγ. Mice fed either 7013 or 2018 had reduced IL-1α, IL-6, and TNFα (Fig. 5C and D). This data suggests that lower glomerulonephritis in mice fed the RD diet is not due to generalized immunosuppression.

These data also support that mice fed the RD diet are not experiencing systemically higher levels of cytokine production, rather, the changes seen are organ specific following LPS stimulation.

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Immune cell phenotype in secondary lymphoid organs was not significantly affected by diet sources in MRL/lpr mice

To determine if the changes in cytokine levels were due to altered cellular populations, we analyzed the cellular composition of different tissues by flow cytometry. We assessed multiple cell populations that have been implicated in lupus pathology, including CD4+ and CD8+ T cells,

T regulatory cells, Th17 cells, and B cell populations in the spleen and mLN both before, and after, stimulation. There were no statistically significant changes in any of the T cell sub- populations in either the spleen or mLN among any of the diet groups (Supplementary Figure 4, available at International Immunology Online).). We identified a trend in changes of the B cell populations in the spleens and mLN among diet groups, with mice fed the RD diet having the lowest number of CD19+ B cells before and after stimulation with anti-CD3/CD28, though these values did not reach statistical significance.

Dietary effect on fecal microbiota

A recent report has shown lupus disease severity in the MRL/lpr mouse model correlates with the relative abundance of specific colonic bacterial groups, with Lachnospiraceae abundance having a positive correlation in lupus severity while Lactobacillaceae has a negative correlation

(267). Due to the interactions of intestinal microbiota with dietary components, we wanted to determine if there were alterations in the relative abundance of colonic bacterial groups shown to be important in lupus development. Bacterial DNA was isolated from fecal samples; both 1 week after weaning and at the end of the study. qPCR was performed with primers specific for total bacteria, Lachnospiraceae, and Lactobacillaceae to amplify the isolated DNA and evaluate relative abundance. The relative amount of total bacterial DNA was not different among diet

107 groups (Supplementary Figure 5, available at International Immunology Online). Abundance of

Lachnospiraceae was shown to be higher in the mice fed the 2018 diet compared to the mice fed the RD diet at both time points, consistent with previous reports of higher Lachnospiraceae abundance in more severe lupus disease (Fig. 6A). Interestingly, Lactobacillaceae DNA was amplified at only negligible levels among all diet groups at both time points, suggesting a lack of

Lactobacillaceae species within the MRL/lpr mice used in this study (Fig. 6B).

Select epigenetic markers of lupus are significantly affected by diet source in MRL/lpr mice qPCR analysis of the expression of selected lupus-related microRNAs (miR-182, miR-155, miR-

31, miR-127, miR-379, miR-148a, miR-183) revealed that diet did not influence the level of above miRNAs in unstimulated splenocytes of MRL/lpr mice (Fig. 7A). To further our understanding of the impact of diet source on the disease development, we next asked if dietary source augments the epigenetic response following activation of splenocytes by LPS. Following

24 hours of stimulation with LPS, there was a higher expression level of miR-148a in mice fed either the 7013 or 2018 diet, and increased level of expression in miR-183 in mice fed the 7013 diet when compared to mice fed the RD diet (Fig. 7B). Further evaluation revealed that LPS stimulation significantly reduced miR-148a expression in splenocytes of mice from all three diets groups (Fig. 7C). The suppression effect is more profound in the RD diet group than that from

7013 and 2018, which contributes to the observation of increased miR-148a in LPS-activated splenocytes from 7013 and 2018 diet when compared to RD diet. Similar to miR-148a, the trends of increased miR-127, miR-183, miR-379, and miR-31 in LPS activated splenocytes from 7013 and 2018 is also attributed to LPS-induced further suppression in the RD diet group (Fig. 7D-G). miR-155, which is a highly LPS sensitive miRNA (270), was, not surprisingly, upregulated in

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LPS-activated splenocytes from all three diet groups to a similar level (Fig. 7H). This observation suggested that different diets may differentially affect splenic cell immune response to secondary stimuli by altering specific miRNA expression at different levels.

Previous work in our lab has shown a connection between estrogen regulated miRNA and lupus disease progression as well (23,116,259). Due to the potential estrogenic activity of exogenous estrogens found in diets 7013 and 2018, we evaluated the serum levels of both lupus-associated miRNA and estrogen regulated miRNA, with no significant changes found, though miR-155 approached significance (p=0.07) (data not shown).

To further our understanding of the contribution of diet source to epigenetic modulation, we also evaluated the global DNA methylation value of non-stimulated and LPS-stimulated splenocytes.

As indicated in Figure 8A, there was no difference in the global DNA methylation level in splenocytes (either un-stimulated or LPS-stimulated) among different diet groups. LPS stimulation significantly increased global DNA methylation levels, compared to unstimulated cells, in splenocytes of mice fed the 7013 or 2018 diets (Fig. 8A). Correspondingly, we also observed a significant increase of Dnmt1 in LPS activated splenocytes of mice fed with 2018 when compared to unstimulated control. While it is not significant, there is also a trend of increased Dnmt1 gene expression in LPS-activated splenocytes of mice fed the 7013 diet (Fig.

8B). miR-148a has been reported to regulate DNA methylation by targeting DNMT1 (271). On the other hand, the expression of miR-148a is reciprocally regulated by the DNA methylation level at its promoter region (272). With the finding of increased global DNA methylation and reduction of miR-148a expression in LPS-activated splenocytes of different diet groups, we next asked if the miR-148a promoter region is differentially methylated between diet groups. We

109 performed CoBRA analysis on three distinct CGCG sites located at the CpG island region of the miR-148a promoter. No methylation change was detected on these three CGCG sites in either unstimulated or LPS-stimulated splenocytes of different diet groups (data not shown). Further investigation of other CpG sites in the promoter region or gene body is necessary to determine if

LPS-induced changes in miR-148a expression among diets are due to altered miR-148a methylation status.

Discussion

The influence of diet in autoimmune diseases has long been established (273-276). Genetically- prone autoimmune mice such as MRL/lpr mice have been extensively used in many laboratories across the world. However, although all laboratories have consistently reported the autoimmune nature of the disease, there have been notable differences in the time of onset, course and severity of the disease including proteinuria. This implies differences in environmental factors such as diet and housing conditions. Since the level of phytoestrogens in diet can influence the lupus disease, in this study, we utilized a diet that was devoid of phytoestrogens (RD) and compared with two commonly fed diets, one based on soy (2018) and the other based on soy and alfalfa (7013). While many of the nutritional parameters of the three diets were comparable, the

RD diet had a higher concentration of carbohydrates, contributing to a higher energy density.

Unlike the RD diet, which is strictly controlled with purified ingredients, 7013 and 2018 are grain-based diets, and thus subject to variations in not only grains, but also could include pesticides and other contaminants.

With well-controlled genetics and housing conditions, in this study, we reported the effects of various diets on the development of lupus-like disease by using a genetically susceptible mouse

110 model carrying the Faslpr mutation, MRL/lpr. We showed that mice fed a casein-based purified ingredient diet had decreased renal deposition of IgG and C3 (Fig. 1C-E), including reduced

IgG1 and IgG3 deposition (Fig. 2). This reduction in immune-complex deposition is supported by alterations in the glomerular infiltrating immune cell phenotypes depending on diet consumed

(Fig. 3). Consistent with the decreased IgG deposition in the kidneys, there was a trend of decreased (albeit not statistically significant) splenic B cells in mice fed the RD diet

(Supplementary Figure 4, available at International Immunology Online). Cytokine profiles, both at the mRNA and protein levels, were largely unchanged for multiple organs, including unstimulated spleen, mesenteric lymph nodes, kidney, and LPS-stimulated mesenteric lymph nodes (Fig. 5A and B, data not shown). The exception to this was that in splenic leukocytes stimulated with LPS for 24 hours, multiple cytokines were reduced at the protein level (Fig. 5C and D). Evaluation of epigenetic factors associated with lupus including lupus-associated miRNA expression and global DNA methylation, revealed that miR-148a, miR-127, miR-183, miR-379 and Dnmt1 expression are influenced by dietary source followed by an inflammatory stimulus LPS (Fig. 7B-G, 8A and B).

Circulating autoantibody levels were also comparable among groups at the majority of time points evaluated. Our data supports that most of the circulating anti-dsDNA IgG is composed of the IgG2a subclass. It is noteworthy that even though the IgG3 subclass constitutes a small percentage of total IgG, previous studies have shown that IgG3 is deposited in the glomeruli of

MRL/lpr mice and is considered to be pathogenic (104,268). Our studies also show IgG3 glomerular deposition in mice fed on 2018, which had the highest glomerulonephritis. This supports the view that while there are minimal differences in the levels of total circulating IgG,

IgG3 can preferentially deposit in the glomeruli of mice fed the diet that promoted the highest

111 levels of glomerulonephritis. Possible explanations for the higher levels of IgG deposition within the glomeruli of mice fed diets 2018 and 7013 include altered intracellular signaling due to the binding of phytoestrogens to estrogen receptor isotypes, alterations in cellular activation profiles due to differences within the microbiota that could not be controlled for, or some contributing pathway to kidney health that was not evaluated. More work needs to be done to determine the mechanism by which immune-complex deposition is altered by specific dietary components, which is beyond the scope of the present study.

The presence of immunoglobulin deposition in the glomeruli was not the only change found among diet groups, as our results showed a disparity in which immune cell phenotypes were present in the glomeruli between diet groups at the time of sample collection. The changes in infiltrating cellular phenotypes by diet may be due to differences in the number of circulating immune cells, alterations in cell signaling, and the presence of deposited inflammatory proteins within the glomeruli.

While the mice fed the casein-based RD diet had the lowest level of glomerulonephritis, these mice also had the highest levels of cytokine production in in vitro LPS-activated splenocytes. It is important to note that while many of these cytokines have conventionally been classified as

“proinflammatory,” current knowledge supports that cytokine activity is context specific. IL-17 and IFN-γ are generally considered to be proinflammatory, however IL-17 can exhibit anti- inflammatory activity in specific context (277), as can IFN-γ (278). Changes in tissue cytokine response were not a result of differences with T, B, or MHC II+ cell numbers between diet groups. Our data supports that the RD diet did not lead to reduced glomerulonephritis through immunosuppression. This is consistent with previous in-vitro reports in which genistein treatment led to reduction in LPS-induced TNF-α and IL-6 in RAW 264.7 macrophages (279).

112

There may be altered cell numbers in cell populations that were not explored in the scope of this study that may contribute to the higher cytokine production in splenocytes of mice fed the RD diet.

Previous studies have linked the gut microbiota to regulation of multiple autoimmune diseases, ranging from diabetes to multiple sclerosis to SLE (230,280-285). Since dietary nutrients influence microbiota signatures, we investigated the effects of the three diets on two groups of bacteria identified to be important to SLE parameters in the MRL/lpr mouse model. Our data showing higher levels of Lachnospiraceae 1 week after weaning, as well as during late stage disease (Fig. 6A), in mice fed the 2018 diet supports a role for Lachnospiraceae influencing glomerulonephritis development and severity. It has been widely reported that dietary fiber, both soluble and insoluble, are able to modulate the gut microbiota, altering the relative abundance between Firmicutes and Bacteroidetes (286,287). The dietary fiber sources are different among diets, with the fiber in the RD diet exclusively consisting of soluble fiber inulin, and the chow diets 7013and 2018 containing a mixture of plant derived soluble and insoluble fibers. These alterations in fiber source and content may also contribute to differences in relative abundance of

Lachnospiraceae seen between the RD diet and the 2018 diet. Finding negligible relative abundance of Lactobacillaceae sp. in all three groups was unexpected (Fig. 6B). It is possible that over time, these mice derived from our breeding colonies have had reductions in

Lactobacillaceae abundance, leading to negligible levels of these bacteria in our breeding colony. Caution should be taken when evaluating microbiota signatures and effect on disease between mice from multiple facilities.

We have previously shown that exogenous administration of 17-β estradiol (estrogen) induced signature miRNA changes in the spleens of both C57BL/6 and lupus-prone NZB/WF1 mice

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(202). In this study, we show that diet source has the ability to modify the splenic lupus- associated miRNA expression after LPS-stimulation, with no changes in baseline or serum miRNA expression profiles in the MRL/lpr mouse model. miR-148a was shown to contribute to lupus CD4+ T cell hypomethylation through direct downregulation of Dnmt1 (271). While this may not lead to a change in numbers of CD4+ T cells, hypomethylation can have a profound impact on cellular signaling and function. miR-183, together with miR-96 and miR-182

(members of miR-183-96-182 cluster), have been shown to be upregulated in multiple mouse models of lupus, potentially leading to downregulation of Foxo1/3 in T cells and MITF in B cells. This may result in subsequent breakdown of T cell tolerance, spontaneous B and T cell activation, enhanced autoantibody production and secretion, and enhanced T helper cell activation (116,288). It is noteworthy that while miR-183 was significantly higher in mice fed the 7013 diet compared to the RD diet, miR-182, was not changed between diet groups.

Reports have shown that the isoflavone genistein can influence the methylation of the ERβ promoter region in prostate cancer cells (289). Due to the reduced miR-148a expression and increased global DNA methylation in LPS-activated splenocytes, we wanted to determine if diet source can influence the methylation levels of the miR-148a promoter region. Evaluation of the methylation status of the CpG island region of the miR-148a promoter revealed no evidence of methylation at the three regions of interest. Soy isoflavones have been shown to regulate Dnmt1 expression in breast cancer cells (290,291), which may in turn regulate multiple miRNA expression levels. Our data showed that consumption of diet 2018 led to increased Dnmt1 expression following LPS stimulation (Fig. 8B). Other epigenetic changes, such as histone modification or methylation of other regulatory protein genes may contribute to alterations in miRNA expression due to dietary ingestion. Further studies are warranted to determine the exact

114 epigenetic mechanisms by which dietary components can exert exacerbating or ameliorating effects on SLE in mouse models.

These results show that the serum miRNA levels do not necessarily correlate with the splenic tissue miRNA levels of lupus-associated miRNAs. Our results support that when evaluating patients with autoimmune disease, as well as mouse models of disease, it is vitally important to evaluate each organ system individually, as what is seen in the serum or secondary lymphoid organs is not necessarily representative of what is occurring in other disease target organs elsewhere in the body.

While the three standard rodent diets were chosen without modification for their variations in phytoestrogen content and protein source, we recognize that the diets are not balanced in all nutrients, and these variables may have contributed to our results. Control must be taken when extrapolating dietary findings in mouse models to human disease influence, as only 30% of the human population has a gut microbiota that allows for the conversion of daidzein to its metabolite equol, while almost all mice have this ability(292). Equol has been shown to have wide ranging effects, including reduced DNA damage after UV exposure and alterations to the immune system (292,293). Our results show that, while controlling for housing, sex, and handling conditions, diet alone can have an impact on immune-complex glomerulonephritis, renal cellular infiltrates, microbiota, and molecular behavior of cells after LPS activation.

Additionally, our studies on the RD diet (in which dietary formulations are meticulously controlled) provided base-line effects on lupus nephritis in MRL/lpr mice. It will now thus be possible to incorporate an exogenous compound of interest (eg. endocrine disrupting chemicals) in the RD diet formulation to investigate the effect of this compound. Our ongoing studies are utilizing this approach to understand the impact of oral exposure to estrogenic chemicals. The

115 results of this study contribute to a better understanding of the role of commercial dietary sources and how diet choice can influence disease phenotype variability in the field of autoimmune lupus.

Abbreviations:

Actb2- Actin beta 2, C3- complement C3, CoBRA- Combined Bisulfite Restriction Analysis, cDCs- conventional dendritic cells, Dnmt1- DNA methyltransferase 1, ERα- Estrogen Receptor

Alpha, ERβ- Estrogen Receptor Beta, IACUC- Institutional Animal Care and Use Committee, miRNA- microRNA, MRL/lpr, MRL/MpJ-Faslpr/J , NZB/WF1- New Zeland Black/White F1 progeny, O.C.T.- Optimal Cutting Temperature, PAS- Periodic acid-Schiff, pDCs- plasmacytoid

Dendritic cells, RD- Research Diet, SEM- Standard Error of the Mean, SLE- Systemic lupus erythematosus , snoRNA- small nucleolar RNA, TBS- Tris-Buffered Saline

Funding

This work was supported by the Virginia-Maryland College of Veterinary Medicine (VMCVM)

Intramural Research Competition (IRC) Grant [grant number 175185]; Interdepartmental funds to SAA, and by the National Institute of Health T32 training grant [grant number

5T32OD010430-09].

Acknowledgements

We thank Melissa Makris for assistance with flow cytometry results. We thank Caroline Moon for her technical assistance with DNA extraction and DNA methylation ELISA work. We thank

Dr. Nicole Lindstrom, Ms. Karen Hall, Betsy S. Midkiff, and other animal care staff members at

VMCVM, Virginia Tech.

116

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Figure 1

123

Figure 1. Diet source influences proteinuria and immune-complex deposition. (A) Mean urine protein over time measured by ChemStix. Urine protein was monitored every other week starting at 8 weeks of age. (B) The graph represents the mean urine protein concentration at the study end-point. Data are shown as mean ± SEM (n = 8 mice for 7013, 9 mice for 2018 and RD groups). (C) Representative micrographs of formalin fixed renal sections stained with PAS. Bar equals 20 µm. Immunofluorescence images of IgG immune complex deposition (green) or

Complement C3 (red) protein deposition, and counter stained with DAPI (blue). Bar equals 100

µm, 20x magnification. (D and E) Mean fluorescence intensity of IgG (D) and C3 (E) per glomerulus determined by Fiji. 15 glomeruli per mouse were analyzed. CGFI=Corrected glomerular fluorescent intensity, ns: not significant, * P<0.05, ** P<0.01, *** P<0.001, one-way

ANOVA. Data are shown as mean ± SEM (n = 4 mice per group).

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Figure 2

Figure 2. Diets 7013 and 2018 increased IgG subclass glomerular deposition. Representative immunofluorescence micrographs of 5 µm frozen renal sections. Immunofluorescence images of

IgG1 (red) and IgG3 (green) protein deposition within glomeruli. Brighter fluorophores (Alexa

Fluor 568 (red) and Alexa Fluor 488 (green) were used in an effort to compensate for both the

125 relatively dim FITC and PE used in Figure 2 experiments, and the reduced binding potential of subclass specific IgG, due to relative quantity of a subclass protein compared to total IgG. (B and

C) Mean fluorescence intensity of IgG1 (B) and IgG3 (C) per glomerulus determined by Fiji. 15 glomeruli per mouse were analyzed. CGFI=Corrected glomerular fluorescent intensity. 10x

Magnification (n = 4 mice per group).

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Figure 3

Figure

3. Glomerular leukocyte infiltration phenotypes. Representative immunofluorescence micrographs of frozen renal sections. Paired immunofluorescent stains of CD11b+ cells (red,

CD11b-PE), CD4+ T cells (red CD4-PE), or neutrophils (red, Ly6G-PE). Images presented are representative images from 3 separate experiments for each group. Bar equals 200 µm. (B-D)

Mean fluorescence intensity of CD11b+ (B), CD4+ (C), and Ly6G (D), per glomerulus

127 determined by Fiji. 15 glomeruli per mouse were analyzed. CGFI=Corrected glomerular fluorescent intensity. (n = 4 mice per group).

128

Figure 4 A. P ro g re s s io n o f  -d s D N A 1 .5 R D

7 0 1 3 m

n 1 .0 2 0 1 8

D 0 .5 O

0 .0 0 5 1 0 1 5 2 0 W e e k s

B.  -d s D N A 2 .5

2 .0

m n

0 1 .5

D 1 .0 O 0 .5

0 .0

D 3 8 R 1 1 0 0 7 2

C.  -d s D N A (1 0 W e e k s )

2 .5 R D 2 .0

m 7 0 1 3

0 1 .5 2 0 1 8

8 3

1 .0

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0 .0

1 a b 3 G 2 2 G Ig G G Ig Ig Ig

D.  -d s D N A (1 4 W e e k s )

2 .5 R D 2 .0

m 7 0 1 3

0 1 .5 2 0 1 8

8 3

1 .0

D O 0 .5

0 .0

1 a b 3 G 2 2 G Ig G G Ig Ig Ig

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Figure 4. Effects of diet source on autoantibody production. Anti-dsDNA autoantibody ELISA.

(A) Anti-dsDNA antibody levels were monitored sequentially every 2 weeks in individual mice by ELISA. (B) Anti-dsDNA antibody levels were determined by ELISA after termination at 16 weeks of age. (C and D) Serum anti-dsDNA IgG subclass (IgG1, IgG2a, IgG2b, IgG3) levels were measured by ELISA at early onset of disease (10 weeks of age) and late stage disease (14 weeks of age). * P<0.05, one-way ANOVA. Data are shown as mean ± SEM (n = 8-9 mice per group).

130

Figure 5

A. B.

S p le n ic m R N A K id n e y m R N A

e v

v 1 0 1 5 e

e R D R D

n 8 7 0 1 3 7 0 1 3

i s

s 2 0 1 8 1 0 2 0 1 8 s

s 6 e

p x

x 4

5 e

v i

i 2

l e

e 0 0 R R g 6  g 6  n - - n - - If IL F If IL F N N T T

C. D. S p le n ic C y to k in e s S p le n ic C y to k in e s

4 0 0 0 0 * 4 0 0 * R D R D 3 0 0 0 0 7 0 1 3 7 0 1 3 2 0 0 0 0 3 0 0

l 2 0 1 8 l 2 0 1 8

m 1 0 0 0 0

m * /

/ * g

4 0 0 0 * g 2 0 0 p p 3 0 0 0 2 0 0 0 * 1 0 0 1 0 0 0 0 0   6   4 0 N F - - L 1 -1 -1 IF N I - IL T IL IL IL

Figure 5. Dietary mediated changes to cytokine production. (A and B) mRNA expression levels shown as fold change compared to RD diet expression levels, using comparative 2-ΔΔCt. ACTB2 was used as the endogenous control gene. One-way ANOVA. Data are shown as mean ± SEM

(n = 8 mice per group). (C and D) Splenocytes were treated with LPS for 24 hours at 500 ng/ml.

Culture supernatant was evaluated using Aushon cytokine Multiplex ELISA. * P<0.05, one-way

ANOVA. Data are shown as mean ± SEM (n = 8 mice per group).

131

Figure 6

A. L a c h n o s p ira c e a e

6 0 *

* 1 w e e k p o s t-w e a n in g

a i

r E n d p o in t

e t

c 4 0 a

a t

o 2 0

% 0

D 3 8 1 1 R 0 0 B. 7 2

L a c to b a c illia c e a e

1 .0

1 w e e k p o s t-w e a n in g

a i

r 0 .8 E n d P o in t

t c

a 0 .6

l a t 0 .4

o T 0 .2 % 0 .0

D 3 8 1 1 R 0 0 7 2

Figure 6. Effects of dietary source on fecal microbiota. (A and B) Real-time PCR analysis of

(A) Lachnospiraceae or (B) Lactobacillaceae relative abundance in feces from mice 1 week after weaning and at termination. Family specific gene amplification was compared to total bacterial DNA amplification to determine relative abundance. * P<0.05, one-way ANOVA.

Data shown are representative of 3 independent experiments. Data are shown as mean ± 95% confidence interval (n = 8 mice for 7013, 9 mice for 2018 and RD groups).

132

Figure 7

A. B.

l e

e v

6 v 1 5 *** e

R D e R D

*** n

7 0 1 3 n 7 0 1 3

i s

4 2 0 1 8 s 1 0 2 0 1 8

s e

p x

x *

2 5

a l

l e

0 e 0

R R 2 5 1 7 9 a 3 2 5 1 7 9 a 3 8 5 3 2 7 8 8 8 5 3 2 7 8 8 1 1 - 1 3 4 1 1 1 - 1 3 4 1 - - R - - 1 - - - R - - 1 - R R i R R - R R R i R R - R i i m i i R i i i m i i R i m m m m i m m m m m i m m m C. D. E.

l m iR -1 4 8 a E x p re s s io n m iR -1 2 7 E x p re s s io n m iR -1 8 3 E x p re s s io n

v v 2 .5 v e p = 0 .0 5 2 2 .5

2 .0 e * ** T0 e

L T0 T0

n n 2 .0 L P S n *

o L P S 2 .0 L P S

o i

i 1 .5

s s 1 .5 s

e 1 .5

p p

p 1 .0

x x

1 .0 x 1 .0

e e

e 0 .5

v v

i 0 .5 i

i 0 .5

e e

0 .0 e 0 .0 0 .0

R R D 3 8 D 3 8 D 3 8 1 1 1 1 1 1 R 0 0 R 0 0 R 0 0 F. 7 2 G. 7 2 H. 7 2

l m iR -3 7 9 E x p re s s io n m iR -3 1 E x p re s s io n m iR -1 5 5 E x p re s s io n

v v 1 .5 v e 8 2 .0 e T0 e

L T0 T0

n n L P S n * L P S

o L P S *

i o

i 1 .5 6

s s

1 .0 s

r p

p 4

p 1 .0

0 .5

e e

e 0 .5 2

e e

0 .0 e 0 .0 0

R R R D 3 8 D 3 8 D 3 8 1 1 1 1 1 1 R 0 0 R 0 0 R 0 0 7 2 7 2 7 2

Figure 7. Dietary source alters miRNA expression in LPS stimulated splenocytes from MRL/lpr mice. (A and B) Real-time RT-PCR analysis of lupus-associated miRNA expression in splenocytes. (A) miRNA expression in non-stimulated splenocytes. (B) miRNA expression in splenocytes after 24 hours of 500 ng/ml LPS stimulation. Expression is shown as relative

133 expression level to mice fed the RD diet. (C-H) miRNA expression in splenocytes without stimulation (T0) and after 24 hours of LPS stimulation (LPS). Data is expressed relative to the non-stimulated mean (n = 4 mice per group). The graphs represent mean + SEMs (n=6 mice per group). * P<0.05, ** P<0.01, *** P<0.001, one-way ANOVA.

134

Figure 8

A. B.

G lo b a l D N A M e th y la tio n D N M T 1 E x p re s s io n

2 0 *** 2 .5 * *** U n stim u la te d U n stim u la te d n L P S

e 2 .0 L P S

o i

1 5 g

l a

y 1 .5

h t

1 0 C

d 1 .0

5 F % 0 .5 0 0 .0

D 3 8 D 3 8 1 1 1 1 R 0 0 R 0 0 7 2 7 2

Figure 8. Methylation of MRL/lpr DNA and Dnmt1 expression increased after LPS stimulation in mice fed diets 7013 or 2018. (A) 5-mC DNA Methylation ELISA. ELISA was performed on

DNA extracted from splenocytes that were non-stimulated or stimulated for 24 hours with either

ConA or LPS. (B) Dnmt1 mRNA expression. RT-PCR was performed on RNA extracted from splenocytes without stimulation and splenocytes following 24 hours of LPS stimulation from the same mice. qPCR was performed on cDNA using a TaqMan assay. The graphs represent mean

± SEMs. * P<0.05, ** P<0.01, *** P<0.001, one-way ANOVA (A) t-test (B). (n=4 mice per group).

135

Table I.- Isoflavone Content per Diet

Isoflavone RD 7013 2018 Daidzein <10.0 ppm <10.0 ppm <10.0 ppm Daidzin <10.0 ppm 88.9 ppm 179 ppm Genistein <10.0 ppm <10.0 ppm <10.0 ppm Genistin <10.0 ppm 99.0 ppm 204 ppm Glycitein <10.0 ppm <10.0 ppm 10.1 ppm Glycitin <10.0 ppm 20.7 ppm 51.9 ppm Total as <10.0 ppm 209 ppm 451 ppm Glucosides

Table I. Isoflavone content of each diet evaluated by LC-MS/MS by Covance Laboratories. The limit of detection for each isoflavone was 10ppm.

136

Table II.- Renal Histopathologic Scores

Diet Glomerular Interstitial Vessel Tubule Kidney Source score Score Score Score Scores RD 0.8±0.7 1.2±1.0 1.8±0.4 0.6±0.7 4.3±2.3 7013 1.4±0.7 1.6±0.7 2.1±0.4 0.7±0.9 5.9±2.0 2018 1.7±1.2 2.0±1.0 2.1±0.6 1.1±1.3 6.9±3.7

Table II. Evaluation and scoring of histopathologic renal sections. H&E and PAS stained slides were evaluated by a board certified veterinary pathologist in a blinded fashion. Individual scores were averaged for each group score for each structure evaluated. Data are shown as mean+/-SD

(n = 8 mice for 7013, 9 mice for 2018 and RD groups).

137

Supplemental Table 1 Primer Sequences

Primer Name Primer Sequence Forward (5’-3’) Primer Sequence Reverse (5’-3’)

Total Bacteria ACTCCTACGGGAGGCAGCAGT ATTACCGCGGCTGCTGGC F340/R514

Lachnospiraceae ACTCCTACGGGAGGCAGC GCTTCTTAGTCAGGTACCGTCAT 338F/491R

Lactobacilliaceae AGCAGTAGGGAATCTTCCA CACCGCTACACATGGAG LabF362/LabR677

148-R1 GGAGTTAGGTGATTATTTGGAGTGT TATCAAATCAACAAATTCCCTCC

148-U2 AGGGAATTTGTTGATTTGATATGA CATCAACCTATCTAACCTAACCACC

148-R3 TGTTTTAGTAGGTAGAGGGAAAT TTCCTAAAACAAAAAATCAAAACC

Table S1 Legend:

Primer sequences used for real-time PCR.

138

Supplemental Figure 1

A. B.

M o u s e w e ig h t M o u s e w e ig h t g a in 1 0 5 0 R D *

4 0 7 0 1 3 )

*** ) g

g 2 0 1 8

( (

*** R D

t 3 0

n i h 7 0 1 3

a 5

g i

G 2 0 1 8 e

2 0

W g

1 0 i

e W 0 0 0 5 1 0 1 5 2 0 5 1 0 1 5 2 0 W e e k s W e e k s C. D.

) F o o d C o n s u m p tio n

g C a lo rie s C o n s u m e d (

4 0 e

s 1 5 0

u R D o R D 7 0 1 3

m 3 0

r 7 0 1 3 2 0 1 8 s

e 1 0 0 e

p n s

2 0 1 8 i

r d

2 0 o

m C

u 5 0 s

n 1 0

d 0 o 0

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F 0 5 1 0 1 5 2 0 W e e k s W e e k s

139

Supplemental Figure 1 Legend: Bodyweight changes and caloric ingestion by MRL/lpr mice fed a purified ingredients diet (RD), a chow diet containing soy and alfalfa (7013), or a chow diet containing soy (2018). (A,B) Mice were weighed weekly starting from the date of weaning at 3 weeks of age. (C) Average grams of food consumed per mouse per week. (D) Average calories consumed per mouse per week, calculated by multiplying average weight of food consumed by energy density per kg. of diet, n=9-10 per group. Data are presented as mean ± SEM. *, p<0.05,

**, p<0.01, ***, p<0.001, one-way ANOVA.

140

Supplemental Figure 2

Supplemental Figure 2 Legend:

Glomerular IgG2a and IgG2b deposition. Representative immunofluorescence micrographs of 5

µm frozen renal sections. Immunofluorescence images of IgG2a (red) and IgG2b (green) protein deposition within glomeruli. Brighter fluorophores (Alexa Fluor 568 (red) and Alexa Fluor 488

141

(green) were used to compensate for both the relatively dim FITC and PE used in Figure 2 experiments, and the reduced binding potential of isotype specific IgG, due to relative quantity of subclass protein compared to total IgG. 10x Magnification (n = 4 mice per group).

142

Supplemental Figure 3

A. B. S e ru m A n ti-c a rd io lip in S e ru m A n ti-S m D 1

3 5

m n

n 2 0

5 3

2 D

D 1

O O 1 0 0

D D 3 8 3 8 R 1 1 R 1 1 0 0 0 0 7 2 7 2

Supplemental Figure 3 Legend:

Anti-cardiolipin and anti-SmD1 autoantibody ELISA. (A) Anti-cardiolipin and (B) anti-SmD1 antibody levels were determined by ELISA after termination at 16 weeks of age. * P<0.05, one- way ANOVA. Data are shown as mean ± SEM (n = 8-9 mice per group).

143

Supplemental Figure 4

Splenic lymphocytes Mesenteric lymphocytes

B 2 2 0 + C D 1 9 + B 2 2 0 + C D 1 9 + 2 .0 2 5

2 0 1 .5

e l

1 5 c e

1 .0

% 1 0 0 .5 5 0 .0 0 D 3 8 1 1 D 3 8 R 0 0 1 1 7 2 R 0 0 7 2

C D 4 + C D 8 - C D 4 + C D 8 -

2 5 1 5

2 0

s l

l 1 0 l

1 5 l

% 1 0 % 5 5

0 0

D 3 8 D 3 8 1 1 1 1 R 0 0 R 0 0 7 2 7 2

C D 4 -C D 8 + C D 4 -C D 8 +

2 5 2 0

2 0

1 5

l l

1 5 l

1 0

% 1 0 %

5 5

0 0

D 3 8 D 3 8 1 1 1 1 R 0 0 R 0 0 7 2 7 2

S p le n ic T re g m L N T re g

1 0 6

l l

l 4 e

e 6

% % 4 2 2

0 0 3 8 3 8 D 1 1 D 1 1 R 0 0 R 0 0 7 2 7 2

144

Supplemental Figure 4 Legend:

Dietary Phytoestrogen exposure does not alter relative cellular abundance in secondary lymphoid organs. Flow cytometry was performed on fresh cellular isolates from either spleen or mesenteric lymph nodes. Cells were incubated with antibodies against CD3-FITC, CD4-ef450, CD8-PerCP- cy5.5, B220-PE, and CD19-APC. The cells were fixed prior to analysis on a BD FACSaria. For cytometric flow analysis, the cells were gated for CD3+. Data are shown as mean ± SEM (n = 8 mice for 7013, 9 mice for 2018 and RD groups).

145

Supplemental Figure 5

B a c te ria l L o a d a t W e a n in g

2 .0

c n

a 1 .5

n u

b 1 .0

v i

t 0 .5

l e

R 0 .0

D 3 8 R 1 1 0 0 7 2

B a c te ria l L o a d a t E n d -P o in t

1 .5

a d

n 1 .0

e 0 .5

l e

R 0 .0

D 3 8 R 1 1 0 0 7 2

Supplemental Figure 5 Legend: Fecal bacterial relative abundance. Relative total bacterial

DNA abundance at 1 week post-weaning (A) or at 14 weeks of age (B). Relative abundance was evaluated using the 2ΔCt method * P<0.05, one-way ANOVA. Data shown are representative of

3 independent experiments. Data are shown as mean ± SEM (n = 4 mice per group).

146

Chapter 4

17-β estradiol and synthetic 17α-ethinyl estradiol exhibit immunologic and epigenetic regulatory effects in NZB/WF1 female mice.

Dai, R., Edwards, MR., Heid, B., S Ansar Ahmed

Department of Biomedical Sciences and Pathobiology, Virginia Maryland College of Veterinary

Medicine, Virginia Tech, Blacksburg, Virginia, USA

This chapter was published in Endocrinology and reproduced with permission.

Dai, R.*, Edwards, MR.*, Heid, B., Ansar Ahmed, S. (2019) 17-β estradiol and 17α-ethinyl estradiol exhibit immunologic and epigenetic regulatory effects in NZB/WF1 female mice.

Endocrinology. 160(1), 101-118. Doi: 10.1210/en.2018-00824. [*co-first author]. and

Rujuan Dai, Edwards, M., Heid, B., & S. Ansar Ahmed. (2018). Supplemental Data from "17-β estradiol and 17α-ethinyl estradiol exhibit immunologic and epigenetic regulatory effects in

NZB/WF1 female mice" [Data set]. University Libraries, Virginia Tech. https://doi.org/10.7294/W4-A15D-1W64

Abstract

17α-ethinyl estradiol (EE), a synthetic analog of natural estrogen 17- estradiol (E2), is extensively used in hormonal contraceptives and estrogen replacement therapy, and has also been found in sewage effluents. Given that E2 is a well-known immunomodulator, surprisingly there has been only limited information on the cellular and molecular immunologic

147 consequences of exposure to EE. To address this fundamental gap, we directly compared the effects of EE with E2 on splenic leukocytes of NZB/WF1 mice during the pre-autoimmune period. We found that EE and E2 have common, as well as distinctive, immunologic effects, with EE exposure resulting in more profound effects. Both EE and E2 increased numbers of splenic neutrophils, enhanced neutrophil serine proteases and myeloperoxidase expression, promoted the production of nitric oxide (NO) and monocyte chemoattractant protein (MCP-1), and altered adaptive immune T cell subsets. However, activation of splenic leukocytes through

TCR or TLR4 revealed not only common (IL-10), but also hormone-specific alterations of cytokines (IFN, IL-1 F IL-2). Further, in EE-exposed mice, TLR9 stimulation suppressed IFN, in contrast to increased IFN from E2-exposed mice. EE and E2 regulated common and hormone-specific expression of immune-related genes. Further, EE exposure resulted in more marked alterations in miRNA expression levels than E2. Only EE was able to reduce global DNA methylation significantly in splenic leukocytes. Together, our novel data revealed that EE and E2 exposure confers more similar effects in innate immune system related cell development and responses, but has more differential regulatory effects in adaptive immune related cell development and responses.

148

Introduction

Estrogen has previously been shown to regulate the delicate balance of the immune system (53,110,294-296). 17β-estradiol (E2) is a naturally produced estrogen and serum levels fluctuate during menstrual cycles and pregnancy and decline during menopause. E2 is able to alter the development, differentiation, function and responses of all types of immune cells, including both innate and adaptive cells (54,55,59,132). Among estrogens, E2 is the most widely studied with respect to immune system regulation, and these effects have been well documented

(53-56). These E2-induced immunoregulatory effects have been shown to influence disease presentation and clinical signs in such categories as autoimmune and allergic diseases

(85,86,117). The potential effects of estrogen on autoimmunity is suggested by clinical observations that lupus flares increase after puberty when estrogen levels are increased compared to prepubertal age and decrease following menopause, when estrogen levels drop (91,130).

Exposure to estrogenic compounds may mimic or block the action of natural hormones by binding to its receptor or interfering with receptor activities(110,112). Estrogens have been shown to alter various functions of the immune system and regulate the responses to stimuli in both normal and autoimmune individuals through estrogen receptor-dependent or independent mechanisms(84,85,126,127,297). E2 treatment, both in vivo and in vitro of splenic leukocytes, has been shown to influence the production of multiple pro-inflammatory cytokines and chemokines and promoted B cell survival following stimulation(59,88,130,131,138,139,298).

Increased B cell survival following estrogen treatment has been associated with a break in B cell tolerance, and promotion of autoantibody production and lupus-related pathology in non- autoimmune mouse strains(86). Increased circulating E2 was also associated with increased autoimmune activity in human males(299).

149

E2 and other estrogenic compounds, such as bisphenol A, exert modulatory effects on the epigenetic regulatory mechanisms such as microRNA (miRNA) and DNA methylation, impacting cellular responses and gene expression(117,201-204,208). We have reported that E2 regulated miRNA expression in both orchidectomized male B6 and NZB/WF1 mice(116,202). A subset of E2-regulated miRNAs including miR-146a, miR-148a, miR-125a, and miR-126 have been implicated in human lupus pathogenesis by targeting lupus-related signaling pathways(200,300-305). These data suggest the potential involvement of miRNAs in E2- mediated effects on lupus. Although E2’s role in regulation of multiple aspects of the immune system, and exacerbation of autoimmune inflammatory processes has been well documented, the effects of 17α-ethinyl estradiol (EE), a synthetic analog of E2, on immune development, differentiation, and function has been minimal. Also, to date, no studies have directly compared the effects of E2 and EE on immune response and/or epigenetic regulation in an autoimmune- prone immune system.

EE is a primary component in common contraceptives, both transdermal and oral, used by females and has been implemented in estrogen replacement therapy (ERT). Other common indications for EE treatment include breast cancer, vasomotor symptoms in menopause, female hypogonadism, hirsutism, acne vulgaris, and dysmenorrhea

(https://www.drugbank.ca/drugs/DB00977). Detection of EE as an aquatic pollutant and in sewage effluents increases the potential exposure to human populations in addition to pharmaceutical administration and is regarded as an environmental endocrine disrupting chemical (EEDC)(306). EE was shown to have a 100-times stronger potency than E2 for estrogen receptor (ER)α nuclear translocation(93). However, resultant immunological functional

150 assays were not performed in that study, and few studies have been published detailing EE’s specific effects on immune system development and function.

In this study, we investigated whether or not EE can induce similar or dissimilar immunologic and epigenetic effects. Further, since estrogen modulation of the immune system can affect responses to infections, we investigated the effects of E2 and EE exposure to on cytokine responses through T cell receptor (TCR)/CD3 complex, toll-like receptor (TLR) 4 (a receptor of bacterial lipopolysaccharide) and TLR9 (recognizes unmethylated double-strand

DNA commonly found in bacteria and viruses) stimulation. To understand the transcriptional and epigenetic regulatory effects of E2 and EE exposure in splenic leukocytes, we also profiled the expression patterns of immune-related genes and miRNAs. Our study shows that exposure to

E2 or EE resulted in distinct differences in action between cellular responses and cell populations generally considered innate vs. those generally considered part of the adaptive immune response.

Materials and Methods

Mice and Subcutaneous implants

Genetically lupus-prone NZB/WF1 (NZBWF1/J, stock# 100008) mice were purchased from vendor, Jackson Laboratory, ME, USA. Wild-type C57BL/6 mice were bred in the Virginia-

Maryland College of Veterinary Medicine (VMCVM) vivarium. All mice were housed in the

AAALAC certified animal facility at the VMCVM, Virginia Tech. Since historically, predominantly females are exposed to these estrogenic compounds through contraceptive use and ERT, and lupus exhibits a strong female sex bias, for this study we investigated the effects of

E2 and EE on female NZB/WF1 mice only. Mice were fed the 7013 NIH-31 Modified 6%

Mouse/Rat Sterilizable Diet (Harlan Laboratory, Madison, WI, USA). Mice were implanted

151 with a silastic capsule of the same size containing either 17β-estradiol or 17α-ethinyl estradiol, or an empty control capsule for the placebo group at 6-7 weeks of age. For the experiments investigating DNA methylation levels, in addition to intact NZB/WF1 female mice, orchiectomized male C57BL/6 mice were implanted with a silastic capsule containing 17β- estradiol or an empty control capsule for the placebo group. Mice were euthanized at 16-17 weeks of age in the early-disease stage, constituting 9-10 weeks of treatment. Care was taken to ensure that all groups were subjected to the same housing, local environment and handling conditions. All animal procedures and experiments were performed in accordance with guidelines of the Institutional Animal Care and Use Committee (IACUC) at Virginia Tech. CO2 was used for euthanasia as required by the approved IACUC protocol.

Tissue preparation, and cellular culture

Whole splenic leukocytes and thymocytes were isolated using standard lab procedures described in detail previously(23,88,202,298). Briefly, the spleens and thymus were dissociated by gently scraping through a steel screen, and the cell suspension was passed through a 70-μm cell strainer to remove undissociated tissue debris. The splenic leukocytes and thymocytes were isolated by lysing red blood cells with ACK-Tris-NH4Cl buffer and then washed with complete RPMI-1640 medium (Mediatech, Inc., Manassas, VA, USA) that was supplemented with 10% charcoal- stripped fetal bovine serum (Atlanta Biologicals, Flowery Branch, GA, USA), 2 mM L- glutamine (HyClone Labs Inc, Logan, UT, USA), 100 IU/ml penicillin and 100 g/ml streptomycin (HyClone), and 1% non-essential amino acids (HyClone). The cells were adjusted to 5x106/ml in complete medium for seeding into cell culture plates. Briefly, the cells were plated into 48-well cell culture plate (0.25ml/well), and stimulated with LPS (500 ng/ml), ConA

( 5g/ml), or TLR9 agonist ODN-1585 (0.5M, synthesized by IDT Inc., Skokie, IL, USA) for

152 the designated time by adding an equal volume of 2x concentration of stimulation medium. For anti-CD3 plus anti-CD28 stimulation, the cells were plated into anti-CD3 (5g/ml, eBioscience) pre-coated cell culture plates and then equal volumes of medium containing anti-CD28

(eBioscience) at 4g /ml (final at 2g/ml) were added for 24hrs.

Flow Cytometry

The relative percentage of CD4, CD8, CD25, CD69 expressing cells in the thymus and spleen, and B220, CD19, IgG, IgM, IgG2a, CD11b+, and Ly6G+ expressing cells in the spleen were quantified by flow cytometric analysis. Stained cells were visualized using a FACSAria flow cytometer (BD Biosciences) and data analyzed using FlowJo version 7 software as described in our previous studies(87,106,118).

Detection of inducible nitric oxide synthase (iNOS) and nitric oxide

Griess assays were used to detect nitric oxide levels in culture supernatants as described(298).

Western blots were used to analyze iNOS protein expression in whole cell extracts as described before(298). The whole cell extracts were prepared by lysing the cell pellet with CelLyticM Cell

Lysis Reagent (Sigma-Aldrich). The anti-iNOS (M19, sc-650; RRID:AB_631831) antibody was purchased from Santa Cruz Biotechnology Inc., Paso Robles, CA, USA. The protein loading control antibody, anti-β-actin antibody (ab8227, RRID:AB_2305186), was obtained from Abcam

Inc., Cambridge, MA, USA. The blot images were captured using a Kodak Image Station 440.

Cytokine/Chemokine ELISAs

The levels of MCP-1 in ConA, anti-CD3 plus anti-CD28, or LPS stimulated culture supernatants were determined with mouse MCP-1 ELISA MAX deluxe kit (Biolegend, San

Diego, CA, USA). The levels of IFN in TLR9 against ODN-1585 stimulated splenic leukocytes culture supernatants were determined with eBioscience Mouse IFN alpha Platinum ELISA kit

153

(Fisher Scientific). The ELISAs were performed per the manufacturers’ protocols. Ciraplex®

Chemiluminescent Assay kits (Aushon Biosystem, Billerica, MA, USA) were used to quantify the levels of IFN-γ, IL-1β, IL-2, IL-6, IL-10, and TNFα in cell culture supernatants per the manufacturer’s instructions(106,201). The images of chemiluminescent array plates were captured with Cirascan image system (Aushon) and the image data was processed with Cirasoft software.

DNA isolation and global DNA methylation analysis

The whole genomic DNA was isolated from splenic cells with DNeasy Blood and Tissue Kit

(Qiagen). The DNA concentration was measured with NanoDrop 2000 spectrophotometer.

The 5-mC DNA ELISA Kit (ZYMO Research, Irvine, CA, USA) was used to measure the global

DNA methylation level as we previously reported(106,201). Briefly, 100ng DNA of each sample was brought up to 100 μl volume with 5-mC coating buffer, denatured at 98°C, and then coated into 96-well assay plate. After washing, the coated DNA was incubated with an antibody mix consisting of anti-5-Methylcytosine antibody and secondary antibody. After antibody incubation, the plate was washed, and HRP developer solution was added to develop color signal. The absorbance was measured by reading the plate at 405nm on a SpectraMax M5 Microplate reader

(Molecular Devices, Sunnyvale, CA, USA). The percentage of 5-mC in each DNA sample was quantified with a standard curve that was generated with kit-provided positive control (100% methylation) and negative control (0% methylation). mRNA and immune-related miRNA gene expression profile analysis

Total RNA, containing small RNA, was isolated from whole splenic leukocytes using a miRNeasy Mini Kit (Qiagen, Valencia, CA, USA) as described in our previous publications

(REF). Briefly, on-column DNase digestion with RNase-free DNase (Qiagen) was performed to

154 remove residual amounts of DNA contamination in the isolated RNA. The RNA concentration was quantified using a NanoDrop 2000 (ThermoFisher Scientific Inc., Wilmington, DE, USA). miRNA and immune-related mRNA expression profiles of freshly-isolated splenic leukocytes from EE-, E2-, and placebo-treated NZB/WF1 mice were analyzed with NanoString nCounter gene expression system (NanoString Technologies, Seattle, WA). Briefly, about 150ng of total RNA (containing small RNA) for each sample were send to NanoString Technologies for mRNA and miRNA expression analysis with nCounter GX mouse immunology kit (contains 547 immune-related genes) and nCounter mouse miRNA V1.5 Assay kit (contain 578 miRNA targets).

The acquired raw data were uploaded to nSolver Nanostring software 3.0 and processed with default quality control settings, and then for data normalization and expression analysis. The data normalization took two steps. First, raw counts were normalized to the six internal positive controls to adjust for systematic variation from assay-to-assay. Second, the positive control normalized counts were further normalized with the geometric mean of 14 reference genes in the

CodeSet (for immune-related genes) or with the geometric mean of the top-100 expressed miRNAs (for miRNA) to adjust for the variation of RNA contents. The scatterplot expression data for mRNA and miRNA were generated in nSolver using group expression values. Ratios for the comparison of treatment groups and associated p-values were generated by the nSolver software. The normalized expression values of the mRNA genes that demonstrated 1.5-fold change or above with p < 0.1 in either the EE or E2-treated group and of the miRNAs that demonstrated 1.5-fold change or above in either EE or E2-treated group were exported from nSolver to excel and were selected to generate the Venn diagram. To visualize the differential expression intensity levels of mRNA and miRNAs in different treatment groups, the normalized expression intensity values of exported genes (mRNAs or miRNAs) from individual samples

155 were Log2 transformed, centered by genes, and then used for cluster analysis to generate the heat map with Java Tree View(307). qRT-PCR analysis of miRNA and mRNA expression

As we described in detail previously(23,116,202), Taqman miRNA assay reagents (Applied

Biosystems, Grand Island, NY, USA) were used to quantify the miRNA expression per the manufacturer's instructions. The iScript one-step RT-PCR with SYBR green kit (Bio-Rad,

Hercules, CA, USA) was used for quantifying the mRNA expression levels of neutrophil serine proteinases including neutrophil elastase (NE), proteinase 3 (PR3), cathepsin G (CG) and myeloperoxidase (MPO) as we previously reported(87). Quantitect 10  PCR primer mixes for mouse NE, PR3, CG, MPO, and -actin genes were purchased from Qiagen. The expression levels of miRNAs and mRNAs were normalized to endogenous small RNA control snoRNA 202, and housekeeping gene -actin, respectively. The data was shown as relative expression level to an appropriate control by using the 2−ΔΔCt formula (Livak method).

Statistical Analysis

All values in the graphs are given as means ± SEM, or as otherwise stated in the figure legend.

To assess statistical significance, one-way ANOVA following by Least Significant Difference

(LSD) method was performed for the group comparisons, unless otherwise specified in the figure legend. The JMP software (Pro13 version) was used for all the statistical analysis.

Graphical presentation of data was performed in Prism Graphpad software (Version 7.04).

156

Results

E2 and EE treatment affect spleen weight

Compared with placebo-treated NZB/WF1 mice, in both 17β-estradiol (E2) and 17α- ethinyl estradiol (EE) exposed mice, a significant increase of absolute spleen weight (Sup. Fig.

1A) and spleen:body weight ratio was observed (Sup. Fig. 1B)(308). Although similar splenomegaly was present in E2-exposed NZB/WF1 mice, there was no evidence of reduced splenic leukocyte numbers (Supp. Fig. 1C) as we previously observed in E2-exposed orchidectomized male B6 mice(87,308). We did observe an increase in splenic leukocyte cell counts in EE-exposed mice; though the value did not reach statistical significance, consistent with increased spleen weight in these mice (Supp. Fig. 1C)(308). This suggests that the majority of the increased spleen weight may be due to accumulation of erythrocytes and/or stromal cell populations rather than leukocytes in the estrogenic compound exposed NZB/WF1 female mice.

We then examined the phenotypic and functional effects of EE and E2 exposure on innate immunity and adaptive immune cells.

Both E2 and EE treatment increase splenic neutrophil numbers and increased neutrophil serine protease and myeloperoxidase mRNA expression.

Here, we demonstrated that both E2 and EE exposure significantly increased the percentage of neutrophils (CD11b+ Ly6G+) in the splenic leukocytes of gonadal intact female

+ + NZB/WF1 mice (Fig.1A). However, the increase of the absolute splenic CD11b Ly6G counts in both E2 and EE treated mice were not significant (Fig. 1B). Further real-time RT-PCR analysis revealed that NSPs including NE, CG, PR3 and neutrophil specific MPO were all significantly increased in the splenic leukocytes from both E2- and EE- treated NZB/WF1 mice at a similar level when compared to placebo controls (Fig. 1C).

157

E2 and EE exposure promote the production of Nitric Oxide and inducible Nitric Oxide synthase

Inducible nitric oxide synthase (iNOS) and nitric oxide (NO) production following LPS or ConA stimulation in splenic leukocytes has previously been shown to be sensitive to estrogen exposure. There was a significant increase of NO production in ConA-activated splenic leukocytes from EE-exposed mice when compared to placebo control mouse splenic leukocytes

(Fig. 2A). While it did not reach statistical significance (p=0.055), there was a clear trend of increased NO in ConA-activated splenic leukocytes from E2-exposed mice when compared to placebo control (Fig.2A). Compared to ConA-activated splenic leukocytes, there was a much lower production of NO in LPS-activated splenic leukocytes (Fig. 2B). Consistent with the NO data, there was an increase of iNOS protein expression in E2- and EE-exposed splenic leukocytes (Fig. 2C). Western blot analysis also showed only a marginal increase of iNOS protein in LPS activated splenic leukocytes of EE-exposed mice (Fig. 2D).

Both E2 and EE-exposure increased Monocyte Chemoattractant Protein-1 (MCP-1) production following stimulation, but to different degrees

MCP-1 production levels were remarkably increased in ConA-, CD3 plus anti-CD28-, or

LPS-activated splenic leukocytes from E2 and EE-exposed NZB/WF1 mice, and there was only minimal level of MCP-1 production in either LPS or ConA-activated splenic leukocytes from placebo-treated NZB/WF1 mice (Fig. 2E-G). EE exposure consistently led to more pronounced

MCP-1 production compared to E2 exposed mice with different stimulants. The MCP-1 data, together with above iNOS/NO (Fig.2A-D) and neutrophil /NSPs (Fig.1) suggested that both E2 and EE affect innate immune cell development and functions, and that EE appears to have more profound effect than E2 with regard to the production of NO and MCP-1.

158

E2 and EE treatment alter the T cell subsets (composition) and activation phenotype in the spleen and thymus

Flow cytometric analysis was performed to evaluate the T cell composition in the spleen and thymus of NZB/WF1 mice following in vivo E2 and EE treatment. As indicated, E2 and EE treatment reduced the percentage of CD4+ single positive (SP) T cells in the spleen. The reductive effect of EE on CD4+ SP T cells was more prominent than E2 (Fig. 3A&E). We further analyzed the expression of T cell activation markers CD25 and CD69 in splenic leukocytes. Both

E2 and EE exposure led to reduced CD4+CD25+ and minimal changes to CD4+CD69+ subsets

(Fig. 3B, C, F,& G), in NZB/WF1 mice when compared to placebo-controls. Gating only on the splenic CD4+ cells, the percentage of CD25+CD69+ (CD4+/CD25+CD69+) was significantly increased (Fig. 3D&H).

E2 had only minimal effects on splenic CD8+ cell populations and activation. However,

EE exposure led to significant differences in CD8+ subsets when compared to both placebo and

E2 groups (Fig. 4A). When compared to placebo controls, in EE- treated, but not E2- treated splenic leukocytes, the percentages of splenic CD8+CD25+ cells were increased with minimal effects on CD8+CD69+ subsets in EE treated, but not E2 treated splenic leukocytes (Fig. 4B, C,

F, &G). The percentage of CD8+CD25+CD69+ cell population was also only significantly increased in EE exposed, but not E2 exposed NZB/WF1 mice (Fig. 4D&H).

Thymic T cell populations were also altered due to EE exposure. The effect of E2 on thymic T cell populations was minimal. No differences were identified in CD4+ SP cells in both

E2 and EE treated mice when compared to placebo controls (Supp. Fig. 2A)(308). Thymic CD8+

SP and double positive CD4+CD8+ cells were significantly decreased in EE exposed mice (Supp.

Fig. 2B&C)(308). EE, but not E2, exposure led to increased CD4+CD25+ cells in the thymus

159 compared to the placebo and E2 groups (Supp. Fig. 2D)(308). Similar to the findings in the splenic cells, the percentage of CD4+CD69+ was not altered, but the percentage of CD25+CD69+ cell in CD4+ T cells (CD4+/CD25+CD69+) was significantly increased in EE-treated mice (Supp.

Fig. 2E&F)(308). The percentage of CD8+CD25+, but not CD8+CD69+ in splenic cells and the percentage of CD25+CD69+ double positive cells in CD8+ SP T cells were increased in EE- exposed mice (Supp. Fig. 2G-I)(308). Together, our data indicated that EE exposures significantly affects T cell composition in the thymus, and promotes T cell activation. Although

E2 exposure on thymic T cell populations had similar trends as EE, the effects were not as pronounced as EE.

The effect of E2 and EE treatment on B cell numbers and immunoglobulin levels is marginal.

We also examined whether estrogen and EE exposure affected B cell development and

+ + function in NZB/WF1 mice. There were no obvious changes of the percentages of CD19 , B220 ,

IgG+, IgM + cells in the spleens of E2- and EE-exposed mice (data not shown). We only

+ observed a significant increase of IgG2a splenic cells in EE-exposed NZB/WF1 mice when compared to placebo controls (data not shown).

E2 and EE exposure leads to differential effects on the induction of various inflammatory cytokines from in vitro activated splenic leukocytes

Our previous studies have demonstrated that 17- estradiol significantly promoted the induction of IFNγ and IL-1β, and IL-6 in LPS stimulated splenic leukocytes from male B6 mice(87,88,202). To our surprise, we observed a significant reduction of IFNγ, and a similar trend in IL-1β, in LPS-activated splenic leukocytes from E2-exposed NZB/WF1 mice when compared to placebo controls. However, IFNγ and IL-1β were significantly upregulated in LPS-

160 activated splenic leukocytes from EE-exposed NZB/WF1 mice when compared to placebo control

NZB/WF1 mice (Fig.5A&B). There were only minimal changes in TNFα and IL-6 production in

LPS-activated splenic leukocytes from both E2 and EE exposed mice when compared to placebo control, while there was a trend of reduction of LPS-induced IL-2 (p=0.096) in EE-treated mice

(Fig. 5C-E). LPS-activation increased IL-10 levels in both EE- and E2- exposed mice similarly when compared to placebo controls (Fig. 5F).

Due to the decreased CD4+ and CD8+ SP T cells in splenic leukocytes, we evaluated a more specific T cell response using anti-CD3 plus anti-CD28, instead of ConA to stimulate the splenic leukocytes in this study. Surprisingly, even with reduced CD4+ T cells in splenic leukocytes, we observed a significant increase of IFN and IL-1β in anti-CD3 plus anti-CD28 activated splenic leukocytes from EE- exposed mice (Fig. 6A& B). We did observe a significant reduction of IL-6, TNF and IL-2 in anti-CD3 plus anti-CD28 activated splenic leukocytes from

EE-exposed mice (Fig.6C-E). E2 exposure had no obvious effect in the induction of IFN, IL-1β,

IL-6, TNF, or IL-2 in anti-CD3 plus anti-CD28 activated splenic leukocytes (Fig. 6A-E). E2 and EE exposure both significantly increased IL-10 production, while E2 showed a stronger magnitude of effect in splenic leukocytes following anti-CD3 plus anti-CD28 stimulation (Fig.

6F).

TLR-driven IFN plays a critical role in the pathogenesis of lupus(42,248,309). We therefore analyzed the production of IFN in splenic leukocytes from E2 and EE-exposed mice in response to TLR9 agonist Class A CpG ODN-1585. As shown in Fig 7A, E2 exposure significantly increased IFN production in ODN-1585 activated splenic leukocytes. IFNα production from EE- exposed mice was significantly lower than that of E2- exposed mice, but the reduction did not reach significance when compared to the placebo-exposed mice. As a class

161

“A” TLR9 agonist, ODN-1585 also has weak stimulatory effect on TLR9-dependent NF-κB signaling to drive the expression of inflammatory cytokines such as IL6. We observed that EE exposure also significantly reduced IL-6 production, while E2 has no obvious effect on the IL-6 in ODN-1585 activated cells (Fig 7B).

E2 and EE exposure has similar and yet distinct effects on the regulation of immune- related gene expression

To better understand the immune regulatory role of E2 and EE, we performed gene expression analysis in splenic leukocytes from NZB/WF1 mice following placebo, E2, or EE exposure with nanostring nCounter GX mouse immunology code set. The scatter plot showed that

E2 and EE treatment altered numerous immune-related genes at a similar pattern (Fig. 8A). The regression analysis revealed that the coefficient of determination (R2) value of gene expression between placebo and E2 is 0.867, and the R2 value between placebo and EE was 0.860. We identified that 190 genes were altered by E2 and 251 immune-related genes were altered by EE at a fold change of 1.5 or above with p <0.1. Of which there were 159 genes were commonly regulated by E2 and EE treatment (Fig. 8B & Supp. Table 1)(308). A heat map was generated to visualize the similar expression change of these 159 genes induced by E2 and EE treatment (Fig.

8C). Together, we demonstrated that E2 and EE exposure has a similar effect on the regulation of immune-related gene expression.

EE has a more profound effect than E2 on the expression of miRNA genes.

We have reported that estrogen regulated miRNAs expression in splenic leukocytes from

E2 treated orchidectomized male B6 mice(202). There is limited data with regards to EE regulation of miRNAs, and these data were collected in tissues/cells other than immune cells(310,311). To understand the epigenetic regulatory role of E2 and EE in NZB/WF1 mice, we

162 profiled the miRNA expression in placebo-, EE-, and E2- exposed splenic leukocytes. The scatter plot showed correlation of miRNA expression between E2 or EE exposed groups and placebo group (Fig. 9A). It is apparent that EE had more profound effects than E2 on the miRNA expression, as the R2 value of miRNA expression between placebo and E2 was 0.852, but the R2 value of miRNA expression between placebo and EE was only 0.777. The R2 between

E2 and EE groups for miRNAs expression was 0.902. Compared to the R2 of 0.962 for immune relate-genes gene expression between E2 and EE groups, E2 and EE have more differential effects on the miRNA expression than on mRNA gene expression. Further analysis demonstrated that 68 miRNAs were altered by E2 and 86 miRNAs were altered by EE at a fold change of at least 1.5 (Fig. 9B & Supp. Table 2)(308). A heat map was generated to show the miRNA expression level changes of 50 miRNAs that are commonly regulated by E2 and EE in the individual samples of different treatment groups (Fig. 9C). It is noteworthy that there was greater biological variation in the same group for miRNAs profiling assays when compared to mRNA profiling analysis data. We then performed Taqman assays to confirm the expression changes of select miRNAs in E2 and EE treatment NZB/WF1 mice (Fig. 9D-I). These specific miRNAs (miR-451, miR-148a, miR-18a, miR-125a, miR-126, miR-145) were selected because they have previously been shown to be regulated by estrogen(202).

Consistent with the previous data in E2-treated B6 mice, here, we showed the upregulation of miR-451, miR-148a, miR-18a in splenic leukocytes of E2-treated NZB/WF1 mice, while the upregulation of miR-451 (p=0.1) and miR-148a (p=0.05) did not reach the statistical significance (Fig. 9D-F). EE treatment significantly upregulated the expression of miR-451, miR-148a, miR-18a at a higher magnitude then E2 (Fig. 9D-F). Unlike the downregulation effect of E2 on miR-125a, miR-126, miR-145 in orchidectomized male B6

163 mice(202), there was only trends of reduction of these three miRNAs in splenic leukocytes of

E2-treated NZB/WF1 mice (Fig. 9G-I). More surprisingly, there was a significant upregulation of miR-145 and miR-126 in splenic leukocytes of EE- treated NZB/WF1 mice when compared to placebo controls (Fig. 9H&I). Together, our data indicated that E2 and EE exposure regulated numerous miRNA expression in splenic leukocytes of NZB/WF1 mice and that EE has more dramatic effects on miRNA expression than E2. Also, EE and E2 have opposite effects on the expression of some miRNAs such as miR-145 and miR-126 (Fig. 9H&I).

EE but not E2, significantly reduces DNA methylation of splenic leukocytes of NZB/WF1 mice.

DNA methylation is an important epigenetic mechanism linking environmental exposure and human disease risk(312-317). Estrogen has been shown to regulate DNA methylation in different types of cancer cells(205,206,208). However, there is limited data with regard to estrogen regulation of DNA methylation in immune cells. By surveying the DNA samples prepared from splenic leukocytes of placebo- and estrogen-treated orchidectomized B6 mice, we found that estrogen treatment significantly reduced global DNA methylation levels in splenic leukocytes (Supp. Fig. 3)(308). Here, we observed a slight, but not significant reduction of global DNA methylation in splenic leukocytes of E2-treated NZB/WF1 mice (Fig. 10A).

However, we observed a dramatic reduction of DNA methylation in splenic leukocytes of EE- treated NZB/WF1 mice when prepared to placebo controls (Fig. 10A). Given that DNA hypomethylation plays an important role in lupus pathogenesis, environmental estrogen exposure may promote the disease development by decreasing the DNA methylation.

To further explore the underlying mechanism of EE exposure induced DNA hypomethylation, we evaluated the expression levels of multiple DNA methyltransferases

164

(DNMTs) since DNA hypomethylation in lupus has been suggested be associated with decreased expression of DNMTs(271,318). Interestingly, we observed that DNMT1 expression was significantly elevated in EE exposed mice compared to both placebo and E2- exposed mice (Fig.

10B). No other DNMTs were altered due to E2 or EE exposure (Fig. 10C&D). Recent studies suggested the involvement of Ten-eleven translocation proteins (TETs), active demethylation enzymes in the regulation of DNA methylation and gene expression(319-323). We therefore checked the expression of TETs in splenic leukocytes of EE-, E2- and placebo-treated mice. No significant differences were observed in the expression TET1, 2 or 3 among different treatment groups. (Fig. 10E-G).

4. Discussion

The immune system is one of the main non-reproductive target organs for estrogenic compounds. There is now overwhelming evidence that estrogens modulate the immune system

(54,59,86,87,117,123,132,298) . Estrogens modulate the immune system through estrogen receptor dependent and independent mechanisms(53,84,85,297). Several studies have shown that the cells of the immune system possess estrogen receptors, and that genetically engineered mice with depletion of estrogen receptor α, β, or both, had specific altered development and/or immune capability of the immune system(53,137,324-328). The estrogen modulation of the immune system, in part, is believed to contribute to sex-based differences in immune capabilities and susceptibility of autoimmune diseases. It is conceivable that estrogen modulation of the immune system can influence responses to external agents such as infectious agents, vaccines and allergens(55,85,86,117,152). Despite the vast body of knowledge on estrogen-modulation of

165 the immune system, it is surprising that there is a paucity of literature on EE-modulation of the immune system, in comparison to E2 or even bisphenol A (BPA).

A very limited number of studies have shown that exposure to EE modulated the immune system through alterations in circulating cytotoxic T cells, B cells, and NK cells, reduced NK cell activity, and altered cytokine, chemokine, and adhesion molecule gene expression(329).

However, reports comprehensively comparing and contrasting the effects of E2 and EE exposure on immune response, gene expression and epigenetic modulation in autoimmune-prone mouse models are lacking. Previous studies have demonstrated an inconsistency between the effects of two estrogenic compounds on TLR9, but not TLR4, responses(122) and gender differences in behavioral response following EE or BPA exposure(330). Our study asked a fundamental question: Are the immunomodulatory effects of EE comparable to E2 or does EE exposure have unique features?

Evaluation of E2 and EE effects on lymphoid organ cell populations revealed differences in splenic neutrophils (Fig. 1), splenic and thymic CD4+ (Fig. 3, Supp. Fig. 2) and CD8+ (Fig. 4,

Supp. Fig. 2) subsets(308). The neutrophil data in this study is consistent with our previous report showing estrogen treatment in B6 mice increased splenic neutrophils and neutrophil serine proteases(87). The mechanisms underlying mildly increased neutrophils in spleens of E2 and EE exposed mice remains unclear. It is possible that neutrophil migratory behavior was altered, increased neutrophil chemoattractants were present in the spleen, to promote neutrophil accumulation, or that myeloid cell maturation and development was promoted at sites of extramedullary hematopoiesis through direct and indirect estrogenic influence. It is plausible that the increased presence of neutrophils in the spleen may interact with T and B cells to influence their function. Since defining the mechanisms underlying neutrophil accumulation and

166 aberration related to estrogen exposure and lupus disease phenotype are beyond the scope of this paper, we will investigate these mechanisms in a separate detailed study.

Consistent with our previous report and others (298,331-333), E2 increased NO, iNOS, and MCP-1 production following stimulation of splenic leukocytes (Fig. 2). EE promoted effects that are similar to E2 in response patterns generally considered part of the innate immune branch.

This does not appear to hold true for adaptive immune responses. Though EE reduced CD4+ and

CD8+ cells in both spleen and thymus, the remaining cells exhibited a more robust activation profile (Fig. 3,4, Supp. Fig. 2)(308). This was also evident in the cytokine response following stimulation (Fig. 5&6). To simulate exposure to infections, we stimulated with ligands for TLR4

(bacterial LPS), TLR9 (ODN 1585 to simulate viral activation) and T cell receptor (through activating CD3 and co-stimulatory CD28 molecules). Remarkably, differential production of cytokines in response to different stimuli and estrogenic exposure were found in multiple cytokines typically considered “pro-inflammatory,” such as IFNγ, IL-1β, and TNFα, among the

E2 and EE exposure groups (Fig. 5&6). EE increased IFNγ and IL-1β and decreased IL-2, whereas E2 did not, in NZB/WF1 mice. Further, in EE-exposed mice, TLR9 stimulation suppressed IL-6 and IFNα, in contrast to increased IFNα in splenic leukocytes of E2-exposed

NZB/WF1 mice, suggesting that these hormones have differential ability to modulate splenic leukocytes to release cytokines (Fig. 7). These studies show that there two estrogenic hormones may differentially alter the signaling through TLR4 or TLR9, or TCR/CD3 to regulate cytokine production. In our previous study using gonadectomized C57BL/6 mice given E2, we found that

IFNγ and IL-1β were significantly upregulated from LPS or ConA-activated splenic leukocytes(88). It is noteworthy that in this study (unlike our previous study) the mice were gonadally intact. It is likely that the existence of endogenous estrogen levels may affect the

167 extraneous estrogen effects, or the difference in strains may have contributed to the differences observed between studies. The common increased production of IL-10 in LPS and anti-CD3 plus anti-CD28 activated splenic leukocytes from both E2 and EE treated mice is consistent with previous reports(87,88,334).

E2 exposure has been shown to regulate numerous gene expression levels and epigenetic regulation in various cell types(202,335,336). In this study, we profiled the immune-related gene expression and miRNA in splenic leukocytes of NZB/WF1 mice following E2 and EE exposure respectively (Fig. 8&9). While both EE and E2 induced a large number of common immune- related gene expression changes, EE and E2 also induced hormone specific immune-related gene expression. With regards to miRNA gene expression, both E2 and EE exposure induced expression change of numerous miRNAs in splenic leukocytes of NZB/WF1 mice (Fig. 9).

However, the magnitude of EE in regulation of miRNAs is more profound than E2 as noted for miR-451, miR-18a, miR-145, and miR-126. In addition, while E2 and EE induced immune related gene expression changes in the same direction, E2 and EE exposures also demonstrated differential effects on selected miRNAs. Whether and how the differences in miRNA expression levels contribute to E2 and EE induced differential immune responses is beyond the scope of this present study, an aspect that will be investigated in separate studies.

DNA methylation is a well acknowledged epigenetic mechanism linking environmental exposure and risk of human diseases, including autoimmune disorders(312-317). However, most studies of estrogenic regulation of epigenetics have been focused on non-lymphoid organs/tissues. Estrogen has been reported to regulate DNMTs and DNA methylation in different types of cancer cells(205,207,208,337). We found that although E2 exposure had a negligible effect on DNA methylation of splenic leukocytes from gonadal intact NZB/WF1 mice, whereas

168

EE-exposure significantly reduced global DNA methylation (Fig. 10A). It is possible that the reduced methylation led to increased expression of specific genes related to inflammation and cytokine production. miR-148a and miR-126 have been shown to target DNMT1, leading to

DNA hypomethylation in lupus CD4+ T cells(271). We observed a strong increase of both miR-

148a and miR-126 in EE, but not E2, exposed splenic leukocytes (Fig. 9E&I). We therefore hypothesized that increased miR-148a and miR-126 may contribute to EE induced DNA hypomethylation via suppressing DNMT1. However, surprisingly, we observed a significant increase of DNMT1 in splenic leukocytes from EE exposed samples (Fig. 10B). No differences were seen in DNMT3a/b, or TET proteins (known to be involved in altering DNA methylation).

Therefore, EE induced DNA hypomethylation is unlikely to have contributions from either reduced DNMTs or increased demethylation enzymes, TETs. Further investigation is warranted to understand the mechanism of EE mediated reduction of global DNA methylation in splenic leukocytes from NZB/WF1 mice. Future studies should investigate the DNA methylation changes on the promoters of specific genes and correlate the methylation status with gene expression changes following E2 or EE treatment.

Our study focused on investigating whether or not the immunomodulatory and epigenetic effects of EE are comparable to E2 in a genetically lupus-prone mouse model of disease. To address this central issue, we limited our studies to only female mice based on the fact that female mice are more susceptible to lupus than males, and that EE is pharmaceutically administered to females. Now that we have established the effects of EE on the innate and adaptive immune systems in female mice, we are also interested in sex differences in response to

E2 and EE exposure. We have begun experiments to elucidate these differences between male and female mouse immune response when exposed to estrogenic chemicals. Future studies

169 should also compare these two chemicals in intact and ovariectomized, or orchidectomized, females and males respectively. This current study only included a single dose implant of each of the two chemicals. The dose of human exposure to estrogenic chemicals is highly variable, and future studies incorporating multiple doses of either E2 or EE, as well as combinations of chemicals, may help identify a safe dose of exposure, or potentially reveal an exaggerated response to chronic low-dose exposure in genetically lupus-prone animals. We were interested in the immunomodulatory effects of these chemicals at pre-disease and developing disease ages.

In summary, in this study, we comprehensively investigated and compared E2 and EE exposure induced immunological, molecular and also epigenetic changes in the spontaneous lupus-prone murine model NZB/WF1 mice. Both E2 and EE exposure leads to similar changes in neutrophil serine protease expression, and gene expression. However, our studies for the first time show that the two very similarly chemically-structured estrogenic compounds are able to exert vastly different effects on cytokine production, cell populations, and epigenetic regulatory mechanisms. These data provide an insightful perspective in understanding the similarity and also the difference of natural estrogen and its synthetic analog, 17α-ethinyl estradiol, in the regulation of immunity and autoimmunity. Our findings are further evidence that each estrogenic compound should be evaluated in a context specific manner, and no assumptions should be made that any particular estrogenic compound will act in a similar manner as other compounds in the same classification.

170

Abbreviations AAALAC Association for Assessment and Accreditation of Laboratory Animal Care

International

B6 C57BL/6 mice

BPA bisphenol A

CG Cathepsin G

ConA Concanavalin A

DNMT DNA methyltransferases

E2 17β-estradiol EE 17α-ethinyl estradiol EEDC estrogenic endocrine disrupting chemical

ER estrogen receptor

ERT estrogen replacement therapy IACUC Institutional Animal Care and Use Committee

IFN Interferon

IL interleukin iNOS inducible nitric oxide synthase

LPS Lipopolysaccharide

MCP-1 Monocyte Chemoattractant protein-1 miRNA microRNA

MPO Myeloperoxidase

NE Neutrophil elastase

NO nitric oxide

NZB/WF1 New Zealand Black x White progeny F1

171

ODN oligodeoxynucleaotides

PR3 Proteinase 3

RT-PCR Reverse transcription polymerase chain reaction

SP single positive

TCP transdermal contraceptive patch TCR T cell receptor

TET ten eleven translocation proteins

TLR4 Toll-like receptor 4

TLR9 Toll-like receptor 9

172

Acknowledgements We thank Melissa Makris for assistance with flow cytometry results. We thank, Ms. Karen Hall,

Betsy S. Midkiff, and other animal care staff members at VMCVM, Virginia Tech.

Financial Support: Preparation of this publication was supported by the Virginia-Maryland

College of Veterinary Medicine (VMCVM) Intramural Research Competition (IRC) Grant (grant number 175185); Interdepartmental funds to SAA, and by the National Institute of Health T32 training grant (grant number 5T32OD010430-09). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or VMCVM.

Correspondence: S. Ansar Ahmed, BVSc, PhD, Department of Biomedical Sciences and

Pathobiology, and Office of Research and Graduate Studies, Virginia-Maryland College of

Veterinary Medicine, Virginia Tech, 245 duck pond drive, Blacksburg, Virginia 24061-0442,

USA. Email: [email protected]

Author Contributions: All authors listed have made substantial, direct, and intellectual contribution to the work and approved it for publication.

Disclosure Summary: The authors have nothing to disclose.

173

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Figure 1

Figure 1: Both E2 and EE exposure increase splenic neutrophil percentages and neutrophil serine proteases (NSPs) mRNAs in NZB/WF1 mice. (A, B) Flow cytometric analysis results of splenic leukocytes from Placebo (n=4), 17-β estradiol (E2, n=5), and 17α-ethinyl estradiol (EE, n=4) exposed mice. Cells were stained with neutrophil surface markers CD11b and Ly6G antibodies. (A,B) Summary graphs showing (A) the percentages (mean ± SEMs) of neutrophils in splenic leukocytes, and (B) total CD11b+Ly6G+ splenic leukocyte counts from Placebo, 17-β

186 estradiol, and 17α-ethinyl estradiol exposed mice. Samples were initially gated for live singlets, followed by gating for CD11b and Ly6G double positive cells. (C) Real-time RT-PCR analysis of the relative mRNA expression levels of neutrophil serine proteases (neutrophil elastase, cathepsin G, and proteinase 3) and myeloperoxidase in splenic leukocytes from placebo, 17-β estradiol, and 17α-ethinyl estradiol exposed mice (n=2). ANOVA with post hoc Tukey’s test was used for statistical analysis of NSP and MPO expression data. Data are expressed as mean ±

SEMs. * p<0.05, ** p< 0.01.

187

Figure 2

Figure 2: 17- estradiol and 17α-ethinyl estradiol exposure increased nitric oxide (NO), inducible nitric oxide synthase (iNOS), and MCP-1 production by activated splenic leukocytes

(A,B) Splenic leukocytes were activated by (A) ConA (5g/ml) or (B) LPS (500 ng/ml) for 48 hours. The nitric oxide level was measured in cell culture supernatants by Griess assays. Data are expressed as mean +/- SEM. ** p< 0.01 (n≥4). (C,D) Splenic leukocytes were activated with

188 either (C) ConA (5g/ml) or (D) LPS (500 ng/ml) for 24 hours and then collected for western blot analysis for the expression of inducible nitric oxide synthase and β-actin (loading control).

(E-G) Splenic leukocytes were activated by (E) ConA (5g/ml), (F) LPS (500 ng/ml) for 24 hours, or (G) anti-CD3 plus anti-CD28 (anti-CD3: 5 µg/ml for plate coating, anti-CD28: 2

μg/ml). MCP-1 levels in cell culture supernatants were measured using ELISA. Data are expressed as mean ± SEM. * p<0.05, ** p< 0.01, *** p<0.001.

189

Figure 3

Figure 3: E2 and EE exposure alters splenic CD4+ cell subsets. Flow cytometric analysis results of splenic leukocytes from Placebo (n=4), 17-β estradiol (E2, n=5), and 17α-ethinyl estradiol

(EE, n=4) exposed mice. Cells were stained with antibodies against the surface markers CD4,

CD25, and CD69. Shown are the representative (A-D) FACS plots accompanied by the by the

190

(E-H) summary graphs showing the percentages (mean ± SEMs) of CD4+ subsets in splenic leukocytes. Samples were initially gated for live singlets, followed by gating for (A) CD4+ cells.

(B,C) Samples were further gated on singlets for CD4+ and CD25+, and CD4+ and CD69+. (D)

Samples were gated on the CD4+ population followed by CD25+ and CD69+ cells. Data is presented as (A-C, E-G) percent of total splenic leukocytes or (D,H) percent of CD4+ splenic leukocytes from placebo, 17-β estradiol, and 17α-ethinyl estradiol exposed mice. Data are expressed as mean ± SEM. * p<0.05, ** p< 0.01, *** p<0.001.

191

Figure 4

Figure 4: E2 and EE exposure alters splenic CD8+ cell subsets. Flow cytometric analysis results of splenic leukocytes from Placebo (n=4), 17-β estradiol (E2, n=5), and 17α-ethinyl estradiol

(EE, n=4) exposed mice. Cells were stained with antibodies against the surface markers CD8,

CD25, and CD69. Shown are the representative (A-D) FACS plots accompanied by the by the

(E-H) summary graphs showing the percentages (mean ± SEMs) of CD8+ subsets in splenic

192 leukocytes. Samples were initially gated for live singlets, followed by gating for (A) CD8+ cells.

(B,C) Samples were further gated on singlets for CD8+ and CD25+, and CD8+ and CD69+. (D)

Samples were gated on the CD8+ population followed by CD25+ and CD69+ cells. Data is presented as (A-C, E-G) percent of total splenic leukocytes or (D,H) percent of CD8+ splenic leukocytes from placebo, 17-β estradiol, and 17α-ethinyl estradiol exposed mice. Data are expressed as mean ± SEM. ** p< 0.01, *** p<0.001.

193

Figure 5

Figure 5: Differential effect of E2 and EE exposure on the production of different cytokines from LPS-activated splenic leukocytes from NZB/WF1 mice. Splenic leukocytes were activated by LPS (500 ng/ml) for 24 hours. Cell culture supernatants were used for ELISA to evaluate the production of cytokines (A) IFNγ, (B) IL-1β, (C) IL-6, (D) TNFα, (E) IL-2, and (F) IL-10 with

Ciraplex® multiplex cytokine assay. Data are expressed as mean ± SEM (n≥4). * p<0.05, ** p<

0.01, *** p<0.001.

194

Figure 6

Figure 6: Differential effect of E2 and EE exposure on the production of different cytokines from anti-CD3+CD28-activated splenic leukocytes from NZB/WF1 mice. Splenic leukocytes were activated by anti-CD3 (5 µg/ml for plate coating) plus anti-CD28 (2 µg/ml) for 24 hours.

Cell culture supernatants were used for ELISA to evaluate the production of cytokines (A) IFNγ,

(B) IL-1β, (C) IL-6, (D) TNFα, (E) IL-2, and (F) IL-10. Data are expressed as mean ± SEM

(n≥4). * p<0.05, ** p< 0.01, *** p<0.001.

195

Figure 7

Figure 7: Differential effect of E2 and EE exposure on the production of cytokines following

TLR9 stimulation. Splenic leukocytes were activated by class A ODN 1585 (0.5 µM) for 24 hours. Cell culture supernatants were used for ELISA to evaluate the production of cytokines (A)

IFNα, and (B) IL-6. Graph data are expressed as mean ± SEM (n≥4). * p<0.05, ** p< 0.01, *** p<0.001.

196

Figure 8

Figure 8: E2 and EE exposure has similar effect in the regulation of immune-related gene expression in splenic leukocytes from NZB/WF1 mice. (A-C) NanoString gene expression analysis of splenic leukocytes of placebo-, E2- and EE- treated NZB/WF1 mice. The raw data was processed and analyzed using nSolver software 3.0. (A) Scatter plot show the means of normalized log intensities of individual gene probes. The coefficient of determination (R2) indicate the correlation level of the expression of all analyzed genes between placebo and 17β- estradiol, or 17α-ethinyl estradiol treatment groups. (B) The Venn diagrams show the number of mRNAs that were regulated by both E2 and EE or specifically by E2 or EE. The mean normalized intensity of the mRNA genes that demonstrated at least 1.5-fold change with p < 0.1

197 in either EE or E2-treated group when compared to placebo-treated group were selected to generate the Venn diagram. (C) Hierarchical cluster heat map shows the expression levels of mRNAs that were regulated by both E2 and EE (159 genes) in different treatment groups.

198

Figure 9

199

Figure 9: EE exposure has more profound effect than E2 in the regulation of miRNA expression in splenic leukocytes from NZB/WF1 mice. (A-C) NanoString nCounter microRNA expression analysis of splenic leukocytes of placebo-, E2- and EE- treated NZB/WF1 mice. Acquired raw data was processed and analyzed using nSolver software 3.0. (A) Scatter plot show the means of normalized log intensities of individual miRNA probe. The coefficient of determination (R2) is reported to show the miRNAs expression correlation levels between placebo and 17β-estradiol, or 17α-ethinyl estradiol treatment group. (B) The Venn diagrams show the number of miRNAs that were regulated by both E2 and EE or specifically by E2 or EE. The mean normalized intensity of the miRNAs that demonstrated at least 1.5-fold change in either EE or E2-treated group when compared to placebo-treated group were selected to generate the Venn diagram. (C)

Hierarchical cluster heat map shows differential expression levels of miRNAs (50) that were commonly regulated by E2 and EE in individual samples of different treatment groups. (D-I)

Real-time RT-PCR analysis of the relative expression levels of miRNAs in unstimulated splenic leukocytes from placebo, 17β-estradiol, or 17α-ethinyl estradiol exposed- mice. Data are expressed as mean ± SEMs (n=4 per group). ** p< 0.01, *** p<0.001.

200

Figure 10

Figure 10: 17α-ethinyl estradiol, but not E2, exposure significantly reduced global DNA methylation and increased DNMT1 in splenic leukocytes from NZB/WF1 mice. (A)

Deoxyribonucleic acid was extracted from unstimulated splenic leukocyte cell pellets. The extracted DNA was used for a 5-methylcytosine ELISA kit to measure global DNA methylation levels from placebo, 17β-estradiol, or 17α-ethinyl estradiol exposed- mice. Real-time RT-PCR analysis of the relative mRNA expression levels of (B-D) DNA methyltransferase (DNMT) 1,

DNMT3a, DNMT3b, (E-G) Ten-eleven translocation methylcytosine deoxygenase (TET) 1,

TET2, or TET3 in unstimulated splenic leukocytes from placebo, 17-β estradiol, and 17α-ethinyl

201 estradiol exposed mice. Data are expressed as mean ± SEMs (n≥4 each group). ** p< 0.01, *** p<0.001.

202

Supplemental Figures and Tables

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0 .0 0 S 2 E o 2 o E E 2 b E b E E o E e e b E E c c e a a c l l P la P P

Supplemental Figure 1: 17-β estradiol and 17α-ethinyl estradiol exposure increase spleen weight. (A) Mouse spleens were aseptically removed and placed in sterile 60mm petri dishes.

Organs were weighed aseptically. (B) Organ to bodyweight ratio were calculated by dividing the weight of the spleen by the total bodyweight of the mouse prior to euthanasia, multiplied by 100 and then expressed as a percentage of total body weight. (C) Single cell suspensions were prepared, samples were diluted 1:10 with PBS and AOPI stain, and cells numbers were counted using an automated cellometer. Data are expressed as mean ± SEM. *** p<0.001.

203

Supplemental Figure 2

Supplemental Figure 2: 17- estradiol and 17α-ethinyl estradiol exposure alters thymic T cell composition. Flow cytometric analysis results of thymic leukocytes from Placebo (n=4), 17-β estradiol (E2, n=5), and 17α-ethinyl estradiol (EE, n=4) exposed mice. Cells were stained with antibodies against the surface markers CD4, CD8, CD25, and CD69. The summary graphs show the percentages (mean ± SEMs) of CD4+ and CD8+ subsets in thymic leukocytes. Samples were initially gated for live singlets, followed by gating for (A) CD4+ cells, (B) CD8+ cells or (C)

CD4+CD8+ double positive cells. (D&E) Samples were further gated on singlets for CD4+ and

CD25+, and CD4+ and CD69+. (F) Samples were gated on the CD4+ population followed by

204

CD25+ and CD69+ cells. (G&H) Samples were further gated on singlets for CD8+ and CD25+, and CD8+ and CD69+. (I) Samples were gated on the CD8+ population followed by CD25+ and

CD69+ cells. Data is presented as (A-E,G&H) percent of total thymic leukocytes or (F) percent of CD4+ or (I) percent of CD8+ thymic leukocytes from placebo, 17-β estradiol, and 17α-ethinyl estradiol exposed mice. Data are expressed as mean ± SEM. * p<0.05, ** p< 0.01.

205

Supplemental Table I

Gene Accession # E2 vs. P value of: EE vs. P value of: EE vs.

Name Placebo E2 vs. Placebo Placebo

Placebo

Abcb10 NM_019552.2 4.97 0.0033 7.16 0.0021

Abl1 NM_009594.3 -1.54 0.0656 -1.55 0.0949

Atm NM_007499.1 -1.69 0.0229 -1.96 0.0179

Batf NM_016767.2 -1.72 0.0398 -1.79 0.0595

Bcl6 NM_009744.3 -2.48 0.0136 -2.01 0.0127

Bst2 NM_198095.2 -1.67 0.0943 -1.77 0.0062

Btk NM_013482.2 -1.52 0.0785 -1.69 0.0209

Btla NM_177584.3 -2.75 0.0249 -3.05 0.0567

Camp NM_009921.2 32.17 0.0401 14.03 0.0386

Casp3 NM_009810.2 1.8 0.0017 1.95 0.0953

Casp8 NM_009812.2 -1.75 0.0271 -1.81 0.0719

Ccl12 NM_011331.2 55.33 0.0047 16.37 0.0024

Ccl24 NM_019577.4 3.46 0.0149 4.92 0.0066

Ccl3 NM_011337.1 -2.47 0.0832 -3.12 0.051

Ccr3 NM_009914.4 1.63 0.0357 2.15 0.0611

Ccr7 NM_007719.2 -2.33 0.0535 -2.91 0.0248

Ccr9 NM_009913.6 -2.88 0.028 -5.71 0.0111

Cd19 NM_009844.2 -1.93 0.0703 -2.32 0.0922

Cd2 NM_013486.2 -2.06 0.0305 -2.84 0.0079

206

Cd22 NM_001043317.2 -2.44 0.059 -2.82 0.0458

Cd226 NM_001039149.1 -1.7 0.0926 -2.07 0.0443

Cd247 NM_001113391.2 -2.07 0.0404 -3.19 0.0193

Cd24a NM_009846.2 3.34 0.0627 4.77 0.0537

Cd27 NM_001042564.1 -2.28 0.0478 -2.95 0.0512

Cd274 NM_021893.2 -1.62 0.0576 -1.98 0.0093

Cd36 NM_007643.3 2.06 0.0347 2.61 0.0228

Cd3d NM_013487.2 -1.76 0.068 -2.53 0.0313

Cd3e NM_007648.4 -2.93 0.0277 -3.1 0.0599

Cd4 NM_013488.2 -2.21 0.0526 -2.75 0.0684

Cd5 NM_007650.3 -2.13 0.0645 -2.82 0.0498

Cd53 NM_007651.3 -2.18 0.015 -2.35 0.0042

Cd55 NM_010016.2 -1.76 0.079 -1.97 0.0089

Cd74 NM_001042605.1 -1.86 0.0228 -1.92 0.0452

Cd79a NM_007655.3 -2.47 0.0267 -3.16 0.0136

Cd79b NM_008339.2 -1.6 0.0957 -1.95 0.0101

Cd8a NM_001081110.2 -2.33 0.0295 -2.87 0.0897

Ciita NM_007575.2 -2.26 0.0862 -2.59 0.0806

Clec5a NM_001038604.1 33.43 0.0656 15.85 0.0875

Crlf2 NM_001164735.1 -1.84 0.0161 -1.9 0.0562

Csf1 NM_001113530.1 -3 0.0244 -2.67 0.0306

Ctsc NM_009982.2 -1.8 0.0225 -1.77 0.0139

Cxcl9 NM_008599.2 -2.19 0.0437 -1.8 0.0378

207

Cxcr3 NM_009910.2 -1.82 0.0827 -1.98 0.071

Ddx58 NM_172689.3 -1.57 0.0256 -1.94 0.0068

Dpp4 NM_001159543.1 -1.6 0.0442 -2.27 0.014

Eomes NM_010136.2 -3.74 0.019 -4.29 0.036

Ets1 NM_001038642.1 -2.48 0.0284 -2.96 0.0558

Fcgr2b NM_001077189.1 -1.71 0.0394 -2.06 0.0489

Fkbp5 NM_010220.3 -3.12 0.0242 -2.74 0.0281

Fyn NM_008054.2 -2.19 0.0257 -2.48 0.041

Gata3 NM_008091.3 -2.7 0.0658 -3.18 0.0203

Gm10499 XM_003086920.1 -1.91 0.0106 -1.86 0.012

H2-Aa NM_010378.2 -1.58 0.0539 -1.81 0.0563

H2-DMb2 NM_010388.4 -1.92 0.0463 -2.01 0.0843

H2-Eb1 NM_010382.2 -1.66 0.0509 -1.72 0.0363

H2-K1 NM_001001892.2 -1.71 0.0884 -1.84 0.0304

H2-Ob NM_010389.3 -2.14 0.0841 -2.48 0.0776

Hif1a NM_010431.1 -1.55 0.0725 -1.72 0.0104

Icam2 NM_010494.1 -1.83 0.004 -2.27 0.0474

Icos NM_017480.1 -1.71 0.038 -1.99 0.0074

Icosl NM_015790.3 -2.1 0.0648 -2.18 0.0967

Ifnar1 NM_010508.1 -1.89 0.0522 -2.05 0.0469

Ikbkb NM_010546.2 -2.16 0.0865 -2.52 0.0547

Ikbke NM_019777.3 -2.12 0.0388 -2.89 0.0412

Ikzf1 NM_001025597.1 -1.89 0.0636 -2.15 0.0016

208

Ikzf2 NM_011770.4 -2 0.0317 -1.86 0.0869

Ikzf3 NM_011771.1 -1.99 0.0405 -2.32 0.0148

Il10ra NM_008348.2 -1.59 0.0494 -1.82 0.0301

Il12rb2 NM_008354.3 -1.81 0.0108 -1.76 0.0118

Il16 NM_010551.3 -1.74 0.0608 -2.09 0.019

Il17ra NM_008359.1 -2.04 0.0797 -2.6 0.0754

Il18r1 NM_001161842.1 -2.14 0.0736 -3.03 0.0273

Il1r1 NM_001123382.1 4.03 0.0527 4.71 0.0002

Il1rl1 NM_001025602.2 5.1 0.0103 6.75 0.0298

Il21r NM_021887.1 -2.94 0.0148 -2.5 0.0308

Il27ra NM_016671.3 -2.34 0.0329 -2.73 0.0325

Il2rb NM_008368.3 -2.04 0.035 -2.21 0.031

Il2rg NM_013563.3 -1.91 0.0263 -2.03 0.0682

Il4ra NM_001008700.3 -2.16 0.0402 -2.29 0.0795

Il6ra NM_010559.2 -1.57 0.0807 -2.07 0.042

Il7r NM_008372.3 -3.91 0.0362 -4.73 0.0211

Irak2 NM_001113553.1 -1.69 0.0434 -2.01 0.0542

Irak3 NM_028679.3 -1.72 0.047 -1.96 0.0642

Irak4 NM_029926.5 -2.06 0.0728 -2.08 0.0439

Irf1 NM_008390.1 -1.86 0.0146 -2.14 0.0171

Irf5 NM_012057.3 -1.54 0.0211 -1.85 0.0456

Irf8 NM_008320.3 -1.57 0.0563 -1.73 0.0093

Irgm1 NM_008326.1 -1.68 0.0245 -1.82 0.019

209

Itga6 NM_008397.3 -1.73 0.0531 -1.91 0.0127

Itgal NM_008400.2 -1.85 0.0419 -2.09 0.0102

Itgam NM_001082960.1 3.36 0.0325 1.92 0.0446

Jak1 NM_146145.2 -1.9 0.0289 -2.06 0.048

Jak3 NM_010589.5 -2.4 0.0324 -2.15 0.077

Klra8 NM_010650.3 -2.96 0.0626 -3.36 0.0045

Klrk1 NM_001083322.1 -2.5 0.0299 -2.18 0.0394

Lck NM_010693.2 -2.46 0.0343 -2.69 0.0288

Lef1 NM_010703.3 -2.39 0.0754 -3.24 0.0618

Ltb NM_008518.2 -1.85 0.0906 -2.02 0.0388

Ltf NM_008522.3 25.09 0.0069 11.8 0.0099

Map4k4 NM_008696.2 -2.03 0.0631 -2.65 0.0561

Mapk11 NM_011161.5 -4.95 0.0206 -2.41 0.0571

Nfkb1 NM_008689.2 -1.8 0.0777 -2.05 0.094

Nfkb2 NM_019408.2 -1.92 0.0305 -1.69 0.0758

Nfkbiz NM_030612.1 -1.74 0.0752 -2.06 0.0049

Notch1 NM_008714.2 -1.84 0.0534 -2.75 0.0297

Notch2 NM_010928.1 -1.91 0.0867 -2.31 0.0812

Npc1 NM_008720.2 -1.63 0.0637 -1.88 0.083

Nt5e NM_011851.3 -2.02 0.0849 -1.82 0.0434

Pax5 NM_008782.2 -2.17 0.0327 -2.64 0.0683

Pecam1 NM_008816.2 -2.1 0.0424 -2.23 0.0568

Phlpp1 NM_133821.3 -2.09 0.0849 -1.95 0.0001

210

Pml NM_008884.2 -1.58 0.0685 -1.6 0.0964

Pou2f2 NM_001163554.1 -1.84 0.0272 -2 0.0568

Ppbp NM_023785.2 2.75 0.0645 7.98 0.0534

Prim1 NM_008921.2 2.46 0.0291 3.11 0.0385

Prkcd NM_011103.2 -1.6 0.0637 -1.89 0.0828

Ptpn22 NM_008979.1 -2.51 0.0746 -2.86 0.001

Ptpn6 NM_013545.2 -1.78 0.0426 -1.91 0.0867

Ptprc NM_011210.3 -1.79 0.0623 -1.95 0.0064

Rela NM_009045.4 -1.67 0.0394 -1.83 0.0269

Relb NM_009046.2 -1.93 0.049 -2.03 0.095

Runx1 NM_001111021.1 -1.88 0.0165 -2 0.0399

Runx3 NM_019732.2 -2.91 0.0197 -3.43 0.0458

S100a8 NM_013650.2 27.56 0.0309 12.5 0.0443

S100a9 NM_009114.2 25.34 0.0404 11.03 0.0529

Sell NM_001164059.1 -1.87 0.0361 -2.53 0.0424

Selplg NM_009151.3 -9.22 0.0239 -9.29 0.0248

Sigirr NM_023059.3 -2.02 0.0149 -2.37 0.0314

Ski NM_011385.2 -1.92 0.0974 -2.54 0.0646

Slamf1 NM_013730.4 -2.22 0.0887 -1.87 0.0076

Smad3 NM_016769.3 -1.72 0.0286 -2.04 0.0237

Smad5 NM_008541.2 -1.6 0.0665 -2.2 0.0297

Socs1 NM_009896.2 -1.79 0.0746 -1.94 0.0304

Stat2 NM_019963.1 -2 0.0209 -2.05 0.0097

211

Stat3 NM_213659.2 -1.89 0.0263 -2.01 0.0523

Stat4 NM_011487.4 -2.47 0.0222 -2.61 0.0061

Stat5b NM_011489.3 -1.74 0.0699 -2.09 0.0849

Syk NM_011518.2 -2.2 0.039 -2.32 0.082

Tal1 NM_011527.2 7.29 0.0553 10.5 0.0509

Tap1 NM_001161730.1 -1.8 0.0295 -2.07 0.0126

Tapbp NM_009318.2 -1.56 0.0282 -1.7 0.0216

Tbk1 NM_019786.4 -1.55 0.093 -1.56 0.0417

Tbx21 NM_019507.1 -4.45 0.0275 -4.12 0.061

Tcf7 NM_009331.3 -2.87 0.0415 -3.65 0.0439

Tfrc NM_011638.3 6 0.0418 6.99 0.0386

Tgfb3 NM_009368.2 6.79 0.016 7.18 0.0378

Tgfbr2 NM_009371.2 -2.21 0.0271 -2.43 0.0579

Tlr1 NM_030682.1 -1.97 0.02 -2.16 0.0103

Tlr9 NM_031178.2 -1.9 0.0435 -2.02 0.0524

Tnfrsf13c NM_028075.2 -1.83 0.0056 -1.96 0.007

Tnfrsf14 NM_178931.2 1.85 0.0182 2.45 0.0122

Tnfrsf1b NM_011610.3 -1.6 0.0544 -1.85 0.0817

Tnfrsf4 NM_011659.2 -1.71 0.0534 -1.7 0.0131

Tnfrsf9 NM_001077508.1 -5.92 0.0017 -4.82 0.0858

Traf1 NM_009421.3 -1.9 0.0255 -1.65 0.0477

Traf3 NM_001048206.1 -1.86 0.0685 -2.27 0.08

Traf5 NM_011633.1 -1.82 0.0656 -2.09 0.0811

212

Tyk2 NM_018793.2 -1.7 0.0978 -1.63 0.0667

G6pdx NM_008062.2 2.06 0.0321 1.7 0.0293

Abcb1a NM_011076.1 -2.09 0.165 -2.24 0.0621

Arhgdib NM_007486.4 -1.29 0.0691 -1.65 0.0415

Atg16l1 NM_029846.3 -1.44 0.089 -1.61 0.0499

Bcl2 NM_009741.3 -1.93 0.0863 -2.27 0.1114

Bcl3 NM_033601.3 -1.68 0.0213 -1.58 0.2838

Blnk NM_008528.4 -1.75 0.0985 -1.81 0.1394

C1qa NM_007572.2 1.1 0.2816 1.92 0.0143

C3 NM_009778.2 3.78 0.0876 2.6 0.1744

C4a NM_011413.2 -1.14 0.4001 11.14 0.0221

C6 NM_016704.2 -1.1 0.4368 1.57 0.09

Card9 NM_001037747.1 2.71 0.0297 1.48 0.1571

Casp1 NM_009807.2 -1.43 0.1284 -1.64 0.0515

Ccbp2 NM_021609.3 -1.15 0.5972 -1.92 0.088

Ccl22 NM_009137.2 2.04 0.2036 1.65 0.0235

Ccl5 NM_013653.1 -2.44 0.0656 -1.99 0.1176

Ccr2 NM_009915.2 -1.03 0.7884 -1.91 0.0203

Ccr5 NM_009917.5 -2.32 0.2454 -2.85 0.0511

Ccr6 NM_001190333.1 -1.64 0.0289 -1.36 0.0052

Cd109 NM_153098.3 -1.88 0.0076 -2.88 0.1603

Cd160 NM_001163496.1 -5.34 0.0607 -2.57 0.1442

Cd164 NM_016898.2 -1.52 0.087 -1.51 0.1244

213

Cd28 NM_007642.4 -2.35 0.1324 -2.78 0.0039

Cd34 NM_001111059.1 4.52 0.1069 3.9 0.0618

Cd40 NM_011611.2 -1.59 0.1455 -1.86 0.0958

Cd48 NM_007649.4 -1.29 0.1828 -1.54 0.0969

Cd59b NM_181858.1 9.43 0.1671 16.51 0.0175

Cd69 NM_001033122.3 -1.53 0.2286 -1.97 0.0671

Cd8b1 NM_009858.2 -1.95 0.1261 -2.69 0.0854

Cd97 NM_011925.1 -1.48 0.0535 -1.76 0.0059

Cdh5 NM_009868.3 -18.79 0.0173 -8.35 0.2312

Cfb NM_008198.2 -5.66 0.1258 -2.4 0.0217

Cfh NM_009888.3 -14.55 0.0631 -12.37 0.1082

Cfp NM_008823.3 1.11 0.2906 1.64 0.029

Clec4a4 NM_001005860.2 -1.54 0.4501 -2.21 0.0746

Clu NM_013492.2 1.66 0.1024 3.65 0.0105

Cr2 NM_007758.2 -1.68 0.1301 -2.04 0.0988

Csf2rb NM_007780.4 -1.13 0.3641 -1.78 0.0896

Ctla4 NM_009843.3 -1.82 0.1202 -1.82 0.0135

Cxcr5 NM_007551.2 -1.4 0.1695 -1.75 0.0285

Cxcr6 NM_030712.4 -1.9 0.2005 -3.51 0.0502

Fasl NM_010177.3 -2.35 0.0291 -2.55 0.3155

Fcamr NM_001170632.1 3.62 0.0209 1.06 0.819

Fcgr1 NM_010186.5 1.38 0.161 2.46 0.0184

Fn1 NM_010233.1 1 0.9912 -2.46 0.0187

214

Folr4 NM_022888.2 -1.92 0.1124 -2.13 0.0571

Gfi1 NM_010278.2 -1.35 0.0975 -1.79 0.0816

Gpr183 NM_183031.2 -1.76 0.1625 -1.93 0.0349

Gzma NM_010370.2 -5.3 0.1355 -4.89 0.0414

Gzmb NM_013542.2 -5.16 0.198 -2.81 0.0878

H2-Ab1 NM_207105.2 -1.88 0.0766 -1.97 0.1118

H2-Ea-ps NM_010381.2 -1.48 0.0763 -1.69 0.0073

H2-Q10 NM_010391.4 -1.5 0.3735 -2.5 0.0683

Hlx NM_008250.2 2.13 0.0536 1.2 0.583

Icam4 NM_023892.2 9.76 0.1104 14.02 0.084

Icam5 NM_008319.2 1.72 0.6646 3.93 0.0263

Ifitm1 NM_001112715.1 -1.48 0.1047 -2.04 0.0015

Ifngr2 NM_008338.3 -1.54 0.1336 -1.71 0.0046

Ikbkap NM_026079.3 -1.45 0.1131 -1.72 0.056

Il12a NM_008351.1 -2.15 0.076 -1.46 0.0018

Il17rb NM_019583.3 -4.16 0.1132 -13.07 0.05

Il2ra NM_008367.2 -1.57 0.1231 -2.21 0.0407

Il6st NM_010560.2 -1.7 0.1516 -2.09 0.0616

Il9 NM_008373.1 7.35 0.0379 2.57 0.5439

Ilf3 NM_010561.2 -1.6 0.1042 -1.51 0.0751

Irf4 NM_013674.1 -1.41 0.2379 -1.67 0.0101

Itga2b NM_010575.2 1.95 0.189 7.91 0.0961

Itga4 NM_010576.3 -1.43 0.0591 -1.64 0.0599

215

Itgax NM_021334.2 -1.36 0.0673 -1.55 0.0464

Itgb2 NM_008404.4 -1.34 0.0241 -1.54 0.0022

Itln1 NM_010584.3 -1.47 0.3865 -1.86 0.0768

Klra4 NM_010649.3 -8.61 0.0571 -6.31 0.1077

Klra7 NM_001110323.1 -4.94 0.1228 -3.4 0.0378

Klrc2 NM_001098669.1 -4.96 0.0758 -1.99 0.6629

Klrd1 NM_010654.2 -2.48 0.1155 -3.39 0.0144

Lcp2 NM_010696.3 -1.71 0.1198 -1.84 0.0024

Lilrb4 NM_013532.2 1.51 0.0923 1.04 0.8132

Ltb4r1 NM_008519.2 8.68 0.0983 3.92 0.1825

Ltb4r2 NM_020490.2 -1.88 0.2192 5.11 0.0609

Ly86 NM_010745.2 -1.41 0.0925 -1.71 0.0027

Map4k2 NM_009006.2 -1.96 0.1597 -2.69 0.0765

Mapkapk2 NM_008551.1 -1.33 0.1145 -1.52 0.0947

Masp2 NM_010767.3 -2.93 0.0301 -2.56 0.2295

Ms4a1 NM_007641.5 -1.44 0.1906 -1.74 0.0037

Mx1 NM_010846.1 -1.6 0.1753 -2.2 0.0609

Ncf4 NM_008677.2 -1.09 0.4168 -1.53 0.0112

Nfatc1 NM_016791.4 -1.74 0.139 -2.1 0.0737

Nfatc2 NM_001037177.1 -2.16 0.0973 -2.35 0.1211

Nod2 NM_145857.2 -1.96 0.0273 -1.45 0.041

Pdcd1 NM_008798.1 -2.17 0.1879 -1.58 0.0338

Pdcd2 NM_008799.2 -1.41 0.1961 -1.51 0.0112

216

Pdgfb NM_011057.3 -1.89 0.0591 -1.26 0.3193

Plau NM_008873.2 4.99 0.1224 4.79 0.0775

Plaur NM_011113.3 -1.29 0.1433 -2 0.0184

Prf1 NM_011073.2 -3.28 0.1518 -4.6 0.0167

Ptafr NM_001081211.1 1.75 0.0805 1.03 0.9335

Rorc NM_011281.2 -1.35 0.0266 -12.21 0.0529

Serping1 NM_009776.3 1.82 0.2131 3.18 0.0774

Sh2d1a NM_011364.3 -2.35 0.1258 -2.68 0.058

Socs3 NM_007707.2 -1.55 0.1394 -1.66 0.0175

Spn NM_001037810.1 -1.44 0.0737 -2.01 0.0105

Stat1 NM_009283.3 -1.66 0.1067 -1.8 0.0028

Stat5a NM_011488.2 -1.49 0.1242 -1.51 0.0736

Stat6 NM_009284.2 -1.79 0.0767 -2.05 0.1042

Tagap NM_145968.2 -1.77 0.1182 -2.38 0.0037

Tcf4 NM_013685.1 -1.43 0.0987 -1.84 0.0815

Thy1 NM_009382.3 -1.75 0.1015 -2.02 0.0288

Tigit NM_001146325.1 -2.23 0.2022 -1.58 0.0313

Tlr4 NM_021297.2 1.25 0.0249 1.53 0.0227

Tmem173 NM_028261.1 -1.54 0.11 -1.94 0.062

Tnfaip3 NM_009397.2 -1.63 0.1402 -1.75 0.0239

Tnfrsf11a NM_009399.3 -1.43 0.2444 -1.67 0.0133

Tnfrsf13b NM_021349.1 -1.54 0.1254 -1.75 0.0225

Tnfrsf17 NM_011608.1 1.92 0.1386 1.63 0.0225

217

Tnfrsf8 NM_009401.2 2.07 0.55 4.66 0.0635

Tnfsf10 NM_009425.2 -1.72 0.1518 -1.58 0.0487

Tnfsf13b NM_033622.1 7.85 0.1042 2.77 0.0174

Tslp NM_021367.1 2.71 0.0175 2.67 0.1263

Vcam1 NM_011693.2 -1.03 0.6211 1.58 0.0443

Xcl1 NM_008510.1 -1.38 0.0569 -1.78 0.009

Zap70 NM_009539.2 -2.27 0.1069 -2.43 0.0158

Zeb1 NM_011546.2 -1.64 0.0638 -1.64 0.1243

Oaz1 NM_008753.4 1.2 0.0414 1.54 0.0135

Polr1b NM_009086.2 -1.54 0.0261 -1.44 0.0268

Supplemental Table I: Gene mRNA expression levels regulated by E2 or EE with associated p- values.

218

Supplemental Table II

Gene Name Accession # E2 vs. P value of: EE vs. P value of:

Placebo E2 vs. Placebo EE vs.

Placebo Placebo mmu-miR-93 MIMAT0000540 1.63 0.0859 2.36 0.0891 mmu-miR-196b MIMAT0001081 53.82 0.0007 45.07 0.0118 mmu-miR-709 MIMAT0003499 2.1 0.0238 2.44 0.0606 mmu-miR-132 MIMAT0000144 -12.94 0.0789 -2.31 0.3159 mmu-miR-144 MIMAT0000156 14.63 0.1307 23.24 0.083 mmu-miR-150 MIMAT0000160 -1.57 0.1232 -2.59 0.0386 mmu-miR-151-3p MIMAT0000161 -2.24 0.1436 -5.3 0.0241 mmu-miR-181a MIMAT0000210 -1.54 0.1762 -2.75 0.0267 mmu-miR-340-3p MIMAT0000586 -4.36 0.4735 -2.68 0.03 mmu-miR-223 MIMAT0000665 3.39 0.0768 1.93 0.1343 mmu-miR-181c MIMAT0000674 -17.37 0.0073 -3.71 0.4823 mmu-miR-451 MIMAT0001632 5.26 0.151 8.22 0.0995 mmu-miR-466a- MIMAT0002107 -1.57 0.2659 -8.67 0.0732

3p+mmu-miR-466b-

3-3p mmu-miR-467b MIMAT0005448 -2.13 0.2244 -37.98 0.0058 mmu-miR-467f MIMAT0005846 -1.54 0.0738 -2.98 0.2106 mmu-miR-669o MIMAT0009421 -3.15 0.1122 -3.12 0.0618 mmu-miR-1961 MIMAT0009434 -5.52 0.3411 -2.17 0.0959

219 mmu-miR-2132 MIMAT0011208 -2.09 0.11 -2.64 0.047 mmu-miR-99a MIMAT0000131 -11.79 0.172 -3.37 0.5376 mmu-miR-145 MIMAT0000157 -1.76 0.131 1.91 0.1093 mmu-miR-152 MIMAT0000162 1.66 0.4022 2.04 0.167 mmu-miR-155 MIMAT0000165 -3.1 0.2473 -11.37 0.5024 mmu-miR-148a MIMAT0000516 1.7 0.2471 1.69 0.1823 mmu-miR-200a MIMAT0000519 -4.69 0.1819 -5.36 0.1968 mmu-miR-15a MIMAT0000526 1.54 0.1342 1.53 0.238 mmu-miR-344 MIMAT0000593 1.89 0.7971 -2.96 0.5365 mmu-miR-345-5p MIMAT0000595 -1.66 0.5698 -1.64 0.5744 mmu-miR-32 MIMAT0000654 -5.81 0.2788 -1.55 0.7933 mmu-miR-33 MIMAT0000667 -2.75 0.5371 -2.72 0.5393 mmu-miR-211 MIMAT0000668 -1.93 0.555 -1.91 0.5584 mmu-miR-361 MIMAT0000704 -1.97 0.4357 -5.08 0.5151 mmu-miR-376a MIMAT0000740 3.75 0.7119 14.88 0.4181 mmu-miR-486 MIMAT0003130 11.02 0.1507 15.15 0.116 mmu-miR-467a MIMAT0003409 -3.32 0.5316 -3.28 0.5336 mmu-miR-669a MIMAT0003477 -2.04 0.2568 -15.92 0.287 mmu-miR-714 MIMAT0003505 -2.19 0.5469 -2.17 0.5497 mmu-miR-674 MIMAT0003740 -1.66 0.5698 -1.64 0.5744 mmu-miR-762 MIMAT0003892 -12.2 0.1378 -4.27 0.4178 mmu-miR-330 MIMAT0004642 -2.19 0.5469 -2.17 0.5497 mmu-miR-342-5p MIMAT0004653 -4.43 0.4929 -9.14 0.1376

220 mmu-miR-362-3p MIMAT0004684 -2.29 0.5447 -2.27 0.5474 mmu-miR-466g MIMAT0004883 -3.73 0.3601 2.08 0.5513 mmu-miR-574-3p MIMAT0004894 5.32 0.3086 18.46 0.1322 mmu-miR-669f MIMAT0005839 -2.6 0.5392 -2.57 0.5416 mmu-miR-1945 MIMAT0009410 -2.19 0.5469 -2.17 0.5497 mmu-miR-1955 MIMAT0009426 -2.7 0.102 -5.37 0.2571 mmu-miR-1960 MIMAT0009433 -3.42 0.5308 2.8 0.586 mmu-miR-1966 MIMAT0009439 -7.89 0.3156 -10.45 0.1689 mmu-miR-2135 MIMAT0011211 -5.23 0.131 -8.16 0.2224 mmu-miR-3475 MIMAT0015219 -3.21 0.5324 -3.18 0.5344 mmu-miR-125a-5p MIMAT0000135 -5.65 0.0065 -1.24 0.5406 mmu-miR-126-3p MIMAT0000138 1.33 0.3317 2.83 0.0058 mmu-miR-142-3p MIMAT0000155 -1.42 0.142 -1.8 0.0025 mmu-miR-342-3p MIMAT0000590 -1.33 0.1533 -1.82 0.0638 mmu-miR-532-5p MIMAT0002889 -1.21 0.5349 -1.53 0.0621 mmu-miR-421 MIMAT0004869 -1.44 0.5931 8.93 0.0942 mmu-miR-467e MIMAT0005293 -9.28 0.0153 -1.5 0.357 mmu-miR-1904 MIMAT0007874 1.13 0.1374 9.52 0.09 mmu-miR-1949 MIMAT0009416 2.32 0.0426 1.29 0.664 mmu-let-7g MIMAT0000121 -1.08 0.6389 -1.55 0.119 mmu-miR-99b MIMAT0000132 -2.35 0.5435 1.14 0.9377 mmu-miR-124 MIMAT0000134 1.13 0.1374 3.03 0.5 mmu-miR-125b-5p MIMAT0000136 -15.56 0.2825 1.34 0.1869

221 mmu-miR-135a MIMAT0000147 1.13 0.1374 4.92 0.1829 mmu-miR-185 MIMAT0000214 1.17 0.5272 1.67 0.303 mmu-miR-24 MIMAT0000219 -1.66 0.3844 1.2 0.2934 mmu-miR-194 MIMAT0000224 -2.62 0.5388 1.01 0.9934 mmu-miR-200b MIMAT0000233 -1.42 0.5635 -1.67 0.4615 mmu-miR-203 MIMAT0000236 -2.57 0.3711 -1.5 0.7735 mmu-miR-143 MIMAT0000247 1.13 0.1374 2.24 0.3731 mmu-miR-30c MIMAT0000514 -1.11 0.5115 -1.51 0.1817 mmu-miR-192 MIMAT0000517 1.13 0.1374 2.01 0.3487 mmu-miR-23a MIMAT0000532 -1.22 0.2037 -1.52 0.1065 mmu-miR-92a MIMAT0000539 1.13 0.1374 1.74 0.2996 mmu-miR-96 MIMAT0000541 -1.35 0.8682 -3.28 0.5336 mmu-miR-328 MIMAT0000565 1.13 0.1374 13.09 0.167 mmu-miR-107 MIMAT0000647 -1.37 0.7148 -5.83 0.358 mmu-miR-10a MIMAT0000648 -1.02 0.958 -1.55 0.1662 mmu-miR-28 MIMAT0000653 -1.42 0.4011 -1.59 0.1182 mmu-miR-139-5p MIMAT0000656 -1.5 0.2141 -2.34 0.1159 mmu-miR-200c MIMAT0000657 -1.58 0.1452 -1.28 0.3825 mmu-miR-7a MIMAT0000677 1.3 0.8959 2.35 0.6828 mmu-miR-365 MIMAT0000711 1.13 0.1374 4.81 0.4399 mmu-miR-433 MIMAT0001420 1.13 0.1374 1.98 0.5 mghv-miR-M1-7-5p MIMAT0001570 1.96 0.4503 1.15 0.5 mmu-miR-484 MIMAT0003127 -1.82 0.243 1.35 0.4583

222 mmu-miR-539 MIMAT0003169 1.13 0.1374 4.29 0.5 mmu-miR-423-3p MIMAT0003454 -1.62 0.3058 -1.2 0.6294 mmu-miR-146b MIMAT0003475 -5.22 0.3561 -1.28 0.7838 mmu-miR-652 MIMAT0003711 1.24 0.8109 1.58 0.6079 mmu-miR-125a-3p MIMAT0004528 -1.83 0.2734 -1.27 0.5722 mmu-miR-202-5p MIMAT0004546 1.13 0.1374 2.46 0.3068 mmu-miR-872 MIMAT0004934 1.23 0.6728 -5.48 0.3512 mmu-miR-873 MIMAT0004936 1.13 0.1374 3.17 0.3572 mmu-miR-1224 MIMAT0005460 -1.4 0.459 -2.05 0.2339 mmu-miR-1197 MIMAT0005858 1.13 0.1374 2.56 0.5 mmu-miR-1906 MIMAT0007872 -7.03 0.1742 -1.2 0.8457 mmu-miR-1964 MIMAT0009437 1.13 0.1374 3.48 0.4225 mmu-miR-1965 MIMAT0009438 1.42 0.7834 -1.71 0.5691 mmu-miR-1839-5p MIMAT0009456 2.53 0.2806 1.29 0.6781 mmu-miR-2140 MIMAT0011216 -1.5 0.2805 -1.57 0.2907 mmu-miR-2141 MIMAT0011217 2 0.2401 1.11 0.7839 mmu-miR-2183 MIMAT0011287 1.13 0.1374 2.01 0.3487

Supplemental Table II: microRNA expression levels regulated by E2 or EE with associated p- values.

223

Chapter 5

Chronic low-dose 17α-ethinyl estradiol oral exposure of autoimmune-prone mice exacerbates kidney disease and suppresses TLR7/9 signaling

Michael R Edwards, Rujuan Dai, Bettina Heid, Catharine Cowan, Thomas Cecere, S. Ansar

Ahmed*

Infectious Disease Research Facility (IDRF), Department of Biomedical Sciences and Pathobiology, VA-MD College of Vet. Medicine, Virginia Tech, Blacksburg, VA

Abstract

Estrogens have been shown to regulate the immune system and exacerbate multiple autoimmune diseases. 17α-ethinyl estradiol (EE), a synthetic analog of 17β-estradiol, is prescribed commonly and found in oral contraceptives and hormone replacement therapies. Few studies have investigated the immunoregulatory effects of exposure to EE, especially in autoimmunity. In this study, we exposed autoimmune-prone female MRL/lpr mice to a human- relevant dose of EE through a relevant route of exposure. We then evaluated autoimmune disease parameters, kidney disease, and response to in vivo pathogenic signals. Since lupus patients are prone to infections, mice were injected with viral (Imiquimod) or bacterial (ODN 2395) surrogates. EE-exposed mice had exacerbated proteinuria, blood urea nitrogen, and

BUN:Creatinine ratios (clinical markers of kidney disease) and increased glomerular immune complex deposition compared to controls. Evaluation of response to TLR7 and TLR9 stimulation revealed suppression of cytokine expression in kidneys and reduced production in splenic leukocytes from EE-exposed mice. EE-exposure led to altered MyD88, IRAK4, and IRF7 mRNA expression, and reduced MyD88 protein levels. Taken together, our data suggests that

224 chronic low-dose oral exposure to EE will enhance clinical renal disease and suppress TLR7 and

TLR9 cytokine production through altered MyD88 in female MRL/lpr mice.

Keywords: Azotemia, lupus, immune-complex, Estrogen Receptor, infection, cytokines, autoantibodies

225

Introduction

Environmental exposure of animals and people to low doses of estrogenic endocrine disrupting chemicals (EEDCs) occurs through a variety of sources, including ingestion of food and water, pharmaceuticals, outdoor activities, pesticides and fertilizers in agricultural applications, industrial chemicals, plastics, sewage, detergents, and cosmetics(338). One such

EEDC, 17α-ethinyl estradiol, a synthetic analog of endogenously produced 17β-estradiol (E2), is a primary component in oral contraceptive pills (OCP) and has been widely used in hormone replacement therapy (HRT). EE is also used as a treatment for breast cancer, vasomotor symptoms in menopause, female hypogonadism, hirsutism, acne vulgaris, and dysmenorrhea(339).

EE has been found in aquatic environments and water sources even after water treatment has been performed(340). The high likelihood of chronic or repeated human exposure to multiple

EEDCs throughout one’s lifetime is a significant health concern. It is likely that there may be subsets of populations, such as autoimmune-prone individuals, who may be particularly susceptible to EEDC exposure.

Estrogens have been shown to alter various functions of the immune system and regulate the responses to stimuli in both normal and autoimmune individuals through estrogen receptor- dependent or -independent mechanisms (55,84-87,94,117,123,297). Estrogen treatment of splenic leukocytes in vitro, has been shown to increase the production of multiple proinflammatory cytokines and chemokines and promoted B cell survival (56,59,130,131,139).

Estrogen treatment of non-autoimmune C57Bl/6 mice has been shown to break B cell tolerance, promote B cell survival, and induce autoantibody production and lupus nephritis(84,134). E2’s role in regulation of multiple aspects of the immune system, and exacerbation of autoimmune

226 inflammatory processes has been well documented while the immunomodulatory effects of EE, although extensively used, are not yet well understood. We recently compared the immunoregulatory effects of pharmaceutical administration of EE and E2 in autoimmune-prone

NZB/WF1 mice. EE has both common and unique immunoregulatory effects on T cell subsets, cytokine production and gene expression(94,308). Binding of an EEDC to a relevant receptor may emulate or inhibit the action and downstream effects of endogenous hormone-receptor binding. It has been reported that EE has a 100-times stronger potency for estrogen receptor

(ER)α nuclear translocation when compared to E2(93). EEDCs generally are considered to have a bi-phasic dose response curve, where low-dose and high-dose exposure can result in opposing functional changes, such as cell proliferation(124). EE’s recent addition to the European Union’s watch list of chemicals found in surface water, by Directive 2013/39/EU, suggests a broader concern with regards to toxicity and disease development following human exposures(341).

Despite the extensive studies in the last few decades, the etiology of systemic lupus erythematosus (SLE) remains poorly understood. Multiple factors, including genetic, epigenetic, hormonal, and environmental factors are thought to interact and drive pathogenesis(3,4,28,106,256,316,342). Although the direct role of estrogens in SLE pathogenesis has not been firmly established, increased lupus flares following puberty and decreased flares following menopause, when estrogen levels rise or fall respectively, suggests a role of estrogen in autoimmune disease severity(91,130). Increased circulating E2 was also associated with increased thyroid autoimmune activity in human males(299). Exposure to EEDCs could potentially disrupt the normal regulation of sex hormones and directly alter cellular functions.

Kidney failure and infections are two of the primary causes of mortality in lupus patients(32,33). Autoantibodies bind to autoantigens and form immune-complexes that can

227 accumulate in the glomeruli, resulting in further damage to kidneys and reduced filtration(34).

The reduced renal filtration leads to accumulation of toxins in the blood stream, including blood urea nitrogen (BUN) and creatinine, and glomerular damage allows protein to leak into the urine.

These chemicals can be used as clinical markers of kidney disease progression in a living patient.

Infections are common and are one of the leading causes of mortality in lupus patients(32,33). Recognition of pathogenic organisms and resultant immune response is often triggered through the binding of a pathogen associated molecular pattern (PAMP) to a complimentary receptor. Two PAMP recognizing receptors, toll-like receptor 7 (TLR7) and

TLR9 have been previously associated with lupus disease severity in mice(42-45). TLR7 knock- out mice had reduced disease severity, while TLR9 knock-out mice had exacerbation of disease parameters(47). Currently, the specific contributions of TLR7/9 signaling to lupus pathogenesis are unknown.

We recently reported that autoimmune-prone mice fed on a purified ingredients diet that is devoid of any phytoestrogens or EEDCs developed markedly decreased lupus nephritis(106).

In this study, this controlled formula diet was customized to include a human-relevant dose of

EE to investigate EE’s effects on lupus parameters. Design of a custom diet that includes an

EDC of interest has the potential to reduce labor, remove variables associated with surgery and recovery, and mitigate errors in oral gavage models. EEDC exposure also scales with weight and diet consumption as the animals grow. Female MRL/lpr mice, a common mouse model utilized to study immune complex glomerulonephritis, were fed either a diet devoid of estrogenic compounds, or an identically formulated diet that contains an environmentally relevant dose of

17α-ethinyl estradiol and monitored through disease development. Following moderate disease development at 12 weeks of age, mice were administered imiquimod (a TLR7 agonist),

228

ODN2395 (a TLR9 agonist), or sterile saline as a negative control to evaluate response to viral or bacterial pathogenic stimulation respectively, in an in vivo model of sterile inflammation. Our study shows that oral exposure to EE, even at a very low dose, can exacerbate azotemia, increase clinical markers of renal disease, and promote glomerular immune complex deposition.

Remarkably, EE exposure also suppressed normal TLR7 and TLR9 cytokine production in response to stimulation.

Materials and Methods

Mice and in vivo administration of TLR ligands

Genetically lupus-prone MRL/lpr (MRL/MpJ-Faslpr/J, stock# 000485) mice were purchased from a single vendor, Jackson Laboratory (ME, USA). All mice were bred and housed in the AAALAC certified animal facility at the Virginia-Maryland College of Veterinary

Medicine (VMCVM), Virginia Tech. Breeder mice were fed diet D11112226 (Research Diets

Inc., New Brunswick, NJ, USA) to minimize the trans-generational effects of EDC exposure from dietary sources. Diet D11112226, a purified-ingredients diet devoid of estrogenic chemicals was selected based on macronutrient sources. The protein source includes casein and L-Cystine, carbohydrates are from corn starch, maltodextrin 10, and dextrose, fiber is from Cellulose

BW200 and inulin, fats are from soybean oil, and vitamins and minerals are from defined mixes.

Due to the female predominance of lupus disease and increased exposure risk of females to EE in the form of prescription medications, for this study, we investigated the effects of chronic low- dose oral exposure to EE on female MRL/lpr mice only. At three (3) weeks of age, mice were weaned onto either the Research Diets Inc. D11112226 control diet, or the identically formulated custom diet D16122101(B) which also incorporated 6.8 ppb of 17α-ethinyl estradiol (EE)

229

(Research Diets Inc.). This dose of EE was calculated based off of a human-relevant exposure dose (30µg/day), and corrected for murine metabolic differences. This dose is within previously published doses of EE exposure in mice(343). Calculations were corroborated with a board- certified toxicologist. Mice were allowed to age and develop autoimmune disease until twelve

(12) weeks of age. Mice were then administered an intraperitoneal injection of 100 uL of sterile saline containing 25 μg Imiquimod (TLR7 ligand) every 48 hours, 5 μg ODN2395 (TLR9 ligand) every 24 hours as previously reported (344,345), or sterile saline alone as a negative control. Mice were terminated at 16 weeks of age and tissues were collected. Throughout the study, mouse weights and diet consumption were measured weekly. Blood was collected by mandibular venipuncture and urine protein was evaluated every two weeks. Care was taken to ensure that environmental conditions for all groups were controlled, including mice being subjected to the same housing, local environment, temperature, ambient humidity, disinfectant used, bedding, and handling conditions. Paper-chip bedding was used to remove the risk of mycoestrogen exposure that can occur from corncob bedding materials(346). Mice were housed with a light cycle of 12 hours of light and 12 hours of dark. All animal procedures and experiments were performed in accordance with guidelines of the Institutional Animal Care and

Use Committee (IACUC) at Virginia Tech. Carbon dioxide asphyxiation followed by exsanguination was used for euthanasia as required by the approved IACUC protocol.

Tissue preparation, and cellular culture

Whole splenic leukocytes were isolated using standard laboratory procedures described in detail previously (23,88,94,106,202,298). Briefly, the spleens were dissociated by gently scraping through a steel screen, and the cell suspension was passed through a 70-μm cell strainer to remove tissue debris. To eliminate the unintended in vitro exposure to estrogens, care was

230 taken to use only charcoal-stripped FBS and phenol red-deficient media. The cells were adjusted to 5x106/ml in complete medium for seeding into cell culture plates. Briefly, the cells were plated into 48-well cell culture plates (0.25ml/well), and stimulated with Imiquimod (5 µg/ml)

(IDT Inc., Skokie, IL, USA), ODN-2395 (0.5M, synthesized by IDT Inc.), ODN Control (IDT

Inc.), or complete media for the designated time by adding an equal volume of 2x concentration of stimulation medium. CD4+ and CD19+ cell were purified from whole splenic leukocytes using the Miltenyi Biotec “CD4+ (L3T4) MicroBeads, mouse,” and “CD19+ MicroBeads, mouse,” per the company’s published manual separation protocol (Miltenyi Biotec Inc., Auburn,

CA, USA). Cell pellets were washed with cold PBS and stored at -80°C.

Measurement of proteinuria

Proteinuria was measured using Siemens Uristix dipsticks (Fisher, Hampton, NH, USA), or Chemistrip-2GP (Roche Diagnostics Corporation, Indianapolis, IN, USA). Urine was collected through manual restraint. The semi-quantitative scale was demonstrated as follows:

“1”, 30 mg/dl; “2”, 100 mg/dl; and “3”, 300 mg/dl “4”, 500 mg/dL or over.

Serum blood urea nitrogen and creatinine measurement

Blood was collected and allowed to clot for 30 minutes at room temperature. The blood samples were centrifuged at 2000xg for 10 minutes and serum was collected into new sterile 1.5 ml Eppendorf tubes. Serum was aliquoted and stored at -80°C until used for assays. Blood urea nitrogen was measured using the commercial kit Urea Nitrogen (BUN) Colormetric Detection

Kit (Thermofisher, Wilmington, DE, USA) as per the company’s published protocol. Serum creatinine was measured using the commercial kit Creatinine Assay Kit (Sigma, St. Louis, MO,

USA) as per the included protocol.

Renal histopathology

231

The left kidneys from the MRL/lpr mice were collected and fixed in 10% buffered formalin and embedded in paraffin as previously described(106,116). Five-micron sections were cut from paraffin embedded tissues, followed by staining with hematoxylin and eosin (H&E) or periodic acid-Schiff (PAS). This work was performed by AAVLD-accredited Virginia Tech

Animal Laboratory Services (ViTALS) at Virginia-Maryland College of Veterinary Medicine,

Virginia Tech. A board-certified pathologist assessed and scored the stained tissue slides in a blinded fashion. A grade of 0 to 4 (0 = perfect, no change; 1 = minimal; 2 = moderate; 3 = marked; and 4 = severe) was given to reflect the glomerular, tubular, interstitial, and vessel inflammation and lesions, respectively. The sum of the individual scores is reported as the total renal score, representing the microscopic changes in each sample.

Renal Immunofluorescence

The right kidneys were sectioned and one half of the cut kidney was immersed in Tissue-

Tek O.C.T. Compound (Sakura Finetek, Torrance, CA, USA) and flash-frozen in a bath of dry ice and isopropanol. Frozen OCT samples were cut to 5µm sections by ViTALS at Virginia-

Maryland College of Veterinary Medicine and unstained slides were stored at -80°C.

Immunofluorescent slide preparation was performed as previously described (REF). The following goat anti-mouse antibodies were used in immunofluorescence analysis: complement

C3-PE (Cedarlane, Burlington, NC, USA); IgG-FITC (Sigma); IgG2a-Alexa Fluor 568, IgG3-

Alexa Fluor 488 (ThermoFisher), anti-TLR7-PE, anti-TLR9-PE. Kidney Sections were examined under a fluorescent microscope as previously described(106). Fiji/ImageJ image processing program (264-266) was used to trace the basement membranes and measure the fluorescent intensity of the selected area. The background fluorescence was then subtracted from the

232 glomerular fluorescent intensity to determine the corrected glomerular fluorescent intensity

(CGFI) value(106). Twenty (20) glomeruli were evaluated per antibody, per sample.

Assay of serum autoantibodies

Serum anti-dsDNA autoantibodies

The female MRL/lpr mice were aged in our facility and blood was collected by submandibular venipuncture every 2 weeks after they reached 7 weeks of age. The serum anti- dsDNA antibody levels were measured by ELISA per our previous reports (23,106,116,118).

The absorbance was measured by reading the plate at 380 nm with a SpectraMax M5 Microplate

Reader (Molecular Devices, Sunnyvale, CA, USA).

Serum anti-cardiolipin

End-point serum anti-cardiolipin levels were measured by ELISA as previously reported

(23,106,116). The absorbance was measured by reading the plate at 405 nm with a SpectraMax

M5 Microplate Reader (Molecular Devices).

Cytokine/Chemokine ELISAs

The levels of MCP-1 and BAFF in media, imiquimod, or ODN 2396 stimulated cell culture supernatants were analyzed using the mouse MCP-1 ELISA MAX deluxe kit (Biolegend,

San Diego, CA, USA) or the Mouse BAFF/BLyS/TNFSF13B Quantikine ELISA Kit (R&D

Systems, Minneapolis, MN, USA). The levels of IFN in serum and splenic leukocyte culture supernatants were determined with eBioscience Mouse IFN alpha Platinum ELISA kit (Fisher

Scientific). The ELISAs were performed per the manufacturers’ protocols. Ciraplex®

Chemiluminescent Assay kits (Aushon Biosystem, Billerica, MA, USA) were used to quantify the levels of IFN-γ, IL-1β, IL-2, IL-6, IL-10, and TNFα in serum and cell culture supernatants per the manufacturer’s instructions(106,201). The images of chemiluminescent array plates were

233 captured with Cirascan image system (Aushon) and the image data was processed with Cirasoft software.

RNA Extraction and qRT-PCR analysis of mRNA expression

Total RNA, containing small RNA, was isolated from whole splenic leukocytes, splenic

CD19+ cells, and renal tissue using a miRNeasy Mini Kit (Qiagen, Valencia, CA, USA) as described in our previous publications (REF). The RNA concentration was quantified using a

NanoDrop 2000 (ThermoFisher). As we described in detail previously(23,106,116,202),

Taqman Gene Expression Assay reagents (Applied Biosystems, Grand Island, NY, USA) were used to evaluate the mRNA expression levels of IFNγ, IL-6, TNFα, MyD88, TRAF6, IRAK4,

IRF7, TLR7, TLR9, and β-actin. The expression levels of miRNAs and mRNAs were normalized to endogenous -actin. The data was shown as relative expression level to an appropriate control by using the 2−ΔΔCt formula (Livak method).

Western blot analysis

Western blots were used to analyze MyD88, IRAK4, TRAF6, IRF7, p-IRAK4, p-IRF7, and β-actin protein expression in whole-cell extracts as described before(88,94). The whole-cell extracts were prepared by lysing the cell pellet with CelLytic M cell lysis reagent (Sigma-

Aldrich) with protease inhibitor. The cell lysis reagent for whole-cell extracts used for analyzing phosphorylated protein expression also included Phosphatase Inhibitor Cocktail I (Abcam,

Cambridge, MA, USA). p65 and PCNA were analyzed in splenic leukocyte nuclear extracts. The

NE-PER Nuclear and Cytoplasmic Extraction Reagents (ThermoFisher) with protease inhibitor and Phosphatase Inhibitor Cocktail I (Abcam) were used to extract nuclear proteins for p65 and

PCNA analysis. The anti-MyD88, -TRAF6, -IRAK4, -p-IRF7 were purchased from Cell

Signaling Technologies (Cell Signaling Technologies, Danvers, MAUSA), anti-IRF7 and anti-

234 p65 antibodies were purchased from Santa Cruz Biotechnology (Paso Robles, CA), and the p-

IRAK4 was purchased from Aviva Systems Biology (Aviva Systems Biology, San Diego, CA,

USA). The protein loading control antibodies, anti–β-actin antibody was obtained from Abcam and anti-PCNA was obtained from Santa Cruz Biotechnology. The blot images were captured using a Kodak Image Station 440.

Statistical Analysis

All values in the graphs are given as mean ± SEM, or as otherwise stated in the figure legend. To assess statistical significance, one-way ANOVA following by Tukey’s post-hoc method was performed for the group comparisons, unless otherwise specified in the figure legend. All statistical analysis and graphical representation of data was performed in Prism

Graphpad software (Version 8.0.2).

Results

Low-dose EE exposure increases clinical parameters of renal disease

Diet consumption and body weight among all groups were comparable throughout the study (data not shown). Female MRL/lpr mice develop severe immune-complex glomerulonephritis by 16 weeks of age(73-77,82). Proteinuria, a measurable indicator of glomerular damage, is typically found to be detectable around 7-9 weeks of age in MRL/lpr female mice, and progressively increases with age. In this study, MRL/lpr mice exposed to low- dose EE diet starting at 3 weeks of age, remarkably, had significantly higher levels of proteinuria as early as four weeks after treatment (7 weeks of age, the first timepoint studied) compared to control-fed mice (Fig. 1A). Typically, at this age untreated MRL/lpr mice do not have high levels of proteinuria. The increased levels of proteinuria in EE-exposed mice compared to control diet

235 fed mice was evident at all time points studied (Fig. 1A). Subsequent administration of TLR7 agonist (Imiquimod) or TLR9 agonist (ODN 2395) to EE-fed mice at 12 weeks of age did not further enhance proteinuria levels when compared to EE-fed mice without TLR7 or 9 stimulants

(Fig. 1B). Interestingly, administration of ODN 2395 to control-fed mice increased proteinuria approximately to the level noticed in EE-fed mice (Fig. 1C) Minimal histopathological differences were observed among groups (Supplemental Table 1). Overall, mice exposed to EE consistently had elevated proteinuria compared to the control group, regardless of stimulatory exposure (Fig. 2A).

Blood urea nitrogen (BUN) and creatinine are molecules found in the serum that are also measurable indicators of kidney disease progression. Mice exposed to EE, regardless of in vivo stimulant, also had increased serum BUN. Of note, all mice fed the control diet had serum BUN levels within the normal range, while all mice fed the EE diet were azotemic and had serum

BUN levels above the normal threshold for mice (33 mg/dL) (Fig 2B). No significant differences in serum creatinine were found among groups (Fig. 2C). Mice exposed to EE and stimulated with the TLR7 agonist imiquimod had a significantly higher BUN:Creatinine ratio when compared to the mice fed the control diet and stimulated with imiquimod (Fig. 2D). Taken together, these data support that mice exposed to EE have azotemia and increased clinical parameters of renal disease compared to the control group.

EE exposure increases glomerular immune-complex deposition

Having observed increased clinical parameters of kidney disease in EE exposed mice, further evaluation of renal structure was warranted. In MRL/lpr mice, akin to human lupus patients, a hallmark of renal disease is deposition of immune complexes in the glomeruli.

Circulating autoantibodies can directly target renal podocytes, or become deposited in glomeruli

236 when bound to antigen as immune complexes, promoting renal damage and exacerbation of kidney disease(34). Total IgG deposition was increased in all groups of mice exposed to EE when compared to the control group (Fig. 3A&C). IgG subclass IgG2a deposition was also increased in EE exposed mice with no secondary stimulation. Interestingly, imiquimod administration increased IgG2a deposition in mice fed on the control diet (Fig. 3B&D).

Complement C3 was also evaluated, and the measurement of fluorescent intensity was no stronger than the background fluorescence (data not shown). The above data show that even a very low dose of EE in the diet promotes increased deposition of IgG and IgG2a in the glomeruli.

EE exposure on serum autoantibodies and cytokine levels

We next evaluated circulating autoantibodies and cytokines in sera of experimental groups. Surprisingly, EE did not increase the levels of anti-dsDNA IgG compared to mice fed on control diet (Fig. 4A). Serial kinetics data suggested that EE exposure led to increased levels of total IgG anti-dsDNA only at week 9 when comparing all groups fed the control diet and all groups fed the EE diet, prior to in vivo TLR7/9 agonist administration (Supplemental Fig. 1A-E).

Administration of secondary TLR9 agonist ODN 2395 however, led to significantly elevated circulating IgG2a anti-dsDNA levels compared to the EE-exposed mice without a secondary stimulation (Fig. 4B). TLR7 agonist imiquimod administration to EE exposed mice resulted in a tendency for increased anti-dsDNA autoantibodies (but this was not statistically significant).

This subclass was not increased when compared to the control group or the control group stimulated with ODN 2395 (Fig. 4B). No differences were found among groups for circulating anti-cardiolipin (Fig. 4C). These data suggest that EE, while minimally affecting the levels of serum autoantibodies, markedly enhanced deposition of autoantibodies in the kidneys.

237

Levels of circulating cytokines can be an indication of a systemic inflammatory response.

Interestingly, no differences were observed in the circulating levels of cytokines considered either pro-inflammatory or anti-inflammatory among the various groups (Fig. 4D-G). When measuring IFNα in the serum, all samples were below detectable limits. These data suggest that although EE exposure appears to modulate the disease severity in a specific target organ, such as the kidney, this level of EE exposure does not promote a broad sustained systemic inflammatory response.

Renal TLR7/9 cytokine production is altered is mice exposed to EE

To further elucidate the effects of EE on kidney disease, we evaluated TLR7, TLR9, and cytokine mRNA expression levels in the kidneys. The relative expression of TLR7 was not different among all groups. Interestingly, although TLR9 was comparable between control and

EE-exposed mice, TLR9 expression was increased in mice fed either control diet or EE diet, and stimulated with ODN 2395 in vivo (Fig. 5A&B). Surprisingly, even with similar TLR7/9 expression levels among groups, cytokine mRNA expression levels of IFN-γ and TNFα were significantly suppressed in mice exposed to EE and stimulated in vivo (Fig. 5C&D). IL-6 mRNA expression followed a similar pattern, but differences among groups did not reach significance

(Fig. 5E). This suggests that localized inflammatory responses to TLR7 or TLR9 agonists are inhibited in kidneys following chronic low-dose oral exposure to EE.

TLR7 and 9 expression in splenic leukocytes is increased in mice exposed to EE

Secondary lymphoid organs are important sources to investigate autoimmune and inflammatory abnormalities in immune cells. Flow cytometry was performed on splenic leukocytes to evaluate T and B cell subsets, IFNγ receptor 1, and BAFF receptor cellular expression. No differences were identified among groups (data not shown). When evaluating cell

238 populations in mesenteric lymph nodes, mice exposed to EE and stimulated with TLR9 agonist had increased CD19+ cells compared to mice exposed to EE and injected with sterile saline without stimulation and after 24 hours of stimulation (Supplemental Fig. 2A&B).

Given that susceptibility to infections is one of the primary causes of morbidity and mortality in lupus patients, we used splenic leukocytes to assess their ability to respond to viral and bacterial infections by using their respective surrogates, TLR7 and TLR9 agonists in vivo as a model of sterile inflammation. Unlike in the kidney, EE increased the mRNA expression of

TLR7 and TLR9 in splenic leukocytes compared to mice fed the control diet (Fig. 6A&B).

CD19+ cells had similar TLR7 expression (Fig. 6C) but increased TLR9 expression (Fig. 6D) in mice exposed to EE, when compared to the control group. TLR9 agonist increased TLR9 relative expression only in mice fed the control diet but not in EE-fed mice (Fig. 6D). Since we had identified suppression of TLR7 and TLR9 cytokine production in response to in vivo stimulation in kidney cells, we evaluated cytokine response of splenic leukocytes. IL-6 protein production was suppressed in splenic leukocytes from mice exposed to EE and stimulated with

TLR9 agonist in vivo (Fig. 6E). However, TLR7 imiquimod was able to induce IL-6 production in EE-exposed mice, suggesting that EE promotes differential TLR responses. IFNγ was not altered in the treatment groups (Fig. 6F).

When measuring IFNα in the cell culture supernatants, all samples were below detectable limits. These results suggest that even though TLR9 expression can be increased in splenic leukocytes due to EE exposure or TLR9 stimulation, EE exposure can suppress TLR9 response to stimulation, as seen in the kidneys.

Splenic leukocyte TLR7 and TLR9 signaling is suppressed by exposure to low-dose EE

239

Since we had identified suppressed TLR7- and TLR9-based IFNγ and TNFα in the kidneys and suppressed TLR9-based IL-6 to stimulation in splenic leukocytes with similar, or increased, expression of TLR mRNA, we next assessed the TLR7 and TLR9 signaling cascade proteins in splenic leukocytes. Mice exposed to EE and stimulated with TLR7 agonist had significantly reduced myeloid differentiation primary response 88 (MyD88) expression when compared to control diet fed mice stimulated with TLR7 agonist (Fig. 7A). Trends in opposing mRNA expression profiles following TLR7 and TLR9 stimulation in vivo were seen in splenic

Interleukin-1 receptor-associated kinase 4 (IRAK4) and interferon regulatory factor 7 (IRF7), as well as CD19+ cell IRAK4 mRNA (Fig. 7B-H). Trends for reduced expression of MyD88 and

IRAK4 were seen in mice exposed to EE and subjected to a secondary stimulant. We next evaluated signaling cascade protein levels by western blot analysis.

The protein levels of major signaling cascade proteins involved in TLR7/9 signaling were evaluated in splenic leukocyte whole cell lysates. Levels of MyD88, IRAK4, phosphor-IRAK4,

TRAF6, and IRF-7 were normalized to β-actin levels, an endogenous loading control. We also evaluated p65 levels in splenic leukocyte nuclear extracts, and normalized to proliferating cell nuclear antigen (PCNA). Consistent with mRNA expression data of MyD88, mice exposed to EE had lower levels of MyD88 present in whole cell lysates (Fig. 8A&B). We observed that imiquimod did not have an effect on MyD88 levels compared to respective controls, but ODN

2395 stimulation led to a subtle, albeit not significant, increase in MyD88 compared to respective dietary control groups. No significant differences were observed in protein levels of the remaining signaling cascade proteins evaluated (Supplemental Figure 3). These data suggest that

TLR9 signaling is suppressed in EE exposed mice through alterations in cellular MyD88 protein levels.

240

Discussion

There is now extensive, indisputable evidence that the immune system is a target for estrogens(53,87,118,134,202,298). All cells of the innate and adaptive immune system are regulated by natural, synthetic, and phytoestrogens. Estrogens can act on these cells by both ER- dependent and ER-independent mechanisms(53,127). While the immunoregulatory effects of natural estrogen, 17β-estradiol (E2) are extensively documented, little is known about immunomodulation by EE despite its extensive use pharmaceutically. We recently reported in a comprehensive study comparing the immune consequences of pharmaceutical administration of

EE with E2 in NZB/WF1 mice(94). Overall, we found that while both E2 and EE had similar immunological effects in several parameters, EE had distinct effects on cytokine production, splenic T cell populations, gene transcription, miRNA expression, and DNA methylation(94).

EE is the chief estrogenic ingredient in oral contraceptive pills and in estrogen replacement therapy. Excretion of EE in sewage effluents and subsequent contamination of water has been reported(306). We hypothesized that a subset of the population that is genetically prone to develop autoimmune diseases may be sensitive to the chronic environmental exposure to EE.

The overall objective of our study was to investigate whether environmental exposure of genetically autoimmune-prone individuals to EE may augment the disease and modulate response to external infectious challenge. Exposure to exogenous estrogens likely occurs most commonly through oral ingestion. We developed a relevant model of oral EDC exposure that involves the use of a grain-free purified-ingredients diet. This eliminates unintended exposure to phytoestrogens and other common EEDC contaminants found in grain-based diet formulations(106). This model reduces mouse handling and the risks associated with daily oral

241 gavage of chemicals of interest, while retaining the exposure of oral mucosa and microbiota to the EDC of interest.

Differential modulation of the immune system by sex steroids is currently thought to contribute to sex-biased immune responses and susceptibility to autoimmune disease development(25,85,132,152,169,342,347,348). It is likely that individual response to vaccinations, infections and external antigenic stimuli can be influenced by the presence of endogenous as well as exogenous estrogenic compounds. The few studies published regarding

EE’s immune modulatory effects detail modulation of NK cells and reduced activity, cytotoxic T cells, B cells, circulating cytokines and chemokines, and adhesion molecules(329). Inconsistent with the high prevalence of indications for treatment with medications containing EE, there is a severe deficiency of published literature regarding EE’s effects on immune system modulation, especially when considering the breadth of knowledge available for immunoregulatory effects of

E2 and bisphenol A (BPA), a different EEDC.

In 2012 and 2013, the World Health Organization (WHO) published reports detailing possible effects of EDC exposure on child health, and the state of the science regarding EDCs which concluded that evaluation of human exposure to EDCs and hormonally active pharmaceutical compounds must be addressed(349). Due to multiple factors, including instrument limitations, focus on aquatic life, variability among water sources, genetic diversity, human behavior and prescription medications, determining an exact environmental exposure dose to EE is extremely difficult. No current consensus exists regarding human daily exposure to EE. In this study, we worked with Research Diets Inc. to formulate a murine diet that contains

6.8 ppb of EE, a dose that was calculated with the help of a board-certified toxicologist (Dr.

Marion Ehrich, personal communication) to correspond to a daily human exposure dose of 30µg

242 of EE. Our previous study showed that when compared to a chow-type diet, feeding MRL/lpr mice a diet devoid of exogenous estrogenic chemicals modulated autoimmune disease parameters, including reduced proteinuria and glomerular immune-complex deposition(106). In this study by controlling for genetics, gender, mouse handling and other environmental variables, we investigated the effects of chronic oral exposure to a very low dose of EE on renal pathology, autoimmune disease parameters, and response to pathogens in a genetically susceptible lupus- prone mouse model of disease. The rationale for the focus on renal disease and response to infectious agents signaling is based on the fact that in lupus, two of the primary causes of mortality are lupus nephritis and infections.

Remarkably, just a low-dose EE exposure of MRL/lpr mice accelerated and sustained renal disease as evidenced by increased proteinuria and BUN when compared to control diet fed mice. EE-fed mice had increased levels of proteinuria at the first time point studied (7 weeks of age) compared to control diet-fed mice (Fig. 1). This finding is notable considering that it is unusual for MRL/lpr mice to have high levels of proteinuria by 7 weeks of age. It is possible that

EE-fed mice may have increased proteinuria even earlier than 7 weeks of age, a time point that was not studied. The increased proteinuria in EE-exposed mice remained to the end point of the study, and was accompanied by increased levels of blood urea nitrogen and BUN:Creatinine ratio (Fig. 2). It is noteworthy that EE-fed mice had increased IgG and IgG2a glomerular immune complex deposition compared to controls (Fig. 3). Histopathology scores do not reflect the observed differences between groups in clinical markers of renal disease (Supplemental

Table 1). In our previous study, the estrogen-free diet formulation used in this study reduced observable histopathologic changes in kidneys of MRL/lpr mice(106). It is possible that by using this dietary formulation the severe renal disease in MRL/lpr mice will be delayed, and

243 histopathologic changes may be identifiable if the mice were able to survive to an older age. It is possible that mice exposed to EE would develop more severe changes on histopathology due to the increased level of glomerular IgG and IgG2a immune complex deposition, since immune complex deposition usually initiates and precedes anatomic alterations in renal tissue (34). This particular strain also suffers from severe lymphadenopathy and dermatitis which lead to wasting or animal welfare concerns, preventing the mice from aging beyond 16 weeks of age.

Regardless, early clinical pathological (proteinuria, BUN, and BUN:Creatinine ratio) and immunofluorescent (immune complex deposition in the glomeruli) renal parameters may be an early prelude to histopathological changes.

Estrogens have previously been shown to promote B cell survival and break tolerance to produce autoantibodies in wild-type mice(84,134). The exact mechanism of how estrogens lead to loss of B cell tolerance is still not fully understood. In this study, we did not observe any differences between groups in circulating BAFF or BAFF production following in vitro stimulation. Chronic exposure to EE may promote the survival of CD19+ B cells or plasma cells, leading to increased potential for autoantibody production. We did find increased CD19+ B cells in mesenteric lymph nodes of mice exposed to EE and stimulated with TLR9 agonist ODN 2395

(Supplemental Fig. 2). Increased numbers of B cells may potentially be present in circulation or in tissues not evaluated. ODN 2395 is a form of double-stranded DNA, and the increased levels of anti-dsDNA autoantibodies was observed in mice exposed to EE and stimulated with ODN

2395. IgG2a subclass is the primary circulating subclass in the MRL/lpr mouse model. This increase in anti-dsDNA IgG2a could be due to EE promoting B cell survival, followed by increased levels of dsDNA circulating throughout the body after administration of ODNs, leading to increased anti-dsDNA production. The mild reduction in circulating anti-dsDNA

244

IgG2a in the EE exposed mice administered sterile saline could potentially be due to increased deposition in the glomeruli and potentially other target tissues. It is plausible that autoantibodies may be redirected from the serum and secondary lymphoid tissues to the target organ (kidney) hence, why we did not find markedly high levels of serum autoantibodies. This also suggests that serum antibody levels may not be a good indicator of an individual’s disease status. We cannot eliminate the possibility that EE promoted local target organ (kidney) in situ production of autoantibodies.

The observed increased autoantibody production can be independent of TLR7/9 signaling, since autoantibody production does not require intact TLR7/9 signaling. Interestingly,

TLR9 deficient MRL mice had increased proteinuria and autoantibody production compared to normal MRL mice(42). TLR9 KO also led to B cell hyperreactivity in C57BL/6 (B6) mice, and

TLR9 controlled TLR7 activity in both B6 and MRL/lpr mice(42,47). Human SLE patients have impaired TLR9 response on PBMCs when evaluated in vitro(248,350). Since we have observed a suppression of TLR9 signaling cytokine production in MRL/lpr and NZB/WF1 mice exposed to

EE, it is possible that human SLE patients that already have an impaired TLR9 response and are exposed to EE could develop a more severe loss of normal TLR9 signaling (94). This could promote exacerbation of lupus disease and increase susceptibility to infections if the immune system cannot appropriately respond to pathogenic CpG DNA. Previous work also suggests that

EE exposure can increase TLR4 response to LPS stimulation in vitro(94). Thus, mice exposed to

EE that had gram negative bacteria as part of their normal microbiota could potentially have a heightened response to released LPS through TLR4 signaling compared to mice fed the control diet.

245

Our data supports that chronic low-dose EE exposure can alter gene expression and protein levels of the MyD88 adaptor molecule in splenic leukocytes (Fig. 7&8). MyD88 is important in the signaling processes of many receptors, and has been considered a “central node” of inflammatory signaling(351,352). It is possible that the responses of receptors not evaluated in this study were suppressed in a similar manner to what we observed in TLR9. Surprisingly,

TLRs and MyD88 are not required for autoantibody production, whereas they are required for αβ and γδ T cells(352). TLR signaling and MyD88 potentially skew anti-DNA towards Th1- mediated production(352). Further evaluation of DCs and T cell and B cell subsets may reveal more distinct differences resulting from EE exposure and explain differences in tissue specific cytokine suppression following TLR7 and 9 stimulation. Of particular interest is the role of EE exposure on the formation and maintenance of germinal centers in the spleen and lymph nodes of autoimmune-prone mice.

The current study evaluated the response of intact female mice to an estrogenic chemical.

Further work in the laboratory is involved in characterizing the sex-specific differential response to oral EE exposure in wild-type and autoimmune-prone mouse models, which is beyond the scope if this manuscript. Further work is also being done to evaluate the specific regulatory effects of EE exposure on ERα’s transcriptional functions and epigenetic modulation at genes of interest. The underlying mechanism for EE’s ability to modulate renal disease is not yet fully understood, and may involve the contribution of epithelial and endothelial cell signaling, infiltration of specific dendritic cell subsets, or be associated with immune cell signaling independent of TLR7 and TLR9. IFNα is an important product following TLR7/9 stimulation, and the mouse model used in this study did not produce detectable levels of IFNα for further evaluation. IFNγ levels, however, were detectable.

246

In summary, we investigated the effects of chronic exposure to a human-relevant dose of

17α-ethinyl estradiol on kidney disease and response to ssRNA viral (TLR7) or bacterial (TLR9) challenge in a mouse model of systemic lupus erythematosus. Chronic low-dose oral exposure to

EE can exacerbate clinical markers of renal disease and promote glomerular IgG and IgG2a immune complex deposition. EE exposure led to impairment of normal TLR7 and TLR9 signaling in kidneys and TLR9 signaling in splenic leukocytes, through alterations of a signaling cascade protein, MyD88. To our knowledge, this is the first study to show that EE exposure reduces MyD88 in a mouse model of SLE. This study allowed us to further develop our oral estrogenic EDC exposure model utilizing a purified ingredients diet with an exogenous estrogenic EDC specifically added in at a defined concentration. These data provide a significant step forward in our understanding of how environmental exposure to an estrogenic EDC can alter the immune system’s ability to respond to infectious organisms and modulate an already autoimmune-dysregulated immune system, exacerbating clinical evidence of renal disease even at an extremely low dose.

247

Abbreviations AAALAC Association for Assessment and Accreditation of Laboratory Animal Care

International

E2 17β-estradiol

EE 17α-ethinyl estradiol

EEDC estrogenic endocrine disrupting chemical

ER estrogen receptor

ERT estrogen replacement therapy

IACUC Institutional Animal Care and Use Committee

IFN Interferon

IL interleukin

IRAK4 Interleukin-1 receptor-associated kinase 4

IRF7 Interferon regulatory factor 7

MCP-1 Monocyte Chemoattractant protein-1

MyD88 Myeloid differentiation primary response 88

NZB/WF1 New Zealand Black x White progeny F1

ODN oligodeoxynucleaotides

PCNA proliferating cell nuclear antigen

RT-PCR Reverse transcription polymerase chain reaction

TLR7 Toll-like receptor 7

TLR9 Toll-like receptor 9

248

Acknowledgements

We thank Melissa Makris for assistance with flow cytometry results. We thank, Ms. Karen Hall,

Betsy S. Midkiff, and other animal care staff members at VMCVM, Virginia Tech.

Financial Support: Preparation of this publication was supported by the Virginia-Maryland

Correspondence: S. Ansar Ahmed, BVSc, PhD, Department of Biomedical Sciences and

Pathobiology, and Office of Research and Graduate Studies, Virginia-Maryland College of

Veterinary Medicine, Virginia Tech, 245 duck pond drive, Blacksburg, Virginia 24061-0442,

USA. Email: [email protected]

Author Contributions: All authors listed have made substantial, direct, and intellectual contribution to the work and approved it for publication.

Disclosure Summary: The authors have nothing to disclose.

249

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Figure 1- EE exacerbates proteinuria in female autoimmune-prone MRL/lpr mice. Urine protein concentration of all mice was monitored every 2 weeks starting at 7 weeks of age and continued throughout the study. Urine concentration was measured using a dipstick. (A) The urine protein concentration of the mice fed either the control diet or EE diet, with only sterile saline administered in vivo is represented. (B) The urine protein concentration of mice fed the control diet with in vivo TLR agonist or saline administration or (C) mice fed the diet containing EE with in vivo TLR agonist or saline administration is shown from 11 weeks of age until the study end point. Graph data are expressed as mean ± SEM (n=9-10 per group). * p<0.05, ** p< 0.01,

*** p<0.001.

260

Figure 2- Clinical markers of azotemia and kidney disease are increased by EE exposure. (A)

Proteinuria concentration as measured at the end of the study (16 weeks of age) using a dipstick

(n=9-10 per group). (B) Blood urea nitrogen (BUN) was measured in the serum of mice. The line at 33 mg/dL represents the upper limit of what is considered normal BUN concentration. (C)

Serum creatinine was analyzed using a commercial colorimetric kit. (D) BUN values were divided by creatinine values to determine the BUN:Creatinine ratio. Graph data are expressed as mean ± SEM (n=5-6 per group). * p<0.05, ** p< 0.01, *** p<0.001.

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Figure 3- EE exposure augments immune complex deposition (A) Representative micrographs of immune-fluorescently labeled IgG and (B) IgG2a immune-complex deposition in OCT frozen renal sections (20x magnification). IgG is shown in green, and IgG2a is shown in purple. (C, D)

Corrected glomerular fluorescent intensity of IgG (C) and IgG2a (D) per glomerulus as measured

262 by Fiji/ImageJ. Twenty glomeruli were analyzed per mouse. Graphical data are expressed as mean ± SEM (n=6 mice per group). * p<0.05, ** p< 0.01, *** p<0.001.

263

264

Figure 4- Serologic parameters of autoimmune disease. (A, B, C) Autoantibodies (A) anti- dsDNA IgG, (B) anti-dsDNA subset IgG2a and (C) anti-cardiolipin were analyzed by ELISA in serum of mice at 16 weeks of age. (D-G) Serum cytokines (D) IFNγ, (E) IL-6, (F) TNFα, and

(G) IL-10 were measured by multiplex ELISA from Aushon at 16 weeks of age. Graph data are expressed as mean ± SEM (n=9-10 per group). ** p< 0.01.

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Figure 5- TLR7/9 expression is unchanged, but cytokine expression is suppressed by EE exposure. (A-E) mRNA expression levels were evaluated using qRT-PCR analysis of kidney

RNA. Expression levels are shown relative to the control group (labeled C) using comparative

-2ΔΔCt. β-Actin was used as the endogenous control gene. One-way ANOVA. Graph data are expressed as mean ± SEM (n=4 per group). * p<0.05.

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Figure 6- EE exposure can increase TLR7/9 expression in splenic leukocytes, but IL-6 production is suppressed following ODN stimulation. (A-D) TLR7 and TLR9 mRNA expression levels were evaluated using qRT-PCR analysis of RNA collected from (A&B) splenic

267 leukocytes and (C&D) splenic CD19+ cells. Expression levels are shown relative to the control group (labeled C) using comparative -2ΔΔCt. β-Actin was used as the endogenous control gene

(n=4 samples per group) (E-F) Splenic leukocytes from Control diet and EE diet fed mice given in vivo sterile saline i.p. were activated with either imiquimod at 5 µg ml-1 or ODN 2395 (0.5

µM) for 24 hours. Cytokine protein levels were analyzed by ELISA from the cell culture supernatants. One-way ANOVA. Graph data are expressed as mean ± SEM (n=5 per group). * p<0.05, ** p< 0.01.

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269

Figure 7- EE exposure leads to differential expression of TLR7/9 signaling cascade proteins. (A-

H) mRNA expression levels were evaluated using qRT-PCR analysis of RNA extracted from unstimulated splenic leukocytes. Expression levels are shown relative to the control group (Log2 fold change from C) using comparative -2ΔΔCt. β-Actin was used at the endogenous control gene.

One-way ANOVA. Graph data are expressed as mean ± SEM (n=5 per group). * p<0.05

270

Figure 8- MyD88 levels are decreased in splenic leukocytes of EE exposed mice. Protein was extracted from unstimulated splenic leukocytes and analyzed by western blot for MyD88 and β- actin (loading control). (A) Representative western blot images of MyD88. (B) Densitometry evaluation of blot images with background subtracted prior to normalization to β-actin as analyzed through Fiji/ImageJ. Graph data are expressed as mean ± SEM (n=5 per group). * p<0.05, ** p< 0.01, *** p<0.001.

271

Supp. Methods

Flow Cytometry

The relative abundance of CD4, CD8, CD25, CD69 expressing cells in the spleen and mesenteric lymph node and B220, CD19, IgG, IgM, IgG2a, CD11b+, BAFFR, CD119 expressing cells in the spleen and mesenteric lymph node were quantified by flow cytometric analysis.

Stained cells were visualized using a FACSAria flow cytometer (BD Biosciences) and data analyzed using FlowJo version 7 software as described in our previous studies(87,106,118).

272

273

Supp. Fig. 1- Circulating anti-dsDNA throughout the study. Autoantibodies anti-dsDNA IgG was analyzed by ELISA in serum of mice at 9, 11, and 15 weeks of age. Data is shown for (A) all groups combined, (B) between diet groups C and EE only, (C) control groups, and (D) EE

Groups. Data in (E) is pooled among all treatment groups fed either the Control diet or EE Diet, prior to administration of TLR ligands. Graph data are expressed as mean ± SEM (weeks 9, 11: n= 5-6, week 15: n=9-10 per group). * p< 0.05.

274

Supp. Fig 2- Relative abundance of CD19+ cells in mesenteric lymph nodes (mLN). Flow cytometry was performed on mesenteric lymph node leukocytes and gated on singlets. Data shown is relative abundance (%) of CD19+ in (A) unstimulated mLN leukocytes and (B) after 24 hours of anti-CD3+anti-CD28 stimulation. Graph data are expressed as mean ± SEM (n=5 per group). * p< 0.05.

275

276

Supp. Fig. 3- Signaling cascade protein levels are unchanged. Protein was extracted from unstimulated splenic leukocytes and analyzed by western blot for IRAK4, p-IRAK4, IRF7, p65, and β-actin (loading control). (A) Densitometry evaluation of IRAK4, (B) p-IRAK4, (C) ratio of p-IRAK4 to IRAK4, and (D) IRF7 blot images with background subtracted prior to normalization to β-actin or (E) p65 normalized to PCNA, as analyzed through Fiji/ImageJ. Graph data are expressed as mean ± SEM (n=5 per group).

277

Supplemental Table I Legend- Scores of formalin-fixed kidney slides. H&E- and PAS-stained slides were analyzed by a board-certified veterinary pathologist in a blinded fashion. Data is represented as mean ± SD for each individual renal tissue structure. Total Kidney scores represents the sum of individual tissue sections scores. (n=9-10 mice per group).

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Chapter 6

Conclusions and Future Directions

The immune system is highly susceptible to regulation or alteration by estrogenic compounds. Estrogen receptorα (ERα) is expressed on the majority, if not all, known immune cells, whereas ERβ has a more selective expression profile in immune-related tissues(1-8). ERs act as ligand-responsive transcription factors and regulate the expression of a wide variety of genes. Both ERs are required in a mammalian organism to maintain homeostasis, and for appropriate development of immune cells and immune-related tissues(9,10). Cells of both the innate and adaptive branches of immunity exhibit altered function in the presence of estrogens(11). ERs are able to exert both ligand-dependent and ligand-independent actions(12).

In general, estrogens promote inflammation, B cell survival and enhance IFNγ production and secretion in humans and mice. However, estrogen can also induce T regulatory cell expansion and upregulate FoxP3, PD-1 and CTLA-4 which are involved in immunosuppression(11,13-16).

Estrogens and estrogen receptors are able to alter a variety of inflammatory and regulatory immune processes, making our need for understanding the role of estrogenic compounds in the etiopathology of multiple autoimmune diseases a primary concern.

There is now recognition that sources for exogenous estrogenic chemical exposures are common and increasing. Pharmaceutical administration through oral contraceptives and treatments involving hormone replacement therapy, industrialization and agricultural chemicals including pesticides and fertilizing chemical exposures, and common use of plastic products in daily life have contributed to environmental contamination of estrogenic products(17). Over

1,500 known compounds are currently listed as endocrine disrupting chemicals. Given the varied

279 and complex effects that estrogens may have on the immune system, few estrogenic chemicals are thoroughly studied with respect to immunomodulation. Of estrogenic chemicals, 17β- estradiol is the most well studied(11,18-20). Studies have begun to investigate the role of exogenous estrogenic chemicals on immune regulation and response, such as BPA. For how commonly 17α-ethinyl estradiol is prescribed and consumed, there exists a stark gap in literature exploring EE’s immunomodulatory abilities. This lack of knowledge is readily apparent in the field of autoimmune diseases, with few studies documenting the effects of EE on an already dysregulated immune system. It is plausible that there exists a subset of the population that is genetically prone to developing autoimmune disease, and is also highly sensitive to hormonal responses, making these individuals uniquely susceptible to the detrimental effects of estrogenic

EDC exposure. To address some of the gaps in knowledge, the work included within the body of this dissertation tested the hypothesis that chronic exposure to an exogenous estrogenic endocrine disruptor will enhance renal disease, exacerbate autoimmune disease parameters, and alter the immune response to pathogenic stimuli. Based on conclusions drawn from the work in

Chapters 3, 4 and 5 of this dissertation, we accept this hypothesis.

The reproductive effects of EE exposure are widely reported in the literature, however immunologic effects are still minimally documented. Concerns of EE exposure have been somewhat debatable following a Nurse’s Cohort study that described minimal differences seen with lupus patients and oral contraceptive use (21). While EE is a major component of oral contraceptive pills, these medications also typically include a progesterone component as well.

Depending on the context and cellular micro-environment, progesterone is generally considered to be anti-inflammatory or immune-suppressive, and may aid in masking or blocking the potentially harmful immunologic effects of EE (22,23). A study performed on a human fallopian

280 tube epithelial cell line showed no differences in TLR1-6 gene expression following in vitro incubation with either estradiol or progesterone (24). However, incubation with both estradiol and progesterone did decrease expression of most TLRs at low concentrations and increased

TLR expression at high concentrations(24). Similarly, TLR2, TLR4, MyD88, and cytokines

TNFα and IL-6 were decreased in newborn mononuclear cells and monocytes exposed to estradiol and progesterone (25). Progesterones have also been shown to suppress anti-viral immunity dependent on MyD88 signaling in dendritic cells (DC). DCs exposed to progesterone and stimulated with TLR7 ligand imiquimod resulted in reduced IL-12p40, IL-10, IL-6, and

TNFα production. Human PBMCs treated with TLR9 ligand CpG produced lower levels of IFNα

(26). These data support findings that humans and non-human primates exposed to progesterone had increased viral susceptibility (26-28). Similarly, our work documented in Chapter 5 is the first study that suggests that chronic exposure to a very low dose of EE leads to diminished

TLR7 and TLR9 responses, and may contribute to a further reduction in anti-viral immunity in genetically susceptible SLE-prone populations.

The concept of the envirome encompasses all exposures that are outside of one’s self. It is as yet unsettled whether the human microbiota, mycobiota, and virome can be included within the envirome, or if normal flora are considered “self”. It is my opinion that microbiota should be considered an environmental contributor to immune system education and maintenance. Current evidence suggests that even if a “signature microbiota” can be used to identify an individual, the microbiota is an ever-shifting community that can be modified by alterations to ones’ physiology. For example, an individual that changes dietary habits or loses a significant amount of adipose tissue can have significant alterations in the relative abundance of gut microbes (29-

33). Germ-free animals that lack a microbiota exist and are able to survive in isolators. The lack

281 of host DNA, the fluidity and equilibrium inherent within the mammalian microbiota, and the presence of germ-free animals suggests that the microbiota is an environmental community, and not inherently part of the host. This distinction supports that host microbiota should be included as a variable when discussing environmental contributions to disease pathogenesis and one’s envirome.

It is possible that estrogenic compounds in the diet, or chronic oral administration of EE can alter the host microbiota, which in turn can regulate the immune system. We evaluated the relative abundance of two bacterial genera that have been associated with SLE disease, both among mice fed various commercial rodent diets, and mice fed a diet containing 6.8 ppb of EE.

Mice fed a diet high in soy isoflavones had a higher abundance of Lachnospiraceae sp. in fecal samples compared to mice fed an estrogen-free purified-ingredients diet(34). We did not find substantial differences in Lactobacillus sp. or Lachnospiraceae sp. relative abundance among the groups of mice fed either an estrogen-free control diet, or a diet containing low-dose EE (Chapter

3, Fig. 6; Appendix A1). It must be noted that our studies were only a snapshot in time. Future studies should conduct an extensive evaluation of microbiota changes in multiple areas of the gastrointestinal tract over time, in association to EE exposure and autoimmunity.

The work described in this dissertation lays the foundation for continued studies dissecting the underlying mechanisms of EE exposure, and the model begun in Chapter 3, and further refined in Chapter 5, can be customized for use with many specific EDCs of interest.

Designing a custom diet that includes an EDC of interest can remove labor, variables associated with surgery and recovery, and user error involved with daily or multiple oral gavage models, as well as stress response or injury associated with handling of mice during the gavage procedure.

Dosing also scales with weight and diet consumption as the animals grow. Future experiments

282 should include multiple doses of EE or a specific EDC of interest to further elucidate the specific dose of EDC that will lead to alterations in the immune system. Careful consideration revolving around extraneous products that are required for animal care and maintenance is also paramount.

Choosing of bedding material, disposable or reusable cages, water source, water delivery method, disinfectant used within the vivarium, proximity of murine predators and related olfactory or auditory stimuli that can induce a stress response, and alterations to circadian rhythm can all influence the outcome of studies attempting to measure relatively minor biologically relevant changes in products of immune cell responses.

Future work investigating the direct immunologic effects of endocrine disrupting chemicals can involve the judicious use of newer technologies and computer modeling to aid in the development of a comprehensive schematic detailing specific environmental factor contributions to autoimmune disease pathogenesis or severity. Based on the data presented within the body of this dissertation, evaluation of ERα and ERβ nuclear translocation and ERE site biding is one next step for extending the current body of knowledge surrounding EDCs and

SLE. Determination of the underlying mechanism for TLR7/9 suppression by low-dose EE, analysis of the thymus and splenic germinal centers following EE exposure, and further elucidation of the mechanisms underlying the altered renal clinical pathology are also vitally important follow-up studies.

Evaluation of ERα and ERβ response to EDC exposure is the logical next step in understanding EE’s role in immunomodulation. To investigate alterations in ER response and functions due to EE exposure, both in vitro and in vivo experiments can be of use. Comparing

ERα and ERβ mRNA expression and nuclear translocation following in vitro exposure of mouse primary splenic leukocytes to E2 or EE can be investigated by western blot analysis in

283 cytoplasmic and nuclear cellular fractions. Use of the model described in Chapter 5 in ERα knock-out mice, ERβ knock-out mice, and GPR30 knock-out mice can help us to understand the receptor-dependent specific effects, and determine if EE is exerting immunomodulatory effects independent of the ERs. Inclusion of mouse models with overexpression of these receptors can also be of potential use in defining the role of these receptors. To explore possible alterations in

ERs binding to EREs, use of transcriptomics, and ChIP-seq techniques can help determine EDC specific functional differences following receptor ligation. Drawing on the current strengths of

Dr. Ahmed’s lab, further exploration into epigenetic alterations, including miRNA expression and specific gene or promoter region dynamic DNA methylation alterations due to EDC exposure can help expand the current knowledge base surrounding estrogenic EDC exposure and altered ER signaling.

In Chapter 5 we describe suppressed TLR7 and TLR9 response to stimuli in EE exposed mice. Further investigations into the mechanism of suppression should be performed. Full TLR signaling and cytokine production following stimulation requires ERα binding to EREs on target genes(35,36). Some EDCs are able to bind ERs and alter the ER-signaling pathways, inhibiting

ERE binding by the ERs(37). Thus, it is possible that EE exposure results in impaired ERE binding by the ERs, contributing to abnormal TLR signaling responses. Other areas of interest include endosome microenvironmental changes, such as possible increased acidity contributing to reduced TLR-ligand binding efficiency, or alterations in the NF-κB pathway that were not explored, such as TRAF3, IRAK1 phosphorylation, MAPK, and p50 nuclear translocation.

Identifying cellular TLR expression patterns through flow cytometry of splenic leukocytes, kidney epithelial cells, and infiltrating immune cells into the kidneys would help define which cells of interest are contributing greatly to TLR7/9 response to stimuli. Narrowing of important

284 signaling components that are altered by EDC exposure could be achieved through TLR signaling cascade arrays or super arrays.

Germinal centers (GC) are vital organizational structures for the development of B cell maturation and antibody production (38). Evaluation of GCs and areas of abnormal extramedullary hematopoiesis (EMH) may contribute to a better understanding of augmented adaptive immune responses due to EDC exposure. We found differences in Bcl6 expression in splenic leukocytes and splenic CD19+ cells that suggests that Bcl6 may be increased in CD4+ T follicular helper cells or follicular dendritic cells (Appendix A2). Histopathologic analysis followed by IFA or IHC for germinal center markers and Bcl6 may aid in identifying structural changes to lymphoid organs in EDC exposed mice. Evaluation of cytokines important for normal germinal center formation and TFH differentiation, such as IL-2, IL-6, and IL-21, during early and mid-late stage autoimmune disease development, may help us identify microenvironment alterations that may inhibit or promote GC formation and abnormal B cell survival (39).

The data presented in Chapters 3 and 5 of this dissertation support a role for EDC exposure in exacerbation of autoimmune glomerulonephritis. A likely contributing factor is increased immune-complex deposition within the glomeruli, predisposing the kidney for further damage due to physical alterations to the glomerular filtration apparatus. Renal infiltrating immune cells can also recognize and respond to immune complex heavy chains. It is currently unknown why the degree of immune complex deposition is higher in EDC exposed mice compared to control diet fed mice, or potentially how the dietary ingredients within the control diet reduce immune complex deposition. Immune complex deposition was not compared between mice fed the diet containing EE and mice fed diet 2018. Mice fed the diet containing EE did have unexpectedly low histopathologic scores, in contrast to the increased clinical markers of

285 renal disease. Of the three proposed mechanisms of immune complex deposition, the trapping of pre-formed complexes seems the most likely. The reduced ability of MRL/lpr mice to clear cellular debris and regulate abnormal cells makes the contribution of neutrophil extracellular traps to nephritis an area that should be explored further. We cannot rule out that infiltrating B cells (in collaboration with T, DC, and neutrophils) in the kidney itself contributed to the immune complex deposition. We showed in Chapter 4 that higher doses of estrogens can promote increased splenic neutrophils in the early disease stage (40). It is possible that low-dose

EE may also increase neutrophil numbers and activation leading to increased nucleosomal content for autoantibodies to complex with. Increased nucleosomal content may also bind to glomerular basement membranes due to charge differences, and promote in situ immune complex formation. Increased in situ complex formation may help explain the disparity observed between the minimal differences in circulating anti-dsDNA and the significant differences in immune complex deposition measured by IFA in Chapters 3 and 5.

A second area of interest in further evaluating the augmented renal disease is in the immune cell infiltrates. Resident and infiltrating immune cells are essential for damage induced by autoimmune disease, and the cytokine milieu is instrumental in determining the type of nephritis that occurs (41). Macrophages are considered the primary immune cell infiltrating

MRL/lpr kidneys and are driven by renal expression of colony stimulating factor-1 (CSF-1) (41).

Evaluation of EE’s effects on CSF-1 expression and macrophage infiltration are important next steps. While TLR2, 3, and 4 are ubiquitously expressed in renal tissue, TLR7 and 9 are considered to be primarily expressed by resident and infiltrating immune cells (42). This suggests that resident dendritic cells, neutrophils, macrophages, and B cells are important for the differences seen in TLR7 and 9 responses observed in Chapter 5. Thus, evaluation of immune

286 cell subsets and identifying phenotypes, such as CX3CR1+CD11c+ DCs by flow cytometry and confocal imaging in renal tissues should provide a better understanding of the cells regulated by

EEDC exposure.

Surprisingly, the previous understanding was that TLR9 is not expressed in non-immune related renal tissues. This was disputed by a study of childhood onset SLE patients (42). TLR9 was found to be expressed in podocytes of patients with active nephritis, but TLR9 was not found in patients in remission, and TLR9 expression did correlate to the level of circulating anti-

DNA autoantibodies. Slit-membrane proteins nephrin, podocin and synaptopodin are normally expressed in healthy tissues, but are decreased in active lupus nephritis (42). Previous studies have shown a strong increase in renal immune complex deposition and immune cell infiltration in mouse models administered TLR7 or TLR9 agonist, with variable effects on measured proteinuria (41). We did not observe the described severe effects of TLR7/9 agonist administration, but there were trends for increased renal damage in mice fed control diet and administered imiquimod or ODN 2395. Currently oligonucleotide antagonists to TLR7 and

TLR9 are being developed as therapeutics (43). Based on data from Chapter 5, environmental chemical exposure may alter the ability of these therapeutics to show efficacy. Based on the data presented in this dissertation, a model of EEDC action on immune cells and kidney tissue is proposed as an appendix (Appendix A3).

Despite the focus of this dissertation utilizing autoimmune-prone mice, my desire is to extend the incorporation of environmental factor models and studies in sex differences into metabolic diseases, including obesity and Type II diabetes mellitus, and nutritional modulation of chronic diseases. I am also interested in exploring wound healing and regenerative medicine.

It is with great humility that I hope for the work contained within this dissertation to be of some

287 benefit to humans and pet owners to make informed decisions regarding the chemicals that are consciously consumed, and to improve awareness regarding the potential immunologic effects of environmental estrogenic endocrine disrupting chemicals ubiquitously present in daily life.

288

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Appendix A1

Figures Relevant to the dissertation

Appendix A1- Fecal relative abundance of bacteria Lactobacillaceae sp. and Lachnospiraceae sp. Fecal DNA was extracted as described in Chapter 3. (A and B) Real-time PCR analysis of

(A) Lactobacillaceae or (B) Lachnospiraceae relative abundance in feces from at termination.

Family specific gene amplification was compared to total bacterial DNA amplification to

295 determine relative abundance. One-way ANOVA. Data shown are representative of 3 independent experiments. Data are shown as mean ± SEM (n = 4-7 mice per group).

296

Appendix A2

Appendix A2- Bcl6 expression is altered in EE exposed mice and following in vivo stimulation.

(A-C) mRNA expression levels were evaluated using qRT-PCR analysis of RNA extracted from splenic leukocytes or purified splenic CD19+ cells. Expression levels are shown relative to the control group (labeled C) using comparative -2ΔΔCt. β-Actin was used as the endogenous control gene. One-way ANOVA. Graph data are expressed as mean ± SEM (n=4 per group). * p<0.05.

297

Appendix A3

Appendix A3- Proposed model of EEDC action within immune cells and kidney tissue.

298

Appendix B

Complete List of Published Works

1. Edwards, M., Dai, R., Heid, B., Cowan, C., Cecere, T., Ansar Ahmed, S. Exposure to

human relevant doses of 17α-ethinyl estradiol alters TLR7 and 9 signaling and

exacerbates clinical azotemia in female MRL/lpr mice. (In Preparation)

2. Dai, R.*, Edwards, MR.*, Heid, B., Ansar Ahmed, S. (2019) 17-β estradiol and 17α-

ethinyl estradiol exhibit immunologic and epigenetic regulatory effects in NZB/WF1

female mice. Endocrinology. 160(1), 101-118. Doi: 10.1210/en.2018-00824. [*co-first

author].

3. Edwards M, Dai R, Ansar Ahmed S. (2018) Our Environment Shapes Us: The

Importance of Environment and Sex Differences in Regulation of Autoantibody

Production. Front Immunol. 9(478). doi: 10.3389/fimmu.2018.00478.

4. Luo XM, Edwards MR, Mu Q, Yu Y, Vieson MD, Reilly CM, Ansar Ahmed , S.,

Bankole, AA. (2018) Gut microbiota in human systemic lupus erythematosus and a

mouse model of lupus. Appl Environ Microbiol 84(4). doi:10.1128/AEM.02288-17

5. Qinghui Mu; Husen Zhang; Xiaofeng Liao; Kaisen Lin; Hualan Liu; Michael Edwards;

S. Ansar Ahmed; Ruoxi Yuan; Liwu Li; Thomas Cecere; David Branson; Jay Kirby;

Poorna Goswami; Caroline Leeth; Kaitlin Read; Kenneth Oestreich; Miranda Vieson;

299

Christopher Reilly; Xin Luo. (2017). Control of lupus nephritis by changes of gut

microbiota. Microbiome. July, 5:73.

6. Edwards, M., Dai, R., Heid, B., Cecere, T., Khan, D., Mu, Q., Cowan, C., Luo, XM.,

Ansar Ahmed, S. (2017). Commercial rodent diets differentially regulate autoimmune

glomerulonephritis, epigenetics, and microbiota in MRL/lpr mice. International

Immunology. June. 29(6):263-276.

7. Xin M. Luo, Michael R. Edwards, Christopher M. Reilly, Qinghui Mu, S. Ansar

Ahmed. “Chapter 8: Diet and Microbes in the Pathogenesis of Lupus.” Lupus. Ed. Wahid

A. Khan. Rijeka: Intech. 2017. https://www.intechopen.com Web. (ISBN 978-953-51-

3180-9, Print ISBN 978-953-51-3179-3)

300