Medicine, Faculty of
Medicine, Department of
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Wu, Dan
Doctor of Philosophy - PhD
University of British Columbia
Adipose tissue (AT)-resident regulatory T cells (Tregs) are important regulators of local and systemic inflammation and metabolism. We have previously found that insulin receptor signaling inhibits Treg suppressive function in vitro, and hyperinsulinemia is associated with alterations in visceral AT Tregs in vivo. To directly test the role of Treg-intrinsic insulin receptor signaling, we generated Foxp3(cre)InsR(fl/fl) mice and fed them either chow or high-fat diet (HFD) to induce hyperinsulinemia and obesity. Compared to Foxp3(cre) mice, Foxp3(cre)InsR(fl/fl) mice had improved glucose tolerance and insulin sensitivity after 13 weeks of HFD. This protective metabolic effect was associated with increased brown AT ST2+ Treg numbers and functions and dampened AT inflammation in Foxp3(cre)InsR(fl/fl) mice, rather than changes in food consumption or energy expenditure. Moreover, Foxp3(cre)InsR(fl/fl) mice were protected from age-associated glucose intolerance and insulin resistance as determined in 52-week-old mice. Unlike in the HFD cohort, visceral AT Treg numbers and functions were greatly reduced in aged Foxp3(cre)InsR(fl/fl) mice, leading to increased inflammation in the visceral AT. Surprisingly, elevated AT inflammation was associated with improved metabolic outcomes. Gene expression analysis revealed differential gene signatures in AT Tregs and total AT inflammation, suggesting that the underlying mechanisms contributing to the metabolic syndromes were distinct between the two insulin resistance models. Together, these data suggest that hyperinsulinemia contributes to metabolic syndrome in part by differentially affecting Treg control of AT inflammation in diet-induced versus age-associated metabolic syndrome. In mice, lean visceral AT is populated with anti-inflammatory cells, notably Tregs expressing the IL-33 receptor ST2. Conversely, obese AT contains fewer Tregs and more pro-inflammatory cells. In humans, however, there is limited evidence for a similar pattern of obesity-associated immunomodulation. We used flow cytometry and mRNA quantification to characterize human omental AT in 29 obese individuals, 18 of whom had T2D. Omental AT Treg frequencies negatively correlated to BMI but were comparable between T2D and non-T2D individuals. Compared to human thymic Tregs, omental AT Tregs had a distinct gene signature. ST2, however was not detectable on omental AT Tregs from lean or obese subjects.
ROLE OF REGULATORY T CELLS IN OBESITY AND INSULIN RESISTANCE by Dan Wu B.Sc., The University of British Columbia, 2013 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) October 2019 © Dan Wu, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: ROLE OF REGULATORY T CELLS IN OBESITY AND INSULIN RESISTANCE submitted by Dan Wu in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Experimental Medicine Examining Committee: Megan Levings Supervisor Bruce Verchere Supervisory Committee Member Fumio Takei University Examiner James Johnson University Examiner Additional Supervisory Committee Members: Alice Mui Supervisory Committee Member iii Abstract Adipose tissue (AT)-resident regulatory T cells (Tregs) are important regulators of local and systemic inflammation and metabolism. We have previously found that insulin receptor signaling inhibits Treg suppressive function in vitro, and hyperinsulinemia is associated with alterations in visceral AT Tregs in vivo. To directly test the role of Treg-intrinsic insulin receptor signaling, we generated Foxp3creInsRfl/fl mice and fed them either chow or high-fat diet (HFD) to induce hyperinsulinemia and obesity. Compared to Foxp3cre mice, Foxp3creInsRfl/fl mice had improved glucose tolerance and insulin sensitivity after 13 weeks of HFD. This protective metabolic effect was associated with increased brown AT ST2+ Treg numbers and functions and dampened AT inflammation in Foxp3creInsRfl/fl mice, rather than changes in food consumption or energy expenditure. Moreover, Foxp3creInsRfl/fl mice were protected from age-associated glucose intolerance and insulin resistance as determined in 52-week-old mice. Unlike in the HFD cohort, visceral AT Treg numbers and functions were greatly reduced in aged Foxp3creInsRfl/fl mice, leading to increased inflammation in the visceral AT. Surprisingly, elevated AT inflammation was associated with improved metabolic outcomes. Gene expression analysis revealed differential gene signatures in AT Tregs and total AT inflammation, suggesting that the underlying mechanisms contributing to the metabolic syndromes were distinct between the two insulin resistance models. Together, these data suggest that hyperinsulinemia contributes to metabolic syndrome in part by differentially affecting Treg control of AT inflammation in diet-induced versus age-associated metabolic syndrome. In mice, lean visceral AT is populated with anti-inflammatory cells, notably Tregs expressing the IL-33 receptor ST2. Conversely, obese AT contains fewer Tregs and more pro-inflammatory cells. In humans, however, there is limited evidence for a similar pattern of iv obesity-associated immunomodulation. We used flow cytometry and mRNA quantification to characterize human omental AT in 29 obese individuals, 18 of whom had T2D. Omental AT Treg frequencies negatively correlated to BMI but were comparable between T2D and non-T2D individuals. Compared to human thymic Tregs, omental AT Tregs had a distinct gene signature. ST2, however was not detectable on omental AT Tregs from lean or obese subjects. v Lay Summary Type 2 diabetes (T2D) accounts for 90% of the 9 million cases of diabetes or prediabetes in Canada. T2D is a result of insulin resistance, which is a hormone that signals tissues like fat, muscle and liver to take up sugar from the bloodstream. The body produces more insulin to compensate for the insensitivity. We think that high levels of insulin can interfere with the functions of a type of immune cells called regulatory T cells (Tregs), whose role is to calm inflammation. Reduced Treg activity could lead to excessive inflammation in fat tissue, and further promoting insulin insensitivity. My research studied the mechanisms that control the function of Tregs in fat tissue. Overall, this research may lead to a new understanding of T2D prevention and have a significant impact on the quality of life of Canadians T2D patients and their families. vi Preface Individual sections of Chapter 1 have been published: - Wu D, Dawson NA, and Levings MK. Obesity-associated adipose tissue inflammation and transplantation. Am J Transplant. 2016. 16: 743-750. I performed literature review and wrote the text represented in my thesis. Levings MK and I edited the final manuscript together along with Dawson NA. The work presented in Chapter 2 is in manuscript preparation. - Wu D, Orban PC, Huang Q, and Levings MK. Ectopic germline recombination activity of the widely used Foxp3-YFP-Cre knock-in mouse: a case report in the generation of Treg-specific InsR knockout mice. Manuscript in preparation. I bred the mice, noticed the breeding problem, and designed experiments to solve the problem. Orban PC assisted with knowledge and PCR designs. Huang Q performed most of the genotyping. The manuscript is written by me and edited by Lam AJ, Orban PC and Levings MK. The work presented in Chapter 3 is in manuscript preparation. - Wu D, Wong CK, Han JM, Orban PC, Huang Q, Gillies J, Mojibian M, Gibson WT, and Levings MK. Regulatory T cell-specific deletion of the insulin receptor prevents diet-induced and age-associated metabolic syndrome. Manuscript in preparation. Han JM initiated the project and assisted in early breeding. Wong CK performed all metabolic cages experiments. Orban PC assisted with the knowledge of genotyping and PCRs. Gillies J performed the western blots to confirm insulin receptor knockout. Huang Q, Mojibian M assisted in glucose tolerance test, insulin tolerance test, and tissue harvest. I performed all other vii experiments, interpreted the data and wrote the manuscript. The final manuscript was edited by Lam AJ, Levings MK and Gibson WT. The entirely of work presented in Chapter 4 has been published: - Wu D, Han JM, Yu X, Lam AJ, Hoeppli RE, Pesenacker AM, Huang Q, Chen V, Speake C, Yorke E, Nguyen N, Sampath S, Harris D. and Levings MK. Characterization of regulatory T cells in obese omental adipose tissue in humans. Eur J Immunol. 2019. 00:1-12. Han JM initiated the project and collected the early patient samples. Yorke E, Nguyen N, Sampath S were the bariatric surgeons. Hoeppli RE collected the thymic T cells samples. Pesenacker AM and Speake C collected the T2D blood samples. Lam AJ optimized the anti-human ST antibody. Huang Q assisted in sample processing. Yu X and Chen V performed statistical analyses. I performed all other experiments and wrote the manuscript. The final version of the manuscript was edited by Harris D and Levings MK, along with other authors. Human samples were collected according to protocols approved by the University of British Columbia Clinical Research Ethics Board, including written informed consent (UBC CREB NUMBER: H13-03283). Animal research protocols were approved by UBC Animal Care Committee (UBC APPLICATION NUMBER: A14-0155, A18-0619). viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiii List of Abbreviations ...................................................................................................................xv Acknowledgements ................................................................................................................... xvii Dedication ................................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................1 1.1 Regulatory T cells ........................................................................................................... 1 1.1.1 Thymically-derived Tregs ........................................................................................... 2 1.1.2 Peripherally-derived Tregs.......................................................................................... 3 1.1.3 Treg mechanisms of suppression ................................................................................ 3 1.2 Type 1 and type 2 immunity ........................................................................................... 5 1.3 Adipose tissue inflammation and insulin resistance ....................................................... 6 1.4 Obesity-associated adipose inflammation....................................................................... 6 1.4.1 The role of adipose tissue in obesity ........................................................................... 7 1.4.2 Immune cell composition in lean VAT ....................................................................... 8 1.4.3 Tregs play a key role in suppressing AT inflammation ............................................ 11 1.4.4 Loss of Immune Regulation in Obese Adipose Tissue ............................................. 12 ix 1.5 Age-associated adipose tissue inflammation and insulin resistance ............................. 13 1.5.1 VAT Tregs in aging mice ......................................................................................... 14 1.6 Insulin signaling ............................................................................................................ 14 1.6.1 Insulin-AKT signaling in Tregs ................................................................................ 15 1.7 Hyperinsulinemia as a causal role in perpetuating insulin resistance ........................... 16 1.8 Objectives ..................................................................................................................... 16 Chapter 2: Ectopic germline recombination activity of the widely used Foxp3-YFP-Cre knock-in mouse: a case report in the generation of Treg-specific InsR knockout mice ........18 2.1 Introduction ................................................................................................................... 18 2.2 Materials and methods .................................................................................................. 19 2.3 Results ........................................................................................................................... 21 2.3.1 .... Unexpected offspring genotype ratio during the breeding of Foxp3YFP-CreInsrfl/fl mice............................................................................................................................................... 21 2.3.2 Conventional two-band PCR strategy does not distinguish mice with germline-recombined alleles ................................................................................................................ 24 2.3.3 A three-band PCR strategy confirm the presence of mice with ectopic germline recombination ....................................................................................................................... 26 2.3.4 .... A quantitative PCR strategy quantifies the extent of ectopic recombination in gDNA............................................................................................................................................... 28 2.3.5 ......... Quantification of ectopic recombination across metabolic tissues and immune cell types ...................................................................................................................................... 30 2.3.6 Spontaneous ectopic Cre recombination occurs as early as the first generation of breeding................................................................................................................................. 32 x 2.4 Discussion ..................................................................................................................... 35 Chapter 3: Regulatory T cell-specific deletion of the insulin receptor prevents diet-induced and age-associated metabolic syndrome ....................................................................................39 3.1 Introduction ................................................................................................................... 39 3.2 Material and methods .................................................................................................... 41 3.3 Results ........................................................................................................................... 46 3.3.1 Generation and Verification of Treg-specific InsR-knockout animals ..................... 46 3.3.2 Treg-specific InsR-knockout mice are protected from diet-induced metabolic syndrome ............................................................................................................................... 48 3.3.3 In diet-induced hyperinsulinemia, InsR-deficient Tregs have increased proliferation and function .......................................................................................................................... 51 3.3.4 Treg-specific InsR-knockout mice are protected from age-associated metabolic syndrome ............................................................................................................................... 55 3.3.5 In age-associated hyperinsulinemia, InsR-deficient Tregs have diminished proliferation and function ..................................................................................................... 55 3.3.6 Mechanisms underlying differential Treg responses to hyperinsulinemia in age-associated versus diet-induced metabolic syndromes ........................................................... 57 3.4 Discussion ..................................................................................................................... 63 Chapter 4: Characterization of Tregs in obese omental adipose tissue in humans ...............66 4.1 Introduction ................................................................................................................... 66 4.2 Materials and methods .................................................................................................. 68 4.3 Results ........................................................................................................................... 74 4.3.1 Obesity and T2D alter metabolic gene expression in human omental AT ............... 74 xi 4.3.2 Obesity and T2D are associated with increased innate inflammation in human omental AT ........................................................................................................................... 77 4.3.3 M1 macrophages accumulate in the omental AT of patients with T2D ................... 78 4.3.4 Obesity is associated with decreased Treg proportions in human omental AT ........ 78 4.3.5 Human omental AT Tregs have a tissue-specific phenotype.................................... 80 4.3.6 Undetectable expression of ST2 on human omental AT Tregs ................................ 85 4.4 Discussion ..................................................................................................................... 87 Chapter 5: Conclusion .................................................................................................................93 5.1 Summary and overall discussion .................................................................................. 93 5.2 Strengths and limitations............................................................................................... 94 5.3 Future directions ........................................................................................................... 95 Bibliography .................................................................................................................................97 Appendix .....................................................................................................................................111 Annotated sequence of the floxed allele near exon 4 of Insr .................................................. 111 xii List of Tables Table 2.1 Antibodies used for FACSorting. ................................................................................. 20 Table 2.2 Genotype ratios of all mice in the second breeding cohort from Figure 6. .................. 36 Table 3.1 Antibodies used for flow cytometry. ............................................................................ 44 Table 4.1 Characteristics of cohort undergoing bariatric surgery. ................................................ 69 Table 4.2 Lean cohort characteristics. .......................................................................................... 70 Table 4.3 Blood collection cohort characteristics. ........................................................................ 70 Table 4.4 Antibodies used for flow cytometry. ............................................................................ 71 xiii List of Figures Figure 1.1 Immune regulation in lean versus obese adipose tissue. ............................................... 9 Figure 2.1 Unexpected offspring genotype ratio during the breeding of Foxp3YFP-CreInsrfl/fl mice. .............................................................................................................................................. 22 Figure 2.2 The conventional two-band genotyping PCR strategy does not distinguish mice with germline-recombined alleles. ........................................................................................................ 25 Figure 2.3 A three-band PCR strategy confirm the presence of mice with ectopic germline recombination. .............................................................................................................................. 27 Figure 2.4 A quantitative PCR strategy quantifies the extent of unwanted ectopic recombination in gDNA. ....................................................................................................................................... 29 Figure 2.5 Quantification of ectopic recombination across metabolic tissues and immune cell types. ............................................................................................................................................. 31 Figure 2.6 A second cohort reveals spontaneous ectopic Cre-mediated recombination as early as the first generation of breeding. .................................................................................................... 34 Figure 3.1 Treg-specific InsR deletion in Foxp3creInsrfl/fl mice. ................................................... 47 Figure 3.2 Additional metabolic characteristics of the high-fat-diet cohort. ................................ 49 Figure 3.3 Foxp3creInsrfl/fl mice are protected from diet-induced metabolic syndrome. .............. 50 Figure 3.4 Metabolic parameters of the high-fat-diet cohort. ....................................................... 52 Figure 3.5 Gating strategy for immune-profiling of brown and visceral adipose tissues using flow cytometry ...................................................................................................................................... 53 Figure 3.6 Gene expression assays measured by nanoString nCounter........................................ 54 Figure 3.7 Foxp3creInsrfl/fl mice are protected from age-associated metabolic syndrome. ........... 56 Figure 3.8 Additional metabolic characteristics of the aging cohort. ........................................... 58 xiv Figure 3.9 Metabolic parameters of the aging cohort. .................................................................. 59 Figure 3.10 Specific deletion of InsR in Tregs leads to gradual loss of Tregs and ST2+ Tregs in the visceral AT but not the spleen................................................................................................. 60 Figure 3.11 Treg and VAT gene signatures differ in age-associated and diet-induced metabolic syndrome. ...................................................................................................................................... 62 Figure 4.1 Gating strategy for flow cytometric analysis of human omental adipose tissue. ........ 73 Figure 4.2 Obesity and T2D alter metabolic gene expression in human omental AT. ................. 75 Figure 4.3 Obesity and T2D are associated with increased innate inflammation in human omental adipose tissue. ............................................................................................................................... 76 Figure 4.4 Flow cytometry analysis of ex vivo macrophage populations from omental AT reveals increased M1 macrophages in T2D. ............................................................................................. 79 Figure 4.5 Flow cytometric analysis of ex vivo Tregs from human omental AT and peripheral blood. ............................................................................................................................................ 81 Figure 4.6 Gene expression analysis of human omental AT Tregs. ............................................. 83 Figure 4.7 Tregs in human omental adipose tissue do not produce proinflammatory cytokines. 84 Figure 4.8 Human omental AT Tregs do not express detectable ST2. ......................................... 86 Figure 4.9 ST2 expression on tonsil B cells is not susceptible to collagenase II digestion. ......... 87 xv List of Abbreviations AT adipose tissue APC antigen-presenting cell BAT brown adipose tissue bp base pair CTLA-4 cytotoxic T-lymphocyte-associated protein 4 FACS fluorescence-activated cell sorting GARP glycoprotein A repetitions predominant protein GITR glucocorticoid-induced tumor necrosis factor receptor-related protein HFD high fat diet HOMA-β homeostatic model assessment of β-cell function IFN-γ interferon gamma IGF1 insulin-like growth factor 1 IGF1-R insulin-like growth factor 1 receptor ILC2 group 2 innate lymphoid cells iNKT invariant natural killer T InsR insulin receptor IPGTT intraperitoneal glucose tolerance test IRS-1 insulin receptor substrate-1 iTreg induced regulatory T cell ITT insulin tolerance test LAG-3 Lymphocyte-activation gene 3 LAP latency associated peptide xvi LPS lipopolysaccharide MAPK Ras–mitogen-activated protein kinase MCP-1 monocyte chemotactic protein 1 NCD normal chow diet NK natural killer cells PHLPP1 PH-domain leucine-rich-repeat protein phosphatase 1 PI3K phosphatidylinositol 3-kinase pTreg peripherally-derived regulatory T cell qPCR quantitative PCR SVF stromal vascular fraction T2D type 2 diabetes Tconv conventional T cell TCR T cell receptor TGF-β transforming growth factor beta Th1 T helper type 1 Th2 T helper type 2 Th17 T helper type 17 TLR Toll-like receptor TNF-α tumor necrosis factor alpha Treg regulatory T cell tTreg thymically-derived regulatory T cell UCP1 uncoupling protein 1 VAT visceral adipose tissue xvii Acknowledgements I would like to thank the following individuals: - Megan Levings o My amazing supervisor, for giving me all the support and I needed, as well maximum intellectual freedom to explore sciences - Bruce Verchere and Alice Mui o My awesome committee members, for all the great suggestions - Jonathan Han o My mentor at the beginning of my PhD, whom I inherited all my projects from - Qing Huang, May Wong and Lisa Xu o The best technicians I have ever worked with - Paul Orban o For all the intellectual-stimulating conversations we had - Avery Lam o A great friend, whom I learned a lot from; my go-to English teacher/editor - Everyone else in the Levings lab o For creating a fun and supportive lab environment - UBC, BCCHRI, CIHR o For their financial support xviii Dedication To my parents, for their unconditional love and support. 1 Chapter 1: Introduction Immune homeostasis is maintained by a balance of effector mechanisms that protect against pathogen infections and regulatory mechanisms that prevent autoimmunity. To date, several types of regulatory cells have been discovered: CD4+CD25+FOXP3+ regulatory T cells (Tregs), IL-10-producing type 1 regulatory T (Tr1) cells, regulatory B cells (Bregs), myeloid-derived suppressor cells (MDSCs) [1]. More recently, a new subset of regulatory innate lymphoid cells (ILCs) has also been identified [2]. Among all these regulatory cells, Tregs are the most well-studied, physiologically relevant cells due to their indispensable role in maintaining peripheral tolerance. This thesis will focus on Tregs and their role in the context of obesity and development of type 2 diabetes. 1.1 Regulatory T cells Tregs are a group of CD4+ cells defined by expression of the transcription factor forkhead box protein 3 (FOXP3). In addition to FOXP3 expression, demethylation of the Treg-specific demethylated region (TSDR) in the FOXP3 locus is often used to define Tregs because in humans effector T cells can transiently express low-to-medium levels of FOXP3 when stimulated [3]. Demethylation of DNA in the TSDR region is crucial to maintain high and stable expression of FOXP3 in Tregs, which is necessary for their suppressive function and lineage stability [4]. Tregs also constitutively express high levels of the high-affinity IL-2Rα chain (CD25), although CD25 is not limited to Tregs. One of the functions of FOXP3 is to suppress IL-2 transcription, meaning that Tregs cannot make their own IL-2 and thus rely on external sources. IL-2 availability is an important positive feedback pathway as IL-2 signaling within Tregs is also important for maintaining high expression of FOXP3 as well as functionally 2 relevant proteins such as cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and glucocorticoid-induced tumor necrosis factor receptor related protein (GITR) [5]. Tregs are primarily generated in the thymus (tTregs), but can also be made at peripheral sites (pTregs), or induced in cell culture (iTregs) when given transforming growth factor beta (TGF-β). The relative role of these different populations of Tregs in vivo remains an outstanding question, but it is clear that both tTregs and pTregs are essential for maintaining immune homeostasis [6]. iTregs generated in vitro can be used as a source for adoptive Treg transfer studies/therapies. However, it is generally thought that iTregs are unstable compared to tTregs and finding methods to improve iTreg stability is an active field of research [7]. 1.1.1 Thymically-derived Tregs All αβ T cells, including CD4+ T cells, undergo negative selection during thymic development. T cells recognizing self-peptides presented by medullary thymic epithelial cells are considered “self-reactive” and receive an apoptotic signal that leads to cell death. While negative selection is a crucial mechanism for establishing central tolerance, the process is not 100% efficient, as healthy individuals have self-reactive T cells in their circulation [8]. Therefore, in addition to thymic negative selection it is crucial that the body has peripheral tolerance mechanisms to prevent autoimmunity. One major mechanism of peripheral tolerance is the de novo generation of tTregs during negative selection. It is believed that CD4+ T cells expressing TCRs with intermediate affinity to self-peptides develop into tTregs, while those with high affinity are deleted and those with low-to-no affinity develop into effector T cells [9]. The exact details of tTreg development remains unclear, although several models have been proposed [10]. 3 1.1.2 Peripherally-derived Tregs In addition to the de novo generation in thymus, Tregs can convert from naïve CD4+ T cells in the periphery. In mice, IL-2 and TGF-β are two essential cytokines for driving this conversion [5]. Other metabolites, such as retinoic acid or short chain fatty acids (e.g. butyrate, propionate and acetate) can also play a role in the development of pTregs [11, 12]. Overall it has been difficult to study pTregs due to the lack of markers that can distinguish them from tTregs. Previously the transcription factor Helios and neuropilin-1 have been suggested to be specific tTregs [13, 14], but later studies shown these proteins can be expressed by pTregs as well [15, 16]. 1.1.3 Treg mechanisms of suppression Tregs function to suppress inflammation in local microenvironments. Although initial antigen-specific TCR interaction is required for activation and subsequent TCR interaction is required for maintenance [17], once activated, Tregs suppress in an antigen-independent manner, which is often referred to as “bystander suppression” [18]. Tregs function in three ways to suppress inflammation: (1) secreting soluble inhibitory cytokines, (2) metabolic dysregulation, and (3) inhibiting antigen-presenting cells (APC) functions. Tregs produce inhibitory cytokines including IL-10 and TGF-β. The effects of these cytokines on effector cells are similar, including suppression of proliferation and cytokine production [19, 20]. In addition, both cytokines can enhance their own expression via a positive feedback loop, creating an inhibitory microenvironment [19, 21]. It is worth mentioning that Tr1 cells also produce high levels of IL-10 [22-24]; these CD4+FOXP3- cells are often characterized by the expression of CD49b and lymphocyte activation gene-3 (LAG3) [24, 25]. Tregs produce 4 both soluble TGF-β, both in its active or latent form that is bound to latency-associated peptide (LAP). In addition, Tregs express membrane-bound TGF-β and glycoprotein A repetitions predominant (GARP) protein, which facilitate the release of TGF-β [26]. In mice, Tregs can secrete IL-35 [27], as well as granzymes A, B and perforin to mediate cytolysis [28, 29]. Tregs can mediate suppression via metabolic dysregulation. Because Tregs constitutively express high levels of CD25, the high-affinity IL-2Rα chain, it is believed that Tregs competitively consume IL-2 in the proximity and therefore deprive the surrounding effector T cells of IL-2 [30]. Furthermore, Tregs express CD39 and CD73 on their surface; these enzymes convert adenosine triphosphate to adenosine in the extracellular space, which can in turn suppress effector cell function by binding to A2A receptors [31, 32]. Tregs can modulate APC function to suppress effector cells. In order for naive T cells to be optimally activated, their CD28 receptors have to bind to CD80 and/or CD86 co-stimulatory molecules on APCs. Tregs express CTLA-4, which has a higher affinity to CD80 and CD86 than does CD28, allowing Tregs to competitively occupy the two co-stimulatory molecules on APCs, ultimately removing them from the surface by transendocytosis [33]. Moreover, Tregs induce expression of indoleamine 2,3-dioxygenase (IDO) in DCs in a CTLA-4-dependent manner. IDO breaks down tryptophan, an essential amino acid for T cell during rapid expansion phase [34]. Although the above mechanisms of actions have been discovered, currently it is unclear which mechanism is being used in given situations. Regardless, these mechanisms enable Tregs to effectively modulate the balance of immune responses, such as between type 1 and type 2 immunity. 5 1.2 Type 1 and type 2 immunity In this thesis, type 1 and type 2 immunity are used to loosely define a set of immune characteristics that are distinct from each other. The usage of the two type of immunity are by no mean to encompass the full landscape of the complicated immune responses found in humans and mice. However, it is a useful concept that facilitates discussions in the thesis. Type 1 immunity refers to an environment skewed towards cytotoxic immune responses including elevated T helper type 1 (Th1) cells, CD8+ T cell, M1 macrophages and natural killer (NK) cell activities [35, 36]. The key cytokine in type 1 immunity is interferon gamma (IFN-γ). The goal of type 1 immunity is to kill infected or cancer cells. The process can elicit tissue damage and further recruitment of the type 1 immune cells, until the immune stimuli are diminished. Note that T helper type 17 (Th17) cells are sometimes considered to be type 1 immune cells due to their similar ability to cause tissue inflammation and damage; however, their set of immune characteristics is distinct from type 1 immunity [37]. Th17 cell activities are often considered to be type 3 immunity, which is characterized by the cytokine production of IL-17A and IL-22 [36]. The term type 2 immunity is used differently in various research fields, but overall tends to encompass the concept of a suppressive environment (characterized by IL-10 and TGF-β) and a low-grade inflammatory environment (characterized by IL-4, IL-5 and IL-13). In this thesis, the term type 2 immunity refers to an environment enriched with elevated T helper type 2 (Th2) cells, eosinophils, M2 macrophages and Tregs. Generally speaking, compared to type 1 immunity, type 2 immunity may cause less tissue damages and hence important in would healing [38, 39]. However, excessive type 2 immune activities can also cause severe tissue damage, including tissue fibrosis [40]. 6 1.3 Adipose tissue inflammation and insulin resistance Insulin resistance is defined as the inability of cells and tissues to respond normally to physiological levels of insulin and adequately activate pathways leading to glucose uptake. Insulin resistance is commonly associated with chronic inflammation, particularly in the adipose tissue (AT). Increasing evidence suggests that chronic AT inflammation caused by infiltration of pro-inflammatory immune cells directly contributes to the development of insulin resistance. Mechanistically, tumor necrosis factor alpha (TNF-α), a pro-inflammatory cytokine, can block insulin receptor (InsR) signaling by causing serine phosphorylation of insulin receptor substrate-1 (IRS-1) in a number of cell types [41-43]. Subsequent studies have shown that mice lacking TNF-α are protected from obesity-associated insulin resistance [44]. Increased TNF-α production is observed in human obesity, and reduced TNF-α levels are associated with human weight loss [45, 46]. In addition, disrupting a number of pro-inflammatory signaling pathways such as the Toll-like receptor (TLR) 4, inflammasome, NF-κB, and JNK pathways have been reported to prevent obesity-associated insulin resistance [47-50]. In humans, obese individuals who are insulin-resistant exhibit a greater degree of AT inflammation than those who remain insulin-sensitive [51]. Taken together, AT inflammation may directly contribute to the development of insulin resistance. 1.4 Obesity-associated adipose inflammation Chronic AT inflammation is frequently associated with obesity. The triggers of this inflammation are thought to include gut-derived antigens, excessive free lipids, accumulation of visceral adiposity, and a loss of local immune regulation [52]. 7 1.4.1 The role of adipose tissue in obesity Mammalian AT is classified into white, brown, or beige tissue based on cellular morphology and expression of uncoupling protein 1 (UCP1), a fatty acid/ATP symporter located in the mitochondria that generates heat from chemical energy [53]. White adipocytes specialize in lipid storage and release, are characterized by a large lipid depot stored as a single droplet, and do not express UCP1. In contrast, brown and beige adipocytes have multiple lipid droplets and express UCP1, and hence generate body heat. Despite their similar phenotypes, brown and beige adipocytes derive from distinct lineages: brown adipocytes arise from Myf5+ pre-adipocytes and are developmentally related to skeletal muscle cells, while beige adipocytes arise from Myf5- pre-adipocytes and are more closely related to smooth muscle cells [53]. In adult humans, brown AT is only found in confined anatomic sites, but white and beige AT is distributed throughout the body, including in the abdominal cavity, allowing for white and beige AT to play a dominant role in the regulation of obesity [54]. The ratio of white to brown and beige adipose tissue is thought to be key for regulating weight, with a higher ratio of beige to white visceral AT (VAT) associated with better metabolic health. It is worth noting that visceral adiposity is more directly associated with metabolic dysfunctions than subcutaneous adiposity, possibly due to VAT being the major site for energy storage and immune-metabolic crosstalk [55, 56]. The process of beige adipocytes appearing in white AT is referred to as “AT browning” and is regulated by a number of factors. For example, norepinephrine can act via the β3-adrenergic receptor to induce AT browning, and this response is impaired in obese humans [57]. A higher proportion of beige AT is thought to be protective from weight gain because higher UCP1 expression increases thermogenesis and whole-body 8 energy expenditure. Indeed, mice lacking UCP1 develop obesity compared to UCP1-sufficient controls [58], obese humans have significantly reduced brown and beige AT proportions [59], and cultured beige adipocytes from obese adults have reduced expression of UCP1 [60]. Thus, in obesity, a progressive loss of beige AT and the accumulation of white AT leads to cellular stress in adipocytes, triggering the release of pro-inflammatory cytokines, and ultimately a change in the immune cell composition in AT. 1.4.2 Immune cell composition in lean VAT Visceral AT contains a wide array of immune cells [61]. In lean individuals, resident immune cells are typically associated with a type 2 immune axis, including M2 macrophages, eosinophils, invariant natural killer T (iNKT) cells, group 2 innate lymphoid cells (ILC2s), Th2 cells and Tregs (Fig 1.1). Moreover, type 2 cytokines have an essential role in maintaining bodily metabolism and a normal balance of white and beige AT: mice lacking IL-4 and IL-13 have reduced UCP1 expression [62], and administration of exogenous IL-4 protects mice from high fat diet (HFD)-induced obesity and insulin resistance [63]. At the cellular level, multiple IL-4-dependent cells maintain an anti-inflammatory state in lean adipose tissue. For example, IL-4- and IL-13-dependent M2 macrophages (also termed alternatively activated macrophages) help maintain a type 2 immune response by producing anti-inflammatory cytokines such as interleukin (IL)-10, as well as norepinephrine, which acts via the β3-adrenergic receptor to promote UCP1 expression and browning [62]. Furthermore, in lean mice, some AT-resident M2 macrophages store iron; this capacity decreases in obese mice and excess iron accumulation in adipocytes leads to cellular toxicity and impaired insulin sensitivity [64]. IL-4-dependent Th2 cells provide positive feedback by producing IL-4, IL-5, and IL-13 to 9 Figure 1.1 Immune regulation in lean versus obese adipose tissue. (A) Lean adipose tissue (AT) is dominated by type 2 immune-regulatory responses. M2 macrophages (also termed alternatively-activated macrophages) that produce norepinephrine (NE) to induce AT browning, thereby promoting mitochondrial biogenesis and UCP1 expression, energy expenditure and overall inhibiting weight gain. Browning can also be induced by group 2 innate lymphoid cell (ILC2)-derived interleukin (IL)-13 and methionine-enkephalin (MetEnk). M2 macrophages are maintained by IL-4, which is produced by eosinophils, invariant natural killer T (iNKT) cells and T helper type 2 (Th2) cells, as well as IL-13, which is made by ILC2s. Lean adipocytes can help M2 macrophage differentiation by secreting adiponectin. The IL-5 dependent eosinophils are sustained by Th2 cells and ILC2s. IL-33 produced by fibroblast reticular cells in lean AT induces potent proliferation of ILC2s and AT-resident Tregs, which express elevated levels of IL-10 compared to their peripheral counterparts. IL-4 and IL-10 are crucial in inhibiting type 1 immune responses in lean AT. (B) In obese AT, type 1 pro-inflammatory immune responses are triggered by adipokines made by enlarged adipocytes experiencing hypoxia and oxidative stress. These adipokines include: leptin, which promotes Th1 polarization; 10 monocyte chemotactic protein 1 (MCP-1), which recruits monocytes and M1 macrophages; IL-6 and tumor necrosis factor alpha (TNF-α), which induce inflammation. Another trigger is elevated concentrations of circulating bacteria-derived lipopolysaccharide (LPS) as a result of the compromised gut barrier integrity in obesity. Together with TNFα, LPS induces M1 macrophage activation and subsequent upregulation of co-stimulatory molecules. Activated M1 macrophages promote Th1 polarization and are highly phagocytic, and therefore engulfing dying adipocytes to form “crown-like structures”. Large amount of Th1 cells and CD8+ effector T cells accumulate in obese AT, and they secrete interferon-γ (IFN-γ) to act on each other and on M1 macrophages to further promote type 1 immune responses. The high amounts of TNF-α in inflamed AT also block insulin signalling both locally in adipocytes, and remotely in hepatocytes, thereby promoting the development of insulin resistance. maintain the type 2 immune response in lean AT. Th2 cells also directly protect from obesity-associated inflammation since adoptive transfer of wild-type but not STAT6-/- CD4+ T cells, which have impaired Th2 differentiation, normalizes HFD-induced, obesity-associated pathologies [65]. The major source of IL-4 in AT is eosinophils and iNKT cells. Using IL-4 reporter mice, Molofsky et al. reported that over 85% of the IL-4 producing cells in lean AT are eosinophils, immune cells that are dependent on IL-5 and IL-13 for growth and survival [66]. Mice lacking eosinophils develop severe HFD-induced obesity and insulin resistance, and exhibit significantly lower numbers of M2 macrophages [66]. iNKT cells resident in AT are also a major source of IL-4. When compared to their peripheral counterparts, AT-resident iNKT cells express higher levels of IL-4 and IL-10, and CD1d-deficient mice, which cannot present lipid antigens to activate iNKT cells, develop spontaneous obesity even when fed a low-fat diet [67]. 11 IL-5 and IL-13 are also required to maintain dominant type 2 immune responses in AT. In lean AT, over 90% of the IL-5- and IL-13-secreting cells are ILC2s [68, 69], and these cytokines work in concert with IL-4 to sustain the normal composition of eosinophils and M2 macrophages. Additionally, ILC2s can act directly on white adipocytes to promote AT browning via an IL-4R-IL-13 axis [70]. When stimulated with IL-33, by producing methionine-enkephalin, an opioid-like peptide that promotes browning in an IL-4R-depedent mechanism [71]. Notably, antibody-mediated depletion of ILC2s leads to increased weight gain and insulin resistance in mice fed a HFD, while transfer of ILC2s into obese mice results in weight loss [72], demonstrating that ILC2s dominantly regulate AT inflammation. Finally, a population of regulatory B cells, characterized by constitutive expression of IL-10, is found in the AT of lean mice [73]. The IL-10-producing B cells comprise two distinct populations: an AT-resident subset and another recruited following HFD challenge [74]. Both populations of regulatory B cells are an important source of IL-10 to restrain diet-induced accumulation of inflammatory M1 macrophages and CD8+ T cells. 1.4.3 Tregs play a key role in suppressing AT inflammation VAT Tregs also adopt a Th2-like phenotype, expressing elevated levels of IL-10, GATA3, CCR4 and the ST2 chain of the IL-33R. In obese AT, Treg expression of these proteins is diminished [75-79]. Diphtheria toxin-mediated depletion of Tregs results in AT inflammation and insulin resistance [75], indicating that Tregs play a key role in maintaining metabolic homeostasis. The distinct phenotype of VAT Tregs appears to be driven at least partly by IL-33 because administration of exogenous IL-33 induces VAT Treg proliferation, restores their classical Th2-like phenotype, and is sufficient to resolve obesity-associated VAT inflammation 12 [77-79]. Although IL-33 also acts on eosinophils and ILC2s, which could then affect Tregs and result in the observed VAT Treg phenotype [69], recent evidence suggests the accumulation of VAT Tregs specifically requires ST2 expression on Tregs [80]. Another major contributor to the VAT Treg phenotype is PPARγ, the master regulator of adipocyte differentiation [81]. PPARγ expression is elevated in VAT Tregs, and mice with a Treg-specific deletion of PPARγ have substantially fewer Tregs in the VAT without any alteration in the spleen [82, 83]. Interestingly, VAT Tregs display a restricted but distinct T cell receptor (TCR) repertoire, indicative of clonal expansions [78]. This suggests that VAT Tregs may be responding to specific local antigens, which have yet to be discovered. In comparison, VAT Foxp3- conventional T cells (Tconvs) have much more diverse TCR repertoires, similar to peripheral CD4+ T cells. 1.4.4 Loss of Immune Regulation in Obese Adipose Tissue In obesity, VAT immune cells respond to a combination of endogenous and exogenous signals, and they switch from an anti-inflammatory phenotype to a pro-inflammatory phenotype. Mechanistically, white adipocytes expand in obesity, resulting in cellular stress due to iron accumulation and hypoxia. Stressed adipocytes secrete a number of pro-inflammatory adipokines, including leptin, monocyte chemotactic protein 1 (MCP-1), IL-6, and TNF-α. Leptin then acts on T cells to stimulate IFN-γ production, thereby promoting Th1 differentiation while inhibiting Th2 responses. MCP-1, IL-6 and TNF-α recruit and activate M1 macrophages, which are highly phagocytic and engulf dying or dead adipocytes, forming “crown-like structures”: morphologically a ring of macrophages surrounding adipocytes. Lipolysis is also increased in obesity, and the release of free fatty acids activates M1 macrophages through Toll-like receptor or Nod-like receptor signalling, further promotes type 1 immune responses [61]. Notably, 13 macrophage-derived TNF-α can further promote lipolysis, resulting in a positive feedback loop [84]. HFD also induces gut inflammation, leading to compromised gut barrier integrity, altered microbiota composition, and endotoxemia which can activate M1 macrophages [85]. Pro-inflammatory B cell accumulation is also evident in obesity: these cells present antigens to T cells and secret pro-inflammatory cytokines and antibodies [86]. Collectively, a pro-inflammatory milieu directly contributes to the downstream metabolic consequences of obesity, such as the development of insulin resistance [61]. 1.5 Age-associated adipose tissue inflammation and insulin resistance In addition to obesity, aging can also lead to the development of insulin resistance, though the underlying mechanisms remain unclear [87]. In contrast to the overwhelming amount of literature describing obesity-associated insulin resistance, studies elucidating age-associated inflammation and insulin resistance are limited. In humans, age-associated insulin resistance is partially attributed to the redistribution of AT: subcutaneous fat decrease while visceral and intramuscular fat increases [88]. Similarly, in rats, prevention of age-associated insulin resistance can be achieved by either the surgical removal of the VAT [89] or calorie restriction to decrease VAT accumulation [90]. In mice, aging animals display impaired glucose tolerance and insulin sensitivity as early as 12 months old [83, 91, 92]. By mRNA analysis, VAT from aged mice express higher levels of pro-inflammatory cytokines including Il1b, Il6, Tnf and lower levels of Pparg [93]. 14 1.5.1 VAT Tregs in aging mice In mice, thymus-derived Tregs seed the VAT during the first week of life [78]. In comparison to the ~10% Treg representation typical of peripheral lymphoid organs, VAT Tregs comprise 15% of the CD4+ T cell compartments in 10-week-old B6 mice, and continue to expand: at 25 weeks old, VAT Treg proportions peak to 40–80%. By ~40 weeks, however, the VAT Treg contracts to 20–30% [94]. While obesity causes a decrease in Treg proportions in the VAT leading to AT inflammation and thus insulin resistance, aging is instead associated with VAT Treg expansion and insulin resistance, suggesting that VAT Tregs may play a different role in the development of age-associated insulin resistance. 1.6 Insulin signaling Insulin and insulin-like growth factor 1 (IGF-1) signaling is an evolutionarily conserved pathway that plays a critical role in promoting nutrient uptake and storage in invertebrates and vertebrates. Insulin promotes: (1) glucose uptake in muscle and AT; (2) glycogen synthesis in the liver; (3) lipogenesis in the liver and AT; and (4) protein synthesis in liver and muscle. In addition, insulin also inhibits glycogenolysis, lipolysis, gluconeogenesis, and ketogenesis in the liver [95, 96]. Produced almost exclusively by the pancreas, circulating insulin is quickly broken down in liver. Its short plasma half-life of ~5 minutes is essential to allow beta cells within the pancreas to respond rapidly to changes in blood glucose [96]. Insulin mainly binds to its own receptor, InsR, but it can also bind to the IGF-1 receptor (IGF-1R) and InsR/IGF-1R heterodimers, albeit with a lower affinity [97]. Upon binding, InsR phosphorylates IRS-1 and 2, which then initiates signal transduction via two main pathways: the phosphatidylinositol 3-kinase (PI3K)-AKT pathway and the Ras–mitogen-activated protein 15 kinase (MAPK) pathway. The PI3K-AKT pathway is responsible for the majority of the metabolic actions of insulin, while the MAPK pathway regulates cell growth and differentiation. The signaling networks downstream of insulin are highly complex, and for this reason the exact mechanisms differentiating the two major pathways are not known [96]. 1.6.1 Insulin-AKT signaling in Tregs PI3K-AKT signaling also plays an important role in the activation of CD4+ T cells. In contrast to Tconvs, however, Tregs activated through their TCR do not efficiently signal through PI3K-AKT. This low PI3K activity in Tregs is crucial for stable expression of the master transcription factor Foxp3 and their suppressive capacity [98-100]. Mechanistically, Tregs express high levels of the phosphatases PTEN and PH-domain leucine-rich-repeat protein phosphatase 1 (PHLPP1), which negatively regulate PI3K [100, 101]. Since InsR signaling also signal through PI3K-AKT, we are interested in whether insulin inhibits Treg function, especially at high concentrations during hyperinsulinemia in obesity. Previously, we have reported that insulin signaling inhibits mouse Treg suppressive function in vitro by dampening their production of IL-10, and in vivo, VAT Tregs express diminished Il10 when the mice are fed a HFD [76]. To date, two studies have dissected the role of insulin signaling in T cells and concluded that insulin signaling on CD4+ Tconvs promotes their inflammatory functions [97, 102]. However, neither study explores the role of insulin signaling in Tregs in the context of hyperinsulinemia. 16 1.7 Hyperinsulinemia as a causal role in perpetuating insulin resistance Hyperinsulinemia is a feature of both obesity-associated and age-associated insulin resistance [83, 103]. Multiple lines of evidence suggest that hyperinsulinemia may have a causal role in the development of insulin resistance. Hyperinsulinemia can precede and promote obesity and insulin resistance [60, 104], and drugs that prevent hyperinsulinemia can induce weight loss [105, 106]. More recently, a series of studies by Johnson and colleagues demonstrates that lowering plasma insulin levels by modulating Ins1 and Ins2 gene dosage in mice protects them from diet-induced obesity and age-associated insulin resistance [103, 104, 107]. Mechanistically, insulin promotes lipogenesis and adipogenesis while inhibiting lipolysis, causing adipose tissue inflammation [108]. If our hypothesis is true that hyperinsulinemia inhibits Treg functions and thus promote AT inflammation, then our findings would further support that hyperinsulinemia plays a causal role in perpetuating insulin resistance. 1.8 Objectives My thesis aims to answer the following research questions: • Does hyperinsulinemia inhibit VAT Treg function in obesity and aging, and thus contribute to the development of metabolic dysfunction? • Do human omental AT Tregs play a similar role to the mouse VAT Tregs in regulating AT inflammation and overall metabolism? My thesis comprises the following chapters: • Chapter 2: Generation of Treg-specific InsR knockout animals and the unexpected ectopic Cre-lox recombination 17 • Chapter 3: Characterization of Treg-specific InsR knockout mice in diet-induced and age-associated metabolic syndrome • Chapter 4: Characterization of human omental AT Tregs in obese individuals 18 Chapter 2: Ectopic germline recombination activity of the widely used Foxp3-YFP-Cre knock-in mouse: a case report in the generation of Treg-specific InsR knockout mice 2.1 Introduction Generation of conditional gene knockout animals using Cre-lox technology has become an important tool for studying gene function in specific tissues and cell types. This technology exploits the function of Cre recombinase, initially discovered in bacteriophage P1, to recombine DNA flanked by a pair of palindromic 34-base pair (bp) DNA sequences called loxP sites. By using tissue- and lineage-specific promoters and enhancers, Cre-lox recombination can be used to limit gene deletion to specific tissues or cell types [109]. Its widespread adoption has resulted in a large number of mouse strains with tissue-specific Cre transgenic or floxed genes generated and deposited at the Jackson Laboratory; today, generating conditional gene knockout mice for most researchers simply means purchasing and breeding the specific Cre and floxed mice. However, users of the Cre-lox technology often overlook during breeding and genotyping the precautions required to detect and avoid common pitfalls, including ectopic recombination [110, 111]. Publications employing Cre-lox mice frequently have incomplete information in breeding and genotyping details [112]. Here we report that Foxp3YFP-Cre mice, widely used to generate models with regulatory T cell (Treg)-specific gene deletion, exhibit spontaneous ectopic recombination at the germline level when crossed to Insrfl/fl mice. Aiming to study the role of the insulin receptor (InsR) in Tregs, we bred Foxp3YFP-Cre mice to the Insrfl/fl mice to generate Treg-specific InsR knockout animals. With a conventional two-band PCR genotyping method, we saw unexpected genotype 19 ratios during breeding, the first sign of germline recombination. Further investigation using a quantitative PCR (qPCR) genotyping method revealed that ectopic recombination occurred immediately following the first cross of the Foxp3YFP-Cre mice to the Insrfl/fl mice. To our knowledge, one other study also using this Foxp3YFP-Cre mouse line reported ectopic recombination in their Foxp3YFP-CreCd28fl/fl mice, in which CD28 deletion was observed in Foxp3- conventional T cells (Tconvs) in addition to Foxp3+ Tregs [113]. Our unexpected findings underscore the importance of testing for ectopic recombination events when using the Foxp3YFP-Cre mice. 2.2 Materials and methods Animals. All animal protocols were approved by the University of British Columbia Animal Care Committee. Foxp3YFP-Cre mice (Jackson Laboratory, 016959) and Insrfl/fl mice (Jackson Laboratory, 006955) were purchased and crossed to generate Treg-specific InsR knockout mice. Tissue harvest and DNA extraction. At the time of weaning at 3 weeks of age, a piece of the ear tissue was notched and processed with the KAPA Express Extract Kit (Sigma-Aldrich, KK7101) according to the manufacturer’s instructions. Briefly, ear tissue was enzymatically digested at 75°C for 10 min and denatured at 95°C for 5 min; the resultant tissue lysate was diluted 10-fold with 10 mM Tris-HCl (pH 8.0–8.5) and stored at −20°C prior to genotyping. To assess the relative degree of ectopic recombination in key metabolic tissues, mice were sacrificed and immediately perfused with PBS. Ear, brain, heart, visceral adipose tissue, liver, and quadricep muscles were collected and homogenized in gentleMACS M tubes using the gentleMACS Dissociator (Miltenyi Biotec), then processed with the KAPA Express Extract Kit. 20 Fluorescence-activated cell sorting. To purify immune cells, mouse spleens were harvested and minced through a 100 µm cell strainer, then incubated with 1 ml red blood cell lysis buffer (0.8% NH4Cl, STEMCELL Technologies). Cells were washed and stained for FACSorting using the following antibodies (Table 2.1). FACSorting was performed on a FACSAria II (BD Biosciences). To quantify ectopic recombination in major immune cell types, we sorted B cells (CD19+CD3-), myeloid cells (CD19−CD3−NK1.1−), NK cells (CD19−CD3−NK1.1+), CD8+ T cells (CD3+CD8+), Tconvs CD3+CD4+Foxp3-YFP−), and Tregs (CD3+CD4+Foxp3-YFP+). Sorted cells were lysed with the KAPA Express Extract Kit and diluted 10-fold for genotyping. Table 2.1 Antibodies used for FACSorting. Marker Fluorochrome Company Catalogue Clone NK1.1 PE BD 557391 PK136 CD3 BV605 BioLegend 100237 17A2 CD19 APC BD 550992 1D3 CD4 eF450 eBioscience 48-0041-82 GK1.5 CD8a PE-Cy7 eBioscience 25-0081-82 53-6.7 Fixable viability Dye eF780 eBioscience 65-0865-14 PCR-based genotyping. PCR was performed on tissue and cell lysates containing gDNA using the KAPA2G Fast HotStart Genotyping Mix (Sigma-Aldrich, KK5621) per the manufacturer’s cycling protocol. Primers are listed in Appendix A. PCR products were resolved on a 2% agarose gel containing SYBR Safe (Thermo Fisher Scientific) and imaged using a UV gel documentation system. 21 Quantitative PCR. To assess the relative quantity of each allele in a gDNA sample, three sets of primers each specifically amplifying only one allele were used (Appendix A). Quantitative PCR was performed with PerfeCTa SYBR Green FastMix (Quantabio, 95074) on a ViiA 7 Real-Time PCR System (Thermo Fisher Scientific). Wildtype Ccr5 and Il10rb were used as gDNA housekeeping genes. Data were normalized to ear tissue lysates from Foxp3YFP-Cre mice, Insrfl/fl mice, and Foxp3YFP-CreInsrwt/rcb mice. To assess Insr mRNA expression, total RNA was isolated from flow-sorted immune cells using the OMEGA E.Z.N.A Total RNA Kit (Omega Bio-Tek, R6834), reverse-transcribed with qScript cDNA SuperMix (Quanta Bio, 95048), and qPCR performed as above. Primers used were: Insr, TGTGACATCTGCCCAGGAGCT and GCATAGGAGCGGCGGATCTTTAG; Sdha, CCTAAACATGCAGAAGTCGATGC and GCCTTCTTGCAATACACTCCCC; Rpl13a, CTCAAGGTTGTTCGGCTGAA and CTGTCACTGCCTGGTACTTCCA. 2.3 Results 2.3.1 Unexpected offspring genotype ratio during the breeding of Foxp3YFP-CreInsrfl/fl mice In our pilot cohort, we intended to use Foxp3YFP-CreInsrfl/fl mice (experimental) and Foxp3YFP-CreInsrfl/wt littermates (control). To generate these mice, we purchased female Foxp3YFP-Cre mice [114] and male Insrfl/fl mice [115] from the Jackson Laboratory and bred them according to the scheme shown in Figure 2.1A. Note that the Foxp3YFP-Cre mouse has YFP-Cre knocked-in to its Foxp3 locus, which is located on the X chromosome; as a result, the female mice require homozygous Cre expression to achieve recombination in 100% of their Tregs. Therefore, the 22 Figure 2.1 Unexpected offspring genotype ratio during the breeding of Foxp3YFP-CreInsrfl/fl mice. (A) Breeding scheme to create a pilot cohort of Foxp3YFP-CreInsrfl/fl mice (experimental) and Foxp3YFP-CreInsrfl/wt littermates (control), with expected genotype ratios. Circles represent females, squares represent males, and and diamonds represent mice of either sex; shading indicates the number of Insrfl alleles. The dashed line indicates F4-generation with an unexpected genotype. (B) The conventional two-band genotyping PCR strategy, in which 23 primers flanking the loxP site that is 3’ of Insr exon 4 amplify different PCR products depending on the presence of a floxed or a wild-type allele (top). Example PCR products resolved on a 2% agarose gel, with each lane representing one mouse (bottom). (C) Punnett square showing the expected F4-generation genotype ratio from Foxp3YFP-CreInsrfl/fl mice and Foxp3YFP-CreInsrfl/wt parents (left). Table comparing expected and actual F3 offspring ratios (right). desired genotypes were hemizygous Foxp3YFP-Cre/y males and homozygous Foxp3YFP-Cre/YFP-Cre females. According to our breeding scheme, all mice of generation F2 and beyond will have a knock-in YFP-Cre on all their X chromosomes (Figure 2.1A). To determine genotype at the Insr loci, PCR was performed with ear notch tissue lysate using a set of genotyping primers previously designed by the group that generated the Insrfl/fl mice [115]. As shown in Figure 2.1B, these primers flank the loxP site located 3’ to Insr exon 4, such that a floxed Insr allele will yield a larger PCR product than that of the wild-type allele (full annotated sequence shown in Appendix A). Mice with both PCR products were considered heterozygous Insrfl/wt, while mice with only a smaller or a larger PCR product were considered homozygous Insrwt/wt or Insrfl/fl, respectively (Figure 2.1B, bottom). However, we observed an unexpected genotype ratio in our F4-generation mice, which are the offspring of F3-generation Foxp3YFP-CreInsrfl/wt and Foxp3YFP-CreInsrfl/fl mice. We anticipated 50% homozygous Insr floxed mice and 50% heterozygous Insr floxed mice in F4 (Figure 2.1C, left); instead, 48% were Foxp3YFP-CreInsrfl/fl, 29% were Foxp3YFP-CreInsrfl/wt, and 23% were apparently Foxp3YFP-CreInsrwt/wt (Figure 2.1C, right; n=62). This was striking, as the F3 cross as shown could not generate Foxp3YFP-CreInsrwt/wt mice (Figure 2.1A, dashed line). We verified these results by genotyping fresh ear notch tissue lysates from both parent (F3) and 24 offspring (F4) mice (data not shown). Additionally, Sanger sequencing confirmed that the unexpected PCR product was indeed the 279-bp sequence of the Insrwt allele. 2.3.2 Conventional two-band PCR strategy does not distinguish mice with germline-recombined alleles The conventional two-band PCR strategy failed to detect one possible genotype: a germline-recombined allele, which would yield no PCR product with these primers, because the forward primer was targeting sequence between the two loxP sites, which would be excised after Cre-lox recombination (Figure 2.2A). Heterozygous mice with an Insrfl/rcb genotype (rcb denotes the allele expected after Cre-mediated recombination) would appear the same as Insrfl/fl mice, with a single 319-bp PCR product from only the floxed allele (Figure 2.2A). Similarly, heterozygous Insrwt/rcb mice would appear the same as homozygous Insrwt/wt mice using the conventional two-band PCR strategy (Figure 2.2A). There was thus a chance that we mistakenly used Foxp3YFP-CreInsrfl/rcb mice instead of Foxp3YFP-CreInsrfl/fl mice as parents in the F3 cross. This would produce F4-generation PCR product ratios consistent with the actual ratios observed (Figure 2.2B). According to this hypothetical cross, the F4 mice would be expected to have equal distribution of Insrfl/fl, Insrfl/rcb, Insrfl/wt, and Insrwt/rcb genotypes (all with Foxp3YFP-Cre) (Figure 2.2B, left). With this PCR strategy, mice with Insrfl/fl and Insrfl/rcb alleles would appear the same by PCR and account for up to 50% of offspring; 25% mice with Insrwt/rcb allele would appear the same as Insrwt/wt mice – all in agreement with our actual F4 genotype ratios (Figure 2.2B, right). Taken together, our hypothetical F3 cross may explain the unexpected F4 genotype ratios as well as the appearance 25 of mice with Insrwt alleles, and suggests that both Insrfl/rcb and Insrwt/rcb genotypes may be present in our breeding cohort. Figure 2.2 The conventional two-band genotyping PCR strategy does not distinguish mice with germline-recombined alleles. (A) The conventional two-band genotyping PCR primers are unable to identify mice with a single germline-recombined Insr allele, which would yield no PCR product from this allele. Insrwt/wt mice (bottom-left) and Insrwt/rcb mice (bottom-right) appear identical using this strategy. Similarly, Insrfl/fl mice and Insrfl/rcb mice appear the same. (B) Punnett square with a hypothetical F3 cross, in which Foxp3YFP-CreInsrfl/rcb mice (mistaken as Foxp3YFP-CreInsrfl/fl) are crossed with Foxp3YFP-CreInsrfl/wt mice (left). Table comparing hypothetical and actual F4 genotype ratios (right). 26 2.3.3 A three-band PCR strategy confirm the presence of mice with ectopic germline recombination To better characterize our mice, we designed new PCR primers to distinguish all three Insr alleles: wild-type, floxed, and recombined. These primers flank both loxP sites surrounding exon 4 such that each allele will yield a PCR product of a different size: wild-type (1983 bp), floxed (2150 bp), and recombined (209 bp) (Figure 2.3A, Appendix A). To validate the primer design, PCR was performed with the cell lysates of Foxp3-Cre-YFP- Tconvs and Foxp3-Cre-YFP+ Tregs fluorescence-activated cell sorted (FACSorted) from the spleens of a Foxp3YFP-creInsrwt/wt, Foxp3YFP-creInsrfl/wt and Foxp3YFP-creInsrfl/fl mouse (Figure 2.3B). In Tregs, we saw the expected PCR products in all 3 genotypes, validating the three-band PCR strategy. However, in the splenic Tconvs from the Foxp3YFP-CreInsrfl/fl mouse, there was an unexpected 209-bp PCR product from the recombined allele in addition to the 2150-bp product from the floxed allele. This could have been due to: (1) Treg contamination of the Tconv population during FACSorting; (2) “ex-Tregs”, which are Foxp3- cells that had previous Foxp3 expression [116]; (3) ectopic recombination activity of the Foxp3-YFP-Cre. However, PCR of ear notch tissue lysates of F4-generation mice yielded a 209-bp recombined product in nearly all samples assayed, confirmed by Sanger sequencing (data not shown). This suggests that even Foxp3YFP-creInsrfl/fl or Foxp3YFP-creInsrfl/wt mice without a predicted germline-recombined Insrrcb allele exhibit frequent ectopic recombination in their ear tissues. Thus, the three-band PCR strategy is unable to distinguish mice with germline Insrfl/fl and Insrfl/rcb genotypes. Nevertheless, the three-band PCR method enabled us to tell apart Insrwt/rcb and Insrwt/wt genotypes. To confirm that the Insrwt/rcb mice had 50% of their genomic DNA (gDNA) 27 Figure 2.3 A three-band PCR strategy confirm the presence of mice with ectopic germline recombination. (A) Primers flanking both loxP sites hypothetically yield three distinct PCR products, distinguishing wild-type, floxed, and recombined Insr alleles. (B) Validation of the new primer design. Foxp3-YFP-Cre- Tconvs and Foxp3-YFP-Cre+ Tregs were FACSorted from the spleens of Foxp3YFP-Cre mice, Foxp3YFP-CreInsrfl/wt mice, and Foxp3YFP-CreInsrfl/fl mice. PCR was performed with cell lysates, and PCR products were resolved on a 1% agarose gel. (C) Punnett square showing the expected offspring genotype ratio of a test cross of Foxp3YFP-CreInsrwt/rcb parents (left). Table comparing expected and actual test cross offspring ratios (right). 28 recombined at their Insr locus in all cells including their germ cells, we performed a test cross of supposedly Foxp3YFP-CreInsrwt/rcb mice from F4 among themselves (Figure 2.3C, left). Their offspring should thus be 33% Foxp3YFP-CreInsrwt/wt and 67% Foxp3YFP-CreInsrwt/rcb (Figure 2.3C, left). No homozygous Foxp3YFP-CreInsrrcb/rcb mice would survive as complete knockout of InsR leads to postnatal diabetes and death [117]. Consistent with the predicted frequencies, 29% of offspring mice were Foxp3YFP-CreInsrwt/wt and 71% were Foxp3YFP-CreInsrwt/rcb (Figure 2.3C, right; n=21). Overall, the emergence of Foxp3YFP-CreInsrwt/rcb mice in the F4 generation demonstrates that ectopic germline recombination occurred before the F4 generation. 2.3.4 A quantitative PCR strategy quantifies the extent of ectopic recombination in gDNA In order to determine the extent of ectopic recombination within each animal, we turned to a qPCR-based method. We designed three pairs of primers, each amplifying only a single allele: wild-type, floxed, and recombined (Figure 2.4A). Primers A&B amplify only the wild-type allele, because the primer A binding site is disrupted by the loxP site; primers C&D amplify only the floxed allele; and primers E&F amplify only the recombined allele, as the potential PCR product from a floxed allele is too large to form within the timeframe of qPCR (Figure 2.4A). Note that all primers target introns, so only gDNA but not mRNA is amplified. For reference genes, we chose wildtype Ccr5 and Il10rb, which should be homozygous for all our mice. By using Foxp3YFP-Cre, Insrfl/fl and Foxp3YFP-CreInsrwt/rcb mice as the normalizing controls for Insrwt, Insrfl and Insrrcb alleles, respectively, we were able to estimate the relative abundance 29 Figure 2.4 A quantitative PCR strategy quantifies the extent of unwanted ectopic recombination in gDNA. (A) Three sets of qPCR primers specifically amplify one Insr allele (wild-type, floxed, or recombined). (B) Validation of the strategy. Quantitative PCR was performed with ear notch tissue lysates with these three primer pairs. The relative amounts of each allele were quantified and normalized to those of a Foxp3YFP-Cre mouse, an Insrfl/fl mouse or a Foxp3YFP-CreInsrwt/rcb mouse. Each column represents one mouse. of the three Insr alleles within each animal (Figure 2.4B). With this qPCR method, we genotyped all F4-generation mice and were not only able to distinguish Insrfl/fl from Insrfl/rcb mice, but were also able to quantify the relative amount of ectopic recombination in the ear 30 tissue gDNA of Foxp3YFP-CreInsrfl/fl mice (Figure 2.4B), enabling us to select mice with minimal ectopic recombination for further breeding and experimentation. Thus, a qPCR genotyping strategy can successfully diagnose the extent of ectopic Cre recombination within animals and mitigate unwanted off-target knockout during Cre-lox mouse breeding. 2.3.5 Quantification of ectopic recombination across metabolic tissues and immune cell types As we noted varying degrees of ectopic recombination in the ear tissue gDNA of Foxp3YFP-CreInsrfl/fl mice and intend to use these mice to study the role of Tregs in the context of metabolism, we asked whether ectopic recombination was present in other immune cell types and in key metabolic tissues, namely the brain, fat, liver and muscle. Using four F4-generation mice with the least ectopic recombination in their ear tissue, we depleted immune cells by whole-animal perfusion with PBS and extracted DNA from brain, visceral adipose tissue, liver, and skeletal muscle. Using the same qPCR method, we observed a small amount of ectopic recombination (2–5% of total gDNA) in all tissues examined in Foxp3YFP-CreInsrfl/fl mice (Figure 2.5A). This is partially attributable to tissue-resident immune cells including Tregs not removed by perfusion. We also examined the extent of ectopic recombination in several FACSorted immune cell types from the spleens of Foxp3YFP-CreInsrfl/fl mice. All examined immune cell types exhibited a limited amount of Insr recombination, with the greatest recombination in myeloid cells and Tconvs (10–15% of their total gDNA) (Figure 2.5B). The analysis of Insr mRNA showed a similar trend (Figure 2.5C). Collectively, mice with the least ectopic recombination in their ear tissue also exhibited limited recombination across multiple metabolic tissues and 31 Figure 2.5 Quantification of ectopic recombination across metabolic tissues and immune cell types. 32 (A-B) Quantitative PCR was performed using three pairs of allele-specific primers on the gDNA obtained from (A) ear, brain, fat, liver and muscle tissue lysates and (B) FACSorted B cells, CD8+ T cells, myeloid cells, NK cells, Tconvs, and Tregs. Data were normalized to the gDNA from ear tissue lysates of a Foxp3YFP-Cre mouse, an Insrfl/fl mouse, or a Foxp3YFP-CreInsrwt/rcb mouse. Error bars represent standard errors (n=4). (C) Insr mRNA expression by qPCR with mRNA from FACSorted immune cells. Data were normalized within each immune cell group to those obtained from Foxp3YFP-CreInsrwt/wt mice. Error bars represent standard errors (n=4). immune cell types. With these results, we were confident in addressing our scientific questions using only the most stringent Treg-specific InsR-knockout animals. 2.3.6 Spontaneous ectopic Cre recombination occurs as early as the first generation of breeding To exclude the possibility that ectopic Cre recombination was due to isolated genetic drift in the original breeders, we purchased -new Foxp3YFP-Cre mice and Insrfl/fl mice from the Jackson Laboratory. In a second cohort, we intended to use Foxp3YFP-CreInsrfl/fl (experimental) as before, but Foxp3YFP-CreInsrwt/wt mice instead as controls and established a revised breeding scheme (Figure 2.6). We incorporated our qPCR method and selected only mice with the least ectopic recombination as breeders for the next generation. All genotype ratios and the incidence of ectopic recombination were documented throughout all four generations (Table 2.2). Other than the offspring Foxp3YFP-CreInsrwt/wt mice, all mice generated displayed varying degrees (2-65%) of ectopic recombination; we considered mice with >40% ectopic recombination to harbor a Insrrcb allele; mice with <5% ectopic recombination to be normal (Insrfl); mice with 5-40% ectopic recombination were denoted with a Insrfl-r. Ectopic Cre recombination was observed as early as the F1 generation, with up to half of male heterozygous Foxp3YFP-CreInsrfl/wt mice exhibiting 33 some degree of ectopic recombination in ear tissues. Spontaneous germline recombination first occurred in the F2 generation with the emergence of Foxp3YFP-CreInsrwt/rcb mice, despite the stringent selection of breeders (Table 2.2). Moreover, the F3 generation yielded a low proportion of mice with the desired genotypes: only 5% of mice were truly Foxp3YFP-CreInsrfl/fl, and 14% truly Foxp3YFP- CreInsrwt/wt, instead of the predicted 25% each (Table 2.2). Collectively, our experience with the Foxp3YFP-Cre mice highlights the importance of careful breeding and quantitative genotyping at every generation to limit the otherwise high frequency of unwanted spontaneous germline recombination. 34 Figure 2.6 A second cohort reveals spontaneous ectopic Cre-mediated recombination as early as the first generation of breeding. Breeding scheme to generate a second cohort of Foxp3YFP-CreInsrfl/fl mice (experimental) and Foxp3YFP-CreInsrwt/wt mice (control). Circles represent females, squares represent males, and diamonds represent mice of either sex; shading indicates the number of Insrfl alleles. The expected genotype ratios, assuming no ectopic recombination, are shown. 35 2.4 Discussion The Foxp3YFP-Cre mouse strain is a mainstay for generating Treg-specific gene knockout mice. Currently, four Foxp3-Cre mice are available from the Jackson Laboratory: (1) B6 Foxp3-EGFP-hCre and (2) NOD Foxp3-EGFP-hCre mice [118], both expressing an EGFP-hCre fusion protein driven by a transgenic Foxp3 promoter; (3) B6 Foxp3YFP-Cre mice with YFP-Cre knocked-in at the endogenous Foxp3 gene [114]; and (4) B6 Foxp3EGFP-Cre-ERT2 mice, requiring tamoxifen to induce Cre activity [119]. Both transgenic and knock-in Foxp3-driven Cre mice are widely used today, with 137 citations and 370 citations on PubMed, respectively, at the time of writing. We selected Foxp3YFP-Cre mice instead of transgenic Foxp3-EGFP-hCre mice to avoid any unexpected phenotypes as a result of random transgene integration of the latter mouse. However, unlike the transgenic Foxp3-EGFP-hCre mice which can be used as hemizygous animals, the female Foxp3YFP-Cre/YFP-Cre mice require homozygous Cre expression to achieve recombination in 100% of their Tregs, as the YFP-Cre is located in the X chromosome downstream of the endogenous Foxp3 coding sequence. To generate female Foxp3YFP-Cre/YFP-Cre mice, we set up our breeding cross using female Foxp3YFP-Cre/YFP-Cre mice and male Insrfl/fl mice, even though evidence suggests a number of other Cre driver strains display increased ectopic recombination frequency when the Cre allele is inherited maternally, presumably due to residual pre-zygotic expression of Cre [120-122]. This is supported by our observation that half of our F1-generation male offspring already exhibited some degree of ectopic Cre recombination in their ear tissue. However, a breeding scheme in which the YFP-Cre allele is only transmitted paternally, would produce female offspring with InsR-knockout in only 50% of their Tregs. Although we did not switch to such a breeding scheme, because we were able to select breeding 36 Table 2.2 Genotype ratios of all mice in the second breeding cohort from Figure 6. Using a qPCR genotyping strategy, mice with >40% ear tissue gDNA containing ectopic recombination were considered Insrrcb; ectopic recombination within 5–40% of ear tissue gDNA was considered Insrfl-r; and <5% ectopic recombination was considered normal (Insrfl). F1 Male (n=18) Female Total (n=18) Foxp3YFP-Cre/yInsrwt/fl 9 (50%) No Data 9 (50%) Foxp3YFP-Cre/yInsrwt/fl-r 9 (50%) No Data 9 (50%) F2 Male (n=66) Female (n=53) Total (n=119) Foxp3YFP-CreInsrwt/fl 8 (12%) 8 (15%) 16 (13%) Foxp3YFP-CreInsrwt/fl-r 23 (35%) 18 (34%) 41 (34%) Foxp3YFP-CreInsrwt/wt 29 (44%) 21 (40%) 50 (42%) Foxp3YFP-CreInsrwt/rcb 6 (9%) 6 (11%) 12 (10%) F3 Male (n=81) Female (n=86) Total (n=167) Foxp3YFP-CreInsrfl/fl 4 (5%) 4 (5%) 8 (5%) Foxp3YFP-CreInsrfl/fl-r 5 (6%) 12 (14%) 17 (10%) Foxp3YFP-CreInsrfl/rcb 11 (14%) 4 (5%) 15 (8%) Foxp3YFP-CreInsrwt/fl 4 (5%) 7 (8%) 11 (7%) Foxp3YFP-CreInsrwt/fl-r 34 (42%) 39 (45%) 73 (43%) Foxp3YFP-CreInsrwt/wt 11 (14%) 11 (13%) 22 (13%) Foxp3YFP-CreInsrwt/rcb 12 (15%) 9 (10%) 21 (12%) F4 (KO) Male (n=88) Female (n=75) Total (n=163) Foxp3YFP-CreInsrfl/fl 43 (49%) 36 (48%) 79 (48%) Foxp3YFP-CreInsrfl/fl-r 45 (51%) 39 (52%) 84 (52%) 37 mice with least ectopic recombination using our qPCR genotyping method, such a scheme might be appropriate when only male Treg-specific knockout mice are required. Dose-dependent Cre toxicity is not a concern in female Foxp3YFP-Cre/YFP-Cre mice somatic cells including Tregs, perhaps due to X-inactivation [123]. In our experience and as reported by the original paper [114], Tregs comprise ~15% of CD4+ cells in the spleen in female Foxp3YFP-Cre mice, similar to wild-type mice, suggesting that knock-in YFP-Cre does not affect Treg development. It is possible that transient YFP-Cre expression in homozygous female gametes and in early embryos prior to X-inactivation, favouring ectopic Cre recombination in homozygous females or their offspring. However, our data suggest otherwise. Across all generations, genotype ratios with recombined Insr alleles were not different between males and females (Table 2.2). Quantitative PCR on the ear tissue lysates revealed that, other than the offspring Foxp3YFP-CreInsrwt/wt mice, all mice generated in the second experimental cohort displayed varying degrees of ectopic recombination, ranging from 2-65% of total gDNA (Table 2.2). For this reason, the qPCR genotyping strategy is superior to the conventional PCR genotyping methods, allowing accurate and stringent selection for mice with least ectopic recombination. Based on the qPCR results, we arbitrary assigned mice with <5% ectopic recombination to be Foxp3YFP-CreInsrfl/fl; mice with >40% ectopic recombination to be Foxp3YFP-CreInsrwt/rcb or Foxp3YFP-CreInsrfl/rcb; mice with 5-40% ectopic recombination to be Foxp3YFP-CreInsrwt/fl-r or Foxp3YFP-CreInsrfl/fl-r. While the Insrrcb allele was likely transmitted vertically (i.e. the recombination took place in the parents), the Insrfl-r genotype was likely a result of ectopic recombination during embryonic development. We did not record any mice with Foxp3YFP-38 CreInsrrcb/fl-r genotype (>65% ectopic recombination); these mice probably could not survive due to the insufficient amount of InsR. The extent to which ectopic Cre-mediated recombination occurs when generating Foxp3YFP-Cre mice harbouring other floxed genes appears to vary. Mice obtained by crossing Foxp3YFP-Cre mice to Rosa26fl-Stop-fl-YFP reporter mice, showed varying degrees (2–10%) of ectopic recombination in a number of immune cell types, including B cells, T cells, myeloid cells, and bone marrow precursor cells [114]. Moreover, in the progeny of Foxp3YFP-Cre and Cd28fl/fl mice, ~30% of Tconvs were CD28-deficient [113]. In the same study, the authors noted unpublished data in which Foxp3YFP-CreBcl6fl/fl mice also exhibited ectopic recombination [113]. Our mice exhibited ectopic recombination at the germline level, perhaps due to the ubiquitous expression of InsR. Clearly, different floxed target genes may exhibit markedly different sensitivities to ectopic Cre-mediated recombination, so “Cre reporter” mice like Rosa26fl-Stop-fl-YFP mice cannot reflect the true specificity of a given Cre mouse line. Therefore, it is critical that researchers closely monitor target gene recombination across multiple cell and tissue types in their Cre-lox mice. At the very least, during breeding, one should employ PCR primers to detect the recombined allele, in addition to both the floxed and wild-type alleles in ear/tail tissues. Ideally, in the experimental animals, qPCR should be performed with the gDNA from a series of cell types and tissues unrelated to the Cre-driver, to confirm the specific deletion. 39 Chapter 3: Regulatory T cell-specific deletion of the insulin receptor prevents diet-induced and age-associated metabolic syndrome 3.1 Introduction Obesity-associated adipose tissue (AT) inflammation is thought to promote insulin resistance and contribute to the development of type 2 diabetes (T2D). In the visceral AT (VAT) of lean mice, type 2 immune cells are more prevalent, these include regulatory T cells (Tregs), T helper type 2 (Th2) cells, M2 macrophages, eosinophils and group 2 innate lymphoid cells (ILC2s). Diet-induced obesity results in the reduction of the type 2 immune cells and a parallel accumulation of proinflammatory type 1 immune cells such as M1 macrophages in the VAT, producing proinflammatory cytokines such as TNF-α, IL-6 and IFN-γ. These cytokines are thought to directly contribute to insulin resistance and ultimately the development of T2D [124-127]. In brown AT (BAT), type 2 immune cells including M2 macrophages, eosinophils and ILC2s respond to cold stimulation by promoting thermogenic gene expression such as Ucp1 in BAT, thereby enhancing energy expenditure [62, 70, 71, 128]. Healthy thermogenesis in BAT is important for preventing obesity, as shown by mice that lack UCP1 develop obesity spontaneously [58]. Therefore, maintaining a healthy immune composition in both VAT and BAT is important for overall metabolic homeostasis. AT-resident CD4+Foxp3+ Tregs (hereafter Tregs) control local immune homeostasis and systemic metabolism. In lean mice, VAT Tregs have a distinct Th2-like phenotype, with elevated expression of PPARγ, IL-10 and ST2 (IL-33R) [75, 76, 79, 82, 94]. In BAT, Treg number correlate positively with beneficial thermogenesis and energy expenditure, particularly upon cold stimulation [129, 130]. In both mice [76, 79, 94] and humans [131] obese AT has reduced 40 proportions of VAT Tregs, and therapies that reverse obesity-associated loss of VAT Tregs improve systemic insulin sensitivity, supporting a model whereby these tissue-resident cells have systemic metabolic effects [75, 79, 132]. In contrast to the apparently beneficial effects of VAT Tregs in diet-induced obesity, in a model of age-associated insulin resistance, VAT Tregs are deleterious, and their depletion leads to improved insulin sensitivity [83]. Obesity is often associated with hyperinsulinemia. We previously reported that insulin diminished IL-10 production from mouse Tregs via an Akt-mTORC1-dependent mechanism, resulting in diminished suppression of inflammatory cytokine production from myeloid cells [76]. Insulin signals partly through the PI3K-Akt pathway, which inhibit Treg function when the level of signaling is too high [100, 133]. Study of the role of the insulin receptor (InsR) in conventional CD4+ T cells (Tconvs), revealed that insulin signaling promoted their inflammatory function in vivo [97, 102]. However, the role of insulin signaling in Tregs in vivo in the context of normal or elevated levels of insulin was unknown. Here, we shown the specific deletion of InsR in Tregs in two hyperinsulinemia models prevented the development of insulin resistance by contrasting mechanisms. In our diet-induced hyperinsulinemia model, insulin inhibited AT Treg Il10 expression and ST2+ Treg proliferation, and the resulting AT inflammation contributed to the development of insulin resistance; however, in our age-associated hyperinsulinemia model, insulin signaling was required for the maintenance of AT Treg number and function, and it was the lack of AT inflammation that was correlated to the development of insulin resistance. Our findings suggest hyperinsulinemia contribute to the development of insulin resistance in part by modulating AT Treg functions. 41 3.2 Material and methods Experimental animals and diet. All animal protocols were approved by the UBC’s Animal Care Committee. Foxp3YFP-Cre mice (Jackson Laboratory, 016959) and InsRfl/fl mice (Jackson Laboratory, 006955) both on C57/Bl6 background were crossed to generate Foxp3YFP-CreInsRwt/wt mice (control) and Foxp3YFP-CreInsRfl/fl mice (Treg-InsR-knockout). To genotype and select for mice with confirmed Treg-specific InsR deletion, quantitative PCR was performed with ear notch DNA using three sets of primers each specifically amplifying only one allele: wildtype (ACAAACTTTATAGGACTAGTGAGGT, ACCACCATGTCCACCTTTCCC), floxed (GCTTGGCTGGACGTAAACTCCT, ACCACCATGTCCACCTTTCCCA), or recombined (ACCGTGCCTAGAGACTCCAAGAC, CTGAATAGCTGAGACCACAG). Wildtype Ccr5 (CAGGCAACAGAGACTCTTGG, TCATGTTCTCCTGTGGATCG) and Il10rb (ATGATTCCACCCCCTGAGA, GAGCTGTGAAAGTCAGGTTCG) were used as gDNA housekeeping genes. For high-fat-diet studies, 3-week-old male mice were fed a high-fat-high-sucrose diet (HFD, 58 kcal% fat and sucrose, Research Diet, D12331) or a normal chow diet (NCD, Lab Diet, 5P76) diet for 13 weeks. For aging studies, male mice were aged to 52 weeks old on NCD. Each study included two independent cohorts. Insulin and glucose tolerance tests. After 13 weeks of diet, for intraperitoneal glucose tolerance tests (IPGTT), the animals were fasted for 6 h and blood glucose was measured (time 0). Mice were then injected intraperitoneally with 1.4 g/kg glucose (Sigma-Aldrich, G7021), followed by blood glucose measurements at 10, 20, 30, 60, 90 and 120 min. For insulin tolerance tests (ITT), mice were fasted for 4 h, followed by basal blood glucose (time 0) measurement. Mice were then injected intraperitoneally with 1.25 U/kg insulin (Novolin ge Toronto), followed by blood 42 glucose measurements at 10, 20, 30, 60, 90 and 120 min. Blood was sampled from mouse tails, and blood glucose levels were assessed using a OneTouch Ultra Glucometer (Johnson & Johnson). Measurement of metabolic parameters. Following HFD for 13 weeks or NCD for 52 weeks, a subset of mice were singly housed in metabolic cages designed to mimic home-cage environment (LabMaster TSE Systems) for 3 consecutive days. O2 consumption and CO2 production were measured via an open circuit indirect calorimetry system, with sensors sampling air from each cage once every 15 min to obtain individual measurements of respiratory function and food and water intakes. Metabolic activity (respiratory exchange ratio, RER) was calculated from the ratio of VCO2 (ml/h) produced to VO2 (ml/h) consumed throughout the day for each mouse. Food/water intakes were monitored every 15 minutes through weight sensors directly associated with food/water dispensers. Mice were allowed 2 days to acclimatize to the metabolic cage conditions. Body composition analysis. Animal body composition was measured in conscious animals using quantitative magnetic resonance technology, which distinguishes differential proton states between lipids, lean tissues and free water (EchoMRI-100 Echo Medical Systems). Insulin ELISA. Blood was drawn from mice fasted for 4 h from the saphenous vein. Heparinized blood was centrifuged at 2,000 g for 15 min at 4°C to separate plasma. Insulin was quantified using STELLUX Chemiluminescent Immunoassays (ALPCO, 80-INSMR-CH01). 43 AT processing. Brown AT and visceral AT from each animal were minced and digested in 0.5 mg/mL collagenase II (Worthington Biochemical, LS004176) at 37°C for 45 min, passed through a 100 μm cell strainer, and centrifuged to obtain Stromal Vascular Fraction (SFV) cells. SVF cells were resuspended in 1 mL red blood cell lysis buffer (0.8% NH4Cl, STEMCELL Technologies, 07800), incubated for 5 min at room temperature, washed with FACS buffer (PBS, 0.5% BSA, 2 mM EDTA), and stained for flow cytometry. A portion of omental AT was homogenized and lysed in gentleMACS M tubes using the gentleMACS Dissociator (Miltenyi Biotec) for RNA analysis. Flow cytometry. For phenotypic analysis, cells were acquired on an LSRFortessa X-20 (BD Biosciences) and analyzed using FlowJo V10 (Tree Star). Antibodies used were listed in Table 3.1. To isolate Tregs and Tconvs, flow cytometric sorting was performed on a FACSAria II (BD Biosciences). Tregs were sorted as CD45+CD4+Foxp3(YFP)+ cells and Tconvs were sorted as CD45+CD4+Foxp3(YFP)- cells. For quantifying the relative amount of phosphorylation on Akt Ser473 following insulin treatment, total splenic CD4+ cells were rested in RPMI with no serum for 4 h (time 0), then stimulated with up 10 µg/ml insulin for up to 30 min. The cells were immediately fixed at time 5, 10, 30 min and the stained using anti-Akt AF647 (pS473, BD 561670, clone M89-61). Immunoblotting. FACSorted splenic Tconvs and Tregs were stimulated with 1:1 anti-CD3/CD28 beads (ThermoFisher, 11452D) for 3 days, then lysed in Laemmli lysis buffer (1% SDS, 10% glycerol, 0.1 M Tris pH 6.8) and boiled for 10 min. The lysates were then sonicated 44 Table 3.1 Antibodies used for flow cytometry. Marker Fluorochrome Company Catalogue Clone CD45 V500 BD 561487 30-F11 NK1.1 PE BD 557391 PK136 Siglec-F BV421 BD 562681 E50-2440 CD3 BV605 BioLegend 100237 17A2 CD19 BV650 BioLegend 115541 6D5 CD4 BV785 BD 563727 RM4-5 CD8a PE-Dazzle594 Biolegend 100762 53-6.7 CD11c APC BD 340544 HL3 CD11b PerCP-Cy5.5 eBioscience 45-0112-80 M1/71 CD206 BV711 BioLegend 141727 C068C2 F4/80 PE-Cy7 eBioscience 25-4801-82 BM8 Ki-67 AF700 eBioscience 58-5698-82 SolA15 Foxp3 FITC eBioscience 11-5773-82 FJK-16s ST2 PerCP-eF710 eBioscience 46-9335-82 RMST2-2 LAP PE eBioscience 12-9821-82 TW7-16B4 for 4 min and the protein contents were quantified using BCA assay (ThermoFisher, 23225). Forty µg of lysates were run on an 8% SDS gel and transferred on to a PVDF membrane. InsR was blotted using anti-InsRβ mAb (Cell signaling, 3020) and vinculin was blotted using anti-vinculin mAb (New England Biolabs, 13901). Gene expression analysis. To measure Insr mRNA using quantitative PCR (qPCR), FACSorted splenic Tconvs and Tregs were stimulated with 1:1 anti-CD3/CD28 beads (ThermoFisher, 11452D) for 3 days. RNAs were isolated using OMEGA E.Z.N.A Total RNA Kit (Omega Bio-45 Tek, R6834), reverse transcribed using the qScript cDNA SuperMix (Quanta Bio, 95048) and quantified by SYBR green–based reaction using PerfeCTa SYBR® Green FastMix (Quanta Bio, 95074) on an ABI ViiA 7 Real-Time PCR System (ThermoFisher Scientific). Data were normalized to Sdha and Rpl13a, and fold changes were calculated with the ∆∆CT method. Primers used were InsR (TGTGACATCTGCCCAGGAGCT, GCATAGGAGCGGCGGATCTTTAG), Sdha (CCTAAACATGCAGAAGTCGATGC, GCCTTCTTGCAATACACTCCCC), Rpl13a (CTCAAGGTTGTTCGGCTGAA, CTGTCACTGCCTGGTACTTCCA). Additional mRNA expression was measured using 2 custom nanoString nCounter codesets. A total of 100 ng of AT RNA or 4 μl of unpurified lysate from sorted Tregs was analyzed on a nCounter SPRINT Profiler (nanoString Technologies). Background subtraction, sum normalization, and log2 transformation of mRNA counts were performed. The hierarchical clustering module (public GenePattern server, Broad Institute) was used to generate heat maps and cluster trees by pairwise average linkage according to Spearman’s correlation coefficient. IL-10 CBA. FACSorted splenic Tconvs and Tregs were stimulated with 1:1 anti-CD3/CD28 beads (ThermoFisher, 11452D) for 5 days in the presence or the absence of insulin, and the supernatant from the last day was analyzed for IL-10 using the BD mouse inflammation kit (BD 552364). Statistical analysis of experimental data. Statistical analyses were performed in GraphPad Prism 7. Comparison of gene expression between two groups was performed with the Mann-Whitney test, and for more than two groups, one-way or two-way ANOVA with a Turkey’s test was 46 performed. P values are adjusted for multiplicity; *p < 0.05; **p < 0.01; ***p < 0.001, ****p < 0.0001. Data are shown as mean ± SEM. 3.3 Results 3.3.1 Generation and Verification of Treg-specific InsR-knockout animals To investigate the role of insulin signaling in Tregs in vivo, we crossed Foxp3YFP-cre mice [114] to InsRfl/fl mice [115], using Foxp3YFP-creInsRwt/wt mice (hereafter Foxp3cre mice) as controls. Western blotting and mRNA analysis confirmed Treg-specific deletion of the InsR, with no significant reduction in protein or mRNA expression in Tconvs (Figure 3.1A&B). To verify that insulin signaling was impaired in Tregs from Foxp3creInsRfl/fl mice, splenic CD4+ T cells were isolated, stimulated with 10 g/ml of insulin for up to 30 minutes, then analyzed using flow cytometry to measure the amount of Akt phosphorylation at Ser473 in gated FoxP3+ Tregs or FoxP3- Tconvs. Consistent with previous work [76], in Foxp3cre mice the amount of insulin-induced Akt phosphorylation was significantly higher in Tregs than in Tconvs; an effect which was specifically abrogated in Tregs from Foxp3creInsRfl/fl mice (Figure 3.1C). Thus InsR-/- Tregs fail to respond to higher-than-physiological levels of insulin, and although high levels of insulin have been reported to signal via the insulin-like growth factor receptor [134], this possibility did not appear to contribute significantly to the Tregs response to insulin. Further, wild-type but not InsR-/- Tregs produced less IL-10 in response to insulin (Figure 3.1D), confirming the functional deletion of the inhibitory effect of insulin on a key Treg suppressive mechanism. 47 Figure 3.1 Treg-specific InsR deletion in Foxp3creInsrfl/fl mice. (A-B) Tregs and conventional T cells (Tconvs) were sorted from spleens of Foxp3cre and Foxp3creInsrfl/fl mice and stimulated with anti-CD3/CD28 beads for 3 days. (A) Cell lysates were analyzed by immunoblotting for Insulin Receptor (InsR) and (B) by qPCR for Insr mRNA expression. (C) Phosphorylation of Akt Ser473 was measured by flow cytometry in Tconvs or Tregs sorted from spleen and stimulated ex vivo with insulin (10 µg/ml) for 30 min. (D) Tregs sorted from spleen were stimulated with the indicated concentration of insulin and anti-CD3/28 beads for 5 days, then supernatants were analyzed for amount of IL-10. Statistical comparisons of groups were performed using 1-way ANOVA and Tukey’s test with multiple comparisons (B) or 2-way ANOVA and Turkey’s test with multiple comparisons (C&D). Data are shown as mean ± SEM; n=3 per group, analyzed in 3 independent experiments. *p<0.05, **p<0.01, ****p<0.0001. 48 3.3.2 Treg-specific InsR-knockout mice are protected from diet-induced metabolic syndrome To explore the role of insulin signaling in Tregs in the context of diet-induced obesity and hyperinsulinemia, 3-week-old male Foxp3cre and Foxp3creInsRfl/fl mice were fed a high-fat-diet (HFD) or a normal chow diet (NCD) for 13 weeks. All HFD mice became obese with no difference between Foxp3cre and Foxp3creInsRfl/fl mice in body weight or fat versus lean mass body composition (Figure 3.2A&B). Regardless of genotype, all HFD mice developed hyperinsulinemia, indicating that Treg-specific InsR deletion did not affect systemic insulin levels (Figure 3.3A). Whereas HFD Foxp3cre mice developed glucose intolerance and insulin resistance, HFD Foxp3creInsRfl/fl mice had significantly improved glucose and insulin tolerance tests (GTT and ITT, respectively), with the latter returning to normal (Figure 3.3B&C, Figure 3.2C). To dissect the underlying reasons contributing to the differences in GTT and ITT, insulin secretion was quantified during the first 20 minutes of the GTT, revealing no difference between HFD Foxp3cre and Foxp3creInsRfl/fl mice. Thus the difference in GTT was not due to differential beta cell function (Figure 3.2D). Additionally, a subset of HFD Foxp3cre and Foxp3creInsRfl/fl mice were placed in metabolic cages to measure oxygen consumption, carbon dioxide production, heat production, cumulative food and water intake. These data revealed no difference between genotypes indicating that differences in GTT were not due to a shift in oxidative substrate preference, increased heat production or altered food/water intake (Figure 3.4). 49 Figure 3.2 Additional metabolic characteristics of the high-fat-diet cohort. Male Foxp3cre and Foxp3creInsrfl/fl mice were fed on a high-fat-diet or normal chow diet for 16 weeks. (A) Weight (n=10 per group). (B) Fat and lean mass measured by EchoMRI at week 16 (n=4-8 per group). (C) Area-under-the-curve of glucose and insulin tolerance tests at Week 16 (n=5-20 per group). (D) Insulin secretion following glucose injection in glucose tolerance test at Week 16 (n=5-8 per group). Statistical comparisons of groups in B and C were performed using 1-way ANOVA and Turkey’s test with multiple comparisons. Data are shown as mean ± SEM, tested in 2 independent experiments. *p<0.05. 50 Figure 3.3 Foxp3creInsrfl/fl mice are protected from diet-induced metabolic syndrome. Male Foxp3cre and Foxp3creInsrfl/fl mice were fed a high-fat-diet (HFD) or normal chow diet (NCD) from 3 weeks of age. Metabolic parameters were measured in 16-20-week-old mice. (A) Fasting serum insulin levels (n=4-5 per group). (B) Glucose and (C) insulin tolerance tests (n=5-20 per group). Statistical comparisons of groups were performed with 1-way ANOVA and Turkey’s test with multiple comparisons using area-under-the-curve shown in Figure 3.2C. (D) Immune-profiling of visceral and brown ATs measured by flow cytometry with gating shown in Figure 3.5. ATMs, adipose tissue macrophages. Numbers are mean of n=5-10 per group. Gene expression 51 measured by nanoString in (E) Tregs sorted from the visceral AT (H) unfractionated brown AT (n=3). (F) Proportion, absolute number, and (G) phenotype of Tregs within CD4+ T cells in brown AT (n=3-10 per group). Statistical comparisons of groups in A, E, F, G and H were performed using 1-way ANOVA and Turkey’s test with multiple comparisons. Data are shown as mean ± SEM, tested in 2 independent experiments. *p<0.05, **p<0.01. 3.3.3 In diet-induced hyperinsulinemia, InsR-deficient Tregs have increased proliferation and function To explore the possibility that the metabolic improvements were due to changes in immune modulation, the immune composition in spleen, hypothalamus, pancreatic lymph nodes, liver, BAT and VAT were examined (Figure 3.5). Among HFD mice, no compositional changes were observed in spleen, hypothalamus, pancreatic lymph nodes or liver (data not shown). Surprisingly, there was also no change in the VAT, with equal proportions and absolute numbers of all immune cell subsets analyzed, including ST2+ Tregs. However, the VAT from HFD Foxp3creInsRfl/fl mice did express significantly lower levels of a variety of inflammatory genes (Figure 3.6A) and consistent with in vitro data in Figure 1D, VAT Tregs from HFD Foxp3cre but not Foxp3creInsRfl/fl mice had reduced expression of Il10 (Figure 3.3E). In contrast to the minimal effect on immune cell composition in VAT, in the BAT of HFD Foxp3creInsRfl/fl mice the proportions of eosinophils and ST2+ Tregs were increased (Figure 3.3D). Specifically, both the proportions and absolute numbers of ST2+ Tregs were ~doubled in Foxp3creInsRfl/fl versus Foxp3cre mice (Figure 3.3F). Further examination of BAT Tregs revealed that Foxp3creInsRfl/fl mice had elevated proportions of LAP+ and Ki67+ cells within the ST2+ but not ST2- Tregs (Figure 3.3G). HFD also resulted in increased expression of inflammatory genes (e.g. Ifng, Tnf, Ccl2, Ccl7 and Ccl8) in BAT of Foxp3cre mice, an effect which was significantly 52 Figure 3.4 Metabolic parameters of the high-fat-diet cohort. Male Foxp3cre and Foxp3creInsrfl/fl mice were fed on a high-fat-diet or normal chow diet for 13 weeks, then singly housed in metabolic cages designed to mimic home-cage environment for 3 consecutive days. Oxygen consumption, carbon dioxide production, heat production, cumulative food and water intake were measured (n=8 per group). Data are shown as mean ± SEM, tested in 2 independent experiments. 53 Figure 3.5 Gating strategy for immune-profiling of brown and visceral adipose tissues using flow cytometry 54 Figure 3.6 Gene expression assays measured by nanoString nCounter. (A) Unfractionated VAT. (B) Unfractionated brown AT. Statistical comparisons of groups in were performed using 1-way ANOVA and Turkey’s test with multiple comparisons. Data are shown as mean ± SEM; n=3-5 per group, tested in 1 experiment. *p<0.05, **p<0.01, ***p<0.01, ****p<0.0001. 55 dampened in Foxp3creInsRfl/fl mice (Figure 3.3H, Figure 3.6B). These results show that Tregs are directly affected by diet-induced hyperinsulinemia in vivo and that, surprisingly, the negative effects of insulin on Tregs seemed to primarily manifest in BAT rather than VAT. 3.3.4 Treg-specific InsR-knockout mice are protected from age-associated metabolic syndrome To further explore the metabolic consequences of insulin signaling in Tregs we next examined mice which had age-associated, rather than diet-induced, insulin resistance and hyperinsulinemia. NCD Foxp3cre and Foxp3creInsRfl/fl mice were aged to 52 weeks, and all developed hyperinsulinemia (Figure 3.7A). Although the two groups had no difference in weight or fat versus lean mass body composition (Figure 3.8A&B), Foxp3creInsRfl/fl mice had significantly improved glucose tolerance and insulin sensitivity (Figure 3.7B&C, Figure 3.8C). As with the HFD mice, these improved metabolic parameters were not related to changes in insulin production, oxidative substrate preference, increased heat production or food/water intake (Figure 3.8D & Figure 3.9). There were also no differences in the immune compositions in spleen, hypothalamus, pancreatic lymph nodes or liver (data not shown). 3.3.5 In age-associated hyperinsulinemia, InsR-deficient Tregs have diminished proliferation and function In contrast to findings in HFD mice, aged Foxp3creInsRfl/fl had significant changes in the immune composition of the VAT but not the BAT (Figure 3.7D). However, contrary to our hypothesis, VAT Treg proportions and numbers, including both ST2+ and ST2- cells were significantly 56 Figure 3.7 Foxp3creInsrfl/fl mice are protected from age-associated metabolic syndrome. Male Foxp3cre and Foxp3creInsrfl/fl mice were fed on normal chow diet and metabolic parameters were measured in 52-60-week-old mice (A) Fasting serum insulin levels. Dash line presents the mean of serum insulin levels measured in healthy and young (16-week-old) mice (n=7). (B) glucose and (C) insulin tolerance tests. Statistical comparisons of groups were performed with 1-way ANOVA and Turkey’s test with multiple comparisons using area-under-the-curve shown in Figure 3.8C. (D) Immune-profiling of visceral and brown ATs measured by flow cytometry with gating shown in Figure 3.5. ATMs, adipose tissue macrophages. Numbers are the mean of n=10 per 57 group. (E) Proportion and absolute numbers of Foxp3+ Tregs with CD4+ T cells from visceral AT. (F) Proportion and absolute numbers of ST2+ cells within Foxp3+ T cells in visceral AT. Gene expression measured by nanoString in (G) Tregs sorted from the visceral AT or (H) in unfractionated visceral AT (n=3-6 per group). Statistical comparisons of groups were performed with the Mann–Whitney test. Data are shown as mean ± SEM; For B, C, D, E and F, n=10 per group; tested in 2 independent experiments. *p<0.05. reduced in Foxp3creInsRfl/fl versus Foxp3cre mice (Figure 3.7E-F). This loss of Tregs in Foxp3creInsRfl/fl mice happened gradually over time and was only observed in the VAT and not in the spleen (Figure 3.10). Additionally, compared to those in aged Foxp3cre mice, VAT Tregs in aged Foxp3creInsRfl/fl mice expressed lower levels of transcripts associated with Treg function (Foxp3, Il2ra, Pdcd1, Il10, Icos, Gzmb, Il10ra, Ikzf2) and with the VAT-Tregs specific signature (Il1rl1, Klrg1, Dgat1, Pparg, Areg, Prdm1, Nr4a1, Adipor2) [94] (Figure 3.7G). Consistent with the apparent reduced Treg function, VAT from Foxp3creInsRfl/fl mice had increased levels of inflammatory transcripts, including Ifng, Tnf, Il1b, Tlr2, Slamf7, Ccl2, Ccl7 and Ccl8 (Figure 3.7H). Together, these data show that in age-associated hyperinsulinemia, InsR signaling promotes VAT Treg expansion and function, but, consistent with a previous report [83], is this context increased VAT Tregs promote undesirable aging-related insulin resistance. 3.3.6 Mechanisms underlying differential Treg responses to hyperinsulinemia in age-associated versus diet-induced metabolic syndromes To further explore why AT Tregs might be beneficial for diet-induced, but detrimental for aging-associated metabolic syndrome, we examined the gene signature of VAT Foxp3cre Tregs from young (16 weeks) lean or obese (13 weeks HFD), or aged (52 weeks) mice. Hierarchical clustering of VAT Treg signature genes revealed striking differences. Consistent with previous 58 Figure 3.8 Additional metabolic characteristics of the aging cohort. Foxp3cre and Male Foxp3creInsrfl/fl mice were fed on a normal chow diet for 52 weeks. (A) Weight (n=3-6 per group). (B) Fat and lean mass measured by EchoMRI (n=3-6 per group). (C) Area-under-the-curve of glucose and insulin tolerance tests at Week 52 (n=10). (D) Insulin secretion following glucose injection in glucose tolerance test at Week 52 (n=4-8 per group). Statistical comparisons of groups in C were performed using 1-way ANOVA and Turkey’s test with multiple comparisons. Data are shown as mean ± SEM, tested in 2 independent experiments. *p<0.05. 59 Figure 3.9 Metabolic parameters of the aging cohort. Male Foxp3cre and Foxp3creInsrfl/fl mice were fed on a normal chow diet for 52 weeks, then singly housed in metabolic cages designed to mimic home-cage environment for 3 consecutive days. Oxygen consumption, carbon dioxide production, heat production, cumulative food and water intake were measured (n=3-4 per group). Data are shown as mean ± SEM, tested in 1 experiment. 60 Figure 3.10 Specific deletion of InsR in Tregs leads to gradual loss of Tregs and ST2+ Tregs in the visceral AT but not the spleen. Male Foxp3cre and Foxp3creInsrfl/fl mice were fed on a normal chow diet for 52 weeks. Flow cytometric quantification of Treg and ST2+ Treg populations in (A) the visceral AT or (B) the spleen. Statistical comparisons of groups were performed using 2-way ANOVA and Turkey’s test with multiple comparisons. Data are shown as mean ± SEM; n=3-6 per group, tested in 1 experiment. *p<0.05. 61 reports [94], in comparison to young lean mice, VAT Tregs from young obese mice expressed lower-than-normal levels of Il2ra, Il1rl1, Il10 and Pparg, but largely retained an expected pattern of Treg gene expression, with high expression of Foxp3 and Ikzf2 (Helios) and functionally-relevant genes, such as Areg, Pdcd1 (PD1), Tbfb1 and Lrrc32 (GARP). On the other hand, VAT Tregs from aged mice had diminished expression of many Treg-related genes including Foxp3, Ikzf2, Lrrc32, Il2ra, Il1rl1, but normal levels of Il10 (Figure 3.11A). Moreover, expression of many other obesity-related and VAT-specific signature genes were different in VAT Tregs from obese versus aged mice, showing that in both cases VAT Tregs were dysfunctional, but in different ways. The distinct features of obesity versus age-associated metabolic dysfunction were further supported by differential gene expression analysis in unfractionated VAT of Foxp3cre mice (Figure 3.11B). For example, obese VAT had increased levels of Ifng, Tlr2 and Tlr4, whereas aged VAT had increased levels of Tnf, Il6, Ccl2, Ccl7, Ccl8 (Figure 3.11B). Thus while AT inflammation was present in both types of metabolic syndrome, the types of cytokines and immune cells were distinct. Together, our data show that under conditions of hyperinsulinemia, intrinsic insulin signaling to Tregs contributes to the development of both diet-induced and age-associated metabolic syndromes (Figure 3.11C). In the context of diet-induced obesity, insulin signaling inhibited Il10 expression in VAT Tregs. In contrast, in aging, insulin promoted expansion of VAT Tregs. This differential effect of Treg-intrinsic insulin receptor signaling directly correlated with levels of AT inflammation: the reduction in AT Tregs in diet-induced obese mice correlated with higher AT inflammation, whereas the expansion of AT Tregs in aged mice correlated with reduced AT inflammation. 62 Figure 3.11 Treg and VAT gene signatures differ in age-associated and diet-induced metabolic syndrome. Visceral AT Tregs or unfractionated visceral AT was isolated from young (16 weeks old), obese (high-fat-diet for 13 weeks), or aged (52 weeks old) Foxp3cre mice. mRNA was isolated and analyzed by nanoString to measure expression of genes associated with Tregs and adipose inflammation. Hierarchical clustering of gene expression data from (A) visceral AT Tregs or (B) unfractionated visceral AT. Each column represents one animal. Additionally, statistical comparisons of single gene expression were performed using 1-way ANOVA and Turkey’s test with multiple comparisons; #<0.05 between the aged and obese groups, †<0.05 between the young 63 and aged groups, ‡<0.05 between the young and obese groups. (C) Schematic diagram summarizing the differential role of insulin signaling in VAT Tregs in the context of diet-induced versus age-associated metabolic syndrome. 3.4 Discussion Extensive work in diet-induced metabolic syndrome has revealed the critical role for AT Tregs at the interface between metabolism and inflammation [135]. The VAT-specific Treg phenotypes, such as the elevated expression of Il10, Pparg, Il1rl1, are required for Tregs to maintain the local immune homeostasis and overall healthy metabolism [94]; the mechanisms driving loss of the VAT-Treg phenotype was unknown . Here we found that one mechanism driving AT Treg dysfunction is insulin: it causes reduced expression of IL-10 restrained expansion of ST2+ cells. These effects were prevented by Treg-specific InsR deletion, resulting in decreased AT inflammation and attenuation of metabolic dysfunction. In contrast to the well-characterized role of AT Tregs in obesity, their role in age-associated metabolic syndrome is largely unexplored. Age-associated insulin resistance is associated with excess VAT, since reduction of VAT mass through surgical means [89], or caloric restriction [90] improves metabolic parameters. Tregs accumulate in VAT with aging [83, 94] but paradoxically were reported to be detrimental since their removal improved overall insulin sensitivity [83]. We confirmed and extended this finding by showing that their expansion is controlled by InsR signaling. Evidence that Tregs-specific InsR deletion blocked Treg expansion and exacerbated VAT inflammation, but ameliorated glucose intolerance and insulin resistance supports the notion that least some aspects of inflammation are favourable in ageing. 64 Indeed proinflammatory signaling in adipocytes is required for tissue remodeling and storage of excess nutrients [136]. Regulation of Tregs by receptor tyrosine kinase signaling is a relatively unexplored area of research. In cancer settings, EGFR-expressing Tregs respond to amphiregulin with enhanced function [137, 138] and similarly stimulation via VEGFR promotes Treg proliferation [139]. Two other studies have investigated the role of InsR in T cells and reported that insulin signaling boost T cell function in the context of adaptive immunity [97, 102]. The first study made use of an inducible InsR-knockdown rat, which express a small interfering RNA upon treatment with doxycycline, leading to complete knockdown of InsR in most organ [102]. In this model, 5 days of whole-body InsR silencing led to metabolic dysfunction including hyperglycemia and impaired glucose uptake in most tissues. Both CD4+ Tconvs and CD8+ cytotoxic T cells from the InsR-knockdown rats had decreased function when stimulated in vitro [102]. Focusing on Tregs, the study found no change in the proportions of Tregs in vivo or Treg function in vitro, following 5-day doxycycline treatment. [102]. A whole-body InsR silencing model has a number of weaknesses with respect to studying the role of InsR signaling on a specific cell type; and a 5-day silencing regime looks at the only acute effect of InsR loss. The second study utilized T cell-specific InsR-knockout mice and reported these mice had reduced Tconv and cytotoxic cell effector function in a viral infection model [97]. The impair T cell function was due to decreased cellular metabolism in the T cells lacking InsR [97]. Taken together, InsR signaling seems to promote pro-inflammatory functions in non-Treg T cells. In conclusion, excessive insulin signaling in Tregs leads to unfavourable outcomes in diet-induced and age-associated insulin resistance. These data further support the hypothesis that hyperinsulinemia has a causal role in the development of T2D, a concept supported by evidence 65 that hyperinsulinemia precedes and promotes obesity and insulin resistance [60, 104], and prevention of hyperinsulinemia induces weight loss [105, 106]. Insulin reduction prevents both diet-induced and age-associated insulin resistance [103, 104, 107], and in conjunction with our data, this effect is likely partially mediated via altered AT Treg expansion/function and consequent changes in AT inflammation. Therapeutic approaches to prevent the deleterious effects of InsR signaling in Tregs may be a new approach to limit the pathological effects of hyperinsulinemia. 66 Chapter 4: Characterization of Tregs in obese omental adipose tissue in humans 4.1 Introduction Obesity-associated visceral adipose tissue (AT) inflammation is thought to promote insulin resistance and contribute to the development of type 2 diabetes (T2D). In lean mice, immune cells resident in visceral AT are typically associated with type 2 immunity, and include regulatory T cells (Tregs), T helper type 2 (Th2) cells, M2 macrophages, group 2 innate lymphoid cells, and eosinophils. Conversely, visceral AT in mice fed with a high fat diet is characterized by a reduction in type 2 immune cells and a parallel accumulation of pro-inflammatory M1 macrophages and cytokines, such as IL-6, TNF-α, CCL2, IFN-γ, and leptin. These inflammatory cytokines are thought to directly contribute to insulin resistance and ultimately the development of T2D [124-127]. Of particular interest is the role of Tregs in maintaining metabolic homeostasis. Evidence that Treg depletion results in AT inflammation and insulin resistance in mice [75, 140] suggests that these cells may be central to maintaining the normal complement of type 2 immune cells. Notably, in mice, visceral AT Tregs acquire a Th2-like phenotype distinct from their peripheral counterparts, with heightened expression of Gata3, Ccr4, and Il10 [94]. This Th2-like phenotype seems to be driven by the IL-33/ST2 pathway, since the majority of visceral AT Tregs in lean mice express the IL-33 receptor ST2, and in vitro or in vivo administration of IL-33 promotes their Th2-like, anti-inflammatory function that protects mice from diet-induced insulin resistance [79, 132]. Consistent with this concept, ST2- or IL-33-deficient mice have reduced visceral AT Tregs and impaired glucose tolerance even when fed a normal chow diet [79]. In vitro, ST2-67 deficient Tregs are as suppressive as their wildtype counterparts in the presence of IL-33; in vivo, however, ST2-deficient Tregs are unable to prevent adoptive T cell-induced colitis [141], suggesting that the IL-33/ST2 axis is critical for Treg proliferation and maintenance in mice. In contrast to these well-established paradigms in mice, the immune landscape of human omental AT is unclear. Limited evidence suggests there is a positive correlation between obesity and immune cell counts in omental but not subcutaneous AT [142]. Focussing on macrophages, the lack of definitive M1 versus M2 markers in human AT macrophages has yielded inconsistent reports on macrophage polarization in obesity. Flow cytometry has been used to show that CD11c+ monocytes accumulate in obese omental AT [142], but CD11c itself is insufficient to identify M1 macrophages in human AT because most macrophages in the crown-like structures of human omental AT also express high levels of CD206 [143]. Notably, CD206 is traditionally considered an M2 marker in mice; in humans, however, it also marks pro-inflammatory macrophages in omental AT [143-145]. Accordingly, the number of CD11c+CD206+ pro-inflammatory macrophages in omental AT has been positively correlated to insulin resistance [143]. On the other hand, another study used immunohistochemistry to show that, compared to normal-weight subjects, obese individuals had increased numbers of CD40+ cells in omental AT, whereas the number of CD206+ cells were unchanged [146]. Overall, more studies are needed to investigate how AT macrophages change in obese humans. Focusing on T cells, there is also no clear evidence that either the proportion or the phenotype of T cells changes with obesity in humans [142, 147, 148]. There are reports of omental AT T cells skewing towards a Th1/Th17 phenotype, with a parallel decrease in Th2-associated cytokines and transcription factors [147, 149-151]. For AT Tregs, there are even fewer studies, with both negative and positive correlations found in small cohorts. Some reports 68 found negative correlations between the amount of FOXP3 mRNA and obesity or insulin resistance [75, 140, 147], whereas others reported positive correlations [148, 152, 153]. To date, only two studies examined human omental AT Tregs using flow cytometry [154, 155]. Moreover, there are no reports on the phenotype of human omental AT Tregs, leading to a gap in knowledge about whether human AT Tregs possess a unique, tissue-specific phenotype that could lead to AT-specific mechanisms of immune regulation. Here we aimed to characterize the immune landscape of human omental AT, examining in particular how the phenotype and frequencies of macrophages and CD4+ T cell subsets change with obesity and in the presence or absence of T2D. 4.2 Materials and methods Patients. Human samples were collected according to protocols approved by the University of British Columbia Clinical Research Ethics Board, including written informed consent. For omental AT, 29 adults undergoing bariatric surgery between 2014 and 2016 were recruited from the Bariatric Surgery Program at Richmond Hospital. Demographic data including age, sex, weight, height, calculated BMI, waist circumference, fasting glucose, and A1C levels were recorded at the time of surgery. A lipid profile (triglycerides, total cholesterol, low-density lipoprotein (LDL) and high-density lipoprotein (HDL)) was obtained prior to surgery (Table 4.1). Omental AT from 6 non-obese controls was collected during hernia repair or laparoscopic cholecystectomy (Table 4.2). For comparisons between omental AT Tregs and blood Tregs, peripheral blood mononuclear cells (PBMCs) were collected from 22 patients with T2D and 10 non-diabetic age-matched controls (Table 4.3). T2D PBMCs were from participants consented under protocols approved by the Benaroya Research Institute Institutional 69 Table 4.1 Characteristics of cohort undergoing bariatric surgery. No T2D (n=11) T2D (n=18) P-value Characteristics Sex – female n (%) 9 (81.8) 15 (83.3) Duration of diabetes to surgery – mean in years (SD) N/A 11.4 (8.2) Age at time of surgery – mean in years (SD) 45.7 (9.7) 47.5 (10.7) 0.81 BMI – mean in kg/m2 (SD) 47.9 (6.9) 48.4 (9.7) 0.99 Waist circumference – mean in cm (SD) 129.9 (12.8) 139.3 (15.1) 0.17 A1C – mean in percentage (SD) 5.4 (0.5) 7.4 (1.2) <0.0001 Fasting glucose – mean in mmol/L (SD) 5.7 (0.7) 8.3 (2.3) <0.001 Lipid Profiles – mean in mmol/L (SD) Total Cholesterol 4.78 (0.71) 4.39 (0.76) 0.24 LDL 2.69 (0.69) 2.34 (0.66) 0.19 HDL 1.29 (0.19) 1.23 (0.49) 0.16 Non-HDL 3.48 (0.87) 3.11 (0.75) 0.15 Triglyceride 1.75 (0.73) 1.82 (0.90) 0.75 Review Board. For comparing omental AT Tregs and thymic Tregs, thymus tissue discarded from pediatric cardiac surgery was dissociated as described [156], and CD25+CD8- Tregs were isolated by sequential CD25 positive selection and CD8 negative selection using custom EasySep reagents (STEMCELL Technologies) according to the manufacturer’s protocols. CD25-CD8- thymic Tconvs were isolated from the CD25- fraction by CD8 negative selection. Omental AT processing. Approximately 10g of omental AT obtained during bariatric surgery or hernia repair or laparoscopic cholecystectomy was minced and digested in 0.5mg/ml collagenase 70 Table 4.2 Lean cohort characteristics. Non-obese (n=6) Sex - Female n (%) 5 (83.3) Age at time of surgery - Mean in years (SD) 63.9 (2.6) BMI - Mean in kg/m2 (SD) 23.8 (2.8) Waist circumference - Mean in cm (SD) 78.5 (9.8) Fasting glucose - Mean in mmol/L (SD) 5.4 (0.4) A1C - Mean in percentage (SD) 5.3 (0.4) Total cholesterol - Mean in mmol/L (SD) 4.97 (1.06) LDL - Mean in mmol/L (SD) 2.72 (0.86) HDL - Mean in mmol/L (SD) 1.45 (0.70) Non-HDL - Mean in mmol/L (SD) 3.34 (0.99) Triglyceride - Mean in mmol/L (SD) 1.12 (1.06) Table 4.3 Blood collection cohort characteristics. No T2D (n=10) T2D (n=22) P-value Sex - Female n (%) 7 (70.0) 14 (63.6) Duration of diabetes at draw - Mean in years (SD) 3.05 (3.51) Age at time of draw - Mean in years (SD) 43.8 (10.1) 39.9 (11.5) 0.85 BMI - Mean in kg/m2 (SD) No data 36.1 (7.7) A1C - Mean in percentage (SD) No data 7.3 (1.2) II (Worthington Biochemical) at 37oC for 45 min, passed through a 100m cell strainer, and centrifuged to obtain SVF cells. SVF cells were resuspended in 1ml red blood cell lysis buffer (0.8% NH4Cl, STEMCELL Technologies), incubated for 5 min at room temperature, washed with FACS buffer (PBS, 0.5% BSA, 2mM EDTA), and stained for flow cytometry using the 71 Table 4.4 Antibodies used for flow cytometry. Marker Fluorochrome Company Catalogue Clone FVD eF780 eBioscience 65-0865-14 CD45 V500 BD 560777 HI30 CD3 APC eBioscience 17-0038-42 UCHT1 CD4 BV605 BD 562658 RPA-T4 CD25 PE-Cy7 BD 557741 M-A251 CD127 eF450 eBioscience 48-1278-42 eBioRDR5 FOXP3 PE eBioscience 12-4777-42 236A/E7 CD14 FITC BD 555397 M5E2 CD68 PE-Cy7 eBioscience 25-0689-42 eBioY1/82A CD206 PerCP-eF710 eBioscience 46-2069-42 19.2 CD11c APC BD 340544 S-HCL-3 CD19 APC BD 555415 HIB19 ST2 Unconjugated R&D MAB523 97203 IL-2 BV421 BD 564164 MQ1-17H12 IL-17A BV785 BD 563745 N49-653 IFN-γ BB700 BD 566935 B27 antibodies listed in Table 4.4. A portion of omental AT was homogenized and lysed in gentleMACS M tubes using the gentleMACS Dissociator (Miltenyi Biotec). RNA was isolated using an EZNA Total RNA kit (OMEGA Bio-tek). Flow cytometry and cell sorting. For phenotypic analysis, cells were acquired on an LSRFortessa X-20 (BD Biosciences) and analyzed using FlowJo V10 (Tree Star). AT macrophages were gated as CD45+CD14+ cells, then subgated as M1 (CD206+CD11c+) or M2 72 (CD206+CD11c-, Figure 4.1A) [83, 143]. To isolate Tregs and Tconvs, flow cytometric sorting was performed on a FACSAria II (BD Biosciences). Tregs were sorted as CD45+CD3+CD4+CD25hiCD127lo cells and Tconvs as CD45+CD3+CD4+CD25loCD127hi cells (Figure 4.1B) directly into RLT lysis buffer (Qiagen). For intracellular cytokine staining, SVF cells treated with 10µg/ml brefeldin A were either left unstimulated or stimulated with 10ng/ml PMA and 500ng/ml ionomycin for 6 hours, then fixed/permeabilized with the Foxp3 / Transcription Factor Staining Buffer Set (eBioscience) and stained with the antibodies listed in Table 4.4. Gene expression analysis. mRNA expression was measured using a custom NanoString nCounter codeset (49 genes including 5 reference genes). 100ng of adipose tissue RNA or 4l of unpurified lysate from sorted Tregs was analyzed as previously described [157]. Background subtraction, sum normalization, and log2 transformation of mRNA counts were performed prior to analysis. Hierarchical clustering. The hierarchical clustering module (public GenePattern server, Broad Institute) was used to generate heat maps and cluster trees by pairwise average linkage according to Spearman’s correlation coefficient. Statistical analysis. For all correlation analyses between gene expression and clinical parameters, Spearman’s nonparametric correlation was used. Comparison of gene expression between two groups were performed with the Mann-Whitney test, and for more than two groups, 73 Figure 4.1 Gating strategy for flow cytometric analysis of human omental adipose tissue. The stromal vascular fraction of human omental adipose tissue was isolated and mononuclear cells analyzed by flow cytometry. (A) Gating strategy for macrophages. Within single, live CD45+ immune cells, total macrophages were gated as CD14+, within which M1 macrophages were subgated as CD11c+CD206+ cells and M2 macrophages as CD11c-CD206+ cells. (B) Gating strategy for Tregs. Within single, live CD45+ immune cells, T cells were gated as CD3+CD4+. To quantify Tregs, the proportions of CD4+FOXP3+ Tregs or CD4+FOXP3- conventional (Tconv) cells were determined. To isolate Tregs for further analysis, CD25hiCD127lo cells were sorted according to the illustrated gating strategy. To confirm that the majority of FOXP3+ Tregs resided within the CD25hiCD127lo fraction in human omental adipose tissue some samples were also stained for FOXP3. Data are representative of 5 independent samples. 74 one-way ANOVA with a Bonferroni post hoc test was performed. * represents P < 0.05; ** represents P < 0.01; *** represents P < 0.001. Error bars in figures represent the SEM. Validation of ST2 mAb specificity. A monoclonal anti-human ST2 antibody (R&D MAB523) was conjugated to PE by the University of British Columbia Antibody Lab. 293T cells were plated in a 24-well plate at 40,000 cells/ml in media (DMEM, 10% fetal bovine serum, 1% penicillin-streptomycin, 2mM L-glutamine) and transfected with a lentiviral vector encoding human ST2 (pLVX-EF1a-IL1RL1-IRES-emGFP, a gift from Drs. Guy Charron and John Rioux, University of Montreal) using jetPRIME reagent (Polyplus Transfections) according to the manufacturer’s protocol. Cells were collected 48 hours later and stained with the PE conjugated anti-human ST2 antibody. Mononuclear cells from human tonsil samples were stained with the same anti-ST2 antibody. In some cases, tonsil samples were exposed to 0.5mg/ml collagenase II (Worthington Biochemical) at 37oC for 45 min. 4.3 Results 4.3.1 Obesity and T2D alter metabolic gene expression in human omental AT An initial cohort of 29 participants with obesity, 18 diagnosed with T2D and 11 without the disease, were recruited (Table 4.1). The cohort had a range of BMIs, from ~30 to ~70 kg/m2. Participants with or without T2D had a similar sex ratio, age, BMI and blood lipid profiles. As expected, fasting glucose and A1C levels were significantly higher in the T2D group. Omental AT was collected at the time of bariatric surgery and an unfractionated portion was processed for mRNA expression analysis using a NanoString custom codeset. We first quantified expression of key metabolic genes thought to regulate insulin resistance (PPARγ), adipose 75 Figure 4.2 Obesity and T2D alter metabolic gene expression in human omental AT. Omental AT was collected from 29 obese patients undergoing bariatric surgery. RNA was isolated from unfractionated tissue and expression of the indicated genes was measured by NanoString nCounter Gene Expression. Each circle represents one subject. (A) Gene expression data were correlated to A1C, waist circumference, and BMI using Spearman’s rank correlation. (B) Gene expression in subjects with (n=11) or without T2D (n=18). Comparisons of gene expression between two groups were performed with the Mann-Whitney test. Correlations between gene expression and clinical variables were performed with Spearman’s rank correlation. * represents P < 0.05; ** represents P < 0.01; *** represents P < 0.001. Error bars represent the SEM. browning (UCP1), and immune function (adiponectin and leptin), and assessed their correlation to relevant clinical parameters. We found that PPARG, UCP1, and ADIPOQ (adiponectin) all negatively correlated to clinical parameters indicative of obesity, whereas LEP (leptin) exhibited the opposite trend (Figure 4.2A). Similarly, omental AT from patients with T2D had lower levels of PPARG, UCP1, and ADIPOQ but higher levels of LEP compared to non-T2D controls 76 Figure 4.3 Obesity and T2D are associated with increased innate inflammation in human omental adipose tissue. Omental AT was collected from 29 obese patients undergoing bariatric surgery. RNA was isolated from unfractionated tissue and expression of the indicated genes was measured by NanoString nCounter Gene 77 (Figure 4.2B). These data are consistent with the concept that obesity and T2D lead to reduced expression of favourable metabolic genes [125, 158]. 4.3.2 Obesity and T2D are associated with increased innate inflammation in human omental AT Visceral AT inflammation in obesity and T2D has been documented in both humans and mice [125-127, 158]. In humans, although macrophages are known to accumulate in omental AT in severe obesity, there are contradictory results regarding their M1 versus M2 phenotype [159]. In our cohort, we found that BMI positively correlated with expression of several pro-inflammatory cytokines and chemokines in omental AT, including CCL2 (MCP1), IL1B, IL6, and CXCL8 (Figure 4.3A) as well as with the anti-inflammatory cytokines IL10 and IL1RN (IL-1RA) (Figure 4.3B). In addition, BMI positively correlated to innate-cell and functional markers such as ITGAX (CD11c), CD1D, TLR2, and CD86 (Figure 4.3C). Furthermore, omental AT from patients with T2D had higher expression of CD68, TLR4, CD86, and IL10 than non-T2D individuals (Figure 4.3D). Together, these data suggest that obesity and T2D both correlate with increased innate inflammation in human omental AT. Expression. Each circle represents one subject. Gene expression of (A) pro-inflammatory cytokines, (B) anti-inflammatory cytokines, and (C) innate immune cell markers were correlated to BMI. (D) Gene expression in subjects with (n=11) or without T2D (n=18). Comparison of gene expression between two groups were performed with the Mann-Whitney test. Correlations between gene expressions and clinical variables were performed with Spearman’s rank correlation. * represents P < 0.05; ** represents P < 0.01; *** represents P < 0.001. Error bars represent the SEM. 78 4.3.3 M1 macrophages accumulate in the omental AT of patients with T2D To more definitively examine how immune cells change with human obesity, we next used flow cytometry to quantify changes in M1 versus M2 macrophages. Using the gating strategy for M1 versus M2 macrophages developed by Wentworth et al. [143], we gated on live CD45+CD14+ cells in the stromal vascular fraction (SVF), and defined M1 macrophages as CD206+CD11c+ cells and M2 macrophages as CD206+CD11c- cells (Figure 4.4A, Figure 4.1A). Consistent with Wentworth et al. [143], the omental AT of patients with T2D exhibited a significantly higher proportion of macrophages, entirely due to an increase in M1 macrophages as other macrophage subpopulations remained unchanged (Figure 4.4B). Accordingly, M1 macrophage proportions in omental AT also positively correlated to A1C levels (Figure 4.4C). Interestingly, we observed that patient age also affects M1 and M2 macrophage proportions in omental AT, with older age associated with an accumulation of M1 macrophages and a concomitant reduction of M2 macrophages (Figure 4.4D). Overall, individuals with T2D have a significant accumulation of M1 macrophages in their omental AT. 4.3.4 Obesity is associated with decreased Treg proportions in human omental AT To examine how obesity and T2D affect Tregs, we used flow cytometry to quantify and characterize Tregs in human omental AT, using omental AT samples from 6 non-obese patients obtained during hernia repair or laparoscopic cholecystectomy as controls (Table 4.2). As comparators, we used the same panel to quantify Tregs in the blood of healthy controls or 79 Figure 4.4 Flow cytometry analysis of ex vivo macrophage populations from omental AT reveals increased M1 macrophages in T2D. In a subset of subjects (no T2D, n=4; T2D, n=8), a portion of the SVF was analyzed by flow cytometry for monocyte/macrophage populations. Each circle represents one subject. (A) Representative gating strategies for the human AT macrophages, pre-gated on live CD45+ cells. (B) Proportions of the indicated macrophage subpopulations. (C) Proportions of M1 macrophages were correlated to A1C levels. (D) Age-associated changes in macrophage proportions. Comparison of population frequencies between two groups were performed with the Mann-Whitney test. Correlations between population frequencies and clinical variables were performed with Spearman’s rank correlation. * represents P < 0.05; ** represents P < 0.01; *** represents P < 0.001. Error bars represent the SEM. 80 patients with T2D (Table 4.3). Tregs were defined as CD45+CD3+CD4+FOXP3+ cells and conventional T cells (Tconvs) as CD45+CD3+CD4+FOXP3- cells (Figure 4.5A, Figure 4.1B). Proportions of CD3+ and CD3+CD4+ cells were comparable between omental AT and blood, as well as in omental AT among all subjects regardless of obesity or T2D status (Figure 4.5B, top panels). Focusing on Tregs, in non-T2D individuals, Treg proportions were significantly higher in omental AT than in blood, while in patients with T2D, AT Treg proportions became comparable to those of blood (Figure 4.5B, bottom panels). Similarly, non-obese individuals also had higher Treg proportions in omental AT than in the blood of non-T2D individuals (Figure 4.5B, bottom panels). These data suggest that omental AT Treg proportions may progressively decline as obese individuals acquire T2D. This possibility is supported by evidence that BMI negatively correlated with Treg proportions in omental AT and, conversely, positively correlated to CD3+ and Tconv proportions (Figure 4.5C). While Spearman’s correlation testing did not reveal a significant correlation between A1C levels and Tregs in omental AT, there was a trend towards a negative correlation (Figure 4.5D). Similar to our findings with AT macrophages, we observed an aging-related effect in CD4+ T cell populations, with aging promoting Tconv accumulation and reducing Treg frequencies in omental AT (Figure 4.5E). Together these results suggest that increased obesity, especially in conjunction with T2D, is associated with decreased Treg proportions in human omental AT. 4.3.5 Human omental AT Tregs have a tissue-specific phenotype To further characterize human Tregs in omental AT, we sorted CD45+CD3+CD4+CD25hiCD127lo cells in omental AT (Fig. 4.6A; for comparison a typical staining pattern in blood is also shown) 81 Figure 4.5 Flow cytometric analysis of ex vivo Tregs from human omental AT and peripheral blood. In a subset of obese subjects (no T2D, n=9; T2D, n=13) and non-obese controls (n=6), a portion of the SVF was analyzed by flow cytometry for Treg and Tconv frequencies. Data were compared to blood from aged-matched controls with (n=10) or without T2D (n=22). (A) Representative flow cytometry data for Tregs, pre-gated on CD45+CD3+CD4+ cells. (B) Quantification of the indicated T cell populations. (C-D) Obesity-associated changes in T cell populations in the omental fat. (E) Aging-associated changes in CD4+ T cells in omental AT. 82 Comparison of population frequencies between multiple groups were performed with one-way ANOVA with a Bonferroni post hoc test. Correlations between population frequencies and clinical variables were performed with Spearman’s rank correlation. * represents P < 0.05; ** represents P < 0.01; *** represents P < 0.001. Error bars represent the SEM. Similar to blood, the CD25hiCD127lo population contains the majority of the FOXP3+CD25hi Treg cells (Figure 4.1B). We then isolated RNA for analysis with a custom NanoString codeset for Treg-related markers. From 10g of omental AT, we isolated on average ~1000 Tregs per sample, limiting our analysis to mRNA quantification. To compare omental AT Tregs to bona fide Tregs free from potential Tconv contamination, we also isolated Tregs from human thymi for parallel NanoString analysis. Hierarchical clustering revealed that the gene signature of omental AT Tregs was closely related to that of thymic Tregs (Figure 4.6B), with both expressing high levels of FOXP3, IL2RA (CD25), and IKZF2 (HELIOS) transcripts (Figure 4.6C). Human AT Tregs and thymic Tregs also had comparable expression of TBX21 (TBET), GATA3, and RORC (Figure 4.6D), indicating that AT Tregs are unlikely to be poised to produce inflammatory cytokines. The expected negligible production of inflammatory cytokines in AT Tregs was confirmed by intracellular straining for IL-2, IFN- and IL-17A (Figure 4.7). In terms of markers of suppressive potential, omental AT Tregs also closely resembled thymic Tregs, with high expression of CTLA4, TNFRSF18 (GITR), and ICOS, and lower expression of GZMB, CCL3, and CCL4 compared to AT Tconvs (Figure 4.6E). Distinct from thymic Tregs, omental AT Tregs had increased expression of ENTPD1 (CD39) but reduced TGFB transcript levels (Figure 4.6E). 83 Figure 4.6 Gene expression analysis of human omental AT Tregs. 84 (Figure 4.6 continued) Ex vivo Tregs from omental AT (n=3) or thymus (n=5) were isolated and mRNA levels measured by NanoString nCounter Gene Expression. (A) Representative staining pattern of human omental AT Tregs. For comparison, a typical staining pattern for Tregs in blood is shown. (B) Hierarchical clustering of Tconvs and Tregs isolated from omental AT or thymus by pairwise average linkage according to Spearman’s correlation coefficient. Each column represents one individual. (C-H) Comparison of gene expression by omental AT Tregs and thymic Tregs: (C) Treg markers, (D) Th1/2/17 transcription factors, (E) Treg functional markers, and (F, G, H) signature genes of mouse visceral AT Tregs. Comparison of gene expression between multiple groups were performed with one-way ANOVA with a Bonferroni post hoc test. Correlations between gene expression and clinical variables were performed with Spearman’s rank correlation. * represents P < 0.05; ** represents P < 0.01; *** represents P < 0.001. Error bars represent the SEM. Figure 4.7 Tregs in human omental adipose tissue do not produce proinflammatory cytokines. The isolated stromal vascular fraction was either left unstimulated or stimulated with PMA/ionomycin for 6 hours. Samples were then stained for CD45, CD3, CD4, FOXP3 and the indicated cytokines. The plots shown are pre-gated as live CD45+CD3+CD4+ cells. Data are representative of 2 independent samples of obese and 1 sample of lean human omental adipose tissue. 85 Extensive work in mice has shown that visceral AT Tregs have a Th2-like phenotype with high expression of Pparg, Gata3, Prdm1 (BLIMP1), Il10, Il1rl1 (ST2), Ccr4, and Cxcl2 [79, 94, 132]. Consistent with this phenotype, human omental AT Tregs had elevated expression of PPARG, PRDM1 (BLIMP1), and CXCL2 compared to thymic Tregs, and increased levels of CCR4 transcript compared to AT Tconvs (Figure 4.6F). In addition to these well-established mouse visceral AT Treg markers, Cipolletta et al. previously reported a comprehensive list of differentially expressed genes in visceral AT versus splenic Tregs in both lean and obese mice [94]. Consistent with this study, genes that were reduced in mouse visceral AT Tregs such as SATB1 and TCF7 were also reduced in human omental AT Tregs compared to thymic Tregs (Figure 4.6G). In contrast, genes highly expressed in mouse visceral AT Tregs, including AREG, ANXA1, CXCR6, KLRG1, PLIN2, PCYT1A, CTSH, and DGAT1, were all either reduced or unchanged compared to omental AT Tconv and/or thymic Tregs (Figure 4.6H). 4.3.6 Undetectable expression of ST2 on human omental AT Tregs Notably, we did not detect any IL10 or IL1RL1 (ST2) expression in purified human omental AT Tregs. IL1RL1 was only detectable in whole AT mRNA, with gene expression not correlating to BMI, A1C (Figure 4.8A), or other clinical parameters (data not shown). To further explore the finding that IL1RL1 (ST2) mRNA was undetectable on obese AT Tregs, we used flow cytometry to measure ST2 protein on FOXP3+ T cells. We first validated antibody specificity by confirming its ability to bind to 293T cells transfected with an ST2-encoding expression vector (Fig. 4.8B). We also confirmed the ability of this antibody to detect endogenous ST2 expressed by a subset of tonsil CD19+ B cells, and that endogenous ST2 was not susceptible to collagenase II digestion using the same protocol used to digest adipose tissue (Figure 4.9). In contrast, 86 Figure 4.8 Human omental AT Tregs do not express detectable ST2. (A) IL1RL1 (ST2) expression in whole omental AT correlated to BMI or A1C (n=29). (B) Validation of the anti-human ST2 flow antibody. Representative flow plots of surface ST2 expression on ST2-overexpressing 293T cells (n=2). (C) Representative flow plots of ST2 expression in human omental AT CD4+ T cells (non-obese, n=6; obese, n=3). 87 Figure 4.9 ST2 expression on tonsil B cells is not susceptible to collagenase II digestion. Mononuclear cells from human tonsil samples were either untreated or treated with 0.5 mg/ml collagenase II at 37oC for 40 minutes. Samples were then stained for CD45, CD3, CD19, ST2. The shown plots were pre-gated as live CD45+ cells. Data are representative of 2 independent experiments. no ST2 expression was detected on CD4+ T cells in either the FOXP3+ or FOXP3- fractions isolated from obese omental AT (Figure 4.8C). Since we and others have shown that mouse Treg expression of ST2 wanes with obesity [79, 94, 132], we also investigated ST2 expression on CD4+ T cells isolated from lean omental AT. Similar to the obese omental AT samples, we were also unable to detect ST2 expression on Tregs or other CD4+ T cells from lean omental AT (Figure 4.8C). 4.4 Discussion Extensive work in mouse AT has revealed a critical role for immunoregulatory cells at the interface between metabolism and inflammation; however, whether similar pathways are 88 relevant in human AT have remained poorly characterized. Here we report the first comprehensive study of how human omental AT immune cells change with increasing obesity and correlate with the presence or absence of T2D. We found that the proportion of AT M1 macrophages is elevated in patients with obesity and T2D, and that although Treg frequencies negatively correlated to BMI, they did not differ between T2D and non-T2D individuals. Human omental AT Tregs express high levels of canonical Treg-defining markers, including FOXP3, CD25, IKZF2, and CTLA4, and possess a unique AT-specific signature characterized by high expression of PPARG, PRDM1 (BLIMP1), and CXCL2, but notably not ST2, IL10, or AREG. Humans have two major visceral adipose depots, omental and mesenteric; increased adiposity of either depot is associated with an elevated risk of insulin resistance [160, 161]. Unlike mesenteric AT, omental AT contains “milky spots”, or clusters of leukocytes [162]. Few studies have characterized the immune composition of the milky spots in human omental AT, but overall the majority have been reported to be T cells, followed by B cells and macrophages [163, 164]. In our study, we found that ~70% of the CD45+ cells are CD3+, whereas ~10% are macrophages, consistent with previous reports. Although milky spots are also found in mouse omental AT [165], it is important to note that most of the studies examining the role of visceral AT in controlling the animal’s overall metabolism studied male gonadal fat pads which lack milky spots [154, 155]. Therefore, it is difficult to make direct comparisons between mouse and human data due to the disparate features of these two tissues. Obesity is known to alter adipokine levels, which can contribute to systemic low-grade inflammation. Specifically, the pro-inflammatory adipokine leptin is elevated in the serum and AT of obese individuals, whereas adiponectin, the anti-inflammatory adipokine, is reduced in serum [166]. Indeed, we confirm that obese AT has elevated leptin expression and report the new 89 finding that adiponectin declines in proportion to the degree of obesity and in the presence of T2D. In addition, we found that UCP1 transcript levels in omental AT were lower in patients with T2D, consistent with the idea that in obesity a progressive loss of beige AT and the accumulation of white AT leads to cellular stress in adipocytes, triggering the release of pro-inflammatory cytokines and ultimately a change in the immune cell composition in AT [125, 158]. How obesity affects the expression of pro-inflammatory molecules in omental AT has previously been investigated using histology and/or RT-PCR [150, 166, 167]. Consistent with these reports, we found that expression of several pro-inflammatory cytokines (CCL2, CXCL8, IL1B, and IL6) had a weak positive correlation with BMI. Interestingly, BMI also positively correlated with two anti-inflammatory cytokines, IL10 and IL1RN (IL-1RA) [166], implying the presence of counter-regulatory mechanisms. Several groups have examined the phenotype of omental AT macrophages in humans [143-145]. Although there is a clear consensus that macrophages accumulate in obesity, their phenotype is less well-defined. CD14+CD206+ macrophages, traditionally considered M2-like, are reported to produce pro-inflammatory molecules and form crown-like structures around inflamed adipocytes [143, 145]. Using a similar gating strategy, we found that patients with T2D exhibited elevated frequencies of M1 (CD14+CD11c+CD206+) macrophages in their omental AT. Our findings further support the notion that these M1 macrophages are important contributors to human omental AT inflammation. In mice, ST2+ Tregs represent a dominant component of the immune cell composition in visceral AT. Lean visceral AT is highly enriched in ST2+ Tregs, with up to 40% CD4+ T cells expressing Foxp3 and more than half of visceral AT Tregs expressing ST2 [76, 79, 132]. Treg 90 proportions are reduced but not eliminated by diet-induced obesity in mice, decreasing to ~20% of CD4+ T cells in obese visceral AT [132]. Our data in human omental AT contrast with these findings in mice: the average proportion of FOXP3+ cells within human omental AT CD4+ T cells was 10% (range of 2–20%). To date, two other studies have examined human omental AT Tregs by flow cytometry, finding ~5% FOXP3+ cells or CD25hiCD127lo cells within the CD4+ gate [154, 155]. As in mice, Treg frequencies in human omental AT negatively correlated with BMI. These data are consistent with Gyllenhammer et al. who found that lower omental AT Treg numbers were associated with worsened homeostatic model assessment of β-cell function (HOMA-β), a measure of β-cell function [154]. Interestingly, non-T2D individuals had significantly higher Treg frequencies in omental AT than in blood, while they were similar between AT and blood in patients with T2D, suggesting a trend towards loss of omental AT Tregs in T2D. This observation is in line with the previous finding that metabolic unhealthy obese individuals have reduced omental AT Treg frequencies compared to lean individuals [155]. Together our results further support the notion that, despite differences in AT Treg phenotype and proportions between mice and humans, AT Tregs may nevertheless be involved in suppressing inflammation-induced insulin resistance. Our observations also indicate an age-related effect on the innate and adaptive immune cell compartments. Older individuals exhibit an overall reduction in the proportion of CD14+ macrophages in their omental AT, and the composition of macrophage subsets additionally alters with age: M1 macrophages increased, while M2 macrophages decreased, with older age. In contrast, in the few studies carried out in mice, older mice exhibited an accumulation of M2 macrophages in the spleen, lymph nodes, and bone marrow [168, 169]. 91 On the other hand, we found that omental AT Treg frequencies decrease with age. In mice, visceral AT Tregs initially accumulate over time, peaking at ~40% of CD4+ T cells at 25 weeks, but then gradually decline to ~10% by 40 weeks of age [94]. Thus, our findings in humans suggest that there is a similar aging-associated reduction in omental AT Tregs and a concomitant shift in macrophage polarization. Further work is needed to explore their potential contribution to aging-associated inflammation. Finally, we report the first data on the phenotype of human omental AT Tregs. We find that they are similar to thymic Tregs in their expression of lineage-defining and functional molecules, including FOXP3, CD25, IKZF2 (HELIOS), ENTPD1 (CD39), and CTLA4. Distinct from thymic Tregs, and similar to mouse visceral AT Tregs [170], they also had high expression of PPARG and PRDM1 (BLIMP1). However, expression of IL1RL1 (ST2) or IL10 was undetectable. This finding is consistent with a previous report that ST2 is exclusively expressed by endothelial cells and not by any immune cells in human omental AT [171]. In support of this conclusion, we also detected abundant levels of IL1RL1 transcript in total omental AT lysate, suggesting that ST2 is expressed outside of the CD4+ T cell compartment. Overall, while omental AT Treg frequencies are reduced in obesity, they appear to be lineage-committed cells that have adopted a tissue-specific profile. The present study fills a knowledge gap in the immune landscape of human omental AT, particularly how the phenotype and proportion of macrophages and CD4+ T cells change with increasing obesity and the presence of T2D. We provide the first phenotypic descriptions of human omental AT Tregs and discover important similarities and differences to the paradigms developed in mice. For instance, high expression of PPARG, which is essential for Treg function in mouse visceral AT [82] suggests that the function of this transcription factor in AT Tregs is 92 highly conserved. On the other hand, the lack of detectable IL1RL1 (ST2) and AREG expression indicates that more research is needed to define the relevance of these proteins in human AT Tregs. Our data increase our understanding of how pathways of immune regulation could be targeted to ameliorate AT inflammation in humans. 93 Chapter 5: Conclusion 5.1 Summary and overall discussion To summarize, my findings further support the central role of AT Tregs in maintaining a healthy metabolic state. In mice, hyperinsulinemia modifies AT Treg numbers and function, leading to metabolic dysfunction. In humans, omental AT Tregs display a distinct phenotype which is similar but not identical to that of the mouse VAT Tregs. Specifically, human omental AT Tregs lack ST2, a key marker VAT Tregs found in mice. Obesity is inversely correlated to Treg proportions in omental AT, but positively correlated to M1 macrophages and AT inflammation. As a technical note, I found ectopic germline recombination in a widely used Foxp3-YFP-Cre mouse; this serves as a reminder that, use of Cre-lox mice should always come with careful testing for the intended tissue-specific recombination. Although the knowledge of AT Tregs in regulating organismal metabolism has substantially increased over the past decade, at the beginning of this work many questions still remained. The essential role of AT Tregs had been established through loss-of-function, gain-of-function and HFD studies, but what caused the changes in AT Tregs in obesity- or aging-associated insulin resistance was unknown. My studies shed the first light on this question, showing that hyperinsulinemia can alter AT Treg numbers and functions in these contexts. We might be able to harness this knowledge and engineer Tregs with increased or decreased insulin sensitivity for developing adoptive transfer therapies to improve obesity- or aging-associated insulin resistance. Moreover, this finding is exciting as it suggests we might be on the way to uncovering other stimuli, such as IGF-1 signaling, that could exert similar effects on AT Tregs. At this point, it is not clear why hyperinsulinemia seems to only affect AT Tregs and not other tissue-resident or peripheral Tregs. Additional studies, for example using RNA sequencing are 94 needed to take a closer look at how subsets of Tregs changes in response to chronic hyperinsulinemia. There has been growing interest in uncovering the role of tissue resident Tregs in regulating various physiological processes, such as AT Tregs in regulating organismal metabolism, muscle Tregs in promoting injury recovery, and gut Tregs in maintaining the local immune homeostasis. In mice, all these tissue Tregs have one common characteristic, that is they all express ST2 and are dependent on IL-33, distinct from the peripheral Tregs [135]. However, based on our findings that human tissue Tregs do not seem to express ST2 [131, 172], it raises the question of whether knowledge from mouse Tregs is directly translatable to human Tregs. While it is exciting to continue learning more about how mouse tissue Tregs use IL-33 and contribute to various physiological processes, it is critically important to study human Tregs in parallel. For example, it is possible that there is a different cytokine/receptor pathway in human Tregs that performs the same function as IL-33/ST2, but has yet to be identified. 5.2 Strengths and limitations In Chapter 3, the strengths of my study include the utilization of Treg-specific InsR knockout animals in two different models of insulin resistance; contrary to many studies that only look at one of the two models. Furthermore, in contrast to the previous two studies that made use of whole-body InsR knockdown and T cell-specific InsR knockout, my study deleted InsR on only the cell type of interest, making data interpretation less complicated. The weakness of the study is the ectopic recombination found in my Foxp3YFP-CreInsrfl/fl mice; minimal InsR deletion is also found in other immune cells despite effort in selecting for the most stringent conditional knockout animals. 95 In Chapter 4, the strength of my study is the use of ex-vivo human omental AT and the thorough characterization of omental Tregs using gene signature analyses. The weakness of the study is the relatively small sample size. 5.3 Future directions The precise mechanisms of how InsR-deficient Tregs achieve the metabolic protective effects in hyperinsulinemia remain unclear. Since the primary actions of insulin include promoting glucose uptake, lipogenesis and protein synthesis, the hypothesis is that hyperinsulinemia alters Treg cellular metabolism. To test this hypothesis, a number of metabolic assays can be performed. To measure glucose uptake following insulin stimulation, 2-NBDG (a fluorescent homologue to glucose that cannot be metabolized) uptake assay can be performed with Tregs from the VATs, BATs and spleens of control or Foxp3creInsRfl/fl mice. To measure changes in oxidative substrate preference, Seahorse assay, mitochondrial staining, metabolic enzyme and substrate assays, can be performed. Changes in ECAR and OCR in Seahorse would suggest a shift in cellular metabolism; since insulin promotes glycolysis, my prediction is that ECAR would increase in the control Tregs but not the InsR-deficient Tregs. Mitochondrial volume, activities and morphologies in Tregs might also change in response to insulin stimulation. Confocal microscopy and mitochondrial dye MitoTracker Green can be used to detect mitochondria fission and fusion. In addition, qPCR to detect changes in gene expression of metabolic enzymes could reveal detailed information regarding the changes in metabolic pathways. Finally, assays to measure metabolic substrate and byproduct availability such as lactate and pyruvate assays could provide definite evidence for the changes in cellular metabolism in Tregs stimulated by insulin. 96 To study the therapeutic potential of InsR-deficient Tregs in the context of hyperinsulinemia and insulin resistance, adoptive therapy of these cells into the HFD-fed or aged mice can be performed. Whether the InsR-deficient Tregs retain their expect phenotypes (i.e. more suppressive in the HFD model and less functional in the aging model) and provide beneficial protections against metabolic dysfunction would be a topic of interest. Next, whether the negative effects of insulin signaling on mouse Tregs are relevant on human Tregs should be addressed by comparing their suppressive capacity in vitro and in a humanized mouse model, with or without presence of excessive insulin. 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J Immunol 2019. 202: 2195-2209. 111 Appendix Annotated sequence of the floxed allele near exon 4 of Insr Annotation (WT intron, Cloning remnant, loxP, Exon4) TCTCAAAAACAAGCTAAAAGGCTCCTGAAGAATGACACCCAAAGTTGACCTCTGATCTCCATAAAGGTGTGTATGTACCCCCAACACACACACACACACACACACACACACACACACACGCCTACACATCACATGCATATGAACATAAAGTTCTATCATGTGATCAATGATTCTATCAACCGTGCCTAGAGACTCCAAGACAAACTTTATAGGACTAGTCGACCTGCAGCCAAGCTAGCTTGGCTGGACGTAAACTCCTCTTCAGACCTAATAACTTCGTATAGCATACATTATACGAAGTTATATTAAGGGTTATTGAATATGATCGGAATTCCTCGAGTCTAGTGAGGTGACAGGGTTGGTAAAATGTGGACTTCAGTTTGATCTCTAGCACCCTTGGGAAAGGTGGACATGGTGGTCTGTGCTTGTTATACCAGCACTGGAGAGGTGTGGTGCTGTCAGATCTGCCCTGTGCAGGGAATGAAGGAAGTTTTTTATACCAGGAAAGGAATTTTGTGTGTTTGTGTGTATGCAGCTATTTGTGAGTGGGTGTAGGGAGATCAGAGGACAACCTCAGATATTATTCTCAGAATTTCACCTACTTTCTTTGTGATAGGGTCTCCCAGTGACCCTGAGCCTGACCTATTAGGCTAGGCTACCTGATCAGCAAACCCCAGGATTCTCTCTCCTTCCTCAGTACTACTCTTACCAATATCTAACAGTGTTTGATATTTCCAAGAAAACCTTTCATTGCGTATGTATTTATTTTTTATGCGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGCGTGCAAGCGCACGCGCGCACAGGGTTAGTATGTGGTGCTCAGTTCTCAGAACTTGCTTTCCTTCTTTCACTTTGTTGGGCCCAGGGATTGAACTCAGGTTAGCAGGCTTGGCTGCAAGCAGGTACCTTTACCCTATGAGCTGTCTTTCCAGCCAATGCCTGACATTTTTAGGTGGGCTATAGGGGTTCAACTGAGGTTCTCATATTTATGAGGTGAATATTCCATGGTCTGAACACTCCCTTAAACCCTGGGCAATGAATTTTAAGGGGATAATTGGCCATTTAAAAACTGTAGGAGAAGGTTGACAATGCCTGGGAATAATTTTGATTGTGGCAATTTAGAGATGAGATGGTGTTGGTACTGAGCACCAGTTGGGAATCTGGCTCTGCATCGTATACTGCCCAAGGTTGCCTCATGACCACTGTGAAGACATATTTGGTGTCAAATATTTGGAATACTATATCAGTGAACTTTCTCTTGCTGTGATAAAACACTCTGACCAAAGGCAGCTTAGGGGAGGACAAGGTTTGTTTCAGCTTATACTGCTTGGTTACATTCTGTCACAGAGTGAAGTCAAATAGAAACATAAAGCAGAAACCACAAAACAGAGTTTCTCAACCTTCCTAGCACGAACCTTTTATACAGATCCTCATGTTGTAATGACCTATAACCATAAAATTATTTATCTGCTACTTCATAAGTGTAGTTTTGCTACTGTTATGAGTCATAATGTTAATATCTGATAGGTAGGATCTCTGATATGTGACCAACAGAGGGGTAGCAGTGCTCAGGCTGAAAACCACTGCCATAGAGCAGTGATGCTAGTTGACTCTGTTTCTAGCTCATGTGCAGCTAGCTTTCTCAGATAGACCAGGCCCACCTGCCTTGGAAATGGCCACCCACAGTGGGCTGGACCCTCCCCCATCAATCAACAATCGAGATAACTCCCCACAGTATAGTCATGGACCAATCTGATTTAGGCAATTCCTCAACTGAGATCCCCCTCATGATGACTCTAGGCTATATCCTGCTGACAGTTAAAAGTTAGCTAGCTCAACTGCTGAGCTCAGCACATCCACTTTAGAGTAACAGACTGAGGTTGCCACTGGGTTCCTGTGAAGAGTGCTGTATTCCAAATGACGGGGTGAGGGAACCCTGTGCACCTCATTGATCTCTTTGCCTGTGCTCCACTCTCAGCTTGATGTGCACCCCATGTCTGGGACCCTGCCCTAAGGTCTGCCAAATCCTCGAAGGTGAGAAGACCATTGATTCTGTGACATCTGCCCAGGAGCTCCGAGGCTGCACTGTGATCAACGGTAGCCTGATCATCAACATCCGAGGGGGCAGTGAGTATTTTGGAGCATCCCCCACTTCCTATGAGCTGTGAGCAGGGAAGCTCGAGATAACTTCGTATAGCATACATTATACGAAGTTATTGCACAGCTCAGGGTATTCCACACATTCCCCTCTTAACTTTCACGGCCAAATTTACTTTGTTCCTGTGGTCTCAGCTATTCAGGAGGCTGAGGCAGGAGGATTGCTTTAATCTAGCAAGTTCAGTCTAGCCTAGATGACATAATCTTGTCCAAAGAAATAAAACAACATAACATGCAAAGTGACACATCCTTTAGTGGCCCCCAAATGAGAGACTTATACCAGGAGTTGCCTTAAAATCCAAAATGAAAATTTTATGGCTCTACCTCTGCCTGAAATTCTTATTTTGCAATCTGCAGCAGGAATCTCTATTCTTTATATTTTTATGTGTATGAGTGTTTTGTCTGGGTATATGTATATACATGGTGTATATGAGGTGTCCACAAAAAAAGAGGGTATATAATACCCTGGAACTGCAGTTAGAGATGA112 CTGTGAGTTACCATGTGGATGTGGGGACTGAACCCAGGCCCTCTGTGATAGCACCTGATTCTCGTAATTAATTCT PCR primers The primers shown in Fig 2.1B. Designed by PMID 9844629 Forward primer GATGTGCACCCCATGTCTG Reverse primer CTGAATAGCTGAGACCACAG Product sizes wt allele = 279 bp floxed allele = 319 bp The primers shown in Fig 2.3A. New PCR primer that give 3 distinct products Forward primer ACCGTGCCTAGAGACTCCAAGAC Reverse primer CTGAATAGCTGAGACCACAG Product sizes wt allele = 1983 bp floxed allele = 2150 bp recombined allele = 209 bp qPCR Primers for estimating relative ectopic recombination (shown in Fig 2.4A) Primers only amplify wt allele (product = 92 bp) ACAAACTTTATAGGACTAGTGAGGT ACCACCATGTCCACCTTTCCC Primers only amplify floxed allele (product = 182 bp) GCTTGGCTGGACGTAAACTCCT ACCACCATGTCCACCTTTCCCA Primers only amplify recombined allele (product = 209 bp) ACCGTGCCTAGAGACTCCAAGAC CTGAATAGCTGAGACCACAG Housekeeping gene 1: Ccr5 CAGGCAACAGAGACTCTTGG, TCATGTTCTCCTGTGGATCG Housekeeping gene 2: Il10rb ATGATTCCACCCCCTGAGA, GAGCTGTGAAAGTCAGGTTCG
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Experimental Medicine
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Role of regulatory T cells in obesity and insulin resistance
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