Kishton Final Dissertation

Metabolism Regulates the Fate and Function of T Lymphocytes

Rigel Joseph Kishton

Department of Pharmacology and Cancer Biology Duke University

Date:______Approved:

______Jeffrey C. Rathmell, Co-Supervisor

______Donald P. McDonnell, Co-Supervisor

______Christopher B. Newgard

______Christopher M. Counter

______Kris C. Wood

Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Pharmacology and Cancer Biology in the Graduate School of Duke University

2016

ABSTRACT

Metabolism Regulates the Fate and Function of T Lymphocytes

Rigel Joseph Kishton

Department of Pharmacology and Cancer Biology Duke University

Date:______Approved:

______Jeffrey C. Rathmell, Supervisor

______Donald P. McDonnell

______Christopher B. Newgard

______Christopher M. Counter

______Kris C. Wood

An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Pharmacology and Cancer Biology in the Graduate School of Duke University

2016

Abstract

The proper balancing of cell metabolic activity is a key requirement to support the energy demands to maintain viability and provide necessary products for biosynthesis and cell proliferation. Cell metabolism has been found to play a crucial role in the immune system, where a successful immune response requires rapid proliferation and successful clearance of dangerous pathogens followed by resolution of the immune response. Additionally, it is now well known that cell metabolism is markedly altered from normal cells in cancer, where tumor cells rapidly and persistently proliferate. In both settings, alterations to the metabolic profile of the cells play important roles in promoting cell proliferation and survival.

It has long been known that many types of tumor cells and actively proliferating immune cells adopt a metabolic phenotype of aerobic glycolysis. In this program, cells import large amounts of glucose and flux it through the glycolytic pathway and produces lactate even under normoxic conditions. However, the metabolic programs utilized by various immune cell subsets have only recently begun to be explored in detail, and the metabolic features and pathways influencing cell metabolism in tumor cells in vivo have not been studied in detail. The work presented here examines metabolism in the settings of T cell leukemia and in regulatory T cells (Treg).

First, we examined the role and regulation of metabolism in the context of malignant T cell acute lymphoblastic leukemia (T-ALL). In our studies focused on the metabolism of T-ALL, we observed that while T-ALL cells use and require aerobic glycolysis, the glycolytic metabolism of T-ALL is restrained compared to that of an antigen activated T cell. We observed that the pro-anabolic growth mTORC1 signaling pathway was limited in primary T-ALL cells as a result of insufficient ATP levels and activation of the AMPK pathway. Consistent with this model, genetic deletion of AMPK in an in vivo murine model of T-ALL resulted in increased glycolysis and anabolic metabolism. Paradoxically, AMPK deletion also increased T-ALL cell death and reduced tumor burden. AMPK acts to promote mitochondrial oxidative metabolism in T-ALL through the regulation of Complex I activity, and the loss of AMPK reduced mitochondrial oxidative metabolism and resulted in increased metabolic stress.

Confirming a limiting role for mitochondrial metabolism in T-ALL, we observed that the direct pharmacological inhibition of Complex I also resulted in a rapid loss of T-ALL cell viability in vitro and in vivo. Taken together, this work establishes an important role for

AMPK to balance metabolic pathways in T-ALL to allow for cell proliferation while controlling metabolic stress and promoting cell viability.

Treg are an immune suppressive T cell subset that, like T-ALL, depend on mitochondrial metabolism. We therefore also examined Treg metabolism and the mechanisms through which it is regulated. We found that Treg are metabolically

heterogenous and Treg switch between primarily oxidative and glycolytic metabolisms and this metabolic switch controls Treg suppressive function. Inflammatory signaling through toll-like receptor (TLR) drives Treg proliferation and glycolysis, yet reduced

Treg suppressive function. We show that Treg cells adopt a metabolic program characterized by oxidative metabolism with active suppression of anabolic signaling and metabolic pathways in order to properly suppress auto-inflammatory disease. We also found that the transcription factor FoxP3, which is highly expressed in Treg cells, drives this phenotype. Perturbing the metabolic phenotype of Treg cells by enforcing increased glycolysis or driving proliferation and anabolic signaling through inflammatory signaling pathways results in a reduction in suppressive function of Tregs.

Overall, this work demonstrates the importance of the proper coupling of metabolic pathway activity with the functional needs of particular types of immune cells. We show that Treg cells, which mainly act to keep immune responses well regulated, adopt a metabolic program where glycolytic metabolism is actively repressed, while oxidative metabolism is promoted. In the setting of T-ALL, oncogenic signaling drives a metabolic program that is unbalanced relative to normal T cells. Consequently,

T-ALL cells fail to maintain sufficient ATP levels to meet the strong demands for biosynthesis as a consequence of activated c-Myc and mTOR pathway signaling. In the setting of malignant T-ALL cells, metabolic activity is surprisingly balanced, with both glycolysis and mitochondrial oxidative metabolism being utilized. In both cases,

altering the metabolic balance towards glycolytic metabolism results in negative outcomes for the cell, with decreased Treg functionality and increased metabolic stress in T-ALL. This work has generated a new understanding of how metabolism couples to immune cell function, and may allow for selective targeting of immune cell subsets by the specific targeting of metabolic pathways.

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Contents

Abstract ...... iv

List of Figures ...... xiii

Acknowledgements ...... xvi

List of Abbreviations ...... xix

1. Introduction ...... 1

1.1 Development of T cells ...... 3

1.2 T cell activation and function ...... 5

1.3 T cell differentiation ...... 7

1.4 Effector and regulatory CD4+ T cells ...... 9

1.4.1 Effector CD4+ T cells ...... 9

1.4.2 Regulatory CD4+ T cells ...... 10

1.5 T cell malignancies ...... 11

1.5.1 Notch pathway signaling and normal T cell development ...... 12

1.5.2 Notch pathway mutations and T-ALL oncogenesis ...... 15

1.6 Glycolytic and oxidative metabolism ...... 16

1.6.1 Glycolysis ...... 16

1.6.2 Mitochondrial oxidative metabolism ...... 18

1.6.3 Aerobic glycolysis ...... 20

1.7 T cell metabolism ...... 21

1.7.1 T cell activation alters metabolism ...... 22

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1.7.2 Metabolism of differentiated T cell subsets ...... 24

1.8 Tumor cell metabolism ...... 28

1.8.1 Aerobic glycolysis in tumor cells ...... 28

1.8.2 Additional metabolic pathways in tumor cells ...... 30

1.8.3 Tumor metabolism as a therapeutic target ...... 31

1.9 The PI3K/AKT/mTOR pathway and the regulation of metabolism ...... 34

1.9.1 The PI3K/AKT/mTOR signaling pathway ...... 34

1.9.2 The PI3K/AKT/mTOR signaling pathway and metabolism ...... 38

1.9.3 PI3K/AKT/mTOR pathway in immune and cancer cells ...... 40

1.10 The AMPK pathway and regulation of metabolism ...... 41

1.10.1 The AMPK signaling pathway ...... 42

1.10.2 AMPK signaling and metabolism ...... 43

1.10.3 AMPK pathway signaling in immune and cancer cells ...... 46

1.11 Questions to be addressed ...... 47

1.11.1 The role and regulation of metabolism in T-ALL ...... 48

1.11.2 Control of metabolism by FoxP3 in Treg ...... 49

2. Materials and Methods ...... 51

2.1 Materials and methods used for T-ALL studies (Chapter 3) ...... 51

2.1.1 Human samples and cell lines ...... 51

2.1.2 Mice ...... 51

2.1.3 T-ALL mouse model ...... 52

2.1.4 Retroviral packaging ...... 53

2.1.5 Drug treatment of mice ...... 53

2.1.6 Human T-ALL cell line culture ...... 53

2.1.7 T Cell Isolation, Stimulation, and Culture ...... 54

2.1.8 Primary T-ALL cell culture ...... 54

2.1.9 Metabolomics ...... 54

2.1.10 PCR Arrays ...... 55

2.1.11 Immunoblotting ...... 55

2.1.12 Metabolic assays ...... 56

2.1.13 Flow cytometry ...... 57

2.1.14 Complex I Activity Assay ...... 57

2.1.15 Statistical Analysis ...... 58

2.2 Materials and methods used for FoxP3 and Treg studies (Chapter 4) ...... 58

2.2.1 Mice ...... 58

2.2.2 FoxP3-ER cell lines ...... 59

2.2.3 Retroviral FoxP3 expression ...... 59

2.2.4 ChIP-seq ...... 59

2.2.5 Microarray gene expression analysis ...... 60

2.2.6 Flow cytometry analyses ...... 61

2.2.7 Gene ontology analyses ...... 61

2.2.8 PCR Arrays ...... 61

2.2.9 Immunoblotting ...... 62

2.2.10 Metabolic Assays ...... 63

2.2.11 iTreg Differentiation ...... 63

2.2.12 Treg suppression assay ...... 64

2.2.13 T cell transfer model of colitis ...... 64

2.2.14 Statistical Analysis ...... 65

3. AMPK is Essential to Balance Glycolysis and Mitochondrial Metabolism to Control T- ALL Cell Stress and Survival ...... 66

3.1 Introduction ...... 66

3.2 Results ...... 69

3.2.1 Primary T-ALL cells exhibit increased glycolysis that is necessary for cell survival and disease progression ...... 69

3.2.2 Glycolysis is selectively restrained in T-ALL ...... 80

3.2.3 Oncogenic Notch regulates glycolytic and oxidative metabolism ...... 83

3.2.4 Oncogenic Notch signaling in T-ALL results in metabolic stress and AMPK pathway activation ...... 89

3.2.5 AMPK signaling suppresses mTORC1 activity in primary T-ALL, resulting in decreased aerobic glycolysis ...... 95

3.2.6 AMPK pathway signaling promotes oxidative metabolism through regulation of mitochondrial Complex I ...... 101

3.2.7 Loss of AMPK signaling or direct pharmacological inhibition of Complex I results in reduced T-ALL cell survival and slowed disease progression ...... 106

3.3 Discussion ...... 114

4. FoxP3 and TLR Signals Balance Treg Metabolism for Proliferation or Suppressive Function ...... 120

4.1 Introduction ...... 120

4.2 Results ...... 123

4.2.1 Natural Treg are metabolically heterogenous ...... 123

4.2.2 Inflammatory TLR signaling drives Treg glycolysis and proliferation but reduces suppressive capacity ...... 125

4.2.3 FoxP3 modulates cell metabolism through regulation of the PI3K pathway . 127

4.2.4 Increased PI3K/Akt/mTORC1 pathway activity and glycolysis oppose Treg function ...... 136

4.3 Discussion ...... 150

5. Conclusion and Future Directions ...... 154

5.1 T-ALL metabolism is distinct from that of normal T cells ...... 154

5.2 AMPK balances T-ALL metabolism to control metabolic stress and promote cell survival ...... 160

5.3 Treg metabolism is dynamically regulated and coupled to immune function ... 165

5.4 Treg metabolism as a potential therapeutic target for modulating Treg function ...... 171

5.5 Concluding Remarks ...... 174

Appendix A ...... 176

Appendix B ...... 195

Appendix C ...... 198

Appendix D ...... 211

Appendix E ...... 214

Appendix F ...... 217

Appendix G ...... 228

Biography ...... 254

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List of Figures

Figure 1.1: T cell Development and Differentiation ...... 8

Figure 1.2: The Notch signaling pathway in normal cells ...... 14

Figure 1.3: Metabolic profile of T cells ...... 27

Figure 1.4: The PI3K/AKT/mTOR Signaling Pathway ...... 38

Figure 1.5: AMPK pathway signaling ...... 45

Figure 3.1: Expression of glycolytic proteins in human T-ALL ...... 70

Figure 3.2: Functional metabolic profiling of primary T-ALL ...... 72

Figure 3.3: Inhibition of glycolysis selectively targets T-ALL cells ...... 74

Figure 3.4: Deletion of Glut1 in T-ALL inhibits disease progression ...... 76

Figure 3.5: Deletion of HK2 in T-ALL slows cancer progression and inhibits aerobic glycolysis ...... 78

Figure 3.6: Cre activation does not result in changes in T-ALL disease progression or PPP activity ...... 79

Figure 3.7: Primary T-ALL metabolism is distinct from that of normal T cells ...... 81

Figure 3.8: Glycolysis is limited in primary T-ALL, while mitochondrial metabolism is similar to activated T cells ...... 82

Figure 3.9: Oncogenic Notch signaling stimulates glycolytic metabolism ...... 84

Figure 3.10: Oncogenic Notch signaling promotes mitochondrial metabolism ...... 86

Figure 3.11: The PI3K pathway and c-Myc mediate Notch effects on metabolism ...... 88

Figure 3.12: c-Myc expression is similar in primary T-ALL and activated T cells ...... 89

Figure 3.13: Oncogenic Notch activates mTOR, yet mTORC1 activity is not elevated ... 90

Figure 3.14: The AMPK pathway is activated in primary murine T-ALL ...... 92

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Figure 3.15: The AMPK pathway is activated in primary human T-ALL ...... 93

Figure 3.16: Oncogenic Notch drives AMPK activation that is observed in T-ALL ...... 94

Figure 3.17: Primary T-ALL is chronically ATP deficient ...... 95

Figure 3.18: AMPK signaling negatively regulates mTORC1 in T-ALL ...... 97

Figure 3.19: AMPK signaling negatively regulates the expression of glycolytic proteins in T-ALL ...... 98

Figure 3.20: AMPK signaling inhibits glycolysis in T-ALL ...... 99

Figure 3.21: AMPK signaling inhibits de novo nucleotide synthesis in T-ALL ...... 100

Figure 3.22: AMPK signaling promotes mitochondrial oxidation in T-ALL ...... 102

Figure 3.23: AMPK signaling promotes functional mitochondria in T-ALL ...... 103

Figure 3.24: AMPK signaling regulates Complex I gene expression in T-ALL ...... 104

Figure 3.25: AMPK signaling regulates Complex I activity in T-ALL ...... 105

Figure 3.26: AMPK loss in T-ALL results in reduced tumor burden ...... 106

Figure 3.27: AMPK loss in T-ALL increases apoptosis but does not alter proliferation 107

Figure 3.28: AMPK loss in T-ALL slows disease progression ...... 109

Figure 3.29: Mitochondrial metabolism and Complex I activity promote T-ALL survival ...... 111

Figure 3.30: Inhibition of Complex I reduces T-ALL tumor burden in vivo ...... 113

Figure 4.1: Proliferative nTreg have increased expression of Glut1 and elevated mTOR signaling ...... 124

Figure 4.2: Inflammatory signaling through TLR1/2 drives Treg glycolysis and proliferation but reduces suppressive function ...... 126

Figure 4.3: FoxP3 expression in T cells alters the expression of genes in multiple metabolic pathways ...... 128

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Figure 4.4: FoxP3 expression in T cells inhibits glycolytic metabolism and promotes mitochondrial oxidation ...... 130

Figure 4.5: FoxP3 expression in FL5.12 cells inhibits anabolic signaling and metabolic gene expression ...... 132

Figure 4.6: FoxP3 expression in FL5.12 cells inhibits anabolic metabolism ...... 134

Figure 4.7: FoxP3 expression in FL5.12 cells promotes oxidative metabolism and survival ...... 135

Figure 4.8: Constitutive Akt expression increases Treg number and percentage but reduces suppressive function ...... 137

Figure 4.9: Constitutive Glut1 expression alters Treg metabolism and phenotype ...... 139

Figure 4.10: Transgenic Glut1 expression alters Treg phenotype ...... 141

Figure 4.11: Transgenic Glut1 expression alters induced Treg functional markers ...... 142

Figure 4.12: Transgenic Glut1 expression alters induced Treg gene expression ...... 143

Figure 4.13: Transgenic Glut1 expression reduces Treg suppressive capacity in vitro .. 145

Figure 4.14: Transgenic Glut1 expression reduces Treg ability to suppress IBD ...... 147

Figure 4.15: Transgenic Glut1 expression reduces Treg FoxP3 expression in IBD ...... 149

Figure 5.1: Model of Treg suppressive function being modulated by inflammatory signals or glycolytic metabolism ...... 167

Acknowledgements

I would like to thank the many people who have given me tremendous support and assistance throughout graduate school.

I would first like to thank my mentor Jeff Rathmell for all of the guidance and help that he gave me throughout my time in the lab. I really appreciate the freedom that

Jeff gave me to work on so many different interesting projects – being in the Rathmell lab was a wonderful opportunity for me to really branch out and learn about many different aspects of cancer and immune biology. During my time in Jeff’s lab, I was fortunate to work with many great people who both helped me scientifically and became my good friends. Andrew, thank you for being a great rotation mentor and for all of your guidance and advice. Alfredo, I always really enjoyed talking science and the world with you. Violet, it was great to have someone in the lab to talk about cancer biology with. Donte, lunch breaks and happy hours haven’t been nearly entertaining without you there. Peter, I really enjoyed getting to know you and talking science (and clinical applications). Sivan, you kept me sane during revisions and I’m so glad that you were able to stay at Duke with me. Marc, I really enjoyed getting to know you and discussing pop culture and movies (especially Team America). Val, you gave me so much good advice on grad school and science. It was great working with you and I’m happy that we became friends! Kelly and I always really had fun on dinner nights with you and Baron! Amanda, thank you for all of your help on my projects! I’ve really

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enjoyed getting to know you and Marshall and it was really great to have you in the lab.

Carson, I enjoyed working with you on the T-ALL project. Thank you for all of your help and I know you have a very bright future ahead of you as a doctor! Nancie, thank you for all of your help and advice and for the use of your breakroom for lunch!

I would also like to thank my committee members for all of their guidance and advice over the last few years. I especially thank Dr. McDonnell and his lab for being so nice to Sivan and I when we moved in. I will always be grateful for all of your kindness!

Next, I would like to thank my family and friends for their continued and unwavering support. Mom, I appreciate my everyday after-work phone calls with you talking about my day. You’ve been so helpful in helping me stay on the right track.

Dad, thank you for your guidance and calm presence. It’s always good to talk to you when I need help or advice. Meg, thank you for helping me see the big picture and not worry too much. Richard, Matt and Nick, thank you all for being good friends. Richard, it has been really great having someone close by in Durham the last couple of years. I really enjoyed grabbing lunch and talking sports with you. Matt, you’ve been a great friend to me for a really long time. It’s always great to hang out and catch up. Nick, I always enjoy hanging out with you and surfing! Thank you and Chelsea for being great and letting me (and Kelly) come visit you in all of the cool places you’ve lived.

Finally, I would like to thank my fiancé (and soon-to-be wife) Kelly. Thank you for being so supportive of me and my work! You have helped me get through so much

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in the last few years. It has been great to have you here with me along the way during this journey.

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List of Abbreviations

2DG 2-deoxyglucose

ADP adenosine diphosphate

AMP adenosine monophosphate

AMPK 5' AMP-Activated Protein Kinase

APC antigen presenting cell

ATP adenosine triphosphate

CPT1α carnitine palmitoyltransferase 1 alpha

CSL CBF-1/Suppressor of hairless/Lag-1

CTL cytotoxic T lymphocyte

DN double negative

DP double positive

DSL delta/serrate/lag

ETC electron transport chain

FAD flavin adenine dinucleotide

FoxP3 forkhead box P3

G6P glucose-6-phosphate

Glut1 glucose transporter

HIFα hypoxia-inducible factor 1 alpha

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HK Hexokinase

IBD inflammatory bowel disease

ICN intracellular Notch

IFNγ interferon gamm iTreg inducible regulatory T cell

MHC major histocompatibility complex mTOR Mechanistic Target of Rapamycin

NAD nicotinamide adenine dinucleotide

NADPH nicotinamide adenine dinucleotide phosphate

NFAT nuclear factor of activated T cells nTreg natural regulatory T cell

PFK1 phosphofructokinase 1

PI3K phosphatidylinositol 3-OH kinase

PPP pentose phosphate pathway

PTEN phosphatase and tensin homology

RAPTOR Regulatory Associated Protein of mTOR

RHEB Ras homolog enriched in brain

RICTOR Rapamycin-Insensitive Companion of mTOR

ROS reactive oxygen species

T-ALL T cell acute lymphoblastic leukemia

T-bet T-box expressed in T cells

TCA tricarboxylic acid

TCR T cell receptor

Teff effector T cell

Treg regulatory T cell

TSC Tuberous Sclerosis Protein

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1. Introduction

The immune system is a multi-layered network that is comprised of the innate and adaptive immune systems. The innate immune system generally functions as the rapid and non-specific first line of defense against pathogens, while the adaptive immune system is a measured, yet more precise defense mechanism (Parkin and Cohen,

2001). The adaptive immune system has long been known to be a critical player in maintaining an organism’s ability to defend itself from pathogenic invasion (Burnet,

1970). More recently adaptive immunity has also been recognized to be an important aspect of the body’s defense against tumor development (Gajewski et al., 2013). T lymphocytes (T cells) are key components of the adaptive immune system and play crucial roles to mount effective immune responses to bacterial, viral and parasitic infections as well as resisting the growth of cancerous lesions. Although there are numerous subsets of T cells, each with specific immune roles that will be discussed in further detail in the following sections, the essential role of the T cell is the specific recognition of pathogen-derived antigens in the body. T cells then aid in the clearing of pathogens by directing the response of other aspects of the immune system or by directly targeting pathogens and pathogen infected cells for destruction.

The requirement for T cells to be able to rapidly proliferate upon the detection of pathogenic antigens is a fundamental aspect of the T cell immune response. Indeed, upon antigen detection, T cells exhibit one of the most rapid proliferation rates in

mammalian cells. This rapid proliferation rate is reflective of the biology of T cells, in which each individual cell possesses an individual specificity for various antigens, meaning that an individual T cell must, upon antigen recognition, divide rapidly to generate sufficient cell numbers to mount a successful immune response (Moon et al.,

2007). Given the biological capacity for normal T cells to divide every 6-8 hours during an immune response (Jelley-Gibbs et al., 2000), the mechanisms that regulate T cell development and function must be closely regulated or adverse effects may result. For instance, an excessive immune response may occur resulting in autoimmune diseases, or equally seriously, T cell proliferation may become deregulated as a result of mutations, resulting in malignant transformation and cancer onset.

As has been recently made clear in the context of tumor cell biology (Hanahan and Weinberg, 2011), rapid cellular proliferation imposes metabolic requirements on the cell that are distinct from those in quiescent, non-proliferating cells. T cells actively participating in healthy immune responses as well as dysfunctional, auto-reactive or cancerous T cells, must maintain energy levels and produce biosynthetic intermediates to proliferate (MacIver et al., 2013). Consistent with this idea, proliferating T cells undergo a process of metabolic reprogramming, in which T cells transition from primarily oxidative to primarily glycolytic metabolism and use of amino acids. This program increases key products that are necessary for cellular proliferation, such as amino acids and proteins, lipids and nucleotides. Interestingly, various subsets of T

cells, which have different biological functions in immune responses, have been shown to each exhibit distinct metabolic properties (Gerriets et al., 2015), which are often required for proper function. Therefore, understanding the metabolic requirements of different types of T cells, along with how metabolism is regulated in these cells, may provide insight into the function and potential to modulate normal and disease-causing

T cells. This could allow for a more complete understanding of how to harness metabolism to promote healthy immune responses and for techniques to design new metabolic based therapeutic interventions to treat autoimmunity or malignantly transformed T cells.

1.1 Development of T cells

The development of T cells is a highly regulated multi-stage process that begins when hematopoietic progenitor cells derived from the bone marrow migrate to the thymus, where they develop into mature T cells (Koch and Radtke, 2011). Upon arrival to the thymus from the periphery, T cells are initially known as double negative (DN) T cells because they do not express the cell surface markers CD4 or CD8. Over the course of four stages as DN T cells, each distinguished by the T cell expression profile of cell surface proteins such as CD25 and CD44, the T cell gains a mature T cell receptor (TCR)

(Godfrey and Zlotnik, 1993). At this point, the DN T cells begin to express both CD4 and

CD8 and move into the double positive (DP) stage of development. Highlighting a key

role for metabolism in this stage of T cell development, DN cells are stimulated by Akt signaling to increase glucose metabolism to promote cell survival (Ciofani and Zuniga-

Pflucker, 2005). Additionally, DN cells that are unable to upregulate glycolysis at this stage are unable to proliferate (Macintyre et al., 2014).

During the DP stage, T cells undergo two successive rounds of selection. The first selection is known as positive selection and selects for T cells that have rearranged their TCR and have the ability to interact appropriately with self-antigens expressed on major histocompatibility complex (MHC) molecules of thymic epithelial cells. T cells that are able to interact properly with these antigens (meaning an interaction that is neither too strong nor too weak), receive pro-survival signals, while T cells that interact inappropriately do not receive these signals and die due to neglect (Robey and Fowlkes,

1994). The second round of selection for T cells in the thymus is known as negative selection and selects against developing T cells that react too strongly to an additional self-antigen. T cells that react too strongly to this self-antigen are given signals that promote apoptosis. Negative selection acts to eliminate auto-reactive T cells that may have the potential to cause autoimmune reactions in the body. T cells that advance through both positive and negative selection mature to become single positive T cells, meaning they express either CD4 or CD8 on their cell surface (Germain, 2002). These mature, single positive T cells then move out of the thymus and into the periphery of the body. Overall, the successful completion of T cell development is crucial to produce

mature T cells that are both capable of successfully mounting an immune response against pathogens, but also are not prone to self-reactivity and subsequent autoimmunity.

In addition to allowing for the successful generation of mature T cells that are functional in immunity, proper progression of T cell development is very important to prevent the development of cancerous T cells. Among the pathways that regulate immature T cells during the developmental process are the phosphatidylinositol 3-OH kinase (PI3K), Akt and Notch signaling pathways (Ciofani and Zuniga-Pflucker, 2005).

These pathways all play important roles in promoting T cell survival during development. Interestingly, mutations in each of these signaling pathways are associated with the development of T cell malignancies, including T cell acute lymphoblastic leukemia (T-ALL) and T cell lymphoma (Demarest et al., 2008).

Illustrating the importance of proper T cell development in this context, an immature phenotype in T cell malignancy is correlated with poor patient outcomes (Jain et al.,

2016).

1.2 T cell activation and function

The functional role of mature T cells in the periphery is to patrol the body and react to the presence of foreign pathogens or abnormally growing tissues. T cell activation occurs when an antigen presenting dendritic cell presents an antigen to the T

cell that is recognized by that T cell’s TCR. This antigen recognition results in a rapid cascade of signals through several cell signaling pathways that mediate growth and proliferation, including the MAPK and nuclear factor of activated T cell (NFAT) pathways (Smith-Garvin et al., 2009). Co-stimulation through a second activation signal, often through dendritic cell interactions with cell surface CD28 (June et al., 1987), drives activity through the PI3K pathway, resulting in further pro-growth signaling and shifts towards anabolic metabolism (Lenschow et al., 1996). The end result of T cell activation is a transition from a quiescent state towards a growth and proliferative phenotype.

As described above, mature T cells may be divided into two broad subsets, distinguished by cell surface expression of CD4 and CD8. While the developmental process for both types of T cells is similar, there are important differences in the functional features of each type of T cell. In a general sense, T cells that express CD4 are thought of as helper T cells, meaning they help to direct the immune response to pathogens upon encounter with antigens. CD8 expressing T cells directly respond to antigen stimulation by acting to cause apoptosis in target cells such as pathogens or infected host cells. Further distinctions within each of these two subsets can be made as a result of T cell differentiation.

1.3 T cell differentiation

The notion of T cell activation is context specific, in that actual T cell stimulation in the body occurs with T cells experiencing particular environmental features. These environmental features influence the fate of the activated T cell, with factors such as the presence of various cytokines, stromal cell interactions, and even nutrient availability directing activated T cells towards various differentiated states.

Both CD8 and CD4 expressing T cells undergo differentiation upon activation, with specialized subsets of T cells arising as a result. CD4+ T cells may differentiate into helper subtypes, among which are included Th1 and Th17, as well as regulatory T cells, or Treg. Helper subtype CD4+ T cells promote an immune response by producing inflammatory cytokines that direct the function of both the innate and adaptive immune systems towards the destruction of pathogens. In contrast to this, Treg cells serve to inhibit the immune response and prevent excessive immune cell activities that can be damaging to the body or even result in autoimmunity. CD8+ T cells may also become differentiated into several subtypes of cells, including cytotoxic T lymphocytes (CTLs) and memory T cells. CTLs act to cause cell death in pathogen infected or abnormally growing cells. Memory CD8+ T cells, on the other hand, are cells that have previously responded to a pathogen in an immune response, and circulate in the periphery to allow for rapid recognition of these previously encounter pathogens (Figure 1.1).

DiﬀerenCaCon(

Thymic( (Development( Eﬀector(CD4+(T(cell( (Th1,(Th17)( Naïve(CD4+(T(cell( Immature(T(cell(

Induced(CD4+(Treg(

Natural(CD4+(Treg(

Cytotoxic(T(Lymphocyte(

Naïve(CD8+(T(cell(

Memory(CD8+(T(cell( Figure 1.1: T cell Development and Differentiation

Immature T cells develop in the thymus to become mature T cells. Mature T cells may express the cell surface markers CD4 or CD8. A small fraction of CD4 expressing cells mature as natural Tregs, characterized by high FoxP3 and CD25 expression. Naïve CD4+ T cells have the ability to differentiated into effector T cells such as Th1 or Th17, or induced Treg, depending on environmental characteristics, such as cytokines, upon TCR stimulation. Major subsets of CD8+ T cells include cytotoxic T lymphocytes (CTLs) and memory CD8+ T cells.

1.4 Effector and regulatory CD4+ T cells

Effector and regulatory CD4+ T cells each have critical roles to play in the adaptive immune system. Effector CD4+ T cells serve to organize the immune response to various pathogen, while regulatory CD4+ T cells act to suppress effector T cell proliferation to prevent harmful inflammation or the development of autoimmune disease. CD4+ T cell subsets are defined by the specific cytokines that each produces and the expression of specific transcription factors associated with particular T cell subsets.

1.4.1 Effector CD4+ T cells

Each subtype of helper CD4+ T cells (e.g. Th1, Th17) plays a specific role in promoting robust immune responses to pathogens. In accordance with these varied roles, functionally, each helper subset acts to regulate and manage immune responses by producing unique inflammatory cytokines that drive specific immune functions. For instance, Th1 cells, which act to promote immune responses to intracellular pathogens, are identified as expressing the transcription factor T-bet (Szabo et al., 2000), and produce the cytokines IL-2 and interferon gamma (Mosmann et al., 1986). In contrast,

Th17 cells play an important role in directing mucosal immunity and are characterized by the expression of the transcription factor RAR-related orphan receptor gamma

(RORγt) and the production of the inflammatory cytokine IL-17 (Ivanov et al., 2006).

Both types of cells may become dysregulated and are linked to the pathogenesis of autoimmune disease, with Th1 cells known to play a role in inflammatory bowel disease

(IBD), inflammatory arthritis and others, while Th17 cells play roles in asthma and multiple sclerosis (Steinman, 2007).

1.4.2 Regulatory CD4+ T cells

In contrast to the inflammatory nature of helper CD4+ T cell subsets such as Th1 or Th17, regulatory CD4+ T cells (Treg) promote tolerance in the immune system and act to inhibit inflammation. Treg may develop in the thymus during the process of normal

T cell differentiation and are referred to as natural Treg (nTreg) when generated in this way (Hsieh et al., 2004). Additionally, Treg may arise due to differentiation of naïve mature T cells in the presence of the cytokine transforming growth factor beta (TGFβ).

These types of Treg are referred to as inducible Treg (iTreg). Both natural and inducible

Tregs are characterized by the expression of the transcription factor Forkhead Box 3

(FoxP3). Additionally, both types of Tregs are observed to express high levels of the cell surface marker CD25 (Fontenot et al., 2003; Hori et al., 2003). Tregs act to inhibit Teff proliferation and cytokine production through several mechanisms, including by producing the tolerogenic cytokine Il-10 (Rubtsov et al., 2008). The presence of functional Treg in the body is critical to prevent the induction of autoimmunity, as

mutations in FoxP3 in both mice and humans results in non-suppressive Tregs and an often fatal autoimmune disease (Bennett et al., 2001; Brunkow et al., 2001).

1.5 T cell malignancies

As discussed above, proper regulation of the cellular signaling pathways involved in T cell development is necessary to prevent the formation of T cell malignancies. Illustrating this, cancers of T cell origin are thought to arise during various stages of T cell development (Crist et al., 1988; Uckun et al., 1997). Cancers of the T cell lineage may present as leukemia or lymphoma. The most commonly seen T cell leukemia in human patients is T cell acute lymphoblastic leukemia (T-ALL). T-ALL is a subset of the broader classification of acute lymphoblastic leukemia (ALL), representing approximately 20% of the total number of ALL cases in the United States

(Goldberg et al., 2003). T-ALL is most commonly diagnosed in children, with a total of approximately 1500 new patient diagnosis in the United States each year (Pui et al.,

2008). T-ALL is generally well treated, although patients that exhibit increased age at diagnosis or induction failure with frontline chemotherapy regimens have a markedly poorer prognosis (Aifantis et al., 2008; Goldberg et al., 2003). Interestingly, T-ALL is now known to be a relatively homogenous type of cancer, in that greater than two-thirds of all cases are linked to activating mutations in the Notch signaling pathway (Weng et al., 2004).

1.5.1 Notch pathway signaling and normal T cell development

As briefly discussed previously, the Notch signaling pathway plays an important role in the development of normal T cells in the thymus. The Notch family of proteins are transmembrane proteins containing an extracellular domain, a transmembrane domain, and an intracellular domain. In a normal cell setting, Notch pathway signaling involves interactions between two adjacent cells. In the case of T cell development, the two interacting cells are thymic stromal cells and the pre-T cell (Hozumi et al., 2008).

Stromal cells express ligands of the Delta/Serrate/LAG2 (DSL) family, which interact with the extracellular domain of Notch. This interaction results in a series of cleavage event by Presenilin-dependent gamma secretase and a metalloprotease of the ADAM family that leads to the release of the intracellular domain of Notch (ICN), which may enter the nucleus and interact with a DNA-bound transcription factor known as CBF-

1/Suppressor of hairless/Lag-1 (CSL) (Bray, 2006; Kovall, 2007). The ICN interaction with CSL results in the displacement of co-repressor proteins and drives the transcription of target genes including Hes1, Deltex1, c-Myc and components of the pre-

TCR (Figure 1.2) (Bray, 2006; Kovall, 2007; Reizis and Leder, 2002). The activity of the

Notch signaling pathway is regulated by E3-ubiquitin ligases, which act to promote the degradation of ICN at the PEST domain (Bray, 2006).

It has been shown in the context of T cell development that Notch signaling provides pro-survival signals to the developing T cell (Ciofani and Zuniga-Pflucker,

2005). Notch pathway signaling has been found to promote the initial lineage commitment of bone marrow cells towards the T cell fate (Pui et al., 1999) and also plays a role in driving the progression of the developing T cell towards a mature state.

Among the effects of Notch signaling in T cells is increased glucose metabolism that helps maintain cell viability. Notch signaling is tightly regulated during T cell development, becoming active at discrete stages of development and subsequently ceasing activity (Thompson and Zuniga-Pflucker, 2011).

! !

Figure 1.2: The Notch signaling pathway in normal cells

A diagram of the signaling events upon Notch ligand binding to Notch receptor. (A) When Notch ligand is not bound to the Notch receptor, co-repressor proteins bind to the transcription factor CSL and repress the transcription of Notch target genes. (B) Notch ligand binding to the Notch receptor results in a series of cleavage events and the generation of intracellular Notch (ICN). ICN localizes to the nucleus, displacing co- repressor proteins. This results in the transcription of target genes such as Hes1, Deltex1 and c-myc. 14

1.5.2 Notch pathway mutations and T-ALL oncogenesis

Mutation of genes in the Notch signaling pathway may give rise to T-ALL. In the T cell leukemia setting, Notch pathway mutations are most commonly observed in

Notch1 protein. Notch1 mutations occur within domains that result in increased abundance of active, intracellular Notch. These mutations may act through driving an increase in the generation of intracellular Notch in a cell-autonomous fashion, as seen with mutations in the heterodimerization domain of Notch1 (Malecki et al., 2006), or may act to increase the stability of already generated intracellular Notch1 by inhibiting degradation mechanisms. Mutations in the PEST domain of Notch have this effect, as do mutations to the E3-ubiquitin ligases such as FBXW7 that promote the degradation of

ICN (Thompson et al., 2008). The increased abundance of intracellular Notch protein that results from cancer-driving mutations in the Notch pathway has the effect of driving the activity of several pathways commonly associated with many cancers. As discussed, the oncogene c-Myc is a direct target of Notch signaling, and the c-Myc expression is elevated in T-ALL (Palomero et al., 2006). Additionally, another target of

Notch signaling, Hes1, has been shown to transcriptionally suppress the expression of the tumor suppressor PTEN, a negative regulator of PI3K pathway signaling, resulting in increased activity of the PI3K pathway in cells with activated Notch signaling (Wong et al., 2012). Interestingly, it is thought that, by themselves, Notch pathway mutations are not sufficient to drive malignant transformation, with a secondary cooperating

mutation being required for disease onset (Chiang et al., 2008). Indeed, mutations in the

PI3K signaling pathway, among others, are frequently observed alongside Notch pathway mutations in T-ALL (Palomero et al., 2007).

1.6 Glycolytic and oxidative metabolism

Cells that proliferate rapidly, such as the cells of the immune system and cancer cells, must couple the biosynthetic demands of cell division with the metabolic activities of the cell. There are several different metabolic pathways that cells may utilize to generate sufficient levels of energy to maintain cell viability and to produce intermediates to support proliferative demands. Although alternate metabolic pathways play distinct and important roles in both immune and cancer cells, the glycolytic pathway and mitochondrial oxidative metabolism through the TCA cycle and oxidative phosphorylation both play crucial and non-redundant roles in meeting the bioenergetics and biosynthetic demands of proliferating cells.

1.6.1 Glycolysis

The glycolytic pathway (also known as glycolysis) begins with the cell taking up glucose from the extracellular environment and continues with the processing of this glucose in the cytoplasm of the cell to generate pyruvate along with other products. The reversible uptake of glucose by the cell is mediated by a group of cell membrane bound

proteins known as the glucose transporters. One subtype of glucose transporter is known as the sodium-independent glucose transporter, of which there are more than a dozen currently recognized isoforms that are expressed in a tissue specific manner. All subtypes of sodium-independent glucose transporters incorporate a series of 12 transmembrane domains that are heavily glycosylated, a modification that mediates plasma membrane localization and functionality (Mueckler et al., 1985). One key member of this subtype is Glut1 (SLC2A1), which is widely expressed across multiple tissue types in mammals (Takata et al., 1990). Glut1 has been found to be the dominantly expressed isoform of glucose transporter in stimulated lymphocytes

(Chakrabarti et al., 1994), and is also commonly overexpressed in a number of cancer cell settings (Carvalho et al., 2011).

After glucose is taken up into the cell, it is phosphorylated by hexokinase. The product of this phosphorylation, glucose-6-phosphate (G6P) may not be transported back out of the cell. There are four currently identified isoforms of hexokinase (HK1-

HK4), with each isoform varying in expression across tissues. Both HK1 and HK2 are known to associate with the mitochondria of the cell, and may play roles in apoptotic signaling pathways in addition to their metabolic functions (Wilson, 2003).

Interestingly, different isoforms of hexokinase may direct glucose to varying fates. HK2 is thought to promote the diversion of glucose from the glycolytic pathway towards

biosynthetic pathways include the pentose phosphate pathway (PPP), which is utilized for the de novo synthesis of nucleotides and amino acids (Patra et al., 2013).

Several enzymatic reactions take place subsequent to the action of hexokinase to phosphorylate glucose that eventually resulting in the conversion of glucose to pyruvate. The fate of pyruvate is a distinguishing feature between glycolysis and mitochondrial oxidative metabolism. Whereas pyruvate is shunted into the mitochondria for further processing in the oxidative metabolic pathway, the fate of pyruvate in glycolytic metabolism is a cytosolic conversion into lactate. This reaction is performed by an enzyme known as lactate dehydrogenase (LDH) and results in the generation of NAD+. Finally, cytosolic lactate may be transported out of the cell by a family of transporters known as the monocarboxylate transporters (MCTs). Overall, the process of glycolysis results in a net gain of 2 ATP molecules per glucose molecule that undergoes complete processing to lactate.

1.6.2 Mitochondrial oxidative metabolism

In addition to being metabolized to lactate in the cytosol of the cell, pyruvate may also be directed to the mitochondria where it can enter into the tricarboxylic acid

(TCA) cycle. Pyruvate that is directed towards this fate is first modified by the pyruvate dehydrogenase complex, which results in the production of acetyl CoA. This pyruvate- derived acetyl CoA enters the TCA cycle and is condensed with oxaloacetate to generate

citrate. Citrate is then processed through a series of reactions in the mitochondria, eventually contributing to the reduction of NAD+ to yield NADH, the reduction of flavin adenine dinucleotide (FAD) to FADH2, and the production of carbon dioxide and an additional molecule of oxaloacetate, which may condense with a new acetyl CoA and undergo the entire TCA cycle again. Overall, the TCA cycle yields up to 8 molecules of

NADH, 2 molecules of FADH2 and 6 molecules of carbon dioxide for each molecule of glucose that is directed towards a mitochondrial fate.

The mitochondrial production of NADH and FADH2 allows for the generation of large amounts of ATP through the use of subsequent reactions known as oxidative phosphorylation. Both NADH and FADH2 may act to donate electrons to the electron transport chain (ETC) of the mitochondria. Overall, the ETC functions to generate an electrochemical gradient by coupling the movement of electrons and the transport of protons from the inner mitochondrial membrane to the intermembrane space.

Subsequently, the electrochemical gradient is utilized by a complex known as F0F1 ATP synthase to synthesize ATP by condensing ADP with inorganic phosphate. While the

ATP production achieved by mitochondrial oxidative metabolism is much higher than with glycolytic metabolism, it is important to note that mitochondrial oxidative metabolism requires oxygen to take place.

Fatty acids may also be oxidized in the mitochondria as a substrate for ETC and

ATP synthesis. Fatty acid oxidation begins with the movement of a fatty acid into the

mitochondria. Short chain fatty acids (those with an acyl chain shorter than 6 carbons in length) may enter the mitochondria through passive diffusion, while longer chain fatty acids require a process known as the carnitine shuttle to enter the mitochondria. Fatty acid chain length and degree of saturation influence the particulars of the metabolic process, but generally speaking, once a fatty acid enters the mitochondria, a series of enzymatic reactions known as beta-oxidation take place, yielding FADH2 and acetyl

CoA, which may be utilized in the TCA cycle. Illustrating the tremendous energy payoff of fatty acid oxidation, a molecule of palmitate, if fully oxidized in the mitochondria, has the potential to generate over 100 ATP molecules. As with the oxidation of glucose- derived pyruvate, however, mitochondria require oxygen in order to perform fatty acid oxidation.

1.6.3 Aerobic glycolysis

As described above, a cell requires the presence of oxygen in order to perform mitochondrial oxidative metabolism. Most cell types will direct glucose towards an oxidative fate when oxygen is abundant, only extensively using glycolysis to produce lactate under conditions of low oxygen tensions. In an exception to this, Otto Warburg noted in the early 1920’s that tumor cells extensively utilize glycolytic metabolism even in the presence of adequate levels of oxygen to allow for the use of oxidative metabolism

(Warburg, 1956). This effect, known as aerobic glycolysis or the Warburg Effect, has

since been observed in a number of cancer cell settings and has more recently been found to be utilized by proliferating cells, including those of the immune system (Pearce and Pearce, 2013).

Compared with metabolic pathways such as oxidative phosphorylation or fatty acid oxidation, glycolysis is an inefficient metabolic mechanism in terms of ATP generation and produces a net of only 2 ATP molecules per glucose molecule processed.

Glycolytic metabolism does, however, provide other key benefits to a proliferating cell.

Glycolysis allows for increased generation of products that are beneficial for cellular growth and proliferation through the diversion of glycolytic intermediates to pathways that produce fatty acids, nucleotides and amino acids (Vander Heiden et al., 2009).

Indeed, many anabolic signaling pathways such as the PI3K and MAPK pathways promote the use of a glycolytic metabolic program (Munoz-Pinedo et al., 2012).

Glycolytic metabolism also has the additional benefit of allowing for the reduction of

NAD+ to NADH, which is a useful co-factor for many cellular enzymatic reactions

(Vander Heiden et al., 2009). It is likely because of these benefits that many proliferating cells adopt a predominantly glycolytic metabolic program.

1.7 T cell metabolism

The metabolic profile of T cells is closely coupled to their immune function. T cells responding to antigen stimulation rapidly alter their metabolism to allow for the

rapid growth and proliferation that is required to initiate and sustain an immune response. Interestingly, various T cell subsets, which play differing roles in managing the immune response, exhibit markedly differing metabolic preferences and requirements. It appears, then, that metabolism may play an important role in regulating T cell immunobiology to allow for the proper generation and functionality of

T cell subsets (Buck et al., 2015).

1.7.1 T cell activation alters metabolism

T cell activation following TCR recognition of a specific MHC-peptide results in dramatic alterations to cellular signaling pathway activities, proliferation rates, functional activities and even cellular morphology. It is not surprising, then, that the metabolic profile of a T cell changes profoundly upon activation (Macintyre and

Rathmell, 2013). Naïve T cells are relatively inactive metabolically, utilizing low levels of a mixture of fuel sources, including glucose, fatty acids and amino acids through a catabolic oxidative metabolism. Naïve T cells have minimal biosynthetic demands, but require ATP and utilize a ATP-efficient metabolic program of oxidizing fuels in the mitochondrial. Activated T cells, on the other hand, must use metabolism to support rapid growth and proliferation (Macintyre and Rathmell, 2013). Reflective of this, T cell activation results in a rapid shift towards anabolic metabolism, with a marked

upregulation of the synthesis of proteins, nucleotides and lipids necessary for proliferation (Gerriets and Rathmell, 2012).

The metabolic program adopted by activated T cells is reminiscent of the

Warburg Effect that is seen in some tumor cells. Activated T cells strongly upregulate the expression proteins involved in the glycolytic pathway, including Glut1 and HK2

(Palmer et al., 2015). Consistent with the increased expression of glycolytic proteins, activated T cells take up high levels of glucose and flux it through the glycolytic pathway to produce lactate (Macintyre and Rathmell, 2013). Previous work from our lab has shown that Glut1 is the most abundantly expressed glucose transporter in activated

T cells and it is required for proper T cell activation (Macintyre et al., 2014). The PI3K pathway has been shown to play an important role in the promotion of glycolytic metabolism during T cell activation (Finlay, 2012).

Activated T cells also increase mitochondrial metabolism compared to their naïve counterparts. Increased flux through the TCA cycle allows for the export of citrate from the mitochondria to promote the de novo of lipids that are necessary for proliferation.

Activated T cells further support TCA cycle flux by increasing the import of glutamine into the cell (Buck et al., 2015). Glutamine may converted to glutamate by an enzyme known as glutaminase, followed by conversion of glutamate into oxaloacetate and entry into the TCA cycle. By utilizing glutamine in this way, it is thought that activated T cells

are able to maintain the production of citrate-derived fatty acids without compromising the ability of the TCA cycle to function (Buck et al., 2015).

In contrast to the upregulation of glycolysis and TCA cycle flux that is seen during T cell activation, fatty acid oxidation is inhibited (Wang et al., 2011). It is currently unclear why this occurs, but it is likely that it is a mechanism to prevent the activated T cell from oxidizing lipids that are important for proliferation. Additionally, it has been shown that increased PI3K pathway activity may repress fatty acid oxidation, while promoting fatty acid synthesis (Patsoukis et al., 2015).

1.7.2 Metabolism of differentiated T cell subsets

Activated T cells that experience distinct cytokine microenvironments and differentiate into the various T cell subsets also exhibit changes in metabolism. Effector and regulatory T cell subsets have different functional roles in immunity, and mounting evidence indicates that metabolic differences between subsets play a role in mediating these functional differences (Buck et al., 2015). Indeed, there is now increasing evidence suggesting that metabolism is directly coupled to immune function in differentiated T cells, bringing about the possibility that metabolism may have potential as a therapeutic target in the case of immunodeficiency or autoimmunity (Gerriets and Rathmell, 2012).

Effector T cells, such as Th1 and Th17, rapidly grow and proliferate in response to encounter with pathogens and have been shown to exhibit increase expression of

glycolytic proteins such as Glut1 and HK2 relative to naïve T cells (Gerriets et al., 2015).

Effector T cells also have increased glucose uptake and glycolytic flux to lactate

(Michalek et al., 2011) and have been shown to utilize and require glutamine for proper generation and function (Klysz et al., 2015). Studies in our lab found that Glut1 expression is required for effector T cell generation and function in vitro and in vivo

(Macintyre et al., 2014). However, it is clear that the metabolic preferences of effector T cell subsets are not uniform, as we have observed differential effects on these subsets with the pharmacological inhibition of lactate production by dichloroacetate, which drives pyruvate into a mitochondrial metabolic fate (Gerriets et al., 2015).

Although it has been recognized that the metabolism of regulatory T cells is somewhat heterogenous (Zeng et al., 2013), generally they have been described to not markedly upregulate the expression of glycolytic proteins and have much lower levels of glucose uptake and glycolytic flux than effector T cells (Michalek et al., 2011). Our lab found that Treg are not dependent on the expression of Glut1 for differentiation, the maintenance of cell viability, or suppressive capacity (Macintyre et al., 2014). Instead,

Treg appear to utilize increased levels of mitochondrial oxidative metabolism, with increased rates of pyruvate oxidation along with fatty acid oxidation. Consistent with

Treg utilizing mitochondrial metabolism, the expression of components of the electron transport chain is elevated in Treg compared with effector T cells (Gerriets et al., 2015).

Additionally, the pharmacological inhibition of the mitochondrial electron transport

chain or fatty acid oxidation results in decreased Treg differentiation and viability, while effector T cells are not affected (Gerriets et al., 2015; Michalek et al., 2011).

Differentiated CD8+ memory T cells also utilize an oxidative metabolic program.

Memory T cells must be long-lived to provide effective long-term immunity against previously encountered pathogens. It has been demonstrated that memory T cells extensively utilize mitochondrial oxidation of glucose and fatty acids, and actually require this metabolic program to properly function. Interestingly, it is thought that some CD8+ memory T cells are generated from activated CD8+ T cells, requiring a metabolic shift from the glycolytic metabolic program exhibited by CD8+ effector T cells towards an oxidative metabolic program. (O'Sullivan et al., 2014). Driving glycolytic metabolism in memory T cells was shown to reduce cell lifespan and result in reduced memory cell function (Sukumar et al., 2013). Collectively, these results suggest a metabolism-lifespan axis, whereby mitochondrial oxidation of pyruvate or fatty acids promotes a long-lived cell phenotype, while glycolytic metabolism promotes proliferation and growth, but also results a terminal effector state with reduced cellular longevity (Figure 1.3).

Eﬀector&CD4+&T&cell& (Th1,&Th17)&

Naïve&CD4+&T&cell& Aerobic&glycolysis& FaBy&acid&synthesis& Glutamine&metabolism&

Induced&CD4+&Treg&

TCA&cycle& FaBy&acid&oxidaEon& TCA&cycle& FaBy&acid&oxidaEon&

Cytotoxic&T&Lymphocyte&

Aerobic&glycolysis& FaBy&acid&synthesis& Glutamine&metabolism& Naïve&CD8+&T&cell&

TCA&cycle& FaBy&acid&oxidaEon& Memory&CD8+&T&cell&

TCA&cycle& FaBy&acid&oxidaEon&

Figure 1.3: Metabolic profile of T cells

Different subsets of T cells exhibit distinct metabolic profiles. Naïve CD4+ and CD8+ T cells are metabolically quiescent, relying on low levels of TCA cycle activity and fatty acid oxidation. Differentiated CD4+ effector T cells extensively utilize aerobic glycolysis, glutamine metabolism and fatty acid synthesis. Induced CD4+ Treg cells utilize TCA cycle metabolism and fatty acid oxidation. Differentiated CD8+ T cells that become cytotoxic T lymphocytes (CTLs) utilize aerobic glycolysis, fatty acid synthesis and glutamine metabolism, while memory CD8+ T cells are thought to revert back to a metabolism based on the TCA cycle and fatty acid oxidation

1.8 Tumor cell metabolism

In contrast to the relative uniformity of the immune system, where subsets of immune cells generally show functional and metabolic similarities across species and individuals, cancer is a heterogeneous disease. Genotypes vary between different cancer types, different cases of the same type of cancer and there is even heterogeneity within a tumor. It is not surprising, then, that tumor cell metabolism also shows considerable heterogeneity in phenotype. However, there are some common features of tumor metabolism that are observed in many different tumor settings. These commonalities include increased glycolytic flux and adoption of aerobic glycolysis, increased usage of glutamine and alterations to mitochondrial metabolism to support growth, proliferation and survival (Hanahan and Weinberg, 2011). A great deal of research has been performed in recent years with the goal of targeting the common metabolic traits of cancer for therapeutic purposes.

1.8.1 Aerobic glycolysis in tumor cells

Similar to what is observed in the rapidly proliferating cells of the immune system, many tumor cells preferentially shunt glucose towards a glycolytic fate and eventual production of lactate in the presence of abundant oxygen supplies (Vander

Heiden et al., 2009). When Otto Warburg first observed this metabolic trait in tumor cells, he hypothesized that it was the result of defective mitochondria in cancer cells that

did not allow for the use of mitochondrial oxidative metabolism. Although most tumor cells have since been found to have functional mitochondria, the usage of aerobic glycolysis is a common feature of many different tumor types that are driven by a number of oncogenic mutations (Koppenol et al., 2011). It is thought that, similar to proliferating immune cells, tumor cells utilize aerobic glycolysis to allow increased production of biosynthetic intermediates that support the rapid proliferation and to helping the cell to maintain supplies of NAD+ that is useful for enzymatic reactions.

Many tumor cells induce aerobic glycolysis by altering the expression or functionality of the proteins in the glycolytic pathway. Proteins that function in the early stages of the glycolytic pathway to allow increased import of glucose into the cell, including several isoforms of glucose transporters and hexokinases, are often observed to be highly expressed in tumor cells (Mathupala et al., 2006; Szablewski, 2013). Another modification to the glycolytic pathway that is often seen in tumor cells is an increase in the activity of 6-phosphofructo-1-kinase (PFK1) (Hennipman et al., 1987). Increased

PFK1 activity allows for more rapid flux of glucose through the glycolytic pathway, and is achieved by many tumor cells through the increased generation of fructose-2,6- bisphosphate, an allosteric activator of PFK1 (Van Schaftingen et al., 1981). Fructose-

2,6,-bisphosphate is produced by a member of the 6-phosphofructo-2-kinase/fructose-

2,6-bisphosphatase (PFKFB) family of proteins known as PFKFB3. PFKFB3 expression is promoted by a number of oncogenic signaling pathways, illustrating how closely

oncogenic signaling is linked with metabolic alterations (Atsumi et al., 2002). Numerous additional proteins that play a role in glycolytic metabolism are also linked with the phenotype of aerobic glycolysis in tumor cells (Vander Heiden, 2011).

1.8.2 Additional metabolic pathways in tumor cells

Tumor cells are not entirely reliant on aerobic glycolysis for supplying metabolic needs. Additional metabolic pathways, such as glutaminolysis, also aid cancer cells in meeting metabolic demands for survival and proliferation. As is seen in the proliferating immune cell setting, glutamine uptake is often increased in cancer cells.

Cancer cells may utilize glutamine as a substrate to support the synthesis of proteins and nucleotides. Additionally, just as in the immune cell setting, glutamine may be converted to glutamate by glutaminase, after which it may be fluxed into the TCA cycle as an additional carbon source. Glutamate may also be useful to tumor cells as a source for the synthesis of glutathione and NADPH, both of which aid the cell in maintaining a favorable redox balance (Lyssiotis et al., 2013).

Alterations in the normal activities of the TCA cycle also occur in many tumor cells. As in immune cells, citrate may be exported from the TCA cycle to provide a substrate for the de novo synthesis of fatty acids. Additionally, the directionality of the

TCA cycle can be reversed in tumor cells to generate more citrate for use as a lipid synthesis substrate (Scott et al., 2011). This process, known as reductive carboxylation,

allows α-ketoglutarate to be converted to isocitrate and then citrate, which may then be exported from the mitochondria. In some tumor cells, the TCA cycle may be altered such that it directly contributes to the cancerous state of the cell. Mutations in succinate dehydrogenase (SDH) and fumarate hydratase (FH) may contribute to oncogenesis

(Pollard et al., 2003). Intriguingly, mutations to isocitrate dehydrogenase (IDH) have been observed that result in the generation of an “oncometabolite”, known as (R)-2- hydroxyglutarate (2HG). 2HG is now known to contribute to the formation and maintenance of tumors (Losman et al., 2013) through inhibition of demethylases that results in DNA hypermethylation and a stem-cell like phenotype in cancer (Figueroa et al., 2010).

1.8.3 Tumor metabolism as a therapeutic target

Researchers and clinicians have considered the possibility of targeting the metabolic traits of cancer cells for therapeutic benefit for some time. While the clinical success of the metabolic inhibition of cancer cells has been decidedly modest to this point in time, efforts are ongoing to increase the specificity and efficacy of interventions targeting metabolism. Currently, there are numerous approaches being utilized to target the aspects of cancer metabolism that may allow for achieving a therapeutic window, where normal cells are not adversely affected by inhibition. These approaches are being developed to target multiple aspects of tumor metabolism.

Pharmacological inhibitors of the glycolytic pathway, such as 2-deoxyglucose

(2DG), an inhibitor of hexokinase activity, have been utilized to successfully induce cell death and slow proliferation of tumor cells in vitro (Zhang et al., 2006). However, the in vivo effects of many of these drugs are modest, demonstrating that careful evaluation of pharmacological inhibitors of metabolism in an in vivo setting is necessary (Maschek et al., 2004). Nevertheless, genetic studies, in which proteins such as Glut1 (Liu et al.,

2014a), HK2 (Patra et al., 2013), and PFKB3 (Telang et al., 2006) are altered in tumor cells in an in vivo setting have demonstrated that the specific inhibition of tumor expressed isoforms of glycolytic proteins may provide useful therapeutic targets.

There has also been considerable pre-clinical success in the pharmacological inhibition of several of the alternative metabolic pathways utilized by tumor cells. The inhibition of glutamine metabolism cancer has been approached from several angles, with promising results. The inhibition of glutamine uptake in tumor cells by the pharmacological inhibitor of the glutamine transporters ASCT2 and LAT1 resulted in reduced cancer cell growth in vitro an in vivo (Kaira et al., 2013; Wang et al., 2013).

Preventing the cytosolic conversion of glutamine to glutamate by the inhibition of glutaminase with bis-2-(5-phenylacetamido-1,2,4-thiodiazol-2-yl)ethyl sulfide (BPTES) also results in a reduction in cancer cell viability and growth (Seltzer et al., 2010).

Additional approaches to alter glutamine metabolism in tumor cells by inhibiting the entry of glutamine-derived α-ketoglutarate into the TCA cycle have also been explored

(Yang et al., 2009). To date, the greatest degree of success at targeting metabolism in tumors has been achieved through targeting the mutated TCA cycle enzyme IDH to inhibit the production of 2HG. The pharmacological inhibition of mutant IDH resulted in reduced tumor growth and viability in vitro and in vivo (Rohle et al., 2013), and initial clinical trials with an inhibitor of mutant IDH have been promising.

In spite of the numerous identified therapeutic targets in cancer metabolism, there are great challenges in successfully utilizing metabolically targeting drugs as a clinical cancer therapy. The similarities that have been outlined between the metabolic profiles of proliferating immune cells and tumor cells illustrate one major issue with targeting tumor metabolism – that of achieving specificity. Compromising the immune system’s ability to mount an anti-tumor response through inhibition of effector T cell metabolism may result in loss of immune control of tumors. Additionally, metabolic inhibition of immune cells could potentially leave cancer patients vulnerable to opportunistic infections.

A secondary challenge in targeting tumor metabolism is that many tumor cells exhibit remarkable metabolic flexibility. Aside from the rare cases where metabolic genes are actually mutated, tumor cells are often able to shift fuel sources to accommodate metabolic demands when a single metabolic pathway is blocked (Liu et al., 2014a). This may necessitate the inhibition of multiple metabolic pathways in tumor cells in order to achieve therapeutic benefits.

1.9 The PI3K/AKT/mTOR pathway and the regulation of metabolism

A critical pathway in the regulation of cell metabolism in many settings, including immune and cancer cells, is the PI3K/AKT/mTOR signaling pathway. The activity of this pathway is closely regulated by a variety of signals, including growth factors or, in the case of T cells, stimulation in response to pathogen presence in the body. Overall, the PI3K/AKT/mTOR signaling pathway serves to integrate extracellular signals and nutrients to coordinate cellular activity and function. Reflecting this, the

PI3K/AKT/mTOR pathway plays key roles in the regulation of numerous cellular functions, including growth, proliferation and survival.

1.9.1 The PI3K/AKT/mTOR signaling pathway

At the top of the PI3K/Akt/mTOR pathway is PI3K. In a classical understanding of PI3K signaling, PI3K is composed of a heterodimer of two core units –a regulatory p85 subunit and a catalytic p110 subunit. Activation and subsequent phosphorylation of an upstream receptor tyrosine kinase allows for the p85 regulatory subunit to bind to phosphorylated tyrosine residues through a Src homology 2 domain (SH2) domain. The bound p85 subunit then recruits the catalytic p110 subunit, generating an active PI3K dimer. Active PI3K functions to phosphorylate phosphoinositide molecules to generate product phospholipids, including phosphatidylinositol (3,4,5)-triphosphate (PIP3), that are attached to the plasma membrane and may recruit proteins that contain a pleckstrin 34

homology (PH) domain. Opposing the function of PI3K is the tumor suppressor phosphatase and tensin homolog (PTEN), which dephosphorylates phosphoinositides in the cell and is known in particular to negatively regulate the levels of PIP3 in the cell

(Vanhaesebroeck et al., 2012). Activating mutations of PI3K and loss of function mutations of PTEN are often seen in cancer, highlighting the important role of PI3K in regulating fundamental cell processes such as proliferation and survival (Yuan and

Cantley, 2008).

The serine/threonine kinase AKT, also known as protein kinase B, binds with high affinity to PIP3, allowing it to interact with other PIP3 binding proteins, including the kinase phosphoinositide-dependent kinase 1 (PDK1). PDK1 phosphorylates AKT on

Thr308, resulting in partial activation of AKT. Further phosphorylation of AKT on

Ser473 results in greater levels of activation. Activated AKT has numerous functions in the cell related to cell growth, proliferation and survival. Reflecting the importance of

AKT in regulating these cellular features, AKT is known to be an oncogene and activating mutations of AKT may result in tumorigenesis (Manning and Cantley, 2007).

One of the many pathways regulated by AKT in the mechanistic target of rapamycin (mTOR) signaling pathway. mTOR is also a serine/threonine kinase and is involved in the regulation of many cellular processes through the phosphorylation of target substrates. In a general sense, the function of mTOR is to couple external cues, such as growth factors, nutrient availability and cellular stresses, to cellular processes

such as cell growth and proliferation, cell survival, metabolism, and protein translation. mTOR exists in two distinct complexes that are defined by association with other proteins. The two mTOR complexes have distinct roles to regulate cellular processes.

When mTOR is found in mTOR complex I (mTORC1), it is joined by the Regulatory-

Associated Protein of mTOR (RAPTOR), mammalian lethal with SEC13 protein 8

(MLST8), along with PRAS40 and DEPTOR. The activity of the mTORC1 complex is sensitive to inhibition with the drug rapamycin. mTOR is also found in mTOR complex

II (mTORC2), which is comprised of Rapamycin-Insensitive Companion of mTOR

(RICTOR), MLST8 and mammalian stress-activated protein kinase interacting protein 1

(mSIN1). mTORC2 complex activity is more resistant to inhibition by rapamycin than is mTORC1 (Laplante and Sabatini, 2012).

mTORC1 plays a role in the regulation of critical pathways including cell growth, protein translation, cell metabolism and autophagy. The activity of mTORC1 is strongly influenced by upstream signals from AKT and the nutritional state of the cell.

AKT acts to promote mTORC1 activity by indirectly regulating the activity of the mTORC1 activating protein Ras Homolog Enriched in Brain (RHEB). RHEB is a small

GTPase that, when bound with GTP, acts to stimulate the kinase activity of mTORC1.

RHEB is negatively regulated by a GAP protein heterodimer known Tuberous Sclerosis

Protein 1/2 (TSC1/2). AKT acts to suppress the activity of TSC1/2, thereby activating

RHEB and mTORC1 (Inoki et al., 2002). Once activated, mTORC1 phosphorylates

substrates such as p70S6K, 4EBP1/2 and others to promote mRNA translation, lipid synthesis and many other anabolic processes.

The mTORC2 complex acts as an upstream activator of AKT signaling in a feed- forward mechanism. It is thought that mTORC2 acts to phosphorylate AKT on Ser473 in order to potentiate the kinase activity of AKT. mTORC2 signaling, like mTORC1, contributes to the growth of the cell through the positive regulation of substrate Sgk1.

Through the activation of AKT, mTORC2 also regulates metabolic activity in the cell

(Figure 1.4) (Sarbassov et al., 2005)

Receptor(Tyrosine( Kinase(

PIP3( PIP3( PDK1( p85( PI3K( PIP3( p110( P( Glucose(metabolism( PIP2( PIP3( AKT( Cell(growth( P( Cell(survival( PTEN(

TSC2( TSC1(

mTOR( mTORC2(

RICTOR( MLST8( Rheb( mSIN1( mTORC1( mTOR( Cell(growth( RAPTOR( Anabolic(metabolism( MLST8( PRAS40( DEPTOR( Glucose(metabolism( Lipid(synthesis( Cell(growth( Protein(translaLon(

Figure 1.4: The PI3K/AKT/mTOR Signaling Pathway

Activation of receptor tyrosine kinases allows for the recruitment of the regulatory p85 subunit of PI3K to SH2 domains. p85 then recruits the catalytic p110 subunit, after which the PI3K dimer phosphorylates PIP2 to generate PIP3. The phosphatase PTEN may convert PIP3 back to PIP2. Increased concentrations of PIP3 recruit Akt, allowing for phosphorylation by PDK1 at Thr308. Active Akt negatively regulates TSC2 to allow for increased Rheb activation of mTORC1. mTORC2 attenuates Akt activity by phosphorylation of Ser473. Akt itself regulates cell glucose metabolism, growth and survival. mTORC1 signaling promotes glycolysis, lipid synthesis and cell growth.

1.9.2 The PI3K/AKT/mTOR signaling pathway and metabolism

It is now well recognized that the PI3K/AKT/mTOR signaling pathway plays a key role in regulating the metabolism of the cell, particularly with regard to anabolic

metabolism. Activated PI3K/AKT/mTOR signaling is often associated with increased glycolytic metabolism, and is thought to be a driver of aerobic glycolysis (Kim and

Dang, 2006). AKT has been shown to play an important role in the cell trafficking and cell surface localization of Glut1 in the context of lymphocytes (Wieman et al., 2007).

AKT and mTORC1 both act to promote the uptake of glucose into the cell, and both may support the phosphorylation of glucose by hexokinase (Buller et al., 2008). Additionally, the PI3K/AKT/mTOR pathway is known to positively regulate the expression of both c-

Myc and HIF1α (Pore et al., 2006; Zhu et al., 2008), both of which act to promote aerobic glycolysis through transcription of additional proteins in the glycolytic pathway such as

LDH (Dang et al., 2008).

The PI3K/AKT/mTOR pathway also plays an important role in regulating lipid metabolism by controlling the balance between lipid oxidation and synthesis. AKT and mTORC1 regulate the activity of the transcription factor Sterol Regulatory Element-

Binding Protein 1 (SREBP1), a key regulator of fatty acid synthesis. AKT and mTORC1 activity drives the nuclear localization of SREBP1, resulting in increased expression of genes that promote fatty acid synthesis, including acetyl-CoA carboxylase (ACC), fatty acid synthase (FASN) and stearoyl-CoA desaturase 1 (SCD1) (Krycer et al., 2010). In contrast, fatty acid oxidation is negatively regulated by mTORC1. In T cells, the loss of

TSC2 and hyperactivation of mTORC1 signaling results in a reduction in Cpt1a

expression, while RHEB deficiency results in low mTORC1 activity and causes increased expression of Cpt1a and higher rates of fatty acid oxidation (Pollizzi et al., 2015).

1.9.3 PI3K/AKT/mTOR pathway in immune and cancer cells

The PI3K/AKT/mTOR pathway plays important roles in the regulation of both immune and cancer cell biology. In the immune cell setting, the PI3K/AKT/mTOR pathway is a critical regulator of the activation of T cells in response to antigen recognition. It is recognized that PI3K/AKT/mTOR pathway signaling is responsible for much of the metabolic reprogramming that occurs after T cell activation (Lenschow et al., 1996). Inhibition of mTORC1 activity with rapamycin has been found to result in suppression of T cell activation and a failure of T cells to induce metabolic reprogramming towards aerobic glycolysis (Finlay et al., 2012; Zheng et al., 2007). The process of T cell differentiation is also regulated by the PI3K/AKT/mTOR pathway. mTORC1 or mTORC2 deficiency in T cells results in impaired differentiation into effector T cell subsets including (Kurebayashi et al., 2012). Rapamycin treatment to inhibit mTORC1 leads to a similar effect (Yuan et al., 2015). Conversely, the

PI3K/AKT/mTOR pathway appears to have differential effects on Treg generation and maintenance. The catalytic domain of PI3K, p110, is expressed in several isoforms, and inhibition of p110δ results in reduced Treg functionality (Patton et al., 2006). Treg also utilize mTORC1 to promote glycolysis and Treg differentiation (Zeng et al., 2013). More

recent work has found that PTEN is required for Treg stability and function. Treg lacking PTEN have been observed to become more proliferative and adopt an effector phenotype. Interestingly, PTEN null Treg, in addition to losing suppressive capacity, become markedly more glycolytic, suggesting that glycolytic metabolism may oppose

Treg suppressive function (Huynh et al., 2015; Shrestha et al., 2015). Taken together, these results show that Treg need mTORC1 to properly differentiate, yet an excess of mTOR signaling results in decreased Treg functionality and lineage stability.

The PI3K pathway plays an important and well document role in tumor cell biology (Thorpe et al., 2015). Many cancer-causing mutations occur in the

PI3K/AKT/mTOR pathway, with the loss of PTEN occurring in a high percentage of human tumors (Hollander et al., 2011). Additional mutations in the pathway known to be associated with human cancer include alterations to PI3K, AKT, TSC2, RHEB, and mTOR (Grabiner et al., 2014; Yuan and Cantley, 2008). All of these mutations confer increased downstream activity in the PI3K/AKT/mTOR pathway and are thought to be a major driver of aerobic glycolysis in tumor cells.

1.10 The AMPK pathway and regulation of metabolism

An additional pathway that plays a key role in coupling cell nutritional status to cell growth and proliferation is the 5’ AMP-Activated Protein Kinase (AMPK) pathway.

In contrast to the PI3K/AKT/mTOR pathway, which generally drives cell growth and

proliferation in response to adequate nutrient levels, the AMPK pathway acts to preserve cell viability and energy levels under conditions of energy stress by opposing anabolic growth signaling and metabolism.

1.10.1 The AMPK signaling pathway

AMPK itself is a serine/threonine kinase that is composed of three subunits.

AMPKα is the catalytic subunit of the complex, while the β and γ subunits serve as regulatory domains for the protein complex. AMPK activity is responsive to the levels of

AMP and ADP in the cell, allowing it to act as an energy sensor. AMP and ADP may both act as allosteric activators of AMPK kinase activity, with increased levels of either molecule resulting in up to ten-fold increases in AMPK activity (Hardie et al., 2012).

AMPK activity is also increased by phosphorylation of Thr172 of the α subunit by upstream kinases LKB1 or CAMKK2. AMP and ADP binding to AMPK act to promote activity of the complex by mediating the phosphorylation of AMPK Thr172 by LKB1

(Hawley et al., 1996). Conversely, phosphorylation of AMPK by CAMKK2 is directly regulated by calcium signaling and AMP and ADP interactions with AMPK in this context do not appear to modulate kinase activity levels (Mihaylova and Shaw, 2011).

Due to the activation of AMPK by AMP and ADP, AMPK is able to play a critical role as a detector of cellular energy status and, in conditions of energy stress, direct cellular action to promote energy generation. Cellular events that result in a depletion of

ATP levels, such as nutrient starvation, hypoxia, or anabolic pathway activity that consumes high levels of ATP result in increased AMPK pathway activity(Hardie et al.,

2012). When activated, AMPK activates ATP-generating pathways, while inhibiting ATP consuming pathways. Put another way, activated AMPK serves to promote catabolic and inhibit anabolic pathways.

Interestingly, AMPK signaling interacts with and regulates the activity of the mTOR signaling pathway. AMPK directly phosphorylates several regulators of the anabolic mTORC1 pathway, resulting in decreased pathway activity. The GAP proteins

TSC1/2, which inhibit of the mTORC1 activator Rheb, is one such target of AMPK.

AMPK phosphorylates several sites on TSC2, including Ser1387. These phosphorylations increase the activity of TSC2 to inhibit Rheb (Inoki et al., 2003). AMPK can exert additional regulatory activity over mTORC1 through the direct phosphorylation of mTOR binding partner RAPTOR. AMPK phosphorylates RAPTOR on several sites, including Ser792, resulting in decreased mTORC1 complex stability and reduced activity

(Gwinn et al., 2008).

1.10.2 AMPK signaling and metabolism

One of the most well studied aspects of AMPK biology is the influence that

AMPK signaling has on the metabolism of the cell. AMPK is now well characterized to play profound and important roles in the regulation of glycolytic, mitochondrial and

fatty acid metabolic pathways. In regulating each of these pathways, AMPK acts to promote energy production to maintain cellular viability. Often, the metabolic role of

AMPK runs counter to that of pro-growth pathways such as the PI3K/AKT/mTOR signaling pathway, which drives anabolism and proliferation (Hardie et al., 2012).

AMPK has long been known to regulate metabolism in the context of exercise, in which AMPK activity drives increased glucose uptake by mediating the cell surface localization of Glut4 (Kurth-Kraczek et al., 1999). AMPK has been found promote glycolytic flux through the positive regulation of PFK1 activity (Wu and Wei, 2012).

Mitochondrial metabolism is another target of AMPK signaling. AMPK promotes mitochondrial biogenesis through phosphorylation and activation of PGC1α.

Functionally, AMPK promotes the uptake of fatty acids into the mitochondria and subsequent oxidation to produce ATP. AMPK achieves this by inactivating acetyl-CoA carboxylase 2 (ACC2), resulting in a decrease in the generation of malonyl-CoA, an allosteric inhibitor of Cpt1a (Mihaylova and Shaw, 2011).

AMPK also negatively regulates many metabolic pathways that are associated with anabolic growth. Many of these growth-directed pathways consume cellular energy, and AMPK acts to stop the flux of metabolites through these pathways to preserve cellular energy stores. Metabolic pathways that are inhibited by AMPK include fatty acid synthesis, cholesterol synthesis, glycogen synthesis and phospholipid synthesis. AMPK regulates these pathways by phosphorylating key enzymes in each

metabolic pathway (such as ACC in the case of fatty acid synthesis) or by regulating the expression of target genes in each pathway (Mihaylova and Shaw, 2011).

Ac+ve%AMPK% Increased% P% PGC1α% mitochondrial% ATP%deple+on% metabolism% AMPK% AMPK%

ACC2% Reduced%lipid% synthesis% TSC2% TSC1%

Rheb% mTOR% mTORC1% RAPTOR% MLST8%

PRAS40% DEPTOR%

Figure 1.5: AMPK pathway signaling

Activation of AMPK as a consequence of ATP depletion or increased concentrations of AMP or ADP results in changes to cellular signaling and metabolism. Activated AMPK promotes mitochondrial metabolism through the positive regulation of PGC1α. AMPK acts to inhibit anabolic growth by repressing lipid synthesis through the inhibition of ACC2. Additionally, AMPK negatively regulates mTORC1 signaling. AMPK may do this by directly phosphorylating TSC2 to inhibit Rheb activity, or by the direct phosphorylation of RAPTOR to destabilize the mTORC1 complex

1.10.3 AMPK pathway signaling in immune and cancer cells

The AMPK pathway has been studied extensively in the context of immune and cancer cells, and plays key roles in each cell type. In the immune cell setting, AMPK has been found to act as a repressor of glycolysis, cell growth and cytokine production of effector T cells (MacIver et al., 2011). However, it was more recently found that AMPK is necessary to maintain T cell viability in some in vivo settings, indicating that AMPK may be required for T cells to respond to metabolic stress that may occurs in immune responses (Blagih et al., 2015). AMPK is also thought to be essential for Treg cell generation and function. Consistent with this notion, activation of AMPK with metformin has been found to modulate the balance of effector and regulatory T cells by promoting Treg and inhibiting autoimmunity (Lee et al., 2015; Michalek et al., 2011).

AMPK has also been found to play an important role in promoting the formation and survival of memory CD8+ T cells (Rolf et al., 2013).

While the upstream kinase LKB1 has a definitive role as a tumor suppressor

(Shaw et al., 2004a) and is frequently mutated in numerous cancer settings (Sanchez-

Cespedes, 2011), the role of AMPK itself in cancer is less clear. On a surface examination, it would seem that AMPK activity would be deleterious to tumor cells growth through inhibition of anabolic growth pathways. AMPK has also been found to repress the growth of B cell lymphoma by inhibiting mTORC1 and aerobic glycolysis

(Faubert et al., 2013). Additionally, AMPK knockdown with RNAi accelerates the

generation of T-ALL (Mavrakis et al., 2010). However, the role of AMPK to promote cell survival and mitigate metabolic stress may also be favorable to tumor cells in some settings. Indeed, several recent studies have found that AMPK helps to protect tumor cells from oxidative stress and nutrient deprivation to promote tumor cell survival (Jeon et al., 2012; Liu et al., 2012). Thus, it seems that the role of AMPK may be context dependent in cancer. In some tumor settings, AMPK may act to slow tumor growth, while in other settings it may promote tumor cell survival under conditions of nutrient stress.

1.11 Questions to be addressed

It is clear that cell metabolism plays a critical role in both immune and cancer cells settings. In the immune cell setting, metabolism plays an important role in the development, activation and differentiation of T cells. Metabolic pathways promote cell survival, cell proliferation and growth and mediate the immune functions of various subsets of T cells. In cancer, metabolic pathways have been altered and utilized to promote tumor cell growth, proliferation and survival. In both settings, key cell signaling pathways are involved with regulating these metabolic choices.

1.11.1 The role and regulation of metabolism in T-ALL

It has now been clearly established that proliferating tumor cells adopt a metabolic program that is greatly different than that utilized by quiescent normal tissue.

However, it has been less clear how the metabolic profile of cancer cells compares to proliferating cell types of the same tissue origin. To explore this question, the work here compares the metabolic program of T-ALL, along with the activity of the cellular signaling pathways that regulate metabolism, to that of both naïve, resting T cells and stimulated, proliferating T cells. It has been hypothesized that the metabolic activity exhibited by many tumor cells is very similar to that of an activated T cell, such that T cells have been proposed as a model for tumor metabolism. Any differences in the metabolic properties of T-ALL cells and activated T cells may identify new targets for therapeutic intervention. It may also be possible to identify therapeutic windows in which tumor cells may be inhibited while normal cells of the immune system are not significantly impacted. Therefore, this work examines the metabolic characteristics of primary T-ALL samples, derived from human patients and a primary murine model of

T-ALL. The examination of primary T-ALL cell metabolism should allow for a more thorough understanding of in vivo cancer metabolic activity.

This study also explores the status of key cellular signaling pathways that regulate cell metabolism in T-ALL. Much of the work that has been performed to examine both cancer cell signaling and the regulation of cell metabolism has also been

performed in an in vitro context using cancer cell lines. It is a certainty that the environment experienced by primary tumor cells is significantly different than that seen in cell culture. Tumor cells may be exposed to different extracellular environments in the in vivo setting, potentially resulting in cellular and metabolic stresses that are not observed in tissue culture.

1.11.2 Control of metabolism by FoxP3 in Treg

Like the potential for distinct metabolic programs of T-ALL cells and activated T cells, previous work from our lab and others has clearly established that Treg cells utilize an oxidative metabolic program that is distinct from the glycolytic metabolic program utilized by effector T cells. However, to date, how Treg metabolism is regulated and whether this regulation is required for Treg function is unclear. This work examines the role of the Treg specific transcription factor FoxP3 and inflammatory signals in regulating the metabolic choices of Treg cells. FoxP3 has previously been found to inhibit the activity of AKT, resulting in decreased expression of Glut1 in T cells

(Basu et al., 2015). This study expands on this possible metabolic and cellular signaling regulation in Tregs to understand how FoxP3 acts to regulate metabolism, and whether it is responsible for the oxidative metabolic program of Treg. Another goal of this work is to determine the role of inflammatory signaling pathways such as the Toll-Like

Receptor (TLR) pathway in modulating Treg metabolism. Finally, this work seeks to

determine how metabolism regulates Treg function. While it is now accepted that Treg do not extensively utilize glycolytic metabolism, it is unclear whether the suppression of glycolytic metabolism is required for proper Treg function. This work will make use of genetic methods to promote glycolysis in Treg to determine the functional consequences of enforced glycolytic metabolism.

2. Materials and Methods

2.1 Materials and methods used for T-ALL studies (Chapter 3)

The following materials and methods were utilized in studies of T-ALL.

2.1.1 Human samples and cell lines

DND-41, HPB-ALL and ALL-SIL cell lines were obtained from DSMZ

(Braunschweig, Germany). Human T-ALL samples were obtained from the Children's

Oncology Group Biobanking Facility (5 samples), the University of North Carolina

Cancer Hospital Tissue Procurement Facility (1 sample), or the Singapore Leukemia

Tissue Bank (2 samples). Patient samples were chosen sequentially based solely on a diagnosis of T-ALL and availability of tumor samples obtained from peripheral blood prior to treatment. Samples were collected according to an IRB-approved tissue collection protocol at each institution, and were de-identified prior to use.

2.1.2 Mice

Mice were obtained from Jackson Laboratory or have been previously described

(Gershon et al., 2013; Macintyre et al., 2014; Michalek et al., 2011). Eight to twelve week old C57BL/6J mice were used throughout. All procedures were performed under

protocols approved by the Duke University Medical Center Institutional Animal Care and Use Committee (IACUC).

2.1.3 T-ALL mouse model

T-ALL was generated in mice as previously described (Herranz et al., 2015), with minor modifications. In brief, lineage negative hematopoietic cells were isolated from murine bone marrow using Miltenyi Lineage Cell Depletion Kit (130-090-858). Isolated cells were cultured for 16 hrs in X-VIVO 15 media (Lonza 04-418Q) with 100 ng/mL murine stem cell factor (Peprotech 250-03), 10 ng/mL murine IL3 (Peprotech 213-13), 10 ng/mL human IL6 (Peprotech 200-06) and 50 ng/mL FLT3-ligand (Peprotech 250-31L).

Cells were then treated with freshly made MSCV-ICN1-IRES-GFP or MSCV-ICN1-IRES-

NGFR or vector control virus along with 8 µg/mL polybrene on retronectin coated plates and centrifuged for 45 min at 1200 rpm. Cells were rested for 2 hr and plated with fresh media with cytokine supplements for 24 hrs. Spinfection protocol was repeated 24 hrs later. 48 hrs after final spinfection, 5 x 104 transduced cells (identified by GFP or NGFR) along with 2 x 105 bone marrow cells for hemogenic support were injected via tail vein into lethally irradiated (9.0 Gy split into two fractionated doses 4 hrs apart) syngeneic mice. Secondary recipient mice were irradiated with 4.5 Gy and received 2 x 105 T-ALL cells via tail vein.

2.1.4 Retroviral packaging

Retroviral constructs were packaged in Plat-E cells. Plat-E cells were maintained in DMEM supplemented with 10% FBS, with 1 µg/mL puromycin and 10 µg/mL blasticidin. To produce virus, Plat-E cells at 70% confluency in a 6 well dish were transfected with construct plasmid along with Lipofectamine 2000 Transfection Reagent

(Life Technologies). Media supernatant was removed 24 hrs post transfection, and fresh virus was filtered and collected 72 and 96 hrs post transfection.

2.1.5 Drug treatment of mice

Mice were dosed with tamoxifen (Sigma T5648) dissolved in corn oil I.P. at a dosage of 75 mg/kg body weight for 4 consecutive days starting 4 days after T-ALL transplantation for survival curve experiments or 10 days after T-ALL transplantation for acute deletion experiments. Mice were dosed with phenformin hydrochloride

(Sigma P7045) dissolved in PBS via oral gavage at a dosage of 100 mg/kg body weight.

2.1.6 Human T-ALL cell line culture

DND-41, HPB-ALL and ALL-SIL cells were cultured between 5 x 105 and 2 x 106 cells per mL media in RPMI 1640 media supplemented with 10% FBS, 2 mM glutamine,

10 mM HEPES and 55 µM β-mercaptoethanol.

2.1.7 T Cell Isolation, Stimulation, and Culture

Murine or human total or CD4 T cells were isolated by magnetic bead negative and cultured in RPMI 1640 media supplemented with 10% FBS, 2 mM glutamine, 10 mM

HEPES and 55 µM β-mercaptoethanol. T cells were stimulated on plates with 5 µg/mL anti-CD3ε and anti-CD28 antibodies (Pharmingen) for 24 or 48 hrs as indicated. Naïve T cells were cultured with 10 ng/mL IL-7. Activated T cells were washed off of stimulation plates and cultured with 5 ng/mL IL-2 for assays.

2.1.8 Primary T-ALL cell culture

Murine or human T-ALL cells were cultured in RPMI 1640 media supplemented with 10% FBS, 2 mM glutamine, 10 mM HEPES and 55 µM β-mercaptoethanol. IL-2 (10 ng/mL) and IL-7 (10 ng/mL).

2.1.9 Metabolomics

Nontargeted metabolomic analyses were performed as described using LC Q

Exactive Mass Spectrometer (LC-QE-MS) (Thermo Scientific) (Liu et al., 2014b).

Metaboanalyst was used to range-scale data and provide PCA and KEGG pathway analysis of metabolites significantly changed (1.5-fold difference,

P<0.05)(www.metaboanalyst.ca/).

2.1.10 PCR Arrays

RNA was isolated from cells using RNeasy Plus Mini kit (Qiagen) following the manufacturers' instructions. 1 µg total RNA was subjected to single-strand cDNA synthesis using the RT2 first strand kit (Qiagen). The cDNA was used in the Glucose

Metabolism and Mitochondrial Energy Metabolism RT2 Profiler PCR arrays according to the manufacturer’s instructions and assayed on a CFX384 (Biorad). Data were analyzed using the RT2 Profiler program supplied by Qiagen and normalized to the geometric mean of the housekeeping genes beta-actin, beta-glucuronidase and heat shock protein

90kDa alpha.

2.1.11 Immunoblotting

Immunoblotting was performed as described previously (Jacobs et al., 2008).

Primary antibodies (1:1000 dilution) were followed by mouse- or rabbit-conjugated horseradish peroxidase (HRP) secondary antibodies (1:8000). HRP-conjugated antibodies (anti-mouse or anti-rabbit IgG HRP conjugate, Promega) were detected by enhanced chemiluminescence detection (Thermofisher). This included the following antibodies: ACC (3662, Cell Signaling), phospho-ACC (3661, Cell Signaling), pan-

AMPKα (2532, Cell Signaling), phospho-AMPKα (2535, Cell Signaling), 4EBP1 (9644,

Cell Signaling), phospho-4EBP1 (2855, Cell Signaling), c-myc (9402, Cell Signaling), phospho-mTOR (5536, Cell Signaling), Activated Notch (ab8925, Abcam), RAPTOR

(2280, Cell Signaling), phospho-RAPTOR (2083, Cell Signaling), S6 (2217, Cell Signaling), phospho-p70 S6K (9204, Cell Signaling), p70 S6K (2708, Cell Signaling), phosphor-TSC2

(5584, Cell Signaling), TSC2 (3612, Cell Signaling). Alternatively, primary antibodies were followed by fluorescently labeled anti-mouse or rabbit antibodies (LiCor) and imaged using the Odyssey infrared imaging system (LiCor). This included the following antibodies: Glut1 (ab652, Abcam), hexokinase 2 (2867, Cell Signaling), hexokinase 1 (ab104835, Abcam), cytochrome C (556433, BD Biosciences), β-actin (A5441,

Sigma), phospho-S6 (4858, Cell Signaling). Western blots were quantified using ImageJ software.

2.1.12 Metabolic assays

Glycolysis and glucose uptake assays using 3H-glucose or 3H-2-deoxyglucose have been described previously (Gerriets et al., 2015; Macintyre et al., 2014; Wang et al.,

2011). Pentose phosphate pathway (PPP) flux and glucose oxidation were determined by the rate of 14CO2 release from 1-14C-glucose as described (Wang et al., 2011). All values were normalized to cell number. OCR and ECAR were measured with an XF24 extracellular flux analyzer (Seahorse Bioscience) as described (Caro-Maldonado et al.,

2014). Suspension cells were attached to culture plates using Cell-Tak (BD Bioscience).

OCR and ECAR were measured in unbuffered DMEM (Sigma-Aldrich) supplemented with 10mM D-glucose (Sigma-Aldrich) and 10mM L-glutamine, as indicated. OCR and

ECAR values were normalized to cell number. For certain experiments, OCR was measured over time following injection of 1 µM oligomycin.

2.1.13 Flow cytometry

The following antibodies were used: anti-human CD4+ VioBlue (Miltenyi), anti- human CD8+ APC (Miltenyi), anti-human CD271 (LNGFR)-APC (Miltenyi) and goat anti-rabbit PE (eBioscience). Cell death was measured by exclusion of 1 µg/ml propidium iodide. Annexin V staining was performed following manufacturer instructions (BD 556547). Intracellular staining for GLUT1, HK2 and HK1 and measurement of surface Glut1 by myc epitope tag (Millipore 05-724) was performed as previously described (Michalek et al., 2011). DCFDA, Mitotracker Green and TMRE staining were measured by manufacturer protocol (Life Technologies). Data were acquired on a MacsQuant Analyzer (Miltenyi Biotec) and analyzed using FlowJo

(TreeStar software).

2.1.14 Complex I Activity Assay

Complex I activity assay was performed following manufacturer instructions

(ab109721, Abcam). In brief, isolated T-ALL cells were lysed and protein was quantified.

125 µg of protein lysate was loaded per well of immunocapture plate. Biological samples were analyzed in duplicate technical samples. Lysate was incubated on plate

for 3 hr, then plate was washed twice and NADH substrate solution was added. OD450 was recorded for each well for 30 minutes and Complex I activity was calculated as the increase in OD450 within each well.

2.1.15 Statistical Analysis

Statistical analyses were performed with Prism software (GraphPad). Data were analyzed using a 2-tailed Student’s t test, and P < 0.05 was considered significant.

Survival curve data was analyzed using Log Rank (Mantel-Cox) test. Statistically significant results are indicated (* p < 0.05) and n.s. indicates select non-significant data.

2.2 Materials and methods used for FoxP3 and Treg studies (Chapter 4)

2.2.1 Mice

Six to eight week old C57BL/6J mice from Jackson Laboratory were used for all experiments unless otherwise indicated. Glut1 and MyrAkt transgenic mice were previously described (Jacobs et al., 2008; Michalek et al., 2011; Rathmell et al., 2003). All procedures were performed under Duke University Medical Center IACUC-approved protocols.

2.2.2 FoxP3-ER cell lines

FL5.12 cells were infected with control or FoxP3-ER expressing lentivirus and selected with NGFR. The FoxP3-ER-NGFR construct was generously provided by M.

Levings (University of British Columbia) and has been previously described (Allan et al.,

2008). Three clones of each were selected based on equivalent NGFR expression. To activate FoxP3-ER, 100 nM 4-hydroxytamoxifen (4-OHT) was added 36 hours prior to experimental procedures.

2.2.3 Retroviral FoxP3 expression

Primary CD4+CD25- T cells were isolated from mice and activated with 3 ng/mL

PMA and 1 µM ionomycin overnight. Cells were then infected with FoxP3-NGFR or

NGFR control retrovirus along with 8 µg/mL polybrene and centrifuged for 90 min at

2500rpm. Cells were rested for 5h and then plated with fresh media and 10 U/mL IL-2 for 72h. FoxP3-NGFR or NGFR control cells were positively selected with PE magnetic beads following NGFR-PE labeling (≥90% purity, Miltenyi Biotec).

2.2.4 ChIP-seq

Foxp3-chromatin complexes were immunoprecipitated from Foxp3- or empty vector-transduced CD4+ T cells as described previously (Chen et al., 2006). Genomic

DNA libraries were constructed from these samples using NEBNext Ultra DNA library

Prep kits (NEB) and sequenced using the HiSeq platform (Illumina). Sequence reads were mapped to mm9 using Bowtie, and HOMER software was used to identify read densities (peaks) enriched significantly (>5-fold) in ChIP samples over input controls.

The top 30% of peaks were highly enriched for the Foxp3 consensus binding sequence

(>85%), and were used as the set of high confidence, genome-wide Foxp3 binding sites.

2.2.5 Microarray gene expression analysis

Total RNA from activated transduced cells was isolated using TRIzol reagent

(Invitrogen), and dissolved in RNase-free water. Total RNA treated with RNase-free

DNase and purified using Qiagen RNeasy columns. Biotinylated antisense cRNA was prepared by two rounds of amplification using the BioArray RNA Amplification and

Labeling system according to the protocol for 10-1000 ng of input RNA. cDNA libraries were amplified and hybridized to Affymetrix GeneChip Mouse Gene 1.0 ST array at the

Nucleic Acid and Protein core facility of the Children’s Hospital of Philadelphia

Research Institute. Array data were normalized and differential expression was determined using Utils, Stats, Samr, RankProd, topGO and Bioconductor. Significance was assessed using significance analysis of microarrays (SAM). Genes differing in intensity by at least 1.5-fold (up or down) with p <0.001 were considered Foxp3- regulated.

2.2.6 Flow cytometry analyses

Flow cytometry was performed on a Miltenyi Macsquant Analyzer. Antibodies utilized included anti-CD4 eFluor450 (eBioscience 48-0041-82), anti-CD25 PE (BD

Biosciences 553866), anti-CD62L PE (eBioscience 12-0621-82), anti-CD69 PE (BD

Biosciences 553237), anti-FoxP3-APC (eBioscience 17-5773-80), anti-FoxP3-PE

(eBioscience 12-5773-80), anti-Helios PE (eBioscience 12-9883-41), anti-ICOS PE

(eBioscience 12-9942-81), anti-KI67 eFluor450 (eBioscience 48-5698-80). Flow cytometry data analysis was performed using FlowJo.

2.2.7 Gene ontology analyses

Functional annotation of the top 200 FOXP3-regulated genes was achieved using

Database for Annotation, Visualization and Integrated Discovery (DAVID) software, and network analyses of the same gene sets were generated using Ingenuity™ software.

2.2.8 PCR Arrays

RNA was isolated from Glut1-transgenic and control Treg cells using RNeasy

Plus Mini kit (Qiagen) following the manufacturers' instructions. 1 µg total RNA was subjected to single-strand cDNA synthesis using the RT2 first strand kit (Qiagen). The cDNA was used in the Glucose Metabolism and T cell Anergy and Immune Tolerance

SuperArray RT2 Profiler PCR arrays according to the manufacturer’s instructions and

assayed on a ViiA 7 (Applied Biosystems). Data was analyzed using the RT2 Profiler program supplied by Qiagen and normalized to the geometric mean of the housekeeping genes beta-actin, beta-2 microglobulin, beta-glucuronidase and heat shock protein 90kDa alpha.

2.2.9 Immunoblotting

Immunoblotting was performed as described previously (Jacobs et al., 2008).

Primary antibodies (1:1000 dilution) were followed by mouse- or rabbit-conjugated horseradish peroxidase (HRP) secondary antibodies (1:8000 dilution). HRP-conjugated antibodies (anti-mouse or anti-rabbit IgG HRP conjugate, Promega) were detected by enhanced chemiluminescence detection (Thermofisher). This included the following antibodies: Akt (9272, Cell Signaling), Cpt1a (15184-1-AP, Proteintech group), Hif-1α

(10006421, Cayman Chemical Company), phosphor-Akt (4060, Cell Signaling), PIK3cg

(ab140310, abcam), phosphor-mTOR (5536, Cell Signaling), PTEN (9552, Cell Signaling), phosphor-p70 S6K (9204, Cell Signaling). Alternatively, primary antibodies were followed by fluorescently labeled anti-mouse or rabbit antibodies (LiCor) and imaged using the Odyssey infrared imaging system (LiCor). This included the following antibodies: Glut1 (ab652, Abcam), hexokinase 2 (2867, Cell Signaling), hexokinase 1

(ab104835, Abcam), cytochrome C (556432, BD Biosciences), β-actin (A5441, Sigma), phosphor-S6 (4858, Cell Signaling).

2.2.10 Metabolic Assays

Glycolysis and glucose uptake assays using 3H-glucose or 3H-2-deoxyglucose have been described previously(Plas et al., 2001; Wieman et al., 2007). Glutamine oxidation, pentose phosphate pathway (PPP) flux and glucose oxidation were determined by the rate of 14CO2 release from U-14C-glutamine, 1-14C-glucose and 6-14C- glucose as described(Wang et al., 2011). All values were normalized to cell number.

OCR and ECAR were measured with an XF24 extracellular flux analyzer (Seahorse

Bioscience) as described(Caro-Maldonado et al., 2014). Suspension cells were attached to culture plates using Cell-Tak (BD Bioscience). OCR and ECAR were measured in unbuffered DMEM (Sigma-Aldrich) supplemented with 10mM D-glucose (Sigma-

Aldrich) and 10mM L-glutamine, as indicated. OCR and ECAR values were normalized to cell number. For certain experiments, ECAR was measured over time following injection of 10mM D-glucose, oligomycin and 2DG. Glycolytic capacity is defined as the difference between the ECAR following the injection of oligomycin and ECAR following glucose injection.

2.2.11 iTreg Differentiation

CD4+CD25- T cells were cultured on irradiated splenic feeder cells (300 Gy) with

2.5µg/mL of anti-CD3 antibody at a ratio of 5:1 in RPMI supplemented with 10% FBS,

sodium pyruvate, penicillin/streptomycin, HEPES and beta-mercaptoethanol. 3ng/mL

TGFβ was added to induce FoxP3+ iTreg. On day 3 post-stimulation, cells were split 1:2 and re-plated with IL-2 alone for an additional 2 days. In some cases, cells were treated with 5 µg/mL PAM3CSK4 for 24 hr beginning on day 4 post-stimulation.

2.2.12 Treg suppression assay

Treg cells were differentiated as described above and cultured 1:1 with CellTrace

Violet (CTV) labeled CD4+CD25- T cells along with 1µg/mL of anti-CD3 antibody and irradiated feeder splenocytes (300Gy). Treg suppression of effector T cell proliferation was determined 72h post stimulation by CTV dilution of target population.

2.2.13 T cell transfer model of colitis

Splenic CD4 T cells were isolated as described above, and naïve effector

(CD4+CD25−CD45RBhi) T cells were sorted (FACSVantage; BD Bioscience). Naïve effector

T cells from were injected i.p. into 6-8 week old C57BL/6 RAG1−/− recipients (1x106 cells/mouse). Thy1.1 naïve effector T cells were used in some experiments. Because the mice were H. pylori negative, and colitis does not occur spontaneously in this setting, disease was initiated two weeks following T cell injection with 200ppm piroxicam

(Sigma-Aldrich) in powdered rodent chow for 3 days to enhance mucosal exposure to enteric bacteria and induce colitis(Hale et al., 2005). 21 days following piroxicam

treatment, Glut1 transgenic or littermate control Treg cells (sorted CD4+CD25+CD45RBlo,

2x105 cells/mouse) were injected as indicated. Mouse weights were monitored tri- weekly. At the end of the experiment, total CD4 and CD4+FoxP3+ Treg in the spleen and mesenteric lymph nodes were analyzed by flow cytometry.

2.2.14 Statistical Analysis

Statistical analyses were performed with Prism software (GraphPad). Data were analyzed using a 2-tailed Student’s t test, and P < 0.05 was considered significant.

Longitudinal data was analyzed by two-way ANOVA followed by Tukey’s test.

Statistically significant results are indicated (* p < 0.05) and n.s. indicates select non- significant data.

3. AMPK is Essential to Balance Glycolysis and Mitochondrial Metabolism to Control T-ALL Cell Stress and Survival

Note: The material described in this chapter was published as a research manuscript

(Kishton et al., 2016).

3.1 Introduction

The field of cancer metabolism has come to understand that resting healthy tissues generally utilize mitochondrial oxidative phosphorylation to produce energy for basic cell maintenance, while cancer cells instead often utilize a metabolic program that is termed aerobic glycolysis (Cantor and Sabatini, 2012; Hanahan and Weinberg, 2011).

The metabolic program of aerobic glycolysis is associated with a large increase in the import of glucose and subsequent flux through the glycolytic pathway to generate lactate even under conditions where the oxygen tension is amenable for oxidative metabolism (Warburg et al., 1927). This metabolic program is thought to allow glycolytic intermediates to flux towards biosynthetic pathways that promote the de novo synthesis of lipids, nucleotides and amino acids that are useful for cells undergoing rapid growth and proliferation (Vander Heiden et al., 2009).

T cell acute lymphoblastic leukemia (T-ALL) is a type of malignancy that is known for rapid onset and proliferation. T-ALL, while generally known to be well treated (Pui et al., 2008), has a very poor prognosis for patients that present with advanced age at disease onset or those patients that relapse after treatment (Bhojwani and Pui, 2013; Oudot et al., 2008). T-ALL is a relatively homogenous disease in terms of

the mutations that are thought to drive disease initiation and maintenance, with activating mutations in the Notch signaling pathway being found in greater than 60% of human T-ALL (Weng et al., 2004). While it is known that Notch can drive glycolytic metabolism in human T-ALL cell (Palomero et al., 2007) and T cells that are developing in the thymus (Ciofani and Zuniga-Pflucker, 2005), recent studies have indicated that

Notch pathway signaling is also able to promote mitochondrial oxidative metabolism in polarized macrophages (Xu et al., 2015) and in T-ALL cell lines (Palomero et al., 2006).

Activated Notch pathway signaling has been shown to promote activity in the PI3K pathway (Palomero et al., 2007) and c-Myc signaling (Palmer et al., 2015; Palomero et al.,

2006) that plays an important role to promote glutamine oxidation in T-ALL (Herranz et al., 2015). Similarly, antigen stimulated normal T cells also exhibit activation of the PI3K and c-Myc signaling pathways, and are characterized as utilizing aerobic glycolysis to grow, proliferate and perform immune functions (Gerriets et al., 2015; Macintyre et al.,

2014; Wang et al., 2011). However, it is currently unclear how the metabolic program of

T-ALL cells compares to that of activated T cells. Differences in the metabolisms of these cell populations may reveal novel T-ALL metabolic vulnerabilities that may be exploited for therapeutic benefit.

Often acting in opposition to the PI3K pathway, 5’ AMP-activated kinase

(AMPK) may inhibit the activity of mTORC1 signaling (Gwinn et al., 2008; Inoki et al.,

2003). AMPK can be activated by the tumor suppressor LKB1 (Shaw et al., 2004b) and

has previously been shown to have growth suppressive functions in in vivo models of cancer (Faubert et al., 2013). Consistent with this, the pharmacological activation of

AMPK with can reduce the growth rates of some tumor cells (Hirsch et al., 2009). In the context of T-ALL, AMPK may act to inhibit the development of cancer (Mavrakis et al.,

2010). On the other hand, recent work has shown that several oncogenes, including Ras and Myc, have the effect of generating metabolic stress (Liu et al., 2012; Moiseeva et al.,

2009), and AMPK may act to promote cancer cell survival under conditions of metabolic stress. Consistent with this idea, genetic deletion of LKB1 confers sensitivity to metabolic stress in the context of lung cancer (Shackelford et al., 2013) and AMPK plays an important role in mitigating metabolic stress to promote cell survival in the context of myeloid leukemia initiating cells (Saito et al., 2015) and differentiated effector T cells in vivo (Blagih et al., 2015).

In this chapter, we analyzed the metabolic preferences of primary T-ALL and normal naïve and activated T cells. Consistent with previous work in the field, primary human T-ALL samples utilized and required aerobic glycolysis to maintain viability.

Interestingly, primary T-ALL glucose metabolism was notably restrained compared to that of normal proliferating T cells. Metabolomics analysis of primary T-ALL cells and activated T cells revealed dramatically different global metabolomics profiles. We found that oncogenic Notch signaling induced metabolic stress in T-ALL, resulting in the activation of AMPK pathway signaling. AMPK acted to suppress mTORC1 signaling

and aerobic glycolysis in T-ALL, while promoting the utilization of mitochondrial oxidative metabolism. We found that AMPK signaling and mitochondrial metabolism were essential for T-ALL cell survival in vivo. Collectively these data show that primary

T-ALL cells make use of a unique metabolic program in which oncogenic Notch signaling drives metabolic stress that activates the AMPK pathway. AMPK plays a dual role to inhibit anabolic metabolism and also promote T-ALL cell survival by promoting the usage of mitochondrial oxidative metabolism.

3.2 Results

3.2.1 Primary T-ALL cells exhibit increased glycolysis that is necessary for cell survival and disease progression

While studies have been performed describing the metabolic characteristics of T-

ALL cell lines (Palomero et al., 2007), the metabolic program utilized by primary T-ALL cells is not well understood. To begin to explore this, we first profiled the expression of the glycolytic proteins Glut1, the primary glucose transporter in T cells (Macintyre et al.,

2014), and Hexokinase 2 (HK2), a hexokinase isoform commonly associated with aerobic glycolysis (Gershon et al., 2013; Wolf et al., 2011), in blood samples isolated from human patients diagnosed T-ALL. Both Glut1 and HK2 expression were increased in T-ALL cells relative to naïve CD4+ T cells isolated from healthy independent human blood donors. The expression of HK1, however, was found to be significantly lower in T-ALL

cells than in naïve T cells (Figure 3.1A-B). The expression pattern of Glut1, HK1 and

HK2 was observed to be similar in a panel of human T-ALL cell lines (Figure 3.1C).

A" Naïve human T cell Primary human T-ALL

Glut1& Hexokinase&II& Hexokinase&I& B" Glut1" Hexokinase"II" Hexokinase"I" * * * 15000 8000 4000 6000 3000 10000 4000 2000 5000 2000 1000

0 0 0

MFI&(geometric&mean)&

Naïve& Primary& &MFI&(geometric&mean)& Naïve& Primary& Naïve& Primary& &MFI&(geometric&mean)& human&T&cell& human&T>ALL& human&T&cell& human&T>ALL& human&T&cell& human&T>ALL&

C" HK1&

HK2&

Glut1&

Ac3n& DND41& cell&line& SIL>ALL& cell&line& cell&line& HPB>ALL& T&cell&&

Naïve&&human&

Figure 3.1: Expression of glycolytic proteins in human T-ALL

(A) Primary human T-ALL samples were compared to healthy naïve CD4+ T cells by flow cytometry for expression of Glut1, Hexokinase 2 and Hexokinase 1. (B) MFI of T- ALL and naïve CD4+ T cells for Glut1, Hexokinase 2 and Hexokinase 1 was calculated. (C) Human T-ALL cell lines were compared to naïve human CD4+ T cells by

immunoblot. Data are representative of at least five independent samples (A), show the MFI of each sample (B) or are representative of two independent experiments (C). Data are shown as the mean and standard deviation (* p < 0.05).

Next, we sought to determine whether T-ALL cells utilized a program of aerobic glycolysis relative to normal resting T cells. We observed that primary human T-ALL cells had increased levels of glucose uptake compared with healthy naïve CD4+ T

(Figure 3.2A). To further compare the metabolic activity of T-ALL cells to normal T cells, we then utilized a well-characterized primary murine model of Notch driven T-

ALL, in which the intracellular domain of Notch1 (ICN) is expressed in hematopoietic progenitor cells (HPCs) by a viral vector, resulting in T-ALL initiation (Chiang et al.,

2008). In brief, the model involves the isolation of lineage negative hematopoietic progenitor cells from the bone marrow of donor mice, followed by 48 hours culture of the isolated cells in X-VIVO 15 media supplemented with IL-3, FLT3L, MSCF and IL-6.

The cells are then transduced with retrovirus expressing ICN and transferred into lethally irradiated syngeneic mice. After 30-60 days, the mice that receive the cell transplant develop a primary T-ALL that can be analyzed directly or transferred into secondary recipient mice. T-ALL cells were isolated using this system, and metabolic traits were characterized first by using the Seahorse Extracellular Flux Analyzer.

Primary murine T-ALL cells had increased oxygen consumption rate (OCR) and lactate

production as measured by extracellular media acidification rate (ECAR) compared with naïve T cells, indicating increased metabolic activity (Figure 3.2B). We observed that primary human T-ALL cells also exhibited increased ECAR compared to healthy naïve human CD4+ T cells (Figure 3.2C).

Figure 3.2: Functional metabolic profiling of primary T-ALL

(A) Primary human T-ALL glucose uptake was compared to naïve human CD4+ T cells. (B) Primary murine T-ALL was compared to naïve T cells by Seahorse Extracellular Flux Analyzer for oxygen consumption rate (OCR) and extracellular acidification rate (ECAR). (C) Primary human T-ALL ECAR was compared to naïve CD4+ T cells. Data

are representative of two independent experiments (A, C) or three independent experiments (B). Data are shown as the mean and standard deviation (* p < 0.05).

While these data demonstrated that primary T-ALL cells utilize aerobic glycolysis, it was still unclear whether T-ALL cells required this metabolic program for survival and proliferation. To examine this, we treated primary human T-ALL samples with the hexokinase and glycolytic inhibitor, 2-deoxyglucose. As primary T-ALL patient samples were comprised of mixed populations of tumor cells (CD4+CD8+) and non- malignant T cells (CD4+ or CD8+), we compared the effects of 2DG on both transformed and non-malignant T cell populations within the same sample. We observed that the percentage of T-ALL cells in the total live cell population was reduced over time with 2- deoxyglucose treatment (a representative patient sample shown in Figure 3.3A). The percentage of non-malignant CD4+ T cells either remained unchanged or increased, indicating that the T-ALL cells were more sensitive to the inhibition of glycolysis that non-malignant T cells. Consistent with non-malignant CD4+ T cells being resistant to the inhibition of glycolysis, isolated resting CD4+ T cells from healthy independent donors did not show reduced cell viability as a consequence of 2-deoxyglucose treatment (Figure 3.3B).

Figure 3.3: Inhibition of glycolysis selectively targets T-ALL cells

(A) Primary human T-ALL samples were cultured +/- 10 mM 2-deoxyglucose and the percent of viable cells that were T-ALL (CD4/CD8 double positive) or CD4+ T cells was measured over time. (B) Naive CD4+ T cells isolated from healthy donors were cultured +/- 10 mM 2-deoxyglucose and viability over time was measured using propidium iodide exclusion flow cytometry analysis. Data are representative of at three independent experiments performed with individual patient samples. Data are shown as the mean and standard deviation (* p < 0.05).

While the pharmacological inhibition of glycolysis with 2-deoxyglucose indicated that primary T-ALL cells need glycolysis to survive in culture, the in vivo role of aerobic glycolysis was still unclear. To directly determine the role of aerobic glycolysis in primary T-ALL in vivo, we conditionally deleted Glut1 in T-ALL. To accomplish this, we first generated T-ALL from a Glut1 floxed, inducible Cre background (Glut1fl/fl; Rosa26CreERT2) in which Cre can be activated by treatment with

tamoxifen to delete Glut1. We transplanted primary T-ALL cells generated on this genetic background into secondary recipient mice and treated these mice with vehicle or tamoxifen and monitored overall survival time. Mice that were treated with tamoxifen to delete Glut1 survived significantly longer than did mice given vehicle treatment

(Figure 3.4A), supporting a primary T-ALL dependence on Glut1. We also studied the effects of acute deletion of Glut1 in T-ALL. In these experiments, secondary recipient mice that received transplants of Glut1fl/fl;Rosa26CreERT2 background primary T-ALL were treated with tamoxifen starting 10 days after T-ALL transplant, and then were sacrificed for analysis after an additional 5 days. Mice that were treated under this protocol had a significant reduction in the expression of Glut1 that coincided with a reduction in the number of T-ALL cells in the spleen of tamoxifen treated mice (Figure

3.4B).

Figure 3.4: Deletion of Glut1 in T-ALL inhibits disease progression

Primary T-ALL was generated on a Glut1 inducible deletion background and transplanted into secondary recipient mice. Mice were (A) treated with tamoxifen 4 days post-transplant and overall survival was monitored or (B) treated with tamoxifen 10 days after transplant and sacrificed after 5 further days for analysis of tumor burden. Data are representative of two independent experiments with at least 4 mice per group. Data are shown as the mean and standard deviation (* p < 0.05).

To ensure that the results we observed were not specific to Glut1, we also conditionally deleted HK2 in T-ALL cells following a similar protocol to what was used for Glut1. The deletion of HK2 resulted in an increase in animal survival time and a reduction in T-ALL disease burden (Figure 3.5A). Previous studies have shown that

HK2 can act to promote the synthesis of nucleotides through the pentose phosphate pathway (PPP) (Patra et al., 2013). Consistent with this, we found that T-ALL cells

isolated five days after HK2 deletion exhibited decreased PPP flux and a significant reduction in nucleotide and nucleoside levels (Appendix A). Further confirming a role for HK2 in glycolytic metabolism in T-ALL, we observed that HK2 deletion resulted in an increase in non-phosphorylated glucose and a reduction in fructose 1,6-bisphosphate that is consistent with slowed glycolytic flux (Figure 3.5C). Importantly, we found that

Cre activation itself did not result in any of the observed effects on mouse survival

(Figure 3.6A), tumor burden (Figure 3.6B-D), or PPP flux (Figure 3.6E).

Figure 3.5: Deletion of HK2 in T-ALL slows cancer progression and inhibits aerobic glycolysis

Primary T-ALL was generated on a HK2 inducible deletion background and transplanted into secondary recipient mice. Mice were (A) treated with tamoxifen 4 days post-transplant and overall survival was monitored or treated with tamoxifen 10 days after transplant and sacrificed after 5 further days for analysis of tumor burden. (B-C) Vehicle or tamoxifen treated T-ALL was isolated and PPP pathway flux was measured by radiolabeled tracer or cells were extracted and analyzed by metabolomics analysis. Select metabolites are shown. Data are representative of at least 3 biological replicates per group. Data are shown as the mean and standard deviation (* p < 0.05).

ns A% Veh. (n=8) B% Tam. (n=8) 400 100 ns# 300 %(millions)%

50 200 100

0 Splenocytes 0 Percent%survival% 0 5 10 15 20 Veh.# Tam.#

Days%elapsed% Vehicle Tamoxifen PPP%Oxida@on% C% D% E% ns ns ns 700# 600# 250 100 500# 200 80 400# 150 60 CPM% 300# 40 100 200# 20 50 %%T8ALL%in%spleen% 100# 0 0 0# Veh.# Tam.# Veh.# Tam.# Veh# Tam# T8ALL%cells%in%spleen%(millions)% Vehicle Vehicle Tamoxifen Tamoxifen Figure 3.6: Cre activation does not result in changes in T-ALL disease progression or PPP activity

Primary T-ALL was generated on a wild type (no floxed alleles);Rosa26CreERT2 background and transplanted into sublethally irradiated recipient mice. Mice were treated with vehicle or tamoxifen for 4 consecutive days to induce Cre activity 4 days after T-ALL transplant and (A) survival was monitored. Other mice were treated with vehicle or tamoxifen 10 days after transplant, sacrificed 2 days after treatment completion (B) and the (C) cellularity of the spleen, (D) number of T-ALL cells in the spleen and (E) percentage of T-ALL cells in the spleen was assessed. T-ALL cells were isolated and the (E) pentose phosphate pathway activity was measured by production of 14CO2 from 1-14C-Glucose. Data are representative of two independent experiments with at least 5 mice per group. Data are shown as the mean and standard deviation. (ns. Not Significant)

3.2.2 Glycolysis is selectively restrained in T-ALL

While we found that T-ALL cells exhibited increased glycolytic activity relative to naïve T cells and required glycolysis for survival and proliferation, how the metabolism of T-ALL cells compared to normally proliferating T cells was still unclear.

Therefore, we next compared T-ALL metabolism to that of normal activated T cells.

Using non-targeted mass spectrometry metabolomics analysis, samples of primary T-

ALL were compared to naïve ex vivo murine T cells and T cells that were stimulated for

24 or 48 hours with plate bound anti-CD3 and anti-CD28. We utilized unsupervised hierarchical clustering and principle component analysis (PCA) to analyze metabolite levels, with each analysis indicating that the metabolic profile of T-ALL cells is notably distinct from that of naïve ex vivo T cells as well as 24 and 48 hr activated T cells (Figure

3.7A, 3.7B). We next performed a pathway analysis of the metabolomics data, which indicated that T-ALL cells had increased concentrations of metabolites associated with the TCA cycle, glutathione metabolism, nucleotide synthesis and fatty acid oxidation when compared to naïve T cells. When compared to 48 hr stimulated T cells, T-ALL cells were found to have increased concentrations of metabolites associated with the oxidation of amino and fatty acids and lower levels of metabolites associated with glycolytic metabolism, amino acid metabolism, nucleotide production, and mitochondrial activity (please see Kishton et al 2016 for raw data).

Figure 3.7: Primary T-ALL metabolism is distinct from that of normal T cells

Naive T cells, T cells that were stimulated in culture for 24 or 48 hrs and primary T-ALL cells were isolated and extracted for metabolomics analysis. Metabolite levels were analyzed by (A) unsupervised hierarchical clustering or (B) PCA analysis using Metaboanalyst software. Data include at least 3 biological replicates per group.

We next utilized direct measurements of metabolic characteristics to compare primary T-ALL cells to naïve and activated T cells. These assays consistently showed that primary murine T-ALL cells had significantly elevated glucose uptake, glycolytic flux through enolase and PPP flux compared to naïve T cells (Figure 3.8A).

Interestingly, the flux through these pathways was significantly lower in primary T-ALL cells than in activated T cells, indicated that T-ALL glycolytic metabolism was operating below the maximal capacity of the T cell lineage. However, we found that T-ALL cell

mitochondrial content and membrane potential were elevated from the levels seen in naïve T cells and were similar to 48 hr stimulated T cells (Figure 3.8B-C).

A$ PPP$Oxida2on$ Glucose$Uptake$ Glycolysis$ 100" 6000" 20000" * * 5000" * 80" 16000" 4000" ns 12000" 60" ns 3000"

* CPM$ CPM$ 40" * 8000" * * 2000" Glycoly2c$rate$ 4000" 20" 1000"

0" (nmol$glucose/million$cells/hr$ 0" 0" Naïve" TALL" 24"hr" 48"hr" Naïve" TALL" 24"hr" 48"hr" Naïve" TALL" 24"hr" 48"hr" ac3v." ac3v." ac3v." ac3v." ac3v." ac3v." B$ C$ Naïve T cell 48 hr. activ. T cell Mitotracker$$ TMRE$$ ns T-ALL ns 4.5" 3.5" * 4" 3" 3.5" 3" * 2.5" 2.5" 2" 2" 1.5" 1.5" 1" 1"

Normalized$MFI$ 0.5" Normalized$MFI$ 0.5" $(geometric$mean)$ $(geometric$mean)$ 0" 0"

Mito"green" TMRE" T@ALL" T@ALL"

Naïve"T"cell" Naïve"T"cell" 48"hr"ac3vated"T" 48"hr"ac3vated"T"

Figure 3.8: Glycolysis is limited in primary T-ALL, while mitochondrial metabolism is similar to activated T cells

Naïve murine T cells, T cells that were activated in culture for 24 or 48 hrs and primary T-ALL were isolated and assayed (A) with radiolabeled tracers to measure glucose uptake, glycolytic flux through enolase and PPP pathway oxidation and (B) mitotracker green and TMRE staining by flow cytometry. (C) Normalized MFI of mitotracker green and TMRE staining is shown. Data is representative of at least 3 independent experiments. Data are shown as the mean and standard deviation (* p < 0.05).

3.2.3 Oncogenic Notch regulates glycolytic and oxidative metabolism

It is likely that Notch may have diverse and context dependent effects on cell metabolism. To examine the metabolic effects of oncogenic Notch in the context of T-

ALL, we retrovirally transduced oncogenic Notch (ICN) into primary murine hematopoietic progenitor cells (HPCs), the cell of origin of Notch-driven T-ALL (Chiang et al., 2008). After retroviral transduction, ICN expressing cells were cultured for 4 days on OP9 bone marrow stromal cells and compared to vector control transduced cells.

ICN expression resulted in an increase in cell size in primary HPCs, as measured by forward scatter flow cytometry analysis (FSC) (Figure 3.9A). In a reciprocal experiment, treatment of the Notch-dependent human T-ALL cell lines DND41 and HPB-ALL with gamma secretase inhibitor (GSI) to block Notch signaling resulted in decreased FSC

(Figure 3.9B). Notch-ICN expression also resulted in increased cell surface localization of

Glut1 in primary HPCs as seen in cells expressing Glut1myc (Figure 3.9C) and also drove elevated total Glut1 and HK2 expression (Figure 3.9D). Conversely, GSI treatment of

DND41 and HPB-ALL cells inhibited the expression of HK2 (Figure 3.9E). We next performed a radiolabeled glucose uptake assay and found that retroviral expression of

Notch-ICN resulted in increased glucose uptake in HPCs (Figure 3.9F).

Figure 3.9: Oncogenic Notch signaling stimulates glycolytic metabolism

A, C-D, F. Lineage negative hematopoietic cells were isolated from the bone marrow of wild type (A, D, F or Glut1myc expressing (C) mice and retrovirally transduced with ICN1 or vector control. Cell size by forward scatter (FSC) (A) and surface Glut1myc expression (C) were measured by flow cytometry. Total Glut1 and HK2 expression were analyzed by immunoblot (D). Virally transduced cells were isolated to measure (F) glucose uptake. B, E. Human T-ALL cell lines DND41 and HPB-ALL were treated with DMSO vehicle or 100 nM Compound E for 96 hrs and (B) cell size was measured by FSC.

Cell lysates were also analyzed by immunoblot for (E) HK2. Data are representative of at least three independent experiments. Data are shown as the mean and standard deviation (* p < 0.05).

We found that the expression of oncogenic Notch also caused changes in the mitochondrial metabolism of both primary HPCs and T-ALL cell lines. Transduction of notch-ICN in HPCs resulted in increased expression of cytochrome C (Figure 3.10A), while GSI treatment of human T-ALL cell lines resulted in decreased cytochrome C expression (Figure 3.10B). The expression of Notch-ICN in primary HPC cells also resulted in increased basal and maximal OCR (Figure 3.10C, 3.10D), mitochondrial content and membrane potential (Figure 3.10E).

Figure 3.10: Oncogenic Notch signaling promotes mitochondrial metabolism

(A, C-E) Lineage negative hematopoietic cells were isolated from the bone marrow of wild type mice and retrovirally transduced with ICN1 or vector control. Cells were analyzed for (A) cytochrome C expression by immunoblot (C) basal and maximal oxygen consumption rates and (E) mitotracker green and TMRE staining by flow cytometry. (B) Human T-ALL cell lines DND41 and HPB-ALL were treated with DMSO vehicle or 100 nM Compound E for 96 hrs and cytochrome c expression was analyzed by immunoblot. Data are representative of at least three independent experiments. Data are shown as the mean and standard deviation (* p < 0.05).

Previous literature has shown that Notch signaling can modulate the activity of both the PI3K/AKT/mTOR and c-Myc signaling pathways (Palomero et al., 2006). To explore whether these pathways were regulating the metabolic changes brought upon by oncogenic Notch signaling, we treated Notch transduced HPCs with the PI3K pathway inhibitors rapamycin, LY294002 and PP242, and the bromodomain inhibitor

JQ1, previously shown to inhibit c-Myc activity (Delmore et al., 2011), and measured effects on metabolism. Each treatment resulted in a significant reduction in cell size

(Figure 3.11A), surface Glut1 (Figure 3.11B) and HK2 expression (Figure 3.11C), showing that multiple signaling pathways contribute to T-ALL glycolytic metabolism.

Figure 3.11: The PI3K pathway and c-Myc mediate Notch effects on metabolism

Murine lineage negative hematopoietic cells were isolated from the bone marrow of wild type (A-C) or Glut1myc expressing (B) mice and retrovirally transduced with ICN1 or vector control. A-C. Cells were treated with vehicle, rapamycin (20 nM), LY29004 (10 µM), PP242 (1 µM) or JQ1 (1 µM) for 16 hrs and (A) cell size, (B) surface Glut1myc expression and (C) hexokinase 2 expression were measured by flow cytometry. Data are representative of at least three independent experiments. Data are shown as the mean and standard deviation (* p < 0.05).

3.2.4 Oncogenic Notch signaling in T-ALL results in metabolic stress and AMPK pathway activation

Because we observed that the PI3K/Akt/mTORC1 and c-Myc pathways contributed to T-ALL metabolism, we next compared these pathways in primary T-ALL and activated T cells. We found that c-Myc expression levels were similar in primary T-

ALL and activated T cells, with both having far more expression than naïve T cells

(Figure 3.12).

c"Myc%

Ac'n% .% ac'v % hr Naïve% Primary%% 48% mouse%T%cell% mouse%T%cell% mouse%T"ALL%

Figure 3.12: c-Myc expression is similar in primary T-ALL and activated T cells

Primary murine T-ALL and naïve and activated T cells were examined for c-Myc expression by western blotting. Figure is representative of three independent experiments.

Because c-Myc expression was similar in activated T cells and T-ALL, and we found that the PI3K pathway mediated at least some of the metabolic changes that we observed in Notch-ICN transduced cells, we next examined the activity of this pathway in T cells and T-ALL. Interestingly, in HPCs, Notch-ICN expression drove an increase in

phospho-mTOR, but the mTORC1 downstream effector phospho-S6 ribosomal protein remained surprisingly low (Figure 3.13).

pmTOR' (S2448)' mTOR' pS6'

S6'

Ac8n' ICN'

Vector'

Figure 3.13: Oncogenic Notch activates mTOR, yet mTORC1 activity is not elevated

Lineage negative hematopoietic cells were isolated from the bone marrow of wild type mice and retrovirally transduced with ICN1 or vector control and analyzed by immunoblot. Data are representative of three independent experiments.

Consistent with this, samples of primary murine T-ALL exhibited increased phospho-mTOR compared to naïve and 48 hr in vitro stimulated T cells, but also had low levels of phospho-S6 and -4EBP1. Additionally, T-ALL cells exhibited a reduction in pS6K levels relative to total S6K (Figure 3.14).

This pattern of activity in the PI3k pathway suggested that the AMPK signaling pathway could be active in T-ALL and acting to suppress mTORC1 activity. Previous

work has shown that oncogenic signals may induce metabolic stresses that activate the

AMPK pathway (Liu et al., 2012). We found that, compared to naïve T cells, phosphorylation of AMPK and the AMPK target, ACC, were indeed increased in both primary murine T-ALL and activated T cells (Figure 3.14). Interestingly, RAPTOR at

Ser792 was phosphorylated in T-ALL cells (Figure 3.14), while this site remained unphosphorylated in activated T cells. Recent work has shown that the phosphorylation of this RAPTOR site by AMPK can act to negatively regulate glycolysis and mTORC1

(Gwinn et al 2008). Consistent with this idea, naïve T cells also exhibited high levels of phospho-RAPTOR S792 that correlate with low glycolysis and mTORC1 activity.

pmTOR% pAMPKα% (S2448)%% AMPKα% mTOR% pRAPTOR%% pACC% %%%(S792)% % ACC% RAPTOR% pTSC2% Ac#n% (S1387)% .% TSC2% ac#v pS6K%% % hr Naïve%

(Thr389)% Primary%% 48% mouse%T%cell% mouse%T%cell% S6K% mouse%TIALL%

p4EBP1%

4EBP1%

pS6%

S6%

Ac#n% .% ac#v % hr Naïve% Primary%% 48% mouse%T%cell% mouse%T%cell% mouse%TIALL%

Figure 3.14: The AMPK pathway is activated in primary murine T-ALL

Primary murine T-ALL and naïve and activated T cells were analyzed by immunoblot. Data are representative of three independent experiments.

Indicating that the activation of the AMPK pathway may be a feature of human T-ALL, phosphorylation of the AMPK substrate ACC was also elevated in two independent primary human T-ALL (Figure 3.15).

pACC$ pACC$

ACC$ ACC$

Ac&n$ Ac&n$ T$cells$$ $T$cells$$ T1ALL$2$ T1ALL$1$ Naïve$human$$ Naïve$human$ Primary$human$ Primary$human$

Figure 3.15: The AMPK pathway is activated in primary human T-ALL

Two independent samples of primary human T-ALL and CD4+ T cells isolated from healthy donors were analyzed by immunoblot. Data are results of two independent experiments.

Indicating a role for Notch signaling in this pathway, Notch-ICN was sufficient to activate AMPK in primary HPCs (Figure 3.16A). Reciprocally, the inhibition of Notch in human T-ALL cell lines by GSI treatment resulted in decreased AMPK pathway activity

(Figure 3.16B).

A" Mouse"HPC" B" pAMPKα' DND.41" HPB.ALL" ' cell"line" cell"line" 'AMPKα' pAMPKα' ' ' pACC' AMPKα'

ACC' pACC'

ICN' ACC'

Ac1n' Ac1n' ' ' ICN' GSI' GSI' Veh Veh

Vector'

Figure 3.16: Oncogenic Notch drives AMPK activation that is observed in T-ALL

(A) Lineage negative hematopoietic cells were isolated from the bone marrow of wild type mice and retrovirally transduced with ICN1 or vector control and analyzed by immunoblot. (B) Human T-ALL cell lines DND41 and HPB-ALL were treated with vehicle or 100 nM Compound E for 96 hrs and analyzed by immunoblot. Data are representative of at least two separate experiments.

We next examined the metabolomics data for the levels of ATP, ADP, and AMP in T-ALL cells compared to naïve T cells to determine if AMPK was being activated in T-

ALL by reduced ATP ratios. We observed that primary murine T-ALL cells had reduced levels of ATP compared to naïve T cells (Figure 3.17A), as well as decreased ratios of

ATP/AMP (Figure 3.17B) and ATP/ADP (Figure 3.17C), suggesting that T-ALL cells experience chronic ATP-insufficiency that may promote AMPK pathway activation.

Figure 3.17: Primary T-ALL is chronically ATP deficient

Murine naïve T cells and purified primary T-ALL cells were and samples were extracted and analyzed using high-resolution LC-QE-MS for metabolomics analysis of (A) ATP levels, (B) ATP/AMP ratio and (C) ATP/ADP ratio. Data are representative of at least four biological replicate samples. Data are shown as the mean and standard deviation (* p < 0.05).

3.2.5 AMPK signaling suppresses mTORC1 activity in primary T-ALL, resulting in decreased aerobic glycolysis

To determine the role for AMPK pathway activity in primary T-ALL we next tested the effects of AMPK loss on primary T-ALL cells in vivo. Previous work has shown that T cells exclusively express the catalytic α1 subunit of AMPK (MacIver et al.,

2011), therefore we generated T-ALL on an AMPKα1fl/fl;Rosa26CreERT2 background to directly test the role of AMPK in T-ALL physiology and survival. Using a similar procedure as with Glut1 and HK2 deletion in T-ALL, we transplanted primary T-ALL into cohorts of sublethally irradiated recipients that were treated with tamoxifen for 4

days after 10 days engraftment. Consistent with AMPKα1 being the major isoform of

AMPKα in T-ALL, tamoxifen treatment resulted in a large decrease in total AMPKα protein expression levels, along with a sharp reduction in the phosphorylation of AMPK target ACC (Figure 3.18). The genetic deletion of AMPK in T-ALL also resulted in a reduction of inhibitory phosphorylation of RAPTOR at the AMPK target (Figure 3.18).

There was also an increase in mTORC1 activity, with elevated levels of phospho-S6 kinase, phospho-S6 ribosomal protein, and phospho-4EBP1 (Figure 3.18).

Vehicle& Tamoxifen&

AMPKα&

1.0& 1.01& 0.92& 0.35& 0.25& 0.39& * pACC&

ACC&

1.0& 1.39& 1.10& 0.47& 0.36& 0.14& * pRAPTOR& &&&(S792)& RAPTOR&

1.0& 0.61& 1.03& 1.66& 1.86& 1.57& * pS6K&

S6K&

1.0& 0.82& 0.96&2.07&1.59& 1.57& * pS6&

&S6&

1.0& 0.77& 0.83&1.66&1.49&1.67& * p4EBP1&

4EBP1&

Ac*n&

Figure 3.18: AMPK signaling negatively regulates mTORC1 in T-ALL

quantifications are normalized to total protein level where applicable or to actin (* p < 0.05).

AMPK deletion also resulted in increased expression of glycolytic proteins, with

Glut1 and HK2 observed as being increased in AMPKα1 deficient T-ALL cells (Figure

3.19).

Vehicle% Tamoxifen% 1.0% 0.84%1.12% 1.31%1.29% 1.65% * Hexokinase%II% 1.0% 0.84% 1.11% 1.56% 1.26% 1.38% *

Glut1%

Ac#n%

Figure 3.19: AMPK signaling negatively regulates the expression of glycolytic proteins in T-ALL

Primary T-ALL was generated on an AMPKα1 inducible deletion background and transplanted into sublethally irradiated recipient mice. Mice were treated with vehicle or tamoxifen for 4 days starting 10 days after transplant and sacrificed 2 days after treatment completion. Purified T-ALL cells from vehicle and tamoxifen treated groups were isolated and analyzed by immunoblot. Data are representative of two independent experiments with three independent T-ALL samples per group. Immunoblot quantifications are normalized to total protein level where applicable or to actin (* p < 0.05).

Functionally, AMPK deletion in vivo resulted in a shift towards glycolytic metabolism in T-ALL, with an observed increase in ECAR (Figure 3.20A), along with an increase in the expression of genes in the PPP (Figure 3.20B, Appendix B). Importantly, alterations in glycolytic flux were not due to Cre activation (Figure 3.20C).

A" ECAR" B" Pentose"phosphate"pathway" C" "ECAR" ns 2.5" Vehicle" Tamoxifen" * 2.00" 2" Taldo1" H6pd" 1.50"

Rpia" /min)" 1.5" G6pdx"

Rbks" mpH 1.00" 1" Tkt" Prps1" 0.50" ECAR"( ECAR"(mpH/min)" 0.5" Rpe" Magnitude"of"gene"expression" 0" 0.00" Veh." Tam." Veh" Tam" Min" Average" Max"

Figure 3.20: AMPK signaling inhibits glycolysis in T-ALL

A-B. Primary T-ALL was generated on an AMPKα1 inducible deletion background and transplanted into sublethally irradiated recipient mice. Mice were treated with vehicle or tamoxifen for 4 days starting 10 days after transplant and sacrificed 2 days after treatment completion. Purified T-ALL cells from vehicle and tamoxifen treated groups were isolated and (A) ECAR was measured. (B) Isolated T-ALL cells from four separate mice in vehicle and tamoxifen treated groups were examined by rtPCR for select PPP gene expression. (C) Primary T-ALL was generated on a wild type (no floxed alleles);Rosa26CreERT2 background and transplanted into sublethally irradiated recipient mice. Mice were treated with vehicle or tamoxifen for 4 consecutive days to induce Cre activity 10 days after T-ALL transplant and T-ALL was isolated and ECAR was measured. Data are representative of experiments with four independent T-ALL samples per group. Data are shown as the mean and standard deviation (* p < 0.05).

Metabolomic analysis indicated an increased abundance of metabolites (Figure

3.21A-C, Appendix C) that are associated with associated with mTORC1-regulated anabolic synthesis of nucleotides (Ben-Sahra et al., 2013; Robitaille et al., 2013). This indicates that AMPK plays an active role in restraining mTORC1 signaling in T-ALL, with consequent repressing in glycolysis and de novo synthesis of nucleotides.

A" N1carbamoyl"aspartate"" Orotate"" * 3" * 3.5" 2.5" 3" 2.5" 2" 2" 1.5" 1.5" 1" 1" 0.5"

Normalized"abundance" 0.5" Normalized"abundance" 0" 0" Veh." Tam." Veh." Tam." B" C" 51phospho1ribosylamine"" 21deoxy1D1ribose"phosphate"" 2" * 2.5" * 2" 1.5" 1.5" 1" 1"

0.5" 0.5" Normalized"abundance" Normalized"abundance" 0" 0" Veh." Tam." Veh." Tam."

Figure 3.21: AMPK signaling inhibits de novo nucleotide synthesis in T-ALL

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groups were isolated and were extracted and analyzed using high-resolution LC-QE- MS. The normalized abundance of select metabolites is shown. Data are representative of an experiment with four independent T-ALL samples per group. Data are shown as the mean and standard deviation. (* p < 0.05).

3.2.6 AMPK pathway signaling promotes oxidative metabolism through regulation of mitochondrial Complex I

AMPK has been shown to be able to drive mitochondrial metabolism in order to mitigate metabolic stress (Lantier et al., 2014). Consistent with AMPK playing this role in primary T-ALL, we observed a reduction in the OCR (Figure 3.22A) and ratio of

OCR/ECAR was significantly lowered following acute AMPKα1 deletion in T-ALL cells

(Figure 3.22B), indicative of a shift towards glycolytic metabolism due to AMPK. We also observed that the deletion of AMPK in primary T-ALL also resulted in a significant reduction in OCR coupled to ATP production, as defined by oligomycin-sensitive OCR

(Figure 3.22C). No decrease was observed in OCR as a result of Cre activation alone

(Figure 3.22D).

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OCR$coupled$ A$ OCR$ B$ OCR/ECAR$ C$ D$ OCR$ to$ATP$produc4on$ ns 525" * 350" * 250" * 600" 450" 300" 200" 500"

375" 250" /min)$ /min)$ 400" 300" 200" 150" 300" pmoles

225" 150" pmoles 100" 200" 150" 100" OCR$( OCR$( OCR/ECAR$ra4o$

OCR$(pmoles/min)$ 50" 100" 75" 50" 0" 0" 0" 0" Veh" Tam" Veh." Tam." Veh." Tam." Veh." Tam."

Figure 3.22: AMPK signaling promotes mitochondrial oxidation in T-ALL

A-C. Primary T-ALL was generated on an AMPKα1 inducible deletion background and transplanted into sublethally irradiated recipient mice. Mice were treated with vehicle or tamoxifen for 4 days starting 10 days after transplant and sacrificed 2 days after treatment completion. Purified T-ALL cells from vehicle and tamoxifen treated groups were isolated and (A) OCR, (B) OCR/ECAR ratio and (C) ATP coupled OCR, defined as oligomycin sensitive OCR, were measured. D. Primary T-ALL was generated on a wild type (no floxed alleles);Rosa26CreERT2 background and transplanted into sublethally irradiated recipient mice. Mice were treated with vehicle or tamoxifen for 4 consecutive days to induce Cre activity 10 days after T-ALL transplant and T-ALL was isolated and ECAR was measured. Data represent an experiment with four independent T-ALL samples per group. Data are shown as the mean and standard deviation (* p < 0.05).

Further, metabolomics analysis showed an increase in the ratio of NAD+/NADH in AMPKα1 deficient T-ALL cells (Figure 3.23A) and we observed an increase in ROS levels (Figure 3.23B), suggesting that AMPK was essential to maintain a sufficient reductive mitochondrial capacity to prevent oxidative stress. Interestingly, we observed that mitochondrial membrane potential as measured by TMRE was increased upon

AMPK deletion (Figure 3.23C).

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Veh. A' NAD+/NADH'' B' Tam. 20" * DCFDA'MFI'' 8000" * 15"

6000"

10" MFI'' 4000"

2000" 5" (geometric'mean)' NAD+/NADH'ra*o' 0" Tam." 0" DCFDA" Veh." Veh." Tam."

C' TMRE'MFI'' * 70

MFI'' 50

(geometric'mean)'

30 Veh ." Tam."

Figure 3.23: AMPK signaling promotes functional mitochondria in T-ALL

Primary T-ALL was generated on an AMPKα1 inducible deletion background and transplanted into sublethally irradiated recipient mice. Mice were treated with vehicle or tamoxifen for 4 days starting 10 days after transplant and sacrificed 2 days after treatment completion. Purified T-ALL cells from vehicle and tamoxifen treated groups were isolated and (A) cells were analyzed using high-resolution LC-QE-MS. The normalized abundance of select metabolites is shown. B-C. T-ALL cells were isolated from vehicle and tamoxifen treated mice for (B) Flow cytometry analysis of DCFDA and (C) TMRE. Data represent (A) an experiment with four mice per group or (B-C) are representative of two independent experiments with n = 8 per group Data are shown as the mean and standard deviation (* p < 0.05).

RT-PCR analysis of mitochondrial gene expression indicated that AMPK selectively regulated the expression of electron transport Complex I, while other mitochondrial complexes were not consistently regulated by AMPK (Figure 3.24,

Appendix D).

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Complex(I(

Max*

Average*

Min* Magnitude*of* *gene*expression*

Complex(II( Complex(III( Complex(IV( ATP(synthase(

Figure 3.24: AMPK signaling regulates Complex I gene expression in T-ALL

Primary T-ALL was generated on an AMPKα1 inducible deletion background and transplanted into sublethally irradiated recipient mice. Mice were treated with vehicle or tamoxifen for 4 days starting 10 days after transplant and sacrificed 2 days after treatment completion. Purified T-ALL cells from vehicle and tamoxifen treated groups were isolated and rtPCR measurement of select mitochondrial energy pathway gene expression was performed. Unsupervised hierarchical clustering of gene expression is shown. Data represent an experiment with four independent T-ALL samples per group.

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Importantly, direct biochemical measurement of Complex I activity levels in cell lysates revealed a reduction in activity in AMPKα1 deficient T-ALL cells (Figure 3.25).

Complex'I'ac1vity'

0.1" Veh. 4" * Tam. 0.08" 3.5" 3" 0.06" 2.5" /min' OD450' 2" 0.04" mOD 1.5" 1" 0.02" 0.5" 0" 0" Veh." Tam." 0" 10" 20" 30" Time'(min)'

Figure 3.25: AMPK signaling regulates Complex I activity in T-ALL

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3.2.7 Loss of AMPK signaling or direct pharmacological inhibition of Complex I results in reduced T-ALL cell survival and slowed disease progression

We next observed the effects of AMPK deletion in T-ALL on disease progression.

In spite of the observed increase in mTORC1 activity upon AMPK deletion in T-ALL, there were reductions in the number and percentages of splenic, lymph node, and bone marrow T-ALL cells following AMPK deletion (Figure 3.26A-C).

A" Spleen" Spleen" * * 200%

80%

150% 60%

100%

(millions)" 50% 40% #"T$ALL"cells"" 0% Percent"T$ALL" 20% Veh.% Tam.% Veh.% Tam.% B" C" Inguinal"LN" Bone"Marrow" * * 8% 10% 6% 8%

6% 4% 4%

(millions)" (millions)" 2%

#"T$ALL"cells"" 2% #"T$ALL"cells"" 0% 0% Veh.% Tam.% Veh.% Tam.%

Figure 3.26: AMPK loss in T-ALL results in reduced tumor burden

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are representative of three independent experiments with at least five mice per group in each experiment. Data are shown as the mean and standard deviation (* p < 0.05).

These reductions in AMPK-deficient T-ALL cells were observed in the context of increased T-ALL cell staining for Annexin V, an indicator of the early stages of apoptosis, and an overall reduction in the percentage of viable cells (Figure 3.27A).

Interestingly, cell proliferation, as measured by Ki67 staining, was not significantly altered and even trended towards an increase in AMPK deficient T-ALL (Figure 3.27B).

A" Veh." Tam." Early"apoptosis" Live"cells" "+/"

* /PI" 90# *

40#

30# 80# #V#

#V#

70# Annexin 20# Annexin

10# nega5ve" 60# PI"nega5ve"

Annexin Annexin 0# 50# Percent" PI# PI# Veh.# Tam.# Percent" Veh..# Tam.#

B" Prolifera5ng"cells" ns 95# 90# 85# posi5ve"

Percent"Ki67"" 80# 75# Veh.# Tam.#

Figure 3.27: AMPK loss in T-ALL increases apoptosis but does not alter proliferation

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least seven mice per group. Data are shown as the mean and standard deviation (* p < 0.05, ns = not significant).

Importantly, we found that the lower T-ALL cell numbers correlated with increased animal survival upon AMPK deletion (Figure 3.28). Interestingly, mice that received tamoxifen to delete AMPK succumbed to disease, albeit more slowly. This may be indicative of T-ALL selecting for cells that did not efficiently delete AMPK. To explore if AMPK is also maintained in human disease, we examined two published data sets (Homminga et al., 2011; Sanghvi et al., 2014) and found that there was no loss of expression of AMPKα1 in human T-ALL compared with control cells. Therefore, in spite of a clear role for AMPK in the suppression of T-ALL anabolic metabolism, AMPK appears essential for primary T-ALL cell survival.

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Veh. 100" * Tam.

50"

Percent'survival' 0" 0" 10" 20" 30" Days'

Figure 3.28: AMPK loss in T-ALL slows disease progression

Primary T-ALL was generated on an AMPKα1 inducible deletion background and transplanted into sublethally irradiated recipient mice. Mice were treated with vehicle or tamoxifen for 4 days beginning 4 days after transplant and given daily 4- hydroxytamoxifen (5 mg/kg body weight) for 5 days beginning 10 days after transplant to maintain gene deletion and overall survival was measured. Data shown is an experiment with ten mice per group. Data are representative of three independent experiments with at least five mice per group. Kaplan-Meier survival curve is shown (* p < 0.05).

Because AMPK promoted T-ALL cell survival and mitochondrial Complex I, we hypothesized that AMPK stimulates mitochondrial function to relieve metabolic stress that would otherwise lead to apoptosis. We, therefore, next tested the effects of the direct pharmacological inhibition of mitochondrial electron transport on T-ALL.

Purified primary murine T-ALL cells, naïve T cells and 48 hr stimulated T cells were treated with low doses of the Complex I inhibitor rotenone (100 nM, Figure 3.29A). We observed that rotenone treatment did not decrease the viability of naïve and stimulated

T cells, but caused rapid T-ALL cell death. While rotenone is a useful tool for studying

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Complex I in in vitro settings, it may not be utilized in in vivo settings. Therefore, we also treated primary murine T-ALL cells, naïve T cells and 48 hr stimulated T cells with the clinically relevant Complex I inhibitor phenformin. We found that phenformin, similar to rotenone, did not impact the viability of naïve and stimulated T cells, but rapidly killed primary T-ALL (Figure 3.29B). Consistent with phenformin acting through the inhibition of Complex I, we also found that phenformin treatment of primary murine T-

ALL reduced mitochondrial oxygen consumption rate (Figure 3.29C) and increased

ECAR (Figure 3.29D).

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A% B% Naïve Rotenone% Phenformin% T cell 150" 150" T-ALL Activ. 100" 100" T cell

50" * * * 50" * * * (PI%exclusion)% 0" (PI%exclusion)% 0" 0" 20" 40" 0" 20" 40" Normalized%percent%alive% Time%(hrs)% Normalized%percent%alive% Time%(hrs)% C% D% OCR% ECAR% * 250" * 1.20" 200" /min)%

/min)% 0.80" 150" mpH

pmoles 100" 0.40" 50" ECAR%( OCR%( 0" 0.00" Veh." Phen." Veh." Phen."

Figure 3.29: Mitochondrial metabolism and Complex I activity promote T-ALL survival

Primary murine T-ALL was generated on a wild type background. T-ALL was isolated and, (A) along with naïve T cells and T cells that were activated in vitro for 48 hrs, were treated with 100 nM rotenone or DMSO vehicle or (B) 100 µM phenformin or PBS vehicle and cell survival was measured over time by propidium iodide exclusion flow cytometry. Viability normalized to vehicle treatment is shown. Isolated T-ALL was treated for 45 minutes with 100 µM phenformin or PBS vehicle and (C) OCR and (D) ECAR were measured. Data are representative of (A-B) three independent experiments or (C) an experiment with three independent primary T-ALL samples per group. Data are shown as the mean and standard deviation (* p < 0.05).

We then examined the impact of treating primary T-ALL with phenformin in vivo. We generated primary wild type T-ALL and transplanted this T-ALL into cohorts

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of secondary recipient mice and dosed daily with phenformin or vehicle for 10 consecutive days. After this treatment, the mice were rested for 2 days then sacrificed for analysis. We found that T-ALL cells from mice that were treated with phenformin had elevated levels of cytoplasmic ROS (Figure 3.30A), consistent with previous reports indicating that Complex I inhibition results in increased ROS levels (Li et al., 2003).

Importantly, we found that phenformin treatment reduced the number (Figure 3.30B) and percentage (Figure 3.30C) of T-ALL cells in the treated mice and also resulted in a significant increase in overall animal survival (Figure 3.30D).

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Veh. DCFDA$MFI$ Phen. 4000% * 3000% 2000% MFI$$ 1000%

(geometric$mean)$ 0% DCFDA% Vehicle% Phenformin% B$ C$ * * 200% 80% 150% 60% 100% 40% (millions)$

#$T7ALL$cells$$ 50% 20% Percent$T7ALL$ 0% 0% Vehicle% Phenformin% Vehicle% Phenformin%

D$ Veh. (n=9) Phenformin%% Phen. (n=7) treatment% * 100

Percent$survival$ 0 0 10 20 30 40 Days$elapsed$

Figure 3.30: Inhibition of Complex I reduces T-ALL tumor burden in vivo

Primary murine T-ALL was transplanted into sublethally irradiated recipient mice. Mice were dosed daily with vehicle (PBS) or phenformin (100 mg/kg body weight) for 10 consecutive days starting two days after T-ALL transplant. (A-C) Mice were rested for 2 days, sacrificed and (A) DCFDA staining, (B) spleen cellularity and (C) T-ALL cell burden in the spleen were analyzed for each group. (D) Mice were monitored for survival. Data are representative of an experiment with at least 7 mice per group. Data are shown as the mean and standard deviation (* p < 0.05).

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3.3 Discussion

The metabolic profile of tumor cells has largely been defined by and compared with that of resting mature tissues. However, a comparison of tumor metabolism with that of normal proliferative cells of similar background may unlock vulnerabilities or specific requirements of tumor cells. In this work, we compared the metabolism of primary T-ALL cells with that of resting and proliferative T cells and found that, while

T-ALL cells utilize glycolysis in a manner consistent with aerobic glycolysis when compared with resting T cells, the glycolytic activity of T-ALL cells is far below the levels of activated T cells. We attribute this, at least in part, to the inhibitory activity of

AMPK pathway signaling. Specifically, we found that oncogenic Notch signaling induced metabolic stress in T-ALL cells that drove AMPK activation. AMPK then acts to limit glycolytic metabolism while promoting mitochondrial oxidative metabolism to mitigate metabolic stress. The genetic loss of AMPK or pharmacological inhibition of mitochondrial metabolism in T-ALL resulted in decreased cell viability and may represent a new approach to treat T-ALL or similar cancers.

The stimulation of effector T cells by antigen recognition drives a metabolic shift towards the usage of aerobic glycolysis to support the rapid proliferation that is required to successfully mount an immune response. Previous work from our lab has shown that, as in some cancers (Liu et al., 2014a), the inhibition of effector T cell

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glycolysis can result in reduced cell viability and loss of immunological function

(Gerriets et al., 2015; Macintyre et al., 2014). Because of the hypothesized similarity between activated T cells and tumor cells in terms of metabolism, activated T cells have been proposed as a model for cancer metabolism (Macintyre and Rathmell, 2013).

However, recent work has made it clear that activated T cells tend to exhibit short lifespans unless they make the transition from a glycolytic metabolism towards an oxidative phenotype (Sukumar et al., 2013; van der Windt et al., 2012). Although it is thought that cancer may select for cells with maximal proliferative capacities, cells with a prolonged capacity to replicate and maintain survival are also favorable for the development and maintenance of a tumor. Consistent with this notion, we found that metabolically, T-ALL cells share characteristics with long-lived subtypes of T cells, utilizing a mixed metabolic program where glycolytic metabolism is balanced with mitochondrial oxidative metabolism.

We believe that there are several factors that may play a role in the observed differences between activated T cell and T-ALL metabolism. Firstly, we compared acutely stimulated T cells with cancer cells that continuously receive oncogenic signals that promote growth and proliferation. There are reports in literature that chronically stimulated T cells, such as those in Systemic Lupus Erythematosus patients, have hyperpolarized mitochondria, suggesting that chronic autoimmunity may lead to mitochondrial compensation (Gergely et al., 2002). In further support of the notion that

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chronic stimulation may play a role in mediating this metabolic phenotype, the immune modulatory protein PD-1 has been reported to restrain T cell glycolysis (Patsoukis et al.,

2015) and can lead to functional exhaustion (Zinselmeyer et al., 2013) in chronic infection or cancer. The specific mechanisms that influence chronically stimulated T cells are unclear at this time, but a recent report indicated that AMPK signaling is necessary to allow some T cell mediated immunity (Blagih et al., 2015). It is possible that AMPK may act similarly in T-ALL. Second, unlike in T cell activation, which is accompanied by a coordinated series of signals that promote T cell proliferation and viability, T-ALL cells experience a single and unbalanced oncogenic pro-growth signal that may lead to metabolic imbalances that result in continuous cell stress. Although we found that oncogenic Notch signaling can promote mitochondrial metabolism, it is possible that the metabolic imbalances imposed by oncogenic signaling may nevertheless lead to ATP insufficiency and metabolic stress.

Our data show a dual role for AMPK to both promote and suppress T-ALL cell metabolic pathways. Previous work has shown that AMPK can act to suppress glycolytic metabolism and the progression of B cell leukemia through the inhibition of

HIF1α (Faubert et al., 2013). Recent work has also shown that AMPK plays a critical role in maintaining AML tumor initiating cell viability when nutrients are limited through promoting the expression of Glut1 (Saito et al., 2015). Our data shows that, in T-ALL,

AMPK acts to inhibit aerobic glycolysis and Glut1 expression while promoting

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mitochondrial metabolism to maintain cell survival. These results suggest that there may be a context specific role for AMPK in cancer. Our findings here are consistent with the previously reported role of AMPK in the T cell lineage, where it can act to inhibit glycolytic metabolism and mTORC1 signaling, but also promote T cell survival (MacIver et al., 2011) and function in vivo (Blagih et al., 2015). In a similar study, an important regulator of AMPK protein stability, ARK5, was found to be critical to mitigating oncogene-induced metabolic stress and promote hepatocellular carcinoma (Liu et al.,

2012). Our findings indicate that AMPK signaling itself is crucial to maintaining cell viability in murine T-ALL. We hypothesize that AMPK pathway activity can protect T-

ALL cells from metabolic stress by reducing the energy demands of anabolic growth pathways such as mTORC1, and by promoting ATP generation through mitochondrial metabolism via the regulation of mitochondrial Complex I and catabolic metabolism.

Consistent with this model, the genetic deletion of AMPK in T-ALL resulted in increased mTORC1 signaling and anabolic metabolism, while also resulting in increased metabolic dysfunction and eventual apoptosis. Any protective effect of increased glycolytic metabolism or PPP activity, therefore, was insufficient to overcome the negative impacts of AMPK loss. It is curious that we observed, despite high AMPK pathway activity in both T-ALL and activated T cells, the AMPK substrate site in RAPTOR to be phosphorylated in T-ALL cells and naïve T cells, yet not in activated T cells,. This result

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suggests there are still unknown mechanisms of regulation of AMPK pathway interactions.

The paradoxical need for AMPK in T-ALL, while also inhibiting anabolic growth pathways, is likely due to AMPK-induced support of mitochondrial metabolism.

Consistent with this notion, the deletion of AMPK resulted in an increase in the

NAD+/NADH ratio and higher cellular ROS levels, demonstrating that AMPK plays a regulatory role in maintaining proper cellular reductive capacity. This observation is in agreement with previous work that indicated that AMPK pathway activity was necessary to maintain tumor cell redox balance (Jeon et al., 2012). In our studies, AMPK deficiency also led to reduced mitochondrial oxidative metabolism that was characterized by alterations in the activity of mitochondrial Complex I. It is likely that some of the effects of AMPK deletion on Complex I are indirect, potentially through

HIF1α (Faubert et al., 2013) which has previously been shown to negatively regulate mitochondrial metabolism (Tello et al., 2011). AMPK may also regulate Complex I activity, rather than directing large changes in complex subunit expression. While previous work has clearly shown that the loss of LKB1 and consequent reduction in

AMPK activation sensitized non-small cell lung cancer cells to mitochondrial inhibition

(Shackelford et al., 2013), we observed that mitochondrial metabolism is itself a vulnerability in T-ALL. T-ALL cells are under basal metabolic stress, and the inhibition

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of mitochondrial Complex I compounded this stress and resulted in rapid T-ALL cell death in vitro and in vivo.

Tumor cells must successfully balance the metabolic demands of rapid cellular proliferation with energetic requirements of basal survival to successfully propagate.

Interventions that deliberately skew this balance towards pro-growth activities may, therefore, result in increased levels of energy stress and result in a loss of tumor cell viability. Consistent with this idea, excessive oncogenic signaling can result in the loss of tumor cell viability (Chen et al., 2015). Oncogene-induced metabolic stress may be a key and potentially limiting feature of oncogenesis. While our findings here are limited to

Notch-induced T-ALL, oncogenes that play a dominant role across many different types of tumors, such as Ras and Myc can also each drive metabolic stress in tumor cells (Liu et al., 2012; Moiseeva et al., 2009). More specific to metabolism, the unrestrained usage of aerobic glycolysis itself may reduce cell life span. Indeed, recent work has shown that rare activating mutations in PI3K that drive increased lymphocyte glycolysis have the interesting effect of resulting in immunosuppression due to an inability to generate long-lived memory cells (Angulo et al., 2013; Lucas et al., 2014). Therefore, it seems that moderated aerobic glycolysis, along with alternative metabolic pathways, may be required for tumor and immune cells to maintain proliferative capacity and viability over the long term. In the specific case of Notch driven T-ALL, mitochondrial metabolism appears limiting and may thus offer new therapeutic targets.

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4. FoxP3 and TLR Signals Balance Treg Metabolism for Proliferation or Suppressive Function

4.1 Introduction

The activation and differentiation of CD4+ T cells into effector (Teff) or regulatory (Treg) subsets leads to a variety of changes in the cell, including shifts in metabolic characteristics of the cell to support the immunological requirements and activities of each cell type (Buck et al., 2015). Recent work has clarified the metabolic programs that various T cell subsets utilize. Teff subsets, including Th1 and Th17 cells, utilize a metabolic program that is reliant on glycolytic metabolism (Gerriets et al., 2015;

Michalek et al., 2011) and the anapleurotic usage of glutamine (Nakaya et al., 2014;

Wang et al., 2011). In contrast, Treg cells prefer a catabolic metabolic program, oxidizing fatty acids and pyruvate in the mitochondria (Beier et al., 2015; Gerriets et al., 2015). The metabolic programs utilized by specific subsets of T cells may be required for proper immune function. In the case of Teff cells, it is clear that the usage of glycolytic metabolism is required for inflammatory function. The pharmacological or genetic inhibition of Teff glycolysis results in decreased proliferation and inflammatory cytokine production. Conversely, Treg cell function is not compromised by the inhibition of glycolysis (Gerriets et al., 2015; Macintyre et al., 2014). CD8+ memory T cells require an oxidative metabolic program to properly function (O'Sullivan et al., 2014; Sukumar et al.,

2013), and there is evidence to suggest that Treg require oxidative metabolism for differentiation and homeostasis (Gerriets et al., 2015; Michalek et al., 2011).

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Recent work has shown that the metabolic reprogramming that occurs during T cell activation and differentiation is coupled to the activities of the cellular signaling pathways that regulate T cell function (Chi, 2012). In particular, several studies have identified the Phosphatidylinositide 3-kinase (PI3K)/Akt/mTORC1 pathway as playing a key role in regulating differentiated T cell metabolism (Huynh et al., 2015; Pollizzi et al.,

2015; Shrestha et al., 2015). The PI3K/Akt/mTORC1 pathway has been shown to induce the expression and cell surface localization of Glut1 in Teff to support cell proliferation and inflammatory functions (Macintyre et al., 2014). In contrast to the role of PI3K pathway signaling in promoting the function of Teff, Treg do not require mTOR

(Delgoffe et al., 2009). However, it has been observed that PI3K/Akt/mTOR signaling can be activated in Treg (Procaccini et al., 2010; Zeng et al., 2013), suggesting metabolic heterogeneity. Treg cells appear to require balanced PI3K/Akt/mTORC1 signaling for proper function. The loss of PTEN or autophagy signaling that resulted in increased downstream PI3K/Akt/mTORC1 pathway activity in Treg has been shown to result in increased Treg numbers, yet reduced Treg suppressive capacity (Huynh et al., 2015;

Shrestha et al., 2015; Wei et al., 2016). Provocatively, this loss of Treg functionality was correlated with an increase in glycolytic metabolism. However, the regulation and direct role of metabolism in regulating Treg function has remained unclear. One possible regulator of metabolism in Treg is the transcription factor FoxP3, which has previously been shown to regulate Glut1 expression by repressing Akt signaling (Basu

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et al., 2015). Other immune cell specific transcription factors, such as Bcl6, have been found to play a role in regulating cell metabolism (Oestreich et al., 2014).

In this chapter, we show that Treg metabolism is coupled to proliferative status and suppressive capacity. We found that Treg are metabolically heterogenous, with proliferating Treg having increased expression of Glut1 and heightened mTOR activity.

We show that TLR signals, which are known to promote Treg proliferation in the context of infection and in the gut, (Peng et al., 2005; Sutmuller et al., 2006; Voo et al., 2014;

Wang et al., 2015) also have effects on Treg metabolism and function. In particular,

TLR1/2 ligand interactions with Treg increased Glut1, glycolysis, PI3K/Akt/mTOR signaling, and proliferation. However, consistent with previous work on the subject,

TLR1/2-stimulated Treg were less effective suppressors (Peng et al., 2005; Sutmuller et al., 2006; Voo et al., 2014). In contrast to TLR, we found that the Treg defining transcription factor FoxP3 acted in opposition to PI3K/Akt/mTOR pathway activity and inhibited glycolytic metabolism while promoting oxidative metabolism. FoxP3 regulated the metabolic balance of Treg by reducing the expression of glycolytic and anabolic growth genes, while conversely promoting the expression of oxidative and catabolic genes.

To examine the role of the regulation of metabolism in Treg, we examined the effects on Treg of the T cell-specific transgenic expression of activated Akt or Glut1

(Michalek et al., 2011), both of which act to promote glucose metabolism. We found that

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increasing glycolysis in Treg resulted in an increase in Treg growth and accumulation.

Treg suppressive capacity and ability to protect from inflammatory bowel disease (IBD), however, were impaired and FoxP3 expression was reduced. Inflammatory TLR signals and FoxP3 thus balance Treg metabolism and function. In the presence of TLR signals,

Treg adopt a glycolytic metabolic program that promotes proliferation at the cost of suppressive capacity. FoxP3, however, promotes Treg oxidative metabolism to favor suppression and inflammatory resolution. Metabolic modulation may now allow new approaches to manipulate Treg function in inflammatory diseases.

4.2 Results

4.2.1 Natural Treg are metabolically heterogenous

While Treg are not thought to be as proliferative as Teff during an immune response (Itoh et al., 1999), there are reports of Treg being more proliferative than naïve

T cells at steady state in the body (Hadis et al., 2011; Killebrew et al., 2011). We found that FoxP3+ natural Treg (nTreg) are indeed more proliferative than resting FoxP3- CD4

T cells under homeostatic conditions based on Ki67 staining (Figure 4.1A). Interestingly, nTreg that were Ki67+ expressed higher levels of Glut1 (Figure 4.1B) and phospho-S6 ribosomal protein than Ki67- nTreg (Figure 4.1C), consistent with increased glycolysis and mTORC1 activity in proliferating nTreg. Additionally, we found that Ki67+ nTreg

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had reduced expression of CD25 (Figure 4.1D), a cell surface marker for Treg stability and function (Huynh et al., 2015).

A FoxP3 negative FoxP3 positive

105 12.1 104 12.1 17 30.2 17 30.2

103 R1-A: APC-A

102

0 FoxP3

0 102 103 104 105 CD4 B1-A: FITC-A Ki67

B 2" * Ki67 Low Ki67 High 1.5" 1"

0.5"

Normalized MFI 0" Glut1 (geometric mean) Ki67 Low Ki67 High C Ki67 Low 2" * Ki67 High 1.5" 1" 0.5"

Normalized MFI 0" pS6 (geometric mean) Ki67 Low Ki67 High D Ki67 Low 3" * Ki67 High 2"

Normalized MFI 0" CD25 (geometric mean) Ki67 Low Ki67 High

Figure 4.1: Proliferative nTreg have increased expression of Glut1 and elevated mTOR signaling

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Murine splenocytes were analyzed by flow cytometry. CD4+ cells were examined for FoxP3 expression and (A) Ki67 expression was analyzed in CD4+FoxP3+ and CD4+FoxP3- populations. (B-D) CD4+FoxP3+ cells were examined for Ki67 expression and CD4+FoxP3+Ki67+ and CD4+FoxP3+Ki67- cells were compared for expression of (B) Glut1, (C) phospho-S6 and (D) CD25. Data are representative of at least 3 independent experiments. Means and standard deviations are shown, * p<0.05.

4.2.2 Inflammatory TLR signaling drives Treg glycolysis and proliferation but reduces suppressive capacity

We next determined the effects that inflammatory signaling has on induced Treg

(iTreg) metabolism and proliferation. Previous work has shown that Treg express TLR1 and 2 at high levels (Heng et al., 2008). Therefore, we treated iTreg with the TLR1/2 agonist Pam3CSK4 and found that this increased the expression of Glut1 and

Hexokinase 2 (Figure 4.2A). We also found that Pam3CSK4 treatment drove an increase in Treg cell size (Figure 4.2B) and promoted glycolytic metabolism as seen by increased lactate production (Figure 4.2C). This was accompanied with the activation of mTORC1 pathway signaling and increased proliferation based on Ki67 staining (Figure 4.2D).

Importantly, these changes by TLR signaling in Treg were accompanied by decreased capacity to suppress the proliferation of effector CD8 T cells (Figure 4.2E).

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A B Vehicle 1" 4.74" PAM3CSK4 HK2"

1" 1.65" Glut1"

Ac2n" . FSC Veh PAM PAM Vehicle C D PAM3CSK4 2.5" *

/min) 2" 1.5" mpH 1" 0.5" ECAR( 0" Ki67 pS6 . Veh PAM PAM Vehicle E PAM3CSK4 Teff: 1 8 2 1 Treg: 0 1 1 1

Vehicle

PAM3CSK4

CTV

Figure 4.2: Inflammatory signaling through TLR1/2 drives Treg glycolysis and proliferation but reduces suppressive function

CD4+CD25- T cells were isolated from the spleens of WT mice and were polarized under Treg skewing conditions for 5 days and treated with vehicle (H2O) or 5 µg/mL Pam3CSK4 for the final 24 hrs. Cells were examined for (A) expression of Hexokinase 2 (HK2) and Glut1 by immunoblot, (B) extracellular media acidification rate (ECAR) using the Seahorse Extracellular Flux Analyzer, (C) Ki67 and phospho-S6 expression by flow cytometry and (D) were functionally examined in an in vitro suppression assay to measure inhibition of effector T cells (Teff) proliferation. Data are representative of three

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independent experiments (A-B, D-E) or two independent experiments with four technical replicates per group (C). Means and standard deviations are shown, * p<0.05.

4.2.3 FoxP3 modulates cell metabolism through regulation of the PI3K pathway

Recent work has shown a potential role for the Treg specific transcription factor

FoxP3 in regulating cell metabolism through modulating PI3K pathway signaling.

FoxP3 expression was found to negatively regulate Akt pathway signaling and Glut1 expression in T cells (Basu et al., 2015), while the genetic deletion of FoxP3 in Treg led to changes in the expression of numerous metabolic genes (Williams and Rudensky, 2007).

Additionally, in vivo Treg activation was found to be associated with a reduction in Akt pathway target gene expression (Arvey et al., 2014).

To directly test the role of FoxP3 in regulating cell metabolism and the PI3K pathway, we retrovirally transduced CD4+ T cells with FoxP3 and examined gene expression changes compared with vector control transduced cells. Pathway analysis of gene expression showed that FoxP3 expression led to significant changes to metabolic pathways including upregulation of lipid and peptide hormone metabolism pathways and repression of glucose and nucleotide metabolism pathways (Figure 4.3A). We also performed ChIP sequencing of FoxP3 bound loci and this indicated that FoxP3 associated with Pyruvate Dehydrogenase Kinase 3 (PDK3) and PIK3cγ (Figure 4.3B).

Importantly, the expression of PDK3 mRNA was reduced in T cells upon FoxP3 retroviral transduction (Figure 4.3C).

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A FoxP3 FoxP3 Lipid, peptide, hormone

metabolism Upregulated

Migration, chemotaxis, adhesion

Immune response, inflammation

Glucose, glycolipid,

Downregulated nucleotide, sterol metabolism FoxP3

B C 1.2 *

0.8

0.6

0.4 expression 0.2 NormalizedPDK3 0 Control FoxP3

Figure 4.3: FoxP3 expression in T cells alters the expression of genes in multiple metabolic pathways

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Primary murine CD4+CD25- T cells were activated and transduced with control or FoxP3 expressing retrovirus and (A) RNA was analyzed by gene expression microarray and DAVID pathway analysis to determine enrichment for FoxP3 upregulated or downregulated genes in selected pathways, (B) analyzed by chromatin immunoprecipitation-sequencing showing FoxP3 associated sites in the pyruvate dehydrogenase kinase 3 (PDK3) and Pik3cγ loci or (C) analyzed by RT-PCR for expression of PDK3. Experiments were performed on biological triplicate samples. Means and standard deviations are shown, * p<0.05.

We next tested the effect of FoxP3 on the expression and activity of metabolic proteins and the PI3K pathway. Consistent with FoxP3 playing a role in inhibiting the

PI3K pathway, FoxP3 expression in T cells resulted in reductions in phospho-Akt, -

S6Kinase and –S6 compared with vector control transduced T cells (Figure 4.4A). We also found that FoxP3 expression was associated with a reduction in the protein expression of Glut1 and HK2, while promoting HK1 protein expression (Figure 4.4B), an expression pattern similar to that observed in Treg (Gerriets et al., 2015). Importantly, these expression level changes resulted in functional metabolic alterations in T cells.

FoxP3 expression led to reductions in glucose uptake (Figure 4.4C) and glycolysis

(Figure 4.4D), while increasing the oxygen consumption rate (OCR) and the ratio of oxygen consumption to lactate production (OCR/ECAR) (Figure 4.4E-F).

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A NGFR FoxP3 B NGFR FoxP3 C 1 0.45 1 0.87 Glucose uptake pAkt Glut1 1 0.12 14000 * Actin pS6K 12000 10000 1 0.71 1 0.61 8000 pS6 Hk2

CPM 6000 1 0.78 1 3.23 4000 HIF1a Hk1 2000 1 1.30 0 Actin PTEN NGFRControl FoxP3FoxP3 Actin

D E F Glycolysis OCR OCR/ECAR

120 3.5 * 350 * * 3.0 300 100 /min) /min) 2.5 250 80 2.0 200 60 mpH 1.5 150

pmoles 40 1.0 100 0.5 50 20 OCR/ECARratio ECAR( 0.0 0 0 OCR ( NGFRNGFR FoxP3FoxP3 NGFRControl FoxP3 NGFRControl FoxP3

Figure 4.4: FoxP3 expression in T cells inhibits glycolytic metabolism and promotes mitochondrial oxidation

Primary murine CD4+CD25- T cells were activated and transduced with control or FoxP3 expressing retrovirus and (A, B) analyzed by immunoblot and (C) Glucose uptake, (D) Glycolytic flux, (E) OCR, (F) OCR/ECAR ratio were measured. Data is representative of three independent experiments. Means and standard deviations are shown, * p<0.05.

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FoxP3 may have context specific functions in the T cell setting, suggesting a detailed metabolic analysis of the effects of FoxP3 should also be performed in a non-T cell setting. Therefore, to isolate the effects of FoxP3 from other T cell factors, we transduced the pro-B cell line FL5.12 with a conditionally active FoxP3-ER expression vector. 4OHT treatment to activate FoxP3 led to reductions in phospho-Sk6, -Akt, and - mTOR, with lower levels of PIKcγ and HIF1α in this setting. Interestingly, PTEN levels were modestly increased by activation of FoxP3 (Figure 4.5A). FoxP3 also regulated the expression of metabolic proteins in this setting, with FoxP3 activation resulting in increased expression of mitochondrial proteins Cytochrome C and CPT1a. Conversely,

FoxP3 expression resulted in decreased Glut1 and HK2 protein expression (Figure 4.5B).

We found that FoxP3 activation resulted in the reduced expression of the majority of glycolytic enzymes (Figure 4.5C, Appendix E).

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A Ctrl FoxP3 B Ctrl FoxP3

1 0.89 1.13 0.49 0.14 0.13 * 1 0.92 0.97 0.81 0.78 0.81 ** pS6K Glut1

1 1.01 1.24 0.50 0.35 0.26 ** 1 1.17 1.08 0.51 0.53 0.30 ** pAkt HK2 Total Akt 1 0.94 0.81 1.21 1.10 1.19 * HK1 1 1.21 1.28 1.44 1.47 1.45 * PTEN Actin 1 0.85 0.60 0.44 0.34 0.41 * pmTOR 1 0.74 0.79 1.66 1.49 1.32 * 1 0.78 0.77 0.63 0.41 0.52 * Cyto c PIK3cγ 1 1.25 0.68 1.83 1.45 1.88 * Actin CPT1A

1 0.89 0.70 0.35 0.26 0.28 * HIF1α Actin Actin

C Ctrl FoxP3

Pgm3 Bpgm Gpi1 Pgm1 Pgm2 Aldoa Eno1 Tpi1 HK2 Pgk1 Pgam2 HK4 HK3 Eno3

Magnitude of gene expression

Min Average Max

Figure 4.5: FoxP3 expression in FL5.12 cells inhibits anabolic signaling and metabolic gene expression

Three individual clones of control and FoxP3-ER expressing FL5.12 cells were treated with 4OHT to activate FoxP3 and examined (A-B) by protein immunoblot or (B) select glycolytic gene expression by rtPCR. Data are representative of three independent

132

experiments (A-B) or an analysis of three independent clones (C). (A-B) Gel bands are quantified, * p<0.05, **p<0.005.

The activation of FoxP3 in the FL5.12 cell setting also resulted in functional metabolic alterations, with a reprogramming of the metabolome resulting in the decreased abundance of many anabolic metabolites (Figure 4.6A, Appendix F).

Additionally, we observed that FoxP3 activation resulted in decreased glucose uptake

(Figure 4.6B), glycolysis (Figure 4.6C), lactate production (Figure 4.6D), and pentose phosphate pathway flux (Figure 4.6E). Consistent with reduced anabolic metabolism,

FoxP3 activation slowed cell proliferation (Figure 4.6F).

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A B C ) D Glucose hr Glycolysis ECAR Control FoxP3 uptake * * * 12000 cells/ 150 2 6

10000 /min) 8000 100 1.5 6000 mpH 1

CPM 4000 50 2000 0.5 0 0 0 glucose/10 GlycolyticRate ECAR( mmol ( FoxP3 FoxP3 FoxP3 Control Control Control E F PPP 2.5E+6 ) * 3000 *

cpm 2.0E+6 2500 * 2000 1500 1.5E+6 1000 * 500 1.0E+6 0 Cellnumber

C-1-glucose( Control 5.0E+5 14 * FoxP3 FoxP3

Metabolite Level Control 0.0E+0 0 20 40 60 80 100 Min Max Hours

Figure 4.6: FoxP3 expression in FL5.12 cells inhibits anabolic metabolism

Three individual clones of control and FoxP3-ER expressing FL5.12 cells were treated with 4OHT. (A) Cells were extracted and analyzed using high-resolution LC-QE-MS. Shown is a heat map with relative levels of metabolites using unsupervised hierarchical clustering. B-F Cells were analyzed for (B) Glucose uptake, (C) Glycolytic flux, (D) Extracellular acidification rate (ECAR), (E) Pentose Phosphate Pathway (PPP) flux and (F) growth rates. Data shows three independent clones per group (A) or is representative of three independent experiments with three independent clones per group (B-F). Means and standard deviations are shown, * p<0.05.

Conversely, oxygen consumption, OCR/ECAR ratio, pyruvate oxidation, and palmitate oxidation all increased with FoxP3 activity in FL5.12 cells (Figures 4.7A-D). FL5.12 cells

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are dependent on the cytokine IL-3 for survival. Interestingly, FoxP3 activation promoted FL5.12 cell survival under IL-3 starvation conditions (Figure 4.7E). Together, these data show that TLR signals promote Treg proliferation and glycolysis while FoxP3 promotes oxidative metabolism and survival under conditions of cell stress.

A B C Pyruvate D Palmitate OCR OCR/ECAR oxidation oxidation * * 500 * 400 * 30000 7000 350 25000 6000 /min) 400 300 20000 5000 300 250 4000 200 15000 200 150 CPM CPM 3000 pmoles 10000 2000 100 100 50 5000 1000

0 OCR/ECARratio 0 0 0 OCR (

Control FoxP3 Control FoxP3 Control FoxP3 Control FoxP3 E 100 Control 80 * FoxP3 60 * 40 20 Viability (%) Viability 0 0 10 20 30 40 50 Hours

Figure 4.7: FoxP3 expression in FL5.12 cells promotes oxidative metabolism and survival

Three individual clones of control and FoxP3-ER expressing FL5.12 cells were treated with 4OHT. (A) Oxygen Consumption Rate (OCR), (B) OCR/ECAR ratio, (C) Pyruvate oxidation, and (D) Fatty acid oxidation of palmitate measured. (E) Cells were washed and cultured in media lacking IL-3 and cell viability was measured over time by PI exclusion flow cytometry. Data is representative of three independent experiments with

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three independent clones per group. Means and standard deviations are shown, * p<0.05.

4.2.4 Increased PI3K/Akt/mTORC1 pathway activity and glycolysis oppose Treg function

To directly test the role of the metabolic and cellular signaling effects of FoxP3 on

Treg function, we characterized Treg that were modified using T cell specific constitutively active Akt- or Glut1-(Michalek et al., 2011) transgenic (tg) mice. We observed that nTreg were found in increased number (Figure 4.8A) and percentage

(Figure 4.8B) in Akt-tg mice, similar to what other groups have previously observed in the PTEN null Treg context (Huynh et al., 2015; Shrestha et al., 2015). Consistent with elevated Akt pathway signaling, the cell size of Akt-tg Treg was increased as measured by FSC (Figure 4.8C). Also consistent with previously reported observations in the

PTEN null setting, Akt-tg Treg had alterations of cell surface markers indicating a more activated, less suppressive phenotype, including reduced CD25 (Figure 4.8D), elevated

ICOS (Figure 4.8E), CD69 (Figure 4.8F) and reduced CD62L staining (Figure 4.8G). Treg that were differentiated in vitro from Akt-tg naïve CD4 T cells had increased glucose uptake (Figure 4.8H) and reduced ROS production (Figure 4.8I), indicative of a metabolic program skewed towards glycolysis. Importantly, Akt-tg Treg had reduced ability to suppress CD8+ effector T cell proliferation compared with control Treg (Figure

4.8J).

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A B C D Control nTreg

Akt Tg nTreg 2.5 * 2 * nTreg nTreg 2 1.5 1.5 1 1 0.5 0.5 0 0 FSC CD25 Normalized#

Normalized% Tg Tg

Akt Tg Akt Tg Control Control Akt Akt Control Control Glucose E F G H uptake I 2000 * 1500

CPM 1000 500 0

ICOS CD69 CD62L ROS WT Akt Tg Tg Akt Control J Teff: 1 4 1 Treg: 0 1 1 Control

Treg

Akt

CTV

Figure 4.8: Constitutive Akt expression increases Treg number and percentage but reduces suppressive function

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A-G. FoxP3+ nTreg from the spleen of control and mAkt-Tg mice were examined for (A) nTreg number, (B) percentage and (C) cell size determined by forward scatter and were measured by flow cytometry. (D) CD25, (E) ICOS, (F) CD69 and (G) CD62L expression in control and mAkt-Tg nTreg were measured by flow cytometry. (H-J) CD4+CD25- T cells were isolated from the spleens of control and mAkt-Tg mice and were polarized under Treg skewing conditions and (H) glucose uptake, (I) ROS production by DCFDA and (J) ability to inhibit of effector T cells (Teff) proliferation in an in vitro suppression assay were measured. Data are representative of three independent experiments (A-G), or two experiments (H-J). Means and standard deviations are shown, * p<0.05.

We next tested whether the direct modification of Treg metabolism could impact

Treg phenotype and functionality by examining Treg from Glut1-tg mice. We found that transgenic expression of Glut1 resulted in metabolic changes to Treg, with Glut1-Tg

Treg expressing increased Glut1 (Figure 4.9A) and exhibiting increased glucose uptake

(Figure 4.9B) and reduced ROS production (Figure 4.9C). We found that nTreg numbers

(Figure 4.9D) and percentages as a total of CD4+ T cells were increased (Figure 4.9E), although previous work has shown that Glut1-tg mice develop autoimmunity with age

(Michalek et al., 2011). We observed that Glut1-tg nTreg had increased cell size (Figure

4.9F), along with reduced expression of CD25 (Figure 4.9G).

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A B Glucose C ROS levels uptake Ctrl Glut1Tg 2000 * 1.2 * 1 1.27 1 1500 0.8 Glut1 0.6 CPM 1000 MFI MFI 0.4 500

Normalized MFI 0.2 0 0 Actin Normalized DCFDA ControlControl Glut1Glut1Tg Tg ControlWT Glut1Glut1TgGlut1 TG Tg Tregs TregsTreg

D E F Control nTreg * * Glut1 Tg nTreg 2.5 20 1.15 * 2 1.1 nTreg 15

1.5 1.05 10

1 nTreg 1

% 5 0.5 0.95 NormalizedFSC

Normalized# 0 0 0.9 Control Glut1 Control Glut1 FSC Control Glut1Tg Tg Tg

G 1.5 *

0.5

0 Control Glut1

CD25 NormalizedCD25 MFI Tg

Figure 4.9: Constitutive Glut1 expression alters Treg metabolism and phenotype

A-C. CD4+CD25- T cells were isolated from the spleens of control and Glut1-Tg mice were polarized under Treg skewing conditions. Cells were examined (A) by immunoblot and (B) glucose uptake and (C) ROS production as measured by DCFDA were assayed. D-G. FoxP3+ nTreg in control and Glut1-Tg mice were examined in spleen by flow cytometry for (D) number and (E) percentage, (F) cell size by forward scatter, and (G) CD25 expression. Data are representative of three independent experiments (A, B) or is shown as normalized between three independent experiments

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While FoxP3 expression was not significantly changed in resting Glut1-tg nTreg

(Figure 4.10A) there was a modestly increased frequency of Helios negative FoxP3+ CD4

T cells (Figure 4.10B) and a decrease in Helios expression in nTregs (Figure 4.11C) of

Glut1-tg animals. This change in Helios expression may correlate with a greater frequency of peripherally induced Treg or lower Treg suppressive capacity (Sebastian et al., 2016).

A B Control nTreg ns Glut1 Tg nTreg 1.2" * 40 1" 31.5 30 0.8" 40.2 0.6" 20 0.4" 10 0.2" 0 Normalized FoxP3MFI 0"

Control Glut1Tg Helios PercentHelios Negative Control Glut1 Tg

1.2 * 1 0.8 0.6 0.4 0.2

NormalizedHelios MFI 0 Control Glut1Tg

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Figure 4.10: Transgenic Glut1 expression alters Treg phenotype

FoxP3+ nTreg from the spleen of control and Glut1-Tg mice were examined for (A) FoxP3 expression and (B) Helios expression and (C) MFI by flow cytometry. Data are representative of four independent experiments. Means and standard deviations are shown, * p<0.05.

We next examined the effect of transgenic Glut1 expression on induced Treg. We found that Glut1-Tg T cells were able to differentiate into Treg with equal efficiency as

WT control T cells (Figure 4.11A), but had increased cell size (Figure 4.11B) and lower levels of CD25 (Figure 4.11C) and ICOS expression (Figure 4.11D).

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A B Control Glut1 Tg

Control 52.2 67.9 Glut1 Tg FoxP3

CD4 FSC C D

1.2 * 1.2 * 1 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 NormalizedCD25 MFI CD25 CtrlWT Glut1Glut1Tg Tg ICOS Normalized MFI ICOS WTCtrl Glut1Glut1Tg Tg Treg Treg Treg Treg

Figure 4.11: Transgenic Glut1 expression alters induced Treg functional markers

CD4+CD25- T cells were isolated from the spleens of control and Glut1-Tg mice were polarized under Treg skewing conditions. Control and Glut1-Tg Treg were assessed for (A) FoxP3+ percentage and (B) cell size as measured by forward scatter. The mean fluorescence intensity (MFI) of (C) CD25 and (D) ICOS were determined by flow cytometry. Data are representative of four independent experiments. Means and standard deviations are shown, * p<0.05.

The expression of several key regulatory genes for Treg function and stability

(Okamura et al., 2009; Park et al., 2015; Smigiel et al., 2014; Williams and Rudensky,

2007; Wohlfert et al., 2011; Zeiser et al., 2007) were also altered in induced and activated

Glut1-tg Treg (Appendix G). While Ccl3 expression increased, FoxP3 itself, Egr2, Gata3,

IL10, PD-1, and both CD30 and CD30 ligand all decreased (Figure 4.12A). Consistent

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with reduced FoxP3 mRNA, we also found that FoxP3 protein was modestly, but significantly, reduced in Glut1-tg induced Treg (Figure 4.12B). Therefore, we found that the elevation of Glut1 expression in Tregs is sufficient to result in phenotypic changes to

Treg that are suggestive of increased growth and anabolism, yet also indicate potential decreased suppressive capacity and lineage stability.

4 Ccl3 1.5 Egr2 1.5 FoxP3 1.5 Gata3 1.5 Icos 1.5 Il10

3 * ** ** ** ** ** 1 1 1 1 1 2 0.5 0.5 0.5 0.5 0.5 1

0 0 0 0 0 0 Control Glut1 Control Glut1 Control Glut1 Control Glut1 Control Glut1 Control Glut1 Tg Tg Tg Tg Tg Tg 1.5 Irf4 1.5 Nfkb1 1.5 Pdcd1 1.5 Sell 1.5 Tnfrsf8 1.5 Tnfsf8

RelativeExpression * ** * * * ** 1 1 1 1 1 1

0.5 0.5 0.5 0.5 0.5 0.5

0 0 0 0 0 0 Control Glut1 Control Glut1 Control Glut1 Control Glut1 Control Glut1 Control Glut1 Tg Tg Tg Tg Tg Tg B

1.2 * 1 0.8 0.6 0.4 0.2 0

Normalized FoxP3MFI CtrlWT Glut1Glut1 TGTg Treg Treg

Figure 4.12: Transgenic Glut1 expression alters induced Treg gene expression

CD4+CD25- T cells were isolated from the spleens of control and Glut1-Tg mice were polarized under Treg skewing conditions. (A) RNA expression analysis of Treg-related

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genes in control and Glut1-Tg Treg was measured by qrtPCR. (B) FoxP3 protein expression was measured by flow cytometry. Data are (A) averaged from three different paired control and Glut1-Tg mice or (B) are normalized between four different paired control and Glut1-Tg mice. Means and standard deviations are shown, * p<0.05, ** p<0.005.

We next examined the effects of increased glucose metabolism on Treg functionality. We found that in vitro differentiated Glut1-tg Treg had reduced capacity to suppress CD8 effector T cell proliferation in vitro (Figure 4.13A).

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A Teff: 1 4 1 Treg: 0 1 1 Control

Treg Glut1-tg

CTV

4:1 Teff/Treg 1:1 Teff/Treg * * 1.6 1.6 1.4 1.4 1.2 1.2 1 1 0.8 0.8 0.6 0.6 0.4 0.4 DivisionIndex DivisionIndex 0.2 0.2 0 0 WTControl Treg Glut1TG Treg Tg CtrlWTControl Treg Treg Glut1TG Treg Tg

Figure 4.13: Transgenic Glut1 expression reduces Treg suppressive capacity in vitro

CD4+CD25- T cells were isolated from the spleens of control and Glut1-Tg mice and polarized under Treg skewing conditions and functionally examined in an in vitro suppression assay to measure inhibition of effector T cells (Teff) proliferation and division index of Teff was calculated by Flowjo flow cytometry analysis software. Data are the result of three independent experiments. Means and standard deviations are shown, * p<0.05.

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We next determined whether Glut1-tg Treg also had diminished suppressive capacity in vivo using a previously established (Hale et al., 2005) murine model of inflammatory bowel disease (IBD). In this model, naïve Treg-depleted CD4+ T cells were adoptively transferred into lymphopenic animals and then IBD was induced by the addition of the inflammatory NSAID piroxicam to chow. After the onset of disease, the mice were injected with control or Glut1-tg nTreg (Figure 4.14A) and the progression of the disease was measured by monitoring of animal weights. Control nTreg were able to rescue the disease, with mice recovering and gaining weight, but Glut1-tg nTreg were not as effective at reversing disease progression (Figure 4.14B). At the experimental endpoint, we found that the Glut1-tg nTreg were less effective in restraining CD4+ Teff accumulation (Figure 4.14C). Interestingly, there were fewer CD4+ FoxP3+ cells in mice that received Glut1-tg nTreg (Figure 4.14D), suggesting deceased FoxP3 expression or lineage stability.

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A B WT Control Glut1Tg Treg Injected 2

0 * 10 20 30 40 50

-2 CD4 CD4

Weight Change (g) Weight -4 Tg Treg Control Treg -6 Teff alone Days CD25 CD25

CD45Rb CD45Rb

C D 1.8 * 2.5 * 1.6 *

) 2

6 1.4 1.2 1.5 1 0.8 1 0.6 CD4 T cells T CD4 T cells (x10 T 0.4 0.5 Normalized#CD4 0.2

0 Normalized%FoxP3+ 0 Teff CtrlWT TregTreg Glut1TG Treg Tg Teff CtrlWT Treg Treg Glut1 TG Treg Tg Treg Treg

Figure 4.14: Transgenic Glut1 expression reduces Treg ability to suppress IBD

RAG1-/- mice were injected with (A) sorted naïve effector (CD4+CD25-CD45RBhi) T cells to induce colitis. After weight loss indicated active disease, control or Glut1-Tg CD4+CD25+CD45RBlo Treg were injected. (B) Mice were weighed three times per week and the change is shown. Animals were assessed for the (C) number of CD4+ T cells in the spleen and (D) the percentage of CD4+ FoxP3+ T cells in each Treg recipient group. Data are representative of at least three independent experiments with at least 4 mice per group in each experiment (A-B), and two independent experiments (C-D; n= 6 mice, Teff alone; n= 9 mice, control Treg; n= 8 mice, Glut1 Tg Treg). Means and standard deviations are shown, with SEM shown in b. * p<0.05.

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Consistent with the reduced functionality of the Glut1-tg Tregs, we found that

FoxP3 expression was significantly reduced in transferred Treg isolated from both the mesenteric lymph nodes (Figure 4.15A) and the spleen of recipient mice (Figure 4.15B).

We further confirmed this reduced FoxP3 expression with the transfer of congenically marked control and Glut1-tg Treg, which also indicated that Glut1-tg Treg did not maintain expression of FoxP3 in the context of IBD (Figure 4.15C). Thus Glut1-tg Treg were less capable of suppressing disease and appear to have lower lineage stability than control Treg.

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A Control nTreg Mesenteric Lymph Nodes Glut1 Tg nTreg 1.4 * 1.2 33.3 23.4 23.4 1 0.8 0.6 0.4 0.2

Normalized FoxP3MFI 0 FoxP3 Ctrl Treg Glut1 Tg Treg B Spleen 1.4" * 33.3 23.4 1.2" 23.4 1" 0.8" 0.6" 0.4" 0.2" Normalized FoxP3MFI 0" FoxP3 Ctrl Treg Glut1 Tg Treg

C Thy1.2 Control nTreg Mesenteric Lymph Nodes Spleen Thy1.2 Glut1 Tg nTreg Thy1.1 Teff

FoxP3 FoxP3

Figure 4.15: Transgenic Glut1 expression reduces Treg FoxP3 expression in IBD

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Thy1.1 naïve effector (CD4+CD25-CD45RBhi) T cells were adoptively transferred into RAG1-/- mice to initiate IBD. Thy1.2 control or Glut1-Tg nTreg (CD4+CD25+CD45RBlo) T cells were sorted and injected after disease was apparent. FoxP3 levels were then assessed by flow cytometry on adoptively transferred Thy1.1 effectors and Thy1.2 CD4 control and Glut1-tg Treg from (A) mesenteric lymph nodes and (B) spleens. (C) FoxP3 expression between Thy1.1 Teff cells, Thy1.2 control Treg and Thy1.2 Glut1-Tg Treg. Data are normalized between two independent experiments. Means and standard deviations are shown. * p<0.05.

4.3 Discussion

While recent work has shown a clear role for metabolism (Buck et al., 2015) and the cellular signaling pathways that modulate it in regulating the functionality of various subsets of T cells, the metabolic regulation of Treg has been unclear. Several groups, including our own, have established that Treg utilize a primarily oxidative metabolic program (Beier et al., 2015; Gerriets et al., 2015; Michalek et al., 2011).

However, the pathways that promote this metabolic program and the functional necessity of oxidative metabolism in Treg have remained unknown. Here we show that

Treg metabolism is dynamically regulated by TLR signaling and FoxP3 to balance Treg proliferation and suppressive capacity.

We found that nTreg are metabolically heterogenous under homeostatic conditions. A greater proportion of nTreg cells are proliferative than non-Treg CD4 T cells. Interestingly, proliferative nTreg cells exhibit differing metabolic and cellular signaling traits than those found in non-proliferative nTreg cells. The proliferative

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nTregs expressed higher levels of Glut1 and phospho-S6 protein than non-proliferative nTregs, with decreased expression of cell surface markers associated with Treg lineage stability. Driving Treg proliferation through TLR1/2 ligation also promoted glycolysis and increased mTOR pathway signaling, while reducing Treg suppressive capacity.

Direct modulation of Treg metabolism through the transgenic expression of Glut1 resulted in increased glycolysis and a reduction in Treg functionality. These results are consistent with recent work that showed that an increase in mTOR pathway signaling that is correlated with increased glycolysis resulted in reduced Treg functionality and lineage stability (Huynh et al., 2015; Shrestha et al., 2015; Wei et al., 2016). This supports a model of Treg biology where proliferation and anabolic metabolism act in opposition to Treg stability and suppressive capacity. This model is consistent with reports indicating that non-functional Treg accumulate in a variety of autoimmune diseases

(Cribbs et al., 2015; Matsuki et al., 2014; Ohl and Tenbrock, 2015).

We show that the transcription factor FoxP3, in contrast to inflammatory signaling, acts to inhibit glycolysis and anabolic growth in Treg. FoxP3 likely accomplishes this through several mechanisms. We show that FoxP3 represses the PI3K pathway, a key regulator of T cell anabolic metabolism and growth, possibly through direct regulation of the expression of PI3K catalytic subunit PIK3cγ. The regulation of

PI3K pathway signaling by FoxP3 likely contributes to the metabolic changes that are driven by FoxP3 expression. However, it seems likely that FoxP3 directly regulates

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metabolism as well, such as through regulating the expression of PDK3. FoxP3 modulation of metabolism is a likely mechanistic explanation for the previously observed metabolic phenotype of Treg.

These results further hint at a mechanistic understanding for how Treg may function during inflammatory responses. Inflammatory signals that are present during an active infection, for instance, could drive Treg glycolysis and proliferation, while inhibiting suppressive function to allow Teff to clear the infection. When the pathogen was cleared by the immune response, inflammatory signaling would decrease, and

FoxP3 would repress glycolytic metabolism and anabolic growth signaling in Treg to promote suppressive capacity and immune response resolution.

Understanding the regulation of Treg functionality by metabolism and inflammatory signals may also prove useful in better understanding tumor cell interactions with the immune system. This may aid in devising new approaches towards targeting tumor associated Treg that suppress immune responses to cancer.

Indeed, there is evidence that inflammatory signals from systemic infections are capable of reaching the tumor microenvironment (Hobohm et al., 2008), and influencing the immune cells present there (Speeckaert et al., 2011). Additionally, there are reports in literature of spontaneous regression of tumors following acute infection (Kostner et al.,

2013). It is possible that the inflammatory signals that are induced during infection may

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be, in some cases, sufficient to inhibit tumor associated Treg function, allowing for a more robust immune response to the tumor.

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5. Conclusion and Future Directions

The studies described here show T cell metabolism is high heterogeneous and both depends on the activating or transforming signals and is also critical for cell functions and fate. There several specific points to highlight. 1) The metabolic profile of malignantly transformed T cells is distinct from that of resting or activated normal T cells. This raises the exciting possibility of achieving a therapeutic window where the inhibition of metabolic pathways that uniquely utilized by cancer cells may avoid off- target effects on normal immune cells. 2) The energy sensor AMPK plays a critical role in promoting T-ALL cell survival by balancing cell metabolism to mitigate metabolic stress. 3) Inflammatory signaling pathways and the transcription factor FoxP3 both play important roles in modulating regulatory T cell (Treg) metabolism to regulate immune function. This illustrates that Treg metabolism is dynamically regulated and is a potential mechanism for coupling Treg functionality to the temporal requirements of the immune system. 4) Treg actively inhibit glycolytic metabolism to promote lineage stability and suppressive capacity. This indicates that Treg metabolism could potentially be a therapeutic target to modulate Treg function in the case of autoimmunity and cancer.

5.1 T-ALL metabolism is distinct from that of normal T cells

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It has been hypothesized that the metabolic profile of tumor cells is very similar to that of an activated, proliferating T cell (Macintyre and Rathmell, 2013). Both types of cells, it was thought, had similar metabolic demands that must be fulfilled to promote rapid cell growth. Studies have clearly shown that tumor cells have increased glucose uptake and glycolytic flux when compared with normal quiescent cells of the same background (Liu et al., 2014a). Similar findings were made in the immunometabolism field with stimulated, proliferative cells in both the innate (Fukuzumi et al., 1996) and adaptive immune settings being found to adopt a metabolic program of increased glucose usage and lactate production (MacIver et al., 2013). However, most studies of cancer cell metabolism have focused on the comparison of cancer cells to normal resting tissue counterparts rather than proliferating normal cells.

In this work, we show that T-ALL cells utilize a metabolic program consistent with aerobic glycolysis when compared with resting normal T cells. However, direct comparison of the metabolic properties of T-ALL cells with that of in vitro activated, proliferating T cells revealed stark differences in metabolism. T-ALL cells utilize glycolysis at levels far below that of normal, activated T cells. There are a number of explanations for this observation. As we described, the activity of signaling pathways such as PI3K/Akt/mTOR that regulate the usage of glycolytic metabolism in the T cell setting is much lower in T-ALL cells than in activated T cells. This is at least partially due to negative regulation of mTORC1 signaling by AMPK. In the setting of T-ALL, we

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observed that the AMPK pathway was activated by oncogenic Notch signaling imposing metabolic stress on the cell. It is likely that the induction of metabolic stress may be a feature of oncogenic transformation. There are reports in literature of additional oncogenes driving metabolic stress that can activate AMPK pathway signaling. As one example, it was found that oncogenic Myc signaling imposes a requirement for AMPK in order to combat ATP insufficiency (Liu et al., 2012). Therefore, it may be a common feature of tumor cells to exhibit surprisingly moderated levels of signaling through anabolic growth pathways due to inhibit by AMPK. This could have the result of decreasing glycolytic activity in tumor cells. To further understand the metabolism of tumor cells in a broader context, studies should be undertaken to assess tumor cell metabolism against normal proliferative cells in multiple tissue origins. This would allow for a better identification of cancer specific metabolic alterations and determine if the results we described here for T-ALL metabolism are applicable to multiple tumor types.

An interesting observation from our studies is that T-ALL cells appear to be reliant on oxidative metabolism. While it has been hypothesized for some time that tumor cells predominantly utilize a metabolic program of aerobic glycolysis, recent evidence has indicated that the in vivo metabolism of tumor cells may incorporate glucose oxidation in the mitochondria. Davidson and colleagues utilized numerous in vivo models of non-small cell lung cancer and observed that the metabolism of the tumor

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cells was largely oxidative (Davidson et al., 2016). While this work did observe increased glucose uptake in the tumor cells, the results were in line with our findings of modest upregulation of glycolysis in T-ALL cells. This was in contrast to their findings in the setting of tissue culture, in which the non-small cell lung cancer cells utilized aerobic glycolysis and little glucose oxidation was observed. Therefore, it appears likely that there are profound metabolic differences between the tissue culture setting and primary in vivo tumor cells, with the in vivo environment potentially promoting oxidative, rather than glycolytic, metabolism in tumor cells. Further studies should be undertaken to understand the dynamics of cancer cell metabolism in vivo. This can be accomplished by the ex vivo isolation of tumor cells and subsequent metabolic comparison to normal tissues, or in cases where it is not possible this due to technical concerns, in vivo metabolic tracer experiments can be performed and tumor and healthy tissue may be compared directly for metabolic characteristics.

Also meriting consideration is recent evidence by Hosios and colleagues that proliferating cells do not derive a majority of their biomass from glucose, but instead utilize amino acids for anabolic growth and proliferation (Hosios et al., 2016). This study found that a majority of the matter in a proliferating cell was derived from amino acids. These results calls into question the hypothesis that the teleological reason for tumor cells to utilize a metabolic program of aerobic glycolysis is to support the production of intermediates for biosynthesis and proliferation. Further work is

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necessary to determine the actual purpose of the glycolytic metabolism that is often seen in tumor cells. These studies could be performed with in vivo metabolomics tracer experiments to determine the fate of glucose in tumor cells. It is possible that the flux of glucose through the glycolytic pathway is primary designed to maintain the redox balance of the cell through the generation of NADH. Our results in T-ALL show that mitochondrial dysfunction leading to alterations in the tumor cell redox balance may have profoundly negative consequences for the cell and may lead to cell death. In line with our results, Jeon and colleagues found that a key role for AMPK in the tumor cell setting is the management of the cellular redox balance by inhibiting the fatty acid synthesis process to conserve cellular NADPH (Jeon et al., 2012). Follow up studies should be performed in the context of T-ALL, in which we have the capability to knockout genes such as prkaa1 (AMPKα1) and hk2, to utilize in vivo metabolomics tracer experiments to determine how T-ALL cells utilize various pathways and metabolic substrates.

Another interesting finding from our metabolic analysis of primary T-ALL cells was the degree to which there was a marked upregulation in the abundance of numerous fatty acids in T-ALL compared to naïve and activated T cells. We found that

T-ALL cells had more than 50 fold increases in abundance of acyl-carnitines including octanoyl carnitine, decanoyl carnitine, and butyryl carnitine. Pathway analysis of enriched metabolites in T-ALL suggested that fatty acid oxidation pathway activity may

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be increased in T-ALL compared with both naïve and activated T cells. Analysis of available T-ALL gene expression datasets provides further support for this hypothesis

(Sanghvi et al., 2014). Primary human T-ALL has increased expression of components involved in beta-oxidation of fatty acids, including cpt1a and acadvl compared with naïve human T cells. Taken together, this data suggests that fatty acid metabolism may be a key pathway in which T-ALL metabolism differs from that of normal T cells. An analysis of the functional role of fatty acid oxidation in T-ALL biology should be performed. T-ALL could be generated on a genetic background allowing for the acute inhibition of fatty acid oxidation, such as a Cpt1aflox/flox background crossed with the

Rosa26CreERT2 background. T-ALL would be generated in primary recipient mice and then transplanted into cohorts of secondary recipient mice as described in Chapter 3.

Cpt1a could be deleted soon after T-ALL transplant to assess the effects of the genetic inhibition of fatty acid oxidation on T-ALL disease progression. Additionally, Cpt1a could be deleted at a later time point and T-ALL could be isolated soon after deletion to examine the acute effects of deletion of T-ALL metabolism and cell viability. The detailed study of fatty acid oxidation and other metabolic pathways that differentiate T-

ALL from normal T cells may allow for novel strategies to specifically target T-ALL.

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5.2 AMPK balances T-ALL metabolism to control metabolic stress and promote cell survival

A major finding of our studies of T-ALL metabolism described in Chapter 3 was that AMPK plays a key role to balance the metabolism of T-ALL cells to promote cell survival and disease progression. We found that, in T-ALL, AMPK suppresses aerobic glycolysis through the negative regulation of mTORC1 and promotes mitochondrial metabolism through regulation of Complex I activity. The role of AMPK in cancer appears to be context specific. The particular oncogenic driver mutation found in a tumor may play an important part in determining the particular role of AMPK in a tumor as well. In our studies, we observed that oncogenic Notch that drives T-ALL results in increased AMPK activity that is required for T-ALL cell viability. Oncogenic

Myc and Ras have been observed to generate metabolic stress in some contexts as well

(Liu et al., 2012; White, 2013). Conversely, LKB1 mutant lung cancer may be an example of a mutation that does not confer a requirement for AMPK activity. LKB1 mutation tumors may not activate AMPK signaling, indicating that AMPK is likely not important in that particular type of cancer. Of note, however, is the observation that LKB1 loss sensitizes lung cancer cells to the inhibition of mitochondrial metabolism (Shackelford et al., 2013). This may indicate that the promotion of mitochondrial metabolism in cancer, whether by AMPK or by alternate mechanisms, may be a key requirement for the maintenance of tumor cell viability and disease progression.

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The role of AMPK in tumors likely should be evaluated based on the cellular origin of the tumor. Previous work has shown that AMPK plays an inhibitory role in the progression of B cell lymphoma (Faubert et al., 2013), yet in other settings (Jeon et al.,

2012; Saito et al., 2015), can also act to promote tumor cell survival through mitigating metabolic stress and promoting proper cellular redox balance. Consistent with our observations in T-ALL, in the normal T cell lineage, AMPK acts to promote cell survival while limiting anabolic growth signaling and metabolic pathways (Blagih et al., 2015;

MacIver et al., 2011). It is possible that the role of AMPK in the healthy cells of the same tissue origin as a tumor may be determinative of the role of AMPK in that tumor.

Therefore, further work should be performed to gain a more complete understanding of the role of AMPK in various cell lineages throughout the body. This could be accomplished by crossing AMPKαflox/flox mice with various tissue specific Cre recombinases. Unlike our studies in T cells, where AMPKα1 is the solely expressed catalytic subunit of AMPKα, other tissues express multiple isoforms of AMPKα. This may require the use of double knockout mice to abolish AMPK activity.

The metabolic role of AMPK in both T-ALL and normal T cells should be further studied. One interesting observation is that naïve T cells and T-ALL cells have increased phosphorylation of AMPK targets RAPTOR Ser792 and TSC2 Ser1387 compared to activated T cells. We found that activated T cells have equivalent amounts of phospho-

AMPK and phospho-ACC as T-ALL, yet the mTORC1 pathway substrates of AMPK are

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differentially phosphorylated. Naïve T cells, which have very low expression of phospho-AMPK and phospho-ACC, have the highest level of RAPTOR and TSC2 phosphorylation by AMPK. This suggests that there may be additional layers of regulation of the AMPK pathway than is currently appreciated. It is possible that there are phosphatases that act on AMPK substrates differentially, or there may be differences in the cellular localization of AMPK and its substrates under various cell conditions.

Further examination of this observation could be performed to identify proteins that interact with these substrates, and how these interactions are regulated. The direct phosphorylation of RAPTOR by AMPK is known to mediate a metabolic checkpoint on glycolysis to promote cell survival (Gwinn et al., 2008). It would be interesting to explore the role of metabolism in regulating the interaction of AMPK and RAPTOR. To assess this, studies of AMPK signaling and interaction with mTORC1 regulatory proteins could be performed using Glut1-Tg T cells. Comparisons of AMPK pathway activity and phosphorylation of target substrates should be performed on resting and in vitro stimulated cells to determine whether increased glycolytic activity is sufficient to alter the dynamics of AMPK regulation of the mTORC1 pathway.

It is interesting that T cells appears to require a balance of metabolism between mitochondrial oxidation and glycolysis to promote cellular longevity. In our studies, we found that AMPK is a mediator of this metabolic balance. Recent work in the context of

CD8+ memory T cells found that enforcing glycolysis in this setting resulted in reduced

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cellular lifespan (Sukumar et al., 2013). Subsequent work observed a connection between increased mitochondrial membrane potential and increased glycolytic metabolism that reduced cellular longevity (Sukumar et al., 2016). One potential explanation lies in the immunological function of T cells. Effector T cells, in order to function properly, must be able to proliferate at rapid rates upon detection of pathogens.

Glycolytic metabolism may allow for a short burst of rapid proliferation to allow for a robust immune response. However, in order to prevent sustained immune responses that have the potential to lead to adverse effects such as autoimmunity, the majority of effector T cells must undergo cell death during the resolution of the immune response.

It is possible that the unrestrained glycolytic metabolism seen in effector T cells contributes to limiting the lifespan of the cells.

The cellular signaling pathways that regulate metabolic balancing in T cells remain unclear. Recent clinical observations imply that the regulation of the

PI3K/Akt/mTOR signaling pathway likely plays an important role in this process.

Human patients that have activating mutations in PIK3R1 (Deau et al., 2014) or PIK3CD

(Angulo et al., 2013; Lucas et al., 2014), the regulatory p85 subunit or the catalytic p110δ subunit of PI3K, respectively, exhibit paradoxical immunosuppression. In each case, the activating mutation of PI3K drove increased pathway activity downstream through Akt and mTOR. Metabolically, mutant T cells were characterized by having increased glycolysis. Interestingly, this cell signaling and metabolic phenotype was found to

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result in the increased generation of short-lived, terminally differentiated effector T cells.

Conversely, the generation of long-lived memory T cells was impaired. These results indicate that negative regulators of PI3K activity may play important roles in T cells to balance cell metabolism and promote longevity and proper immune cell function.

Proteins such as PIK3IP1 (PI3K interacting protein 1), which inhibits the activation of p110 catalytic subunits of PI3K, may play important roles in properly modulating cell signaling in the T cell context (DeFrances et al., 2012).

The necessity of metabolic balancing in T cells to promote cell longevity also has implications for cancer research. In our studies, we found AMPK deletion that drove a shift towards glycolysis correlated with decreased T-ALL cell viability. However,

AMPK has alternate functions in addition to the regulation of metabolism, making it difficult to make a direct conclusion that the observed metabolic shifts were causative of decreased T-ALL cell viability. Further studies could be performed to determine whether the direct modulation of glucose metabolism towards glycolysis is sufficient to result in reduced tumor cell lifespan. Generating T-ALL on a Glut1-Tg background and comparing the tumor initiation kinetics with that of WT background cells could accomplish this. Alternatively, WT T-ALL could be generated and genetically modified with retroviral vectors to overexpress or knockdown various metabolic enzymes to drive glycolysis or mitochondrial oxidation. The results of these metabolic modifications on

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tumor cell viability and disease outcome would have the potential to reveal whether a metabolic program of glycolysis is associated with decreased cancer cell lifespan.

5.3 Treg metabolism is dynamically regulated and coupled to immune function

Treg are thought to use an oxidative metabolic program, which we showed leads to reduced anabolic growth but enhanced cell survival in the T-ALL context.

Nevertheless, Treg have been shown to be proliferative in some settings. Therefore, we sought to determine how Treg metabolism was regulated and how metabolism and the pathways regulating it affected Treg biology. Our findings, as reported in Chapter 4, indicate an important role for metabolism in the regulation of Treg function. Previous work has clearly shown that the metabolic profile of Treg cells is distinct from that of

Teff (Beier et al., 2015; Gerriets et al., 2015; Michalek et al., 2011), with Treg extensively utilizing mitochondrial oxidation of glucose and fatty acids in contrast to Teff that predominantly utilizing glycolysis. We show in this work that this metabolic program of mitochondrial oxidation is driven and maintained by FoxP3 expression to promote

Treg suppressive capacity. Inflammatory signals, such as TLR ligand binding, act in opposition to FoxP3 and drive glycolytic metabolism that reduces Treg suppressive capacity. This indicates that Treg are dynamically regulated through inflammatory and metabolic signals, coupling Treg function to environment factors. It has long been

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known that Treg are responsive to excess inflammatory cytokines, with the key immune function of Treg to recognize inflammatory sites in the body and suppress inflammatory cells (Bettelli et al., 2006). However, our work shows that inflammatory signals that are indicative of pathogen presence in the body are able to inhibit Treg suppressive capacity. This dynamic regulation of Treg would allow for a robust Teff response when pathogens are present through the inhibition of Treg function, followed by Treg acting to suppress Teff function as the pathogens are cleared and Treg regain suppressive capacity (Figure 5.1).

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Treg% Teﬀ% Tolerogenic% Treg%mediated%% %% suppression% s,muli%

Pathogens% Inﬂammatory%signaling% Glycolysis%

Reduced%suppression%

Non;suppressive%Treg% Teﬀ%mediated% %immune%response%

Figure 5.1: Model of Treg suppressive function being modulated by inflammatory signals or glycolytic metabolism

Treg act to suppress the aberrant activity of Teff under homeostatic conditions. During infection, pathogenic signals may promote Treg glycolysis and reduction of Treg suppressive capacity. This would result in increased Teff proliferation and increased immune response against pathogens.

Several questions remain unanswered with regard to the role of metabolism in regulating this process. While our data shows that enforcing glycolysis in Treg results in

Treg suppressive capacity, the extent to which this applies in the context of TLR signaling is not clear. One interesting point is whether glycolysis itself is directly

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coupled to the loss of Treg suppressive capacity in the context of inflammatory TLR signaling. There are several experiments that could be performed to gain further insight into this point. First, skewed Treg should be treated with TLR1/2 ligand (as in Figure

4.2) and treated with a glycolytic inhibitor such as 2-deoxyglucose. It is possible that the inhibition of glycolysis could result in Treg being refractory to TLR signaling and maintaining suppressive capacity. A second approach would be to utilize genetic methods to address questions about the role of glycolysis in mediating the loss of Treg suppressive capacity after TLR treatment. Treg could be generated by in vitro polarization of CD4+ T cells isolated from Glut1flox/flox;CD4Cre mice, in which T cells lack

Glut1 expression. Our lab has previously reported that these cells were capable of being efficiently polarized towards Tregs despite the lack of Glut1 expression (Macintyre et al.,

2014). Once polarized, WT control and Glut1KO T cells could each be treated with

PAM3CSK4 to stimulate TLR1/2 signaling. Presumably, Glut1KO Tregs would be unable to upregulate glycolysis in response to this and the effects of TLR1/2 stimulation in this setting could be assessed.

An additional interesting question is whether the loss of Treg suppressive capacity upon induction of inflammatory signaling is critical to the immune response. If

Treg suppressive capacity is not reduced upon pathogen invasion, it is possible that Teff would be suppressed and unable to mount an immune response sufficient to clear the pathogen. To address this, the adaptor protein MyD88, required to couple TLR

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signaling to downstream signaling pathways (Arancibia et al., 2007), could be knocked out in Treg by crossing MyD88flox/flox mice with FoxP3Cre mice to generate a strain of mice with MyD88 knockout Tregs. The Tregs in these mice would then be unable to respond to TLR signals. These mice could be inoculated with pathogens known to stimulate TLR, such as gram-negative bacteria, and the effects of TLR signaling loss in

Treg on the immune response to the pathogen could be observed. If the previously described in vitro experiment identified a role for Glut1 expression in mediating the loss of suppressive capacity in Treg after treatment with inflammatory stimuli, the role of

Glut1 in Treg could also be examined in vivo. Glut1flox/flox mice could be crossed to

FoxP3Cre mice to generate Glut1 knockout Tregs. These mice could then also be inoculated with pathogens to stimulate TLR and the effects of the loss of Glut1 in Treg on the immune response to the pathogen could be observed. It is possible that this series of experiments would establish a clear role for the upregulation of glycolysis in Treg and subsequent reduction in Treg suppressive capacity as a key mediator of a robust systemic response to pathogens by Teff.

Finally, the dynamic regulation of Treg suppressive capacity by inflammatory signaling and metabolic pathways may provide a new therapeutic avenue towards reducing Treg suppressive capacity in the tumor setting. Treg have been shown to accumulate in tumors, and act to potently inhibit the immune response to tumors

(Curiel, 2007). Recent work has shown that reducing Treg mediated suppression of Teff

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in the tumor environment allows for an increase in immune response against the tumor, and can result in tumor regression (Joshi et al., 2015). Due to these results, there is now considerable interest in the development of novel methods to inhibit Treg in cancer.

However, current methods of accomplishing this involve the systemic depletion of Treg

(Rech and Vonderheide, 2009). An approach to instead inhibit the function of tumor localized Treg could be more efficacious. One method of reducing Treg ability to repress the anti-tumor immune response would be to reduce Treg suppressive capacity through inflammatory signaling. An approach to explore this possibility would be to utilize a mouse model of melanoma in which Treg function can easily be assayed

(Workman et al., 2011). In this model, CD4+CD25- T cells (nTreg depleted T cells) and

CD8+ T cells are transplanted alone or along with Treg into Rag1-/- mice. The mice are then given an intradermal inoculation of B16 melanoma cells and the growth of the tumor is monitored. The presence of functional Treg results in increased tumor growth relative to mice that received only Teff. This protocol could be modified to include injections of PAM3CSK4 pre-treated Treg or direct injection of PAM3CSK4 into the tumor to determine the effect of inflammatory signaling in Treg in the tumor context.

Additionally, Glut1-Tg Treg could also be utilized in this setting to examine the role of

Treg metabolism in mediating Treg suppression in the tumor environment.

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5.4 Treg metabolism as a potential therapeutic target for modulating Treg function

The work described here also further delineates the metabolic pathways that are utilized by Treg. Previous work found that Treg rely predominantly on the mitochondrial oxidation of glucose and fatty acids for survival, while utilizing glycolytic metabolism at much lower levels than Teff (Beier et al., 2015; Gerriets et al., 2015;

Michalek et al., 2011). However, the functional consequences of this metabolic program were still unclear. We show here that Treg metabolism is regulated extensively by

FoxP3, which acts to promote mitochondrial oxidative metabolism and represses glycolysis. FoxP3 appears to regulate metabolism through several mechanisms, including direct transcriptional regulation of glycolytic genes such as PDK3, and through the inhibition of the PI3K/Akt/mTOR pathway. We found that the maintenance of low levels of glycolysis is important for proper Treg function, as enforcement of glycolytic metabolism by transgenic expression of Glut1 resulted in reduced Treg suppressive capacity. However, there are numerous questions that remain unanswered with regard to the role of metabolism in Treg biology.

One important area that should be explored is to understand the mechanisms by which alterations in Treg metabolism impact suppressive function. While it is clear that transgenic expression of Glut1 results in a reduction in Treg suppressive capacity, it is not known how this occurs. One potential explanation would be that increased glycolytic metabolism acts in a feed-forward loop to promote activity through the

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PI3K/Akt/mTOR pathway, where increased activity has been linked to a loss of Treg suppressive function. There is recent evidence that increased concentrations of glucose are sufficient to promote anabolic signaling and growth in tumor cells (Han et al., 2015).

It is possible that a similar mechanism could drive elevated anabolic signaling in Glut1-

Tg Treg, resulting in a loss of suppressive capacity in that context. To examine this, WT and Glut1-Tg Treg could be compared for PI3K/Akt/mTOR signaling pathway activity.

Another experiment would be to polarize Tregs in differing concentrations of glucose and examine both suppressive capacity and PI3K/Akt/mTOR signaling pathway activity.

Recent work has shown that flux through the glycolytic pathway may itself have immunomodulatory effects. It has been proposed that one mechanism of metabolic regulation of effector T cell function is through the regulation of IFN-γ mRNA translation by GAPDH. Under conditions of low glycolytic activity, GAPDH binds to the 3’ UTR of IFN-γ mRNA and prevents protein translation. Flux through the glycolytic pathway in these cells causes GAPDH to dissociate from IFN-γ mRNA and drives increased inflammatory cytokine production (Chang et al., 2013). It is possible that a glycolytic enzyme, such as GAPDH, is also acting in a similar role in Treg to promote Treg function and lineage stability. The possibility of glycolytic enzymes interacting directly with FoxP3 or other modulators of Treg suppressive capacity should be explored.

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An additional future direction of studies in Treg is the exploration of the role of oxidative metabolism in Treg function. While our work here shows that glycolytic metabolism acts in opposition to Treg function and is inhibited by FoxP3, it is unclear to what degree oxidative metabolism is required for Treg function. Additionally, the mechanism through which FoxP3 promotes mitochondrial oxidation is currently unknown. Previous work has shown that inhibitors of mitochondrial electron transport chain, including rotenone, may reduce Treg viability and differentiation (Gerriets et al.,

2015). Likewise, inhibition of fatty acid oxidation with etomoxir also inhibits Treg function and differentiation (Beier et al., 2015; Michalek et al., 2011). Curiously, metformin treatment, which acts to inhibit mitochondrial Complex I, promotes Treg differentiation (Michalek et al., 2011). In each cases, pharmacological inhibitors were utilized to determine the importance of oxidative metabolic pathways to Treg.

Pharmacological inhibitors often have off-target effects, therefore it would be beneficial to utilize genetic methods to determine the role for glucose and fatty acid oxidation in

Treg. To examine the role of glucose oxidation in Treg, a commercially available strain of mouse in which Ndufs4 is floxed to allow for genetic deletion, could be crossed to a

FoxP3Cre genetic background mouse to generate Ndufs4 KO Treg. The loss of Ndufs4 has been shown to result in inability to assemble Complex I properly and loss of oxidative metabolism (Valsecchi et al., 2012). The functionality of nTreg could be assessed in this model and CD4+CD25- T cells could be polarized towards Treg in vitro

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to measure any defects in that setting. An analysis of the role of fatty acid oxidation in

Treg could be performed by generating Cpt1a KO Treg by crossing Cpt1aflox/flox mice to a

FoxP3Cre background. This would inhibit the transport of medium and long chain fatty acids into the mitochondria of Treg. A caveat to this experimental setup is that Treg would still be able to oxidize short chain fatty acids. Further complicating matters, a recent study showed that macrophages that were sensitive to the inhibition of Cpt1a with etomoxir showed no effect from the genetic loss of Cpt2 and consequent reduction in fatty acid oxidation (Nomura et al., 2016). Therefore, it is certainly possible that the effects of etomoxir on Treg are due to off-target drug interactions.

5.5 Concluding Remarks

The work presented here examines the role and regulation of metabolism in two different T cell settings. In the setting of malignantly transformed T-ALL, we found that oncogenic Notch signaling drives metabolic stress that results in AMPK pathway activity. AMPK promotes mitochondrial metabolism and suppresses glycolysis in T-

ALL to maintain cell survival. In the Treg setting we found that inflammatory signals and FoxP3 act in opposition to modulate Treg metabolism. FoxP3 drives an oxidative metabolic program that is coupled to Treg suppressive capacity, while inflammatory signals drive glycolysis and proliferation, with a consequent loss of Treg suppression. In both cases, the proper balancing of metabolic pathways has crucial implications for the

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function of the cell. T-ALL cells require both glycolysis and mitochondrial oxidative pathways to proliferate and maintain cell viability. It is interesting that the metabolic programs utilized by T-ALL cells and Treg are similarly oxidative. Instead of a being similar to activated T cells, a more apt comparison for the metabolic traits of T-ALL cells might be to those of a proliferative Treg. In each cell setting, we found that the usage of an oxidative metabolic program was advantageous for cell function and stability. The reliance of T-ALL cells on oxidative metabolism supports further investigation into the use of inhibitors of mitochondrial metabolism as potential therapeutics in cancer. In the context of Tregs, this work shows that metabolism plays a key role in modulating suppressive function and may act as a mechanistic link between inflammatory signals and Treg immune function. The modulation of Treg metabolism may be a new therapeutic strategy to promote suppressive function in the context of autoimmunity, and inhibit Treg suppression in the tumor setting.

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Appendix A

Primary HK2flox/flox;Rosa26CreERT2 T-ALL was transplanted into secondary recipient mice. Mice were treated with vehicle or tamoxifen ten days after transplant and sacrificed five days after tamoxifen treatment. T-ALL cells were isolated and extracted for high resolution LC-QE- MS metabolomics. Range scaled area counts are reported.

Vehicle 1 Vehicle 2 Vehicle 3 Vehicle 4 Tamoxifen 1 Tamoxifen 2 Tamoxifen 3 Tamoxifen 4

Label 0 0 0 0 1 1 1 1

24-oxo-1alpha-23- 25- 11906 64006 10000 10000 1628460 1051060 2184324 963598 trihydroxyvitamin D3 7alpha-hydroxy-3- oxo-4- cholestenoic acid anion_24-oxo- 1alpha-25- 25652 10000 10000 10000 526584 224111 1003457 558247 dihydroxyvitamin D3_25- hydroxyvitamin D3-26-23-lactol

C27H46O3(6) 10000 10000 10000 10000 138946 130617 470124 146139

25- hydroxyvitamin 21028 10000 10000 10000 23181 23436 233384 238763 D3-26-23-lactone 1- methylnicotinami 7617106 6860926 7231021 10130770 24356522 27148702 50856172 32470645 de 20- hydroxycholestero l_(24S)-24- hydroxycholestero l_7alpha-hydroxy- 5beta-cholestan- 286122 10000 25817 10000 25828 10000 10000 1181320 3-one_25- hydroxycholestero l_26- hydroxycholestero l

Hypotaurine 846088 987769 642282 1678876 2994519 2774023 2185291 2003812

2-oxoglutaramate 236327 244596 112750 363020 339425 697320 775768 464909 dCTP 66583 95847 93020 110295 258034 127964 303063 156373

Prostaglandin E3 111280 13184 216145 139857 121094 536958 146067 284906 agmatinium(2+) 72340 139149 100395 160046 281340 277071 215278 164676

C27H44O3(5) 914439 978899 851351 881057 1391722 1675006 2063794 1381183 spermidine 1430901 1041331 1325522 1723220 2042666 2005034 2454543 2548055 dialdehyde-2

176

Galactosylglycerol 6245594 40011831 12574118 22798315 29411312 47710109 42310906 13519131

Acetyl adenylate 564873 755405 717280 420268 1043981 644378 800215 1442332

UDP(3-) 5388206 6515429 4680594 7213012 11341938 7196297 11914790 7292171 lactose_sucrose_ 2057468 1584513 1282385 1221770 2851533 3682537 1811832 1342360

N- phosphocreatinat 2106996 1656958 1686815 3812541 4999061 2286215 4100400 3176862 e(2-)

Ornithine 1920532 3551695 1978490 3833699 5872827 3172283 4876250 3805960 propionyl- 642061008 554521792 431332564 755771285 977092114 793134405 1142133013 811418677 carnitine D-ribofuranos-5- 552148 888510 603213 507974 595667 1298588 1286730 803470 yl-ADP(2-) N- acetylputresciniu 3253626 7907191 6374894 7612641 9601790 8372534 14147973 6905603 m N(omega)-(L- 1049805 1086199 847149 1096112 1929198 1583973 1735573 1072993 Arginino)succinate succinyl carnitine 8780645 10225539 8762325 17818456 17691265 16162519 21848342 14376436

3-Hydroxy- N6N6N6- 27719 1488714 364320 557419 952178 1136502 1214678 427002 trimethyl-L-lysine NgNGdimethylLar 36791473 104215911 41009136 56822797 97277914 96018641 120431487 49473852 ginine

Pyridoxamine 274027 646066 380922 304033 801515 342954 699453 550405

5- methoxytryptoph 288712 218829 175267 355931 477047 481532 389813 194725 ol

UDP_neg 19724298 29687310 22435474 29517276 43775785 34046182 46071112 25523449 fructoselysine 3- 342154 354190 394239 460087 389624 792234 525406 578027 phosphate sn-Glycero-3- 727961698 599522395 437475285 687027710 1011481345 720417586 1073902953 775801484 phosphocholine arginine 212342690 268913884 195054968 309031162 450024924 329434812 353594344 293731836

Methionine 5762294 10121278 7818917 15618806 12272907 15107489 13751096 14878265 sulfoxide Flavin adenine dinucleotide 10000 192366 132823 93473 140907 86893 345712 34861 oxidized

2-deoxycytidine 5197623 16288729 4987111 8639273 16466116 18331502 7780664 6580229 erythro-5- hydroxy-L- 140793 300302 187513 257850 342491 354982 329826 210535 lysinium(1+) Phosphodimethyl 13628475 10906129 8798395 19485179 16888121 15708876 23084168 17519890 ethanolamine lipoamide 1142276 984223 838703 1421070 1617466 1843380 1446591 1104138

L-pipecolic acid 13631100 22885828 10681894 16100558 22502321 19375987 30067361 14269909

UDP-D- 304563 669108 448394 957778 827521 966903 805779 637717 glucuronate

177

L-Fucose 1- 31930249 32318564 25210806 33405571 46897536 44597067 41492642 33572068 phosphate

UMP 18079592 25530022 14429211 25833723 26239655 27450496 38194297 20688238 lysine 26812290 43609387 27613745 42470257 50956450 41861726 61654351 33444981 cytosine 56512222 89016477 40348371 72999198 122652280 108080657 54436794 60492163

CDP_neg 6509551 9013988 6214255 8904687 10735418 8998376 13035115 7701101 glutaryl carnitine 1294388 1313173 1259013 1589279 2062041 1571517 2015949 1505433

D-glucose 34202473 28819988 26350019 29102579 35275829 43715475 39865973 36283594

D-Glucono-delta- 34202473 28819988 26350019 29102579 35275829 43715475 39865973 36283594 lactone choline 1502673172 2421790871 1418906645 2060863752 2613391819 2562562048 2601337370 1911497216 cytidine 247980281 290384036 215866044 324563040 348498061 382539280 409542018 262303330 tiglyl carnitine 4816186 6252836 5152624 8844936 9843236 7910859 7693153 6778719 histidinol 159891 354752 250039 328304 340020 249639 512035 298720 quinonoid dihydrobiopterin_ 67- dihydrobiopterin_ 6-Lactoyl-5678- 71898744 50369456 32604848 41053004 80694410 56375376 63650059 49112418 tetrahydropterin_ O2-4a-cyclic- tetrahydrobiopteri n 5-Phospho-beta- 266093 555819 218149 221599 377816 509823 420182 299696 D-ribosylamine

UMP_3-UMP 64103943 99315886 56325540 91086684 93179236 117598317 123921896 61159537

L-lysinium(1+) 36363904 52684719 35764613 45725480 60179071 59147695 59853529 37362831

L-valine 39251876 65680922 45508086 61464694 72221161 55019095 84986750 56606145

L-argininium_D- 64710278 62623048 57163270 71745871 95037815 83237167 74793530 70730752 argininium 2-keto-3-deoxy-D- glycero-D- 77452839 54838647 33841289 83741192 118409911 82341179 52210913 62177970 galactononic acid 9-phosphate o- 1424106 1698657 1458553 2058530 2268330 2100465 2211411 1789297 methylhippurate anthranilate 55811160 41172662 36586318 49432157 56387912 53549893 70672823 49499943 putreanine 3203459 7053751 3288523 4382061 6876907 5403187 6709478 3348688

UDP-D- glucose_UDP-D- 2910900 3759913 2982210 2786513 5372361 4007419 3929790 2178006 galactose acetoacetic acid_2-Oxobutyric 126660215 189587236 120561838 176119654 209089421 179558498 228903059 137287414 acid

178

CDP- 49033326 40439070 34688451 50203680 61612318 42075075 61377220 48662271 ethanolamine(1-)

Nicotinamide 38559890 37952941 26615911 40822694 36661832 39244536 58772968 41683399 adenosine 3-5- bismonophosphat 166779387 171137133 131942908 250503191 235647265 177663136 254160346 213083241 e(PAP)_Dgdp_ADP phenylacetaldehy 141574 119025 24428 212302 87067 156714 219848 143197 de

L-alanyl-L-leucine 2914731 5398092 3693280 4135015 5009607 4679788 5621617 4359991 choline 6797290513 6686595467 5201421031 8654380135 8723259303 7687637389 9820171301 6994220524 phosphate(1-) 2-aminoacrylic 153934656 135614719 106800672 203781357 207633604 149125395 198365653 173805794 acid 2-deoxy-D-ribose 5-phosphate_2- 1074900 1909676 788604 1483576 1922274 1795566 1666148 993111 Deoxy-D-ribose 1- phosphate aspartate 792396243 729836645 560497252 1093878164 1100134339 808010740 1045433659 885795382

CDP- 46193304 49085593 41686192 43699798 63747436 57623754 56755897 39729040 ethanolamine N-acetyl- 4596404 3468164 2380378 5081410 5922941 5189539 4167693 3431400 glutamine 4- trimethylammoni 219428 287165 260062 338008 343821 288552 362613 334545 obutanal

Riboflavin 613975 630021 322501 284318 588665 964102 343703 330077 homocysteic acid 1962895 1531411 1480180 1660174 1765648 2908740 1825881 1480579 dCMP 5283984 5223946 4172815 5325797 7216764 5658641 5894817 5276172

NADH_pos 350731 209602 231863 479479 355616 443461 437039 284299

N-acetyl- 1650954 1578108 1124415 2159143 2413043 1821047 1757650 1792610 glutamate 4-Imidazolone-5- 7869252 7226003 6405778 10618105 10379389 9104270 9966902 8910591 propanoate

Glycylleucine 3625331 3300343 3038423 3612191 4665469 3095310 5363202 3077864

Citicoline 3625331 3300343 3038423 3612191 4665469 3095310 5363202 3077864

2- Hydroxybutyrate_ 3- Hydroxybutyrate_ 149590425 199773528 134791681 192259217 222941113 189862112 240961159 152305098 3-hydroxy-2- methylpropanoat e

NeuNGc 228856 368595 10000 98295 243571 292610 228423 70477

5-guanidino-2- 3610161 3567593 3784534 3875353 4844054 3259365 5087811 4295314 oxopentanoic acid

CMP(2-) 14973242 19434387 12209647 17057261 18403556 21729020 22274063 12527145

56- 7807227 6854808 7228110 10183358 9962923 7054558 10680606 9877678 dihydrothymine

179

(R)-mevalonate 671823 533986 349879 695930 668495 504867 738602 714970

5-amino-1-(5- phospho-D- 467347 165158 250204 388351 437510 302388 353481 382978 ribosyl)imidazole lipoate_(2S3R)-3- hydroxybutane- 123- 4344038 5015431 3851868 4723460 5678090 5283897 6128822 3722514 tricarboxylate_2- methylcitrate butyryl carnitine 397000766 548699494 435797948 695377763 760623257 552500506 547419251 549609055

1-Pyrroline-5- 468639 701666 544224 842830 761226 587176 911657 703330 carboxylate S-[2-carboxy-1-(1 H-imidazol-4- 834870 1165861 1478676 1845288 2059459 1594545 1006786 1501970 yl)ethyl]-L- cysteine

L-aspartate(1-) 1729343650 1739318039 1402580456 2256921068 2308828645 1913634952 2207479191 1818658595

ADP_dGDP_neg 166381509 212603286 163938414 250450717 244958367 239035446 243057899 188312281 indole-3- acetate_(5- 1027286 329227 318415 369973 926911 470374 149661 811707 hydroxyindol-3- yl)acetaldehyde serine 19585289 9355874 11272842 18275125 24199821 9074800 18067940 16079458 glutathione 135787413 116250971 102118855 112034620 166585023 174541879 109781899 85683905 disulfide_neg glutathione 227885962 161542371 137383120 200748712 275311256 214681544 191884865 154717572 disulfide_pos phenylpyruvate 57543 54193 165593 146630 73098 100074 168497 145188

3-Sulfino-L- 68043235 101892234 76923867 104581433 103153920 122784703 106581007 70916503 alanine N6N6N6- 50075354 91600141 59101563 79072378 93530470 89962325 81754242 55862271 Trimethyl-L-lysine 3-oxo-8(R)- hydroxy- hexadeca-6E10Z- dienoate_3-oxo- 462470 259780 432617 288590 644235 348721 408393 252636 8(S)-hydroxy- hexadeca-6E10Z- dienoate (-)-trans- carveol_alpha- pinene 3825151 3040211 2483971 3151000 4082026 3023608 3574624 3615117 oxide_perillyl alcohol L- arabinitol_xylitol_ 6160064 5616714 4643894 6054356 6487656 6756426 7083237 5311819 D-ribitol leukoaminochrom e_dopamine o- 12959775 11223587 8076624 12590975 15745282 10784155 13325137 11289018 quinone-1

D-glucarate(2-) 46897397 48858154 37563333 52309804 54376317 50181941 61322710 45312564

CMP-N-acetyl- beta- 24972170 19089460 15325042 22696524 30616778 23747431 21422334 17111082 neuraminate(2-) N1N8- 211055 290337 285869 206476 369729 193992 282925 274176 diacetylspermidin

180

L-cysteinylglycine 7901509 9226326 6088639 11107697 11410744 7503362 11430527 8302552

Fumarate_Maleic 95315361 91401453 71605319 109605394 113886762 97116346 116595054 84691139 acid

D-glucarate 36311157 34734287 28442011 36382139 41428367 38764999 39109689 32839799

ADPmannose_GD P-L-fucose_ADP 769392 1155469 851174 1436431 1148142 1240101 1394691 909318 alpha-D-glucoside glutathionate(1-) 599323227 662796828 454395887 839113745 822581551 541599508 863674224 602054156 hydroxyproline 104280846 72021612 81669778 119300098 115495229 84744654 97127850 120242456 trans-Hexadec-2- 215739072 258001385 228223827 327602960 259183509 336001089 316298474 225431454 enoyl carnitine (R)-2-hydroxy-4- methylpentanoate 17136662 23755319 17121549 22578102 24178864 21562629 26267465 16842441 _Hydroxyisocaproi c acid 2- methylbutyrylglyci 33473761 30843694 26417724 36354015 36895091 30959642 40182886 31892193 ne_isovalerylglyci ne sarcosine 36835368 31621139 26975955 43721066 41239405 35433291 44446628 31882513

L-glutamine_3- Ureidoisobutyrate 16656465 12720869 11571414 15695434 20465449 10658150 16586790 14466021 _Glycylsarcosine 4- (trimethylammoni 1896668 2682988 1996852 2557004 2870253 2270237 2575542 2291825 o)butanoate_acet ylcholine orotate 2686681 3312909 2364328 3100323 3134367 2970061 3655460 2794739 tetradecenoyl 128921469 143883654 160514822 207679179 165506377 190789474 213373286 131260533 carnitine betaine aldehyde 14749450 12012634 10189717 17523833 15248148 12052855 17118255 14932996

1-piperideine-6- carboxylate_1- 1105213 1100262 959950 1007214 1301095 1099730 1207345 935023 piperideine-2- carboxylate thromboxane 1349597 1331298 1998262 1306173 1080746 2204954 1359410 1859267 A2(1-)

NAD+_neg 6875775 7899600 7197213 8857013 10437103 9389158 7575440 6004819

(S)-malate(2-) 742793913 663425987 505721318 763039603 801539172 693356724 846609783 548054860 tyraminium 8286210 12648479 7210371 32548604 10269140 7838945 36620356 10765405

UDP-alpha-D- 492274 1071007 604341 1004502 828489 1260538 872014 458650 xylose(2-) Phosphoenolpyru 730390 355274 380443 393404 609655 537394 426561 428176 vate creatine 1555607253 1212664395 1048869671 1632088724 1594975024 1316755026 1577602630 1357434792

N-Formimidoyl-L- 3160902 2022670 2592296 3041657 3068634 2656583 2859684 3015312 glutamate

181

N-acetyl-L- 1371331 2051918 1766472 2794753 1823242 2185538 2549652 2002014 asparagine D-Glucuronate 1- 734638 855731 845963 1006293 1266744 697626 868102 847402 phosphate

12S-HHT 577918 218775 170950 272058 427474 375119 331213 189133

UDP-N-acetyl-D- galactosamine_U DP-N-acetyl- 81973316 118161551 73303195 86943032 106907935 115441024 95166176 66917661 alpha-D- glucosamine uracil 20862068 19648669 18686820 21389897 23160170 20516342 21681851 20435322 malonic dialdehyde_acryla 12572289 9524165 8029023 12212262 12436063 9738411 12492643 10314511 te_methylglyoxal

NAD(1-) 42916748 32899013 30813239 50697193 54330027 33393336 42310040 36683759 glyoxylate 2866629 1589351 1642029 2686989 2595751 1660943 2655094 2370981

3-methyl-2- oxobutanoate_2- 78164945 92100218 70598580 87020180 92087995 80579177 104747379 68745450 keto-isovalerate

Oxaloacetic acid 738859909 612702133 485032665 728753388 758341209 647299028 783226970 518891843

N-acetyl-D- glucosamine 6- phosphate_N- Acetyl-D- glucosamine 1- phosphate_N- 5831474 5347392 3997802 5907599 7251850 5835645 5467778 3661163 Acetyl-D- galactosamine 1- phosphate_N- acetyl-D- mannosamine 6- phosphate 3- hydroxyhexadeca 6275092 7074573 6363934 7091909 6231469 10442717 7178380 4359721 noylcarnitine

L-cystathionine 3103843 3144284 2840394 3379640 4033157 3335240 3180892 2565140

2-keto-3-deoxy-D- glycero-D- 6438543 6975773 5894700 7177841 6924382 7598949 7548282 5755072 galactononic acid L-xylonate_L- 86242420 73942899 67040541 83149771 87029574 85172284 83823533 69328343 lyxonate Linoleyl carnitine_Linoelai 785154476 965728306 848569538 1111949359 955931211 1169945354 956446651 794736712 dyl carnitine pyruvate 100730153 109356106 86922984 116257252 114570679 106849889 121321346 88830165 ecgonine 351963 409910 372162 211701 369944 280952 427464 325770

4- hydroxybenzoate_ 93991629 82471159 82857992 101397651 118903211 85461979 92723759 79199235 Gentisate aldehyde imidazole-4- 2657596 1809803 1703072 2127449 2509041 1542655 2129201 2464785 acetaldehyde 1- acetylimidazole_1 2657596 1809803 1703072 2127449 2509041 1542655 2129201 2464785 H-Imidazol-4- ylacetaldehyde

182

xanthosine 17942716 31031644 26146084 13498813 12458401 40006963 30825936 8945975

(R)-carnitine 12439674096 10057182185 9189514218 12152818667 12018351738 10094076301 12135754691 11360328653 sphingosine_(2S)- 1-hydroxy-3- 1301506 1914844 2423771 1677690 2095951 1777994 1727261 2010150 oxooctadecan-2- aminium itaconate_Mesaco nate_Citraconic 64961894 69401130 58363082 82241042 68513394 60481987 89395217 67520493 acid (2R)-2-hydroxy-3- (phosphonatooxy) propanoate_D- 8400065 7649882 6493904 8177286 9708526 8443979 7094817 6670634 Glycerate 2- phosphate 3- 8400065 7649882 6493904 8177286 9708526 8443979 7094817 6670634 phosphoglycerate N-acetyl-L- 10011281 8913889 8642833 10955406 10552991 10313448 9694001 9332594 glutamate(2-) spermidine 731876 597365 580704 962018 739377 542518 740022 943480 dialdehyde-1 nicotinamide 19088327 15579607 14279519 20611529 21419718 14409732 17328810 18570948

3- Phosphonooxypyr 8318180 7342114 6386168 8028223 9443651 8167793 6853760 6524464 uvate N-acetyl-seryl- 8084560 7640564 7474859 10745302 8069236 8590103 10804797 7501009 aspartate perillyl aldehyde 10465502 7875142 7690242 8666830 9738493 8698071 8728151 8377709

8(R_S)-hydroxy- hexadeca- 230908 191298 269807 66423 180133 136367 261880 198194 2E6E10Z-trienoate

L-threonate 685012739 563341706 546007944 647712692 671849288 651694402 586396547 589958514 dTMP_neg 2916637 4041926 2151398 3227525 3076936 4322844 3316186 1885364

Thiamine 1025866 926953 858328 951893 1118253 1149492 856852 718803 pyrophosphate tetradecanoyl carnitine_myristo 459751843 546823698 466770440 670412648 506420277 645633374 635153015 398631478 yl carnitine glycine betaine 6765996011 6473701789 4582966017 7329955759 6968663572 5477065621 6622625486 6565292765

D-glycerate 39488400 13404850 23557414 29405720 29196438 38391212 19511365 20552063

2- phenylethanamini 14996704 10900032 9616313 16101297 13907680 13559332 16358861 8658382 um 3-dehydro-L- gulonate_D- 946836 348485 699688 531925 623375 909960 502066 532465 glucuronate_L- iduronate

Deoxyuridine 2506302 2338615 2750532 2611618 2797186 2988576 2133400 2450565

4-nitrophenolate 15299086 13693260 10494261 14771100 14632692 13112260 14371867 12913767

O-acetylcarnitine 2625769049 2868822547 2042609876 2664010974 2770310804 2541586835 3015049363 1995694328

183

L-3- Cyanoalanine_56- 4649739 3538160 3417070 4961204 4849791 3340560 3965379 4543702 dihydrouracil oxalate(2-) 129926190 73448187 75236528 104946022 108609956 98555122 88859341 90550335 nonanoate 288936322 292077496 297008622 311135114 302036485 317267549 308304646 270870397 octenoyl carnitine 36941362 40277754 33928456 47291499 44259597 40566992 46948530 27869226

3-methyl-2- oxopentanoate_4- methyl-2- 797787530 849912316 739599309 917907301 898301013 786409148 932098273 713027408 oxopentanoate_2- ketohaxanoic acid 1011-dihydro-12- 1646256 3320317 4495907 3605400 3025482 3319573 3036794 3752723 epi-leukotriene B4 glycolate 32202295 21175742 22604799 30687662 32419245 21878575 25242350 27552830 dGTP_ATP 28453235 30295366 21288393 28619955 37687416 16470553 32960928 21872615

2-oxoadipate(2-) 1725871 1250582 988956 941794 1618028 1207576 1081233 1011443 histamium 849901 1069329 727991 1554859 1275792 898974 1136260 896982 glutathione_neg 433387896 523841501 408688430 550496665 515416887 439367403 556372495 407835818

L-Iditol_D- 15685343 10162658 12567795 12938110 13095312 11288106 10789581 16084375 glucitol_galactitol Elaidic carnitine_Vacceny 608073415 862588085 731798436 973667352 787324742 964963366 790897161 622978929 l carnitine

GDP_neg 14482618 20860494 14531128 22380546 16959361 19434000 20975405 14631583 beta-hydroxy- beta- 58614298 59062056 63651529 77133497 74802891 59593235 63626717 59059309 methylbutyrate decanoyl carnitine 66299890 68529192 76409360 91460794 75154706 83226066 88330271 53920253 caprate 169441259 181539973 182653287 186512908 179471781 191883029 184556968 158899629 calcidiol_7alpha- hydroxycholest-4- en-3-one_3beta- hydroxy-5- cholestenal_27alp ha-hydroxy-8- 279762601 36264342 379770542 553106278 139282823 406383509 379938892 312374090 dehydrocholester ol_27alpha- hydroxy-7- dehydrocholester ol

Taurine 4261100313 3770177659 3346882778 4416662047 4218516355 3615027561 4229137986 3575648812 omega hydroxy hexadecanoate (n- 5937082 6706627 7539668 5486773 6239402 7197620 6492560 5460559 C16-0) citrulline 14699856 6299242 7338052 13768656 10949119 7686209 11971074 11029724 propionate_(S)- lactaldehyde_(R)- 20124725 18672792 14648360 20243752 21271211 17038363 18785290 15698534 lactaldehyde_hydr oxyacetone

184

Pyruvic acid_3- Oxopropanoic 663633715 565858363 457694275 588144987 621950210 535574652 601803720 485718602 acid 4-hydroxy-2- nonenal_3_4- 24727761 24904093 24322130 26337978 25038058 25636934 25149816 22804159 epoxynonanal (R)-S- lactoylglutathiona 1199941 3775638 2153456 4909254 2170383 2232522 5266216 2102192 te(1-) D-Glucono-1-5- lactone 6- 7275807 6922716 6318213 8437847 8183600 7530393 7090018 5483541 phosphate 3- methylphenylaceti 9097331 8739168 9290963 11428459 9704440 8918460 10445506 8523361 c acid aldehydo-D- xylose_D- ribose_D- xylulose_L- 4164414 2402524 2939304 3319912 3336795 3319439 3163599 2670750 xylulose_D- ribulose_L- arabinose 3- hydroxyhexadece 13902308 13516983 14482929 14970500 13414397 14196266 15696960 12046754 noylcarnitine w- hydroxydecanoica 17893830 18566416 18759746 20535780 18752444 20032129 18584288 16323907 cid hexose-phosphate 7284150 6986167 6377739 8304258 8115616 7601065 6909886 5503854 laurate 158352391 169657647 174204160 150710856 164547712 212405697 133654446 122133197

3-oxopropanoate 46211866 29752279 28887777 42885751 36896986 26701562 42082936 37456842

N(2)-acetyl-L- 640118 206452 415742 201647 799942 181896 186131 245573 ornithine octanoyl carnitine 198884864 209123226 200508891 236470139 206945092 221126230 246929795 139224538 alpha-linolenyl carnitine_gamma- 12157640 12758907 12939795 17508354 12557825 15752216 14460578 10541750 linolenyl carnitine

CTP(4-) 1563163 2427903 1594243 1634794 2184356 1235879 2297371 1194241

2-Aminooctanoic 2667353743 2225754029 2183699311 3164005391 2810540943 2145412328 2511663028 2327461898 acid glutamate_O- 1294879597 1184622491 909401690 1736858611 1428464155 1081798838 1374077044 1017439960 acetyl-L-serine 2- methylglutaconic 7217503 4549063 4846128 5353357 6536655 4676107 4767744 4949781 acid Succinic semialdehyde_2- oxobutanoate_2- 63915487 42617553 40307608 45687926 57333565 39540549 43008949 43526093 methyl-3- oxopropanoate

NADH(2-) 1922130 2140804 1359955 1669128 1983756 1915451 1625664 1230370 quinolinate(2-) 3501372 3114610 3180813 3755764 3342133 2843375 3421874 3293411

Oxaloacetate 913969 903123 1031608 1151221 919967 813811 1091271 978070 limonene_(+)- 2589621 2029528 1453860 2124671 2275767 2110539 1730411 1673866 alpha-pinene

185

CDP-choline 4211410 4557939 3779602 3430847 4304324 4845088 3648414 2378921 omega hydroxy tetradecanoate 954488 1052024 989696 905041 799938 1198337 1051934 653498 (n-C14-0)

UTP(4-) 5157319 6518221 5304636 4916510 7358911 3564464 6270084 3561283

L-asparagine 45768474 23432391 25776967 52432576 41201860 21108968 30725450 46477708 pendtadenoyl 9057018 12493461 9262255 13339217 11755925 10343975 13191474 6389263 carnitine

CDPcholine 86137328 67332783 52972905 80389065 77306455 61035494 76011363 56367472 putrescine 886066 363499 436123 440575 546376 258711 668475 532074

4- hydroxyphenylace tate_2- Hydroxyphenylace 12673736 11176527 10688879 13035478 12057902 10386510 12094489 10328673 tate_3_4- Dihydroxyphenyla cetaldehyde hexadecanediocac 3147757 2826758 3426146 3095190 3041009 3244746 2971519 2527217 id gulonate 20394571 17246381 15587398 17055331 17205448 17519445 16526338 14980425

Hydroxyphenylace 9756744 8440300 7915971 9842028 9149754 7747676 9229112 7707257 tic acid 2- deoxyguanosine_a 4801563 5759588 2285851 2741685 3478603 5406219 3825986 1947272 denosine creatinine 147962773 143117968 82344127 117234416 122001228 116646106 130434838 92219116 dopaminium(1+) 386308 311241 290350 279359 346816 356816 257481 230275

3- hydroxyanthranila 1882108 1583552 1385690 1852499 1680536 1413332 1767069 1421792 te 3-hydroxy- isovaleryl 20611714 27138756 19147866 26242935 24178117 23114777 22894761 16965476 carnitine

Alanine 43174259 30626553 29620186 48344742 39641043 27247723 36474023 38276291

4-oxo-2-nonenal 9479378 9589312 9495114 10390771 9627710 9228309 9441609 8013842

NN- dimethyldopamin 8451466 6471529 5652398 6985608 7643784 6895439 5881308 5246172 equinone_(-)- Salsolinol-1 acetoacetate 4371532 3328001 3054773 3383954 3576099 2523724 4198249 2843807

D-fructose 2905151 1347602 1535570 1835598 1735140 1700947 1503573 2145251

N-acetyl-L- alanine_5-amino- 2-oxopentanoic acid_5-Amino-4- oxopentanoate_L- 50119199 33518846 41769319 51330185 44218283 39390896 42582749 37953818 Glutamate 5- semialdehyde_tra ns-4-hydroxy-L- proline

186

sedoheptulose 17- 394931 449020 427006 307041 413996 392397 337944 319836 bisphosphate(4-) phenylacetate_(4- hydroxyphenyl)ac 15049600 13328351 14467456 16056981 13810759 13106422 14002358 13689525 etaldehyde 5- Methylthioadenos 133743698 94217574 86402526 133154095 135554241 102308427 81193151 94772787 ine 4(R)-hydroxy- dodec-6Z- enoate_4(S)- 34532183 35869590 37894088 40015298 35220055 38169151 33810078 29832217 hydroxy-dodec- 6Z-enoate succinate(2- )_Methylmalonic 341348495 335928713 263437758 351248364 342925720 304126137 290290382 254749688 acid

Coenzyme A 3209043 2382588 1478988 4306443 2513413 1643773 3389646 2940998

Atrolactic acid_Phenyllactic 3791779 3823314 3973607 4583067 4518351 3365471 3671674 3325391 acid S-adenosyl-L- 1604072 3154599 1741392 2803183 2522250 1811924 2723806 1501535 methionine citrate_isocitrate_ 949633740 995804913 792208285 1025891321 1026316252 779307994 969760388 683006189 Diketogulonic acid 4-hydroperoxy-2- 64989412 63319544 66588650 70479037 66700069 61765345 61620764 53759269 nonenal

Pyridoxine 939549 998886 483811 683492 913286 770734 626261 543125

Taurodeoxycholic 393009 283041 253591 193581 271976 192050 330698 235057 acid

L-threonine 44156667 30414001 29260139 46102898 40251125 24618134 37379647 35079401 homoserine 44156667 30497892 29268248 46102898 40236474 24598190 37364528 35079401

2-oxoglutarate(2-) 10479957 9177339 9616620 10803479 11188101 7454901 9475294 8542295 alpha-D-Ribose 1- phosphate_alpha- D-Ribose 5- phosphate_D- xylulose 5- 11886747 10747959 8499508 10918240 9428151 12385173 9118868 7518170 phosphate_D- ribulose 5- phosphate_D- Xylulose 1- phosphate Taurodeoxycholic acid_taurochenod 393009 283041 253591 210180 283437 192050 330698 235057 eoxycholate sebacicacid 52636364 49714700 57515663 54175661 53094348 51218449 46354969 44443082

N- acetylneuraminat 787706 875763 417098 770242 874149 562536 702501 456640 e D-Glucosamine 6- 2036872 1973373 1532761 1614157 1952677 2284563 1299113 977962 phosphate Dodecanedioic 7670389 6889572 7240928 7478739 7457475 7071866 6322155 5774836 acid

(R_s)-lactate 970872511 647397857 594986018 729686267 743212497 618042049 688220896 624416555

187

L-glutamate(1-) 1164207793 1087864828 965961794 1394625799 1165462983 1023948426 1104215361 891948195 myristate 87908931 106297287 110312688 90145057 80646057 122269935 83779017 70721374

Malonate_3- 38727811 24191538 30853866 37497554 34854719 27631982 24361557 31950944 hydroxypyruvate 3-oxo-6(R)- hydroxy-tetradec- 8-cis-enoate_3- 3059109 3315834 3127883 3319210 3134264 3188935 2970451 2306439 oxo-6(S)-hydroxy- tetradec-8Z- enoate

IMP 862819466 891784242 580360706 986890429 798114833 781243568 782942494 638150354

AMP_dGMP 858951806 886096258 576502622 981024556 793544246 776854241 777284026 633895516

3(S)6(R)- dihydroxy- tetradec-8Z- enoate_3(S)6(S)- 7049639 4805708 6953222 6247839 5554676 6083310 5288065 5693260 dihydroxy- tetradec-8Z- enoate omega hydroxy dodecanoate (n- 4124002 4476962 4674137 4310718 4041700 4497232 3873392 3390518 C12-0) 5-oxoprolinate_L- 1-Pyrroline-3- hydroxy-5- 149899388 142247710 124910487 173543719 148516111 135294466 135478151 111233746 carboxylate_Pyrog lutamic acid

L-proline 1026848449 703622607 699536554 1099856197 951371512 605319474 784129647 829613046

N-acetyl-L- aspartate_2- 1354914841 1070244291 1106898266 1485238883 1272482296 1053735208 1064758157 1110663677 Amino-3- oxoadipate shikimate 22416166 14249798 17380764 18207903 22665263 13996471 14453847 13601068

L- 1392933784 1875294252 1669386539 2194238736 1582140327 1978245712 1569577653 1239088158 palmitoylcarnitine spermidine(3+) 3414865 5387639 5143293 6779004 4330001 3759365 5553061 4748036

N- methylethanolami 37192196 23357066 28769110 37155600 30757771 22001859 27741577 31708414 nium phosphate(1-) cis- aconitate_dehydr 13618863 13261403 11978653 14638708 13585645 11173175 12977270 9589580 oascorbide Glycerol 3- 141219213 133586795 89973432 121643157 115425147 135027304 89350444 89128570 phosphate glyceraldehyde 3- phosphate_Glycer 141219213 133586795 89973432 121643157 115425147 135027304 89350444 89128570 one phosphate

ATP_dGTPneg 28557388 33231222 25899892 28691651 35982726 19366549 28400379 18871725 heptadecanoyl 14188654 21638687 19764436 24895554 18490368 20805986 18102540 13489314 carnitine

Oxalosuccinic acid 983658720 904776990 778091076 1001881249 960366869 714322922 890054460 665637722 trans-45-epoxy- 2462184 2137078 2291701 2459989 2056805 2049242 2313623 1800859 2(E)-decenal

188

3- methylcrotonoylgl 2386458 1404598 1679957 1896073 2164911 1211067 1261456 1811585 ycine pentadecanoate 37716473 43301326 51301996 45007305 36601311 44268736 37530811 36233360

13-hydroxy-alpha- 794432 626995 819632 1151767 654780 996629 834560 467352 tocotrienol (4-hydroxy-3- methoxyphenyl)ac 9833126 8728581 9185442 10952250 9450329 8160193 8399373 7619350 etaldehyde palmitate 830760867 1636240955 1841308401 1274187635 982936333 1689584651 996542660 1181689476

9_10- hydroxyoctadec- 12(Z)- 2749498 2221691 2658986 1961897 2108542 2199471 2180224 1840476 enoate_12_13- hydroxyoctadec- 9(Z)-enoate gamma-L- Glutamyl-L- 1286503 1871955 1260756 1600708 1354938 1038341 1726883 1104283 cysteine allantoin 162692647 188066786 121698394 151849335 149887426 168086095 136814950 85510583 indole 4613684 3432144 3847061 4653215 4179489 2384393 3847935 3885481

Xanthine 28899298 49641071 26483463 17789399 18945053 38232616 34419361 14417985 adenine 45960534 38163777 32442058 44483860 37430378 31711785 34474895 34487855 dGMP_AMP_3- 602188414 701897940 502468015 635731456 530508522 680127501 485342210 396244944 AMP 4- acetamidobutano ate_isobutyrylglyc 31700404 25868700 22048586 31041170 30073107 19362486 24549085 20635342 ine_6-amino-2- oxohexanoic acid_L-allysine methyl indole-3- 519170 371459 440217 552367 501927 421398 331552 354184 acetate 1-Methyl- 29641151 17702821 24627472 31271770 25793503 15860750 21117275 25403317 Histidine

Diphosphate 905415199 504073395 691906969 752458322 681756710 516982123 531962582 705023251

13-carboxy-gama- 289697 136792 72040 61865 110165 153979 78512 134952 tocopherol

L-erythrulose 16954848 8283283 13636058 15481391 15933969 8374270 9864626 12151919

2- aminomuconate(2 9998188 8467487 8140827 10419981 8251148 8266648 7339431 7596699 -) 23- dihydroxybenzoic 389061 344328 396380 619277 488208 196794 557618 242189 acid N-acetyl-L- 665925 380749 572112 1157132 520105 545846 792345 492077 cysteine 2- 405574572 342162824 307003130 392650740 369655700 265195420 318047936 269931768 Phosphoglycolate 2-Isopropylmalic 16749202 10967430 17988812 17225746 14239562 11411462 11137361 16153836 acid linoelaidic acid (all trans C18- 457058303 726680608 587980276 399886346 336389377 787500967 405761138 296095763 2)_linoleate_octa

189

decadienoate (n- C18-2) 9(10)- EpOME_12(13)- 3723831 3366873 3891124 3742958 2788941 3815705 2993973 2744244 EpOME

(R)-Pantothenate 300373905 231130314 308585705 347058818 273167701 234895018 216864224 269698755 benzoate 2458686 1579488 1518791 1813132 2107606 1366282 1320205 1379547 carnosine 809357 389568 627376 501015 527225 479998 428562 513377 glycine 22223459 22377301 16033456 24470032 20185368 12989166 20974787 17012396 adipic acid 133760781 31915678 115837992 140356878 161215015 46048091 39580175 105713409

6- phosphonatooxy- 427627 440772 260987 224063 412105 367988 169716 178875 D-gluconate L-2-amino-3- 3303498 1560569 2359963 2972728 2521836 1810778 1660805 2478032 oxobutanoic acid L-isoleucine_L- 180892845 130345111 146354133 157751983 138413514 129974864 126872019 114891831 leucine

L-tyrosine 22310717 13018844 14763681 18493441 17237025 12046125 14323827 13212386

L-methionine 14223028 9424826 9541376 11848734 9360633 8619179 10188936 9046670 tryptophan 27568254 17042405 17823310 23151623 22394683 15596339 16536738 16169933 stearoylcarnitine 59996116 100487264 106320179 114122424 72163945 97519956 74541093 70311794

15-hydroxy- (8Z11Z13E)- 225819 331568 464935 430083 201979 431179 321834 242007 eicosatrienoate

L-tryptophan 15378988 12174766 13658374 14015774 13438798 12156508 10673612 9246927

912-Oct-13- diepoxy- octadecanoate_11 5477758 3464047 2295731 2411312 3015549 3491773 2424077 2315985 -HpODE_13(S)- HPODE Sedoheptulose 7- 7124248 7209417 5746922 7743019 6118318 7180456 5522900 4104603 phosphate trans-4- coumarate_keto- 5125651 3124576 3164203 4022469 4054123 2886961 2907641 2809013 phenylpyruvate_ci s-2-coumarate palmitoleate 84542357 84766446 74106984 58019862 40925930 101314255 60276107 44537886

S-Adenosyl-L- 9561374 7494073 6254625 8577773 7897319 6534206 6129193 5514713 homocysteine timnodonic acid C20-5n-3_1314- epoxy-retinol_4- 19292648 28533110 20345519 19542879 12911129 30900469 16338645 11459173 hydroxyvitamin A1_14-hydroxy- 414-retro-retinol 35-dihydroxy-34- dihydro-14- 2155741 3506538 1806624 2258448 2366398 2488430 1436185 1649015 benzothiazine 6(R_S)-hydroxy- tetradeca-2_4E8Z- 1157428 1165529 1289318 1144603 988403 1102463 1073481 712284 dienoate

190

D-Alanyl-D- 385620 510640 302146 409612 304208 301283 374127 321427 alanine 12_13-epoxy-9- hydroperoxy-10E- 140946 66663 194191 96376 155420 74534 57857 113788 octadecenoate D- glucuronolactone 6255611 4836695 4802730 5525562 4972386 3845778 4894543 3530279 _L-ascorbate stearate 194484422 432081912 629371306 399320492 256165042 445409788 234560350 387243493

3alpha-7alpha- 12alpha- 474229 939035 1064930 1250715 660028 852354 927830 529091 trihydroxy-5beta- cholestan-26-al salsolinol 1- 748338 905126 916161 753713 705968 793957 561707 572693 carboxylate 3(S)10(R)-OH- octadeca-6-trans- 1580946 874377 1093891 636503 1024109 772780 860687 652371 412-cis-trienoate 3- Methylimidazolea 1808082 619840 1430887 1602502 1178654 427517 1095354 1609022 cetic acid

L-histidine 39416731 21164651 27492388 33037052 31199616 17774170 23139120 23156382

2-oxo-4- methylthiobutano 716117 255914 459487 817870 412583 280987 345404 726839 ate

Ascorbic acid 7726327 5137274 5557937 6318154 5546716 4241246 5217871 4233549 uridine 70885809 107735720 76588218 52354778 45442262 90650808 68142149 33628621 tetradecenoate 30435506 40771061 31308410 28444037 20581898 37880705 26449171 16329758 (n-C14-1)

Biotin 359205 191385 164271 245270 146288 201248 249404 145315

Pyridoxal 4249760 3194262 2491164 2964361 2922679 2797407 2329541 1919080 phytanate_arachi 747130 1409111 2544853 1293804 1012498 1405314 823863 1380602 date

Aconitic Acid 7543214 5009165 5431637 6190903 5393818 4105199 5083946 4052994 margarate 14381991 23176885 29154494 21006469 13742607 22220965 13735082 17780273

L- kynurenine_Form yl-5- 834881 390371 454339 604019 604475 412857 343549 393588 hydroxykynurena mine L-fucose_L- 3009924 1521929 2058795 1495995 1826736 1466762 1347719 1546472 Fuculose Homovanillate_3- (4- hydroxyphenyl)lac tate_3-Methoxy- 5162853 4693817 3721677 4297463 3688511 3184182 3952796 2798753 4- hydroxyphenylglyc olaldehyde NN- dimethyldopamin 29095385 33105102 23497352 41235759 26931211 18471408 26618544 24547579 equinone_(-)- Salsolinol-2 leucine-isoleucine 24619581 19225283 18415729 22586245 19135327 14914909 15788141 14324937

191

inosine 57520429 42510332 40522935 60502003 39592018 50256236 27569697 34230548

6(S)-hydroxy- tetradeca-2E4E8Z- trienoate_6(R)- 780271 521030 798893 802539 632720 551428 522515 482544 hydroxy- tetradeca-2E4E8Z- trienoate N- Ribosylnicotinami 86948 392005 114848 158548 197835 296997 10000 60261 de trans-caffeate_3- (4- 431939 232833 351471 357772 300865 224823 235553 269043 hydroxyphenyl)py ruvate trans- vaccenate_elaidat 340144875 702923147 599307697 437929609 267555149 652499766 341008268 294738511 e_oleate

Cytidine 216629120 156867419 122239084 142995086 129329035 145785491 104381760 98147845

11-cis-eicosenoate 7625083 23135036 23399280 16891014 8257232 22180743 10279028 12250530

NADP+(3-) 7767022 2784872 4774667 7505711 4642446 2917990 4374716 5068026 clupanodonic acid_docosa- 38119565 68862375 54763395 44418611 27452048 64212532 37519359 24012276 4_7_10_13_16- pentaenoic acid 2-Carboxy-23- dihydro-56- 1235252 725841 969048 925592 584698 706462 582787 990547 dihydroxyindole_L -dopaquinone dihomo-gamma- 24121034 43003666 36851301 30123771 15908613 41533653 25069470 16794188 linolenic acid (n-6) Aminobutanoate_ NN- 35184449 17706615 15205804 22391940 19362752 13412944 17334785 16645953 dimethylglycine N(8)- acetylspermidiniu m_N(1)- 5144737 1132106 908442 398149 3906805 436032 553511 609428 acetylspermidiniu m ethamp[c] 1893875249 1762563070 1471141275 1767432350 1606148099 1217754276 1236603997 940719916

N- benzoylglycinate_ 13656850 5601793 10367334 9280298 7644701 6462074 5655311 8310964 adrenochrome 3- (methylthio)propi 1615005 509076 1704398 1166257 871421 665306 723336 1293325 onate

3-Sulfopyruvate 1486717 1122934 1196570 4310976 1868106 1651013 1148757 1078433 pristanic 819163 1362599 1921937 1552556 860538 1263101 709642 1107834 acid_pristanate

5-Hydroxyisourate 718461 277635 424177 558441 418463 274377 200984 481375

Uric acid 5633508 2166728 2246618 2192588 2235338 1895342 1841434 2433790 dopaminochrome 4906944 3328465 2885807 3320666 2671419 3160653 1934555 2110790

D-Fructose 16- 8941580 8110210 9289021 6650631 6540004 5535984 4423090 5623122 bisphosphate

192

naphthalene 373052 223439 198859 371066 373827 95159 108801 196984 suberic acid 6484710 2119081 7005157 3183752 4777322 2269143 2421946 2881550

3- hydroxypropionat e_D- 591290844 131803969 371175668 457616972 299583644 173804802 159093740 380335648 glyceraldehyde_gl ycerone imidazol-4- 34726272 33381682 24152371 27971680 19479890 27176315 15769566 14947171 ylacetate_thymine cholesterol sulfate 546776 913141 652778 312056 409455 584548 371570 177920

3-carboxy-alpha- 966907 1265083 600893 494707 547948 559463 584495 425084 chromanol 1314-dihydroxy- 865295 756315 682913 1132428 308319 776387 689382 381403 retinol

L-phenylalanine 10571315 7488058 8198139 8814186 6748050 5533534 5032541 4565754

Hypoxanthine 4565848990 3900178482 2945675215 2864922930 2015417943 3153900110 2084830416 1632498453 hypoxanthine 401907608 336073419 325186190 270801598 181679362 296968740 189626276 154903636

3-oxo-10(R)- hydroxy-octadeca- 6E8E12Z- trienoate_3-oxo- 1232802 434979 214572 543631 537485 428213 245267 269516 10(S)-hydroxy- octadeca- 6E8E12Z-trienoate guanosine 13429224 9764946 8820638 9711274 4706215 8583853 5756479 4944423

5- Methoxyindoleace 832552 339568 679019 517594 400050 386004 197807 369966 tate (S)-3- sulfonatolactate(2 382652 366474 303422 167694 139939 151419 238981 166408 -) cervonic acid C22- 6 n- 201612288 333420386 253447108 179531096 113455152 224287380 132005131 81522264 3_docosahexaeno ate 23- bisphosphonato- D-glycerate_3- Phospho-D- 455233 297111 590119 937310 299392 202226 386512 387341 glyceroyl phosphate(2_3BP G or1_3BPG) thiosulfate(2-) 1262803 1178340 289513 595407 526121 643794 454344 219151

Thymidine 2214199 2209261 2090109 1851522 1017424 1880360 946023 706833 leukotriene B4(1-) 258473 222563 119995 333435 15878 63202 42640 380951 coumarin 994711 367960 535951 702829 232924 276315 343438 544356

D-Glucosamine 4531000 5515413 1599692 2165663 2177044 1338036 1987330 1914767

(S)-dihydroorotate 13299781 434739 8627154 14004027 5574238 1280050 3543154 8977709

193

GAR 710771 498392 385001 664140 375353 265192 190801 288083

Guanine 755458432 355410308 513539737 428371223 224121512 304209887 211301703 260524902

2-acetamido-5- 1170567 233855 695836 601337 353475 261301 219787 436985 oxopentanoate 13-cis- retinoate_913-cis- retinoate_all- trans-retinoate_9- 274615 170718 10000 30990 58004 33893 125313 10000 cis-retinoate_4- OH-retinal_4-OH- 9-cis-retinal_4- OH-13-cis-retinal 8(R)-hydroxy- hexadeca- 2E4E6E10Z- tetraenoate_8(S)- 249751 279871 378763 190614 75812 103899 167954 123330 hydroxy- hexadeca- 2E4E6E10Z- tetraenoate Glycylphenylalani 1009185 119668 320653 196589 206374 154201 125841 201966 ne arachidonate_eico 248203214 402434887 326068350 195290819 91019742 194414401 107792352 68969144 satetranoic acid 3-hydroxy butyryl 625214948 409635098 372192002 599503721 234775471 167534388 144433714 97123425 carnitine 5-oxo- (6E8Z11Z14Z)- 612580 495615 640982 384887 180013 185179 125987 62932 eicosatetraenoic acid 5- hydroxytryptophol _12- dehydrosalsolinol 200808 18141 205625 10000 25457 10000 10000 10000 _NN- dimethylindolium olate-1 N-Carbamoyl-L- 2243357 10000 10000 695035 10000 10000 10000 10000 aspartate

194

Appendix B

Secondary recipient mice were transplanted with T-ALL from an AMPKα1flox/flox;Rosa26CreERT2 background primary cancer and treated with vehicle or tamoxifen. T-ALL cell were isolated and metabolic gene expression was determined by Glucose metabolism qPCR Array qrt-PCR array. Ct values for each independent mouse are provided.

Vehicle Vehicle Vehicle Vehicle Tamoxifen Tamoxifen Tamoxifen Tamoxifen Symbol 1 2 3 4 1 2 3 4 Acly 23.37 23.22 23.54 23.24 23.71 24.04 23.6 23.36 Aco1 26.57 26.54 26.63 26.48 26.87 27.04 26.5 26.65 Aco2 24.03 24.01 24.05 24.16 24.18 24.6 24.19 24.28 Agl 28.61 28.62 28.54 28.39 28.4 28.95 28.22 28.47 Aldoa 22.04 21.89 21.57 21.89 22.2 22.88 22.16 22.29 Aldob 35 35 35 35 35 35 35 35 Aldoc 31.46 32.07 32.09 31.28 31.37 31.95 31.91 31.57 Bpgm 26.01 26.22 26.18 25.91 26.28 26.59 26.03 26.23 Cs 27.83 27.94 27.45 27.79 27.85 28.32 27.71 28.05 Dlat 26.06 25.74 26.05 25.63 26.05 26.27 26.21 26.09 Dld 24.19 23.84 23.62 23.68 24.22 24.58 23.79 24.04 Dlst 24.41 24.81 24.32 24.39 24.6 25.36 24.27 24.55 Eno1 21.35 21.23 21.16 21.31 21.34 21.63 21.42 21.57 Eno2 32.31 32.73 33.25 32.93 33.26 33.39 33.02 32.42 Eno3 29.2 29.18 29.35 29.03 29.52 29.66 28.97 29.2 Fbp1 35 34.52 34.32 30.42 32.2 31.31 33.06 33.57 Fbp2 34.84 35 35 34.74 35 35 35 35 Fh1 24.4 24.22 24.19 24.26 24.59 24.75 24.43 24.6 G6pc 35 35 35 35 35 35 35 35 G6pc3 26.55 26.71 26.55 26.88 27.06 27.28 26.67 26.82 G6pdx 24.57 24.56 24.31 24.45 24.97 25.3 24.4 24.63 Galm 28.65 28.26 28.66 27.93 28.57 28.49 28.45 28.24 Gapdhs 34.23 35 35 34.25 34.72 35 34.33 34.57

195

Gbe1 28.24 28.32 28.12 28.15 28.29 28.55 27.64 28.05 Gck 35 35 35 35 35 35 35 35 Gpi1 22.57 22.59 22.48 22.54 22.93 23.31 22.52 22.7 Gsk3a 23.75 23.85 24.06 23.74 24.05 24.2 24.01 24.02 Gsk3b 25.36 25.34 25.54 25.36 25.61 25.91 25.19 25.39 Gys1 27.63 27.78 28.09 27.69 28.09 28.13 27.87 28.27 Gys2 35 35 35 35 35 35 35 35 H6pd 28.08 28.09 28.12 28.07 28.57 28.51 27.72 28.16 Hk2 28.85 28.32 28.39 28.18 28.49 29.21 28.9 29.06 Hk3 30.32 30.64 31 30.61 30.91 31.03 29.58 30.03 Idh1 27.95 28.07 27.93 28.2 28.53 29.02 27.86 27.98 Idh2 24.89 24.95 25.09 24.85 25.23 25.34 24.95 24.91 Idh3a 23.33 23.07 23.04 23.17 23.46 23.75 23.48 23.55 Idh3b 23.85 23.68 23.57 23.79 23.67 24.11 23.64 23.99 Idh3g 23.1 23.23 23.19 23.59 23.38 23.67 23.14 23.28 Mdh1 23.34 23.13 23.27 23.52 23.39 23.77 23.52 23.55 Mdh1b 35 35 35 35 35 35 35 35 Mdh2 23.1 23.01 22.94 23.22 23.23 23.42 23.27 23.31 Ogdh 24.09 24.08 24.2 24.16 24.41 24.62 24.16 24.29 Pck1 33.23 33.42 31.16 32.55 32.07 31.44 31.47 32.15 Pck2 30.15 29.97 31.41 30.58 30.62 30.47 29.95 30 Pcx 30.52 30.94 31.44 30.54 31.24 31.52 31.19 31.41 Pdha1 23.16 23.35 22.92 23.22 23.3 23.59 23.11 23.5 Pdhb 24.29 24.36 24.25 24.36 24.49 24.78 24.39 24.53 Pdk1 25.61 25.51 25.34 25.67 25.59 26.08 25.59 25.55 Pdk2 34.33 34.17 35 34.21 34.46 34.86 33.84 34.25 Pdk3 25.51 25.37 25.6 25.61 25.67 25.99 25.67 25.89 Pdk4 35 35 35 35 33.92 35 34.49 35 Pdp2 26.56 26.84 27.02 26.76 27.19 27.28 26.87 27.04 Pdpr 26.52 26.53 26.58 26.71 26.81 27.08 26.39 26.93 Pfkl 25.85 25.46 25.34 25.61 25.81 26.3 25.78 26.32 Pgam2 28.75 28.77 28.78 28.7 28.85 29.14 28.43 28.86 Pgk1 22.7 22.46 22.28 22.83 22.82 23.23 22.8 23.01

196

Pgk2 35 35 35 35 35 35 35 35 Pgm1 25.83 25.87 26.08 26.12 26.27 26.32 25.87 26.07 Pgm2 27.43 27.39 27.3 27.19 27.47 27.96 27.29 27.39 Pgm3 27.19 27.13 27.28 27.3 27.64 27.82 27.05 27.43 Phka1 35 35 34.56 35 34.1 33.95 32.84 34.48 Phkb 27.43 27.46 27.88 27.4 27.73 27.78 27.53 27.78 Phkg1 32.82 33.79 33.58 33.49 34.91 33.98 34.29 35 Phkg2 25.83 25.97 25.96 25.96 26.23 26.4 25.7 25.71 Pklr 32.13 32.26 31.13 31.34 31.45 31.47 30.28 30.6 Prps1 24.31 24.06 24.12 24.56 24.45 24.87 24.34 24.54 Prps1l1 35 35 35 35 35 35 35 35 Prps2 23.29 23.35 23.46 23.45 23.49 23.92 23.55 23.52 Pygl 31.71 31.41 31.71 31.64 31.98 32.11 30.81 31.27 Pygm 30.09 30.47 30.33 30.36 31.01 30.95 29.62 29.83 Rbks 27.17 27.04 26.79 26.91 27.39 27.43 26.89 27.01 Rpe 24.68 24.5 24.8 24.93 24.91 25.21 24.74 25.05 Rpia 24.43 24.25 24.5 24.33 24.68 24.78 24.29 24.52 Sdha 23.43 23.34 23.37 23.45 23.74 24.01 23.44 23.73 Sdhb 24.5 24.36 24.35 24.5 24.57 24.83 24.61 24.6 Sdhc 24.06 23.94 24.11 24.14 24.34 24.5 24.26 24.23 Sdhd 27.63 27.49 27.54 27.55 28.04 28.21 27.71 27.83 Sucla2 25.25 25.29 25.29 25.4 25.56 25.87 25.41 25.63 Suclg1 27.72 27.38 26.62 27.22 27.72 27.99 27.9 27.8 Suclg2 24.96 24.82 24.66 25.12 25.09 25.4 25.05 25.18 Taldo1 25.16 25.08 26.53 24.97 25.27 25.44 26.65 25.22 Tkt 21.72 21.52 21.26 21.51 21.97 22.08 21.58 21.63 Tpi1 23.93 23.42 23.31 23.67 23.73 24.25 24.07 24.11 Ugp2 25.42 25.6 25.45 25.43 25.64 25.88 25.33 25.54 Actb 18.44 18.48 19.5 18.48 18.8 18.98 18.28 18.82 B2m 19.21 19.22 19.51 18.75 19.55 19.72 19.24 19.31 Gapdh 20.79 20.68 20.55 20.55 20.9 21.24 20.85 20.9 Gusb 25.59 25.59 25.62 25.92 25.91 26.05 25.75 25.99 Hsp90a 19.64 19.45 19.33 19.65 19.84 20.06 19.8 19.89 b1

197

Appendix C

Primary AMPKα1flox/flox;Rosa26CreERT2 T-ALL was transplanted into secondary recipient mice. Mice were treated with vehicle or tamoxifen ten days after transplant and sacrificed five days after tamoxifen treatment. T-ALL cells were isolated and extracted for high resolution LC-QE-MS metabolomics. Range scaled area counts normalized using Metaboanalyst are reported.

Vehicle 1 Vehicle 2 Vehicle 3 Vehicle 4 Tamoxifen 1 Tamoxifen 2 Tamoxifen 3 Tamoxifen 4

Label 0 0 0 0 1 1 1 1

2-deoxyguanosine_adenosine 3241996 6129916 9224971 1237713 66916184 71673527 23122108 2295246

24-oxo-1alpha-23-25- 33038.59 115651.13 98271.83 94285.42 712974.34 236718.85 319672.49 193770.78 trihydroxyvitamin D3 C27H46O3(6) 41177.4 25502.34 26226.39 13293.96 106063.76 91697.44 174597.67 41313.04

1011-dihydro-12-epi- 844013.5 781653.9 1141654 967275.4 2292337.1 3452142.8 4613280 3332763.3 leukotriene B4 O-Phospho-L-serine 1789998 1170103.8 927174.1 702192.6 4836337.9 2722969.4 2298200.9 4635430.3

N-Carbamoyl-L-aspartate 2446463 2735871 1616380 10000 4211605 5301974 5423418 4750237

(S)-3-sulfonatolactate(2-) 180547.18 86065.73 16853.25 138225.7 199963.5 73504.2 28640.46 851400.38

D-Glucuronate 1-phosphate 506405.9 478727.3 605959.4 261541.2 945370 1094078.6 1239152.9 1069644 thiamine(1+) 120349.36 345826.64 55815.42 10000 407221.98 223402.02 545353.89 32230.46

3-oxo-8(R)-hydroxy-hexadeca- 308547.9 381372.4 330207.6 243220 451007.9 772118.9 885147.9 705839.4 6E10Z-dienoate_3-oxo-8(S)- hydroxy-hexadeca-6E10Z- dienoate Xanthurenic acid 1155346.6 1325922.8 885404.6 217783.6 1343427.7 2520440.7 1368401.5 2227420.8

2-Carboxy-23-dihydro-56- 73924.09 106841.14 158637.77 340334.27 192851.11 110971.53 351574.1 748286.73 dihydroxyindole_L- dopaquinone orotate 6831981 6478439 4094550 2708187 13024514 10652117 6606165 9297627

N-acetyl-D-glucosamine 66509.52 186914.66 209023.09 10000 359923.09 336090.5 22232.11 141261.25

2-keto-4-methylthiobutyrate 588588.6 167140.9 383111.3 100069.5 660195.9 701629.1 444970.5 444314.7

7alpha-hydroxy-3-oxo-4- 227862.8 212618.8 126450.6 250812.7 455894.2 272094.8 494441 255483.7 cholestenoic acid anion_24- oxo-1alpha-25- dihydroxyvitamin D3_25- hydroxyvitamin D3-26-23- lactol N2-Formyl-N1-(5-phospho-D- 1162566.3 1467674.7 984020.9 10000 1545024.9 1876441.2 1726663.8 1337634.9 ribosyl)glycinamide 2-deoxy-D-ribose 5- 1140348.5 1323218.4 1181804.2 604486.8 1611926.3 1719093.6 2441190.2 1791929.9 phosphate_2-Deoxy-D-ribose 1-phosphate Dihydroxyacetone 10972295.5 13537848.1 7752456.27 69931.23 12983199.58 13551472.65 16594796.49 13206354.16 phosphate_D-glyceraldehdye- 4 1 3-phosphate Pyridoxine 60069417 72347053 36389680 2052095 90364888 77310472 67381980 60508863 butyryl carnitine 132077829 135575644 724675720 1124021303 2083382879 2145377701 1763632393 1784269769 8 2 dopaminochrome 8873370 9938400 6148328 1234378 12978955 13797095 9693834 8081274

4_6- 601942.7 551003.7 391148.4 107315.5 650403.6 703535.9 674858.7 661927.8 Dihydroxyquinoline_quinoline -4_8-diol N(omega)-(L- 5537065 5936875 4157546 8421192 10025531 10477235 9681078 7359571 Arginino)succinate

198

N- 11853030 16210448 12275581 15062360 16087363 15466819 16474650 38270643 benzoylglycinate_adrenochro me Pyridoxal 7675826 8252137 5210769 1847666 10163033 10550761 7471480 7108152 fructoselysine 3-phosphate 1312490.9 1049272 679345.7 555302.7 1579618.8 1455022.2 1423334.8 1045999.6

L-methionine 56072297 66924289 35073807 6953862 66080851 60666424 58035921 66829183

L-isoleucine_L-leucine 509770077 616194432 296916550 133842807 634053257 549677251 637854809 546571340

Carbamoyl phosphate 412266.59 280615.41 72265.37 111616.74 416107.78 364332.44 317533.61 233254.07 inosine 82717434 134647280 87503275 12387272 100001297 172047274 107251655 99059982

3-hydroxypropionate_D- 678933132 144049080 557898366 507267090 1017015341 1357298695 1246076055 1165694574 glyceraldehyde_glycerone 7 docosanedioicacid 405537.3 621772.4 560123 536442.7 330488.4 982328.8 639545.2 1211739.9

L-glutamine_3- 118701657 111405122 665970611 25917674 1286983909 1093443803 873032313 1198880272 Ureidoisobutyrate_Glycylsarco 2 0 sine L-3-Cyanoalanine_56- 34442778 30959657 24971843 12501430 41595685 38975570 32437684 37467860 dihydrouracil hypoxanthine 253851114 319851016 189900100 25577515 302576035 317263780 287669097 241111686 dTMP_neg 5681209 6291231 4686819 2567057 5853093 8084361 7071496 6780197

L-asparagine 480337001 425464277 313517460 196865072 567653461 533090357 360951506 570250387 guanosine 11278677.7 13706873.4 8450805.9 832381.9 12084720.3 12229283.6 12952190.9 11888184.2

Riboflavin 1411487.7 1460127.31 784414.08 57785.11 1478579.71 1112286.39 1509468.72 1221946.17

Pyruvic acid_3-Oxopropanoic 136525966 152915048 569259059 608105634 1757825152 1512040577 1315251636 1221750438 acid 4 5 (R)-mevalonate 8346557.9 7744246.9 4124094.4 743087.5 8745565.2 7064172.1 5589310.1 8400197.4 dCMP 5504699 3526044 3197601 6056228 7773848 7026371 4498854 6348952

L-histidine 106820235 122613245 67205707 34054010 120421040 115303350 104656274 122894776

Hypoxanthine 310411845 369499247 1825723647 270381229 3710836019 3445915522 2779697239 2519242047 4 1 11-cis-eicosenoate 3505636 6086169 3471586 2336017 3312197 6882938 7073664 4204253

1-methylnicotinamide 23447730 32242787 28058997 16316741 41039915 28254581 26810165 43243935

D-fructose 6485999 9008746 9365869 1911122 12649703 5109121 12009192 7497172 trans-4-coumarate_keto- 10756487 13326794 5852550 2976573 13134027 10577839 10520252 11569652 phenylpyruvate_cis-2- coumarate benzoate 5416631.2 5956522.1 2402947.4 866970.4 5638696.5 4705637 4717596.9 5303598

D-ribofuranos-5-yl-ADP(2-) 1058579.1 1454218.9 1569977.2 643964.8 1801614 1177682.8 1429513.9 2151567.2 coumarin 2218662.5 2710399.7 1318352.3 498450.3 2797168.9 1969278 2336212.9 2250432

5-Phospho-beta-D- 1967524 1676974 1465035 1587220 2707563 2440389 1886245 2241013 ribosylamine clupanodonic acid_docosa- 7405984 9316661 10209971 8539395 11476628 13562353 14020165 9712720 4_7_10_13_16-pentaenoic acid stearoylcarnitine 172834096 156216210 160128273 81297625 156355849 152395548 229585474 245369944

D-glucuronolactone_L- 312328349 184111018 260868147 7009130 322358649 43231775 543004654 127872638 ascorbate Aconitic Acid 315995883 188230154 264168176 7784031 327114393 45636892 547461885 131475702

Ascorbic acid 316649011 188597653 264663032 7884193 327662174 45788542 548172448 131731603

L-alanyl-L-leucine 9601210 10908061 5818576 3353826 12515264 10391266 3284161 13834772 hydroxyproline 256549108 216235566 160079649 301003119 383847476 325674848 209150072 334674245 choline 487486285 303417316 1546732324 1396168924 5231074020 4118851791 2442539468 2661191192 3 6

199

gama-L-glutamyl-L-alanine_5- 1284146.6 1635037.2 1081825.5 304206.7 2118118.4 1463500.7 528360.2 1621565.7 L-Glutamyl-L-alanine-1 Glycerol 3-phosphate 89480882 103398529 65586132 8384478 81614918 97050971 83588891 92465122 glyceraldehyde 3- 89480882 103398529 65586132 8384478 81614918 97050971 83588891 92465122 phosphate_Glycerone phosphate (R_s)-lactate 153968710 178692097 869241885 807561322 1956209042 1718265215 1496694057 1478443022 1 6 2-oxoglutarate(2-) 30164663 28787808 21103269 20849195 37859139 32578997 36787700 26145678

L-tyrosine 47880588 56786718 26363461 12415739 53206242 42486458 43862287 48470629

3-(methylthio)propionate 967859.2 1855050.1 870839 2176924.2 1520169 2226111.4 1525645.4 2397529 alpha-D-Ribose 1- 11538564 14345237 11303923 3641355 12725647 12691946 15239652 12583493 phosphate_alpha-D-Ribose 5- phosphate_D-xylulose 5- phosphate_D-ribulose 5- phosphate_D-Xylulose 1- phosphate uridine 139096472 162890617 113694629 8794549 133133940 150602142 151427342 116304069

3-dehydro-L-gulonate_D- 478781.8 421080.7 380494.5 546695.7 586693.1 468822.5 475898.1 837620.7 glucuronate_L-iduronate Guanine 435589692 571166760 346728124 37237181 433733808 433898156 417815860 509671994

L-tryptophan 30624433 35307235 18336748 11351004 30320101 29485851 37098943 26106578 methyl indole-3-acetate 1232930.8 1368471.7 601336.8 370571.8 1342110 1113972.7 1189940.6 938412.8 creatine 227030652 202926385 1611589751 1432328559 2803310502 2056535361 1557016720 2982638122 5 2 Hypotaurine 1760238 1218690 1041724 3198949 2351277 2481906 2680601 1672026 stearate 240188004 287151708 282841441 354380940 267607304 440511409 446354893 321917860 succinate(2-)_Methylmalonic 484170707 498292630 327596092 270166561 478119353 538815127 462814658 523314696 acid serine 163576764 145179301 103809328 113614488 192300114 170024558 113483138 191095303

N-acetyl-L-alanine_5-amino-2- 70228658 58021717 43757840 94419860 102420321 87624715 68285254 79233659 oxopentanoic acid_5-Amino- 4-oxopentanoate_L- Glutamate 5- semialdehyde_trans-4- hydroxy-L-proline Thymidine 4554761.9 5672941.5 3836828.1 354819.8 3493661 4550706.4 6369336.7 3797197.1 dihomo-gamma-linolenic acid 3580864 7290333 4464534 3503932 4590996 6551418 7326948 5248879 (n-6) IMP 896822938 955520581 670157206 295914721 776574377 974284723 766637350 1012077444

AMP_dGMP 892452470 952300460 669682396 294146284 773128681 970409624 763441352 1009036184

CTP(4-) 18669157 9680746 9312558 8077647 18109469 10119080 14226809 14507354

L-argininium_D-argininium 128587129 140023484 100668872 133249919 173760143 165381733 154686284 128647484 agmatinium(2+) 294654.21 222419.58 80336.89 312977.24 378529.59 367820.88 168818.59 208519.3 223215623 197625119 L-glutamate(1-) 1628010411 1925393548 2521758906 2599759472 2090027833 2339932892 3 5 dGMP_AMP_3-AMP 506688775 580574577 467265234 195398744 429384284 600867392 603619264 513040211 arginine 505708282 431773934 233222573 544921569 690297846 556411866 399689890 451388783

UMP_3-UMP 183740937 202000963 182226836 87000207 177833230 228170638 179232285 213715633 homoserine 138890644 124324384 88669150 110107872 162140549 144120976 104471044 151546280

L-threonine 139015941 124241596 88669150 110107872 162173382 144120976 104346296 151546280 5-oxoprolinate_L-1-Pyrroline- 3-hydroxy-5- 299670196 266829436 216062536 242657066 331798642 339737502 277004626 297178610 carboxylate_Pyroglutamic acid tryptophan 51442069 60332086 28813151 24361209 57237966 47397484 50575498 45200769

200

calcidiol_7alpha- hydroxycholest-4-en-3- one_3beta-hydroxy-5- cholestenal_27alpha-hydroxy- 204281825 131055404 205602290 174960125 170795586 141247076 232256377 317439742 8- dehydrocholesterol_27alpha- hydroxy-7-dehydrocholesterol glutathione disulfide_neg 190866766 215809298 202423598 151157281 220867776 272370580 228748618 191215686 phytanate_arachidate 868253.5 1429615.3 1020331.4 1582533.1 898341.7 1482381.4 1965483.5 1535802.4

NADH_pos 793780.6 555400.1 105610 454901.7 552985 332862.9 481400.6 913902.3 glutathione disulfide_pos 290846808 281735540 214788165 238976637 335662343 362219684 226514551 301117709 tetradecanoyl 289126624 309119044 252328954 214481426 313487786 288662112 304425990 364554737 carnitine_myristoyl carnitine Cytidine 268858219 285040563 179213222 38206462 258363937 243154996 205013817 211682210 arachidonate_eicosatetranoic 14515726 22912851 21685732 12836647 17817170 22564246 27615595 17649559 acid glutamate_O-acetyl-L-serine 286030930 245243057 1837469013 2335237339 3120895703 3003072446 2087356250 3074836733 8 9 35-dihydroxy-34-dihydro-14- 7599361.9 5287308.9 1779913.1 599907.8 6549815.3 6229944.7 2175130.5 3195366 benzothiazine Deoxyinosine 2355992.5 3136358.8 2111414.9 531296.6 2332713.1 2854835.2 2621734.1 1850842.5

N6N6N6-Trimethyl-L-lysine 102576487 71278994 35592287 93865226 124571067 91004478 64886113 78602291 adenine 61287195 69562764 57114737 49257895 78064908 65861464 68737820 67137970

1314-dihydroxy-retinol 291380 502777.9 429554.5 351723.1 361477.3 374121.7 609604.2 502512.3

3- 14784523 18363090 14846417 9136170 15705676 15406800 16582960 19297368 hydroxyhexadecanoylcarnitine UMP 39658975 40421232 33232744 19200209 36353191 41793313 26429460 50651629 imidazol-4-ylacetate_thymine 94043811 94560958 50581700 5069169 73033176 76511288 74609164 61121375

CMP(2-) 19050118 19404692 17594709 11584789 17670247 22968482 18326926 19526274

Galactosylglycerol 38221817 13743891 5412708 21788910 37202381 26765563 17175114 10380598

(S)-malate(2-) 185436244 173554878 1397749810 1711388676 2067644909 2086320066 1673298067 1913132308 1 2 palmitate 899111297 106648298 909078313 973536585 922904747 1303465232 1258032137 957368467 5 Oxaloacetic acid 179052112 170688571 1397522413 1682186274 2005468644 2034397829 1640629795 1890153035 1 5 ATP_dGTPneg 308106395 161818195 148264106 147574046 277338952 154264707 229278434 217862636

8(R)-hydroxy-hexadeca- 886141.5 902896.3 1435366.3 341117.9 946670.2 968314.2 1329581.4 845475.9 2E4E6E10Z-tetraenoate_8(S)- hydroxy-hexadeca-2E4E6E10Z- tetraenoate 2-keto-3-deoxy-D-glycero-D- 56283712 70416220 53743575 21720706 53905681 62476504 63082493 52347356 galactononic acid 9-phosphate indole 6960252 7492440 4491106 5278442 7880161 6564970 6635900 6666663 histamium 1479502.9 1300484 1435107.1 969784.4 1354802 2736668.6 764135 1073091.7 glycine betaine 837250442 454544653 4719042903 9792437334 11532074505 7609724252 4469536008 7680745060 8 2 Phosphoenolpyruvate 1410036.4 1582306.8 895638.1 810871.7 1704780.2 1008682.9 1084020.3 1560467.5

CMP-N-acetyl-beta- 32764628 31499452 23795092 28217155 36993740 38599195 23704540 33094569 neuraminate(2-) 3(S)6(R)-dihydroxy-tetradec- 5369870 6983316 4774018 9924268 7291363 8239893 8882552 6364220 8Z-enoate_3(S)6(S)-dihydroxy- tetradec-8Z-enoate 5-guanidino-2-oxopentanoic 2776520 4056237 3125326 3290608 3860946 4256146 2613378 4249071 acid N-phosphocreatinate(2-) 16264928 11837104 12200112 30808127 28562072 16398716 14149026 21284562

2-Phosphoglycolate 164600103 158422961 1469117207 567982233 1520383677 1623904199 1391737450 1414805781 3 9 L-palmitoylcarnitine 110662559 112065890 1047287521 751402123 1005533796 1007197042 1213361377 1309101056 6 3

201

UTP(4-) 92013176 48397871 46951186 36170367 81827416 43202259 63682775 62895767 creatinine 251400516 229565709 155138272 236381464 311132994 257197564 198091359 214369950

NAD+_neg 16582443 14637809 11773197 10171701 15883662 14694528 15509700 13649131 propionate_(S)- 27245243 25236055 17016135 19320568 25681505 26635558 20729903 26701298 lactaldehyde_(R)- lactaldehyde_hydroxyacetone leukoaminochrome_dopamin 34957775 34329405 17934505 23725170 37902999 35054145 23482999 28155639 e o-quinone-1 NAD(1-) 74030560 56091553 39201998 46334265 71435311 56515551 44195811 69911726 citrate_isocitrate_Diketogulon 257747384 239754012 1865390233 2245711812 2716145655 2689534362 2460190875 2323160946 ic acid 2 5 dGTP_ATP 269520706 122008866 89608768 134426114 240821489 114218946 129963847 201468348

L-kynurenine_Formyl-5- 1197013.3 1263419.7 699057.5 1221355.2 1416933.9 1334616.4 1069578.5 1057936.8 hydroxykynurenamine Oxalosuccinic acid 247429351 239320726 1884570894 2257796997 2620503613 2631769385 2440362321 2335194254 9 1 3-carboxy-alpha-chromanol 935953.2 883584.4 1146013.3 682632.1 892035.2 933310.1 1368321.7 863718.6

Fumarate_Maleic acid 199623172 181969295 145008950 205332966 223653852 214234986 175041992 195165216

Biotin 410912.5 759602.5 241592 39305.4 316320.6 386419.4 386761.7 510711.5

Elaidic carnitine_Vaccenyl 619152350 688594178 603740724 306150668 502945499 577134462 715170891 648727604 carnitine (2R)-2-hydroxy-3- 26200108 34294202 24676407 12058561 33499623 25996845 23496653 24040185 (phosphonatooxy)propanoate _D-Glycerate 2-phosphate 3-phosphoglycerate 26200108 34294202 24676407 12058561 33499623 25996845 23496653 24040185

3-Phosphonooxypyruvate 25742848 33993345 24533873 11955741 33047756 25673078 23283953 23833446

8(R_S)-hydroxy-hexadeca- 298092.6 498018.8 294681.4 353426.1 356228.5 351257.4 621052.3 256785.3 2E6E10Z-trienoate cis- 40861127 39990507 33189382 37728436 40455335 42812220 45401417 37335589 aconitate_dehydroascorbide sarcosine 70998090 58303936 39880663 89352434 87434605 66080082 52887701 76009512

D-Alanyl-D-alanine 847976.9 440624.5 368434.3 1102266.1 801227.4 405615.8 808487.1 996998.2 sphingosine_(2S)-1-hydroxy-3- 2185738 2662030 3160171 2051615 2263065 2431240 2544171 3735308 oxooctadecan-2-aminium S-(2-Methylpropanoyl)- 843961.7 2126706.73 1299908.31 35271.12 1514702.62 1170793.7 1138876.08 867310.49 dihydrolipoamide heptadecanoyl carnitine 18174981 17917316 17394752 13655390 14685500 17358521 20877865 19953024 allantoin 156478144 150358523 115067440 239622674 189700139 159773510 201190762 167292062

UDP-N-acetyl-D- 225588659 219903373 188154080 138357778 225134159 209743394 197247394 200986300 galactosamine_UDP-N-acetyl- alpha-D-glucosamine D-glucose 99043913 93398048 77800060 50102082 95826274 80190155 83123114 85409319

D-Glucono-delta-lactone 99043913 93398048 77800060 50102082 95826274 80190155 83123114 85409319

1-Pyrroline-5-carboxylate 884665 579261.7 243223.5 692269.9 865220.9 578452.4 479124.5 656790

L-valine 75822719 47394083 28643094 55127928 74429246 52234652 34693748 60636757

5-Methylthioadenosine 404495334 541542988 484211912 268909330 433927423 429019605 558817827 400158673 nicotinamide 25808403 21609638 17378613 21811274 25949029 21824412 19372872 25678540 cytidine 450636650 406151418 326976813 475345169 487489718 497622338 400387180 392163793

25-hydroxyvitamin D3-26-23- 531563.6 454774.5 353504.7 151393.9 413339.4 346501.7 446630.2 390578.8 lactone Nicotinamide 40478932 42727565 46505996 75639997 83009567 46795146 34517824 54986327 phenylpyruvate 69938.17 215191.32 204685.5 142618.94 129328.86 92633.34 207561.32 243639.91

S-[2-carboxy-1-(1 H-imidazol- 1228477.8 942395.6 316376.8 1245389 1163132.5 1107413.3 938747.1 756953.9 4-yl)ethyl]-L-cysteine

202

thromboxane A2(1-) 1186956 1198249 1017522 1792624 1347268 1701077 1314929 1147421

Malonate_3-hydroxypyruvate 51606303 52739787 48644257 43258909 47463644 66725529 30758390 63167121

NADPH(4-) 1027849.1 577901 434119.4 10000 539911.9 384546.3 801389 447613.4 cervonic acid C22-6 n- 27526689 41221411 41383384 39081406 33653196 41683644 51609648 31204721 3_docosahexaenoate UDP-D-glucose_UDP-D- 144992957 130673015 119340836 18852760 112613563 108951790 112721462 104196580 galactose 3-Methylimidazoleacetic acid 4246816 3867776 3558732 1422323 3043962 3341459 2279205 5185835

Acetyl adenylate 12522.63 10000 105781.3 273143.72 213065.43 60628.72 37293.14 111985.73

N-methyl-467-trihydroxy- 453932.95 653042.67 597444.02 610984.81 842292.58 726052.15 89251.54 781121.17 1234-tetrahydroisoquinoline Alanine 78606492 67053310 47465803 117571776 99896732 76456824 53254983 97492176

N-Formimidoyl-L-glutamate 4229470 4612960 3780831 5245413 4973829 4800575 3809162 5185727 spermidine dialdehyde-1 568181.7 411199.4 278635.1 1062098.5 640122.1 443617.2 491744 859263.2 dopaminium(1+) 536665.3 769322.8 448116.7 843904.7 824840.1 919914.2 443829.6 531331.5

CDPcholine 95186679 82725903 62542685 61719402 91068627 86322957 54925808 83832534 gulonate 14234940 15121716 10611100 16561866 14680557 13548623 12178111 18228863

GDP-alpha-D-mannose(2-) 1772610.3 1602161.4 1270155.7 263580.7 1234581.6 842146.9 1588716.2 1418077.7 pristanic acid_pristanate 1293372 1656605 1319546 1449365 1013772 1690125 1942323 1268646

N-acetylneuraminate 3868813.7 3287881.5 2723445.9 610449.6 3965887 2828772.1 1450633 2592901.7

2-deoxycytidine 5161375 2460173 388760 2559317 3764366 3166575 2242326 1729231 limonene_(+)-alpha-pinene 3881092 4565967 3343881 3573435 3675080 4823035 3514289 3778463 phenylacetate_(4- 24317095 26471122 28687270 19493810 23795917 25320911 24101466 28489257 hydroxyphenyl)acetaldehyde betaine aldehyde 16500037 16324618 12514034 20540118 17848048 15405399 11702443 22736462

Diphosphate 622091585 724047474 788087697 572368846 645420547 650505297 613476496 859689452

56-dihydrothymine 7606020 6982036 5074274 10089324 8275306 7688556 5739725 8542896

3- 22227323 23263048 24310176 18144593 22449906 19551325 23494318 23874610 hydroxyhexadecenoylcarnitine Succinic semialdehyde_2- 74431338 61584410 49741577 53490678 76154293 60847653 50211895 55871897 oxobutanoate_2-methyl-3- oxopropanoate 6-phosphonatooxy-D- 2826993.3 3478808 2325989.9 521907.8 2463752.3 2051766.3 2904132.4 1872298.1 gluconate 2-oxoadipate(2-) 1891212.2 1235301.6 889325 975628.1 1921134.2 1305138 877830.5 957601.8

N-acetyl-D-glucosamine 6- 17020981 15405366 13479403 7962737 14235705 15305608 13311356 11641563 phosphate_N-Acetyl-D- glucosamine 1-phosphate_N- Acetyl-D-galactosamine 1- phosphate_N-acetyl-D- mannosamine 6-phosphate malonic 16881824 14218554 11496548 19298455 19063934 14761336 13181711 15594322 dialdehyde_acrylate_methylgl yoxal L-phenylalanine 6484118 6902998 3903789 12118606 6261021 6132222 5326363 11878827

1-Methyl-Histidine 41027221 46678017 29752656 74533198 52322631 51126640 40126986 49355984

S-adenosyl-L-methionine 12751483 11958280 8701129 3776409 10898083 9027061 7331966 9913495

CDP-choline 5618524 5580089 5412112 3036999 5241028 5488785 5116036 3775082

GAR 2463922 3139371 3113603 1058547 4655305 2250904 1264127 1590403

D-Glucosamine 6-phosphate 2058976 2505241 2788503 2398499 3348514 2653992 1713793 2013678

N-acetyl-glutamate 3160107 3271322 2337613 2464645 4026290 3522677 729268 2924804

203

UDP-D-glucuronate 4663386 3640199 3310334 1412176 3067405 2566260 4258994 3093873 linoelaidic acid (all trans C18- 135266370 277990104 78229395 104458825 111820358 192377516 164753012 123247134 2)_linoleate_octadecadienoat e (n-C18-2) ADPmannose_GDP-L- 3246239 2811127 2447848 2136271 2677937 2114084 3048776 2728376 fucose_ADP alpha-D-glucoside glyoxylate 4080229 3149978 2546761 4440703 4115772 3583060 2429818 3967527

12S-HHT 2165742 2435782 1662643 1277365 1394847 2939865 1973547 1163634 aldehydo-D-xylose_D- 3238911 3410852 2819091 4549988 3787771 3728043 2954166 3415586 ribose_D-xylulose_L- xylulose_D-ribulose_L- arabinose N-acetyl-L-asparagine 1585922 1677410 1943260 3000112 1761956 2162682 2455562 1734219 glutaryl carnitine 4930805 5426336 4529650 2138631 4825089 4160215 3650084 4189389 trans-Hexadec-2-enoyl 121357678 146092434 114276014 89101873 108489815 107896270 121253656 126806771 carnitine 4- 55033768 48212239 34394834 42941830 55280763 40087234 32471993 48980234 acetamidobutanoate_isobutyr ylglycine_6-amino-2- oxohexanoic acid_L-allysine NeuNGc 1423307.5 1171384.5 1119728.4 163893.8 1360413.4 626651.9 417096.8 1388646.8 pendtadenoyl carnitine 8827663 9947690 7224179 6646479 7174537 8998071 7543851 8205115

1-piperideine-6- 1436798.1 956372.7 810531.2 998626.3 1395544.9 958613 744957.8 995114.4 carboxylate_1-piperideine-2- carboxylate hexose-phosphate 37308490 48507638 34602821 14158865 33194820 33593819 32024589 31952853 naphthalene 1135795.9 1160182.9 975386.1 805104.6 963904.8 928303.8 1108729.8 959640.4

2-Isopropylmalic acid 9135002 15947383 12808362 17167935 10233207 16281607 13326866 13483479 cytosine 45081529 23774397 8500182 27074037 34526891 28200588 18438819 19779576

2-keto-3-deoxy-D-glycero-D- 13623325 12722912 11382052 8397838 12097447 11366122 10349406 10759258 galactononic acid spermidine dialdehyde-2 2767932 1946372 1079952 1769319 1749526 2485936 1120338 1943281

CDP_neg 30040795 21288880 21246768 22007681 24725822 20987239 19308238 26183868

Xanthine 23864296 76221646 16758353 18817224 32809691 34487163 38105939 25236900

(-)-trans-carveol_alpha-pinene 5982882 5752235 4461112 5402603 4589687 5894035 5006742 5286895 oxide_perillyl alcohol carnosine 675615.5 846941.1 780577 585998 593739.8 709965.7 894111.1 578379.5

D-Glucono-1-5-lactone 6- 37804471 48826605 35231743 16627453 33350625 33399152 32936715 32972568 phosphate sn-Glycero-3-phosphocholine 196048429 180226304 1666399505 702938471 1777602188 1526185404 998886555 1541657361 0 9 uracil 12499616 10852804 9120927 26396667 16133761 14129585 11999146 13782843 laurate 163428776 241994458 150586841 164136907 149902151 188037962 193831736 153658741 sedoheptulose 17- 1546059.3 1820296.3 1060682.9 451729 1369887.9 1020685.6 1365249 885991.7 bisphosphate(4-) Aminobutanoate_NN- 32274037 31308723 20592152 30833628 31557254 27269887 20239360 30119796 dimethylglycine anthranilate 41200676 36698310 24282057 53212146 46484516 37246741 30945589 32485448

(R)-Pantothenate 706003304 819278472 858125677 603268455 676125840 676001805 719044974 755507723

D-Glucosamine 1637028 747206.3 429734.4 1728493.5 1375022.8 805935.9 703994.2 1411545.6 imidazole-4-acetaldehyde 2612528 2088254 1706280 1760284 2669684 1653571 1262403 2136361

1-acetylimidazole_1H- 2612528 2088254 1706280 1760284 2669684 1653571 1262403 2136361 Imidazol-4-ylacetaldehyde quinolinate(2-) 3280490 3164678 3002118 3519127 3280578 2955654 2675834 3341545

4-nitrophenolate 15352025 14727812 12965664 15054775 14421544 14553285 12012815 13787657

204

S-Adenosyl-L-homocysteine 6162352 6695813 4566277 7312952 5779699 6465463 3685397 7325941

4-trimethylammoniobutanal 251099.9 186690.3 116874.8 308236.5 238347.6 223251.3 131294 217527.6

GDP_neg 44818555 35708356 33209761 33834487 37512180 28959864 34727163 37156420 leucine-isoleucine 16923769 16583366 9823355 24803302 18959074 13818283 13280676 17747540 glycine 55722278 45900525 33623830 72911172 56678422 48626839 37567897 51918492

D-glucarate 24764386 29853279 23297844 41836347 33871334 29234311 22424775 26167843

2- 39370625 37327084 33485900 52654399 39653445 38940462 30262084 42878986 methylbutyrylglycine_isovaler ylglycine 4-Imidazolone-5-propanoate 9743483 8519657 7191085 13246888 10129480 8064162 7485962 10356544

Sedoheptulose 7-phosphate 9081689 9637359 7774124 7107914 7895690 8416138 7884874 7054464 decanoyl carnitine 54615451 64445490 47981783 61525910 50398182 46131544 48612372 66917978

2-methylglutaconic acid 7559394 6883659 5564217 5771344 7545947 5725749 5176486 5441013

D-Fructose 16-bisphosphate 32801852 46569974 29078327 7966719 28678221 26462805 32390989 19913078 timnodonic acid C20-5n- 2307301 2638110 2979361 5350947 3115965 3288673 2983387 2820729 3_1314-epoxy-retinol_4- hydroxyvitamin A1_14- hydroxy-414-retro-retinol CDP-ethanolamine 97952556 89080427 78344910 108293591 96585512 90128836 83166022 73379241 glycolate 30907618 32841021 26589883 33765421 30776744 28415392 23908611 30857518 lipoate_(2S3R)-3- 3564206 2961109 2838834 5451960 3655736 2911872 2803252 4232579 hydroxybutane-123- tricarboxylate_2-methylcitrate Taurine 549475606 531705008 4533294177 6291594094 5421249800 5286775505 4443749495 4711904548 4 3 Perillic acid 2231363 2502354 2441747 2295287 2140653 2589558 1885932 2049064

ADP_dGDP_neg 738134533 606727896 534659645 451160292 561540407 523001249 502347491 542470422 adenosine 3-5- 551808906 450979538 339681688 359433504 430367608 380073146 265243611 479042102 bismonophosphate(PAP)_Dgd p_ADP Thiamine pyrophosphate 1305815 1286152 1115755 1526439 1221133 1185619 1276753 1097620

UDP-alpha-D-xylose(2-) 2831312 2608440 2271049 1676738 2177188 1977327 2311151 2105654 itaconate_Mesaconate_Citrac 92597196 83328374 67455265 106812642 84598141 85081375 70117232 79437783 onic acid Linoleyl carnitine_Linoelaidyl 315483755 358898168 295318878 312267433 241324821 279033199 324441453 323459052 carnitine omega hydroxy 833478.3 1628976.3 1071592.5 1127244.4 822418.3 1183819.9 1256723.3 969245 tetradecanoate (n-C14-0) 4-hydroxybenzoate_Gentisate 86137598 88315457 76510750 108902558 76266419 79502228 92574540 78017681 aldehyde adipic acid 100089283 130575592 110561835 156772878 109777542 119390260 110168991 111816597

Methionine sulfoxide 10710805 13954942 9911362 9750871 9825932 10713248 8472698 11115609 choline phosphate(1-) 964973371 912603122 6537002371 13328283476 9729143469 10026565890 6454846531 8764440237 7 3 omega hydroxy 7327546 10510249 6814045 6224704 6201243 7391207 7971427 6264515 hexadecanoate (n-C16-0) Dodecanedioic acid 7285667 8295029 7164495 8722862 6396650 9377710 6352021 6216263 putreanine 4878848 3053470 1572544 4614265 4465363 3319273 2114902 2815596

6(R_S)-hydroxy-tetradeca- 1009043 1506661.5 1025297.5 1153015.4 930310.6 1051703.5 1401595.3 843525.6 2_4E8Z-dienoate L-proline 196541155 169377498 1266266732 3489867074 2226753842 1892177097 1346627057 2054420776 9 2 4(R)-hydroxy-dodec-6Z- 32713050 35136179 34758539 41212398 29738169 36596236 33233258 28516105 enoate_4(S)-hydroxy-dodec- 6Z-enoate UDP(3-) 39866622 33564333 28324745 19927424 30238776 25467922 17737441 34922717

205

perillyl aldehyde 13474309 14022157 9960041 13659665 10185354 13941010 11379825 9904044 w-hydroxydecanoicacid 16547864 18289009 16426861 20867593 15072537 17048281 17312537 14622411

3-hydroxyanthranilate 2134923 2002564 1664416 1941124 1715902 1941310 1444256 1772722

CDP-ethanolamine(1-) 86972240 67842136 50146971 105945906 84856291 71371358 46950487 72749226

912-Oct-13-diepoxy- 9092324 7966530 4902432 3955178 4473292 8621060 5211209 4670903 octadecanoate_11- HpODE_13(S)-HPODE acetoacetate 3659096 3771571 4018716 4808182 3462175 3408074 4030432 3497774

(R)-carnitine 125863760 124577230 10311300814 17199945401 12398320829 11346642565 9473089793 13321482947 75 32 L-lysinium(1+) 39284178 32852612 23727954 55534146 39353898 35512473 32432097 26407244

N-methylethanolaminium 27502348 25723233 26225797 55009795 34343171 28036045 19384472 36429359 phosphate(1-) Flavin adenine dinucleotide 349700.59 375078.36 124363.96 74526.31 149959.85 99793.16 122892.81 438278.21 oxidized 4-hydroxyphenylacetate_2- 14572637 13219081 10265498 13778462 10538974 12340869 10370917 12236450 Hydroxyphenylacetate_3_4- Dihydroxyphenylacetaldehyde Atrolactic acid_Phenyllactic 3681808 3674698 3070368 5275040 3072006 3039170 4233038 3402307 acid 4-oxo-2-nonenal 10262852 9981736 9544751 12151356 8855502 10846201 8100852 8843277

L-aspartate(1-) 212755012 175581044 1472288399 3423453693 2265523097 1808045521 1645338809 1949822603 3 2 3-Sulfopyruvate 3682539 2563041 4368737 1169709 6059557 1252649 868389 2108213 nonanoate 349031156 347036791 347514884 381819116 302108007 394316888 217257416 329929596

4-hydroxy-2-nonenal_3_4- 30372227 30588683 30390766 32616306 26652896 33429019 20011698 28016980 epoxynonanal UDP_neg 185262800 145454793 143571106 109460922 138722571 126826275 105775565 137230514

L-fucose_L-Fuculose 1460106 1782814 2042132 1710979 1481180 1709991 1442412 1450331 omega hydroxy dodecanoate 3692695 4675575 3813925 4866254 3441115 4054786 4091478 3229496 (n-C12-0) octanoyl carnitine 131410767 161315190 116754249 156659828 115884504 123988798 110208168 140088387

NADPH 932061.6 607407.2 552890.3 10000 470319.1 342514.9 524456.7 479723 hexadecanediocacid 3562103 4402235 3330608 4165004 2690172 4295382 3502164 2866869 caprate 199768070 208347170 203804247 228181638 179787594 228052205 135095683 180666467 tetradecenoyl carnitine 56614869 63422684 49565099 68652730 53371073 40236794 46897305 64628293 dCTP 1543161.1 853440 715206.5 1219177.4 1078898.6 649697.7 1091442.9 907719.2

L-pipecolic acid 18484042 11505322 8023921 17986328 16153166 11582256 8746077 11708576

3-oxo-10(R)-hydroxy- 3657242 3172794 2052038 1540994 1627206 3521773 2346544 1455455 octadeca-6E8E12Z- trienoate_3-oxo-10(S)- hydroxy-octadeca-6E8E12Z- trienoate (R)-2-hydroxy-4- 30158836 22998555 17526987 31316669 21505723 26007090 15709173 24260135 methylpentanoate_Hydroxyis ocaproic acid shikimate 16750811 20277194 12962717 23550684 12403386 22997222 13175683 14424378 trans-45-epoxy-2(E)-decenal 2330645 2379705 2323116 2780846 1951815 2404681 2008980 2007336

15-hydroxy-(8Z11Z13E)- 70736.41 554278.96 169123.57 198154.63 156486.6 220314.85 330849.77 137572.27 eicosatrienoate 3-oxo-6(R)-hydroxy-tetradec- 3831906 4300213 3575535 4366737 2810027 4736945 2990149 3133591 8-cis-enoate_3-oxo-6(S)- hydroxy-tetradec-8Z-enoate O-acetylcarnitine 682083796 647383557 5240188282 7923087751 6174855612 5404168829 4595347822 6316390314 5 0 acetoacetic acid_2-Oxobutyric 251635682 175758894 116723028 235354982 177600007 200075387 101388532 183268825 acid

206

3-methylphenylacetic acid 11185012 10320743 9757682 13266421 8917744 10733860 8135901 10032135 tiglyl carnitine 9187163 9611009 4502987 15382940 8858301 9074596 6969943 7914393

3-oxopropanoate 42820921 39960091 34613214 58015321 41477118 35601794 29470828 42111822

N-acetyl-glutamine 7055007 5305335 3246582 11015010 6744709 5516959 4566851 5723280

C27H44O3(5) 2217995 1768173 1338271 2816949 1701705 2033603 1201001 1941466

2-aminoacrylic acid 182973867 151501942 119552547 291575278 187694675 144107423 110069187 187478166

Hydroxyphenylacetic acid 11645871 10407215 7646878 10603021 7765356 9364477 7538707 9340619

3-methyl-2-oxobutanoate_2- 113578872 92341859 74758129 116785549 85213373 96640828 56833497 96271799 keto-isovalerate D-glucarate(2-) 35615548 36927083 28804666 62700967 41612142 36257727 26741000 33474265 homocysteic acid 623676.2 1775882.8 1481798.4 2760020.4 1803519.8 2157049.9 802074.6 813475.8

2-Hydroxybutyrate_3- 266991243 192531794 141132702 259954533 190428613 215138714 111872699 203265635 Hydroxybutyrate_3-hydroxy- 2-methylpropanoate lactose_sucrose_ 2154259 1905951 1226613 1086552 1179215 1228768 1167510 1759179

N-Acetylneuraminate 9- 193048.9 352973.69 400294.84 46883.65 146522.84 247318.16 185732.05 251496.96 phosphate 4-hydroperoxy-2-nonenal 76215517 72212780 64925654 90588173 58430269 72914768 55017860 67715442

3-methylcrotonoylglycine 2620234 2459556 1746980 2567215 2858377 1936513 1022985 2025777

Homovanillate_3-(4- 4606947 3963803 3221519 5971111 3493560 4099215 3347667 3890382 hydroxyphenyl)lactate_3- Methoxy-4- hydroxyphenylglycolaldehyde beta-hydroxy-beta- 99795402 80574211 58396181 114212460 69926054 90254374 41867188 90327082 methylbutyrate N-acetylputrescinium 4219706 3057630 2008303 6062668 3643943 3295586 2298988 3467668

NN- 297342124 185966890 170804286 138748181 224924308 217521955 31475044 181905478 dimethyldopaminequinone_(- )-Salsolinol-2 3-hydroxy butyryl carnitine 413139587 389749396 414380812 882909474 561769891 300003784 311425139 557346067 sebacicacid 65247420 72562106 61973594 76119781 49843600 78833118 33062349 65308692

2-acetamido-5-oxopentanoate 726820.1 1255844.5 1077644.6 948424.1 454505.4 974017.8 523602.7 1346307.7

3-methyl-2-oxopentanoate_4- 113562987 927030589 801858386 1251612640 840389562 1007655761 553428919 984014599 methyl-2-oxopentanoate_2- 0 ketohaxanoic acid (4-hydroxy-3- 9220953 9542552 7296213 12380216 7154128 7987213 8608747 7833003 methoxyphenyl)acetaldehyde trans- 128261877 266627375 69796664 91565031 86258107 136076157 133495685 100196995 vaccenate_elaidate_oleate L-xylonate_L-lyxonate 56449359 64527241 50809973 117024521 72312935 61001827 51469069 51924681

L-arabinitol_xylitol_D-ribitol 6271020 6490955 4550221 9091596 5624552 6227020 4885728 4900890 aspartate 910551647 765809999 629183842 1564381896 928679192 730430757 575760794 930803870

L-cystathionine 1431310 1524615 1008402 4941738 1749116 1573143 2283027 1661047

6(S)-hydroxy-tetradeca- 1191964.5 1975370.5 1473571.9 1035559.5 1059011.2 1127232.5 1509553.3 888146.3 2E4E8Z-trienoate_6(R)- hydroxy-tetradeca-2E4E8Z- trienoate ethamp[c] 173297323 159531527 1379924747 2392474494 1516969828 1461846034 1329686184 1423627690 6 2 lysine 30985305 22184795 14463999 47522124 28980746 23625849 19061642 21273353 succinyl carnitine 17635958 17585678 12720390 30717772 15670462 17283718 12357401 18079475

L-threonate 355216423 410352925 332625477 809467055 458116002 519694441 228659749 322062634 pyruvate 159891643 123853907 95718162 175337634 111053125 146024150 52413282 134534254

207

L-Fucose 1-phosphate 29389384 29678861 27797262 59835168 33570482 31343101 24678539 27592375

L-erythrulose 14521975 16051645 10672297 22964967 10832260 15743090 8387723 16023625

5-methoxytryptophol 283244.2 232056.8 308235.7 493801.5 343782.2 185440.2 254951.4 257704.8 lipoamide 936291.6 871563.2 888972 2346640.1 1347010.4 1049484.7 690337.9 888303.4

NADP+(3-) 4402747 3090091 1850676 7447829 4147077 2428100 2090036 4535731 alpha-linolenyl 8351118 9759598 8002531 9953444 6674537 6651499 7063586 7841279 carnitine_gamma-linolenyl carnitine 3-hydroxy-isovaleryl carnitine 18691768 14096133 11878302 29232012 13961916 14573155 11937863 16903783

Deoxyuridine 1001283 1183087.6 1042699.1 2234463.4 962551.7 1130253.6 834195.6 1274353.3

N(2)-acetyl-L-ornithine 2167680.2 2391067.9 932266.2 3431473.4 1937549.1 1771036 1546686.1 1576801.1

NN- 20437766 21958278 16147930 34354991 22246471 21453178 9681713 16856702 dimethyldopaminequinone_(- )-Salsolinol-1 23-bisphosphonato-D- 2542449.1 1771352 1264587.1 2732021.1 1776173.1 977605.7 1281938.9 2242877.7 glycerate_3-Phospho-D- glyceroyl phosphate(2_3BPG or1_3BPG) 9_10-hydroxyoctadec-12(Z)- 5325421 3955461 2471726 2215044 2227082 2997986 2752873 2459618 enoate_12_13- hydroxyoctadec-9(Z)-enoate 2-oxoglutaramate 155901.78 105200.67 59828.25 894567.93 267093.64 166401.48 257359.03 211480.76

13-hydroxy-alpha-tocotrienol 936981.1 1427100.9 1129227 799317.8 698202.2 475223.2 809889.4 1199829 oxalate(2-) 59399033 75663691 61502840 167361571 80269241 82687191 35602504 68489934

Phosphodimethylethanolamin 10661699 10078551 6972191 27527690 11385501 10594178 7558084 10978159 e cholesterol sulfate 341621.3 583996.8 264665.3 531973.2 339053.1 268250.3 395038.5 260047.1

L-cysteinylglycine 13818383 8221177 7118468 13950711 10698443 6227983 7143091 7501837 glutathione_neg 574392647 342137610 315752268 658946323 440147906 281967813 371364615 287677730 glutathionate(1-) 100457482 565446109 447372367 1046434773 771932601 446224737 479377222 528780920 3 3-Sulfino-L-alanine 142546316 108882184 67233268 217593948 110630870 120602978 53298509 104805156 spermidine(3+) 2899178 3276667 2652765 8804480 2176436 3070164 3143262 4374685 margarate 14124158 42809503 13288604 18795621 12431200 18094549 18660199 14406764

Coenzyme A 5710554 3774072 2167882 8094034 5362007 3078344 1554863 4077480 salsolinol 1-carboxylate 1125768.2 1169712.6 777343.6 2305824.4 880230.3 1335946.1 497329.3 1096116.4

NgNGdimethylLarginine 36413489 21987944 10952709 66009120 33712705 26204056 16208266 19393837

13-carboxy-gama-tocopherol 297990.5 751422 153566.8 226159.8 181321.5 358816.6 304764 147292.8

2-Aminooctanoic acid 230980120 224985605 1696774200 4928675531 1868072862 2066076437 1587683438 2208890801 2 7 L-2-amino-3-oxobutanoic acid 1924977 2255497 2513679 5498759 2077682 1971041 1837881 2517680 myristate 68457135 200776436 63036987 76589482 62455553 73320435 82766168 61830122

Oxaloacetate 1225884.2 1133003.1 942147.8 2191893.4 879627.7 948255.2 1003474.3 933214.5

NADH(2-) 7810274 6927454 4784158 6696276 4526198 5524856 3198157 4667008 octenoyl carnitine 28012037 29839448 27697806 53858207 25919495 24175763 21149961 23940049

Uric acid 1144081.8 2329733.1 1221409.8 2470320.5 1195799.5 929555.3 1258331.5 1346878.6

3alpha-7alpha-12alpha- 1140478.4 1020145.9 905409.5 1792699.4 644786 1134206.7 474385.5 915072.6 trihydroxy-5beta-cholestan- 26-al 23-dihydroxybenzoic acid 631712 305703.3 384851.2 601377.2 377256.6 301528.7 259277.1 306518.3

208

Taurodeoxycholic 394074.5 541738.1 467517.1 892797 420583.2 279774.8 518670.2 249126.4 acid_taurochenodeoxycholate L-Iditol_D-glucitol_galactitol 9887240 11689337 8525608 16293080 7688753 8227331 7134430 6539660

Taurodeoxycholic acid 387647.7 524634.5 467517.1 882833.3 407882.6 279774.8 505265 237115.1 suberic acid 7690670 12329424 4415458 5741991 3535935 4970075 3249576 7110055

3(S)10(R)-OH-octadeca-6- 6873545 3029386 2054893 1708212 1774904 3183601 2070318 1467929 trans-412-cis-trienoate N-acetyl-L-cysteine 1064787.1 877498.5 1090591.3 1659410.9 789838.8 1018647 243831 849189.7 propionyl-carnitine 489644266 336470040 465587916 1018289541 381548647 439173872 299423007 304031498

Ornithine 5048159 3051827 2067950 12522092 4771358 3022211 2608236 3460746

N1N8-diacetylspermidine 113789.39 20929.31 10000 200913.17 85681.85 19624.28 33737.01 70168.28 putrescine 1864722.5 303826.6 405189.5 609254.7 900988.6 377504.2 198311.8 446180

Pyridoxamine 450951.51 147695.53 143602.54 261853.5 246425.36 145191 111626.63 98811.47 pentadecanoate 44395477 144926902 41318873 50011450 36619825 43801515 47517023 36079325 gamma-L-Glutamyl-L-cysteine 1534327.3 819359.1 606261.2 1119702.8 685780.9 458529.1 687521.8 531494

5-amino-1-(5-phospho-D- 722256.8 1065085.4 1485455.2 689173.5 539209.9 676136.1 418962.7 654026.4 ribosyl)imidazole N(8)- 10939504.4 1535988.5 1034294.7 734196.1 7002114.6 430167.2 374616 314619.8 acetylspermidinium_N(1)- acetylspermidinium 2-phenylethanaminium 32018308 16947754 13608362 41385460 13264138 21729673 5549328 17878009

D-glycerate 3066364 6303380 5445340 23149484 5735666 7587028 3275680 4325076

Glycylphenylalanine 287724.2 764031.2 945409.3 456535.2 256615.3 329692.3 301918.5 460059

2-oxo-4-methylthiobutanoate 10000 10000 10000 566463.79 50546.92 110117.36 153801.09 11800.92

3-Hydroxy-N6N6N6-trimethyl- 260884.3 28766.19 10000 539491.02 217137.84 100349.59 25978.52 114002.95 L-lysine 12_13-epoxy-9-hydroperoxy- 3148850.7 898406.2 562268.9 228236.8 347878.2 1232852.4 745812.2 280468.3 10E-octadecenoate N-acetyl-L-glutamate(2-) 3327848 3215495 2628050 13786061 3517084 3106849 2337154 3169376 thiosulfate(2-) 1667383.3 1371001.8 1109872.4 1329418.7 654278 717289.9 585195.3 897074.3

Glycylleucine 18701259 4548075 5182185 5995614 5295045 4447958 2789337 5243420

Citicoline 18701259 4548075 5182185 5995614 5295045 4447958 2789337 5243420

4- 1385441 995488.2 432656.2 4070525.7 1033791.8 866159.3 544113.7 1040661.3 (trimethylammonio)butanoate _acetylcholine N-acetyl-seryl-aspartate 4127279 3569386 3376478 16171916 3376727 3219757 3085691 3133038

Prostaglandin E3 255802.82 315956.7 171637.89 43380.96 72056.45 105864.29 64769.16 107472.59 tyraminium 146776710 13925795 10474671 50871291 11771083 12698943 7249976 54235934 bilirubin(2-) 250587.99 207200.95 277072.52 327832.64 162385.15 109300.89 11013.86 116627.55

9(10)-EpOME_12(13)-EpOME 18094914 7563807 2215992 2438420 2615973 2998575 3270447 2249793 tetradecenoate (n-C14-1) 5397480 53925710 5552407 7591952 5790568 6224194 6848125 6020239 xanthosine 270110.2 522869.3 239658.7 5132801.5 496369.8 526075.9 497520.3 391888.5 citrulline 4566705 4879801 2666424 53321223 5765822 4749670 3374820 4804963

(R)-S-lactoylglutathionate(1-) 4873900.9 2905803.3 1466965 15749239.7 1351936.1 2224963.4 698391.8 2742821.5

2-aminomuconate(2-) 845382 609239.5 794682.4 8953840.5 812409.4 703222.5 600206.8 893228.5 o-methylhippurate 381773.4 340833.9 226643.9 8638098.5 489583.3 329787.5 354795.3 1344411.4

209

palmitoleate 15921000 361620959 11282019 24791698 14056669 17459152 19782666 13458141

N-acetyl-L-aspartate_2- 31257069 28340509 27273506 1511392731 24580018 23780261 19869948 23398989 Amino-3-oxoadipate quinonoid 2535181.1 2426513.9 2440897.7 55004982.6 577615.1 746538.9 1149637.2 867705.6 dihydrobiopterin_67- dihydrobiopterin_6-Lactoyl- 5678-tetrahydropterin_O2-4a- cyclic-tetrahydrobiopterin indole-3-acetate_(5- 146834.19 108616.58 17025.39 7757780.51 60700.62 23835.76 47913.14 38620.94 hydroxyindol-3- yl)acetaldehyde

210

Appendix D

Secondary recipient mice were transplanted with T-ALL from an AMPKα1flox/flox;Rosa26CreERT2 background primary cancer and treated with vehicle or tamoxifen. T-ALL cell were isolated and metabolic gene expression was determined by qrt-PCR array. Ct values for each independent mouse are provided.

Symbol Vehicle Vehicle Vehicle Vehicle Tamoxifen Tamoxifen Tamoxifen Tamoxifen 1 2 3 4 1 2 3 4 Atp12a 35 35 35 35 35 35 35 35 Atp4a 33.75 33.28 33.78 34.13 33.65 33.22 32.73 33.51 Atp4b 35 35 34.84 34.9 35 35 35 34.93 Atp5a1 21.49 21.44 21.72 22.08 21.3 21.58 21.63 21.75 Atp5b 20.63 20.55 20.95 21.39 20.41 20.67 20.88 20.89 Atp5c1 22.21 22.2 22.46 22.78 22.18 22.15 22.36 22.27 Atp5d 23.2 23.09 23.39 23.78 23.09 23.19 23.4 23.41 Atp5f1 22.35 22.27 22.44 22.85 22.25 22.11 22.48 22.45 Atp5g1 22.9 22.68 23.21 23.23 22.31 22.44 22.79 22.77 Atp5g2 22.41 22.54 22.97 23.2 22.43 22.36 22.4 22.46 Atp5g3 21.25 21.23 21.6 22.01 21.03 21.16 21.34 21.31 Atp5h 21.62 21.97 22.03 22.56 21.44 21.5 21.43 21.73 Atp5j 21.89 21.54 22.23 22.41 21.57 21.52 21.86 21.87 Atp5j2 22.29 22.24 22.49 22.75 22.27 22.25 22.42 22.46 Atp5o 23.01 22.94 23.3 23.5 22.83 22.93 23.14 23.11 Atp6v0a2 24.25 24.37 24.57 24.7 24.46 24.25 24.19 24.36 Atp6v0d2 33.28 33.63 33.17 33.22 33.47 32.6 32.54 32.42 Atp6v1c2 32.88 32.57 32.99 32.87 33.22 33.12 32.32 32.88 Atp6v1e2 35 35 35 35 35 35 35 35 Atp6v1g3 35 35 34.21 35 35 34.85 35 35 Bcs1l 27.63 27.73 27.9 28.19 28.43 27.48 27.59 27.92 Cox11 28.65 28.74 29.16 29.15 28.74 29.93 28.37 28.88 Cox4i1 21.48 21.32 21.73 21.9 21.2 21.19 21.32 21.3 Cox4i2 31.01 31.47 31.38 30.88 31.77 30.6 30.86 31.41 Cox5a 22.71 22.9 23.15 23.43 22.62 22.73 22.96 23.01 Cox5b 31.79 31.54 31.54 31.8 31.33 31.19 31.57 31.54 Cox6a1 22.1 22.25 22.59 22.6 22.33 22.21 22.41 22.34 Cox6a2 34.24 33.77 33.26 34.35 33.7 34.22 32.37 32.72 Cox6b1 22.27 22.21 22.66 22.83 22.17 22.25 22.34 22.4 Cox6b2 28.13 27.83 28.56 28.58 28.14 28.47 28.46 28.48

211

Cox6c 21 21 21.26 21.53 20.9 20.91 20.97 21.08 Cox7a2 33.17 34.58 33.73 33.9 33.35 33.24 32.96 33.66 Cox7a2l 22.53 22.76 23.02 23.24 22.73 22.83 22.55 22.67 Cox7b 22.86 23 23.52 23.82 22.71 22.92 23.06 23.14 Cox8a 23.71 23.71 24.18 24.51 23.65 23.63 24.03 23.87 Cox8c 35 35 35 35 35 35 35 35 Cyc1 23.92 23.76 23.91 24.35 23.64 23.89 24.06 24.16 Lhpp 28.36 28.63 28.4 28.64 28.57 28.62 28.5 28.52 Ndufa1 23.12 23.11 23.41 23.74 23.21 23.22 23.3 23.3 Ndufa10 23.75 23.72 24.2 24.3 23.68 24.05 23.85 23.89 Ndufa11 24.35 24.26 24.64 25.01 24.26 24.39 24.4 24.49 Ndufa2 24.91 24.97 25.37 25.65 25.05 25.03 25.18 25.16 Ndufa3 23.42 23.62 23.75 24.06 23.71 23.36 23.72 23.51 Ndufa4 22.11 21.99 22.29 22.65 21.98 22.02 22.13 22.14 Ndufa5 23.71 23.72 24.12 24.41 23.62 23.7 23.9 24.03 Ndufa6 23.14 23.24 23.56 23.9 23.32 23.26 23.42 23.42 Ndufa7 23.78 23.76 24.13 24.26 23.86 23.89 23.78 23.91 Ndufa8 23.79 23.81 24.16 24.18 23.86 23.71 24.04 23.82 Ndufab1 29.28 29.07 29.4 29.72 29.02 28.88 29.4 29.44 Ndufb10 24.81 24.7 25.15 25.59 24.75 24.86 25.01 25.08 Ndufb2 23.43 23.39 23.98 24.09 23.66 23.56 23.76 23.78 Ndufb3 23.55 23.68 24.13 24.31 23.48 23.38 23.66 23.64 Ndufb4 24.23 24.25 24.57 24.82 23.99 24.34 24.28 24.32 Ndufb5 23.79 23.78 24.25 24.28 23.74 23.58 23.85 23.86 Ndufb6 23.94 23.92 24.23 24.47 24.01 24.14 24.31 24.29 Ndufb7 23.48 23.56 24.09 24.15 23.23 23.23 23.34 23.33 Ndufb8 23.24 23.12 23.42 23.61 23.05 23.05 23.27 23.21 Ndufb9 23.15 23.15 23.38 23.8 23.12 23.33 23.33 23.32 Ndufc1 23.32 23.31 23.35 23.94 23.32 23.11 23.5 23.48 Ndufc2 22.39 22.28 22.73 23.03 22.19 22.35 22.47 22.51 Ndufs1 24.97 24.98 25.24 25.46 24.74 25.18 25.26 25.41 Ndufs2 23.66 23.7 24.19 24.31 23.72 23.86 24.01 24.05 Ndufs3 24.07 24.04 24.34 24.63 24.06 24.18 24.28 24.2 Ndufs4 24.2 24.13 24.64 24.66 24.14 24.14 24.44 24.29 Ndufs5 23.36 23.22 23.9 24.1 23.1 23.03 23.49 23.35 Ndufs6 23.17 22.99 23.46 23.71 22.85 23.13 23.25 23.16 Ndufs7 24.33 24.2 24.79 24.98 24.09 24.41 24.56 24.46 Ndufs8 24.64 24.62 25.09 25.27 24.47 24.59 24.79 24.66

212

Ndufv1 24.58 24.35 24.84 24.9 24.19 24.6 24.67 24.66 Ndufv2 23.42 23.33 23.76 23.87 23.36 23.56 23.45 23.65 Ndufv3 24.04 24.11 24.41 24.57 23.87 23.88 23.89 23.99 Oxa1l 24.7 25.18 25.23 25.73 24.65 24.96 24.84 25 Ppa1 24.54 24.49 24.82 25.34 24.4 24.57 24.92 25.1 Ppa2 25.58 25.45 25.9 26.04 25.44 25.49 25.78 25.94 Sdha 23.36 23.31 24.03 23.91 23.52 23.44 23.67 23.7 Sdhb 24.39 24.24 24.91 24.91 24.28 24.36 24.67 24.53 Sdhc 24.17 24.15 24.69 24.99 24.1 24.17 24.35 24.35 Sdhd 27.91 27.54 28.24 28.32 27.52 27.9 27.85 27.93 Uqcr11 26.74 26.33 27 27.13 26.25 26.31 26.56 26.46 Uqcrc1 23.06 22.97 23.3 23.49 22.93 23.06 23.22 23.19 Uqcrc2 23.29 23.06 23.64 23.71 24.23 23.2 24.38 23.39 Uqcrfs1 22.73 22.69 23.17 23.35 22.5 22.64 22.74 22.76 Uqcrh 25.11 24.98 25.72 25.72 25.1 24.97 25.3 25.24 Uqcrq 22.66 22.45 23.07 23.15 22.27 22.52 22.57 22.62 Actb 18.51 18.34 19.12 19.16 18.52 18.38 18.54 18.28 B2m 19.17 19.24 19.5 19.7 19.59 18.82 19.15 19.15 Gapdh 20.61 20.5 20.88 21.35 20.35 20.49 20.91 20.96 Gusb 25.43 25.3 25.86 25.86 25.35 25.46 25.64 25.6 Hsp90ab 19.67 19.48 20.02 20.24 19.35 19.75 19.83 20.01 1

213

Appendix E

Control and FoxP3-ER expressing FL5.12 cells were treated with 4-OHT and metabolic gene expression was determined by qrt-PCR array. Ct values for each cell line are provided.

Gene Symbol Control line 1 Control line 2 Control line 3 FoxP3 line 1 FoxP3 line 2 FoxP3 line 3 Acly 24.16 25.54 23.55 24.43 24.28 23.54 Aco1 35 35 35 35 35 35 Aco2 27.25 28.08 26.25 26.99 27.06 25.83 Agl 26.69 27.45 25.47 25.47 25.69 24.95 Aldoa 21.05 22.1 19.83 20.65 21.04 20.01 Aldob 35 35 35 35 35 35 Aldoc 35 34.16 33.46 33.89 33.04 32.53 Bpgm 29.18 30.16 27.69 28.32 28.17 27.47 Cs 29.66 30.56 27.92 28.31 28.38 27.64 Dlat 26.84 27.51 25.32 26 26.06 25.23 Dld 24.53 25.48 23.39 24.03 23.98 23.12 Dlst 25.02 25.67 23.59 23.85 24.08 23.63 Eno1 21.3 22.26 19.97 20.92 21.22 20.44 Eno2 35 35 35 35 35 35 Eno3 27.23 28.4 25.68 26.04 25.36 24.27 Fbp1 35 35 35 35 35 35 Fbp2 35 35 35 35 35 35 Fh1 24.53 25.48 23.92 24.38 24.42 23.76 G6pc 35 35 35 35 35 35 G6pc3 26.19 27.09 25.22 25.18 25.14 24.19 G6pdx 25.4 26.43 23.89 24.13 24.08 23.23 Galm 35 35 35 35 35 35 Gapdhs 35 35 35 35 35 35 Gbe1 29.1 30.24 28.13 28.44 28.6 28.23 Gck 35 31.9 31.17 34.62 31.42 28.62 Gpi1 23.38 24.38 22.08 22.53 22.64 21.72 Gsk3a 26 27.02 24.26 25.08 24.88 23.93 Gsk3b 26.53 27.59 25.52 25.89 25.66 24.92 Gys1 26.9 28.04 25.69 26.02 26.22 25.36 Gys2 35 35 35 35 35 35 H6pd 28.45 29.11 26.61 27.15 27.28 26.14 Hk2 26.52 27.31 25.21 26.06 26.12 25.81

214

Hk3 31.48 32.01 28.88 29.33 29.08 28.25 Idh1 26.9 27.92 25.66 26.1 26.02 25.44 Idh2 25.1 26.09 24.04 24.39 24.31 23.72 Idh3a 24.44 25.26 22.77 23.59 23.52 22.56 Idh3b 24.87 25.5 23.25 23.97 24.09 23.27 Idh3g 23.85 24.8 22.7 23.18 23.22 22.4 Mdh1 25.54 26.22 24.49 25.16 25.21 24.12 Mdh1b 35 35 35 35 35 35 Mdh2 23.59 24.1 22.55 23 23.12 22.33 Ogdh 25.28 26.34 23.88 24.64 24.62 23.44 Pck1 35 35 35 35 35 35 Pck2 26.11 27.04 24.75 25.24 25.65 24.82 Pcx 28.19 28.86 26.27 26.67 27.29 26 Pdha1 25.54 26.33 24.01 24.52 24.62 23.85 Pdhb 25.57 26.68 24.59 25.07 25.01 24.25 Pdk1 27.21 28.04 25.76 26.92 26.81 26.64 Pdk2 35 35 35 35 35 35 Pdk3 25.11 25.81 24.26 24.81 25.05 23.95 Pdk4 35 35 35 35 35 35 Pdp2 28.08 28.63 26.7 27.11 27.49 26.65 Pdpr 29.29 29.94 27.6 28.08 28.28 26.98 Pfkl 24.38 25.41 22.81 23.75 24.08 23.25 Pgam2 31.13 32.1 30.05 29.95 29.94 28.81 Pgk1 22.41 23.25 21.17 21.82 22.11 21.66 Pgk2 35 35 35 35 35 35 Pgm1 25.58 26.58 24.61 24.92 25.02 24.15 Pgm2 26.08 27.05 25.07 25.52 25.45 25.08 Pgm3 28.45 29.61 27.28 27.72 27.85 26.54 Phka1 35 35 35 35 35 35 Phkb 27.26 28.46 26.05 26.38 26.17 25.26 Phkg1 34.73 35 33.48 34.3 34.18 32.66 Phkg2 27.66 28.71 26.44 26.83 26.88 25.51 Pklr 35 35 35 35 35 35 Prps1 24.71 25.81 23.58 23.86 23.73 23.05 Prps1l1 35 35 35 35 35 35 Prps2 26.28 27.23 25.02 25.27 25.23 24.57 Pygl 35 35 35 33.65 35 35 Pygm 34.02 35 33.68 33.78 34.12 33.63

215

Rbks 28 29 27.44 27.66 27.9 26.94 Rpe 26.07 26.56 24.7 25.23 25.37 24.29 Rpia 24.54 25.49 23.64 24.31 24.17 23.54 Sdha 24.33 25.35 23.13 23.65 23.64 22.9 Sdhb 25.36 26.38 24.5 25.25 25.15 24.37 Sdhc 24.19 25.39 23.26 23.82 23.67 22.9 Sdhd 28.41 29.62 27.49 27.86 27.85 27.17 Sucla2 26.05 27.02 25.01 25.53 25.63 24.71 Suclg1 28.42 29.63 26.53 27.26 27.09 26.77 Suclg2 25.36 26.35 24.22 24.9 24.83 24.21 Taldo1 25.74 26.71 24.73 27.4 25.07 24.31 Tkt 22.23 23.37 21.16 21.56 21.5 20.69 Tpi1 22.8 23.69 21.64 22.47 22.53 22.04 Ugp2 26.77 27.76 25.46 25.85 25.94 24.67 Actb 19.7 20.95 18.45 18.68 18.58 17.31 B2m 23.53 24.64 22.97 23.1 23.02 21.41 Gapdh 21.21 22.61 19.85 20.62 20.94 19.65 Gusb 26.02 26.83 25.07 25.33 25.27 24.35 Hsp90ab1 21.16 22.13 19.81 20.28 20.39 19.57

216

Appendix F

Metabolomic analysis of control and FoxP3 activated FL5.12 cells. Control and FoxP3- ER FL5.12 cells were treated with tamoxifen for 24 hours and subject to non-targeted mass spectrometry-based metabolomics. Data were range scaled using Metabolanalyst.

Label 0 0 0 1 1 1 Control 2 Control 4 Control 5 FoxP3 1 FoxP3 6 FoxP3 9

(R)-mevalonate 14363765 13518540 14575580 18549269 20320374 19183979

Caprate 216522150 226110516 223807189 261369017 260620930 250817857

Pyridoxal 15961160 21044246 21436412 32345244 30823379 31580745 4-hydroxy-2-nonenal_3_4- epoxynonanal 27781337 29615289 29378881 32263050 33545891 32755711

Dopaminochrome 5558710 9244650 10313400 18387313 17871902 15936284 L-glutamine_3- Ureidoisobutyrate_Glycylsarc osine 2096718143 2077948508 2233923992 2651589887 3156018634 2963609596

Nonanoate 258233127 291235956 288752608 329567195 341344884 328493631

UDP-alpha-D-xylose(2-) 4191590 5248152 4113675 2987286 2637301 2376433 gamma-L-Glutamyl-L-cysteine 67376025 61473024 51933133 41039523 34783016 28792617

(S)-dihydroorotate 21918487 23249121 23469589 14406407 17194915 10264753

L-cysteine 6113776 5540684 4950631 4070595 2647685 2647293 UDP-D-glucose_UDP-D- galactose 330186586 355821148 324324141 290968558 238595389 255353826 UDP-N-acetyl-D- galactosamine_UDP-N-acetyl- alpha-D-glucosamine 387197896 397560889 394125250 366363141 328662474 332740634 succinyl carnitine 4112806 4555640 3921174 4859728 5477452 5741045 leukoaminochrome_dopamin e o-quinone-1 16275350 22774020 23967427 28314677 31317341 31214921

N-acetyl-L-aspartate_2- Amino-3-oxoadipate 12606438 10464191 9175817 14525082 17175144 19634902

Cytosine 74869344 108826287 119682374 161112361 160800130 140447078

O-acetylcarnitine 435862396 355362819 386520559 309897476 317481723 283872759

D-Glucosamine 6-phosphate 21325963 23205819 20914862 18809350 14331923 13186435

(S)-2-[5-Amino-1-(5-phospho- D-ribosyl)imidazole-4- carboxamido]succinate 1652387.96 1778604.07 1953747.74 1249841.06 393563.05 87493.46

N-acetyl-glutamate 3320537 2755512 2503398 2162264 1974684 1944850

Pyridoxine 126949787 203967103 210824843 252621180 280853515 290644872

N-Carbamoyl-L-aspartate 40253102 42872668 46248282 36936579 34336453 28276077

L-alanyl-L-leucine 5082334 4042386 4909283 3880271 3100177 3352598

217

Dihydroxyacetone phosphate_D-glyceraldehdye- 3-phosphate 16405603 18972934 19796435 15848258 13039243 11857441

3-carboxy-alpha-chromanol 1858277 2185349 2266942 1753278 1666816 1718531

(R)-carnitine 1488423422 1451892341 1365694917 1526102699 1561812154 1569537006

(2R)-2-hydroxy-3- (phosphonatooxy)propanoate _D-Glycerate 2-phosphate 25532993 32843505 31205631 39352909 53910687 41730799

3-phosphoglycerate 25245663 32497268 30794173 38873684 53291253 41241124 perillyl aldehyde 8587254 8425272 8092421 7802293 7526842 8003369 methyl indole-3-acetate 2396124 2657735 2371810 2775438 3021409 2745133

UDP(3-) 58636547 60847539 55589794 53113621 37902532 39730448

L-threonate 284360802 277649654 241632797 222853367 222840966 172448451 5-oxoprolinate_L-1-Pyrroline- 3-hydroxy-5- carboxylate_Pyroglutamic acid 1810038449 2405884615 2496854358 2803283177 3135094061 2844972370

L-arabinitol_xylitol_D-ribitol 45953410 47203403 48428050 33545884 43714441 28375917

L-glutamate(1-) 5106543142 4874619176 4696929109 4497700101 4368819841 3728724428

2-Aminooctanoic acid 198420774 200503487 182491510 210457012 212464288 205573698 Aminobutanoate_NN- dimethylglycine 243106951 241636852 238923877 205786913 235556327 205477930

S-adenosyl-L-methionine 80070610 60261068 65965261 58948085 32088860 39679069

L-methionine 107063108 130003046 109170870 133992067 166383605 139725222

6(R_S)-hydroxy-tetradeca- 2_4E8Z-dienoate 1635860 1578991 1553567 1333982 1516487 1469752

L-phenylalanine 414349690 473629024 415799999 484640628 531537290 477068543

4-oxo-2-nonenal 10969758 10630040 10326340 11034434 11270581 11076669 thiosulfate(2-) 4194821 5256072 6166955 4355366 2880493 2972536

UDP-D-glucuronate 34088479 22647344 19754075 18817366 8653322 9879256 glutamate (total) 9554845349 9012430857 8648919634 8343192785 8053624331 6848550382 glutamate_O-acetyl-L-serine 4448302207 4137811681 3951990525 3845492684 3684804490 3119825954

(R)-S-lactoylglutathionate(1-) 29233311 29602064 21342711 17078557 15639984 22791429

2-phenylethanaminium 43383016 45868981 28578844 16075505 14912971 32374461

L-palmitoylcarnitine 10772471 6556928 13910945 13002438 20716575 19512293 choline phosphate(1-) 2605173541 2471900755 2207810592 2293846697 1772620268 1630416342

L-valine 4267442194 4583336902 4161204728 4061321454 4011650556 3571093033

D-glucarate 442536.1 756857 899360 2210561.3 865750.3 1949919.7 glycine betaine 4329846957 4644881876 4241814165 4143344122 4083042785 3644393793

NADH_pos 2543244 2192870.3 1292383.1 1598243.3 286300.8 10000

218

5-Methylthio-5-deoxy-D- ribulose 1-phosphate_5- Methylthio-5-deoxy-D-ribose 1-phosphate 2173252.5 1718063 890547.4 585879 546068.8 993388.5

NADH(2-) 13947757 8267470 11339346 7862357 7522193 2656177

(S)-3-sulfonatolactate(2-) 1471268 1227551 1018762 1754797 1502489 1487490

L-cysteinylglycine 68321101 66176239 61639173 60411697 58344692 46728962 4-hydroxybenzoate_Gentisate aldehyde 137863885 121779905 124358250 141462401 138953961 136889623 lipoate_(2S3R)-3- hydroxybutane-123- tricarboxylate_2- methylcitrate 5601802 5893101 6727613 7225720 6684962 8673142 gama-L-glutamyl-L-alpha- aminobutyrate 1395717 1377906 1434064 1929716 1479551 2290605 tetradecanoyl carnitine_myristoyl carnitine 13452297 11618922 23768177 20298400 33228682 29305931 propionate_(S)- lactaldehyde_(R)- lactaldehyde_hydroxyacetone 61935097 66501677 65671668 61992866 62265371 58834604 adipic acid 430311450 375341761 390571305 433980965 497488750 429694643 hydroxyproline 2168710744 2057303458 2163495069 1986519241 1909671783 1439048227

N-acetylputrescinium 131812247 129523236 138763718 185572429 152592423 142182238

3-hydroxypropionate_D- glyceraldehyde_glycerone 5479095595 6383064563 5823397100 5114826818 5591445793 4452148537

(R_s)-lactate 5479095595 6383064563 5823397100 5114826818 5591445793 4452148537 glutathionate(1-) 6792104517 6626579560 6167124256 6185301877 5880672123 4793170966 limonene_(+)-alpha-pinene 2639581 2442459 2702049 3327473 3223862 2610433 choline 705391636 790752006 738479585 888957427 1480638563 2432797328

Biotin 21879181 31927256 33278633 22704263 23636028 18474570 propionyl-carnitine 114295440 125183340 93706512 102197371 87678198 76722874

NADP+(3-) 15216745 10549661 7663255 8402596 4411765 6664056

N-acetyl-L-glutamate(2-) 24765554 18559047 19458004 18078526 17658125 14399418

Sedoheptulose 7-phosphate 11824758 9721842 9985200 9091768 8340227 3272910 phenylacetaldehyde 7372374 7335214 6616427 6324415 6825816 6589804

UDP_neg 580800075 459485210 363247443 418187524 284576470 257468536 putreanine 160211814 149295486 136274717 156874320 166258722 192087666

1-Pyrroline-5-carboxylate 57406084 57916695 52532902 53168448 47975206 34188467 cis- aconitate_dehydroascorbide 44746759 31298851 34389246 33502169 24237866 25140168 itaconate_Mesaconate_Citrac onic acid 170239859 118826625 108793577 107333170 92540724 89429421

Cytidine 190253072 275927825 308477304 214626402 192119158 144445519

L-tryptophan 61977370 61699468 58012388 64958764 66658137 61248230 citrate_isocitrate_Diketogulo nic acid 4074278087 3131804871 3248414480 3321926636 2370750561 2474619930

219

dopaminium(1+) 2523419 3101265 3093905 4357592 4114598 2885274 butyryl carnitine 744088346 917046720 801359201 807986260 568395666 604933877 inosine 173886551 153849933 102453704 130789560 80409612 59035089

L-isoleucine_L-leucine 1639044528 1799341131 1626457062 1812089606 2103138646 1774540647 glutathione_neg 8178295391 7820324547 7529148006 7566220051 7205068473 5793286185 25-hydroxyvitamin D3-26-23- lactone 319443.6 285036.9 411332.8 316220 225414.5 111623.3 NN- dimethyldopaminequinone_(- )-Salsolinol-1 1263120 2345667 2940384 4418030 5875140 2210080 thromboxane A2(1-) 2340163 3430790 4419702 2067491 2877748 1771390 4(R)-hydroxy-dodec-6Z- enoate_4(S)-hydroxy-dodec- 6Z-enoate 44919418 41470963 40425161 46996529 44667374 43631730

CDP_neg 110862651 71965355 62839191 73685807 41633865 34818787

Glycerol 3-phosphate 51367857 45642891 63521533 43007379 47513565 42248523 serine 89469874 85617581 105478710 115933400 126071601 94395018

NgNGdimethylLarginine 72668810 73892037 67924337 62150662 69208021 43220195 tryptophan 88670500 96352197 84533401 93986944 96080598 97512055

N6N6N6-Trimethyl-L-lysine 236141224 247128867 218387127 211582914 227920078 176755707

N-acetyl-L-asparagine 2826319 3225317 2995321 2587348 2918010 1783736 succinate(2-)_Methylmalonic acid 2044178735 2214495476 2185242802 2070889812 2083016874 1896373260

4- (trimethylammonio)butanoat e_acetylcholine 793621522 828018491 777783206 853594645 848038122 803822152 citrulline 16972542 19547518 24112161 19133111 15211704 12471326

Xanthine 6746809 10658464 13411728 16585067 105672876 38819189 w-hydroxydecanoicacid 25491524 22502549 24553507 28108271 25651104 25031419

CDP-ethanolamine(1-) 664723.9 569483.1 903616.5 829668.9 1543274.9 3258007.8 N(omega)-(L- Arginino)succinate 44905223 38212480 46683480 43438362 31943195 20885829

N-acetyl-glutamine 13284116 10486669 12244910 11774633 9647971 7229982

L-proline 5261338536 5202303240 4952870829 5042506916 4485788487 3095201644

Glycylleucine 11346240 11396293 12533111 11291474 11156701 9410232

Citicoline 11346240 11396293 12533111 11291474 11156701 9410232 1-deoxy-1-(N6-lysino)-D- fructose 506889.3 897581.2 673852.5 739411 1532817.1 1027980.5 cytidine 107961922 148655337 197891957 119795388 119298005 71353267 sphingosine_(2S)-1-hydroxy- 3-oxooctadecan-2-aminium 45358810 39030708 41676009 51724051 68724114 42606338

5-S-glutathionyl-dopamine 1305460 1415326 1338707 2428555 2308897 1186759 hexose-phosphate 40479614 45274916 39632104 40990608 37761178 33224446

220

Ascorbic acid 24979393 24788064 27711431 25002950 24139253 20224739

CDP-ethanolamine 1067712.3 723571.5 1264820.4 1236364.4 2163463.7 5152212

L-Iditol_D-glucitol_galactitol 28349708 33320788 30640699 31161456 39175402 34544920 N-acetyl-L-alanine_5-amino- 2-oxopentanoic acid_5- Amino-4-oxopentanoate_L- Glutamate 5- semialdehyde_trans-4- hydroxy-L-proline 574623603 517264943 554760482 510128124 525573192 366473943 glyoxylate 12289026 11778491 11637593 12155475 10360275 10061645 D-glucuronolactone_L- ascorbate 24424178 24080772 27110932 24448485 23652360 19635697

O-Phospho-L-serine 30365194 32221638 23486736 22198870 28367980 14877609

UTP(4-) 522195844 239024453 181774713 270152434 56940018 109063382 2-keto-3-deoxy-D-glycero-D- galactononic acid 9- phosphate 108977259 95420190 86659099 80424931 97431554 47379522 Elaidic carnitine_Vaccenyl carnitine 1794330 1167689 2704480 2244711 11715426 3755283

Coenzyme A 12027122 14864089 11257826 13827159 6838042 7710506

L-histidine 69967695 79079824 80317548 89122513 77553220 81361513

N(2)-acetyl-L-ornithine 6104505 8462348 9365022 8806838 9510143 9579440

GDP-alpha-D-mannose(2-) 9796557 10464887 13942466 11592435 6988663 8269142

GDP_neg 89590589 54728771 49560070 63175364 30759048 39165422

Succinic semialdehyde_2- oxobutanoate_2-methyl-3- oxopropanoate 235822033 264207033 226253185 245735879 257187899 300194230

L-lysinium(1+) 111648995 125255962 109585343 118442543 152579608 120932770

(S)-malate(2-) 7088299471 7335914824 7496382436 7607072807 6474674869 5576332253 indole-3-acetate_(5- hydroxyindol-3- yl)acetaldehyde 8504580 10925988 5248016 2874134 4336517 8561281 7alpha-hydroxy-3-oxo-4- cholestenoic acid anion_24- oxo-1alpha-25- dihydroxyvitamin D3_25- hydroxyvitamin D3-26-23- lactol 956890.7 610449.2 702523.4 671027.6 688843.6 309447.9 o-methylhippurate 447982505 436624560 399641148 453126289 493180540 426728318

3-hydroxy butyryl carnitine 36988322 33132340 35578453 39466883 24829965 17410304 quinonoid dihydrobiopterin_67- dihydrobiopterin_6-Lactoyl- 5678-tetrahydropterin_O2- 4a-cyclic-tetrahydrobiopterin 413576330 481760183 407376469 431367488 497207060 487933835

Hypoxanthine 1963745613 1976749232 1393041991 1426969600 1824937978 828565844

ATP_dGTPneg 1056056838 622080803 391691868 651456867 232119625 355620546

221

tetradecenoyl carnitine 696106.4 615046.2 1403489.6 879716 2558817.3 1281434.4 dGMP_AMP_3-AMP 212116339 238330881 304242092 243757076 605707748 307355303 pristanic acid_pristanate 3455633 3021572 4005285 3891696 5263652 3446777 suberic acid 14095711 18284508 27695588 10682118 19784400 12939664 dGTP_ATP 503570514 322889351 223831096 359734274 127974083 199202209 lysine 257585251 348166520 326757315 299037914 418737936 363920493

S-[2-carboxy-1-(1 H-imidazol- 4-yl)ethyl]-L-cysteine 3463432 2565695 2812528 2425331 3017523 2156297

IMP 220844080 240592202 293786968 241474231 569222410 292520876

AMP_dGMP 220854740 240605440 293800879 241474231 569222410 292520876 coumarin 4091226 4229513 3662743 3721354 3895613 3772185 malonic dialdehyde_acrylate_methylgl yoxal 54171344 57664054 54192530 53620111 55813223 47056504

2-Hydroxybutyrate_3- Hydroxybutyrate_3-hydroxy- 2-methylpropanoate 413884747 435775514 407083933 443330215 413246256 444444248

L-pipecolic acid 84324832 90663872 85696395 89407989 104675324 86358692 histamium 2154346 1884064 7331816 4177694 10654413 5286204

NAD(1-) 167272478 180854726 159384519 177556926 149926777 133580190 indole 7985994 9179435 8609131 8817624 8872550 9309459

D-Fructose 16-bisphosphate 16046573 18684249 16945590 19275726 10951228 12994580

Nicotinamide 91255303 154646214 143370436 144236132 186401535 138029057 dCTP 6884082 4220225 4014181 5636573 2260815 2814941 arachidonate_eicosatetranoic acid 4128411 5355554 6661431 4327363 22292074 7223261 NN- dimethyldopaminequinone_(- )-Salsolinol-2 415849574 574480466 483452112 469010198 390946284 451347824 glutathione disulfide_pos 214527208 179670652 189988804 224365312 232740813 184111555 erythro-5-hydroxy-L- lysinium(1+) 413885.7 477560.6 429901.9 456138.1 597045.9 436870.6

3-dehydro-L-gulonate_D- glucuronate_L-iduronate 8257837 9825363 5583985 9860173 7870898 10544269 hexadecanediocacid 3275172 3520885 4230677 2773360 3786305 3205771 cervonic acid C22-6 n- 3_docosahexaenoate 1323938 1153467 1544447 1170769 6232704 1605008

(R)-2-hydroxy-4- methylpentanoate_Hydroxyis ocaproic acid 137228751 149495606 136986934 143797855 143523004 151182414 CMP-N-acetyl-beta- neuraminate(2-) 21167910 18538721 18982307 22592344 22639679 18411133

CTP(4-) 107591327 22474761 30323975 49236850 6169101 12548065

222

12S-HHT 1681282 1721217 2144820 2056653 1859441 2152301 ADPmannose_GDP-L- fucose_ADP alpha-D- glucoside 6878483 7191913 5434233 6715570 5306381 5316620 glutathione disulfide_neg 235238701 208365163 225717469 234786999 278005045 216441626

3-Sulfino-L-alanine 136493054 159805617 148216710 218694818 161684570 138649848

D-glycerate 7875080 9033083 11298081 7093056 9589469 7772451 N-acetyl-D-glucosamine 6- phosphate_N-Acetyl-D- glucosamine 1-phosphate_N- Acetyl-D-galactosamine 1- phosphate_N-acetyl-D- mannosamine 6-phosphate 11144457 8892008 10152079 10718005 10159707 11653635 bilirubin(2-) 265065 213571 229622.6 283328.1 151036.9 117806.6

Perillic acid 2519173 2513871 2755156 2417970 2365281 2653320 6-phosphonatooxy-D- gluconate 5436032 5695032 6016114 7085146 4513153 11863540

2-aminoacrylic acid 74360146 76456263 71317222 69853117 77872036 47601867

Fumarate_Maleic acid 700652851 692713786 696157713 736660895 653115307 488022834

D-Erythrose 4-phosphate 1605517 1508697 1089024 1126527 1441501 1083180

N2-Formyl-N1-(5-phospho-D- ribosyl)glycinamide 2233611 3580965.1 4330277.6 2708128 3681324.8 824086.5 alpha-D-Ribose 1- phosphate_alpha-D-Ribose 5- phosphate_D-xylulose 5- phosphate_D-ribulose 5- phosphate_D-Xylulose 1- phosphate 16859903 14803143 13379605 13222556 19865630 18068017

3-methyl-2-oxobutanoate_2- keto-isovalerate 166583072 161911291 188669862 200461805 170166622 179095718 aspartate 452434757 458825946 410416642 408244405 475086214 284373106

UMP_3-UMP 139936868 202308618 273003476 199802292 492601203 199879120

UMP 24669287 33023721 48972490 34373576 75582142 36338533 xanthosine 1261076.7 1063201.9 1081391.1 1179167.7 1068398.8 876502.3

Hydroxyphenylacetic acid 11896179 11404895 10668994 11129661 16059221 11020349 creatine 336110547 357835644 302824760 338111938 342036793 359265115 phytanate_arachidate 5130955 4042061 5850853 4485693 5368391 3133625

ADP_dGDP_neg 917778169 826934822 726793664 861545785 729954206 699829192 gulonate 138454387 143989550 126213399 151008953 121389072 95648842

L-tyrosine 131184690 138019976 118165252 130369105 138456213 133491754 omega hydroxy hexadecanoate (n-C16-0) 14222289 16229750 16970933 12912814 16202839 15291614 clupanodonic acid_docosa- 4_7_10_13_16-pentaenoic acid 840738.4 535484.8 1224818.6 503151.3 3202005.7 917719

5-Methylthioadenosine 139925288 157408173 238014006 289542679 225632809 142427658

223

tyraminium 196206283 329556699 160210413 21349982 15806815 359327664

L-argininium_D-argininium 504127586 569904003 523328458 543924534 641380543 512646849 trans-45-epoxy-2(E)-decenal 1846951 2117131 1947610 1737649 1673323 2131202

L-aspartate(1-) 1664918428 1900551022 1647631182 1640605508 1900978069 1129042676

CMP(2-) 19112573 19567578 27419842 22469232 52094223 16873838

Oxaloacetate 2576123 2360180 2192300 2481323 2211797 2067474

NAD+_neg 85521949 74331269 78258934 86700212 68874843 66985759

4-Imidazolone-5-propanoate 13828345 12294058 11396025 12417737 12736477 9828788

6(S)-hydroxy-tetradeca- 2E4E8Z-trienoate_6(R)- hydroxy-tetradeca-2E4E8Z- trienoate 2234789 2379343 2895761 2477017 2191181 2369708 uridine 216708297 259828288 288167971 208155201 278196941 212149803

2-methylglutaconic acid 30514828 40458762 32177824 27826052 43258108 45539366

D-glucose 113200292 107967483 115097067 117076396 107909102 100184762

L-threonine 254720421 253775067 279557706 300274786 320745764 227841565

Urocanate 5608738 15505846 16534867 8069969 13920065 7271746 stearoylcarnitine 952307.7 1939833.6 2064251.3 665044.1 2226706.1 827282.7

Dodecanedioic acid 7832318 8354013 8561354 8336846 8739280 8213628 lipoamide 1686564 1887314 1799607 2159545 2602482 1355170 homoserine 254537666 253554556 279299375 299828578 319602270 226976871 ecgonine 8189039 13555714 13081271 9903121 6692772 13191993

8(R)-hydroxy-hexadeca- 2E4E6E10Z-tetraenoate_8(S)- hydroxy-hexadeca- 2E4E6E10Z-tetraenoate 284530.1 357011.8 473604.5 351385.7 275652.2 367505.7

Diphosphate 718798328 733185845 638669431 531826018 729033706 697706536 benzoate 8236354 8832619 6878547 7687359 7714143 7410216

3-hydroxyanthranilate 3331949 3595209 3671111 3287680 3514030 3543895 cholesterol sulfate 11262038 7524368 9216492 9131730 9907705 5722109 uracil 58134138 70944962 68407483 70935140 61399739 52315683 glycine 222862607 217246988 225554211 281727202 278353988 173852070 pentadecanoate 60067497 54538585 84887958 67434911 89154983 65618507 gama-L-glutamyl-L-alanine_5- L-Glutamyl-L-alanine-1 3632348 4088286 4100506 2961585 4806466 2840008 palmitoleate 16300373 22603299 32903400 17532712 74421807 16442268 pyruvate 147080607 175888327 163569587 168353398 159728895 176188623

NADPH(4-) 66579.31 555377.81 2582987.35 2772283.69 2516241.94 47415.12 salsolinol 1-carboxylate 6819325 7935195 6914481 7575176 7162207 5749545

CDPcholine 6558019 5361766 5562125 4137512 7618392 3238862 stearate 210744314 208951854 304033020 202965306 275434633 164157007

224

Riboflavin 2582286 3578363 2315586 2148366 3582097 3923280 agmatinium(2+) 1386732 1582285 1564745 1587370 1598912 1474965 creatinine 18102508 21525737 22680127 30512196 21502536 17365023

3-oxo-6(R)-hydroxy-tetradec- 8-cis-enoate_3-oxo-6(S)- hydroxy-tetradec-8Z-enoate 4966575 5137273 4951638 4729057 5239805 5468625

2-oxoglutarate(2-) 293848219 314755846 229585035 382117269 170590674 424389899 guanosine 15842505 14367455 9807608 17594311 12026653 14420480

Guanine 72625527 87421501 70633727 58649146 113122558 85714251

N-methyl-467-trihydroxy- 1234-tetrahydroisoquinoline 71988781 90953153 97090506 126927581 116380630 54901088

Hypotaurine 56503833 51544316 42219511 46280496 52199015 44329078

N-Formimidoyl-L-glutamate 27593795 24895172 23984893 23831702 28714717 19615187 spermidine dialdehyde-1 19740567 20461551 20189809 18784352 21767038 18149623 trans-4-coumarate_keto- phenylpyruvate_cis-2- coumarate 24193077 23714936 21156192 22593015 24619407 23495906 sn-Glycero-3-phosphocholine 2215365408 2076603250 1419604906 2214784748 1834002852 2059060342 912-Oct-13-diepoxy- octadecanoate_11- HpODE_13(S)-HPODE 5120233 6326932 6038274 4375349 11736574 4852411 sarcosine 1554985592 1803718863 1929530569 1849563317 1890634034 1179336652 leucine-isoleucine 64932947 66844333 63411510 61232336 70650280 57499331 phenylacetate_(4- hydroxyphenyl)acetaldehyde 19976203 20201667 17896685 16828633 18935000 20467016

D-fructose 107776204 141887111 97646087 80635361 136467633 172244490 tetradecenoate (n-C14-1) 4883923 5733622 5482469 4889737 7750924 4845916

(R)-Pantothenate 1121580483 1150414940 1215157785 1384632789 1055730273 815903838 adenosine 3-5- bismonophosphate(PAP)_Dgd p_ADP 334839634 369358450 345179520 387558307 341040172 345255599

Carbamoyl phosphate 2169906 1924068 2656663 2645372 2498515 1977309

5-methoxytryptophol 8973795 8819977 6781212 7171906 7403729 8896805 omega hydroxy tetradecanoate (n-C14-0) 2099206 2544727 2415121 1921667 2638350 2195006 laurate 203398983 222477469 251704577 214653904 343354504 182045178 1011-dihydro-12-epi- leukotriene B4 1500721 1578216 3751377 1764747 2503498 1600902

4-hydroperoxy-2-nonenal 90769265 93048771 77340920 81821199 91243564 95591887 2- methylbutyrylglycine_isovaler ylglycine 8168050 8858918 8726532 8702078 8908149 7642085

Alanine 2121676921 2498177309 2608406853 2626401225 2519763097 1721745051

225

Phosphoenolpyruvate 5767910 10022646 6416276 5099197 6121081 9022125

35-dihydroxy-34-dihydro-14- benzothiazine 6555114 9615125 8941536 6142115 2127401 13166960 omega hydroxy dodecanoate (n-C12-0) 8091262 8133183 8760826 7669158 9312559 8535633

Methionine sulfoxide 13640072 16227113 16682710 18267414 16735099 13320139 palmitate 522697088 529127244 921360000 524482521 818240666 458220176 dTMP_neg 7006522 6670692 9387914 6899867 12437573 5899829 sebacicacid 10203413 12967399 19838472 9099666 18601670 11346411

L-xylonate_L-lyxonate 33063653 30380053 33878708 30993523 33975443 30979809

N-acetyl-seryl-aspartate 6062283 4872818 5238863 5808975 5605269 5128303

2-oxoglutaramate 12957231 16368024 14515224 15100797 15411241 12066068 Atrolactic acid_Phenyllactic acid 12867224 12318228 12824076 11761150 13418855 13306147

(4-hydroxy-3- methoxyphenyl)acetaldehyde 14092322 13647753 14162767 12974292 14848763 14583678 imidazol-4-ylacetate_thymine 5850418 5658088 4435983 6798917 5245373 2922736

NADPH 10000 826433.1 1726963.9 1821846.7 1154433.2 136560.3 dTMP 723108.7 819243.3 1232942.8 823097.6 1449037.6 715255.9 trans- vaccenate_elaidate_oleate 77151829 85481424 173105416 80622322 242563304 61937769

Galactosylglycerol 16260408 23943603 27434841 8971898 59692379 11189368 timnodonic acid C20-5n- 3_1314-epoxy-retinol_4- hydroxyvitamin A1_14- hydroxy-414-retro-retinol 1643876 1553989 3043446 1415370 2578648 1835544

9(10)-EpOME_12(13)-EpOME 3738882 4760939 6654232 3518990 9945903 3295866 myristate 89087273 97931219 131492980 92594851 148292596 92842456 Phosphodimethylethanolami ne 3209856 2507713 4081773 5732424 2353434 2500352 3-oxo-10(R)-hydroxy- octadeca-6E8E12Z- trienoate_3-oxo-10(S)- hydroxy-octadeca-6E8E12Z- trienoate 1546044 1764486 3318024 2326317 2345367 2314196 decanoyl carnitine 866877.8 1160845 1336392.5 1077789.9 1599493.8 852039.1 margarate 15040677 14789608 25315112 15837840 23253409 13205951

5-amino-1-(5-phospho-D- ribosyl)imidazole 15750763 21120907 9011644 10924821 18134691 19327150

(-)-trans-carveol_alpha- pinene oxide_perillyl alcohol 4161858 4146507 4120088 4178519 3835375 4338236 allantoin 18251360 17626468 15179966 12187804 21421423 16209992

11-cis-eicosenoate 2992378 2674145 4953445 2593462 6253366 2381914 5-guanidino-2-oxopentanoic acid 3608712 4032120 6019506 5462057 4677869 3844250

226

Adrenaline 18961577 28846400 15554345 10479249 31678949 18676675 linoelaidic acid (all trans C18- 2)_linoleate_octadecadienoat e (n-C18-2) 62685027 102502369 215127932 74654241 289730802 46656707

Ornithine 44318305 51228817 51581252 50667182 54654597 43156419

9_10-hydroxyoctadec-12(Z)- enoate_12_13- hydroxyoctadec-9(Z)-enoate 3737838 4102313 4395076 3290369 5373444 3779274 L-3-Cyanoalanine_56- dihydrouracil 43445985 41769497 50407048 47605039 52812712 36853636 shikimate 51240906 63953136 61163518 49671437 58079457 70732856 dihomo-gamma-linolenic acid (n-6) 2117812 1593527 3715447 1791430 4274661 1650132 thiamine(1+) 30017132 39911869 33574221 23181721 44056400 37609287

3(S)10(R)-OH-octadeca-6- trans-412-cis-trienoate 2168252 2162872 2642728 2243126 2122514 2645557 Flavin adenine dinucleotide oxidized 1640960 1528937.5 1303542.1 2034702.5 987861.2 1410705.6

2-Deoxyribose 2723295 2723092 3623247 3325780 3505695 2292977 orotate 41095198 53823833 45086357 38665416 61679348 40389042 gama-glutamylglutathione 1832448.3 1592251 1187566.4 424558.2 3846204.8 442493

5-hydroxytryptophol_12- dehydrosalsolinol_NN- dimethylindoliumolate-1 1596752 2191818 1683841 1183352 2352202 1969200

Taurine 4169110330 4232012395 3573224824 3907412061 4005276823 4078571257

L-asparagine 469992025 460461252 579468025 549531476 559719346 396216582

56-dihydrothymine 138950924 146000185 137114769 139444239 137036193 145842908 anthranilate 39557014 48532146 49713165 25092230 85779877 25907806 arginine 3488131023 3626162938 3356554766 3648141670 3810228619 3020070982

4-nitrophenolate 17865054 18965598 18385751 17277041 18272227 19655117 L- Prolinylglycine_Glycylproline 9930400 11924184 12740402 11767023 14071623 8748590

227

Appendix G

Fold-change gene expression of Glut1 transgenic to control in six replicate littermate pairs. Treg induced from Glut1 transgenic and littermate controls were analyzed by qrt- PCR array for immune suppression-related genes. Numbers reflect the fold-change caused by Glut1 expression.

Gene Replicate 1 Replicate 2 Replicate 3 Replicate 4 Replicate 5 Replicate 6 Btla 0.988571429 1.06043956 0.411111111 0.503075031 0.566325266 1.141763636 Cblb 0.680375544 1.085829653 0.234066469 0.772281932 0.909217677 1.338181836 Ccl3 0.616999832 4.268929504 2.112465712 2.805259204 1.851694915 1.488888889 Ccr4 0.470878368 0.766731154 1.488657982 0.55755666 0.238603503 0.424633908 Cd27 0.870190362 1.045938267 0.208193618 0.64940857 0.853046547 1.257252313 Cd28 0.831853846 1.020164616 0.311003544 0.631651557 0.823418855 1.113634919 Cd40 1.33089521 0.96179294 0.683971664 0.465604778 0.435486321 0.917181367 Cd40lg 0.841717124 1.26997954 0.260446423 0.484043518 0.921267454 1.347484014 Cd70 0.981132075 2.456967213 0.951851852 0.765837838 0.525714286 2.066810345 Cdk2 0.851697866 1.194816745 0.950660664 0.67511898 0.718317667 0.889634791 Cdk4 1.068366982 1.160545656 1.079230089 1.163279954 0.569081262 1.044109275 Cma1 0.844619909 7.845126695 0.023847842 6.122820579 1.345940227 1.932238599 Csf1 0.794610651 0.989493161 0.505204921 1.555954518 0.436394558 0.539772727 Csf2 2.301310044 2.131782946 1.04947283 0.627306273 0.443514644 0.553475936 Ctla4 0.711728814 1.047391166 0.377619382 0.737236974 0.65008426 1.078680637 Dgka 0.834740707 1.19151893 0.180241252 0.667204658 0.954421907 1.399853449 Dgkz 1.026975522 1.158935643 0.408669349 0.501110702 0.890509724 1.028307259 Egr2 0.858319039 0.53958298 0.550551987 0.780442804 0.875744869 0.753872633 Egr3 1.686695279 0.733546618 0.511235072 0.845766293 0.323687031 0.640901771 Eomes 1.339330694 1.696850987 0.167352051 1.428154986 1.605200544 1.006383992 Fas 1.012139655 1.096420729 0.476322073 0.643140087 0.911743972 1.039785401 Fasl 0.911040227 0.964363864 0.310761078 0.596360592 0.789403974 1.170246619 Fos 1.260831889 0.702614169 0.355020796 0.380546634 0.925758073 0.789090525 Foxp1 0.87139772 0.945926605 0.432269936 0.49079764 0.936724768 1.252031723 Foxp2 1.422857143 1.06043956 0.555555556 0.503075031 0.948275862 1.338461538 Foxp3 0.868981904 0.932905066 0.630253689 0.670446975 0.656422169 0.909584685 Gata3 0.752830281 0.841360751 0.457233466 0.711625629 0.621444535 0.900795201 Gzmb 0.166708998 5.61328391 0.304560012 10.08529682 1.704340704 1.88856074 Hdac9 0.328515112 1.905141844 0.16 0.503075031 2.568627451 1.791952895

228

Icam1 1.475705756 0.899251812 0.645731515 0.688340012 0.909851478 0.926127446 Icos 0.904004073 0.925177115 0.536258269 0.732653536 0.783875974 0.840473484 Ifng 1.030466641 1.184105846 0.552858684 2.131851284 1.687202178 0.573605819 Il10 0.740978847 0.790561165 0.660214819 0.662117965 0.877825619 0.507785467 Il10ra 1.174783221 0.83670953 0.434673619 0.803407787 0.888660481 0.93257068 Il13 0.109701966 6.677272727 4.578402367 2.981710331 1.181619256 1.391736802 Il15 0.104091456 1.78021978 0.166919575 0.771628313 1.364632238 1.045576408 Il17a 0.151277824 1.595162986 0.206896552 0.659359856 1.435349941 0.271770164 Il1a 0.988571429 2.139784946 0.088095238 2.042354934 0.546948357 2.528599606 Il2 0.635167464 1.06043956 0.522222222 0.503075031 1.617021277 1.891304348 Il2ra 1.231488122 0.612083177 2.273635709 0.835203166 0.911742161 0.641389176 Il2rb 1.123032802 0.733973294 0.614293995 0.768010232 0.979218207 2.022692602 Il31 0.988571429 0.518817204 0.022551929 0.503075031 0.948275862 9.078431373 Il4 0.11517976 1.817391304 2.752767528 1.213521904 0.840466926 1.016060862 Il5 1.88 1.053763441 0.112449799 0.503075031 0.530884808 3.105990783 Il6 2.007858546 0.603902439 0.06147541 4.267036694 0.706762418 1.775641026 Il7r 0.604328016 1.085083359 0.124828995 0.672304995 1.142109568 1.016264369 Ing4 0.906506152 1.263819927 0.334483213 0.516626808 0.941927135 0.89768822 Irf4 1.024131031 0.773139188 0.85322722 0.859863635 1.013749125 0.924192394 Itch 0.882335442 1.163768534 0.408960045 0.581223042 0.979215502 0.950847038 Itga1 0.488991295 1.05258467 0.556338028 1.275678867 0.744985673 1.006688963 Jak1 0.895274115 1.072368853 0.288772031 0.60550048 1.090280323 1.375802096 Jak3 0.91092334 1.237819525 0.50104128 1.320599786 0.746749929 0.910213562 Jun 0.864775414 0.5086682 0.217938646 0.683588575 1.243750032 0.796240135 Lat 1.091537066 1.306590258 0.210255019 0.613514662 0.375709861 1.89781679 Lep 0.988571429 1.06043956 0.411111111 0.503075031 0.948275862 1.891304348 Lgals3 1.246950952 0.50937252 0.833353678 0.542314548 1.135792022 0.708714437 Lta 2.020093771 0.721874672 2.20380123 0.841597741 0.535759484 0.956776394 Mef2a 1.140289456 0.904883011 0.437698337 1.000149228 0.956408844 1.08318135 Nfatc1 0.897136431 0.974545684 0.346518222 0.923514585 0.97582694 0.959446639 Nfatc2 0.996818387 0.899877145 0.441962216 0.639138299 0.928963104 1.106707816 Nfatc3 0.948948242 1.154925189 0.415811993 0.721065798 1.017267677 1.102878828 Nfkb1 0.895273137 0.860232231 0.852044828 0.72910515 0.883131912 0.843392528 Nhlh2 0.988571429 1.06043956 0.411111111 0.503075031 0.948275862 1.891304348 Notch1 0.918529897 0.947238203 0.412365147 0.663067517 1.261975044 0.947553496 Pdcd1 0.885408406 1.132018379 0.651569317 0.090090889 0.673971037 0.582875822 Prf1 1.652258694 0.649708738 0.210011603 0.749692497 1.318406206 1.039774727 Prkcg 2.046181172 0.380102041 0.388949079 0.300984936 0.306122449 1.170180723

229

Ptger2 0.55174801 1.315844701 0.609929078 0.503075031 0.683146067 1.060842964 Ptgs2 0.988571429 1.472527473 0.827423168 0.402955665 0.384615385 0.430693069 Rnf128 0.684133346 2.203560372 0.21028396 5.032367587 0.771422261 1.241682848 Sell 0.9052566 0.955148997 0.20964266 0.586903087 0.822279268 0.846906165 Stat3 0.939783533 0.967141801 0.463615887 0.792344009 0.990824622 1.033311177 Stat6 1.031267294 1.306476133 0.500693481 0.503075031 0.276905132 0.835211474 Tbx21 1.115839243 0.221978022 0.369304556 0.503075031 2.014760148 0.813253012 Tgfb1 1.050009411 1.048842861 0.880258777 1.017621242 0.835492124 0.941004556 Tnfrsf10b 0.569002962 1.34140653 0.590092802 0.053578854 0.666030129 0.973358706 Tnfrsf14 0.976977747 1.125684877 0.222212055 0.753772461 1.099385822 1.024041439 Tnfrsf18 0.67380198 0.865616595 1.323174077 0.416734379 0.573038472 1.062360802 Tnfrsf4 1.040594017 1.036555364 1.08222746 0.557562512 0.304749683 1.10901158 Tnfrsf8 0.922345483 0.670347515 0.795965865 0.963714637 0.777063237 0.55942623 Tnfrsf9 0.875631284 1.024413116 2.30376667 0.903870639 0.642020754 0.000384053 Tnfsf10 1.102210879 0.988834655 0.928522427 0.660757461 0.660983834 0.856352389 Tnfsf14 0.594209354 1.412926391 0.759672131 1.18779806 0.691011236 0.848070413 Tnfsf8 0.865976349 0.807650223 0.376572227 0.816322255 0.72232133 0.797901552

230

References

Aifantis, I., Raetz, E., and Buonamici, S. (2008). Molecular pathogenesis of T-cell leukaemia and lymphoma. Nature reviews Immunology 8, 380-390.

Allan, S. E., Song-Zhao, G. X., Abraham, T., McMurchy, A. N., and Levings, M. K. (2008). Inducible reprogramming of human T cells into Treg cells by a conditionally active form of FOXP3. Eur J Immunol 38, 3282-3289.

Angulo, I., Vadas, O., Garcon, F., Banham-Hall, E., Plagnol, V., Leahy, T. R., Baxendale, H., Coulter, T., Curtis, J., Wu, C., et al. (2013). Phosphoinositide 3-kinase delta gene mutation predisposes to respiratory infection and airway damage. Science 342, 866-871.

Arancibia, S. A., Beltran, C. J., Aguirre, I. M., Silva, P., Peralta, A. L., Malinarich, F., and Hermoso, M. A. (2007). Toll-like receptors are key participants in innate immune responses. Biological research 40, 97-112.

Arvey, A., van der Veeken, J., Samstein, R. M., Feng, Y., Stamatoyannopoulos, J. A., and Rudensky, A. Y. (2014). Inflammation-induced repression of chromatin bound by the transcription factor Foxp3 in regulatory T cells. Nat Immunol 15, 580-587.

Atsumi, T., Chesney, J., Metz, C., Leng, L., Donnelly, S., Makita, Z., Mitchell, R., and Bucala, R. (2002). High expression of inducible 6-phosphofructo-2-kinase/fructose-2,6- bisphosphatase (iPFK-2; PFKFB3) in human cancers. Cancer research 62, 5881-5887.

Basu, S., Hubbard, B., and Shevach, E. M. (2015). Foxp3-mediated inhibition of Akt inhibits Glut1 (glucose transporter 1) expression in human T regulatory cells. Journal of leukocyte biology 97, 279-283.

Beier, U. H., Angelin, A., Akimova, T., Wang, L., Liu, Y., Xiao, H., Koike, M. A., Hancock, S. A., Bhatti, T. R., Han, R., et al. (2015). Essential role of mitochondrial energy metabolism in Foxp3(+) T-regulatory cell function and allograft survival. FASEB journal : official publication of the Federation of American Societies for Experimental Biology 29, 2315-2326.

Ben-Sahra, I., Howell, J. J., Asara, J. M., and Manning, B. D. (2013). Stimulation of de novo pyrimidine synthesis by growth signaling through mTOR and S6K1. Science 339, 1323-1328.

231

Bennett, C. L., Christie, J., Ramsdell, F., Brunkow, M. E., Ferguson, P. J., Whitesell, L., Kelly, T. E., Saulsbury, F. T., Chance, P. F., and Ochs, H. D. (2001). The immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome (IPEX) is caused by mutations of FOXP3. Nature genetics 27, 20-21.

Bettelli, E., Carrier, Y., Gao, W., Korn, T., Strom, T. B., Oukka, M., Weiner, H. L., and Kuchroo, V. K. (2006). Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells. Nature 441, 235-238.

Bhojwani, D., and Pui, C. H. (2013). Relapsed childhood acute lymphoblastic leukaemia. The Lancet Oncology 14, e205-217.

Blagih, J., Coulombe, F., Vincent, E. E., Dupuy, F., Galicia-Vazquez, G., Yurchenko, E., Raissi, T. C., van der Windt, G. J., Viollet, B., Pearce, E. L., et al. (2015). The energy sensor AMPK regulates T cell metabolic adaptation and effector responses in vivo. Immunity 42, 41-54.

Bray, S. J. (2006). Notch signalling: a simple pathway becomes complex. Nature reviews Molecular cell biology 7, 678-689.

Brunkow, M. E., Jeffery, E. W., Hjerrild, K. A., Paeper, B., Clark, L. B., Yasayko, S. A., Wilkinson, J. E., Galas, D., Ziegler, S. F., and Ramsdell, F. (2001). Disruption of a new forkhead/winged-helix protein, scurfin, results in the fatal lymphoproliferative disorder of the scurfy mouse. Nature genetics 27, 68-73.

Buck, M. D., O'Sullivan, D., and Pearce, E. L. (2015). T cell metabolism drives immunity. The Journal of experimental medicine 212, 1345-1360.

Buller, C. L., Loberg, R. D., Fan, M. H., Zhu, Q., Park, J. L., Vesely, E., Inoki, K., Guan, K. L., and Brosius, F. C., 3rd (2008). A GSK-3/TSC2/mTOR pathway regulates glucose uptake and GLUT1 glucose transporter expression. American journal of physiology Cell physiology 295, C836-843.

Burnet, F. M. (1970). The concept of immunological surveillance. Progress in experimental tumor research 13, 1-27.

Cantor, J. R., and Sabatini, D. M. (2012). Cancer cell metabolism: one hallmark, many faces. Cancer discovery 2, 881-898.

232

Caro-Maldonado, A., Wang, R., Nichols, A. G., Kuraoka, M., Milasta, S., Sun, L. D., Gavin, A. L., Abel, E. D., Kelsoe, G., Green, D. R., and Rathmell, J. C. (2014). Metabolic reprogramming is required for antibody production that is suppressed in anergic but exaggerated in chronically BAFF-exposed B cells. J Immunol 192, 3626-3636.

Carvalho, K. C., Cunha, I. W., Rocha, R. M., Ayala, F. R., Cajaiba, M. M., Begnami, M. D., Vilela, R. S., Paiva, G. R., Andrade, R. G., and Soares, F. A. (2011). GLUT1 expression in malignant tumors and its use as an immunodiagnostic marker. Clinics 66, 965-972.

Chakrabarti, R., Jung, C. Y., Lee, T. P., Liu, H., and Mookerjee, B. K. (1994). Changes in glucose transport and transporter isoforms during the activation of human peripheral blood lymphocytes by phytohemagglutinin. Journal of immunology 152, 2660-2668.

Chang, C. H., Curtis, J. D., Maggi, L. B., Jr., Faubert, B., Villarino, A. V., O'Sullivan, D., Huang, S. C., van der Windt, G. J., Blagih, J., Qiu, J., et al. (2013). Posttranscriptional control of T cell effector function by aerobic glycolysis. Cell 153, 1239-1251.

Chen, C., Rowell, E. A., Thomas, R. M., Hancock, W. W., and Wells, A. D. (2006). Transcriptional regulation by Foxp3 is associated with direct promoter occupancy and modulation of histone acetylation. J Biol Chem 281, 36828-36834.

Chen, Z., Shojaee, S., Buchner, M., Geng, H., Lee, J. W., Klemm, L., Titz, B., Graeber, T. G., Park, E., Tan, Y. X., et al. (2015). Signalling thresholds and negative B-cell selection in acute lymphoblastic leukaemia. Nature 521, 357-361.

Chi, H. (2012). Regulation and function of mTOR signalling in T cell fate decisions. Nature reviews Immunology 12, 325-338.

Chiang, M. Y., Xu, L., Shestova, O., Histen, G., L'Heureux, S., Romany, C., Childs, M. E., Gimotty, P. A., Aster, J. C., and Pear, W. S. (2008). Leukemia-associated NOTCH1 alleles are weak tumor initiators but accelerate K-ras-initiated leukemia. The Journal of clinical investigation 118, 3181-3194.

Ciofani, M., and Zuniga-Pflucker, J. C. (2005). Notch promotes survival of pre-T cells at the beta-selection checkpoint by regulating cellular metabolism. Nature immunology 6, 881-888.

Cribbs, A. P., Kennedy, A., Penn, H., Amjadi, P., Green, P., Read, J. E., Brennan, F., Gregory, B., and Williams, R. O. (2015). Methotrexate Restores Regulatory T Cell

233

Function Through Demethylation of the FoxP3 Upstream Enhancer in Patients With Rheumatoid Arthritis. Arthritis & rheumatology 67, 1182-1192.

Crist, W. M., Shuster, J. J., Falletta, J., Pullen, D. J., Berard, C. W., Vietti, T. J., Alvarado, C. S., Roper, M. A., Prasthofer, E., and Grossi, C. E. (1988). Clinical features and outcome in childhood T-cell leukemia-lymphoma according to stage of thymocyte differentiation: a Pediatric Oncology Group Study. Blood 72, 1891-1897.

Curiel, T. J. (2007). Tregs and rethinking cancer immunotherapy. The Journal of clinical investigation 117, 1167-1174.

Dang, C. V., Kim, J. W., Gao, P., and Yustein, J. (2008). The interplay between MYC and HIF in cancer. Nature reviews Cancer 8, 51-56.

Davidson, S. M., Papagiannakopoulos, T., Olenchock, B. A., Heyman, J. E., Keibler, M. A., Luengo, A., Bauer, M. R., Jha, A. K., O'Brien, J. P., Pierce, K. A., et al. (2016). Environment Impacts the Metabolic Dependencies of Ras-Driven Non-Small Cell Lung Cancer. Cell metabolism 23, 517-528.

Deau, M. C., Heurtier, L., Frange, P., Suarez, F., Bole-Feysot, C., Nitschke, P., Cavazzana, M., Picard, C., Durandy, A., Fischer, A., and Kracker, S. (2014). A human immunodeficiency caused by mutations in the PIK3R1 gene. The Journal of clinical investigation 124, 3923-3928.

DeFrances, M. C., Debelius, D. R., Cheng, J., and Kane, L. P. (2012). Inhibition of T-cell activation by PIK3IP1. European journal of immunology 42, 2754-2759.

Delgoffe, G. M., Kole, T. P., Zheng, Y., Zarek, P. E., Matthews, K. L., Xiao, B., Worley, P. F., Kozma, S. C., and Powell, J. D. (2009). The mTOR kinase differentially regulates effector and regulatory T cell lineage commitment. Immunity 30, 832-844.

Delmore, J. E., Issa, G. C., Lemieux, M. E., Rahl, P. B., Shi, J., Jacobs, H. M., Kastritis, E., Gilpatrick, T., Paranal, R. M., Qi, J., et al. (2011). BET bromodomain inhibition as a therapeutic strategy to target c-Myc. Cell 146, 904-917.

Demarest, R. M., Ratti, F., and Capobianco, A. J. (2008). It's T-ALL about Notch. Oncogene 27, 5082-5091.

234

Faubert, B., Boily, G., Izreig, S., Griss, T., Samborska, B., Dong, Z., Dupuy, F., Chambers, C., Fuerth, B. J., Viollet, B., et al. (2013). AMPK is a negative regulator of the Warburg effect and suppresses tumor growth in vivo. Cell metabolism 17, 113-124.

Figueroa, M. E., Abdel-Wahab, O., Lu, C., Ward, P. S., Patel, J., Shih, A., Li, Y., Bhagwat, N., Vasanthakumar, A., Fernandez, H. F., et al. (2010). Leukemic IDH1 and IDH2 mutations result in a hypermethylation phenotype, disrupt TET2 function, and impair hematopoietic differentiation. Cancer cell 18, 553-567.

Finlay, D. K. (2012). Regulation of glucose metabolism in T cells: new insight into the role of Phosphoinositide 3-kinases. Frontiers in immunology 3, 247.

Finlay, D. K., Rosenzweig, E., Sinclair, L. V., Feijoo-Carnero, C., Hukelmann, J. L., Rolf, J., Panteleyev, A. A., Okkenhaug, K., and Cantrell, D. A. (2012). PDK1 regulation of mTOR and hypoxia-inducible factor 1 integrate metabolism and migration of CD8+ T cells. The Journal of experimental medicine 209, 2441-2453.

Fontenot, J. D., Gavin, M. A., and Rudensky, A. Y. (2003). Foxp3 programs the development and function of CD4+CD25+ regulatory T cells. Nature immunology 4, 330- 336.

Fukuzumi, M., Shinomiya, H., Shimizu, Y., Ohishi, K., and Utsumi, S. (1996). Endotoxin- induced enhancement of glucose influx into murine peritoneal macrophages via GLUT1. Infection and immunity 64, 108-112.

Gajewski, T. F., Schreiber, H., and Fu, Y. X. (2013). Innate and adaptive immune cells in the tumor microenvironment. Nature immunology 14, 1014-1022.

Gergely, P., Jr., Grossman, C., Niland, B., Puskas, F., Neupane, H., Allam, F., Banki, K., Phillips, P. E., and Perl, A. (2002). Mitochondrial hyperpolarization and ATP depletion in patients with systemic lupus erythematosus. Arthritis and rheumatism 46, 175-190.

Germain, R. N. (2002). T-cell development and the CD4-CD8 lineage decision. Nature reviews Immunology 2, 309-322.

Gerriets, V. A., Kishton, R. J., Nichols, A. G., Macintyre, A. N., Inoue, M., Ilkayeva, O., Winter, P. S., Liu, X., Priyadharshini, B., Slawinska, M. E., et al. (2015). Metabolic programming and PDHK1 control CD4+ T cell subsets and inflammation. The Journal of clinical investigation 125, 194-207.

235

Gerriets, V. A., and Rathmell, J. C. (2012). Metabolic pathways in T cell fate and function. Trends in immunology 33, 168-173.

Gershon, T. R., Crowther, A. J., Tikunov, A., Garcia, I., Annis, R., Yuan, H., Miller, C. R., Macdonald, J., Olson, J., and Deshmukh, M. (2013). Hexokinase-2-mediated aerobic glycolysis is integral to cerebellar neurogenesis and pathogenesis of medulloblastoma. Cancer & metabolism 1, 2.

Godfrey, D. I., and Zlotnik, A. (1993). Control points in early T-cell development. Immunology today 14, 547-553.

Goldberg, J. M., Silverman, L. B., Levy, D. E., Dalton, V. K., Gelber, R. D., Lehmann, L., Cohen, H. J., Sallan, S. E., and Asselin, B. L. (2003). Childhood T-cell acute lymphoblastic leukemia: the Dana-Farber Cancer Institute acute lymphoblastic leukemia consortium experience. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 21, 3616-3622.

Grabiner, B. C., Nardi, V., Birsoy, K., Possemato, R., Shen, K., Sinha, S., Jordan, A., Beck, A. H., and Sabatini, D. M. (2014). A diverse array of cancer-associated MTOR mutations are hyperactivating and can predict rapamycin sensitivity. Cancer discovery 4, 554-563.

Gwinn, D. M., Shackelford, D. B., Egan, D. F., Mihaylova, M. M., Mery, A., Vasquez, D. S., Turk, B. E., and Shaw, R. J. (2008). AMPK phosphorylation of raptor mediates a metabolic checkpoint. Molecular cell 30, 214-226.

Hadis, U., Wahl, B., Schulz, O., Hardtke-Wolenski, M., Schippers, A., Wagner, N., Muller, W., Sparwasser, T., Forster, R., and Pabst, O. (2011). Intestinal tolerance requires gut homing and expansion of FoxP3+ regulatory T cells in the lamina propria. Immunity 34, 237-246.

Hale, L. P., Gottfried, M. R., and Swidsinski, A. (2005). Piroxicam treatment of IL-10- deficient mice enhances colonic epithelial apoptosis and mucosal exposure to intestinal bacteria. Inflammatory bowel diseases 11, 1060-1069.

Han, J., Zhang, L., Guo, H., Wysham, W. Z., Roque, D. R., Willson, A. K., Sheng, X., Zhou, C., and Bae-Jump, V. L. (2015). Glucose promotes cell proliferation, glucose uptake and invasion in endometrial cancer cells via AMPK/mTOR/S6 and MAPK signaling. Gynecologic oncology 138, 668-675.

236

Hanahan, D., and Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. Cell 144, 646-674.

Hardie, D. G., Ross, F. A., and Hawley, S. A. (2012). AMPK: a nutrient and energy sensor that maintains energy homeostasis. Nature reviews Molecular cell biology 13, 251-262.

Hawley, S. A., Davison, M., Woods, A., Davies, S. P., Beri, R. K., Carling, D., and Hardie, D. G. (1996). Characterization of the AMP-activated protein kinase kinase from rat liver and identification of threonine 172 as the major site at which it phosphorylates AMP- activated protein kinase. The Journal of biological chemistry 271, 27879-27887.

Heng, T. S., Painter, M. W., and Immunological Genome Project, C. (2008). The Immunological Genome Project: networks of gene expression in immune cells. Nat Immunol 9, 1091-1094.

Hennipman, A., Smits, J., van Oirschot, B., van Houwelingen, J. C., Rijksen, G., Neyt, J. P., Van Unnik, J. A., and Staal, G. E. (1987). Glycolytic enzymes in breast cancer, benign breast disease and normal breast tissue. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine 8, 251-263.

Herranz, D., Ambesi-Impiombato, A., Sudderth, J., Sanchez-Martin, M., Belver, L., Tosello, V., Xu, L., Wendorff, A. A., Castillo, M., Haydu, J. E., et al. (2015). Metabolic reprogramming induces resistance to anti-NOTCH1 therapies in T cell acute lymphoblastic leukemia. Nature medicine 21, 1182-1189.

Hirsch, H. A., Iliopoulos, D., Tsichlis, P. N., and Struhl, K. (2009). Metformin selectively targets cancer stem cells, and acts together with chemotherapy to block tumor growth and prolong remission. Cancer research 69, 7507-7511.

Hobohm, U., Stanford, J. L., and Grange, J. M. (2008). Pathogen-associated molecular pattern in cancer immunotherapy. Critical reviews in immunology 28, 95-107.

Hollander, M. C., Blumenthal, G. M., and Dennis, P. A. (2011). PTEN loss in the continuum of common cancers, rare syndromes and mouse models. Nature reviews Cancer 11, 289-301.

Homminga, I., Pieters, R., Langerak, A. W., de Rooi, J. J., Stubbs, A., Verstegen, M., Vuerhard, M., Buijs-Gladdines, J., Kooi, C., Klous, P., et al. (2011). Integrated transcript and genome analyses reveal NKX2-1 and MEF2C as potential oncogenes in T cell acute lymphoblastic leukemia. Cancer cell 19, 484-497.

237

Hori, S., Nomura, T., and Sakaguchi, S. (2003). Control of regulatory T cell development by the transcription factor Foxp3. Science 299, 1057-1061.

Hosios, A. M., Hecht, V. C., Danai, L. V., Johnson, M. O., Rathmell, J. C., Steinhauser, M. L., Manalis, S. R., and Vander Heiden, M. G. (2016). Amino Acids Rather than Glucose Account for the Majority of Cell Mass in Proliferating Mammalian Cells. Developmental cell 36, 540-549.

Hozumi, K., Mailhos, C., Negishi, N., Hirano, K., Yahata, T., Ando, K., Zuklys, S., Hollander, G. A., Shima, D. T., and Habu, S. (2008). Delta-like 4 is indispensable in thymic environment specific for T cell development. The Journal of experimental medicine 205, 2507-2513.

Hsieh, C. S., Liang, Y., Tyznik, A. J., Self, S. G., Liggitt, D., and Rudensky, A. Y. (2004). Recognition of the peripheral self by naturally arising CD25+ CD4+ T cell receptors. Immunity 21, 267-277.

Huynh, A., DuPage, M., Priyadharshini, B., Sage, P. T., Quiros, J., Borges, C. M., Townamchai, N., Gerriets, V. A., Rathmell, J. C., Sharpe, A. H., et al. (2015). Control of PI(3) kinase in Treg cells maintains homeostasis and lineage stability. Nature immunology 16, 188-196.

Inoki, K., Li, Y., Zhu, T., Wu, J., and Guan, K. L. (2002). TSC2 is phosphorylated and inhibited by Akt and suppresses mTOR signalling. Nature cell biology 4, 648-657. Inoki, K., Zhu, T., and Guan, K. L. (2003). TSC2 mediates cellular energy response to control cell growth and survival. Cell 115, 577-590.

Itoh, M., Takahashi, T., Sakaguchi, N., Kuniyasu, Y., Shimizu, J., Otsuka, F., and Sakaguchi, S. (1999). Thymus and autoimmunity: production of CD25+CD4+ naturally anergic and suppressive T cells as a key function of the thymus in maintaining immunologic self-tolerance. Journal of immunology 162, 5317-5326.

Ivanov, II, McKenzie, B. S., Zhou, L., Tadokoro, C. E., Lepelley, A., Lafaille, J. J., Cua, D. J., and Littman, D. R. (2006). The orphan nuclear receptor RORgammat directs the differentiation program of proinflammatory IL-17+ T helper cells. Cell 126, 1121-1133.

Jacobs, S. R., Herman, C. E., Maciver, N. J., Wofford, J. A., Wieman, H. L., Hammen, J. J., and Rathmell, J. C. (2008). Glucose Uptake Is Limiting in T Cell Activation and Requires CD28-Mediated Akt-Dependent and Independent Pathways. J Immunol 180, 4476-4486.

238

Jain, N., Lamb, A. E., O'Brien, S., Ravandi, F., Konopleva, M., Jabbour, E., Zuo, Z., Jorgensen, J., Lin, P., Pierce, S., et al. (2016). Early T-cell precursor acute lymphoblastic leukemia/lymphoma (ETP-ALL/LBL) in adolescents and adults: a high-risk subtype. Blood.

Jelley-Gibbs, D. M., Lepak, N. M., Yen, M., and Swain, S. L. (2000). Two distinct stages in the transition from naive CD4 T cells to effectors, early antigen-dependent and late cytokine-driven expansion and differentiation. Journal of immunology 165, 5017-5026.

Jeon, S. M., Chandel, N. S., and Hay, N. (2012). AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress. Nature 485, 661-665.

Joshi, N. S., Akama-Garren, E. H., Lu, Y., Lee, D. Y., Chang, G. P., Li, A., DuPage, M., Tammela, T., Kerper, N. R., Farago, A. F., et al. (2015). Regulatory T Cells in Tumor- Associated Tertiary Lymphoid Structures Suppress Anti-tumor T Cell Responses. Immunity 43, 579-590.

June, C. H., Ledbetter, J. A., Gillespie, M. M., Lindsten, T., and Thompson, C. B. (1987). T-cell proliferation involving the CD28 pathway is associated with cyclosporine- resistant interleukin 2 gene expression. Molecular and cellular biology 7, 4472-4481.

Kaira, K., Sunose, Y., Ohshima, Y., Ishioka, N. S., Arakawa, K., Ogawa, T., Sunaga, N., Shimizu, K., Tominaga, H., Oriuchi, N., et al. (2013). Clinical significance of L-type amino acid transporter 1 expression as a prognostic marker and potential of new targeting therapy in biliary tract cancer. BMC cancer 13, 482.

Killebrew, J. R., Perdue, N., Kwan, A., Thornton, A. M., Shevach, E. M., and Campbell, D. J. (2011). A self-reactive TCR drives the development of Foxp3+ regulatory T cells that prevent autoimmune disease. Journal of immunology 187, 861-869.

Kim, J. W., and Dang, C. V. (2006). Cancer's molecular sweet tooth and the Warburg effect. Cancer research 66, 8927-8930.

Kishton, R. J., Barnes, C. E., Nichols, A. G., Cohen, S., Gerriets, V. A., Siska, P. J., Macintyre, A. N., Goraksha-Hicks, P., de Cubas, A. A., Liu, T., et al. (2016). AMPK Is Essential to Balance Glycolysis and Mitochondrial Metabolism to Control T-ALL Cell Stress and Survival. Cell metabolism 23, 649-662.

Klysz, D., Tai, X., Robert, P. A., Craveiro, M., Cretenet, G., Oburoglu, L., Mongellaz, C., Floess, S., Fritz, V., Matias, M. I., et al. (2015). Glutamine-dependent alpha-ketoglutarate

239

production regulates the balance between T helper 1 cell and regulatory T cell generation. Science signaling 8, ra97.

Koch, U., and Radtke, F. (2011). Mechanisms of T cell development and transformation. Annual review of cell and developmental biology 27, 539-562.

Koppenol, W. H., Bounds, P. L., and Dang, C. V. (2011). Otto Warburg's contributions to current concepts of cancer metabolism. Nature reviews Cancer 11, 325-337.

Kostner, A. H., Johansen, R. F., Schmidt, H., and Molle, I. (2013). Regression in cancer following fever and acute infection. Acta oncologica 52, 455-457.

Kovall, R. A. (2007). Structures of CSL, Notch and Mastermind proteins: piecing together an active transcription complex. Current opinion in structural biology 17, 117-127.

Krycer, J. R., Sharpe, L. J., Luu, W., and Brown, A. J. (2010). The Akt-SREBP nexus: cell signaling meets lipid metabolism. Trends in endocrinology and metabolism: TEM 21, 268-276.

Kurebayashi, Y., Nagai, S., Ikejiri, A., Ohtani, M., Ichiyama, K., Baba, Y., Yamada, T., Egami, S., Hoshii, T., Hirao, A., et al. (2012). PI3K-Akt-mTORC1-S6K1/2 axis controls Th17 differentiation by regulating Gfi1 expression and nuclear translocation of RORgamma. Cell reports 1, 360-373.

Kurth-Kraczek, E. J., Hirshman, M. F., Goodyear, L. J., and Winder, W. W. (1999). 5' AMP-activated protein kinase activation causes GLUT4 translocation in skeletal muscle. Diabetes 48, 1667-1671.

Lantier, L., Fentz, J., Mounier, R., Leclerc, J., Treebak, J. T., Pehmoller, C., Sanz, N., Sakakibara, I., Saint-Amand, E., Rimbaud, S., et al. (2014). AMPK controls exercise endurance, mitochondrial oxidative capacity, and skeletal muscle integrity. FASEB journal : official publication of the Federation of American Societies for Experimental Biology 28, 3211-3224.

Laplante, M., and Sabatini, D. M. (2012). mTOR signaling in growth control and disease. Cell 149, 274-293.

Lee, S. Y., Lee, S. H., Yang, E. J., Kim, E. K., Kim, J. K., Shin, D. Y., and Cho, M. L. (2015). Metformin Ameliorates Inflammatory Bowel Disease by Suppression of the STAT3

240

Signaling Pathway and Regulation of the between Th17/Treg Balance. PloS one 10, e0135858.

Lenschow, D. J., Walunas, T. L., and Bluestone, J. A. (1996). CD28/B7 system of T cell costimulation. Annual review of immunology 14, 233-258.

Li, N., Ragheb, K., Lawler, G., Sturgis, J., Rajwa, B., Melendez, J. A., and Robinson, J. P. (2003). Mitochondrial complex I inhibitor rotenone induces apoptosis through enhancing mitochondrial reactive oxygen species production. The Journal of biological chemistry 278, 8516-8525.

Liu, L., Ulbrich, J., Muller, J., Wustefeld, T., Aeberhard, L., Kress, T. R., Muthalagu, N., Rycak, L., Rudalska, R., Moll, R., et al. (2012). Deregulated MYC expression induces dependence upon AMPK-related kinase 5. Nature 483, 608-612.

Liu, T., Kishton, R. J., Macintyre, A. N., Gerriets, V. A., Xiang, H., Liu, X., Abel, E. D., Rizzieri, D., Locasale, J. W., and Rathmell, J. C. (2014a). Glucose transporter 1-mediated glucose uptake is limiting for B-cell acute lymphoblastic leukemia anabolic metabolism and resistance to apoptosis. Cell death & disease 5, e1470.

Liu, X., Ser, Z., and Locasale, J. W. (2014b). Development and quantitative evaluation of a high-resolution metabolomics technology. Analytical chemistry 86, 2175-2184.

Losman, J. A., Looper, R. E., Koivunen, P., Lee, S., Schneider, R. K., McMahon, C., Cowley, G. S., Root, D. E., Ebert, B. L., and Kaelin, W. G., Jr. (2013). (R)-2- hydroxyglutarate is sufficient to promote leukemogenesis and its effects are reversible. Science 339, 1621-1625.

Lucas, C. L., Kuehn, H. S., Zhao, F., Niemela, J. E., Deenick, E. K., Palendira, U., Avery, D. T., Moens, L., Cannons, J. L., Biancalana, M., et al. (2014). Dominant-activating germline mutations in the gene encoding the PI(3)K catalytic subunit p110delta result in T cell senescence and human immunodeficiency. Nature immunology 15, 88-97.

Lyssiotis, C. A., Son, J., Cantley, L. C., and Kimmelman, A. C. (2013). Pancreatic cancers rely on a novel glutamine metabolism pathway to maintain redox balance. Cell cycle 12, 1987-1988.

Macintyre, A. N., Gerriets, V. A., Nichols, A. G., Michalek, R. D., Rudolph, M. C., Deoliveira, D., Anderson, S. M., Abel, E. D., Chen, B. J., Hale, L. P., and Rathmell, J. C.

241

(2014). The glucose transporter Glut1 is selectively essential for CD4 T cell activation and effector function. Cell metabolism 20, 61-72.

Macintyre, A. N., and Rathmell, J. C. (2013). Activated lymphocytes as a metabolic model for carcinogenesis. Cancer & metabolism 1, 5.

MacIver, N. J., Blagih, J., Saucillo, D. C., Tonelli, L., Griss, T., Rathmell, J. C., and Jones, R. G. (2011). The liver kinase B1 is a central regulator of T cell development, activation, and metabolism. Journal of immunology 187, 4187-4198.

MacIver, N. J., Michalek, R. D., and Rathmell, J. C. (2013). Metabolic regulation of T lymphocytes. Annual review of immunology 31, 259-283.

Malecki, M. J., Sanchez-Irizarry, C., Mitchell, J. L., Histen, G., Xu, M. L., Aster, J. C., and Blacklow, S. C. (2006). Leukemia-associated mutations within the NOTCH1 heterodimerization domain fall into at least two distinct mechanistic classes. Molecular and cellular biology 26, 4642-4651.

Manning, B. D., and Cantley, L. C. (2007). AKT/PKB signaling: navigating downstream. Cell 129, 1261-1274.

Maschek, G., Savaraj, N., Priebe, W., Braunschweiger, P., Hamilton, K., Tidmarsh, G. F., De Young, L. R., and Lampidis, T. J. (2004). 2-deoxy-D-glucose increases the efficacy of adriamycin and paclitaxel in human osteosarcoma and non-small cell lung cancers in vivo. Cancer research 64, 31-34.

Mathupala, S. P., Ko, Y. H., and Pedersen, P. L. (2006). Hexokinase II: cancer's double- edged sword acting as both facilitator and gatekeeper of malignancy when bound to mitochondria. Oncogene 25, 4777-4786.

Matsuki, F., Saegusa, J., Nishimura, K., Miura, Y., Kurosaka, M., Kumagai, S., and Morinobu, A. (2014). CD45RA-Foxp3(low) non-regulatory T cells in the CCR7-CD45RA- CD27+CD28+ effector memory subset are increased in synovial fluid from patients with rheumatoid arthritis. Cellular immunology 290, 96-101.

Mavrakis, K. J., Wolfe, A. L., Oricchio, E., Palomero, T., de Keersmaecker, K., McJunkin, K., Zuber, J., James, T., Khan, A. A., Leslie, C. S., et al. (2010). Genome-wide RNA- mediated interference screen identifies miR-19 targets in Notch-induced T-cell acute lymphoblastic leukaemia. Nature cell biology 12, 372-379.

242

Michalek, R. D., Gerriets, V. A., Jacobs, S. R., Macintyre, A. N., MacIver, N. J., Mason, E. F., Sullivan, S. A., Nichols, A. G., and Rathmell, J. C. (2011). Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets. Journal of immunology 186, 3299-3303.

Mihaylova, M. M., and Shaw, R. J. (2011). The AMPK signalling pathway coordinates cell growth, autophagy and metabolism. Nature cell biology 13, 1016-1023.

Moiseeva, O., Bourdeau, V., Roux, A., Deschenes-Simard, X., and Ferbeyre, G. (2009). Mitochondrial dysfunction contributes to oncogene-induced senescence. Molecular and cellular biology 29, 4495-4507.

Moon, J. J., Chu, H. H., Pepper, M., McSorley, S. J., Jameson, S. C., Kedl, R. M., and Jenkins, M. K. (2007). Naive CD4(+) T cell frequency varies for different epitopes and predicts repertoire diversity and response magnitude. Immunity 27, 203-213.

Mosmann, T. R., Cherwinski, H., Bond, M. W., Giedlin, M. A., and Coffman, R. L. (1986). Two types of murine helper T cell clone. I. Definition according to profiles of lymphokine activities and secreted proteins. Journal of immunology 136, 2348-2357.

Mueckler, M., Caruso, C., Baldwin, S. A., Panico, M., Blench, I., Morris, H. R., Allard, W. J., Lienhard, G. E., and Lodish, H. F. (1985). Sequence and structure of a human glucose transporter. Science 229, 941-945.

Munoz-Pinedo, C., El Mjiyad, N., and Ricci, J. E. (2012). Cancer metabolism: current perspectives and future directions. Cell death & disease 3, e248.

Nakaya, M., Xiao, Y., Zhou, X., Chang, J. H., Chang, M., Cheng, X., Blonska, M., Lin, X., and Sun, S. C. (2014). Inflammatory T cell responses rely on amino acid transporter ASCT2 facilitation of glutamine uptake and mTORC1 kinase activation. Immunity 40, 692-705.

Nomura, M., Liu, J., Rovira, II, Gonzalez-Hurtado, E., Lee, J., Wolfgang, M. J., and Finkel, T. (2016). Fatty acid oxidation in macrophage polarization. Nature immunology 17, 216-217.

O'Sullivan, D., van der Windt, G. J., Huang, S. C., Curtis, J. D., Chang, C. H., Buck, M. D., Qiu, J., Smith, A. M., Lam, W. Y., DiPlato, L. M., et al. (2014). Memory CD8(+) T cells use cell-intrinsic lipolysis to support the metabolic programming necessary for development. Immunity 41, 75-88.

243

Oestreich, K. J., Read, K. A., Gilbertson, S. E., Hough, K. P., McDonald, P. W., Krishnamoorthy, V., and Weinmann, A. S. (2014). Bcl-6 directly represses the gene program of the glycolysis pathway. Nature immunology 15, 957-964.

Ohl, K., and Tenbrock, K. (2015). Regulatory T cells in systemic lupus erythematosus. European journal of immunology 45, 344-355.

Okamura, T., Fujio, K., Shibuya, M., Sumitomo, S., Shoda, H., Sakaguchi, S., and Yamamoto, K. (2009). CD4+CD25-LAG3+ regulatory T cells controlled by the transcription factor Egr-2. Proc Natl Acad Sci U S A 106, 13974-13979.

Oudot, C., Auclerc, M. F., Levy, V., Porcher, R., Piguet, C., Perel, Y., Gandemer, V., Debre, M., Vermylen, C., Pautard, B., et al. (2008). Prognostic factors for leukemic induction failure in children with acute lymphoblastic leukemia and outcome after salvage therapy: the FRALLE 93 study. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 26, 1496-1503.

Palmer, C. S., Ostrowski, M., Balderson, B., Christian, N., and Crowe, S. M. (2015). Glucose metabolism regulates T cell activation, differentiation, and functions. Frontiers in immunology 6, 1.

Palomero, T., Lim, W. K., Odom, D. T., Sulis, M. L., Real, P. J., Margolin, A., Barnes, K. C., O'Neil, J., Neuberg, D., Weng, A. P., et al. (2006). NOTCH1 directly regulates c-MYC and activates a feed-forward-loop transcriptional network promoting leukemic cell growth. Proceedings of the National Academy of Sciences of the United States of America 103, 18261-18266.

Palomero, T., Sulis, M. L., Cortina, M., Real, P. J., Barnes, K., Ciofani, M., Caparros, E., Buteau, J., Brown, K., Perkins, S. L., et al. (2007). Mutational loss of PTEN induces resistance to NOTCH1 inhibition in T-cell leukemia. Nature medicine 13, 1203-1210.

Park, H. J., Park, J. S., Jeong, Y. H., Son, J., Ban, Y. H., Lee, B. H., Chen, L., Chang, J., Chung, D. H., Choi, I., and Ha, S. J. (2015). PD-1 upregulated on regulatory T cells during chronic virus infection enhances the suppression of CD8+ T cell immune response via the interaction with PD-L1 expressed on CD8+ T cells. J Immunol 194, 5801- 5811.

Parkin, J., and Cohen, B. (2001). An overview of the immune system. Lancet 357, 1777- 1789.

244

Patra, K. C., Wang, Q., Bhaskar, P. T., Miller, L., Wang, Z., Wheaton, W., Chandel, N., Laakso, M., Muller, W. J., Allen, E. L., et al. (2013). Hexokinase 2 is required for tumor initiation and maintenance and its systemic deletion is therapeutic in mouse models of cancer. Cancer cell 24, 213-228.

Patsoukis, N., Bardhan, K., Chatterjee, P., Sari, D., Liu, B., Bell, L. N., Karoly, E. D., Freeman, G. J., Petkova, V., Seth, P., et al. (2015). PD-1 alters T-cell metabolic reprogramming by inhibiting glycolysis and promoting lipolysis and fatty acid oxidation. Nature communications 6, 6692.

Patton, D. T., Garden, O. A., Pearce, W. P., Clough, L. E., Monk, C. R., Leung, E., Rowan, W. C., Sancho, S., Walker, L. S., Vanhaesebroeck, B., and Okkenhaug, K. (2006). Cutting edge: the phosphoinositide 3-kinase p110 delta is critical for the function of CD4+CD25+Foxp3+ regulatory T cells. Journal of immunology 177, 6598-6602.

Pearce, E. L., and Pearce, E. J. (2013). Metabolic pathways in immune cell activation and quiescence. Immunity 38, 633-643.

Peng, G., Guo, Z., Kiniwa, Y., Voo, K. S., Peng, W., Fu, T., Wang, D. Y., Li, Y., Wang, H. Y., and Wang, R. F. (2005). Toll-like receptor 8-mediated reversal of CD4+ regulatory T cell function. Science 309, 1380-1384.

Plas, D. R., Talapatra, S., Edinger, A. L., Rathmell, J. C., and Thompson, C. B. (2001). Akt and Bcl-xL promote growth factor-independent survival through distinct effects on mitochondrial physiology. J Biol Chem 276, 12041-12048.

Pollard, P. J., Wortham, N. C., and Tomlinson, I. P. (2003). The TCA cycle and tumorigenesis: the examples of fumarate hydratase and succinate dehydrogenase. Annals of medicine 35, 632-639.

Pollizzi, K. N., Patel, C. H., Sun, I. H., Oh, M. H., Waickman, A. T., Wen, J., Delgoffe, G. M., and Powell, J. D. (2015). mTORC1 and mTORC2 selectively regulate CD8(+) T cell differentiation. The Journal of clinical investigation 125, 2090-2108.

Pore, N., Jiang, Z., Shu, H. K., Bernhard, E., Kao, G. D., and Maity, A. (2006). Akt1 activation can augment hypoxia-inducible factor-1alpha expression by increasing protein translation through a mammalian target of rapamycin-independent pathway. Molecular cancer research : MCR 4, 471-479.

245

Procaccini, C., De Rosa, V., Galgani, M., Abanni, L., Cali, G., Porcellini, A., Carbone, F., Fontana, S., Horvath, T. L., La Cava, A., and Matarese, G. (2010). An oscillatory switch in mTOR kinase activity sets regulatory T cell responsiveness. Immunity 33, 929-941.

Pui, C. H., Robison, L. L., and Look, A. T. (2008). Acute lymphoblastic leukaemia. Lancet 371, 1030-1043.

Pui, J. C., Allman, D., Xu, L., DeRocco, S., Karnell, F. G., Bakkour, S., Lee, J. Y., Kadesch, T., Hardy, R. R., Aster, J. C., and Pear, W. S. (1999). Notch1 expression in early lymphopoiesis influences B versus T lineage determination. Immunity 11, 299-308.

Rathmell, J. C., Elstrom, R. L., Cinalli, R. M., and Thompson, C. B. (2003). Activated Akt promotes increased resting T cell size, CD28-independent T cell growth, and development of autoimmunity and lymphoma. Eur J Immunol 33, 2223-2232.

Rech, A. J., and Vonderheide, R. H. (2009). Clinical use of anti-CD25 antibody daclizumab to enhance immune responses to tumor antigen vaccination by targeting regulatory T cells. Annals of the New York Academy of Sciences 1174, 99-106.

Reizis, B., and Leder, P. (2002). Direct induction of T lymphocyte-specific gene expression by the mammalian Notch signaling pathway. Genes & development 16, 295- 300.

Robey, E., and Fowlkes, B. J. (1994). Selective events in T cell development. Annual review of immunology 12, 675-705.

Robitaille, A. M., Christen, S., Shimobayashi, M., Cornu, M., Fava, L. L., Moes, S., Prescianotto-Baschong, C., Sauer, U., Jenoe, P., and Hall, M. N. (2013). Quantitative phosphoproteomics reveal mTORC1 activates de novo pyrimidine synthesis. Science 339, 1320-1323.

Rohle, D., Popovici-Muller, J., Palaskas, N., Turcan, S., Grommes, C., Campos, C., Tsoi, J., Clark, O., Oldrini, B., Komisopoulou, E., et al. (2013). An inhibitor of mutant IDH1 delays growth and promotes differentiation of glioma cells. Science 340, 626-630.

Rolf, J., Zarrouk, M., Finlay, D. K., Foretz, M., Viollet, B., and Cantrell, D. A. (2013). AMPKalpha1: a glucose sensor that controls CD8 T-cell memory. European journal of immunology 43, 889-896.

246

Rubtsov, Y. P., Rasmussen, J. P., Chi, E. Y., Fontenot, J., Castelli, L., Ye, X., Treuting, P., Siewe, L., Roers, A., Henderson, W. R., Jr., et al. (2008). Regulatory T cell-derived interleukin-10 limits inflammation at environmental interfaces. Immunity 28, 546-558.

Saito, Y., Chapple, R. H., Lin, A., Kitano, A., and Nakada, D. (2015). AMPK Protects Leukemia-Initiating Cells in Myeloid Leukemias from Metabolic Stress in the Bone Marrow. Cell stem cell 17, 585-596.

Sanchez-Cespedes, M. (2011). The role of LKB1 in lung cancer. Familial cancer 10, 447- 453.

Sanghvi, V. R., Mavrakis, K. J., Van der Meulen, J., Boice, M., Wolfe, A. L., Carty, M., Mohan, P., Rondou, P., Socci, N. D., Benoit, Y., et al. (2014). Characterization of a set of tumor suppressor microRNAs in T cell acute lymphoblastic leukemia. Science signaling 7, ra111.

Sarbassov, D. D., Guertin, D. A., Ali, S. M., and Sabatini, D. M. (2005). Phosphorylation and regulation of Akt/PKB by the rictor-mTOR complex. Science 307, 1098-1101.

Scott, D. A., Richardson, A. D., Filipp, F. V., Knutzen, C. A., Chiang, G. G., Ronai, Z. A., Osterman, A. L., and Smith, J. W. (2011). Comparative metabolic flux profiling of melanoma cell lines: beyond the Warburg effect. The Journal of biological chemistry 286, 42626-42634.

Sebastian, M., Lopez-Ocasio, M., Metidji, A., Rieder, S. A., Shevach, E. M., and Thornton, A. M. (2016). Helios Controls a Limited Subset of Regulatory T Cell Functions. J Immunol 196, 144-155.

Seltzer, M. J., Bennett, B. D., Joshi, A. D., Gao, P., Thomas, A. G., Ferraris, D. V., Tsukamoto, T., Rojas, C. J., Slusher, B. S., Rabinowitz, J. D., et al. (2010). Inhibition of glutaminase preferentially slows growth of glioma cells with mutant IDH1. Cancer research 70, 8981-8987.

Shackelford, D. B., Abt, E., Gerken, L., Vasquez, D. S., Seki, A., Leblanc, M., Wei, L., Fishbein, M. C., Czernin, J., Mischel, P. S., and Shaw, R. J. (2013). LKB1 inactivation dictates therapeutic response of non-small cell lung cancer to the metabolism drug phenformin. Cancer cell 23, 143-158.

247

Shaw, R. J., Bardeesy, N., Manning, B. D., Lopez, L., Kosmatka, M., DePinho, R. A., and Cantley, L. C. (2004a). The LKB1 tumor suppressor negatively regulates mTOR signaling. Cancer cell 6, 91-99.

Shaw, R. J., Kosmatka, M., Bardeesy, N., Hurley, R. L., Witters, L. A., DePinho, R. A., and Cantley, L. C. (2004b). The tumor suppressor LKB1 kinase directly activates AMP- activated kinase and regulates apoptosis in response to energy stress. Proceedings of the National Academy of Sciences of the United States of America 101, 3329-3335.

Shrestha, S., Yang, K., Guy, C., Vogel, P., Neale, G., and Chi, H. (2015). Treg cells require the phosphatase PTEN to restrain TH1 and TFH cell responses. Nature immunology 16, 178-187.

Smigiel, K. S., Srivastava, S., Stolley, J. M., and Campbell, D. J. (2014). Regulatory T-cell homeostasis: steady-state maintenance and modulation during inflammation. Immunol Rev 259, 40-59.

Smith-Garvin, J. E., Koretzky, G. A., and Jordan, M. S. (2009). T cell activation. Annual review of immunology 27, 591-619.

Speeckaert, R., van Geel, N., Vermaelen, K. V., Lambert, J., Van Gele, M., Speeckaert, M. M., and Brochez, L. (2011). Immune reactions in benign and malignant melanocytic lesions: lessons for immunotherapy. Pigment cell & melanoma research 24, 334-344.

Steinman, L. (2007). A brief history of T(H)17, the first major revision in the T(H)1/T(H)2 hypothesis of T cell-mediated tissue damage. Nature medicine 13, 139-145.

Sukumar, M., Liu, J., Ji, Y., Subramanian, M., Crompton, J. G., Yu, Z., Roychoudhuri, R., Palmer, D. C., Muranski, P., Karoly, E. D., et al. (2013). Inhibiting glycolytic metabolism enhances CD8+ T cell memory and antitumor function. The Journal of clinical investigation 123, 4479-4488.

Sukumar, M., Liu, J., Mehta, G. U., Patel, S. J., Roychoudhuri, R., Crompton, J. G., Klebanoff, C. A., Ji, Y., Li, P., Yu, Z., et al. (2016). Mitochondrial Membrane Potential Identifies Cells with Enhanced Stemness for Cellular Therapy. Cell metabolism 23, 63-76.

Sutmuller, R. P., den Brok, M. H., Kramer, M., Bennink, E. J., Toonen, L. W., Kullberg, B. J., Joosten, L. A., Akira, S., Netea, M. G., and Adema, G. J. (2006). Toll-like receptor 2 controls expansion and function of regulatory T cells. J Clin Invest 116, 485-494.

248

Szablewski, L. (2013). Expression of glucose transporters in cancers. Biochimica et biophysica acta 1835, 164-169.

Szabo, S. J., Kim, S. T., Costa, G. L., Zhang, X., Fathman, C. G., and Glimcher, L. H. (2000). A novel transcription factor, T-bet, directs Th1 lineage commitment. Cell 100, 655-669.

Takata, K., Kasahara, T., Kasahara, M., Ezaki, O., and Hirano, H. (1990). Erythrocyte/HepG2-type glucose transporter is concentrated in cells of blood-tissue barriers. Biochemical and biophysical research communications 173, 67-73.

Telang, S., Yalcin, A., Clem, A. L., Bucala, R., Lane, A. N., Eaton, J. W., and Chesney, J. (2006). Ras transformation requires metabolic control by 6-phosphofructo-2-kinase. Oncogene 25, 7225-7234.

Tello, D., Balsa, E., Acosta-Iborra, B., Fuertes-Yebra, E., Elorza, A., Ordonez, A., Corral- Escariz, M., Soro, I., Lopez-Bernardo, E., Perales-Clemente, E., et al. (2011). Induction of the mitochondrial NDUFA4L2 protein by HIF-1alpha decreases oxygen consumption by inhibiting Complex I activity. Cell metabolism 14, 768-779.

Thompson, B. J., Jankovic, V., Gao, J., Buonamici, S., Vest, A., Lee, J. M., Zavadil, J., Nimer, S. D., and Aifantis, I. (2008). Control of hematopoietic stem cell quiescence by the E3 ubiquitin ligase Fbw7. The Journal of experimental medicine 205, 1395-1408.

Thompson, P. K., and Zuniga-Pflucker, J. C. (2011). On becoming a T cell, a convergence of factors kick it up a Notch along the way. Seminars in immunology 23, 350-359.

Thorpe, L. M., Yuzugullu, H., and Zhao, J. J. (2015). PI3K in cancer: divergent roles of isoforms, modes of activation and therapeutic targeting. Nature reviews Cancer 15, 7-24.

Uckun, F. M., Gaynon, P. S., Sensel, M. G., Nachman, J., Trigg, M. E., Steinherz, P. G., Hutchinson, R., Bostrom, B. C., Sather, H. N., and Reaman, G. H. (1997). Clinical features and treatment outcome of childhood T-lineage acute lymphoblastic leukemia according to the apparent maturational stage of T-lineage leukemic blasts: a Children's Cancer Group study. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 15, 2214-2221.

Valsecchi, F., Monge, C., Forkink, M., de Groof, A. J., Benard, G., Rossignol, R., Swarts, H. G., van Emst-de Vries, S. E., Rodenburg, R. J., Calvaruso, M. A., et al. (2012).

249

Metabolic consequences of NDUFS4 gene deletion in immortalized mouse embryonic fibroblasts. Biochimica et biophysica acta 1817, 1925-1936. van der Windt, G. J., Everts, B., Chang, C. H., Curtis, J. D., Freitas, T. C., Amiel, E., Pearce, E. J., and Pearce, E. L. (2012). Mitochondrial respiratory capacity is a critical regulator of CD8+ T cell memory development. Immunity 36, 68-78.

Van Schaftingen, E., Jett, M. F., Hue, L., and Hers, H. G. (1981). Control of liver 6- phosphofructokinase by fructose 2,6-bisphosphate and other effectors. Proceedings of the National Academy of Sciences of the United States of America 78, 3483-3486.

Vander Heiden, M. G. (2011). Targeting cancer metabolism: a therapeutic window opens. Nature reviews Drug discovery 10, 671-684.

Vander Heiden, M. G., Cantley, L. C., and Thompson, C. B. (2009). Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029-1033.

Vanhaesebroeck, B., Stephens, L., and Hawkins, P. (2012). PI3K signalling: the path to discovery and understanding. Nature reviews Molecular cell biology 13, 195-203.

Voo, K. S., Bover, L., Harline, M. L., Weng, J., Sugimoto, N., and Liu, Y. J. (2014). Targeting of TLRs inhibits CD4+ regulatory T cell function and activates lymphocytes in human peripheral blood mononuclear cells. J Immunol 193, 627-634.

Wang, Q., Tiffen, J., Bailey, C. G., Lehman, M. L., Ritchie, W., Fazli, L., Metierre, C., Feng, Y. J., Li, E., Gleave, M., et al. (2013). Targeting amino acid transport in metastatic castration-resistant prostate cancer: effects on cell cycle, cell growth, and tumor development. Journal of the National Cancer Institute 105, 1463-1473.

Wang, R., Dillon, C. P., Shi, L. Z., Milasta, S., Carter, R., Finkelstein, D., McCormick, L. L., Fitzgerald, P., Chi, H., Munger, J., and Green, D. R. (2011). The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity 35, 871-882.

Wang, S., Charbonnier, L. M., Noval Rivas, M., Georgiev, P., Li, N., Gerber, G., Bry, L., and Chatila, T. A. (2015). MyD88 Adaptor-Dependent Microbial Sensing by Regulatory T Cells Promotes Mucosal Tolerance and Enforces Commensalism. Immunity 43, 289- 303.

Warburg, O. (1956). On the origin of cancer cells. Science 123, 309-314.

250

Warburg, O., Wind, F., and Negelein, E. (1927). The Metabolism of Tumors in the Body. The Journal of general physiology 8, 519-530. Wei, J., Long, L., Yang, K., Guy, C., Shrestha, S., Chen, Z., Wu, C., Vogel, P., Neale, G., Green, D. R., and Chi, H. (2016). Autophagy enforces functional integrity of regulatory T cells by coupling environmental cues and metabolic homeostasis. Nature immunology 17, 277-285.

Weng, A. P., Ferrando, A. A., Lee, W., Morris, J. P. t., Silverman, L. B., Sanchez-Irizarry, C., Blacklow, S. C., Look, A. T., and Aster, J. C. (2004). Activating mutations of NOTCH1 in human T cell acute lymphoblastic leukemia. Science 306, 269-271.

White, E. (2013). Exploiting the bad eating habits of Ras-driven cancers. Genes & development 27, 2065-2071.

Wieman, H. L., Wofford, J. A., and Rathmell, J. C. (2007). Cytokine stimulation promotes glucose uptake via phosphatidylinositol-3 kinase/Akt regulation of Glut1 activity and trafficking. Molecular biology of the cell 18, 1437-1446.

Williams, L. M., and Rudensky, A. Y. (2007). Maintenance of the Foxp3-dependent developmental program in mature regulatory T cells requires continued expression of Foxp3. Nat Immunol 8, 277-284.

Wilson, J. E. (2003). Isozymes of mammalian hexokinase: structure, subcellular localization and metabolic function. The Journal of experimental biology 206, 2049-2057.

Wohlfert, E. A., Grainger, J. R., Bouladoux, N., Konkel, J. E., Oldenhove, G., Ribeiro, C. H., Hall, J. A., Yagi, R., Naik, S., Bhairavabhotla, R., et al. (2011). GATA3 controls Foxp3(+) regulatory T cell fate during inflammation in mice. J Clin Invest 121, 4503-4515.

Wolf, A., Agnihotri, S., Micallef, J., Mukherjee, J., Sabha, N., Cairns, R., Hawkins, C., and Guha, A. (2011). Hexokinase 2 is a key mediator of aerobic glycolysis and promotes tumor growth in human glioblastoma multiforme. The Journal of experimental medicine 208, 313-326.

Wong, G. W., Knowles, G. C., Mak, T. W., Ferrando, A. A., and Zuniga-Pflucker, J. C. (2012). HES1 opposes a PTEN-dependent check on survival, differentiation, and proliferation of TCRbeta-selected mouse thymocytes. Blood 120, 1439-1448.

251

Workman, C. J., Collison, L. W., Bettini, M., Pillai, M. R., Rehg, J. E., and Vignali, D. A. (2011). In vivo Treg suppression assays. Methods in molecular biology 707, 119-156. Wu, S. B., and Wei, Y. H. (2012). AMPK-mediated increase of glycolysis as an adaptive response to oxidative stress in human cells: implication of the cell survival in mitochondrial diseases. Biochimica et biophysica acta 1822, 233-247.

Xu, J., Chi, F., Guo, T., Punj, V., Lee, W. N., French, S. W., and Tsukamoto, H. (2015). NOTCH reprograms mitochondrial metabolism for proinflammatory macrophage activation. The Journal of clinical investigation 125, 1579-1590.

Yang, C., Sudderth, J., Dang, T., Bachoo, R. M., McDonald, J. G., and DeBerardinis, R. J. (2009). Glioblastoma cells require glutamate dehydrogenase to survive impairments of glucose metabolism or Akt signaling. Cancer research 69, 7986-7993.

Yuan, L. F., Li, G. D., Ren, X. J., Nian, H., Li, X. R., and Zhang, X. M. (2015). Rapamycin ameliorates experimental autoimmune uveoretinitis by inhibiting Th1/Th2/Th17 cells and upregulating CD4+CD25+ Foxp3 regulatory T cells. International journal of ophthalmology 8, 659-664.

Yuan, T. L., and Cantley, L. C. (2008). PI3K pathway alterations in cancer: variations on a theme. Oncogene 27, 5497-5510.

Zeiser, R., Nguyen, V. H., Hou, J. Z., Beilhack, A., Zambricki, E., Buess, M., Contag, C. H., and Negrin, R. S. (2007). Early CD30 signaling is critical for adoptively transferred CD4+CD25+ regulatory T cells in prevention of acute graft-versus-host disease. Blood 109, 2225-2233.

Zeng, H., Yang, K., Cloer, C., Neale, G., Vogel, P., and Chi, H. (2013). mTORC1 couples immune signals and metabolic programming to establish T(reg)-cell function. Nature 499, 485-490.

Zhang, X. D., Deslandes, E., Villedieu, M., Poulain, L., Duval, M., Gauduchon, P., Schwartz, L., and Icard, P. (2006). Effect of 2-deoxy-D-glucose on various malignant cell lines in vitro. Anticancer research 26, 3561-3566.

Zheng, Y., Collins, S. L., Lutz, M. A., Allen, A. N., Kole, T. P., Zarek, P. E., and Powell, J. D. (2007). A role for mammalian target of rapamycin in regulating T cell activation versus anergy. Journal of immunology 178, 2163-2170.

252

Zhu, J., Blenis, J., and Yuan, J. (2008). Activation of PI3K/Akt and MAPK pathways regulates Myc-mediated transcription by phosphorylating and promoting the degradation of Mad1. Proceedings of the National Academy of Sciences of the United States of America 105, 6584-6589.

Zinselmeyer, B. H., Heydari, S., Sacristan, C., Nayak, D., Cammer, M., Herz, J., Cheng, X., Davis, S. J., Dustin, M. L., and McGavern, D. B. (2013). PD-1 promotes immune exhaustion by inducing antiviral T cell motility paralysis. The Journal of experimental medicine 210, 757-774.

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Biography

Rigel Joseph Kishton

Born to Margaret and Joseph Kishton on February 6, 1985 in Wilmington, NC.

Education

University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, B.A. in Psychology, with Honors, May 2007

University of North Carolina at Wilmington, Wilmington, North Carolina, M.S. in Chemistry, May 2011

Duke University, Durham, North Carolina, Ph.D. in Molecular Cancer Biology, September 2016

Publications

Kishton RJ, Barnes CE, Nichols AG, Cohen S, Gerriets VA, Siska PJ, Macintyre AN, Goraksha-Hicks P, de Cubas AA, Liu T, Warmoes MO, Abel ED, Yeoh AEJ, Gershon TR, Rathmell WK, Richards KL, Locasale JW, Rathmell JC. AMPK is essential to balance glycolysis and mitochondrial metabolism to control T-ALL cell stress and survival. Cell Metab. 2016 Apr 12;23(4): 649-62. PMID: 27076078

McFadden K, Hafez AY, Kishton R, Messinger JE, Nikitin PA, Rathmell JC, Luftig MA. Metabolic stress is a barrier to Epstein-Barr virus mediated B-cell immortalization. Proc Natl Acad Sci U S A 2016 Feb 9;113(6):E782-90. PMID: 26802124

Kishton RJ, Rathmell JC. Novel therapeutic targets of tumor metabolism. Cancer J. 2015 Mar-Apr; 21(2):62-9 (Review). PMID: 25815845

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Gerriets VA, Kishton RJ, Nichols AG, Macintyre AN, Inoue M, Ilkayeva O, Winter PS, Liu X, Priyadharshini B, Slawinska ME, Haeberli L, Huck C, Turka LA, Wood KC, Hale LP, Smith PA, Schneider MA, MacIver NJ, Locasale JW, Newgard CB, Shinohara ML, Rathmell JC. Metabolic programming and PDHK1 control CD4+ T cell subsets and inflammation. J Clin Invest. 2015 Jan;125(1):194-207. doi: 10.1172/JCI76012. Epub 2014 Dec. PMID: 25437876

Liu T, Kishton RJ, Macintyre AN, Gerriets VA, Xiang H, Liu X, Abel ED, Rizzieri D, Locasale JW, Rathmell JC. Glucose transporter 1-mediated glucose uptake is limiting for B-cell acute lymphoblastic leukemia anabolic metabolism and resistance to apoptosis. Cell Death Dis. 2014 Oct 16;5:e1470. doi: 10.1038/cddis.2014.431. PMID: 25321477

Kishton RJ, Miller SE, Perry H, Lynch T, Patel M, Gore VK, Akkaraju GR, Varadarajan S. DNA site-specific N3-adenine methylation targeted to estrogen receptor-positive cells. Bioorg Med Chem. 2011 Sep 1;19(17):5093-102. doi: 10.1016/j.bmc.2011.07.026. Epub 2011 Jul 22. PMID: 21839641

Gerriets VA*, Kishton RJ*, Johnson MO, Siska PJ, Nichols AG, Warmoes MO, MacIver NJ, Locasale JW, Wells AD, Rathmell JC. FoxP3 and Inflammatory Signals Balance Treg Metabolism for Proliferation or Suppressive Function. In revision. *Authors contributed equally

Siska PJ, Kishton RJ, Weinberg JB, Rathmell JC. Suppression of Glut1 and Glucose Metabolism by Decreased Akt/mTORC1 Signaling Drives T Cell Exhaustion in B Cell Leukemia. In revision.

Gerriets VA, Danzake K, Kishton RJ, Eisner W, Nichols AG, Saucillo DC, Shinohara ML, MacIver NJ. Leptin Directly Promotes T Cell Glycolytic Metabolism to Drive Th17 Differentiation in Autoimmunity. In revision.

Zeng H, Cohen S, Guy C, Neale G, Brown SA, Cloer C, Kishton R, Do M, Li MO, Rathmell JC, Chi H. Critical roles of mTORC1 and mTORC2 signaling and anabolic metabolism in follicular helper T cell differentiation. In revision.

O’Neill LA, Kishton RJ, Rathmell JC. A guide to immunometabolism for immunologists. Nature Reviews Immunology. Invited review, submitted.

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Awards and Honors

2014-2017 Predoctoral Ruth L. Kirschstein National Research Service Award (NRSA), National Cancer Institute, National Institutes of Health, “Targeting aerobic glycolysis in T-ALL”. F31CA183529

2016 Keystone Symposia Conference Travel Award, Duke University Conference Award to assist in travel to Keystone Symposia on Immunometabolism in Inflammatory Disease.

2012 Poster Award Winner, Duke University Department of Pharmacology and Cancer Biology Annual Retreat

2010 Graduate Travel Award, UNC Wilmington

2009-2010 Most Outstanding Teaching Assistant, UNC Wilmington Chemistry Department

2008 Undergraduate Research Fellowship Award, UNC Wilmington

2007 Graduated with Distinction, UNC-Chapel Hill

2003-2007 Robert Byrd Scholarship

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