Glutaminolysis: a Hallmark of Cancer Metabolism by Lifeng Yang

ABSTRACT

Glutaminolysis: A Hallmark of Cancer Metabolism by Lifeng Yang

The goals of these projects are to study the critical role of glutamine (Gln) in ovarian cancer growth, metastasis, drug resistance and sources of glutamine in tumor microenvironment.

1): Gln‐addicted cancer cells are dependent on glutamine for viability, and their metabolism is reprogrammed for glutamine utilization through the tricarboxylic acid (TCA) cycle. 1): we have uncovered a missing link between cancer invasiveness and glutamine dependence. Using isotope tracer and bioenergetic analysis, we found that low‐invasive ovarian cancer (OVCA) cells are glutamine independent, whereas high‐invasive OVCA cells are markedly glutamine dependent. Consistent with our findings, OVCA patients’ microarray data suggest that glutaminolysis correlates with poor survival. Notably, the ratio of gene expression associated with glutamine anabolism versus catabolism has emerged as a novel biomarker for patient prognosis. Significantly, we found that glutamine regulates the activation of STAT3, a mediator of signaling pathways which regulates cancer hallmarks in invasive OVCA cells. Our findings suggest that a combined approach of targeting high‐invasive OVCA cells by blocking glutamine's entry into the TCA cycle, along with targeting low‐invasive OVCA

cells by inhibiting glutamine synthesis and STAT3 may lead to potential therapeutic approaches for treating OVCAs.

2): Reactive stromal cells are an integral part of tumor microenvironment (TME) in tumors and interact with cancer cells to regulate their growth and survival.

Although targeting stromal cells could be a viable therapy to regulate the communication between TME and cancer cells, identification of stromal targets which make cancer cells vulnerable has remained challenging and still elusive.

Here, we identify a previously unrecognized mechanism whereby metabolism of reactive stromal cells is reprogrammed through upregulated glutamine anabolic pathway. This dysfunctional stromal metabolism confers atypical metabolic flexibility and adaptive mechanisms in stromal cells allowing them to harness carbon and nitrogen from noncanonical sources to synthesize glutamine in nutrient-deprived conditions existing in TME. We demonstrate that targeting cancer associated fibroblasts (CAFs), a major component of reactive stroma that expresses high glutamine synthetase (GLUL), disrupts metabolic crosstalk between stromal and cancer cells. Our work underscores reliance of cancer cells on CAFs and presents a synthetic lethal approach to target tumor stroma and cancer cells simultaneously for desirable therapeutic outcomes.

Acknowledgments

First and foremost, I would like to express my thankfulness to my thesis advisor Dr. Deepak Nagrath for his scientific guidance for my PhD study. He is always so patient with me and teach me not only how to pursue my career and also my life. Because of his care, work, support, and communications with me, I get what I need for my future career and life. I really appreciate that he always makes projects better and better, and dig our potential to achieve higher and higher. Thanks to the freedom that he gave me, and always back me up!

I would like to thank to my thesis committee: Dr. Zygourakis, Dr.

Bennett, Dr. Marini for their advice during my thesis proposal and support on my

PhD defense.

Thanks to my lab colleagues. Christine and I worked at same project for nitric oxide effect on Warburg effect, and it was a great opportunity that she gave.

Bahar and I worked together in lab for more than 5 years, and she was nice for many suggestions. Thanks to Hongyun’s care and support in lab and her efforts in my first project. Thanks to Joelle, always taking care of lab, and collaborating with me for different projects. Thanks to Abhinav, who really offers great help to teach me about data analysis, modify my paper, and work together for different projects. Thanks to my other previous labmates: Nadege, Thasni, Aaron.

A special acknowledgement goes to my collaborators. Without them, all my works cannot go further and complete. Thanks to Tyler, Mangala, Dahai,

Tsz-Lun (So), Cecil, Imelda, and their advisor Drs. Ram, Sood, Mok, Lu, Liu,

v and Dr. Marini. They are always ready to help me with experiments set up, design and I learn a lot from discussion with them. Thanks to Dr. Chacko and

Daniel’s help for GCMS and LCMS machine and troubleshooting.

I thanks to my hard working undergraduates and high school student.

Stephen helped me with setting up experiments and developed protocols. Julia and Sun Hye were so careful for all experiments she performed and got perfect data. Katherine and Lisa were so nice, diligent and always ready to offer help when I needed. Thanks to the help from Joseph and Ryuichi for my CAFs project and continuous work. Thanks to Pavan and Josh’s help for all the works that they do. Thanks to undergraduates and high school students’ help during summer holidays. Their joining brought a lot of energy and happiness to our lab!

I would like to thank the amazing staff in our department: Barbara, Ania,

Leewanda, Karen and Pam. Thanks to other department professors for their help in my courses and my PhD qualify presentation. Thanks to CHBE GSA’s host of different events.

I would like to thank my friends with their advice, and their time we hanged out and played together. Their care enriched my life.

Thanks to my family for their support and respect on my own decision, and give me freedom to let me pursue what I am interested. Both my father and mother set life example for me to follow. I have witnessed them working from day to night in order to create a little more and a little more for our living expense. It is a great study environment that they build up, and expand using their selfish love vi and “never-give up” efforts, so I can learn what I want without any distraction. I

Love you, Dad (Mr. Yang), and Mom (Mrs. Shen). I thank my grandma, hoping she is happy in heaven, and my grandpa, hoping he can have a happy life.

Thanks to all people I love and love me!

Contents

Acknowledgments ...... iv

Contents ...... vii

List of Figures ...... xi

List of Tables ...... xiv

List of Equations ...... xv

Nomenclature ...... 1

Introduction ...... 5 1.1. Ovarian Cancer ...... 5 1.2. Glutaminolysis: Hallmark of Cancer Metabolism ...... 6 1.2.1. Oncogenic and microenvironment regulation of Gln metabolism ...... 7 1.2.2. Glutaminolysis: supporting rapid cell proliferation beyond carbon provision ...... 10 1.2.3. Gln Induces Cell Signaling Affecting Cell proliferation, Apoptosis and Drug Resistance ...... 18 1.2.4. Gln Sources ...... 19 1.2.5. Assessing Gln Uptake and Metabolism in vivo ...... 24 1.2.6. Drug Application targeting Gln Metabolism ...... 25 Metabolic shifts towards glutamine regulates tumor growth, invasion and bioenergetics in ovarian cancer ...... 26 2.1. Glutamine dependence in ovarian cancer cells is correlated with cancer invasiveness and patient survival ...... 27 2.2. Glutamine differentially increases TCA cycle metabolite abundances, in high-invasive versus low-invasive ovarian cancer cells ...... 35 2.3. High-invasive cells are glutamine dependent for increased respiration, proliferation, and redox balance ...... 45 2.4. Ratio of Glutamine Catabolism over Anabolism Correlates with Invasiveness ...... 53 2.5. Clinical significance of glutamine catabolism and therapeutic effectiveness of GLS1 siRNA in ovarian cancer models ...... 58

viii

2.6. Glutamine regulates cancer cell Invasiveness through STAT3 activity in high-invasive ovarian cancer cells ...... 61 2.7. Glutamine dependent anaplerosis regulates glycolysis in high-invasive cancer cells through STAT3 phosphorylation ...... 69 2.8. Discussion ...... 74 Targeting stromal glutamine synthetase disrupts tumor microenvironment- regulated cancer cell growth ...... 81 3.1. Upregulated Glutamine Anabolic Pathway in CAFs Compared to NOFs .. 83 3.2. CAFs but not NOFs maintain Gln-addicted cancer cell growth under Gln deprivation condition ...... 95 3.3. Higher metabolic flexibility in CAFs compared to NOFs induces adaptive mechanisms for harnessing carbon and nitrogen from atypical sources towards Gln synthesis ...... 101 3.4. Crosstalk between stromal-epithelial cell augments dysregulated metabolism in CAFs and supports nucleotides and TCA cycle metabolites levels in cancer cells ...... 109 3.5. Orthotopic ovarian cancer mouse model highlights stromal GLUL and OVCA GLS1 as potential therapeutic targets ...... 117 3.6. Discussion ...... 120

Future studies ...... 124 4.1. Role of Gln in DNA methylation for cytokine secretion ...... 124 4.2. Role of malic enzyme in drug resistance ...... 127

Materials and methods ...... 129 5.1. Cell lines and cell culture ...... 129 5.2. Material ...... 130 5.3. Proliferation assay ...... 130 5.4. Clonogenic assay ...... 131 5.5. Matrigel invasion assay ...... 131 5.6. Migration wound-healing assay ...... 132 5.7. Metabolic footprinting ...... 132 5.7.1. Glucose consumption ...... 132 5.7.2. Lactate secretion ...... 132 5.7.3. Amino acid uptake and secretion ...... 133 ix

5.8. Protein assay ...... 133 5.9. Isotope labeling analysis using GC-MS ...... 134 5.9.1. Metabolites extraction ...... 134 5.9.2. Derivatization ...... 134 5.9.3. GC/MS measurements...... 134 5.10. RNA purification and amplification for Illumina Microarrays...... 135 5.11. LC MS ...... 135 5.12. GLS and GLUL gene silencing by small interfering RNA ...... 136 5.13. Quantitative real-time RT-PCR ...... 136 5.14. Average gene expression of pathways analysis...... 137 5.15. Transcriptional Factors Analysis ...... 137 5.16. In-vivo models and tissue processing ...... 140 5.17. Immunohistochemistry ...... 141 5.18. Survival network ...... 141 5.19. Survival analysis ...... 142 5.20. Ovarian cell line microarray ...... 142 5.21. XF bioenergetics assay ...... 143 5.22. ATP measurements ...... 143 5.23. Mitochondrial membrane potential assay ...... 144 5.24. Reactive oxygen species assay ...... 144 5.25. NADPH measurements ...... 144 5.26. NADPH/total NADP measurements ...... 145 5.27. GSH/GSSG measurements ...... 145 5.28. Transfection ...... 146 5.29. Western blot ...... 146 5.30. TCGA data ...... 147 5.31. 13C Metabolic Flux Analysis ...... 147 5.32. Metabolic model and data processing ...... 150 5.33. Linear Regression analysis for estimating percentage contribution of extracellular CAF-secreted Gln to intracellular glutamate in OVCA...... 151 5.34. Statistical analysis ...... 153 x

References ...... 154

Appendix A ...... 191

Appendix B ...... 195

List of Figures

Figure 1.1: Oncogenic signaling on Gln metabolism...... 10

Figure 1.2: Roles of Gln in fulfilling cell proliferation and energetic requirements...... 16

Figure 1.3: Multiple Gln sources to maintain intracellular Gln level in cancer cells...... 23

Figure 2.1: Gln addiction and cancer invasiveness are positively correlated in OVCA...... 31

Figure 2.2: Nutrient deprivation on OVCA proliferation...... 33

Figure 2.3: Gene expression for central carbon metabolism in breast and kindney cancer...... 34

Figure 2.4: Gln driven TCA cycle metabolite abundance, reductive carboxylation, and oxidative phosphorylation is significantly higher in high-invasive than low-invasive OVCA cells...... 41

Figure 2.5: Differential metabolism in high-invasive and low-invasive OVCA cells...... 43

Figure 2.6: Gln has pleiotropic role of positively regulating respiration and maintaining redox balance selectively in high-invasive OVCA cells...... 51

Figure 2.7: Glc deprivation on OVCA proliferation and oncogene expression in OVCAR3 and SKOV3...... 52

Figure 2.8: Regulating Ratio of Gln catabolism over anabolism reduces tumorigenicity...... 56

Figure 2.9: Low Glc and galactose effect on OCR and ECAR...... 57

Figure 2.10: Glutaminase activity is required for tumor growth and invasion in high-invasive OVCAs...... 60

Figure 2.11: Gln mediates oncogenic transformations in high-invasive cells by regulating STAT3 activity...... 66

Figure 2.12: Glc or Gln deprivation on OVCA cellular signaling phosphorylation...... 69

xii

Figure 2.13: Gln’s effect on metabolic rewiring in high-invasive OVCA cells is through STAT3 phosphorylation...... 72

Figure 2.14: Glc deprivation on amino acids uptake and secretion rate...... 73

Figure 2.15: Gln’s entry into TCA cycle regulates ovarian cancer invasiveness...... 79

Figure 2.16: ERK inhibiton on STAT3 serine phosphorylation...... 80

Figure 3.1: Upregulated Gln anabolic pathway in CAFs compared to NOFs...... 88

Figure 3.2: Differential gene expression levels in CAFs compared to NOFS and tumor compartment...... 89

Figure 3.3: Gene expression level for tumor epithlial compartment and stroma compartment...... 90

Figure 3.4: Gln deprivation on metaboic reprogramming in CAFs and NOFs...... 94

Figure 3.5: CAFs but not NOFs maintain Gln-addicted cancer cell growth under Gln deprivation condition...... 99

Figure 3.6: Rescue effect of CAFs for growth rate of OVCA under Gln deprivation condition...... 100

Figure 3.7: CAFs’ higher metabolic flexibility compared to NOFs induced adaptive mechanisms for harnessing carbon and nitrogen from atypical sources towards Gln synthesis...... 107

Figure 3.8: CAFs utilize different carbon and nitrogen sources for Gln synthesis...... 109

Figure 3.9: Crosstalk between stroma-epithelial cells augments dysregulated metabolism in CAFs and supports nucleotides and TCA cycle metabolites levels in cancer cells...... 115

Figure 3.10: Metabolic reprogramming for CAFs and OVCA when cocultured...... 117

Figure 3.11: Orthotopic ovarian cancer mouse model highlights stromal GLUL and OVCA GLS1 as potential therapeutic targets...... 119 xiii

Figure 3.12: Effect of mGLUL siRNA on mouse cell line GLUL expression level...... 120

Figure 4.1: Gln deprivation on IL6 mRNA and secretion...... 126

Figure 4.2: Role of glutaminolysis on cellular signaling cascade in OVCA...... 127

List of Tables

Table 1.1: ATP Yields for Processes involved in glutaminolysis...... 13

Table 1.2: Composition of nucleotides from different precursors ...... 17

Table 2.1: Amino acids uptake and secretion in OVCAR3 and SKOV3...... 44

Table 3.1: NOF2 metabolic fluxes using U-13C Glucose labeling ...... 92

Table 3.2: CAF1 metabolic fluxes using U-13C Glucose labeling ...... 93

Table 5.1: Transcriptional factors regulating glycolysis and Gln anabolism ...... 140

List of Equations

Equation 5.1 ...... 148

Equation 5.2 ...... 148

Equation 5.3 ...... 148

Equation 5.4 ...... 149

Equation 5.5 ...... 149

Equation 5.6 ...... 149

Equation 5.7 ...... 149

Equation 5.8 ...... 151

Equation 5.9 ...... 152

Equation 5.10 ...... 152

Equation 5.11 ...... 152

Nomenclature

OVCA Ovarian Cancer

CAF Cancer associated fibroblasts

NOF Normal ovarian fibroblasts

Gln Glutamine

Glc Glucose

TCA tricarboxylic acid

OCR Oxygen consumption rate

ECAR Extracellular acidification rate

OXPHOS Oxidative phosphorylation

MFA Metabolic flux analysis

MID Mass isotopomer distribution

ECM Extracellular matrix

UPLC Ultra performance liquid chromatography

GCMS Gas chromatography mass spectrometry

LCMS Liquid chromatography mass spectrometry

MMP Mitochondrial membrane potential

PPP Pentose Phosphate Pathway

ROS Reactive oxygen species

TMA Tissue microarray

FAO Fatty acid oxidation

GMP Guanosine monophosphate

2 AMP Adenine monophosphate

IMP Inosine monophosphate

UMP Uridine monophosphate

2DG 2-deoxy-D-glucose

2HG 2-hydroxyglutarate

BCAA Branch chain amino acid

αKG α-ketoglutarate

OAA Oxaloacetate

NAM Nicotinamide

NIC Nicotinic acid

GLS Glutaminase

GDH Glutamate dehydrogenase

GOT Glutamate oxaloacetate transaminase

GPT Glutamate pyruvate transaminase

BCAT Branch chain amino acids transaminase

PSAT Phosphoserine transaminase

GS/GLUL Glutamine synthetase

ME Malic enzyme

IDH Isocitrate dehydrogenase

FASN Fatty acid synthetase

CS Citrate synthetase

ACO Aconitase

SDH Succinate dehydrogenase

3 FH Fumarate hydrase

PDH pyruvate dehydrogenase

MTHFD Methylenetetrahydrofolate dehydrogenase

EGCG Epigallocatechin gallate

AOA Aminooxyacetate

BPTES Bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl)

ethyl sulfide

GPNA L-γ-glutamyl-p-nitroanilide

BCH 2-amino-bicyclo-(2,2,1)-heptane-2-carboxylate

MSO methionine sulfoximine

ETC Electron transport chain

FCCP Carbonyl cyanide-4-(trifluoromethoxy)

phenylhydrazone

BSO Buthionine sulphoximine

DOPC 1,2-Dioleoyl-sn-Glycero-3-phosphatidylcholine

TMRM Tetramethylrhodamine, methyl ester

LDON 6-Diazo-5-oxo-L-norleucine

PI3K Phosphatidylinositol 3-kinase class I

PKB Serine/threonine protein kinase

FOXO Forkhead box O transcriptional factor

STAT3 Signal transducer and activator of transcription

Erk Extracellular signal-regulated kinases

4 JaK Janus kinase

RTK Receptor tyrosine kinase

EGFR Epidermal growth factor receptor

CDC2 Cell division cycle protein 2

MAPK Mitogen-activated protein kinases

AMPK AMP-activated protein kinase

FAK Focal adhesion kinase

SERPINE Serpin Peptidase Inhibitor, Clade E

COL5A1 Collagen, Type V, Alpha 1

THBD Thrombomodulin

PLAU Plasminogen Activator, Urokinase

5 Chapter 1

Introduction

Ovarian cancer is ranked the most lethal gynecologic malignancy in the world. Its high lethality is due to failure to identify the cancer in its early stages.

By the time most cases are discovered, the cancer has already matured into its final stages, and metastasis to the peritoneum and other organs has occurred

(1)(2)(3). Another difficulty is finding the right prognosis and treatment as they vary according to the cancer’s different histopathologic subtypes and its stages.

In recent findings, dysregulated energetics in cancer cells is hailed as an emerging hallmark of tumor progression. Ovarian cancer is able to reprogram its metabolism to adapt to the microenvironment and promote tumor progression. A better understanding of ovarian cancer’s rewired metabolism is therefore required to provide efficient and effective treatments.

1.1. Ovarian Cancer

According to data collected by American Cancer Society, ovarian cancer is one of the leading causes of mortality in the United States with approximately

6 22,280 new cases and 14,240 estimated deaths in 2016. Ovarian cancers develop from three major ovary cell types: germ cells, endocrine and interstitial cells, and epithelial cells (4). However, less than 10% of ovarian cancers have a germal and endocrine histology with the majority of ovarian cancer thought to develop from ovarian surface epithelial cells or subsurface cells (5). According to different histological grade, molecular phenotype, and genotype, ovarian cancer can be divided into two major groups. One is type I cancer, which grows slowly and is resistant to conventional chemotherapy but responds to hormonal manipulation. Conversely, type II, a late stage cancer, grows aggressively and is resistant to hormonal treatment although around 70% can respond to a combination of platinum- and taxane-based chemotherapies (6). Personalized treatments are hence a necessity to combat the differences between these subtypes. One breakthrough in cancer research field is that cancer cells have dysregulated energetic metabolism, which distinguish themselves from normal epithelial cells, as well as other type of cancer cells. Consequently, studying the distinct metabolic reprogramming among various ovarian cancer cells will deliver more information for personalized prescription.

1.2. Glutaminolysis: Hallmark of Cancer Metabolism

With a conceptual progress in the last decade, the hallmarks of cancer together build up a logical framework to understand the initiation and progression of these neoplastic diseases. With two emerging hallmarks added, the new structure now includes sustaining proliferative signaling, evading growth

7 suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, activating invasion and metastasis, reprogramming energy metabolism, and evading immune destruction (7). In particular, dysregulated energy metabolism paves the foundation for cell duplication by acquiring nutrients from frequently nutrient-poor microenvironment. To examine tumorigenesis associated metabolic reprogramming on establishing and maintaining tumorigenic states, researchers organized metabolic flux changes into six hallmarks (8). One of the emerging hallmarks of cancer metabolism is deregulated uptake of amino acids, especially glutamine (8).

1.2.1. Oncogenic and microenvironment regulation of Gln metabolism

In contrast to normal tissues’ intrinsic capacity to control cell growth and division by regulating homeostasis of growth factor signaling, cancer cells have sustained proliferative signaling (7). As uncontrolled chronic tumor growth involves creation of cell building blocks, neoplastic cells must breakdown nutrients for a rewired energy metabolism to fulfill biosynthetic demands. Glucose and Gln are two principle nutrients consumed in this process as they are responsible for cellular survival and proliferation.

Revealed by Otto Warburg in the 1920s, proliferating cells prefer to convert glucose into lactate instead of entering into the TCA cycle even in the presence of oxygen (9)(10). This is known as aerobic glycolysis or the Warburg effect. The mechanism behind this effect is still under debate. One theory suggests mitochondrial dysfunction. The findings that fumarate hydratase (FH)

8 and succinate dehydrogenase (SDH) mutations in hereditary leiomyomatosis, renal cell carcinoma, and gastrointestinal stromal tumor correlate with the poor prognosis of patients’ survival suggest dysfunctional mitochondria in tumor cells.

However, because the majority of tumor cells have intact mitochondria, a current widely accepted theory is that aerobic glycolysis can accumulate abundant glycolytic intermediates to redirect into biosynthetic anabolism (11). For the proliferative requirements, cancer cells prefer glycolysis instead of glucose oxidation.

Glutaminolysis refers to the process in which cells convert glutamine to glutamate and then further into tricarboxylic acid (TCA) cycle metabolites, generating ATP and NADPH. This was first discovered in HeLa cells which consumes 10 to 100 times more glutamine compared to other amino acids

(12)(13). To interpret the phenomena, researchers found that the reprogrammed energy metabolism is associated with accumulated oncogenic alterations, which also enable other cancer hallmarks (Fig. 1.1). The transcriptional factor, c-myc, binds to the promoter regions of ASCT2 and SN2, glutamine transporters, thereby enhancing glutamine uptake (14). Furthermore, c-myc can promote the expression of mitochondrial glutaminase through transcriptional repression of miR-23a and miR-23b (15) or can directly target the genes in dNTP metabolism to enhance glutamine as the amide donor for nucleotides synthesis (16). K-ras, another principle driver of glutamine utilization, elevates gene expression associated with glycolysis and glutamine metabolism (17). In particular, K-ras downregulates the expression level of glutamate dehydrogenase (GLUD) and

9 increases that of glutamate-oxaloacetate transaminase (GOT) to achieve Gln’s supply of NADPH through malic enzyme (ME) (18). Tumor suppressor p53, the

“guardian of the genome”, can activate GLS2 expression level and lower intracellular reactive oxygen species to protect cells from genomic damage

(19)(20). Other tumor suppressors, Rb and LKB1, can alter glutamine uptake corresponding to E2F-3’s direct regulation of ASCT2 or HIF-1α (21)(22). Besides the regulation by intracellular oncogenes and tumor suppressors, glutamine metabolism is also influenced by the extracellular tumor microenvironment. For example, Interleukin-4, paracrine secreted by immune cells, can increase the expression level of ASCT2 in breast cancer (23). Because of poor angiogenesis, solid tumors have a low oxygen content, resulting in their hypoxia environment and activating HIF signaling. Activation of HIF1 can promote SIAH2 targeted ubiquitination of OGDH2, a key enzyme for α-ketoglutarate conversion into other

TCA cycle metabolites. Thus, hypoxia blocks glutamine oxidation and instead enhances glutamine reductive carboxylation (24). Meanwhile, accumulation of lactate, a byproduct from glycolysis, not only results in a ‘reversed’ pH gradient that enables tumor progression but also activates c-myc, which directly affects glutaminolysis (25).

Figure 1.1: Oncogenic signaling on Gln metabolism. Expression levels of Enzymes in Glutaminolysis pathway are regulated by multiple oncogenes and tumor suppressor. Tumor suppressor Rb and LKB1 inhibit ASCT2 and SN2 expression. Oncogene c-Myc enhance expression of ASCT2, GLS1 and TDG to promote GS transcriptional level. DNA damage or other stress will result in the activation of p53, which transcriptionally regulates GLS2 expression. mTOR inhibits SIRT4, which inhibits GLUD1, and Kras also inhibits GLUD1, but enhances GOT1 expression. GS can be transcriptional regulated by GATA3, and FOXO3/4, whose phosphorylation is controlled by PI3K/PKB/AKT signaling cascade.

1.2.2. Glutaminolysis: supporting rapid cell proliferation beyond carbon

provision

It has been widely accepted that glutamine’s anapelerosis into TCA cycle maintains mitochondrial function and backup cell proliferation. However, recent findings indicate that both Gln and glucose accounts for less than 10% of cell

11 mass from isotope ratio mass spectrometry results by using 13C labeled glucose or Gln. On the other hand, non-glutamine amino acids are the major carbon sources for mammalian cells’ mass, since protein is the major contributor for cell mass (26). Even though glucose and Gln have small contributions towards cell biomass, the role of glucose and glutamine cannot be replaced by other amino acids because of their functions beyond providing carbon for biosynthesis.

Along with Gln’s conversion into TCA cycle intermediates as a carbon donor, glutamine can generate NADH and FADH2 through glutamate dehydrogenase, succinate dehydrogenase, and malate dehydrogenase (Fig 1.2).

Knowing NADH and FADH2 link to mitochondrial electron transport chain (ETC) to create an electrochemical gradient for ATP production, researchers have found that addition of glutamine can largely increase the oxygen consumption rate in Kras-promoting tumor genesis (27). Therefore, with one mole of fully oxidized glutamine, cells can generate 9 or 12 moles ATP (Table 1.1). The imbalance between electron transfer and oxygen consumption rates creates the reactive oxygen species (ROS), serving bi-edged effects. A highly oxidative intracellular environment will cause DNA damage and denaturation of protein, while certain amounts of ROS is required to promote tumorigenesis. To detoxify intracellular ROS and control it into a range for tumor progression, the redox defense power NADPH is generated through three primary metabolic pathways.

The most direct route is through oxidative pentose phosphate pathway where glucose is shunt into ribulose-5-phosphoate, the precursor of ribose-5 phosphate for nucleotides synthesis. Through oxidative pentose phosphate pathway, one

12 mole of glucose-6-phosphate can generate 2 moles of NADPH. Accumulating evidence shows that the folate-dependent NADPH production is vital to the sustainment of cellular NADPH/NADP+. Under the reaction carried by methylenetetrahydrofolate dehydrogenase MTHFD1/2, this serine driven one carbon metabolism generates 2 moles of NADPH per methylene tetrahydrofolate complete oxidation. The other important NADPH source is through malic enzyme which will convert malate to pyruvate. There are mounting studies showing the importance of malic enzymes in tumor growth as a NADPH machinery (28)(29).

As explained above, glutamine is the major source of malate through glutamine oxidation (30)(18). To distinguish and calculate NADPH contribution from each pathway, Fan et al used the deuterium tracer 3-2H glucose, [2,3,3-2H] serine, and

[2,3,3,4,4-2H] glutamine to incubate HEK293T cell. They found that the pentose phosphate pathway, one carbon metabolism, and malic enzyme account for around 30%, 40% and 30% respectively of overall NADP+ reduction (31). Other pathways such as IDH have a minimal influence on NADPH production.

Energy Process Type of Molecule Total ATP

Produced per Glutamine produced

Gln- α-ketoglutarate 1 NADH (GDH) 3/0

/0NADH (GOT,GPT)

TCA cycle 2 NADH 6

1 FADH2 2

1 GTP 1

13 Total 12/9

Table 1.1: ATP Yields for Processes involved in glutaminolysis. ATP Yields for Processes involved in glutaminolysis. A total of 12 ATP or 9 ATP is generated when glutamine is oxidized into oxalacetate

Besides the redox control, NADPH is required for the fatty acids synthesis.

Take de novo synthesis of palmitate for example, one mole of palmitate needs 14 moles of NADPH from its precursor acetyl-coA. The cytoplasmic acetyl-CoA is generated from mitochondrial citrate exported out of the mitochondria and split into acetyl-CoA and oxaloacetate with ATP citrate lyase. Commonly, citrate is formed inside mitochondria combining acetyl-CoA from pyruvate dehydrogenase and oxaloacetate from glutaminolysis or pyruvate carboxylase(12)(32). In a normoxia condition, glucose is the major source for acetyl-CoA and the de novo synthesized fatty acids. However, under hypoxia condition or with dysfunctional mitochondria, cancer cells will shift glutamine oxidation towards glutamine reductive carboxylation to make up for the loss of citrate. In these circumstances, glutamine will directly supply five carbons to citrate, and in turn generate glutamine derived free fatty acids(33)(34)(35). The mechanism of Gln reductive carboxylation is triggered by either a change in the ratio of α-ketoglutarate over citrate because of loss function of pyruvate dehydrogenase (PDH), or the overexpression of NNT transferring NADH proton to NADP+ to form NADPH, which is necessary for the IDH reverse reaction(36)(37)(38). Furthermore, to detoxify H2O2, glutathione is converted into oxidized glutathione dimer, which can

14 be recovered by NADPH(39). To synthesize reduced glutathione monomer, gamma-glutamylcysteine is synthesized from L-glutamate and cysteine via the enzyme gamma-glutamylcysteine synthetase, with glycine addition on the c- terminal of gamma-glutamylcysteine by glutathione synthetase. Clearly, Gln is the direct donor for glutamate and the uptake of cysteine is through efflux of glutamate into microenvironment through xc-cysteine/glutamate antiporter (Fig.

1.2)(40).

Gln has amido and amino nitrogen, allowing its influences on both, nucleotide synthesis and other nonessential amino acids’ generation. 5- phosphoribosyl-α-pyrophosphate (PRPP), generated from pentose phosphate pathway, is converted into phosphoribosyl-β-amine (PRA) with the amido nitrogen from glutamine. Moreover, amido nitrogen from glutamine can also be transferred to formylglycinamide ribonucleotide (FGAR) to form formylglycinamidine ribonucleotide (FGAM). To generate ATP, GTP, UTP and

CTP, aspartate is necessary as the nitrogen donor for all nucleotides, and as the carbon donor for UTP and CTP (Table 1.2) (11). For the inefficiency of aspartate transporter in mammalian cells, the majority of intracellular aspartate is generated from glutaminolysis, where glutamine donates amino nitrogen through

GOT1/2 and donates carbon through glutamine oxidative reaction

(41)(28)(42)(43). Besides GOT, alanine and phosphoryl serine can be generated via GPT or PSAT(44)(45) using nitrogen derived from glutamine. Glutamate can also be converted into proline and ornithine(46). Collectively, Gln can provide

15 both carbon and nitrogen to other non-essential amino acids for protein synthesis.

The breakthrough finding that cytoplasmic and mitochondrial IDH1/2 are recurrently mutated in cancer highlights the success of high-throughput sequencing and triggers new metabolic drug discovery to target the IDH1/2. More than 90% of glioblastoma with IDH1/2 mutation includes IDH1 Arg132 mutations and IDH2 Arg140 and Arg172 mutations(47)(48). Contrary to researchers’ early thought of loss of function of IDH only, the mutation of IDH surprisingly converts

α-ketoglutarate into D(R)-2-hydroxyglutarate (2HG), a powerful oncometabolite that competitively binds to α-ketoglutarate dependent dioxygenases. Therefore, the decreased activity of DNA demethylases and histone demethylases results in a prominent CpG island hypermethylation, and thus leads to genome-wide histone and DNA methylation alterations(49)(50)(51). Furthermore, α- ketoglutarate dependent dioxygenases also include a family of prolyl hydroxylase enzymes, which regulate key metabolic modulator HIF1α levels(51). Recent findings show that α-ketoglutarate can also be converted into L-2- hydroxyglutarate via malate dehydrogenase and lactate dehydrogenase A

(LDHA) in hypoxia and exhibit a similar inhibitory effect on HIF1α and TET enzymes(52)(53). Through the reaction by α-ketoglutarate dehydrogenase, α- ketoglutarate is converted into succinate. Because of the structural similarity of succinate and fumarate to α-ketoglutarate, they can also inhibit dioxygenases activity. The loss of succinate dehydrogenase or fumarate hydratase causes the accumulation of succinate or fumarate, and thus alters the global

16 methylation(54)(55)(56). In fumarate hydratase defective systems, fumarate can also bond to glutathione in vitro and vivo to generate succinated glutathione and amplify mitochondrial ROS signaling(56).

Figure 1.2: Roles of Gln in fulfilling cell proliferation and energetic requirements. Gln is uptaken into cytoplasm and converted into glutamate. The amido nitrogen will be transferred into de novo nucleotides synthesis. Glutamate can be exported out of cytoplasm and exchanged with cysteine, which generates glutathione together with intracellular glutamate and glycine. Glutathione is used to maintain redox homeostasis. Furthermore, glutamate can generate other amino acids including ornithine, proline, as well as serve as amino nitrogen to synthesize other amino acids such as alanine, aspartate. The aminotransferase GOT/GPT can convert glutamate into α-ketoglutarate, which enters into TCA cycle to generate NADH, ATP and NADPH. Citrate will be exported out of mitochondria and broke down into OAA and acetyl-coA, which is precursor for de novo fatty acids synthesis.

17 Number of Carbon Nitrogen dNTP precursors Precursor contribution contribution ATP/dATP 5-Phosphoribosyl-α- 1 5 - pyrophosphate 2 N10-formyl-tetrahydrofolates 2 - 1 Bicarbonate 1 - 1 Glycine 2 1 1 Aspartate - 2 2 Glutamines - 2 Total 10 5 GTP/dGTP 5-Phosphoribosyl-α- 1 5 - pyrophosphate 2 N10-formyl-tetrahydrofolates 2 - 1 Bicarbonate 1 - 1 Glycine 2 1 1 Aspartate - 1 2 Glutamines - 3 Total 10 5 UTP/CTP 1 5-Phosphoribosyl-α- 5 - /dCTP pyrophosphate 1 Bicarbonate 1 - 1 Aspartate 3 1 1 Glutamine - 1 Total 9 2 dTTP 1 5-Phosphoribosyl-α- 5 - pyrophosphate 1 Bicarbonate 1 - 1 Aspartate 3 1 1 Glutamine - 1 1 5,10-Methylene-tetrahydrofolate 1 1 Total 10 2

Table 1.2: Composition of nucleotides from different precursors (11)

18 1.2.3. Gln Induces Cell Signaling Affecting Cell proliferation, Apoptosis

and Drug Resistance

Gln, a key component and precursor for nucleotides, lipids and protein orchestrates intracellular signaling to promote tumor survival and progression as well. Because of the efflux of glutamine out of cytoplasm to facilitate leucine uptake, leucine activates mTOR, which integrates with other stimuli to regulate protein synthesis, ribosomal biogenesis, autophagy and cancer metabolism

(57)(58)(59)(60). Additionally, leucine can directly bind to glutamine dehydrogenase (GDH), an aforementioned reaction converting glutamate into α- ketoglutarate, thus promoting glutaminolysis (61). Overexpression of GDH will lead to mTOR hyper-activation, and addition of cell permeable form dimethyl α- ketoglutarate induces mTOR translocation to lysosome, an essential step for mTOR activation(62)(63)(64). Furthermore, glutaminolysis can promote mTOR translocation recruiter RagB GTP loading via increasing 50% of GTP/GDP ratio(62). mTOR, as a positive feedback, further stimulates glutamine metabolism as a transcriptional repressor of SIRT4 for GDH inhibition(65). Especially during

DNA damage, SIRT4’s blockage of GDH activity could possibly shut down glutamine’s anaplerotic entry into TCA cycle to guarantee adaptive DNA damage response(66)(67). Recently, researchers found that Gln deprivation will induce a dramatic increase of the inflammatory factor IL8 secretion through mTOR-JNK dependent chemokine secretion pathway(68). In their report, Gln deprivation can still maintain mTOR activity. The differences between two observations could be due to the distinct nutrients starvation procedures. Acute Gln deprivation will

19 cause CD95-mediated caspase cascade apoptosis and cell shrinkage, and can specifically kill the c-Myc dependent cancer cells while keeping normal cells alive

(69)(70).

Cisplatin is one of the most effective chemo-drugs and is the first line option for treating ovarian cancer. After a series of aquation reactions, activated cisplatin will either enter into the nucleus to bind with nucleophilic N7 sites of purine base, or accumulate in the mitochondria to adduct with DNA and protein, and in turn, induce cell cycle arrest and apoptosis(71) (72). However, endogenous nucleophile GSH derived from glutamine can bind covalently with cisplatin to prevent its binding to DNA, therefore to promote cisplatin detoxification and conferring cisplatin-resistance in these cells (72). In addition,

GSH can also antagonize ROS, generated from DNA damage. Therefore, the addition of γ-glutamylcysteine synthetase inhibitor BSO counters effects of GSH, leading to a positive synergistic effect with cisplatin on the tumor treatment outcome (73).

1.2.4. Gln Sources

The multifaceted functions of Gln in tumor initiation and progression drew researchers’ attention to discover the original sources of glutamine. In healthy subjects, skeletal muscle is usually the source of plasma glutamine, and under stress, human lungs also have the capacity to release comparable amounts of glutamine (74)(75). The positive effect of obesity on the regional amino acid concentration around adipose tissue indicates that adipocytes can also have a

20 significant effect on glutamine release (76). Moreover, recent studies reveal that adipocyte can cause leukemia cell resistance to L-asparaginase treatment by secreting glutamine (77). However, Gln in the tumor microenvironment is still scarce for the amplified consumption by cancer cells and poor supply from plasma to tumor region. It seems impossible for large tumors to form because of lack of sufficient nutrients. Conversely, the intracellular oncogenic rewired signaling and multiple extracellular supplies make it possible for tumors to grow without restriction. The phosphatidylinositol 3-kinase class I (PI3K) can be activated by a plethora of extracellular stimuli which further activates serine/threonine protein kinase (PKB) (78)(79). PKB can phosphorylate forkhead box (FOX) O transcriptional factors, which have been proven to positively regulate the expression level of glutamine synthetase (GS), the major enzyme for intracellular glutamine synthesis (Table 1.1) (80). By targeting thymine DNA glycosylase with oncogenic Myc, this reaction promotes the demethylation on

GLUL promoter, and upregulates GS expression level (81). GATA3, a master regulatory of transcriptional factors, can directly bind to the GS promoter in breast luminal cancer cells, and can cause their glutamine independence for growth (82). In the glutamine-restricted glioblastoma, glutamine anabolism through GS can fuel de novo purine biosynthesis, guaranteeing cell proliferation

(83). The FOXO-induced GS expression can also enhance autophagy through the lysosome degradation procedure, another source for glutamine generation

(Table 1.3) (80). Under nutrient or growth factors depleted conditions, the poor cellular energy state will inhibit mTOR by sensing the changes in AMP activated

21 protein kinase. The repression of mTOR signaling has been substantiated by many studies that it will induce the formation of autophagosomes, which engulf protein aggregates, damaged organelles, lipid droplets and intracellular pathogens (84)(85)(86). Therefore, this self-eating process can convert cellular waste into building block units to maintain cellular metabolism and homeostasis under stress conditions. The autophagy defective from the loss of Atg7 will cause cells’ incapability to employ lysosome degraded glutamine to sustain mitochondrial metabolism during a starvation condition and speed up cell death

(87).

Ras transformed cells can scavenge unsaturated fatty acids from extracellular lysophospholipids and thereby result in 4 fold higher intracellular fatty acids than cells in lysophospholipids free medium (88). Identically, researchers discovered that cancer cells can uptake extracellular fluid through macropinocytosis, an endocytic process in which extracellular nutrient is taken up and internalized to cells through a giant vesicle (89). This process is also stimulated by Ras- and Src-driven cellular structural remodeling (89). Because of the highly abundant soluble protein (2%) in the plasma and tissue interstitial fluid, macropinocytosis become an unignorable nutrient source, especially under nutrient deprivation conditions. Under low glutamine concentration medium, heat- inactivated, 13C labeled protein derived from yeast can contribute to 10% of intracellular glutamine, and other TCA cycle metabolites (89). Moreover, the addition of 5% BSA will double the intracellular concentration of leucine, and rescue the growth arrest due to leucine deprivation (90). Cells process

22 extracellular protein through lysosome enzymes to manufacture essential building blocks. Therefore, addition of bafilomycin A1, an inhibitor of vacuolar- type H+ ATPase on lysosome, will block the replenishing effect of extracellular protein on intracellular amino acid content. Besides macropinocytosis, other forms of endocytosis can also directly supply nutrients to cancer cells.

Exosomes, cell derived vesicles secreted by cancer-associated fibroblasts, can act as metabolite cargo to support prostate cancer growth under low nutrient conditions, via a Kras-independent mechanism (91). As cancer cells grow, they recruit non-malignant stromal cells in their vicinity forming a complex tumor microenvironment (92) . These reactive stromal cells co-evolve and continually interact with cancer cells and become an integral part of their physiology.

Recently published studies have shown that alterations in metabolic phenotypes in CAFs are mediated by secreted cytokines, extracellular acidification, and reactive oxygen species from tumor epithelial cells. The high level reactive oxygen species generated from cancer epithelial cells and induced inflammation have been shown to be the mechanism for fibroblasts’ metabolic transformation into catabolic metabolism phenotype, providing rich energetic metabolites including lactate, ketones, free fatty acids and amino acids to loopback for cancer epithelial cells proliferation and epithelial-mesenchymal transition (Fig. 1.3)

(93)(94).

Figure 1.3: Multiple Gln sources to maintain intracellular Gln level in cancer cells. Some cancer cells have their own capacity to generate Gln through Gln anabolism. Under stress conditions, cells will form autophagosome, which engulf dysfunctional mitochondria, protein and other organelles, and deliver all organelles into lysosome. Cancer cells can also uptake exosomes secreted by other surrounding cells, and protein from microenvironment through endocytosis. All these nutrients will also be delivered to lysosome. Lysosome contains

24 abundant enzymes which can digest all nutrients into small molecules, containing Gln. Furthermore, surrounding cells can secrete energy rich metabolites includes lactate, ketones and lysophospholipids from their own central carbon metabolism or lysosome degradation. These small molecules will be uptaken through transporters in cancer cell membrane, and reutilized to generate Gln.

1.2.5. Assessing Gln Uptake and Metabolism in vivo

The wide application of positron emission tomography (PET)-based imaging through the monitoring of cells intaking a radioactive fluorine-labeled glucose analog, 18F-flurodeoxyglucose makes it possible to localize tumor sites and record drug response in vivo (95)(96). However, high uptake glucose in normal brain tissue causes suboptimal glioma detection (97). Therefore, a novel method needed to be explored. Venneti et. al applied glutamine analog 4-18F-

(2S,4R)-fluoroglutamine (18F-FGln) to specifically delineate gliomas in vivo since there is a high uptake rate of glutamine in gliomas compared to a minimal uptake in normal brain (98). This discovery brings 18F-labeled glutamine into early clinical studies (99). Nonetheless, in vivo application is still limited to the brain as

Kras-induced non-small cell lung tumors are less dependent on glutaminase compared to in vitro cultured cells (100). Application of CRISPR/Cas9-based knockout Gls1 confirms that pharmacological inhibition of glutaminase in vivo exhibits no effect on the tumor cells, whereas researchers cannot even generate

Gls1 knockout cells in vitro because of their high dependence on Gls1 for cell duplication (100). Meanwhile, different degrees of perfusion into human tumor microenvironments also indicates the heterogeneity in tumor metabolism

25 between tumor compartments (101). Therefore, more studies are required to obtain better understanding of tumor metabolism in vivo.

1.2.6. Drug Application targeting Gln Metabolism

As the critical roles of glutaminolysis involve in cancer cell metabolism, cell signaling, and redox balance, they pave the discovery for potential therapeutic applications in treating cancer. By applying benzylserine and l-γ- glutamyl-p-nitroanilide (GPNA), which inhibits the activity of a facile glutamine transporter SLC1A5, suppressed tumor cell proliferation in vitro and in vivo was observed (102)(103). The emergence of small molecule inhibitors such as

BPTES, CB-839, and compound 968, open up new avenues of metabolism- targeting drugs by block GLS activity (104)(105)(106). BPTES and its derivative

CB-839 allosterically inhibits the dimer-to tetramer transition of GLS (104)(107), and compound 968 treatment represses activity of Rho GTPases, therefore suppressing GLS activity (108). Preclinical trials of all drugs have shown promising futures as possible metabolic therapies for breast cancer and lymphoma. CB-839 is currently in Phase I clinical trials. Epigallocatechin gallate

(EGCG), inhibitor for GDH and AOA, a non-specific inhibitor of transaminases, is also effective in reducing tumor proliferation in preclinical studies(109)(110). Most recently, AG-221, AG-120, AG-881, inhibitors of IDH mutations have been developed and are currently being evaluated in multiple clinical studies.

Chapter 2

Metabolic shifts towards glutamine regulates tumor growth, invasion and bioenergetics in ovarian cancer

Glutamine can play a critical role in cellular growth in multiple cancers.

Glutamine addicted cancer cells are dependent on glutamine for viability, and their metabolism is reprogrammed for glutamine utilization through the tricarboxylic acid

(TCA) cycle. Here, we report a novel mechanism by which cancer cells maintain their invasiveness. We reveal that Gln, rather than Glc, may play a critical role in high‐invasive ovarian tumor growth and in overall patient survival. Our data show that dependence on Gln in cancer cells is strongly correlated with cancer invasiveness. Low‐invasive cells are found to be Gln independent, whereas high‐ invasive cells are extremely Gln dependent. We found significant correlation between patient survival and gene expression in the glutaminolysis/glycolytic

metabolic network; genes involved in glutaminolysis and TCA cycle metabolic pathways are highly expressed in patients with poor survival, whereas glycolytic genes are associated with better patient survival. Using isotope tracers and bioenergetics, we uncover Gln's differential modulation of cancer cell hallmarks including deregulated cellular energetics(7)(111)(112), reductive carboxylation for biosynthesis(34) and shifts in energy metabolism (12), between low‐ and high‐ invasive cancer cells. Moreover, we show that Gln orchestrates cell survival, cancer progression and rewiring of metabolic pathways in high‐invasive cancer cells by interacting with cellular networks that regulate homeostasis and cellular functions. We identified that Gln withdrawal selectively reduces phosphorylated

STAT3 expression in high‐invasive cancer cells. Furthermore, forced expression of STAT3 in Gln‐deprived cells can abrogate Gln‐induced cell death. Our study uncovers previously undescribed roles of Gln in low‐invasive versus high‐invasive

OVCA cells. This may provide avenues for targeting cancer cells not only in a stage‐specific manner, but also by treating heterogeneous populations uniformly with drug cocktails to target mutually compensatory metabolic pathways in tumors.

2.1. Glutamine dependence in ovarian cancer cells is correlated

with cancer invasiveness and patient survival

To understand Gln dependence in ovarian cancer cells, we first analyzed cellular proliferation in an array of eight ovarian cancer (OVCA) cell lines under complete medium, Gln-deprived and Glc-deprived conditions. We found that

OVCAR3, IGROV1 and OVCA429 are Gln independent, OVCAR8 and OVCA420 are moderately Gln dependent, and SKOV3, SKOV3ip and Hey8 are highly Gln dependent (Fig. 2.1A). Consistent with cellular proliferation in normoxia, OVCAR3 and SKOV3 showed similar behavior under hypoxia, where OVCAR3 is Gln independent and SKOV3 is Gln dependent (Fig. 2.9A). However, growth in all

OVCA cell lines is Glc dependent (Fig. 2.1B). Clonogenic growth experiments confirmed that Gln is critical for SKOV3 cell growth but has no influence on

IGROV1 OVCA cells (Fig. 2.1C). To uncover the linkage between Gln dependence and cellular invasion and migration, we performed transwell invasion and wound healing assays, respectively. Interestingly, we found that Gln dependence is correlated with the invasion and migratory capacity of both cancer cell lines (Fig.

2.1D-E, Fig. 2.9B). Gln independent OVCA cell lines OVCAR3, IGROV1 and

OVCA429 are low-invasive; highly Gln dependent OVCA cell lines SKOV3,

SKOV3ip and Hey8 are high-invasive, while OVCAR8 and OVCA420 display intermediate behavior (Fig. 2.1D-E). We did not find differential effects of Glc deprivation with respect to invasiveness in OVCA cell lines (Fig. 2.9C). Further, the expression of cell cycle proteins, specifically CDC2 and cyclin D1, correlates with cell proliferation levels in both Gln and Glc deprivation conditions (Fig. 2.1F).

Following Glc depletion, phosphorylated CDC2 decreases only in low-invasive

OVCA cells (OVCAR3) and moderately invasive OVCA cells (OVCA420) (Fig.

2.1F, Fig. 2.9D). The high-invasive OVCA cells (SKOV3 and SKOV3ip), respond negatively whenever Glc or Gln is depleted from the culturing medium. Notably,

cyclin D1 decreases only under Gln deprivation in high-invasive OVCA cells.

Furthermore, loss of E-cadherin expression in high-invasive OVCA cells is consistent with their higher metastatic potential (Fig. 2.1D). These results present evidence that high-invasive OVCA cells are Gln addicted whereas low-invasive

OVCA cells are Gln independent.

To determine whether the Gln dependent phenotype of high-invasive OVCA cells in our cell lines models has clinical significance, we analyzed gene expression and survival data from OVCA patients within The Cancer Genome Atlas (TCGA).

We built a network of the glucose and Gln metabolism within the TCA cycle and calculated the Cox regression values for each of the enzymes in the network to determine how expression levels correlate with survival(113). The correlation of metabolic network genes with ovarian cancer patient survival shows poor outcome in patients with a higher expression level of genes involved in glutaminolysis, such as glutaminase (GLS1), glutamate dehydrogenase (GLUD1) and glutamate oxaloacetate transaminase (GOT1, GOT2), as well as genes involved in TCA cycle metabolic pathways and pyruvate conversion to acetyl CoA like pyruvate dehydrogenase (PDH), citrate synthetase (CS), aconitase (ACO2), and succinate dehydrogenase B (SDHB) (Fig. 2.1G).This is in contrast to genes involved in Glc uptake and entry into the TCA cycle. These data suggest that increased glutaminolysis supports mitochondrial TCA cycle activity in patients with high- invasive OVCA, resulting in the observation that glutaminolysis rather than glycolysis correlates with poor patient survival. Our findings are in contrast with

previous studies that show correlation of poor outcome with high expression of glycolysis genes in some cancers(114)(115). To confirm if our findings are specific to OVCA, we analyzed the TCGA data from breast and kidney cancer patients.

The gene expression network in kidney cancer patients shows that high expression of Glc metabolism genes correlates with poor survival, while in breast cancer patients TCGA data shows that both Glc and Gln metabolism correlate with poor survival (Fig. 2.10A-B).

Figure 2.1: Gln addiction and cancer invasiveness are positively correlated in OVCA. (A) Gln deprivation effect on proliferation rate of a panel of OVCA cells for 24, 48, and 72 hours. OVCAR3, IGROV1, OVCA429 cells were Gln independent; OVCA420 and OVCAR8 cells were moderately Gln dependent; and SKOV3, SKOV3ip and Hey 8 cells were Gln dependent. n≥15. (B) Glucose deprivation effect on proliferation rate of OVCA cell lines for 24, 48, 72 hours. n≥15. (C) Gln

deprivation effect on clonogenic formation in IGROV1 and SKOV3. n=6. (D) Matrigel invasion assay was conducted to characterize invasiveness of OVCA cell lines. OVCAR3, IGROV1 and OVCA429 cells were noninvasive, OVCA420 and OVCAR8 cells were moderately invasive, while SKOV3, SKOV3ip and Hey 8 were highly invasive OVCA cell lines. n≥6. (E) Correlation between proliferation rate at 72 hours of OVCA cell lines under Gln depleted conditions with their corresponding number of invaded cells. Data in A-D are expressed as mean ± SEM, *P<0.05, **P<0.01, ***P<0.001. In D, 1-way ANOVA with Tukey tests was used to compare between cell lines, using OVCAR3 as the control. (F) Western blot analysis of cell cycle proteins linked with growth rate (CDC2, Cyclin D1) and protein linked with metastatic potential (E-cadherin) in OVCA cell lines. β-actin was used as the loading control. (G) Genes in the glutaminolysis and TCA cycle metabolic pathways are associated with higher risk in ovarian cancer patients. Shown is a metabolic network and the genes (rectangles) that produce the enzyme catalyzing the reactions in the network. The genes in the network are colored by their correlation with ovarian cancer patient survival based on calculating their Cox hazard values (color key). The gene expression and clinical data were fitted with a cox proportional hazard model to determine the hazard ratio for each gene in Gln/glucose metabolic network. A higher hazard ratio (poor outcome) was observed in gene products that catalyze reactions in Gln catabolism and higher expression of glycolytic genes culminated in better patient survival (low hazard ratio).

Figure 2.2: Nutrient deprivation on OVCA proliferation. (A) Gln deprivation effect on proliferation rate of OVCAR3 and SKOV3 cells under hypoxia (1% O2) for 24, 48, and 72 hours. (B) Migration ability of a panel of OVCA cells determined using wound healing assay at 12, 24 hours. (C) Relationship between proliferation rate under glucose deprivation and ovarian cancer cells’ invasive capacity. (D) CDC2 phosphorylation level in different medium conditions for OVCAR3, OVCA420, SKOV3 and SKOV3ip cells. Data in A-D are expressed as mean ± SD, n≥6, *P<0.05 **P<0.01, ***P<0.001. In B, 1-way ANOVA with Tukey test was used to compare between cell lines, using OVCAR3 as the control.

Figure 2.3: Gene expression for central carbon metabolism in breast and kindney cancer. Genes in the glutaminolysis and TCA cycle metabolic pathways are associated with higher risk in breast cancer patients (A) and kidney cancer patients (B).

color indicates the hazard ratio where red indicates high hazardousness, green indicates low harzardousness.

2.2. Glutamine differentially increases TCA cycle metabolite

abundances, in high-invasive versus low-invasive ovarian

cancer cells

Recently, we identified that during migration, high- and low-invasive OVCA cells exhibit differing consumption of pyruvate. This discrepancy may be due to differential metabolite pools available in cancer cells(116). Here, we asked whether high- and low-invasive OVCA cells have different metabolite abundances. Since both Glc and Gln can contribute to TCA cycle metabolites, we also questioned whether high- and low-invasive OVCA cells utilize different sources (Glc or Gln) to maintain their metabolite pool. To answer this, we performed 13C GC-MS based

13 13 isotope tracer analysis using labeled U- C5 Gln and/or a 1:1 mixture of U- C6 Glc

13 and 1- C1 Glc (Fig. 2.2A). This analysis can reveal the contributions of a substrate

13 within a particular metabolic pathway. For instance, a 1:1 mixture of U- C6 Glc and 1-13C labeled Glc will provide both M3 (three 13C labeled carbon) and M1 (one

13C labeled carbon) pyruvate. Furthermore, M3 pyruvate will be converted into M2 and M3 malate and fumarate (M2 is produced in the first complete TCA cycle and

M3 is from the second cycle); whereas M1 pyruvate will result in M1 and M2 malate and fumarate (M1 is produced in the first complete TCA cycle and M2 is from the second cycle). Conversely, the unlabeled M0 malate and fumarate will be obtained

mainly from non-Glc sources (i.e. Gln etc.). Thus, the relative abundance of isotopic labels between different cell lines reveals the preferential utilization of Glc towards replenishment of the TCA cycle metabolite pool (Fig. 2.2A).

The mass isotope distribution (MID) of malate and fumarate using labeled Glc indicated that OVCAR3, the low-invasive OVCA cell line, has a higher conversion rate of Glc into TCA cycle intermediates than SKOV3, the high-invasive OVCA cell

13 13 line. Interestingly, using a mixture of U- C6 Glc and 1- C1 labeled Glc, we found that the percentage of M2 and M3 malate is significantly higher in low-invasive

OVCA cells (OVCAR3), while that the percentage of M0 malate is significantly lower (Fig. 2.2B). Similar results were obtained for fumarate (Fig. 2.2C). In addition, we found that OVCA cells convert Glc into glutamate, and that this conversion flux is higher in the low-invasive OVCA cell line, OVCAR3 (Fig. 2.2D).

The Gln contribution to TCA cycle metabolite pools was estimated using

13 labeled U- C5 Gln and its conversion to M5 glutamate, M4 fumarate, M4 malate, and M4 citrate (Fig. 2.2E). The relative isotope abundances between different cell lines reveal Gln utilization for TCA cycle metabolite pool. Interestingly, we found that high-invasive OVCA cells (SKOV3) use Gln to produce 40% of their total TCA intermediate pool, while Gln contributes to only 15% of the TCA intermediate pool for low-invasive OVCA cells (Fig. 2.2F). This data supports the hypothesis that high-invasive OVCA cells preferentially use Gln for their TCA cycle metabolite pools whereas low-invasive cells utilize Glc.

Recently, reductive carboxylation was found to promote tumor growth by supplying citrate for fatty acid synthesis(33)(34). In this process Gln, through an α- ketoglutarate (α-KG) intermediate, produces citrate for lipogenesis using isocitrate dehydrogenase 1/2 (IDH1/2). The rate of Gln-dependent reductive carboxylation

13 is estimated by U- C5 Gln derived M5 citrate, M3 malate, M3 fumarate, M3 aspartate (Fig. 2.2E). The high-invasive cancer cells (SKOV3) have at least two folds higher Gln-dependent reductive carboxylation flux compared to low-invasive cancer cells (OVCAR3) (Fig. 2.2G). In contrast, low-invasive OVCA cancer cells have higher pyruvate-dependent reductive carboxylation flux using pyruvate carboxylase (PC). Glc entry into the TCA cycle via pyruvate carboxylation involves an alternate pathway where pyruvate is directly converted to oxaloacetate (Fig.

2.11A). PC converts M3 pyruvate into M3 oxaloacetate, which condenses with acetyl-CoA to produce M3, M4 and M5 citrate (Fig. 2.11A). OVCAR3 has higher pyruvate carboxylation flux, compared to high-invasive cancer cells, to support its

Gln independent growth(117) (Fig. 2.11B). Additionally, we found that the M4 malate and fumarate levels, obtained through pyruvate carboxylation, are significantly higher in low-invasive OVCA cells (OVCAR3) (Fig. 2.2B-C).

Next, we determined whether differential utilization of Gln and Glc in high- and low-invasive cancer cells could be the result of atypical metabolic reprogramming of these cells. We measured the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of several OVCA cell lines. Surprisingly, we observed that the low-invasive OVCA cells (OVCAR3) have higher levels of basal oxidative

phosphorylation (OXPHOS, indicated by OCR) and higher levels of basal glycolysis (indicated by ECAR) than high-invasive OVCA cells (Fig. 2.2H-J).

Although most cancer cells exhibit high levels of glycolysis and functional mitochondrial oxidative phosphorylation, cancer cells from many tumors have dysfunctional mitochondria and increased glycolytic capacity. Using oligomycin, the protonophoric uncoupler FCCP, the electron transport inhibitor antimycin, and the Glc analogue 2-deoxyglucose (2-DG), we measured parameters indicative of basal, maximal and reserve mitochondrial and glycolytic capacities for OVCAR3,

OVCA420, SKOV3, and SKOV3ip cells. Low-invasive OVCA cells had higher basal, maximal and reserve mitochondrial capacity than high-invasive OVCA cells

(Fig. 2.2I). However, reserve glycolytic capacity did not change with increasing invasiveness (Fig. 2.2J). Essentially, low-invasive OVCA cells are in a higher energy-generation state than high-invasive OVCA cells. We then investigated the relative contribution of glycolysis and OXPHOS toward energy maintenance. To understand the cell invasiveness-sensitized energetic shift, we used oligomycin, an inhibitor of ATP synthetase that blocks the electron transport chain (ETC), to force cancer cells to shift their energy generation pathway from OXPHOS to glycolysis. The cellular ATP content did not reduce with addition of oligomycin (Fig.

2.11C-D). Therefore, the increased levels of glycolysis (indicated through lactate secretion) are likely a consequence of OXPHOS inhibition(118). From these data, we can conclude that the dominant pathway of energy generation shifts from glycolysis to OXPHOS with increasing degrees of invasiveness (Fig. 2.2K). Similar

results were obtained by independently estimating the ratio between basal OCR and basal ECAR of different cancer cells using extracellular flux analysis. Indeed, the value of OCR/ECAR increases with increasing degrees of invasiveness (Fig.

2.11E).

To further verify differential metabolic rewiring of glycolysis and glutaminolysis in high- and low-invasive OVCA cells, we used GC-MS to quantify relative metabolite abundances in OVCAR3 and SKOV3 using targeted metabolomic analysis. Remarkably, low-invasive OVCA cells had higher quantities of glycolysis

(pyruvate and lactate) and TCA cycle (citrate, malate, and fumarate) metabolites than high-invasive OVCA cells (Fig. 2.2L). This further confirms the increased OCR and ECAR observed in low-invasive cancer cells. Glutaminolysis levels, measured using metabolic isotope abundances of glutamate and aspartate, were markedly higher in high-invasive OVCA cells than in low-invasive OVCA cells (Fig. 2.2L).

Through secretomics of uptake/secretion of primary and secondary amino acids using ultra-high-performance liquid chromatography (UPLC), we found that high- invasive OVCA cells (SKOV3) had a higher Gln uptake flux, and a higher glutamate, alanine, aspartate, ornithine secretion flux through Gln supported anaplerosis pathway when compared to low-invasive OVCA cells (OVCAR3) (Fig.

2.2M. Fig 2.11F-G). Notably, SKOV3 has a higher uptake flux of (a) cysteine, which may be used for glutathione generation; (b) serine, lysine, and secondary amino acids (AABA) which may support cell proliferation. However, SKOV3 has a lower uptake rate of branched chain amino acids (BCAA; isoleucine and leucine); these

metabolites can increase mitochondrial TCA cycle activity (Fig. 2.11F-G, Table

2.1). Lower uptake of BCAA rules out BCAA catabolism as a part of the TCA cycle in high-invasive cells. The above results conclusively show that as invasiveness increases in OVCA cells, cells shift their dependence on Glc onto Gln, with respect to generation of TCA cycle metabolites.

Figure 2.4: Gln driven TCA cycle metabolite abundance, reductive carboxylation, and oxidative phosphorylation is significantly higher in high-invasive than low-invasive OVCA cells.

13 (A) Schematic of carbon atom transitions using 1:1 mixture of C6 glucose and 1-13C labeled glucose. This allows estimations of the contribution of glucose towards TCA cycle metabolites and synthesis of glutamate and Gln. Black color represents labeled carbon on 1st TCA cycle, yellow color represents labeled carbon on 2nd TCA cycle, and blue color represents labeled carbon on 3rd TCA cycle. (B-D) Glucose’s contribution to TCA metabolites and glutamate pool. Comparison of mass isotopomer distribution (MID) of malate (B), fumarate (C), glutamate (D) in high-invasive (SKOV3) and low-invasive (OVCAR3) cells 13 13 cultured with a 1:1 mixture of C6 glucose and 1- C labeled glucose. (E) 13 Schematic of carbon atom transitions using C5 labeled Gln. Green color represents Gln’s direct effect on canonical TCA cycle, red color represents Gln’s effect on TCA cycle through reductive carboxylation. (F) Gln’s contribution to TCA cycle metabolites pool. Comparisons of MID of M5 glutamate, M4 fumarate, 13 M4 malate, M4 citrate in OVCAR3 and SKOV3 cultured with a C5 labeled Gln. (G) Mass isotopomer analysis of isotopomers linked with reductive carboxylation. M5 citrate, M3 malate, M3 fumarate, and M3 aspartate analysis indicates significantly higher reductive carboxylation fluxes in high-invasive than in low- invasive OVCA cells. (H) Basal OCR and ECAR were measured for OVCAR3, OVCA420, SKOV3, and SKOV3ip cell lines. Basal OCR is a measure of OXPHOS activity, and basal ECAR is a measure of glycolysis activity. (I) Using oligomycin, FCCP, and antimycin we estimated mitochondrial functional state in OVCA cells through maximal and reserve mitochondrial capacities. (J) Using 2DG we estimated glycolytic functional state in OVCA cells through maximal and reserve glycolytic capacities. (K) Percentage of OXPHOS and glycolysis contribution to cancer cell's energetic demand. (L) Relative metabolite abundances measured using GC-MS in OVCAR3 and SKOV3 cells. (M) Extracellular uptake/secretion fluxes of amino acids involved in glutaminolysis (Glutamine: Gln; Glutamate: Glu; Alanine: Ala; Aspartate: Asp) were measured using UPLC. Data in A-M are expressed as mean ± SEM, n≥9, *P<0.05, **P<0.01, ***P<0.001. In I and J, 1-way ANOVA with Tukey test was used to compare between cell lines, using OVCAR3 as the control.

Figure 2.5: Differential metabolism in high-invasive and low-invasive OVCA cells. (A) Schematic of carbon atom transitions undergoing pyruvate carboxylation 13 13 using 1:1 mixture of C6 glucose and 1- C labeled glucose. Blue circle

represents labeled carbon through PC. M3 and M1 pyruvate is directly converted into oxaloacetate by combining with CO2 through pyruvate carboxylase. (B) 13 13 Citrate isotopomer distribution using 1:1 mixture of C6 glucose and 1- C labeled glucose medium. (C) Effect of varying oligomycin (OM) concentrations (200, 500, 1000 ng/ml) on glycolysis. (D) Oligomycin’s effect on ATP content. (E) Basal OCR/ECAR of OVCAR3, OVCA420, SKOV3ip and SKOV3 cells. (F) Uptake/secretion rate of cysteine (Cys), ornithine (Orn), proline (Pro) and serine (Ser) of OVCAR3 and SKOV3 cells in complete medium. (G) Uptake/secretion rate of essential amino acids for OVCAR3 and SKOV3 in complete medium. His, histidine; Thr, threonine; Lys, lysine; Met, methionine; Val, valine; Ile, isoleucine; Leu, leucine; Phe, phenylalanine; Trp, tryptophan Data in B-G are expressed as mean ± SD, n≥9, *P<0.05, **P<0.01, ***P<0.001.

Table 2.1: Amino acids uptake and secretion in OVCAR3 and SKOV3.

2.3. High-invasive cells are glutamine dependent for increased

respiration, proliferation, and redox balance

The above experiments show that high-invasive cancer cells exhibit higher utilization of Gln for maintenance of their TCA cycle metabolite pool. We further investigated whether Gln increases mitochondrial respiration in high-invasive

OVCA cells. Adding Gln to Gln deprived cells dramatically increases OCR in high- invasive OVCA cell lines (SKOV3 and SKOV3ip); whereas there was no observable change in the OCR of low-invasive OVCA cells (OVCAR3 and

IGROV1) and only a marginal OCR increase in moderately-invasive OVCA cells

(OV420) (Fig. 2.3A). To establish whether the effects of Gln addition are due to its entry into the TCA cycle, we added aminooxyacetate (AOA), an inhibitor of glutamate dependent amino-transferase, to block glutamate conversion to α- ketoglutarate (α-KG). The addition of AOA decreases OCR significantly in high- invasive cancer cells. Respiration is markedly rescued in high-invasive cancer cells by addition of dimethyl α-KG, a cell penetrant form of -KG. In OVCAR3, IGROV1 and OVCA420, α-KG still increases OCR, since there is sufficient mitochondrial reserve capacity in these cell lines (Fig. 2.3A).

Next, we determined whether high-invasive cancer cells have increased glutaminolysis levels to support proliferation. Using 6-diazo-5-oxo-L-norleucine (L-

DON), a Gln analog which inhibits Gln conversion into glutamate, we found that high-invasive OVCA cells are highly sensitive to decreased glutamate, while only

a marginal decrease in proliferation is observed in low-invasive OVCA cells (Fig.

2.3B). To confirm the importance of Gln in supporting growth through the TCA cycle, we used AOA and epigallocatechin gallate (EGCG, an inhibitor of glutamate dehydrogenase) to inhibit glutamate’s conversion into α-KG. A large decrease in the growth of high-invasive OVCA cell lines was observed following addition of

AOA and EGCG (Fig. 2.3C-D). These results suggest that high-invasive cell lines are significantly more Gln dependent than low-invasive OVCA cells. To further substantiate that the supportive effect of Gln on cell growth occurs through glutaminolysis and entry of Gln into the TCA cycle, we added cell permeable dimethyl α-KG into the culture medium along with EGCG. Indeed, α-KG rescued the proliferation rate of high-invasive OVCA cells, whereas α-KG had no effect on the growth of low-invasive OVCA cells (Fig. 2.3C). We confirmed that this rescue of proliferation is mediated through Gln entry into the TCA cycle. The addition of ammonia and increasing concentration of other nonessential amino acids, including aspartate, asparagine, serine, glycine, and alanine failed to rescue the high-invasive OVCA cells from Gln depletion (Fig. 2.12A).

We used 2-deoxyglucose (2-DG), a glycolysis inhibitor, to illustrate the effects of glycolysis on cell proliferation and metabolism. 2-DG reduced cell proliferation in both high-invasive (SKOV3) and low-invasive (OVCAR3) OVCA cells (Fig.

2.12B). Interestingly, adding Glc markedly decreased the OCR in low-invasive

OVCA cells, demonstrating competition between glycolysis and oxidative phosphorylation for ATP generation (Fig. 2.12C). High-invasive cells showed a

modest OCR decrease induced by Glc depletion. Consistent with previous results, this data confirms that Glc is a critical nutrient for low-invasive OVCA cells, and that these cells are metabolically reprogrammed towards high Glc sensitivity (Fig.

2.12B-C).

It has been shown previously that a significant portion of Gln is diverted towards

13 lactate secretion, via malate and malic enzyme(12). Because U- C6 Glc produces

13 M3 pyruvate and lactate through glycolysis (Fig. 2.3E), whereas U- C5 Gln produces M3 pyruvate and lactate through glutaminolysis and malic enzyme (Fig.

2.3F), we analyzed 13C labeled isotopologues of lactate and pyruvate using labeled

Glc and Gln as tracers. The lactate secretion in Glc-depleted and complete media indicates that in both OVCAR3 and SKOV3 cancer cells, Glc is the major source of lactate (Fig. 2.3G). There were significantly lower levels of M3 pyruvate, M3

13 lactate, and M6 citrate, from U- C5 Gln, than M3 pyruvate, M3 lactate, and M2

13 13 citrate, from 1:1 mixture of U- C6 Glc and 1- C labeled Glc, for both cell lines (Fig.

2.3H-I). These data suggest that lactate is mainly derived from Glc rather than Gln in OVCA cells. Interestingly, increased M6 citrate from labeled Gln indicates that malic enzyme is important for NADPH generation in high-invasive OVCA cells (Fig.

2.3F, 2.3I). In contrast, low-invasive cancer cells had increased oxidative pentose phosphate pathway flux (PPP) compared to high-invasive cancer cells (Fig. 2.3J), which was estimated using M0 and M1 labeling of lactate in cells cultured with 1:1

13 13 13 mixture of U- C6 Glc and 1- C labeled Glc. 1- C labeled Glc loses the labeled carbon when it is routed through oxidative PPP, and then is converted into M0

pyruvate(17). These results support the observation that Glc is critical in increasing

PPP flux for generating NADPH in low-invasive cells.

Previous studies have suggested that c-myc and k-ras can induce oncogenic transformations and increase Gln utilization in tumors (17)(14). However, c-myc protein expression levels have already been shown to be similar in both high- invasive (SKOV3) and low-invasive cell lines (OVCAR3), and no Kras mutations were detected in previous reports(119). Consistent with published data, we did not observe significant changes in the expression levels of c-myc, and k-ras in

OVCAR3 and SKOV3 cell lines (Fig. 2.12D-E). Thus, we can conclude that the increased glutaminolysis observed in high-invasive OVCA cells is not due to increased expression of oncogenes c-myc and k-ras (Fig. 2.12D-E). Our data suggests that the increased glutaminolysis is associated with metabolic reprogramming induced by an increase in cancer cell invasiveness.

The above data demonstrates that Gln facilitates mitochondrial function in high- invasive OVCA cells. We then proceed to determine whether Gln is involved in maintaining mitochondrial membrane potential (MMP) and whether it increases antioxidant activity via glutathione under oxidative stress. Using tetramethylrhodamine methyl ester (TMRM), a MMP sensitive fluorescent dye, we measured MMP after adding H2O2 to the culture medium for ROS generation.

Indeed, in high-invasive OVCA cells (SKOV3), MMP decreased significantly under

Gln deprivation conditions. In contrast, there was no change in the MMP of low- invasive OVCA cells (OVCAR3) during Gln deprivation (Fig. 2.3K), suggesting that

Gln affects MMP only in high-invasive cancer cells. In agreement with previous results, Gln addition doubles the ATP content in high-invasive cancer cells, but shows only a marginal effect in low-invasive OVCA cells (Fig. 2.3L). Gln addition dramatically reduces ROS induced by H2O2, in high-invasive cancer cells

(SKOV3). However, in low-invasive cancer cells, Gln does not reduce ROS levels

(Fig. 2.3M). To further distinguish the differential role of Gln with respect to cancer invasiveness, we studied the role of Gln in NADPH and reduced glutathione (GSH) generation. NADPH is critical for the synthesis of GSH, an important antioxidant, by glutathione reductase. We observed that Gln deprivation decreases NADPH levels in high-invasive OVCA cells (SKOV3 and SKOV3ip), but not in low-invasive

OVCA cells (OVCAR3) (Fig. 2.3N). Thus, Gln serves as an important source for

NADPH, which decreases ROS, in high-invasive OVCA cells. Furthermore, the ratio of NADPH to total NADP (NADPH/total NADP), an indicator of the availability of reducing equivalents (NADPH), was markedly sensitive to Gln deprivation in high-invasive cells but not in low-invasive OVCA cells (Fig. 2.3O). To confirm Gln’s role in scavenging ROS, we measured the levels of glutathione. Gln deprivation decreases total glutathione and GSH level in SKOV3 (Fig. 2.3P). Interestingly, Glc deprivation does not have a significant effect on MMP, ATP generation, or ROS in high-invasive OVCA cells (Fig. 2.12F-I).

Figure 2.6: Gln has pleiotropic role of positively regulating respiration and maintaining redox balance selectively in high-invasive OVCA cells. (A) Oxygen consumption rate (OCR) was measured in high-invasive (SKOV3 and SKOV3ip), low-invasive (IGROV1 and OVCAR3) and moderately-invasive (OVCA420) cells in media containing Gln, aminooxyacetate (AOA), and α- ketoglutarate (α-KG). OCR was normalized with value before injections. (B) Analysis of proliferation of OVCA cell lines with range of concentrations of 6- diazo-5-oxo-L-norleucine (L-DON), a Gln analog which inhibits Gln's conversion to glutamate. Data were normalized with complete media conditions without L- DON. (C) Analysis of proliferation of OVCA cell lines with range of concentrations of epigallocatechin gallate (EGCG, an inhibitor of glutamate dehydrogenase, which converts glutamate into α-KG) used to inhibit glutamate entering the TCA cycle. α-KG was used to rescue reduction of cell proliferation by EGCG. Data were normalized with complete media conditions without EGCG. (D) Analysis of proliferation of OVCA cell lines when glutaminolysis was inhibited with range of concentrations of AOA. Data were normalized with complete media conditions without AOA. (E) Lactate and citrate generation from glucose through direct glycolysis or through both oxidative pentose phosphate pathway (PPP) and glycolysis. Pink circles represent unlabeled carbons from oxidative PPP. (F) Lactate and citrate generation from glutaminolysis. Blue circles represent labeled carbons from malic enzymes, and green circles are labeled carbons from canonical TCA cycle. (G) Glucose deprivation effect on lactate secretion rate. (H) Comparison of M3 pyruvate and M3 lactate derived from either glucose (1:1 mixture of U-13C6 glucose and 1-13C1 glucose) or Gln (U-13C5 Gln) in high- and low-invasive OVCA cells. Since all conditions have complete media, their total pyruvate and lactate content should be the same. (I) Comparison of M2 citrate labeling from glucose and M6 citrate labeling from Gln in OVCAR3 and SKOV3 cells. Total pyruvate and lactate content in all conditions should be equal as explained for (H). (J) Oxidative pentose phosphate pathway fluxes estimated using the mathematical relation (percentage of unlabeled M0 lactate - percentage of labeled M1 lactate) to represent relative oxidative PPP fluxes in OVCAR3 and 13 13 SKOV3 cells cultured with 1:1 mixture of C6 glucose and 1- C labeled glucose. (K) Role of Gln in maintaining mitochondrial membrane potential (MMP) levels in low-and high-invasive cells. (L) Gln maintains ATP content selectively in high- invasive OVCA cells. (M) Gln reduces ROS induced by H2O2 in high-invasive cells but not in low-invasive cells. (N) Gln maintains NADPH level in high- invasive OVCA cells. Data in A-N are expressed as mean ± SEM, n≥9, **P<0.01, ***P<0.001. (O) Gln increases ratio of NADPH / total NADPH ratio selectively in high-invasive OVCA cells. Glucose is unable to provide enough reducing

equivalents in high-invasive OVCA cells. (P) Gln / glucose deprivation’s effect on total glutathione and GSSG’s level in cancer cells. Data in O-P (n≥6) are expressed as mean ± SEM, *P<0.05, **P<0.01, ***P<0.001.

Figure 2.7: Glc deprivation on OVCA proliferation and oncogene expression in OVCAR3 and SKOV3. (A) Addition of nitrogenous sources (NH4+) and amino acids increasing tumor growth (Ala, Asp, Asn, Ser, Gly) could not rescue Gln deprivation in short term / 48 hours. (B) Effect of different 2-DG concentrations on OVCAR3 and SKOV3’s proliferation. (C) Effect of glucose on OCR. Data in A-C are expressed as mean ± SD, n≥9. (D,E) Western blot of c-MYC (D) and k-Ras (E) expression levels in low-invasive OVCA (OVCAR3) and high-invasive (SKOV3) OVCA cells. n≥3. (F) Glucose's effect on mitochondrial membrane potential. (G,H) Glucose's effect on NADPH and ROS generation. (I) Glucose deprivation’s effect on ATP content.

2.4. Ratio of Glutamine Catabolism over Anabolism Correlates

with Invasiveness

To reveal the genetic cause behind dependence on Gln, we first analyzed the relative expression of Gln pathway genes in ovarian cancer cell lines using

KyotoOv38, an ovarian cancer line gene expression dataset. Interestingly, glutaminase (GLS1, which converts Gln into glutamate) expression is approximately four fold higher in high-invasive OVCA cells (SKOV3) compared to low-invasive OVCA cells (OVCAR3) (Fig. 2.4A). Moreover, low-invasive OVCA cells (OVCAR3) have a higher expression of Gln synthetase (GLUL) which catalyzes condensation of glutamate and ammonia into Gln (Fig. 2.4A). The

Western blot results confirm that high-invasive OVCA cells have relatively higher expression level of GLS1, but lower expression level of Gln synthetase (Fig. 2.4B).

This metabolic reprogramming results in the Gln independence of low-invasive cancer cells. To further validate our findings that metabolic shifts in Gln utilization regulate cancer progression, we analyzed gene expression profiling and clinical outcome data from published studies. A correlation was discovered between progression free survival of OVCA patients and the ratio of expression levels of

Gln catabolism-related genes to those of Gln anabolism-related genes. Based on the noticeable effects of glutaminase expression levels on the prognosis of cancer patients, we found that competing activities of Gln catabolism and Gln anabolism act as potential biomarkers for predicting patient survival. The gene expression ratios GLS1/GLUL, GLUD1/GLUL and combined ratio (GLS1+GLUD1)/GLUL are

considered as valid representations of the Gln catabolic versus anabolic activities.

Interestingly, patients above the 75th percentile of GLS1/GLUL and GLUD1/GLUL ratios had remarkably poorer prognoses than patients below the 25th percentile

(Figs. 2.4C-E). Therefore, the prevalence of Gln catabolism over Gln anabolism

((GLS1+GLUD1)/GLUL) in OVCA patients has a conspicuous influence on their prognosis.

We further explored the reasons behind Gln independence in low-invasive

OVCA cells, by converting "Gln independent" cells to a "Gln dependent" phenotype. Through investigation of cellular metabolism, energetics, and invasiveness, we have demonstrated that low-invasive OVCA cells are Gln independent and high-invasive cells are Gln dependent. Our isotope labeling data reveal that low-invasive OVCA cells (OVCAR3) produce glutamate from other nutrient sources especially Glc (Fig. 2.2D). Using labeled Glc, the glutamate isotope distribution data illustrates that low-invasive OVCAR3 cells have a significantly higher percentage of labeled (M2, M3, M4 and M5) glutamate than high-invasive OVCA cells (SKOV3) (Fig. 2.2D). This data suggests that although low-invasive OVCA cells are Gln independent, they use Glc for glutamate production to compensate for the lack of Gln.

We then performed nutrient-sensitized energetic shift experiments to confirm our premise that shifting a cell’s metabolic focus from glycolysis to mitochondrial respiration will make low-invasive OVCA cells Gln dependent(27)(120). To convert

Gln independent cancer cells into Gln dependent cells, we replaced 11 mM Glc in

the culture medium with 11 mM galactose. Galactose enters glycolysis through the

Leloir pathway, bypassing the hexokinase step, and is converted into glucose-6- phosphate at a very low rate compared to Glc(27). Hence, cells shift their energy metabolism towards mitochondrial glutaminolysis in the presence of galactose to compensate for reduced glycolysis (120). We used galactose to assess whether shifting cellular energy metabolism towards the mitochondrial TCA cycle will give

“Gln independent” cells a “Gln dependent” phenotype. The growth rate of low- invasive OVCA cells decreases when Glc is replaced with galactose in the presence of Gln (Fig. 2.4F). Cell proliferation further decreases when Gln is depleted from the media, confirming the Gln dependent phenotype of low-invasive cells when in the presence of galactose. Gln deprivation in high-invasive cells

(SKOV3), in the presence of galactose, results in more than 90% cell death, further confirming the strong dependence of these cells on Gln (Fig. 2.4F).

As an alternative strategy for converting “Gln independent” cells to a “Gln dependent” phenotype, we reduced glycolysis levels by decreasing either Glc concentration (reducing 11 mM Glc to 1.1 mM Glc) or using 2-DG (inhibiting glycolysis). Both low Glc concentration and 2DG converted low-invasive OVCA cells to Gln dependent phenotype (Fig. 2.4G). To determine the reason behind this increase in Gln importance in the presence of galactose or low Glc levels, we measured OCR and ECAR. Galactose and low Glc medium shifts the primary energy pathway of OVCAR3 and SKOV3 from glycolysis to OXPHOS, as evident from the increased OCR/ECAR. The increased OCR/ECAR enhances the function

of Gln in the TCA cycle (Fig. 2.4H; Fig. 2.13A-B). We further reasoned that Glc is used for Gln synthesis in low-invasive cancer cells, causing their Gln independent phenotype. Thus, these cells use a de novo synthesis mechanism for Gln synthesis and are not sensitive to Gln withdrawal. Indeed, when both Gln uptake and Gln synthesis pathways are inhibited, Gln pools in both high- and low-invasive

OVCA cells are depleted, resulting in significant cell growth reduction for all OVCA cells (Fig. 2.4I).

Figure 2.8: Regulating Ratio of Gln catabolism over anabolism reduces tumorigenicity. (A) Gene expression levels of Gln synthetase (GLUL) and glutaminase (GLS1) were determined using KyotoOv38, a database for gene expression of ovarian cancer cell lines. (B) Western blot of the GLUL and GLS1 protein expression

level for ovarian cancer cell lines. (C-E) Progressive free survival rate for ovarian cancer patients, categorized according to GLS1/GLUL (C), GLUD1/GLUL (D), (GLS1+GLUD1)/GLUL (E). The survival analysis is executed from comparison of upper quartile and lower quartile patients. (total patients n=539). (F, G) Converting Gln independent OVCA cells into Gln dependent by replacing glucose with galactose (F) or low glucose concentrations and 2-DG (10 mM) (G). (H) Galactose and low glucose’s effect on OCR/ECAR. (I) Combined approach of targeting high-invasive OVCA cells by blocking glutamine's entry into the TCA cycle along with targeting low-invasive OVCA cells by inhibiting glutamine synthesis leads to pronounced reduction of cell growth. Targeting Gln synthetase using MSO under Gln deprivation decreases OVCA cell growth. Arbitrary Units (AU) is proportional to cell number (n≥10).

Figure 2.9: Low Glc and galactose effect on OCR and ECAR. (A,B) Galactose and low glucose’s effect on OCR (A) and ECAR (B) values. Data in A-B are expressed as mean ± SEM, n≥9, *P<0.05, **P<0.01, ***P<0.001.

2.5. Clinical significance of glutamine catabolism and

therapeutic effectiveness of GLS1 siRNA in ovarian cancer

models

To further evaluate the clinical significance of the ratio of Gln catabolism over anabolism, we assessed GLS1 and GLUL protein expression in epithelial ovarian cancer samples (n= 139) using tissue microarrays (TMA) and correlated it to survival outcome. High GLS1 protein expression was associated with worse overall survival, whereas high GLUL protein expression was associated with better survival (Fig. 2.5A). In concordance with our previous results obtained from gene expression data from TCGA, we found that the ratio of Gln catabolism over anabolism (GLS1/GLUL) through protein expression was also significantly related to worse overall survival (p<0.001).

Next, we examined the biological effects of GLS1 siRNA in Gln-independent

(IGROV1) and Gln-dependent (SKOV3ip1) cells in mouse models. Before initiating in vivo experiments, we checked the efficacy of siRNA for silencing GLS1 in

SKOV3ip1 cells using targeted GLS1 siRNA. Transfection of SKOV3ip1 cells with the targeted siRNA resulted in >70% decrease in GLS1 mRNA levels (Fig. 2.5B).

Trypan blue dye assay confirmed that transfection of cells with siRNAs did not affect cell viability after 48 hours of transfection, suggesting that siRNA was not toxic to cells. SiRNA was incorporated into the neutral nanoliposome DOPC (1,2-

Dioleoyl-sn-Glycero-3-phosphatidylcholine); for therapy experiments, siRNA

administration was started 1 week after tumor cell injection subcutaneously. Mice were divided into the following 2 groups (n = 5 mice per group): a) control siRNA-

DOPC, and b) GLS1 siRNA-DOPC. All of the animals were euthanized two weeks after inoculation. As seen in Fig. 2.5C-E, treatment with GLS1 siRNA-DOPC in Gln independent IGROV1 tumor bearing mice did not affect tumor volume or deep tumor infiltration through the muscle layers compared to control siRNA treatment.

In contrast, GLS1 gene silencing in Gln dependent SKOV3ip1 tumor-bearing animals showed significant reduction in both tumor weight (60%; p = 0.007), and tumor volume (75%; p = 0.008) when compared to control siRNA-DOPC treated groups. Moreover, GLS1 siRNA-DOPC blocked tumor infiltration into surrounding tissues (Fig. 2.5F-H).

Figure 2.10: Glutaminase activity is required for tumor growth and invasion in high-invasive OVCAs. (A). Kaplan-Meier curves of disease-specific survival for patients with epithelial OVCA (n=139) based on GLS1 and GLUL protein expression. The log-rank test (two sided) was used to compare difference between groups. (B) Following transfection with either GLS1 siRNA or control siRNA, mRNA levels were assessed with qRT-PCR. Therapeutic efficacy of siRNA-mediated GLS downregulation: (C) tumor nodule (D) volume (E) pattern of invasion of low- invasive OVCA cell line (IGROV1); (F) tumor nodule (G) volume and (H) pattern of invasion of SKOV3ip1 cells in nude mouse models. Following subcutaneous injection of nude mice with 2.0 x 106 IGROV1 or SKOV3ip1 cells, mice were randomly allocated to one of the following groups: control siRNA-DOPC or GLS1 siRNA-DOPC. Treatment was started three days after tumor cell injection and

siRNA-liposomes were administered twice weekly at a dose of 150 g/kg body weight and continued for two weeks. At the time of sacrifice, mouse weight, tumor weight and tumor volume were recorded. Statistical analysis for tumor weights was performed by Student’s t test. **P = 0.007 and **P = 0.008 compared with control siRNA-DOPC.

2.6. Glutamine regulates cancer cell Invasiveness through

STAT3 activity in high-invasive ovarian cancer cells

To explore the underlying mechanisms of Gln induced increased invasiveness, we first assessed the effect of Gln on the invasive capacity of OVCA cells.

Consistent with our working model showing the selective role of Gln in high- invasive cancer cells, we found that Gln directly maintains cancer cell’s invasiveness. Gln significantly increases the invasive and migratory capacity of

SKOV3, but not of OVCAR3 (Fig. 2.6A, Fig. 2.14A). On the other hand, glucose deprivation only has an effect on migratory capacity (Fig. 2.14A-B). Interestingly, inhibiting the entry of Gln into the TCA cycle with LDON, AOA, EGCG, or Bis-2-(5- phenylacetamido-1,3,4-thiadiazol-2-yl) ethyl sulfide (BPTES, glutaminase inhibitor) significantly reduces the invasive capacity of the high-invasive SKOV3 cells (Fig. 2.6A). In line with these findings, the invasive capacity of SKOV3 was restored upon addition of α-KG under Gln depletion conditions. Similar results were obtained upon addition of α-KG when Gln entry into the TCA cycle was blocked by EGCG. Using rotenone (inhibits complex I of mitochondrial electron transport chain (ETC)) we confirmed that reducing TCA cycle activity indeed

decreases the invasiveness of OVCA cells. Collectively, these results demonstrate that the pleiotropic role of Gln in tumor growth, invasion and survival within high- invasive OVCA cells is dependent on glutaminolysis and entry of Gln into the TCA cycle.

To further test the hypothesis that Gln mediates oncogenic transformations in high-invasive cells by regulating growth factor pathways, we assessed the levels of phospho-proteins in key signaling pathways (Fig. 2.6B, Fig. 2.14G).

Confirmation that glutaminolysis has a differential effect on growth factor pathways of high-invasive ovarian cancer cells would further support our premise that Gln maintains invasiveness in OVCA cells by regulating these pathways. During Gln starvation conditions, we observed that the greatest differential effect between high- and low-invasive cells occurs in the STAT3 pathway (Fig. 2.6B). Signal transducer and activator of transcription 3 (STAT3) is a latent cytoplasmic transcription factor activated in response to growth factors and cytokines(121)(122). STAT3 is highly expressed in OVCA and has been shown to confer drug resistance, induce growth promoting effects and is implicated in the malignant transformation of cancer cells(123)(124)(125). The tyrosine phosphorylation of STAT3 through JAK (Janus kinases), RTK (receptor tyrosine kinases), and non-RTK activates STAT3, which then translocate to the nucleus and promotes gene expression to mediate oncogenic transformations, including aerobic glycolysis(126). In contrast, the serine phosphorylation of STAT3 has been found to promote oxidative phosphorylation in mitochondria through interaction

with mitochondrial complexes (127)(128). We observe that in complete medium,

STAT3 phosphorylation at tyrosine 705 (Y705) is elevated in the high-invasive cell lines (Fig. 2.6B). Gln deprivation significantly decreases the levels of Y705 in the high-invasive OVCA cells, but has no effect in low-invasive OVCA cells (Fig. 2.6B,

Fig. 2.14C), while Glc deprivation reduces Y705 levels in the intermediately- invasive cells as well as the high-invasive cell lines (Fig. 2.14C). The level of Y705 parallels the abundance of Jak1 and phospho-Src in similar conditions (Fig. 2.6B).

Epidermal growth factor Receptor (EGFR), JAK tyrosine kinase, Src tyrosine kinase and Erk1/2 play a dominant role in STAT3 phosphorylation and are linked with cancer cell proliferation, metabolic regulation, and metastasis(129)(130)(131)(132)(122). As the degree of invasiveness increases, phosphorylation of EGFR and Erk 1/2 also increases when in complete medium

(Fig. 2.6B). Glc deprivation has no effect upon STAT3 phosphorylation of serine

727 (S727) in any of the cells lines whereas Gln deprivation results in a significant decrease of S727 level in the high-invasive cell lines (Fig. 2.6B, Fig. 2.14D). The deprivation of other amino acids, such as serine, glycine, asparagines, and aspartate, did not affect the STAT3 phosphorylation in OVCA cells (Fig. 2.14F).

Since Gln depletion decreases STAT3 phosphorylation, we inquired whether overexpression of STAT3 could attenuate the Gln deprivation induced reduction in proliferation. Significantly, when STAT3 is overexpressed in SKOV3 cells through transfecting with a constitutively active form of STAT3 (stat3c) we found that it rescues high-invasive OVCA cells (SKOV3) from Gln deprivation induced

reduction of cellular growth (Fig. 2.6C). Importantly, this confirms our premise that

Gln exerts oncogenic transformations in high-invasive cancer cells by differentially activating STAT3 in these cells compared to their low-invasive counterparts.

Strikingly, in line with our previous findings in which we found that Gln entry into the TCA cycle maintains invasiveness, we found that levels of Y705 are maintained by α-KG and oxaloacetate (OAA) under Gln deprivation conditions (Fig. 2.6D-E).

Inhibiting TCA cycle activity through prevention of Gln entry into the TCA cycle, using BPTES or through rotenone inhibition of the ETC, results in a decrease of

Y705 levels (Fig. 2.6F). This further implicates glutaminolysis in maintaining growth factor signaling. We found that OAA and α-KG fail to rescue S727 levels. Similarly, the addition of BPTES and rotenone produced no change in S727 levels, indicating that serine S727 levels are not dependent on TCA cycle activity (Fig. 2.6D-F).

AG490, a Jak inhibitor, is known to modulate the phosphorylation level of

STAT3. Notably, we found that AG490 substantially decreases the invasive capacity of high-invasive cancer cells (Fig. 2.6G) but has a marginal effect on low- invasive cells. Furthermore, in high-invasive OVCA cells, Gln/Glc deprivation results in increased phosphorylation levels of p38 MAPK, a protein kinase central to regulation of MAPK signaling pathway cascades; increased phosphorylation level of ACC, an enzyme integral to lipid metabolism regulated through AMPK signaling pathway; as well as decreased FAK phosphorylation levels for cellular adhesion. Additionally, Glc/Gln deprivation did not have significant effect on

TSC2/GSK3 signaling, which regulates energy metabolism. However, Glc but not

Gln deprivation enhanced autophagy in all cell lines indicated by increased LC 3β-

II and AKT phosphorylation, which regulate Glc metabolism in cancer cells (Fig.

2.14G). We further analyzed the relative expression of STAT3 target genes in ovarian cancer cell lines using the KyotoOv38 dataset. Consistent with our observations, STAT3 is found to be important for OVCA invasion since it targets the expression of invasion-related genes, including THBS1, SERPINE1/2,

COL5A1, THBD, and PLAU (Fig. 2.6H). Indeed, STAT3 inhibition decreases cell proliferation and enhances cell death within low- and high-invasive OVCA cells in both Glc and Gln deprivation conditions (Fig. 2.6I). A combination of STAT3 inhibition and nutrient deprivation (Glc or Gln) has a stronger effect due to the pleiotropic roles of both STAT3 and nutrients. In line with these findings, inhibiting both STAT3 activity (through addition of static, a STAT3 inhibitor) and conversion of Gln into glutamate (through BPTES, a glutaminase inhibitor) results in a pronounced reduction of viability in high-invasive cancer cells (Fig. 2.6J).

Figure 2.11: Gln mediates oncogenic transformations in high-invasive cells by regulating STAT3 activity. (A) Comparison of OVCAR3 invasive capacity in Gln depleted and complete media conditions. Invasive capacity of SKOV3 is measured under complete media, Gln depleted, and drugs inhibiting Gln’s entry into TCA cycle. L-DON, BPTES, EGCG, AOA, and Rotenone decrease SKOV3’s matrigel invasive capacity. α-ketoglutarate (α-KG) addition under glutamine deprivation or EGCG

conditions rescues SKOV3’s invasive capacity. Data in A (n≥4), are expressed as mean ± SEM, *P<0.05, **P<0.01, ***P<0.001. (B) Activation of Stat3 through tyrosine-705 phosphorylation (Stat3.pY705) is elevated in high-invasive OVCA cells. The phosphorylation level of EGFR and Erk 1/2 increases with increasing degree of invasiveness. Tyrosine kinase signaling pathway activities can be affected by metabolic stress and in high-invasive cells that are glutamine and glucose deprived. Stat3.pY705 is reduced along with total Jak levels. Stat3.pY705 levels are only reduced in the OVCA420 cell lines upon glucose deprivation while the regulation of Stat3 phosphorylation by metabolic stress is absent in the low-invasive cell line, OVCAR3. Stat3 serine-727 phosphorylation (Stat3.pS727) is also reduced along with phosphorylation level of Erk 1/2 in the high-invasive cell lines and only in response to glutamine deprivation. β-actin as loading control. (C) Cell proliferation in high-invasive OVCA cell line is reduced when glutamine starved. Proliferation can be partially rescued by overexpression of transgenic Stat3 and a constitutively active mutant of Stat3, Stat3c, mean ± SD, n=3, *P<0.05. β-actin as loading control. (D) α-KG addition rescues Jak1- STAT3 phosphorylation, but cannot rescue Stat3 serine-727 phosphorylation. β- actin as loading control. (E) OAA addition rescues Jak1-STAT3 tyrosine phosphorylation. BPTES and rotenone inhibit STAT3 phosphorylation at tyrosine 705 (Y705). β-actin as loading control. (F) Inhibition of Stat3 decreases OVCA metastasis. Treatment of SKOV3 cells with AG490 results in reduced invasion. (G) Gene expression levels of targets of STAT3 involved in invasion were determined using KyotoOv38 in five OVCA cells. High-invasive OVCA cells had higher gene expression of invasive genes. (H) The addition of AG490, a Stat3 inhibitor enhances the effect of glucose and glutamine deprivation on proliferation in OVCA cell lines. (I) The addition of Stattic, inhibitor of STAT3, has a combinational cytotoxicity effect on both cell lines. Data in H, I are expressed as mean ± SEM, n≥8, *P<0.05, **P<0.01, ***P<0.001.

Figure 2.12: Glc or Gln deprivation on OVCA cellular signaling phosphorylation. (A) Glucose/Gln deprivation’s effect on cancer cells’ migratory ability. (B) Glucose deprivation’s effect on OVCAR3 and SKOV3’s invasive capacity. (C-E) STAT3 Y705 phosphorylation level in glutamine / glucose deprivation conditions. STAT3 S727 phosphorylation level in glutamine / glucose deprivation medium conditions. (F) The effect of serine, glycine, asparagine, and aspartate deprivations on STAT3 phosphorylation. Cell culture medium is MEM and was adjusted to similar nutrients component as RPMI. And different amino acids are deprived. (G) Glucose/glutamine deprivation effect on protein expression and phosphorylation level for OVCAR3, OVCA420, SKOV3, SKOV3 ip.

2.7. Glutamine dependent anaplerosis regulates glycolysis in

high-invasive cancer cells through STAT3 phosphorylation

To expand our findings on the differential regulation of Gln metabolism in high- invasive cancer cells, we examined both, the effect of Gln on glycolysis and the effect of Glc on glutaminolysis. We hypothesized that Gln’s effects on the metabolic rewiring of high-invasive OVCA cells acts through STAT3 phosphorylation. We first cultured OVCA cells in complete and Gln-depleted media to measure Glc uptake and lactate secretion levels. Notably, we observed competitive regulation of Glc by Gln in high-invasive cancer cells. Gln addition significantly down regulates glycolysis (Glc uptake and lactate secretion) in high- invasive cancer cells, but has no effect on low-invasive OVCA cells (Fig. 2.7A-B).

Similar conclusions were drawn from experiments on OVCA429 and OVCAR8 cells (Fig. 2.15A). To confirm whether the effect of Gln on glycolysis acts through its entry into the TCA cycle, we added AOA, an inhibitor of Gln entry into the TCA

cycle. Addition of AOA selectively increases glycolysis in SKOV3 but not in

OVCAR3 (Fig. 2.7C-D). The addition of α-KG reverses this metabolic coupling in high-invasive cells (SKOV3). The reduced glycolysis observed in low-invasive cells

(OVCAR3) upon α-KG addition is the result of increased TCA cycle activity

(Fig.2.3A). We next cultured OVCA cells in Glc-depleted and complete media and used an UPLC to measure uptake/secretion fluxes of Gln, glutamate, aspartate and alanine. Glc deprivation increases Gln uptake in both high- and low-invasive

OVCA cells. Conversely, only in high-invasive OVCA cells is there a significant increase in the secretion of glutamate, aspartate, and proline, which can be derived from Gln (Fig. 2.7E-F, Fig. 2.15B-C). Moreover, there is an increased uptake of cysteine (used for glutathione generation) and arginine (used in the urea cycle and protein synthesis) in high-invasive OVCA cells (Fig. 2.15D-E). For OVCAR3, Glc deprivation decreases alanine secretion due to decreased pyruvate generation

(Fig. 2.7E). Further, Glc deprivation increases serine uptake in both cell lines (Fig.

2.15D-E). This may be due to the absence of Glc mediated serine synthesis for the SOG (serine, one-carbon cycle, glycine synthesis) pathway to generate

NADPH, ATP and purines. Therefore, cancer cells will uptake more exogenous serine to meet their nutritional requirement for survival(133)(134).

To delineate STAT3 induced metabolic alterations from Gln driven metabolic pathways in high-invasive cancer cells, we used AG490 to measure glycolytic and

OXPHOS activity in OVCA cells. We found that AG490 decreases STAT3 serine phosphorylation in high-invasive OVCA cells (SKOV3) and marginally increases

STAT3 tyrosine phosphorylation in low-invasive OVCA cells (OVCAR3) (Fig.

2.7G). Consistent with our hypothesis, inhibition of STAT3 phosphorylation decreases OCR in high-invasive OVCA cells (Fig. 2.7H-J) along with a compensatory increase in glycolysis. Surprisingly, we found that AG490 decreases glycolysis in low-invasive OVCA cells, whereas it increases glycolysis in high- invasive OVCA cells (Fig.2.7K-L). Taken together, these results show that STAT3 interacts with the metabolic switch that shifts low-invasive OVCA cells from glycolysis to OXPHOS. Furthermore, AG490 induces metabolic reprogramming in high-invasive OVCA cells by decreasing STAT3 serine phosphorylation, thereby decreasing OXPHOS and increasing glycolysis.

Figure 2.13: Gln’s effect on metabolic rewiring in high-invasive OVCA cells is through STAT3 phosphorylation.

(A, B) Effect of Gln deprivation on glucose uptake and lactate secretion fluxes in OVCAR3, OVCA420, SKOV3, SKOV3ip cells. (C, D) Influence of AOA, - ketoglutarate (α-KG) on glucose uptake and lactate secretion fluxes in OVCA cells. Data in A-D are expressed as mean ± SEM, n≥9. ***P<0.001. (E, F) Effect of glucose deprivation on Gln, glutamate, aspartate, and alanine uptake/secretion fluxes in high- and low-invasive OVCA cells. Data in E-F are expressed as mean ± SEM, n=6. *P<0.05, **P<0.01. (G) AG490’s effect on tyrosine and serine phosphorylation of STAT3 in low-invasive (OVCAR3) and high-invasive (SKOV3) OVCA cells. (H) AG490 shifts low-invasive OVCA cells from glycolysis to OXPHOS, whereas reverse was true for high-invasive cells. (I) AG490 increases OCR for OVCAR3 and decreases OCR for SKOV3 cells. (J) AG490 decreases ECAR for OVCAR3 and increases ECAR for SKOV3 cells. (K, L) AG490 decreases glucose uptake (K) and lactate secretion rate (L) for OVCAR3 and increases glucose uptake and lactate secretion rate for SKOV3. Data in H-L are expressed as mean ± SEM, n≥6, ***P<0.001.

Figure 2.14: Glc deprivation on amino acids uptake and secretion rate.

(A) Gln’s effect on glycolysis for OVCA429 and OVCAR8 cells. (B-E) Amino acid uptake/secretion rate after glucose deprivation measured using UPLC in OVCAR3 (B,D) and SKOV3 (C, E) cells. Data in A-E are expressed as mean ± SEM, n≥6.

2.8. Discussion

The role of Gln in regulating cancers with varying degree of invasiveness has been poorly understood. Understanding the differential role of Gln with respect to increasing cancer invasiveness could lead to the development of a metabolic regulation strategy for targeting heterogeneous populations of cancer cells. Recent studies have indicated a differential metabolic wiring in high-invasive cancer cells compared to low-invasive cancer cells(135). High-invasive cancer cells were shown to have increased expression of monoglycerol lipase, a protein which regulates the fatty acid network that initiates oncogenic signaling and promotes tumor pathogenicity(135). For OVCA cells under anoikis conditions, we recently reported that pyruvate increases the migratory capacity of highly invasive OVCA cells’ migration ability, thus implicating mitochondrial activity in the development cancer metastasis. Kim et. al showed that inhibiting mitochondrial function reduces cancer cells’ invasive capacity(136)(137). Here, we reveal that Gln is a key mitochondrial substrate for driving cancer metastasis. Our work defines the essential role of Gln in high-invasive OVCA cells and presents a viable therapeutic strategy to target OVCA tumors with variable degrees of invasiveness.

We show here for the first time that high-invasive OVCA cells are markedly dependent on Gln for cell proliferation, whereas low-invasive OVCA cells are Gln- independent for growth. As the degree of cancer invasiveness increases, nutrient dependence shifts from Glc to Gln (Fig. 2.8). In both low and high-invasive OVCA cells, we found that Glc contributes more than 50% of pyruvate (M3 pyruvate and

13 13 lactate was greater than 25% in 1:1 mixture of C6 Glc and 1- C labeled Glc). On the other hand, in high-invasive OVCA cells, Gln contributes more than 50% of glutamate and 40% of TCA cycle fluxes, whereas in low-invasive OVCA cells Gln contributes around 15% of glutamate and TCA cycle fluxes. Moreover, Gln addition increases the oxygen consumption rate in high-invasive cells (greater than 80% in

SKOV3 and SKOV3ip) (Fig. 2.3A). A highlight of our in vitro mechanistic studies and in vivo model is the finding that the inhibition of glutaminolysis is more detrimental to high-invasive OVCA cells than their low-invasive counterparts.

GLS1-targeted siRNA significantly decreased cancer growth and invasion in mice bearing ovarian tumors derived from glutamine dependent (high-invasive,

SKOV3ip1) compared to glutamine independent (low-invasive, IGROV1) cells. Our results substantiate the hypothesis that Gln is essential for anaplerosis in the TCA cycle and cell survival only in high-invasive cancer cells. In particular, Gln increases glutathione synthesis and reduces ROS differentially in high-invasive cells compared to low-invasive cells. Thus, high-invasive cells are dependent on

Gln, which protects mitochondrial integrity by synthesizing glutathione to reduce

ROS and promote cancer cell survival. Moreover, we found that Gln promotes

cancer invasion in high-invasive cells. However, with increasing cancer invasiveness, there is a decrease in both glycolytic and basal mitochondrial capacity. This could be due to a shift in the role of nutrients (Glc and Gln) from energy generation to biosynthesis.

Herein, we provide previously unidentified evidence that Gln maintains invasive cancer phenotypes by regulating factors controlling the oncogenic transformations in cancer cells. STAT3 is involved in cellular differentiation, anti-apoptotic response, metastasis, and large-scale signaling system(138)(139)(140). The constitutive canonical tyrosine phosphorylation of STAT3 is required for transcriptional downstream activation of several oncogenes in the nucleus(141)(142)(143). We believe our results are the first to show that Gln deprivation regulates the phosphorylation of STAT3, an oncogenic transcription factor, and thereby induces rewiring of cancer metabolic pathways (Fig. 2.8). As the degree of cancer invasiveness increases, the STAT3 phosphorylation cascade is altered. In low-invasive OVCA cells, Src and Jak1 increase STAT3 tyrosine phosphorylation. Whereas, EGFR along with Src and Jak1, controls the tyrosine phosphorylation levels of STAT3 in high-invasive OVCA cells. Our results suggest that the tyrosine phosphorylation levels of STAT3 are dependent on Gln’s entry into the TCA cycle, which further confirms our findings that glutaminolysis positively regulates invasiveness in OVCA cells. Furthermore, Erk1/2 is highly phosphorylated in high-invasive OVCA cells, resulting in promotion of STAT3 serine phosphorylation. Nutrients also play important roles in regulation of cell

signaling pathways and rewire cancer metabolism (Fig. 2.8). Deprivation of both

Glc and Gln decreases tyrosine phosphorylation of STAT3, whereas Gln deprivation lead to decreased STAT3 serine phosphorylation levels through reduction in Erk 1/2 phosphorylation level; validated by addition of Erk1/2 phosphorylation inhibitor PD98059 (Fig. 2.16).

Having established that Gln deprivation reduces STAT3 phosphorylation levels and cellular growth, we found that overexpression of STAT3 can restore reduced proliferation that was observed under Gln deprivation conditions. Based on our results, we postulate that Gln regulates mitochondrial respiration through selective serine phosphorylation of STAT3 in high-invasive OVCA cells. Under Gln deprivation conditions serine phosphorylation of STAT3 is reduced, which in turn downregulates mitochondrial respiration and increases glycolysis through compensatory pathways. In low-invasive cancer cells, we concluded that respiration is not regulated through serine phosphorylation, since we did not find expression of serine phosphorylated STAT3 even under complete media conditions. Also in low-invasive OVCA cells, both STAT3 serine and tyrosine phosphorylation pathways are insensitive to Gln deprivation; hence Gln does not regulate Glc uptake. Glc deprivation decreases tyrosine phosphorylation of STAT3 in OVCA cells, thereby activating the signaling pathways downstream of RTKs, which include Akt phosphorylation and MAPK p38 phosphorylation (Fig.

2.14G)(144). This upregulates the metabolism of other available nutrients, including Gln and serine. For high-invasive OVCA cells, inhibiting Jak with AG490

decreases serine phosphorylation of STAT3 and thus decreases mitochondrial respiration (OCR). Further, compensatory pathways enhance glycolysis by increasing Glc uptake in SKOV3 cells. In low-invasive cells, AG490 marginally increases tyrosine phosphorylation of STAT3. We observe increased respiration and decreased glycolysis in low-invasive cells when STAT3 is inhibited. Future studies are needed to determine the mechanisms by which Gln deprivation negatively regulates STAT3 phosphorylation.

Finally, our work provides insights into the observation that low-invasive OVCA cells are Gln independent. In low-invasive but not in high-invasive OVCA cells, glucose is converted into glutamine to meet its glutamine requirements. We provide evidence, using galactose and low Glc medium conditions, that low- invasive cells shift their energy metabolism to meet energetic demands. Our work extends this concept by showing that restricting de novo Gln synthesis may be effective in targeting low-invasive OVCA cells. A combined approach of targeting high-invasive OVCA cells by blockading Gln entry into TCA cycle pathways, along with targeting low-invasive cancer cells by inhibiting Gln synthesis and STAT3, may provide opportunities for addressing heterogeneity in tumors.

Figure 2.15: Gln’s entry into TCA cycle regulates ovarian cancer invasiveness. Schematic showing the shift in nutrient utilization in TCA cycle with increasing degree of invasiveness. Low-invasive OVCA cells are glucose dependent for their TCA cycle pool. With increasing invasiveness in cancer cells, dominant

nutrient which feeds the TCA cycle shifts from glucose to Gln. In high-invasive OVCA cells, Gln dominates the TCA cycle. In low-invasive OVCA cells, glucose activates Jak1, which activates STAT3 by tyrosine phosphorylation, thereby regulating glycolysis in cancer cells. In high-invasive OVCA cells, besides glucose’s role in activating STAT3 tyrosine phosphorylation, glutamine activates JAK1 through TCA cycle to further activate STAT3 by tyrosine phosphorylation and thus regulate glycolysis. Further, Gln activates Erk1/2, which subsequently activates STAT3 by serine phosphorylation selectively in high-invasive OVCA cells. The serine phosphorylation of STAT3 enhances oxidative phosphorylation in mitochondria by interaction with mitochondrial complexes I and II, thereby increasing TCA cycle activity in high-invasive OVCA cells.

Figure 2.16: ERK inhibiton on STAT3 serine phosphorylation. STAT3 serine phosphorylation level at the addition of PD98059, a MEK/ERK pathway inhibitor. Here the ERK 1/2 as the loading control.

Chapter 3

Targeting stromal glutamine synthetase disrupts tumor microenvironment- regulated cancer cell growth

The transformation of healthy epithelial cells into aggressive tumors is a gradual process that is characterized by cancer hallmarks such as drug resistance, dysregulated energy metabolism, and metastatic proclivity(7)(145)(146). As cancer cells grow, they recruit stromal cells, forming a complex TME. These reactive stromal cells co-evolve and continually interact with cancer cells becoming an integral part of their physiology and are indispensable for their survival(93)(147). Increasing evidence suggests that reactive stroma is not an innocent bystander(148)(149), but rather mediates a network of paracrine signals conferring resistance to cancer cells in nutrient- deprived conditions observed in TME(150). Targeting reactive stromal cells is emerging as an attractive and viable therapy to regulate the channels of communication between stromal and cancer cells(151)(152)(153). To target non- autonomous mechanisms of cancer cell aberrations, the mechanistic

underpinnings of reactive stroma vis a vis quiescent or normal stroma is required.

TME consists of several non-cancerous cells such as fibroblasts, endothelial cells, pericytes, and immune cells embedded within extracellular matrix proteins and vasculature. These interactions present numerous opportunities for targeting

TME for effective therapy. Since CAFs are the most populous cells in the TME and are genetically stable relative to cancer cells, therapies targeting them would theoretically be more effective(154).

The objective of this study was to examine if altered reactive stromal metabolism is the driver for regulating cancer growth in its harsh microenvironment and if targeting this aberration could create metabolic vulnerability in cancer cells by disrupting the metabolic crosstalk between stromal and cancer cells. Our study revealed the fundamental metabolic differences between CAFs and normal ovarian fibroblasts (NOFs) which support tumor growth and progression. Comparing the gene expression profiles of fibroblastic stromal components from a series of advanced stage, high-grade serous ovarian adenocarcinomas to NOFs revealed strikingly higher Gln anabolic pathway in

CAFs compared to NOFs. We also demonstrated, for the first time, that CAFs had remarkably higher metabolic flexibility compared to NOFs. Dysregulated

CAF metabolism induced adaptive mechanisms for harnessing carbon and nitrogen from atypical sources to synthesize Gln in environments where Gln is scarce. Significantly, co-targeting highly expressed stromal Gln synthetase (GS) with highly expressed cancer cells’ glutaminase (GLS), in an orthotopic intra-

ovarian mouse model revealed that the metabolic interdependence of CAF and

OVCA cells conferred a synthetic lethality in tumor and stromal compartments.

Our work underscores the reliance of cancer cells on stromal CAFs, presenting an opportunity to target tumor stroma and cancer cells simultaneously to improve therapeutic outcomes.

3.1. Upregulated Glutamine Anabolic Pathway in CAFs

Compared to NOFs

To analyze metabolic reprogramming in reactive stroma, we analyzed expression of genes encoding enzymes in central carbon metabolism of the fibroblastic stromal components microdissected from a series of advanced stage, high-grade serous ovarian adenocarcinomas and compared that to normal ovarian fibroblasts13. Interestingly, we found that CAFs had significantly higher expression of glutamine pathway genes, especially GLUL, responsible for Gln synthesis and genes encoding amino acid transferases (GOT1, BCAT1, GOT2), which facilitate the intracellular glutamate synthesis (Fig. 3.1a-b, Fig. 3.2a-b).

Further, CAFs had increased expression of genes encoding glycolysis, tricarboxylic acid (TCA) cycle, electron-transport chain (ETC) (Fig. 3.1a, Fig.

3.2c), which increased their metabolic activity. Consistent with these expression results, transcription factors known to target these enzymes were also indeed upregulated (Fig. 3.1c).

To confirm that the increased gene expression of metabolic enzymes is associated with stromal compartment, we compared expressions of microdissected paired fibroblastic stromal and epithelial components. We found that stromal CAFs had significantly higher expression of GLUL and higher average expression of Gln anabolic pathway genes (Fig. 3.1d-e, Fig. 3.3a).

However, tumor stroma had similar expression of glycolysis genes as tumor epithelial cells (Fig. 3.2d). The GS staining in OVCA patient-derived tumor tissues further substantiated that the fibroblastic stromal component had higher expression of GS compared to its epithelial compartment (Fig. 3.1f). The increased GS expression in reactive stromal CAFs implied that CAFs could survive under Gln-deprivation conditions. Indeed, patient-derived CAFs were

Gln-independent for proliferation while NOFs were Gln-addicted (Fig. 3.1g).

Previously, we showed that high grade OVCA cells were Gln-dependent for cell proliferation and metastasis(155). This hyper-nutrient-dependency of cancer cells depletes nutrients from TME and suggests that Gln anabolic metabolism of stromal CAFs may play a role in maintaining growth of these cancer cells by secreting Gln. To verify Gln secretory capacity of reactive stroma, we measured the concentration of Gln in Gln-free spent media and found that patient-derived

CAFs secreted Gln at a rate of around 25 pmoles/K cells/hour, while NOFs barely secreted any detectable Gln (Fig. 3.1h). Within complete medium containing physiological concentration of Gln, OVCA cell lines, HeyA8 and

SKOV3, uptake around 100 pmole/K cells/hour of Gln for sustaining cell growth

(Fig. 3.1i). The physiological ratio of CAFs to cancer is reported to be between 1-

10(156)(157)(158), thereby suggesting that CAFs in TME are well-equipped to maintain the Gln uptake flux of Gln-addicted cancer cells.

To obtain the mechanistic underpinnings of the deregulated CAF metabolism,

13 13 13 we performed C labeled metabolic flux analysis ( C-MFA) using U- C6-

Glucose (Fig. 3.4a) to estimate metabolic fluxes in CAFs and NOFs. We found that NOFs were sensitive to Gln-depletion stress, and there was significant reduction in glutamate, -ketoglutarate (-KG), malate, aspartate, and citrate pools in NOFs, whereas CAFs suffered only slight reduction in the metabolite pools (Fig. 3.4b). The consumption and secretion profiles of CAFs revealed an increase in the uptake of asparagine and branched-chain amino acids (BCAA) from the medium and reduction in glutamate secretion (Fig. 3.4c). Through quantified intracellular fluxes we found that in NOFs under Gln deprivation, TCA cycle fluxes were affected significantly and conversion of -KG to succinate, fumarate, malate and oxaloacetate (OAA) was reduced by 45% as compared to

Gln-replete condition (Fig. 3.4d). Further, there is no Gln synthesis in NOFs. In contrast, in CAFs under Gln-deprivation, the increased contribution of glucose- derived carbon to citrate and -KG along with increase aspartate, asparagine intake contributes to glutamine synthesis (Fig. 3.4e). On comparing normalized fluxes in CAF 0Q to those in NOF 0Q, we observed that all TCA cycle fluxes are

2.5 to 4 times higher (Fig. 3.1j, Table 3.1, Table 3.2). The above results establish

a major metabolic reprogramming of reactive stroma, evident through upregulated Gln metabolism in CAFs compared to quiescent NOFs.

Figure 3.1: Upregulated Gln anabolic pathway in CAFs compared to NOFs. (a): Differential expression of genes encoding metabolic enzymes in cancer- associated fibroblasts (CAFs, n = 33) relative to normal ovarian fibroblasts (NOFs, n = 8). All CAF samples were derived from microdissected ovarian tissues from ovarian cancer patients. (b): Expression level of BCAT1,GOT1,GOT2,GLUL in CAFs and NOFs. (c): Network of transcription factors regulating Gln anabolic and glycolysis pathway genes in CAFs relative to NOFs. (d): Glutamine synthetase, GLUL expression levels in paired tumor epithelial and stromal compartments. Lines connecting tumor and stromal data points signify tumor and stromal samples derived from the same patient. (Wisconsin test). (e): Average pathway expression of Gln anabolism levels in paired tumor epithelial and stromal compartments. (Wisconsin test). (f): Representative IHC staining image comparing GLUL protein expression between stromal and tumor compartments. (g): Proliferation after 72 hours, of patient- derived NOF1, NOF2 and CAF1-5, under Gln deprivation relative to nutrient-rich media. (h): Gln secretion rate of three patient derived CAF1-3 and patient- derived NOF2 under Gln deprivation conditions. (i): Gln uptake rate for highly- invasive OVCA cell-lines, HeyA8 and SKOV3, in nutrient-rich medium with physiological Gln concentration. (j): Intracellular fluxes in Gln-deprived CAFs 13 (CAF 0Q) relative to Gln-deprived NOFs (NOF 0Q) quantified using U- C6 glucose tracer experiments and 13C-Metabolic flux analysis. Line thickness is proportional to flux values (relative to pyruvate flux to TCA) in NOF 0Q conditions. Confidence intervals for 13C-MFA fluxes estimated using Monte-Carlo sampling are reported in Extended Data Table 1. For all graphs if not specified, error bars indicate mean ± s.e.m. for n≥3 independent experiments. Two tailed student t-test.

Figure 3.2: Differential gene expression levels in CAFs compared to NOFS and tumor compartment. (a-b): Clustermap of expressions of genes encoding glycolysis (a) and glutamine metabolism (b) enzymes in microdissected NOFs (NS samples) and CAFs (TS samples) derived from ovarian cancer patients. (c): Expression level of HKII, PDHB, COXI in CAFs and NOFs. (d): Average expression of glycolysis genes in paired tumor epithelial and stromal compartments. Lines connecting tumor and stromal data points signify tumor and stromal samples derived from the same patient. (Wisconsin test). Errors bars indicate mean ± s.e.m. for n≥3 independent experiments.

Figure 3.3: Gene expression level for tumor epithlial compartment and stroma compartment.

(a): Expression of genes encoding central carbon metabolic enzymes in paired tumor epithelial and stromal compartments. Lines connecting tumor and stromal data points signify tumor and stromal samples derived from the same patient. n=33. (Wisconsin test).

NOF Glutamine Deprived Reaction NOF 0Q GLU_C == AKG -0.24 GLU_C == AKG CIT_M == AKG + CO2_out 0.99 CIT_M == AKG + CO2_out PYR_X == PYR_M 0.23 PYR_X == PYR_M 0.5*SUC_M + 0.5*SUC_M == 0.5*FUM + 0.5*FUM 0.59 0.5*SUC_M + 0.5*SUC_M == 0.5*FUM + 0.5*FUM 0.5*FUM + 0.5*FUM == MAL 0.59 0.5*FUM + 0.5*FUM == MAL MAL == OAC 0.07 MAL == OAC GLN_C == GLU_C -0.13 GLN_C == GLU_C PYR_C == PYR_M 1.00 PYR_C == PYR_M GLN_X == GLN_C 0.03 GLN_X == GLN_C GLU_X == GLU_C -0.08 GLU_X == GLU_C AS_X == AS_C 0.16 AS_X == AS_C OAC + GLU_C == AS_C + AKG -0.16 OAC + GLU_C == AS_C + AKG GLC == G6P 3.13 G6P == PYR_C + PYR_C 3.13 PYR_C == LAC 5.26 PYR_M == ACCOA_M + CO2_out 0.99 OAC + ACCOA_M == CIT_M 0.99 AKG == 0.5*SUC_M + 0.5*SUC_M + CO2_out 0.59

MAL == PYR_M + CO2_out 0.52 0.5202*GLU_C+0.4339*GLN_C == BM 0.37 PYR_M + CO2_in == OAC 0.76 -GLC 3.13 LAC 5.26 GLN_X -0.03 GLU_X 0.08 -AS_X 0.16 -PYR_X 0.23 BM 0.37 GLUD1 (GLU -> AKG) -0.08

PYR_C -> PYR_M (net) 0.79

Table 3.1: NOF2 metabolic fluxes using U-13C Glucose labeling

CAF Glutamine Deprived Reaction CAF 0Q GLU_C == AKG 0.78 GLU_C == AKG CIT_M == AKG + CO2_out 2.50 CIT_M == AKG + CO2_out PYR_X == PYR_M 0.72 PYR_X == PYR_M 0.5*SUC_M + 0.5*SUC_M == 0.5*FUM + 0.5*FUM 2.27 0.5*SUC_M + 0.5*SUC_M == 0.5*FUM + 0.5*FUM 0.5*FUM + 0.5*FUM == MAL 2.27 0.5*FUM + 0.5*FUM == MAL MAL == OAC 1.50 MAL == OAC GLN_C == GLU_C -0.23 GLN_C == GLU_C PYR_C == PYR_M 1.00 PYR_C == PYR_M GLN_X == GLN_C -0.23 GLN_X == GLN_C GLU_X == GLU_C 0.00

GLU_X == GLU_C AS_X == AS_C 1.01 AS_X == AS_C OAC + GLU_C == AS_C + AKG -1.01 OAC + GLU_C == AS_C + AKG GLC == G6P 8.86 G6P == PYR_C + PYR_C 8.86 PYR_C == LAC 16.72 PYR_M == ACCOA_M + CO2_out 2.50 OAC + ACCOA_M == CIT_M 2.50 AKG == 0.5*SUC_M + 0.5*SUC_M + CO2_out 2.27 MAL == PYR_M + CO2_out 0.78 0.5202*GLU_C+0.4339*GLN_C == BM 0.00

-GLC 8.86 LAC 16.72 GLN_X 0.23 GLU_X 0.00 -AS_X 1.01 -PYR_X 0.72 BM 0.00 GLUD1 (GLU -> AKG) 1.79

PYR_C -> PYR_M (net) 0.17

Table 3.2: CAF1 metabolic fluxes using U-13C Glucose labeling

Figure 3.4: Gln deprivation on metaboic reprogramming in CAFs and NOFs.

(a): Schematic showing fate of U-13C glucose enriching TCA cycle metabolites. (b): Intracellular concentrations of metabolites in CAFs and NOFs under Gln- deprivation relative to nutrient-rich medium. (c): Extracellular fluxes of amino acids in CAF1 cells in Gln-deprived and nutrient-rich medium. (d): Intracellular fluxes in Gln-deprived NOFs (NOF 0Q) relative to NOFs in nutrient-rich medium 13 13 (NOF 1Q) quantified using U- C6 Glucose tracer experiments and C-Metabolic flux analysis. Line thickness is proportional to flux values in NOF 1Q conditions. Color key represents increased (red) or decreased (green) fluxes in conditions compared to control. Blue arrows represent fluxes that are reversed in the condition compared to control and grey arrows represent no significant change in fluxes between conditions. (e): Intracellular fluxes in Gln-deprived CAFs (CAF 13 0Q) relative to CAFs in nutrient-rich medium (CAF 1Q) quantified using U- C6 glucose tracer experiments and 13C-Metabolic flux analysis. Line thickness is proportional to flux values in CAF 1Q conditions. Color key represents increased (red) or decreased (green) fluxes in conditions compared to control. Blue arrows represent fluxes that are reversed in the condition compared to control and grey arrows represent no significant change in fluxes between conditions. For all graphs, error bars indicate mean ± s.e.m. for n≥3 independent experiments. Two tailed student t-test.

3.2. CAFs but not NOFs maintain Gln-addicted cancer cell growth

under Gln deprivation condition

To substantiate the role of CAFs in maintaining OVCA cell growth under

Gln-deprivation we measured the proliferation of GFP-labeled high-grade OVCA cell lines, HeyA8 and SKOV3, in direct co-cultures with patient-derived CAFs or

NOFs. Remarkably, CAFs rescued the proliferation rate of cancer cells under

Gln-deprivation while NOFs had no effect (Fig. 3.5a-c). To exclude the possibility that soluble factors could rescue proliferation and confirm that rescue is through

CAF-secreted Gln, we performed cocultures with L-asparaginase, which

hydrolyzes extracellular glutamine to glutamate and ammonia, thereby blocking uptake of CAF-secreted Gln by the cancer cells. As expected, CAF-mediated rescue of cancer cell proliferation was attenuated, thereby confirming the role of

CAF-secreted Gln. To understand the effects of CAF-derived Gln in cancer cells, we measured gene expression of cancer cells in monocultures and cocultures with CAFs using Illumina microarrays. Gene set enrichment analysis

(GSEA(159)(160)) of transcriptional changes revealed that expression of cell cycle genes was higher in cancer cells cocultured with CAFs as compared to monocultures, whereas those of apoptosis were lower (Fig. 3.5d). CAFs also enhanced the expression level of unsaturated fatty acids synthesis, an important pathway of lipid bilayer synthesis for cell division, in cancer cells (Fig. 3.6a).

Comparing HeyA8 gene expression in Gln-replete and Gln-deprived medium we observed higher enrichment scores of cell cycle, unsaturated fatty acids synthesis genes (Fig. 3.6b-c). Furthermore, we used the conditioned medium

(CM) derived from NOF or CAFs to treat cancer cells, and the results corroborated our findings that CM from CAFs rescued the proliferation, while CM from NOFs could not, while the addition of L-asparaginase into the CM abrogated the rescue (Fig. 3.6d). Both CAFs and NOFs cannot support cancer cell survival under glucose-deprivation, since CAFs and NOFs were themselves dependent on glucose for for proliferation (Fig. 3.6e-f). To exclude the possibility that Gln is directly catabolized from cell autophagy induced protein degradation, we used the autophagy inhibitor, chloroquine. We found that chloroquine does not inhibit

the rescue effect of CAFs on cancer cell growth under Gln-deprivation conditions

(Fig. 3.5g-h).

To associate GS, a central enzyme regulating glutamine anabolism, with

CAF-mediated rescue of cancer cells under Gln-deprived conditions, we added

MSO, a GS inhibitor in cocultures. Upon inhibition of the GS activity via MSO, cancer cells in Gln-deprived media could not survive without Gln secreted by

CAFs (Fig. 3.5e-f). Similarly, inhibiting GS expression by siRNA-GLUL in CAFs, significantly impacted rescue of cancer cell growth under Gln deprivation (Fig.

3.5g, Fig. 3.6i). To ascertain that CAF-mediated rescue is conferred via CAF- secreted Gln and not due to a possible CAF-induced overexpression of GLUL in cancer cells, we silenced GLUL in HeyA8 cells using siRNA-GLUL. As expected, knocking down GLUL in cancer cells had no effect on CAF-mediated rescue of cancer cell growth under Gln-deprivation (Fig. 3.5h). These data provide evidence that stromal CAF-secreted Gln maintains cancer cell growth under nutrient stressed TME.

Figure 3.5: CAFs but not NOFs maintain Gln-addicted cancer cell growth under Gln deprivation condition. (a): Fluorescence microscopy images comparing growth of GFP-labeled HeyA8 and SKOV3 cells co-cultured with CAFs or NOFs under Gln deprivation. (b): Relative proliferation rates of GFP-labeled SKOV3 co-cultured with CAFs or NOFs under Gln deprivation quantified from fluorescence intensities. GFP fluorescence values under Gln free condition are normalized with one in nutrient- rich medium. (c): Relative proliferation rates of HeyA8 co-cultured with CAFs or NOFs at different seeding ratios under Gln deprivation and treated with L- asparaginase. Proliferation rates are normalized to HeyA8 co-cultured with NOFs in nutrient-rich media (d): Gene set enrichment analysis (GSEA) of cell cycle, apoptosis and DNA mismatch repair genes in HeyA8 co-cultured with CAFs with respect to mono-cultured HeyA8 in Gln deprived medium. Gene expressions are measured 48 hours after culturing in respective media (e): Gln secretion rates of CAF1 treated with MSO, a glutamine synthetase inhibitor relative to untreated control. (f): Relative proliferation rates of Gln-deprived HeyA8 treated with MSO cultured with and without CAFs normalized to HeyA8 mono-cultured in nutrient- rich medium (g): Fluorescence microscopy images and quantified growth rates of mono- and co-cultured HeyA8 transfected with GLUL siRNA in Gln-rich and Gln- deprived media (h): GLUL protein expression in HeyA8 cells treated with shGLUL and their proliferation rates when co-cultured with CAFs in Gln-rich and Gln- deprived media. Errors bars indicate mean ± s.e.m. for n≥3 independent experiments. Two tailed student t-test.

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Figure 3.6: Rescue effect of CAFs for growth rate of OVCA under Gln deprivation condition. (a): Gene set enrichment analysis (GSEA) of unsaturated fatty acid biosynthesis genes in Gln-deprived HeyA8 co-cultured with CAFs with respect to mono- cultured HeyA8 in Gln deprived medium. Gene expression are measured 48 hours after culturing in respective media. (b-c) GSEA of cell cycle (b) and unsaturated fatty acid biosynthesis (c) genes in mono-cultured HeyA8 in nutrient- rich medium with respect to mono-cultured HeyA8 in Gln deprived medium. Gene expression are measured 48 hours after culturing in respective media. (d) Growth rate of HeyA8 and SKOV3 cultured in condition medium from NOF or CAF and treated with L-asparaginase relative to HeyA8 or SKOV3 in nutrient-rich media. (e): Growth rate of glucose-deprived HeyA8 co-cultured with NOF2 or CAF1 relative to mono-cultured HeyA8 in nutrient-rich medium. (f) Growth rate of NOF2

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and CAF1 under glucose deprivation relative to nutrient-rich medium. (g) Proliferation rate of HeyA8 and SKOV3 co-cultured with CAF4 and treated with chloroquine relative to untreated HeyA8 or SKOV3. (h): Proliferation rate of HeyA8 co-cultured with CAF1 and treated with various concentrations of chloroquine relative to untreated HeyA8. (i) Expression of GLUL mRNA on transfection with GLUL siRNA in CAF1. Errors bars indicate mean ± s.e.m. for n≥3 independent experiments.

3.3. Higher metabolic flexibility in CAFs compared to NOFs

induces adaptive mechanisms for harnessing carbon and

nitrogen from atypical sources towards Gln synthesis

To dissect the contribution of dominant substrates towards carbons of glutamate and Gln and to identify the potenial metabolic vulnerability in reactive stroma, we cultured CAFs with 13C labeled substrates to estimate their contribution towards Gln synthesis (Fig. 3.7a). Metabolic tracing revealed that the relative abundance of M2 citrate and M2 glutamate, directly derived from U-13C glucose, was significantly enhanced in CAFs under Gln-deprivation as compared to complete media (Fig. 3.7b-c). In the case of NOFs, Gln-deprivation dramatically decreased glucose’s conversion to TCA cycle metabolites and glutamate (indicated by heavy isotopologues of metabolites) (Fig. 3.7b-d).

Interestingly, non-glucose substrate contribution, measured through M0 levels was much higher in glutamate indicating dominant contribution from other carbon sources in Gln-deprived CAFs. Culturing CAFs under Gln-deprivation with 2mM

13 U- C5 glutamate revealed that CAFs incorporated over 60% extracellular

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glutamate for Gln synthesis (Fig. 3.7e). Suprisingly, supplementing lactate in culture media not only increased intracellular levels of TCA cycle metabolites, glutamate and Gln, but also enhanced Gln secretion (Fig. 3.8a-b). Although glucose contributed to around 50% of citrate and 30% of intracellular glutamate and Gln in CAFs, adding lactate dramatically decreased the glucose contribution to TCA cycle metabolites, glutamate and Gln, displacing glucose as the major

13 carbon source for Gln precursors (Fig. 3.7f-g, Fig. 3.8c-d). Using U- C3-lactate revealed high lactate contribution towards M2 Gln and M3 alanine in CAFs (Fig.

3.7g, Fig. 3.8e-g). Even with labeled lactate in the media, at least 30% citrate is found to be unlabeled indicating that other sources provide carbon for Gln’s backbone in CAFs (Fig. 3.8c). Apart from glucose and lactate, acetate, branched chain amino acids (BCAAs) and fatty acids are anaplerotic sources of acetyl-

CoA. Both, labeled acetate and leucine were found to not provide significant acetyl-CoA for Gln synthesis (Fig. 3.8h). To reveal if fatty acids oxidation (FAO) can account for unlabeled glutamate, we cultured CAFs in U-13C glucose with 10

µM etomoxir, a FAO inhibitor. The fractional enrichment of extracellular M2 Gln doubled and intracellular M2 glutamate significantly increased, thereby suggesting that FAO inhibition increased glucose’s contribution to glutamate synthesis, and Gln secretion (Fig. 3.8h-i). Our results demonstrate that fatty acids, glucose and lactate were the major sources of acetyl-CoA and TCA metabolites.

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Oxaloacetate (OAA) is an important anaplerotic point-of-entry in the TCA cycle, and is derived either from pyruvate via pyruvate carboxylase (PC), or from asparate or asparagine through glutamic-oxaloacetic transaminase (GOT). The relatively low M5 citrate with U-13C glucose labeling ruled out PC activity in CAFs

(Fig. 3.8c). We then analyzed the aspartate and asparagine transporter expression level in CAFs and compared it to NOFs. Interestingly, CAFs had significantly higher expression of aspartate transporter slc1a3 and asparagine transporter slc38a2, suggesting their significance in supplying OAA (Fig. 3.8j).

13 13 Next, we used the [1,4]- C-aspartate or U- C4-asparagine to trace their contribution in the TCA cycle (Fig. 3.8k). Interestingly, aspartate provided 40% of

M2 citrate, 60% of M2 malate in CAFs, as well as 40% of M1 glutamate (Fig.

13 3.7h). Using U- C4 asparagine, we observed that asparagine can also contribute to 34% aspartate which further contributes to 20% of citrate, malate, and 15% of glutamate pools (Fig. 3.7h, Fig. 3.8l). Therefore, asparagine can also be a major source of OAA for TCA cycle through aspartate in CAFs. Collectively, glucose only accounted for 19% of acetyl-CoA synthesis while lactate contributed to the majority (48%) (Fig. 3.7i). Aspartate and asparagine contributed to 69% and 30%

OAA, respectively (Fig. 3.7i). Approximately, 50% of intracellular glutamate in

CAFs was derived from intracellular Gln anabolic pathway while the remaining half was from the microenvironment (Fig. 3.7i). The hyperactive Gln anabolism of

CAFs was maintained by high glucose, lactate, aspartate, asparagine and

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glutamate intake mediated by upregulated transporter expression levels in CAFs compared to NOFs (Fig. 3.7i, Fig. 3.8j).

To identify the substrates contributing towards nitrogen supply for Gln synthesis in CAFs we used 15N labeled BCAA, aspartate, alanine, serine and ammonia. Analysis of intracellular glutamate and CAF-secreted Gln showed that branched chain amino acids (leucine, 20%; isoleucine, 20%; valine, 6%) contributed significantly to amine (Fig. 3.7j-l). We found that aspartate is another major nitrogen donor as it can contribute to 40% of amine in the glutamate pool while consuming α-KG and producing OAA via GOT. Alanine is deaminated by glutamate pyruvate transaminase (GPT) and amino group is transferred to glutamate and subsequently to glutamine. Although only 10% of glutamate pool was derived from labeled alanine it is interesting to note that 30% of Gln nitrogen is from alanine. Serine contribution of nitrogen was almost negligible to glutamate and Gln (Fig. 3.7k-l). Since GS exclusively utilizes free ammonia to generate glutamine from glutamate, we saw 5% ammonia incorporation into glutamate and

80% incorporation into Gln, and its addition increased Gln secretion (Fig. 3.7k-l,

Fig. 3.8b). In NOFs, the contribution to intracellular glutamate from BCAA, alanine and ammonia was less than 10% and negligible from all other nitrogen sources (Fig. 3.7m). This highlights the disparate nitrogen metabolism in CAFs and NOFs that allowed for efficient acquisition of nitrogen in CAFs for intracellular glutamate synthesis (Fig.3.7n). Removal of BCAAs, or aspartate and asparagine from Gln free medium was detrimental to not only Gln synthesis but

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also to the growth of CAFs (Fig. 3.7o, Fig. 3.8m). To provide further support for the idea that targeting stromal glutamine anabolic pathway disrupts the cancer growth, we deprived asparagine and aspartate from coculture medium and observed a marked decrease in cancer cell proliferation (Fig. 3.7p). The addition of either asparagine or aspartate can completely reverse the proliferation (Fig.

3.7p). Treatment with exogenous malate rescued the growth arrest suggesting that aspartate and asparagine are imperative in maintaining glutamine synthesis in CAFs (Fig. 3.7p). Treating cocultures with 10 mM gabapentin, an inhibitor of

BCAT1, abolished the positive effect of CAFs on cancer cells under Gln deprivation (Fig. 3.7q, Fig. 3.8n). The addition of aminooxyacetic acid (AOA), an inhibitor of amino transferase had a similar effect as gabapentin (Fig. 3.7q, Fig.

3.8n). Hence, targeting incorporation of any of these nitrogen donors can reverse the supportive effects of CAFs on cancer cells.

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Figure 3.7: CAFs’ higher metabolic flexibility compared to NOFs induced adaptive mechanisms for harnessing carbon and nitrogen from atypical sources towards Gln synthesis. (a): Schematic describing the fate of different stable-isotope labeled nutrient sources in CAFs used for synthesizing Gln. (b-d): Fractional enrichments of 13 citrate (b), glutamate (c), malate (d) in CAF1 and NOF2 cultured with U- C6 13 Glucose under nutrient-rich condition or Gln starvation. (e): Contribution of U- C5 Glutamate to Gln secretion in spent medium after incubating with labeled 13 13 glutamate for 24 hours. (f): U- C6 Glucose or U- C3 Lactate incorporation into intracellular glutamate after 24 hours of incubation with labeled substrates. (g) U- 13 13 C6 Glucose or U- C3 lactate incorporation into intracellular and extracellular Gln. (h): Contribution of aspartate and asparagine to intracellular malate, citrate 13 13 and glutamate using 1,4- C2 Aspartate and U- C4 Asparagine after 24-hour isotope incubation. (i): Schematic demonstrating different nutrients sources contributions to acetyl-coA, OAA, glutamate and Gln in CAFs. (j): Schematic showing transfer of amine group for glutamate and Gln synthesis. (k-m): Contribution of 15N labeled substrates to intracellular glutamate in CAF1 (k), extracellular Gln in CAF1 (l) and intracellular glutamate in NOF2 (m). (n): Absolute intracellular concentrations of labeled glutamate derived from 15N labeled BCAAs, serine, aspartate, alanine and ammonia in CAFs and NOFs. (o): Gln secretion rates in CAFs under Gln deprivation combined with removal of BCAAs or aspartate from media relative to CAFs in only Gln-deprived medium. (p) Proliferation of Gln-deprived HeyA8 co-cultured with CAF1 supplemented with combinations of asparagine, aspartate and malate relative to HeyA8 co-cultured with CAF1 in Gln free medium. (q): Proliferation of HeyA8 co-cultured with CAF1 treated with Gabapentin (branch-chain aminotransferase, BCAT inhibitor) and AOA (aminotransferase inhibitor) relative to mono-cultured HeyA8 in Gln free medium Error bars indicate mean ± s.e.m. for n≥3 independent experiments.

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Figure 3.8: CAFs utilize different carbon and nitrogen sources for Gln synthesis. (a): Intracellular citrate, glutamate, Gln levels in Gln-deprived CAFs supplemented with lactate relative to Gln-deprived CAFs. (b): Relative extracellular Gln concentration in spent-media of CAFs treated with lactate or 13 13 ammonium under Gln-deprived media. (c): U- C6 Glucose or U- C3 Lactate incorporation into intracellular citrate after 24 hours of incubation with labeled 13 13 substrates in CAF and NOF. (d): Contribution of U- C6 Glucose or U- C3 Lactate to intracellular citrate, glutamate and Gln in CAF1. (e-g): MIDs of extracellular alanine (e) in CAF1 and alanine (f) and extracellular Gln (g) in CAF3 13 13 13 in presence of U- C6 Glucose or U- C3 Lactate labeled tracers. (h): U- C6 Glucose incorporation into extracellular Gln with and without fatty acid oxidation 13 (FAO) inhibitor, etomoxir. to reveal FAO contribution to extracellular Gln. U- C2 13 acetate and U- C6 leucine contribution to extracellular Gln secreted by CAFs. (i): Effect of Etomoxir on the MID of intracellular glutamate in CAF1 in presence of 13 U- C6 Glucose. (j): Gene expression of glucose, lactate and amino acid 13 transporters in CAFs and NOFs. (k): Schematic showing 1,4- C2 Aspartate and 13 13 U- C4 Asparagine enriching TCA cycle metabolites. (l): Percentage of U- C4 asparagine conversion into aspartate in CAF1. (m): Proliferation of CAF1,2,4 under Gln deprivation combined with removal of BCAAs or aspartate and asparagine from media relative to CAFs in only Gln-deprived medium. (n): Proliferation of SKOV3 co-cultured with CAF1 treated with Gabapentin (branch- chain aminotransferase, BCAT inhibitor) and AOA (aminotransferase inhibitor) relative to mono-cultured SKOV3 in Gln free medium. Error bars indicate mean ± s.e.m. for n≥3 independent experiments. Two tailed student t-test.

3.4. Crosstalk between stromal-epithelial cell augments

dysregulated metabolism in CAFs and supports nucleotides

and TCA cycle metabolites levels in cancer cells

We next asked if neighboring cancer cells influenced the utilization of the carbon substrates by CAFs in order to support their Gln anabolic characteristics.

We found that cancer cells increased accumulation of lactate, pyruvate, along

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with TCA cycle metabolites like citrate and -ketoglutarate in CAFs when they were co-cultured as compared to monocultures under Gln-deprivation (Fig. 3.9a).

This was confirmed through stable isotope tracing kinetic flux experiments with

13 U- C6 glucose. CAFs co-cultured with cancer cells had higher fractional labeling of glutamine and glutamate as compared to CAFs in monoculture (Fig. 3.9b-d,

Fig. 3.10a-b). Cancer cells enhanced TCA cycle activity in CAFs, by increasing their glucose contribution to TCA cycle to maintain glutamate and citrate levels for Gln synthesis (Fig. 3.10c-d). This effect is independent of GLUL expression in cancer cells as both control and HeyA8-shGLUL showed similar fractional labeling (Fig. 3.9b-d).

To test whether metabolic changes in cancer cells were induced by CAF- secreted Gln, we performed metabolic isotope tracing studies of cancer cells when they were cocultured with CAFs or were cultured alone. As expected, extracellular Gln secreted by CAFs was found to be rapidly depleted in the coculture media in presence of cancer cells (HeyA8) cells due to high Gln consumption by these cancer cells (Fig. 3.9e). The metabolic effects of CAF- secreted Gln on cancer cells was also recapitulated at the transcriptional levels using GSEA. This analysis revealed that purine and pyrimidine biosynthesis pathways genes were highly enriched (Fig. 3.9f-g). Concurrently, gene expression of lysosomal pathways responsible for protein degradation under nutrient stress were reduced (Fig. 3.10e). Gap junction genes that regulate exogenous uptake of amino acids uptake were also upregulated (Fig. 3.10f). The

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reduced expression of lysosome and endocytosis genes was indicative of a switch in nutrient acquisition of cancer cells to extracellular sources under Gln deprivation that are potentially provided by CAFs (Fig. 3.10e-g). These transcriptional changes were recapitulated in Gln-deprived cancer cells by supplementing Gln, thereby suggesting that CAF-derived Gln is sufficient to rescue OVCA cells in nutrient-stressed conditions (Fig. 3.10h-j).

To ascertain the utilization of CAF-secreted Gln by cancer cells, we measured intracellular Gln level in cancer cells with or without coculture with

CAFs and observed a dramatic increase of intracellular Gln concentration in cancer cells in cocultures with CAFs (Fig.3.9h). Furthermore, we found that TCA cycle metabolite levels in cancer cells were increased (Fig. 3.9i, Fig. 3.10k).

Another important role of Gln in OVCA is to maintain nucleotide synthesis and we hypothesized that CAF-derived Gln is essential for its maintenance in Gln- deprived cancer cells. Gln enters the TCA cycle after being metabolized to -KG and its amine group is incorporated into purine and pyrimidine precursors18-20.

We supplied Gln-deprived OVCA with either exogenous -KG, GITUAC

(guanine, inosine, thymine, uracil and adenosine) or GAIUMP (GMP, AMP, IMP and UMP) to unearth if they could individually rescue OVCA growth. Interestingly,

-KG did not show any rescue when administered alone proving that the anaplerotic role of Gln into TCA cycle is not sufficient to support tumor growth.

Concurrently, nucleotides precursors could only partially rescue proliferation.

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However, complete proliferation rescue in cancer cells was observed upon addition of GMP, AMP, IMP, UMP along with α-KG (Fig. 3.9j).

Paradoxically, we saw high enrichment of M4 and M5 CAF-secreted Gln under Gln-deprivation in cocultures and not in monocultures (Fig. 3.9c-d). We postulated that cancer cells could be secreting glutamate which when used by

CAFs led to secretion of high mass isotopologues Gln. Indeed, we found that the glutamate secreted by cancer cells is highly enriched as M4 and M5 glutamate in

Gln free medium (Fig. 3.10l-m). This was also supported by observation of negligible secretion of M4 and M5 glutamate by CAFs in monocultures (Fig.

3.10l-m). To further understand the role of cancer cells in modulating CAFs’ Gln synthesis, we estimated the relative contributions of cancer cell-derived glutamate and endogenous glutamate synthesized to glutamate pools within

CAFs. Using a parameter regression-based balance model (see Appendix A), we found that both endogenous and exogenous glutamate contributions were important, since extracellular glutamate contributed around 40% of total glutamate, and the remaining was contributed by endogenous glutamate synthesis (Fig. 3.9k). On estimating the contribution of cancer cell-synthesized glutamate towards glutamate in extracellular medium, we found it to be approximately 65% after 48 hours of cocultures (Fig. 3.9l). These results suggest that glutamate derived from cancer-cells contribute to at least 25% of glutamate in CAFs for Gln synthesis. We then investigated whether cancer cells could modulate CAF’s utilization of nitrogen sources and found that there was an

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increase in M1 glutamate derived from 15N BCAAs (Fig. 3.10n) in CAFs when they are in coculture compared to monoculture. However, we did not find any increase in other nitrogen donors towards glutamate in CAFs cocultured with cancer cells (Fig. 3.10o). We next evaluated if CM from cancer cell could have similar effects of coculture in CAFs. We found that CM significantly increased

CAFs’ glutamine secretion rate (Fig. 3.10p). These results collectively demonstrate that cancer cells enhanced the capacity of CAFs to use different nutrient sources for synthesizing Gln in order to support cancer cell survival in stressed microenvironments. To clearly demonstrate the influence of CAF- secreted Gln on cancer cell metabolism, we formulated a similar balance model as described above, for intracellular glutamate in HeyA8 cells cocultured with

CAFs (see Appendix A) (Fig. 3.10q-r). Notably, we found that more than 60% of intracellular glutamate in cancer cells is derived from CAF-secreted Gln (Fig.

3.9m). These results revealed a novel metabolic crosstalk between reactive stromal and cancer cells, where stromal cells provided glutamine for cancer cells and cancer cells secreted glutamate and lactate for glutamine synthesis in stromal cells (Fig. 3.9n).

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Figure 3.9: Crosstalk between stroma-epithelial cells augments dysregulated metabolism in CAFs and supports nucleotides and TCA cycle metabolites levels in cancer cells. (a): Intracellular concentrations of lactate, pyruvate, alanine, citrate, α- ketoglutarate and glutamate in Gln-deprived CAF1 co-cultured with HeyA8 cells relative to mono-cultured CAF1 (b-d) Dynamic isotope labeling of M2 (b), M4 (c), M5 (d) extracellular Gln secreted by CAF1 cultured with or without HeyA8 and HeyA8 GLUL KD cells. (e): Gln concentration in spent medium of HeyA8 and HeyA8 GLUL KD co-cultured with CAF1. (f-g) GSEA analysis of purine (f) and pyrimidine (g) synthesis genes of HeyA8 co-cultured with CAFs with respect to mono-cultured HeyA8 cells. (h): Intracellular concentration of Gln in HeyA8 co- cultured with CAF1 relative to mono-cultured HeyA8. (i): Intracellular concentrations of glutamate, pyruvate, lactate and TCA cycle metabolites in HeyA8 co-cultured with CAF1 relative to mono-cultured HeyA8 or SKOV3. (j): Proliferation rates of Gln-deprived HeyA8 supplemented with α-ketoglutarate or nucleotides precursor relative to HeyA8 in nutrient-rich medium. (k): Contribution of extracellular glutamate and CAFs’ intracellular glutamate synthesis to intracellular glutamate in CAF estimated using a linear regression model using MID measurements at 24 and 48 h. (l) Contribution of OVCA-secreted glutamate to extracellular glutamate in medium estimated using a linear regression model using MID measurements at 24 and 48 h. (m) Contribution of CAF-secreted extracellular Gln to intracellular glucose synthesis in OVCA estimated using a linear regression model using MID measurements at 24 and 48 h. (n): Schematic showing the mutual transfer of metabolites between CAFs and OVCA. OVCA cells secrete heavy isotopologues of glutamate (M4, M5) and lactate (M3) when 13 cultured with U- C6 glucose. The secreted lactate and glutamate is absorbed and metabolized by CAFs to synthesize lighter glutamine isotopologues (M2), which is secreted by CAFs and subsequently absorbed by OVCA for glutaminolysis and nucleotides synthesis. Circle with colors indicate its 13C labeled, blank circle indicates 12C. For all graphs, error bars indicate mean ± s.e.m. for n≥3 independent experiments.

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Figure 3.10: Metabolic reprogramming for CAFs and OVCA when cocultured. 13 (a-b): Dynamic isotope labeling of M4 (a), M5 (b) glutamate using U- C6 Glucose in CAF1 cultured with or without HeyA8 cells. (c-d): Contribution of glucose to glutamate (c) and citrate (d) for CAFs when co-cultured with HeyA8 and SKOV3 under Gln deprivation condition. (e-g) GSEA of lysosome (e), gap junction (f) and endocytosis (g) regulating pathways in Gln-deprived HeyA8 co-cultured with CAF with respect to Gln-deprived HeyA8. (h-j): GSEA of purine (h), pyrimidine (i) synthesis and lysosome regulating (j) pathways in mono-cultured HeyA8 in Gln- rich medium with respect to HeyA8 in Gln-deprived medium. (k): Relative metabolite level for SKOV3 co-cultured with CAF1 relative to mono-cultured SKOV3. (l-m): Dynamic isotope labeling of M4 (l) and M5 (m) extracellular 13 glutamate in spent media using U- C6 glucose in mono-cultured HeyA8 or CAF1 cultured with or without HeyA8. (n): Nitrogen contribution of 15N BCAA (leucine, isoleucine and valine) to intracellular glutamate (M1 isotopologue of glutamate) in CAF1 cultured with and without HeyA8. (o): Dynamic isotope labeling of extracellular M1 Gln using 15N Aspartate in CAF1 in mono-culture and co- cultured with HeyA8 or HeyA8 GLUL KD cells. (p): Effect of condition medium from OVCA on Gln secretion of CAF2. (q-r): Mass isotopologue distributions 13 (MIDs) of intracellular glutamate (q) and α-ketoglutarate (r) from U- C6 glucose in HeyA8 cultured with and without CAF1. Errors bars indicate mean ± s.e.m. for n≥3 independent experiments.

3.5. Orthotopic ovarian cancer mouse model highlights stromal

GLUL and OVCA GLS1 as potential therapeutic targets

On the basis of our extensive in vitro co-culture experiments, we next examined whether targeting the reactive stroma (GLUL) and ovarian cancer cells

(GLS1) simultaneously could result in enhanced therapeutic effect. We used our well-characterized chitosan nanoparticle delivery system for these experiments21-

22. For this purpose, we devised a targeted therapy in a well characterized orthotopic mouse model of ovarian carcinoma. Following surgical implantation of

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SKOV3 cells directly into the left ovary, mice (n = 10 per group) were randomized into one of four groups: 1) control siRNA-CH, 2) human GLS (hGLS) siRNA-CH,

3) murine GLUL (mGLUL) siRNA-CH, and 4) hGLS + mGLUL siRNA-CH. We did in fact find that the combinational therapy to target the tumor stromal and tumor epithelial compartment significantly improves the therapeutic outcomes of tumor- bearing mice (Fig. 11a-e). By using human GLS siRNA to silence GLS in SKOV3 tumor cells, or mouse GLUL siRNA to repress GLUL expression in the mouse stromal cells, we found that single treatment can decrease tumor weight, number of tumor nodules and the percent of positive Ki67 staining cells (Fig. 11b-d, Fig.

3.12a). Most importantly, the combination therapy significantly improves treatment efficiency, as seen from dramatic decrease in tumor weight, and number of tumor nodules (Fig. 5b-d). Furthermore we also observed a significant decline in metastases to other organs, including mesentery, pelvis, omentum, peritoneum, peri-spleen and peri-hepatic tissue (Fig. 11e). The efficacy of mGLUL siRNA was verified by using IHC staining to measure GLUL intensity.

Reduced staining in tissue treated with mGLUL siRNA and Combo siRNA signified decrease of GLUL expression when the mGLUL siRNA was injected into mice (Fig. 11f). Our results, therefore, highlight the synthetic lethality of targeting stromal GLUL and tumor GLS and expose vulnerabilities in the metabolic interaction between stromal CAFs and cancer cells for developing clinically relevant therapy.

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Figure 3.11: Orthotopic ovarian cancer mouse model highlights stromal GLUL and OVCA GLS1 as potential therapeutic targets. (a): Mice treated with control siRNA, human GLS siRNA, mouse GLUL siRNA and combination of hGLS and mGLUL siRNA. n=10 for each group. (b): Weight of tumors extracted from mice subjected to control siRNA, human GLS siRNA, mouse GLUL siRNA and combination of hGLS and mGLUL siRNA. (c): Number of tumor nodules in mice subjected to control siRNA, human GLS siRNA, mouse GLUL siRNA and combination of hGLS and mGLUL siRNA. (d): Metastasis of ovarian tumor to different organ sites in mice treated with control siRNA, human GLS siRNA, mouse GLUL siRNA and combination of hGLS and mGLUL siRNA. (e): Ki67 staining to quantify proliferative tumor cells in mice tumors subjected to control siRNA, human GLS siRNA, mouse GLUL siRNA and combination of hGLS and mGLUL siRNA. (f) IHC staining indicating expression of GLUL in stromal cells of mice treated with control siRNA, mouse GLUL siRNA and combination of hGLS and mGLUL siRNA. For all graphs, error bars indicate mean ± s.e.m. for n≥3 independent experiments.

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Figure 3.12: Effect of mGLUL siRNA on mouse cell line GLUL expression level. (a): GLUL gene expression levels after transfection with mouse GLUL siRNA. Error bars indicate mean ± s.e.m. for n≥3 independent experiments.

3.6. Discussion

The TME, specifically CAFs, have been found to play a multifaceted role in tumor initiation and development. Failure of traditional therapy is, in part, due to our limited understanding of how the TME can facilitate the rapid progression or recurrence of tumors. Targeting tumor stroma for therapeutic purpose is a

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burgeoning idea that has gained traction in the recent past. In fact, metabolic interactions of cancer cells with TME has been associated as one of the emerging hallmarks of cancer(8). Under stress conditions, cancer cells have been reported to enhance enzymatic acitivity for increasing anabolic metabolism(83), lysosome degradation to recycle intracellular waste(161)(162) or intake lysophospholipids, albumin, from microenvironment(88)(89). We recently identified that TME-secreted exosomes can provide metabolites to cancer cells under nutrient-stress(91). However, the identification of stromal pathways that can be exploited to make cancer cells vulnerable has remained elusive. In order to decode such stromal targets, we adopted an orthogonal approach where differential metabolic transformations in reactive stromal vs normal stroma were identified. Remarkably, we found Gln anabolic pathway to be distinctly upregulated in CAFs as compared to NOFs. Through comprehensive metabolic isotope tracing and flux analysis, we identified that CAFs have extraordinary metabolic flexibility that helps them adapt to harness carbon and nitrogen from atypical carbon and nitrogen sources for Gln synthesis under Gln- deprivation. Contrary to the conventional wisdom, CAFs used more lactate (48%) than glucose (19%) for acetyl-CoA synthesis. Similarly, aspartate (69%) and asparagine (30%) were major sources for oxaloacetate in CAFs, instead of pyruvate through pyruvate carboxylation(117)(32). Our results were in line with the metabolic hallmark, where cancer cell-secreted metabolites were posited to induce adaptations in stroma by altering the microenvironment. There is

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mounting evidence that indicates acidification of the extracellular space by tumor cells, not stroma influences the phenotype, intracellular signaling and metabolic programming of other cells in their vicinity(8)(163)(164)(165). We found that

BCAA (40%) and aspartate (40%) were the major substrates contributing to the nitrogen supply for Gln synthesis in CAFs. This was also confirmed by increased expression of GOT gene.

Further, disrupting Gln anabolism in CAFs via pharmacological inhibitors or nutrient deprivation proves deleterious to OVCA growth. In addition to inherent reprogramming in CAFs, cancer cells also had a noticeable influence on CAFs’ metabolism when in coculture. We found that due to the metabolic pressure applied by cancer cells, CAFs increased their incorporation of glucose-derived carbon into TCA metabolites and BCAA-derived nitrogen to glutamate. Our data suggested that cancer cells influenced CAFs’ metabolism to maximize Gln synthesis, and in a symbiotic manner the CAF-derived Gln influenced the cancer cell metabolism and growth. Interestingly, our analysis of microarray data of cancer cells transcriptome showed that coculturing with CAFs reversed the transcriptional programming induced by nutrient-stress. There are potential therapeutic opportunities resulting from targeting stromal metabolic pathways.

Our study demonstrates that Gln anabolism in CAFs is a potential target for exploiting systemic metabolic vulnerabilities in ovarian tumors. Previously, researchers have focused on discovering drugs that block intracellular Gln catabolism in cancer cells, which led to the development of inhibitors such as

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BPTES, CB839 and other such potent drugs(166)(167)(168)(107). However, recently environmental influences on non-small cell lung cancer have been reported(101)(100). These a results limit efficacy of targeting tumors with glutamine catabolism monotherapy. Our study uncovered a novel target of Gln source in the stroma. As illustrated by our data from orthotopic intra-ovarian mouse model, combination therapy to simultaneously target Gln anabolism in

CAFs and Gln catabolism in cancer cells may prove to be a viable synthetic lethal approach to target tumors systemically for achieving desirable therapeutic outcomes.

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Chapter 4

Future studies

In these projects, we presented roles of Gln to promote OVCA proliferation, metastasis, STAT3 signaling, and cisplatin resistance. Furthermore, an important Gln source is revealed, that CAFs harness non-canonical carbon and nitrogen precursors to synthesize Gln, and directly supply to highly aggressive OVCA to maintain tumor progression. α-ketoglutarate has predominant functions in activating dioxygenases, which modify tumor DNA methylation. Future studies are required to explore role of Gln on tumor epigenetics.

4.1. Role of Gln in DNA methylation for cytokine secretion

Previous results have shown that Gln deprivation will dramatically increase IL6 secretion, and addition of α-ketoglutarate will decrease the IL6 mRNA expression level and secretion for SKOV3 (Fig. 5.1). However, for Gln independent cell OVCAR3, Gln deprivation will not affect cytokine secretion. A similar study is shown that nutrient stress will cause IL8 secretion in cancer cells

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(68). However, we observed other interesting targets that Gln controls (Fig. 5.2).

Furthermore, Shanware et al. only explain role of Gln, instead of glutaminolysis

(68). Therefore, a novel and more comprehensive study can be pursued to link metabolites with cellular inflammation. Moreover, we recommended to conduct a global methylation experiment to show a direct link of Gln to DNA methylation.

Analysis of methylation results could give us a hint to unravel the relationship among metabolites, DNA methylation, and secretomics.

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Figure 4.1: Gln deprivation on IL6 mRNA and secretion.

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(a-b): Effect of Gln deprivation and α-ketoglutarate on IL6 secretion (a), IL6 mRNA(b) for SKOV3. (c-d): Effect of Gln deprivation on IL6 secretion (c), IL6 mRNA(d) for OVCAR3.

Figure 4.2: Role of glutaminolysis on cellular signaling cascade in OVCA.

4.2. Role of malic enzyme in drug resistance

We also reported that cisplatin resistant cell lines have a capacity to couple

NADPH/NADP+ and GSH generation. To further explain it, we recommended conducting ME knocked down experiment to prove that NADPH generation

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through ME pathway is critical for drug resistant cells. A mouse model can be set up to prove that targeting ME pathway is sufficient to sensitize cisplatin for cisplatin resistant cell lines.

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Chapter 5

Materials and methods

5.1. Cell lines and cell culture

OVCAR3, SKOV3, Hey8A were purchased from ATCC. OVCAR8 was purchased from NCI on behalf of Rice University. IGROV1, OVCA429,

OVCA420, SKOV3ip, IGROV1 par, IGROV1 cp20, A2780 par and A2780 cp20 cells were obtained from ovarian cell line core at MD Anderson Cancer Center.

Cells were grown in RPMI 1640 (10% FBS, pyruvate free, 100 U/ml penicillin and streptomycin). Cells used in these experiments were cultured below 75 passages.

Ovarian CAFs were derived from advanced stage high-grade serous ovarian cancer samples and normal ovarian fibroblasts (NOFs) were derived from normal ovaries obtained from patients with benign gynecologic malignancies. Both CAFs and NOFs were kindly provided by Dr. Jinsong Liu and

Dr. Samuel Mok from MD Anderson. All tissue samples were collected under the approval of the institution review board (IRB). Patient-derived CAFs cultures

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were evaluated by immunofluorescent microscopy and confirmed the expression of CAFs-specific markers including alpha smooth muscle actin (-SMA) and fibroblast activation protein (FAP). Ovarian cancer associated fibroblasts cells were cultured in 1:1 mixture of MCDB105 (Sigma Aldrich) and M199 (Invitrogen) supplemented with 10% fetal bovine serum (Sigma Aldrich), 1ng/ml epidermal growth factor (EGF, Sigma Aldrich) and were used at low passage for the

o described experiments. All cells were incubated in 5% CO2, and 37 C incubator.

5.2. Material

Dialyzed FBS was purchased from Invitrogen. Stable Isotopes were purchased from Cambridge Isotopes Laboratories. 6 well inserts were purchased from Corning. Chemical drugs were purchased from cayman chemicals, sigma, vwr, and fisher scientific.

5.3. Proliferation assay

The cells were seeded in 96‐well plates overnight, and medium was replaced with specific conditions (regular medium, or medium with Gln deprivation/Glc deprivation, or medium with different drug conditions) for 24, 48 and 72 h. The cell numbers were measured spectrophotometrically by using cell counting kit‐8 (Dojindo) at 450 nm.

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5.4. Clonogenic assay

The cells (300 cells) were seeded in six‐well plates overnight, and medium was replaced with specific conditions for 1 week. Colonies were fixed and stained with 0.5% crystal violet and 8% glutaraldehyde for more than 30 min. Wells were washed with water and colonies were counted.

5.5. Matrigel invasion assay

Invasion assays were conducted using BD Matrigel culture inserts. Briefly,

24‐well 8.0‐μm pore size polyethylene terephthalate membrane inserts (BD

Biosciences) were washed twice with RPMI medium and then coated with 20 μl of reduced growth factor Matrigel (1:6 dilution; BD Biosciences) and incubated for

30 min in a 5% CO2 incubator for gel formation. OVCA cells were trypsinized, and 100,000 cells in 200 μl of fresh medium were plated into the upper chamber.

Next, 300 μl of medium was added to the lower chamber, and the plate was incubated for 20 h. After incubation, medium in the lower chamber was aspirated, and invaded cells were treated with 5% glutaraldehyde in PBS for 15 min to fix the cells and then washed in PBS solution three times. Next, 0.5% toluidine blue in 2% sodium carbonate was added to cells for 20 min at room temperature.

Subsequently, inserts were washed three times in PBS solution. The noninvaded cells on the inner surface of upper chambers were carefully removed by using a

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cotton swab. Finally, invaded cells were counted under 20 × magnification for at least three fields per insert.

5.6. Migration wound-healing assay

OVCA cells were seeded in 6‐well plates. After confluency, cell monolayers were scratched using a 200‐μl pipette tip, washed and cultured in different medium conditions. Images were taken 12/24 h and processed using

Image J software (National Institutes of Health).

5.7. Metabolic footprinting

5.7.1. Glucose consumption

Glucose consumption assay was performed using Wako Glucose kit according to the manufacturer's protocol. Briefly, a 2‐μl sample and 250 μl of reconstituted Wako glucose reagent were added to a 96‐well assay plate and incubated while shaking at 37°C for 5 min. The change in absorbance, indicating the presence of glucose, was measured at 505 nm by using a spectrophotometer

(SpectraMax M5; Molecular Devices).

5.7.2. Lactate secretion

Lactate secretion assay was determined using the Trinity Lactate Kit according to manufacturer's protocol. Briefly, lactate reagent was reconstituted

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with 10 ml of milliohm water and diluted 1:4 in 0.1 M Tris solution (pH 7.0). Media samples were diluted 1:10 in PBS, and lactate reagent was added to the diluted samples in an assay plate. The plate was protected from light and incubated for

1 h before the absorbance was read on a spectrophotometer at 540 nm.

5.7.3. Amino acid uptake and secretion

Ultra‐high‐performance liquid chromatography was used to assess amino acid uptake and secretion using Waters Acquity UPLC device. Briefly, media samples were deproteinized, and MassTrak Reagent was added to the samples, along with Borate Buffer/NaOH. Samples were then heated and analyzed using the Waters ACQUITY UPLC system. Eluents were prepared according to Waters’ protocol. MassTrak AAA eluent A concentrate was diluted 1:10 in milliQwater, and MassTrak AAA eluent B was inputted in undiluted form. Flow rate of eluents was 0.4 ml/min, and UV detection was at 260 nm.

5.8. Protein assay

Protein assays are used to do normalization in our experiment and is done according to Bicinchoninic Acid Protein Assay protocol (Thermo Fisher). In brief,

200 µl protein reagent was added to a 96-well assay plate and mix with samples or standard, and then incubated at 37°C for 30 min. The absorbance was read on a spectrophotometer at 562 nm.

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5.9. Isotope labeling analysis using GC-MS

5.9.1. Metabolites extraction

Cells were seeded in 6 well plates overnight, and next day, medium were

13 13 15 replaced with fresh medium containing U- C6 glucose or other C, N labeling nutrients. After 24/72 hours, medium were removed, cells were washed with cold

PBS, and added with 400 µl ice-cold methanol. Same volume of water containing

1 µg of norvaline was added before cells were scraped using cell lifter. 800 µl of chloroform was added into the tubes, and vortexed at 4°C for 30 min, centrifuged at 7,300 rpm for 10 min at 4°C. The aqueous layer was collected for metabolite analysis.

5.9.2. Derivatization

Aqueous samples were dried using speedvac and dissolved in 30 μl of 2% methoxyamine hydrochloride in pyridine (Pierce) before sonicating for 10 mins.

Samples were kept in 37°C for 2 hours followed by 1 hour at 55°C after addition of 45 μl of MBTSTFA+1% TBDMCS (Pierce). Samples were transferred into vials containing 150 μl of insert.

5.9.3. GC/MS measurements

GC/MS analysis was performed using an Agilent 6890 GC equipped with a 30-m Rtx-5 capillary column for metabolites samples, connected to an Agilent

5975B MS. For metabolite samples, the following heating cycle was used for the

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GC oven: 100°C for three minutes, followed by a temperature increase of

5°C/min to 300°C for a total run time of 48 min. The abundance of relative metabolites was calculated from the integrated signal of all potentially labeled ions for each metabolite fragment.

5.10. RNA purification and amplification for Illumina

Microarrays.

Cells were seeded in 6 well plates, and RNA was extracted using the

Quick-RNATM MiniPrep. RNA amplification was carried by Illumina® TotalPrepTM

RNA amplification kit, according to manufacturer’s instructions. HumanHT-12 v4

Expression BeadChip Kit (Illumina) was used for hybridization and imaging, according to manufacturer’s protocol

5.11. LC MS

50 µl samples were mixed with 20 µl 0.1 mM sodium tetraborate buffer and 40 µl of 20mM dansyl chloride solution for 30 mins at RT. 300 ul of ice-cold acetonitrile were added, mixed, and incubated for 30 mins at 4°C. Samples were centrifuged at 13000 RPM for 5 mins to remove the protein. Transfer the supernatant to other tubes, and dry for 2 hours to make sure it dry. Add 60 µl of buffer A (water/ acetonitrile/formic acid (95:5:0.1) and sample were centrifuged

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for 10 mins at 13000 RPM. Supernatant was transferred to LCMS tubes and analyzed by LC-MS/MS utilizing a TSQ Quantum Ultra System.

5.12. GLS and GLUL gene silencing by small interfering RNA

Small interfering RNA (siRNA) targeted to GLUL was purchased from

Sigma. In vitro transient transfection was performed as described previously 41.

Briefly, cells were transfected with GLUL specific or scrambled (control) siRNA using lipofectamine reagent (Invitrogen, Carlsbad, CA). Cells were harvested at selected time intervals, to measure mRNA levels of GLUL using quantitative reverse transcriptase-PCR (qRT-PCR).

5.13. Quantitative real-time RT-PCR

Total RNA was isolated using Qiagen RNAeasy kit (Qiagen). cDNA was synthesized using the SuperscriptTM First-strand kit (Invitrogen) as per the manufacturer’s instructions. Real-time qRT-PCR was performed to validate the expression of GLUL mRNA in SKOV3ip1 ovarian cancer cells using an 7000

Sequence Detection System (Applied Biosystems, Foster City, CA) with the

SYBR GreenERTM qPCR Supermix kit (Invitrogen, Carlsbad, CA) as per the manufacturer’s instructions.

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5.14. Average gene expression of pathways analysis

Genes for Gln anabolism pathway include GLUL, GLUD1/2, GOT1/2,

GPT, GPT2, PSAT, BCAT1/2. Genes for glycolysis pathway include ACSS1/2,

ADH1A/B/C, ADH4/5/6/7, AKR1A1, ALDH1A3, ALDH1B1, ALDH2, ALDH3A1/2,

ALDH3B1/2, ALDH7A1, ALDH9A1, ALDOA, ALDOB, ALDOC, BPGM, DLAT,

DLD, ENO1/2/3, FBP1/2, G6PC, G6PC2, GALM, GAPDH, GCK, GPI, HK1/2/3,

LDHA, LDHAL6A, LDHAL6B, LDHB, LDHC, PCK1/2, PDHA1/2, PDHB, PFKL,

PFKM, PFKP, PGAM1/2/4, PGK1/2, PGM1/2, PKLR, PKM2, TPI1. To analyze the pathway gene expression level, average gene expression is calculated for all involved genes for one sample, and get average value to represent this sample’s average gene expression of pathway.

5.15. Transcriptional Factors Analysis

The transcription factor - target gene interaction data were downloaded from pathway commons version 7(171). All binary interaction data were downloaded and then filtered to included only the interaction type 'controls- expression-of'. FOXO3 and GATA3 are from recent published papers. Detail information is presented in Table 5.1. from type to source controls-expression- FOXO3 of GLUL (80) controls-expression- GATA3 of GLUL (82) controls-expression- AHR of HK2 PCv7

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controls-expression- AR of GOT2 PCv7 controls-expression- ARNT of HK2 PCv7 controls-expression- ARNT of LDHA PCv7 controls-expression- ATF1 of LDHA PCv7 controls-expression- ATF2 of LDHA PCv7 controls-expression- ATF3 of LDHA PCv7 controls-expression- CEBPA of GOT1 PCv7 controls-expression- CEBPB of GOT1 PCv7 controls-expression- CREB1 of LDHA PCv7 controls-expression- CREBBP of HK2 PCv7 controls-expression- CREBBP of LDHA PCv7 controls-expression- CYP26A1 of PDHB PCv7 controls-expression- CYP26A1 of SLC16A1 PCv7 controls-expression- E4F1 of HK2 PCv7 controls-expression- EGR3 of HK2 PCv7 controls-expression- ELK1 of BCAT2 PCv7 controls-expression- EP300 of HK2 PCv7 controls-expression- EP300 of LDHA PCv7 controls-expression- ESRRA of GOT1 PCv7 controls-expression- FOXA2 of HK2 PCv7 controls-expression- GABPB2 of BCAT2 PCv7 controls-expression- GTF2A2 of BCAT2 PCv7 controls-expression- GTF3A of BCAT2 PCv7 controls-expression- HIF1A of HK2 PCv7

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controls-expression- HIF1A of LDHA PCv7 controls-expression- HNF4A of HK2 PCv7 controls-expression- HNF4A of SLC16A4 PCv7 controls-expression- LEF1 of SLC16A1 PCv7 controls-expression- MAX of BCAT1 PCv7 controls-expression- MAX of LDHA PCv7 controls-expression- MAZ of BCAT2 PCv7 controls-expression- MAZ of GOT1 PCv7 controls-expression- MAZ of LDHB PCv7 controls-expression- MAZ of SLC16A4 PCv7 controls-expression- MYC of BCAT1 PCv7 controls-expression- MYC of LDHA PCv7 controls-expression- MYC of SLC16A1 PCv7 controls-expression- NEUROD1 of HK2 PCv7 controls-expression- NF1 of GOT1 PCv7 controls-expression- NR3C1 of GOT2 PCv7 controls-expression- PAX4 of HK2 PCv7 controls-expression- PCBP1 of HK2 PCv7 controls-expression- PDX1 of HK2 PCv7 controls-expression- RREB1 of GOT1 PCv7 controls-expression- SP3 of LDHB PCv7 controls-expression- SREBF1 of HK2 PCv7 controls-expression- SRF of LDHA PCv7

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Table 5.1: Transcriptional factors regulating glycolysis and Gln anabolism

5.16. In-vivo models and tissue processing

Female athymic nude mice were purchased from the National Cancer

Institute, Frederick Cancer Research and Development Center (Frederick, MD) and maintained according to guidelines set forth by the American Association for

Accreditation of Laboratory Animal Care and the US Public Health Service policy on Human Care and Use of Laboratory Animals. All mouse studies were approved and supervised by the MD Anderson Cancer Center Institutional

Animal Care and Use Committee. All animals used were between 8 and 12 weeks of age at the time of injection. To determine the therapeutic efficacy of

GLS and GLUL gene silencing, we used well characterized orthotopic model of ovarian carcinoma. To establish the tumors, SKOV3ip1 ovarian cancer cells

(750,000) were trypsinized and suspended in 50 µl of Hanks balanced salt solution (HBSS; Gibco, Carlsbad, CA) and injected directly into the left ovary of anaesthetized female nude through a 1.5-cm intraperitoneal incision. Seven days after cell injection, mice were randomly divided into 4 groups: 1) CH/Control siRNA 2) CH/hGLS siRNA or 3) CH/ mGLUL siRNA or 4) CH/hGLS + mGLUL siRNA (n=10 mice per group). To assess tumor growth, treatment began by injecting CH/siRNA nanoparticles twice weekly (150 μg/kg body weight) through intravenous injection. Mice were monitored daily for adverse effects of therapy and were sacrificed when they became moribund (6-7 weeks after cell injection).

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At the time of sacrifice, mouse weight and tumor weight was recorded. Tumor tissue was harvested and either fixed in formalin for paraffin embedding, or frozen in optimum cutting temperature medium (OCT; Miles, Inc., Elkhart, IN) to prepare frozen slides, or snap-frozen in liquid nitrogen for lysate preparation. The individuals who performed the necropsies, tumor collections, and tissue processing were blinded to the treatment group assignments.

5.17. Immunohistochemistry

Paraffin-embedded tumor sections were heated, deparaffinized, and antigen retrieval was performed by steaming, and endogenous peroxides were blocked with 3% hydrogen peroxide in methanol. Nonspecific proteins were blocked with 4% fish gelatin in PBS. Slides were incubated in primary antibody

(1:100), and the secondary antibody (ready-to-use) was followed by streptavidin horseradish peroxidase (ready-to-use). Slides were quantified by counting the number of positively staining cells per 200× field.

5.18. Survival network

Cox proportional hazard regressions were run on the TCGA ovarian data using the ‘survival’ package in R. Tumor gene expression data and overall survival data from the TCGA ovarian cancer cohort (n = 360, 192 events) were

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used for the survival analysis. The computed hazard ratio was used to color the genes whose products are known to catalyze each reaction step in the glucose and Gln metabolic network.

5.19. Survival analysis

Survival analysis was done on progression‐free survival data of OVCA patients obtained from cBioPortal for Cancer Genomics

(http://www.cbioportal.org). Gene expression values used were reported from

Affymetrix U133A Microarray data in the cBioPortal database. Patient data

(n = 539) with valid gene expression levels were used to estimate medians and bounds for upper and lower quartiles. Patients were categorized into two groups based on whether the values of gene ratios were above the upper quartile bound and below the lower quartile bound. GLS1 and GLUD1 expression levels were normalized with their respective median values before calculating the

(GLS1 + GLUD1)/GLUL ratio. Kaplan–Meier survival graphs were plotted, and log‐rank tests were performed using SigmaPlot.

5.20. Ovarian cell line microarray

The KyotoOv38 OVCA cell line gene expression dataset (GSE29175) was downloaded from the Gene Expression Omnibus. Gene expression was normalized relative to the mean expression over all cell line arrays. The relative

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expression of Gln pathway genes involved in tumor invasion is displayed graphically by heatmap with cell lines ranked by order of invasiveness.

5.21. XF bioenergetics assay

Mitochondrial OCR and ECAR were conducted using a Seahorse XF24

Analyzer. The cells were seeded in Seahorse 24‐well microplates and cultured at

37°C with 5% CO2. The following day, the media was replaced with 700 μl of assay medium composed of RPMI without FBS and sodium bicarbonate and incubated at 37°C without CO2 for 1 h. The basal OCR and ECAR were measured. To measure cells’ mitochondrial capacity, drugs were injected to the final concentration as 2 μg/ml of oligomycin, 2.5 μM of carbonylcyanide‐ ptrifluoromethoxyphenylhydrazone (FCCP) and 2 μM of antimycin A. To measure cells’ glycolytic capacity, drug was injected to the final concentration as 11 mM glucose, 20 mM 2‐DG.

5.22. ATP measurements

The intracellular ATP content was measured for the different cell types using the Cell Titer‐Glo Luminescent cell viability assay (Promega). The cells were seeded in white 96‐well plates at 10 K cells per well. The following day, the media were replaced with specific medium (complete RPMI, RPMI without

Gln/glucose) and incubated for 24 h. For oligomycin's effect on intracellular ATP,

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the cells were then incubated for 7 h by using RPMI with different concentration of oligomycin. The ATP content was thereby measured according to the manufacturer's instructions, and ATP content was normalized with cell number.

5.23. Mitochondrial membrane potential assay

Cells were seeded in 96‐well plates to 90% confluency. Medium was replaced with medium with 0.03% H2O2 (with Gln/without Gln, with glucose/without glucose) for 1 h. Then, wells were loaded with tetramethylrhodamine methyl ester (TMRM) in 37°C for 15 min and washed with

PBS twice to remove residue TMRM. Values were read at 548/574.

5.24. Reactive oxygen species assay

Cells were seeded in 96‐well plates to 90% confluency. Fresh medium with H2DCFDA was loaded in 37°C for 30 min. Remove cell‐permeant 2′,7′‐ dichlorodihydrofluorescein diacetate (H2DCFDA) medium and wash twice with

PBS. PBS with 0.03% H2O2 (with Gln/without Gln, with glucose/without glucose) was loaded and the fluorescence values at 485/538 after 1 and 2 h were read.

5.25. NADPH measurements

66.6 μl 1‐methoxy N‐methyl dibenzopyrazine methyl sulfate (PMS:

0.5 mM in DMSO) and 3.2 ml 2,3‐bis‐(2‐methoxy‐4‐nitro‐5‐sulfophenyl)‐2H‐

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tetrazolium‐5‐carboxanilide (XTT, 251 mM in DMSO) was mixed to prepare

XTT/1‐methoxy PMS solution. Cells were seeded in 96‐well plates. Different fresh medium (with Gln/without Gln, with glucose/without glucose) was loaded for

2 h and replaced with corresponding medium with 0.2% XTT/1‐methoxy PMS solution for another 1 h, and absorbance value at 450 nm with 650 nm was measured as the reference.

5.26. NADPH/total NADP measurements

Cells were seeded in six‐well plates overnight and replaced with specific medium (RPMI, RPMI without Gln or glucose) for 24 h. The cells were lysed. The lysed samples and cycling buffer (100 mM Tris–HCl, 0.5 mM MTT,

2 mM PES, 5 mM EDTA) were mixed and added to clear 96‐well plates. For

NADPH extraction, we heated samples for 30 min at 60°C. 1.6 U of glucose‐6‐ phosphate dehydrogenase was added to each well, and the plates were incubated in the dark for 30 min. Afterward, 10 mM glucose‐6‐phosphate was added to each well, and absorbance was read at 570 nm each minute for 5 min.

5.27. GSH/GSSG measurements

The GSH/GSSG assays were conducted according to the manufacturer's protocol of Promega GSH/GSSG Glo™ assay kit. Briefly, cells were seeded in white 96‐well plates overnight, and medium was replaced with

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different fresh medium (with Gln/without Gln, with glucose/without glucose) for

3 h. Medium was removed and cells were lysed with either total or oxidized glutathione reagent (50 μl). Then plates were shaken for 5 min and added with

50 μl luciferin generation reagent to incubate 30 min at room temperature. 100 μl of luciferin detection reagent was added into well, and plates were shaken briefly, equilibrated for 15 min and read luminescence.

5.28. Transfection

Prior to transfection, 2 × 105 SKOV3 cells were plated in 6‐well plate in

1 ml of RPMI + 10% FBS without antibiotics. The cells were transfected with

2.5 μg DNA and 5 μg lipofectamine 2000 for 16 h. The transfected cells were seeded in to 96‐well plates and treated with experimental media conditions.

Cellular proliferation was measured at 24‐h timepoints using the Cell Counting

Kit‐8 reagent (Enzo life sciences).

5.29. Western blot

Following treatment with drugs and different media conditions, cells were washed and then lysed for 20 min on ice with lysis buffer [50 mM HEPES pH 7.4, 150 mM NaCl, 1 mM ethylene glycol tetraacetic acid (EGTA), 10 mM sodium pyrophosphate, 100 mM NaF, 1.5 mM MgCl2, 10%(v/v) glycerol, 1%(w/v)

Triton X‐100, protease inhibitor cocktail (Sigma) and phosphatase inhibitor

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(Sigma)]. Cell debris was removed by centrifugation, and the total protein content of the cell lysate was determined by Bradford assay. Five to 50 μg of total protein was separated by SDS–PAGE and subsequently blotted to nitrocellulose membranes. Protein and phospho‐protein levels were probed with diluted primary antibodies (Cell Signaling) and HRP‐conjugated secondary antibodies

(Thermo Scientific) and detected by chemiluminescence and exposure to autoradiography film.

5.30. TCGA data

The microarray gene expression data from patient tumor samples made publically available by TCGA were downloaded and analyzed for the relative expression of metabolic genes. Patient tumor samples were annotated based on the clinical data made available and were grouped based on whether the patients presented with venous or lymphatic tumor invasions. The relative levels of gene expression were analyzed using a two‐tailed Student's t‐test.

5.31. 13C Metabolic Flux Analysis

Flux analysis was performed using a MATLAB-based software developed within our lab using KNITRO® optimization toolbox. 13C atom transitions were modeled according to the Atom Mapping Matrix (AMM)(172) and Isotopomer

Mapping Matrix (IMM)(173) method. Reversible reactions were modeled as two

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irreversible forward and reverse fluxes (��, ��) and transformed into net and normalized exchange fluxes (��, �̃��ℎ)(174)(175) are described by equations (1) to (3).

Equation 5.1

�� = �� − �� … (1)

Equation 5.2

��ℎ = min(��, ��) … (2)

Equation 5.3

��ℎ �̃��ℎ = … (3) ��ℎ + �

� is a constant chosen in the order of magnitude of net fluxes. Since we normalize all net fluxes with respect to glucose uptake we choose a value of � =

2. Mass balance of intracellular metabolites is applied assuming pseudo steady- state because most intracellular metabolites have a high turnover rate of intracellular pools. The stoichiometric matrix �, is formed according to the metabolic model and the equation for mass balance for vector � =

[��, ��] becomes equation (4).

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Equation 5.4

�� = 0 … (4)

AMMs and IMMs are used to generate carbon atom balances for each reactant-product in every reaction in the metabolic network. In addition to the flux variables �, isotopomer distribution vectors (IDVs, represented by vector �) are used to describe mass isotopomer fractions of intracellular metabolites.

Equation 5.5 � = [�, �] … (�)

The isotopomer mass balances derived using IMMs are non-linear since the rate of production of isotopomers is proportional to the flux and mass isotopomers of precursor metabolites. The mass balance of metabolite B will therefore be

Equation 5.6 �� = � = � . (�� × ��) − � . �� … (�) � �� →� �→� �→

Assuming measurements are made after isotopic steady-state is reached, the general form of the isotopomer mass balance equations for metabolite i, over all N reactions can be represented as

Equation 5.7

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� � � � ��(�) = ∑ ��. �� ∏ ��→�. � + ∑ ��. � = � … (�) �=� ��>� �=�,� <� �, �� ( ��<� )

To solve the equations (4) and (7) for the flux distribution of metabolic network, minimizing the variance weighted sum of square of errors between measured quantities and unknown variables in the system. We convert the IDV to respective mass isotopologue distributions (MIDs) in order to compute error between model and measurements from GC-MS.

5.32. Metabolic model and data processing

Flux analysis was conducted on CAFs and NOFs labeled with U-13C6-

Glucose under nutrient-rich media (1Q) and glutamine (0Q) conditions. The metabolic model contained 30 reactions and 21 metabolites involved in glycolysis, TCA cycle and glutamine pathways. MID measurements obtained from GC-MS for glutamate, -ketoglutarate, fumarate, malate, citrate, pyruvate and lactate are used for 13C-MFA. MIDs are corrected for natural abundance using IsoCor (176) before used in 13C-MFA. Fluxes in the model are bounded by extracellular fluxes obtained for glucose and glutamine uptake and lactate secretion.

Global optimum solution for 13C-MFA is obtained by solving the minimization from at least 50 randomly generated initial guess vectors. Veracity

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of the solution is checked using the 2 goodness-of-fit test for parameter fitting problems. Furthermore, 95% confidence intervals for fluxes are estimated by performing Monte Carlo simulations. For MC simulations, at least 500 samples are generated by introducing normally distributed random error to measurements and the 13C-MFA problem is solved for each sample. Lower and upper bounds of the confidence intervals for each flux are selected from the 500 normally distributed solutions which lie within probability of 0.025 and 0.975, respectively.

5.33. Linear Regression analysis for estimating percentage

contribution of extracellular CAF-secreted Gln to intracellular

glutamate in OVCA.

We assumed that the intracellular glutamate in OVCA under co-culture system is generated by metabolizing extracellular Gln secreted by CAFs and intracellular glutamate synthesis from glucose via TCA cycle. It is also assumed that OVCA do not uptake extracellular glutamate. We formulate an equation to balance the mass isotopologue distributions (MIDs)

Equation 5.8

(�� ) (�� 1) (�� 2) �� = � ∗ �� + � ∗ ��

152

For intracellular glutamate in CAFs described for Fig. 6j, the equation is formed as,

Equation 5.9

(�� ,��) (�� ) (�� ,��) �� = � ∗ �� + � ∗ ��

For glutamate in shared coculture media described for Fig. 6k, the equation is formed as,

Equation 5.10

(�� ) (�� ,��) (�� ,��) �� = � ∗ �� + � ∗ �0

For intracellular glutamate in OVCA cells described for Fig. 7m, the equation is formed as,

Equation 5.11

(�� ,��) (�� ) (�� ,��) �� = � ∗ �� + � ∗ ��

It was assumed that intracellular citrate was the precursor for endogenous glutamate synthesis and extracellular glutamate in cancer cells co-cultured with

CAF, and extracellular Gln MID to estimate the contribution of CAF-secreted Gln

153

on glutamate synthesis in cancer cells. We used measured MIDs of extracellular

Gln, along with intracellular glutamate and citrate for the model.

Here, Mi represents different mass isotopologue which is i units heavier than unlabeled species. x represents the percentage contribution of source 1 to final product in OVCA, y represents the percentage contribution of source 2 to final product. Take final product as intracellular glutamate, source 1 as intracellular citrate, source 2 as extracellular glutamine as example, M0 intracellular glutamate can be generated by M0 intracellular citrate and M0 extracellular Gln; M2 intracellular glutamate can be generated by M2 intracellular citrate and M2 extracellular Gln; M4 intracellular glutamate can be generated by

M4 intracellular citrate and M4 extracellular Gln; M5 intracellular glutamate can be generated by M5+M6 intracellular citrate and M5 extracellular Gln. Using a two-variable and two parameters linear regression for M0, M2, M4, M5 of glutamate, we estimate the value for x and y.

5.34. Statistical analysis

Some statistical analyses were performed using Student's t‐test, and these data were reported in bar graphs as means ± SEM. For multiple comparisons, one‐way ANOVA with Tukey's tests was used for statistical analysis.

154

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Appendix A

Primers for q-PCR:

Genes Primers G6PD-F TGAGCCAGATAGGCTGGAA G6PD-R TAACGCAGGCGATGTTGTC ME1-F ACAGATAATATTTTCCTCACT ME1-R CTACTGGTCAACTTTGGT ME2-F ATTAGTGACAGTGTTTTCCTA ME2-R CTATTCTGTTATCACAGG NAMPT-1F TCACGGCATTCAAAGTAGGA NAMPT-1R GAGTTCAACATCCTCCTGGC NAMPT-2F TCTGTCTTCTTTTCACGGCA NAMPT-2R GCAGAAGCCGAGTTCAACAT UCP2-F GGTGGTCGGAGATACCAAAG UCP2-R CTCGGGCAATGGTCTTGTAG GAC-F GATCAAAGGCATTCCTTTGG GAC-R TACTACAGTTGTAGAGATGTCC GCLC-F ACATCTACCACGCCGTCAAG GCLC-R ACAGGACCAACCGGACTTTT GSR-F ACAAGCTGGGTGGCACTT GSR-R CAACTTGGAAAGCCATAATCA Nrf2-F TCATGATGGACTTGGAGCTG Nrf2-R CATACTCTTTCCGTCGCTGA ATP-7A-F CTGCGTAGCTCCAGAGGTTT ATP-7A-R TGGTCCAAACACAGGAATTG MRP1-F TGACCGAGGCTACATTCAGA MRP1-R ATATGCCCCGACTTCTTTCC MRP2-F AGACGCAGTCCAGGAATCAT MRP2-R CATAGGAAGCCCAAGGGAAT E-cadherin (CDH1)-F TCGACACCCGATTCAAAGTGG E-cadherin (CDH1)-R TTCCAGAAACGGAGGCCTGAT N-cadherin-F ATT GGA CCA TCA CTC GGC TTA N-cadherin-R CAC ACT GGC AAA CCT TCA CG Twist1-F GGC ACC ATC CTC ACA CCT CT Twist1-R GCT GAT TGG CAC GAC CTC T

192

Snail-F TATGCTGCCTTCCCAGGCTTG Snail-R ATGTGCATCTTGAGGGCACCC MMP-1-F GATGTTCAGCTAGCTCAGGAT MMP-1-R AAGGGATTTGTGCGCATGTAG MMP-2-F CTTCTTCAAGGACCGGTTCAT MMP-2-R GCTGGCTGAGTAGATCCAGTA GLUL-F CCTGCTTGTATGCTGGAGTC GLUL-R GATCTCCCATGCTGATTCCT GLS-F GCTGTGCTCCATTGAAGTGA GLS-R GCAAACTGCCCTGAGAAGTC GLS2-F ATCAGAAAGTGGCATGCTGT GLS2-R GCCTTTAGTGCAGTGGTGAA GLUD1-F GGGATTCTAACTACCACTTGCTCA GLUD1-R AACTCTGCCGTGGGTACAAT GOT1-F CAACTGGGATTGACCCAACT GOT1-R GGAACAGAAACCGGTGCTT GPT1-F CATGGACATTGTCGTGAACC GPT2-R TTACCCAGGACCGACTCCTT ACL-F GAAGGGAGTGACCATCATCG ACL-R TTAAAGCACCCAGGCTTGAT NDUFV1-F AAGGTTCCCTGAGTCGAGGT NDUFV1-R TTGTGAGGATCATGGCGTAA SDHB-F AAATGTGGCCCCATGGTATTG SDHB-R AGAGCCACAGATGCCTTCTCTG COX1-F TCGCATCTGCTATAGTGGAG COX1-R ATTATTCCGAAGCCTGGTAGG UQCRH-F ACTGGAGGACGAGCAAAAGA UQCRH-R TGATGCCCAGATGATGAAGA CYTB-F TGAAACTTCGGCTCACTCCT CYTB-R AATGTATGGGATGGCGGATA ATP5F1-F ATCGCCTGGACTACCACATC ATP5F1-R TTGGCAATGGTCTCCTTTTC ATP5J-F GTCCTTCGGTCAGCAGTCTC ATP5J-R AGATGCCTGTCGCTTTGATT HKII-F TGGAGGGACCAACTTCCGTGTGCT HKII-R TCAAACAGCTGGGTGCCACTGC PFK2-F AATCCCATCACCATCTTCCA

193

PFK2-R AGCGGGGTGACACTATTGCGT PDK-F CGGATCAGAAACCGACACA PDK-R GGATCAGAAACCGACACA GLUT1-F GCTGTGCTTATGGGCTTCTC GLUT1-R CACATACATGGGCACAAAGC CXCL1-F AACCGAAGTCATAGCCACAC CXCL1-R GTTGGATTTGTCACTGTTCAGC CXCR2-F AACATGGAGAGTGACAGCTTTG CXCR2-R TTCACATGGGGCGGCATC CXCL2-F CGCCCAAACCGAAGTCATAG CXCL2-R CGCCCAAACCGAAGTCATAG Bax-F TGG AGCTGCAGAGGATGATTG Bax-R GAAGTTGCCGTCAGAAAACATG Bcl2-F CATGCTGGGGCCGTACAG Bcl2-R GAACCGGCACCTGCACAC SNAI2-F ATGAGGAATCTGGCTGCTGT SNAI2-R CAGGAGAAAATGCCTTTGGA ZEB1-F GGCATACACCTACTCAACTACGG ZEB1-R TGGGCGGTGTAGAATCAGAGTC ZEB2-F AATGCACAGAGTGTGGCAAGGC ZEB2-R CTGCTGATGTGCGAACTGTAGG NADsyn1-F GGATGATACAGGACCTGACAAAG NADsyn1-R CCGAGGCGTTGGTGATGAT NMNAT2-F CACCTGTGATCGGACAGCC NMNAT2-R GTGCCCAGATTGGCATTCTC NAPRT1-F GCAGCCTTTGTGGCCTATG NAPRT1-R AAGTTGGGGAGACCACTCCTC Bcl-Xl-F CGTGGAAAGCGTAGACAA Bcl-Xl-R GTGGGAGGGTAGAGTGGAT MMP3-F ATGGACAAAGGATACAACAGGGA MMP3-R TGTGAGTGAGTGATAGAGTGGG GLUL-mouse-F CCGCCTCGCTCTCCTGACC GLUL-mouse-R CGGGTCTTGCAGCGCAGTC GLUL-mouse-2F TTATGGGAACAGACGGCCAC GLUL-mouse-2R AAAGTCTTCGCACACCCGAT Gapdh-mouse-F GCACAGTCAAGGCCGAGAAT Gapdh-mouse-R GCCTTCTCCATGGTGGTGAA

194

18s-mouse-F AGA AAC GGC TAC CAC ATC CAA 18s-mouse-R GGG TCG GGA GTG GGT AAT TT

195

Appendix B

Small molecules used in this study

Small Molecules Chemical Structures Functions

BPTES Inhibit GLS

AOA Inhibit amino

transferases

EGCG Inhibit GDH

Oligomycin Inhibit ATP

synthase

FCCP Uncoupling

agent

Antimycin Inhibits complex

III

MSO Inhibits Gln

synthetase

196

2DG Competitor of

glucose

Rotenone Inhibit complex I

BSO Inhibit GSH

synthesis

Cisplatin Chemo-drug

L-DON Glutamine

antagonist

Gabapentin Inhibit BCAT

AG490 Inhibit JAK

Stattic Inhibit STAT3