The Interaction Between Pancreatic Stellate Cells and Tumour During Pancreatic Cancer

THE INTERACTION BETWEEN PANCREATIC STELLATE CELLS AND TUMOUR DURING PANCREATIC CANCER

A Thesis Submitted to the University of Manchester

For the Degree of Doctor of Philosophy

In the Faculty of Biology, Medicine and Health

2019

Ahlam M. A. Sultan

School of Medical Science / Division of Cancer Sciences

TABLE OF CONTENTS

TABEL OF CONTENTS ...... 1-9

LIST OF TABELS ...... 10-11

LIST OF FIGURES ...... 12-19

LIST OF ABBREVATION ...... 20-24

ABSTRACT ...... 25

DECLARATION ...... …...26

COPYWRITE ...... 27

ACKNOWLEDGEMENTS ...... 28

CHAPTER-1

1. Introduction ...... 29-62

1.1. Thesis Overview ...... 29-30

1.2. Pancreatic Cancer an Overview ...... 31

1.3. Molecular Genetics and Pathology of PDAC ...... 32-34

1.4. Tumour Microenviroment (TME) ...... 35-40

1.4.1. Extracelluar Matrix ...... 36

1.4.2. Pancreatic Tumour Associated Cells ...... 36-40

1.4.2.1. Pancreatic Stellate Cells ...... 36-38

1.4.2.2. Myofibroblasts ...... 39

1.4.2.3. Inflammatory and Immune Cells ...... 39-40

1.4.2.4. Neural Cells ...... 40

1.4.2.5. Endothelial Cells ...... 40

1.4.2.6. Pericytes...... 40

1.5. Cancer Cell Metabolism ...... 41-50

1.5.1. Introduction ...... 41

1.5.2. Cell Metabolism ...... 41-42

1.5.3. Cancer Cell Metabolic Remodeling ...... 42-50

1.5.3.1. The Warburg Effects ...... 42-44

1.5.3.2. Cancer Cell Metabolic Flexibility ...... 44-46

1.5.3.3. Genetic Alteration of Cancer and Metabolic Remodeling ...... 46-49

1.5.3.3.1. HIF1-Alpha ...... 46-47

1.5.3.3.2. PI3K/AKT, mTOR ...... 47

1.5.3.3.3. AMPK/LKB1 ...... 47-48

1.5.3.3.4. TP53 ...... 48

1.5.3.3.5. Myc ...... 48-49

1.5.3.4. Alteration in Metabolizing Enzyme in Cancer ...... 49-50

1.6. Calcium Signalling and Cancer Metabolism ...... 51-56

1.6.1. Introduction ...... 51

1.6.2. Calcium Signalling and its Machinary ...... 51-53

1.6.3. Calcium Signalling and Cancer ...... 53-54

1.6.4. PMCA and PDAC cells Metabolism ...... 54-56

1.7. Tumour Microenviroment and Cancer Metabolism ...... 57-59

1.7.1. Hypoxia ...... 57

1.7.2. PH ...... 57

1.7.3. Autophagy ...... 58

1.7.4. Stromal Cells Metabolic Coupling with the Tumour and the Reverse Warburge Effect ...... 58-59

1.8. Summary ...... 60

1.9. Experimental Aim...... 61-62

1.9.1. Specific Aim ...... 61-62

1.9.1.1. Results CH3 ...... 61

1.9.1.2. Results CH4 ...... 61

1.9.1.3. Results CH5 ...... 62

CHAPTER-2

2. Materials and Methods ...... 63-86

2.1. Cell Culture...... 63-64

2.2. Preparation of Conditioned media with Serum ...... 65

2.3. Preparation of Serum Free Conditioned media with Low Glucose ...... 65

2.4. Stable Transfection of RFP or GFP genes into MiaPaCa-2 Cells ...... 65-69

2.4.1. Bacterial Transformation, Preparation of Competence Cells ...... 65-66

2.4.2. Isolation and Amplification of DNA ...... 66

2.4.3. DNA Quantification ...... 66

2.4.4. Generation of MiaPaCa-2 Stably Expressing RFP or GFP ...... 66-69

2.5. Fura-2 Imaging and Calcium Overload assay ...... 70

2.6. Calibration of Resting Intracellular Calcium ...... 70-71

2.7. Sulforhodamine-B (SRB) Cell Proliferation Assay ...... 71

2.8. Luciferase-Based ATP Assay ...... 71-72

2.9. Measuring the Glucose Concentration of Conditioned Media ...... 72

2.10. Measuring the Lactate Concentration of Conditioned Media ...... 73

2.11. Measuring the Mitochondrial Mass and Function by Immunofluorescence…… ... 73-74

2.12. Studying the Expression of Alpha-Smooth Muscle Actin by Immunoflurorescentce… ...... 74

2.13. Studying the Expression of Glucose and Lactate Transporter of Cells in Co-culture by Immunofluorescence ...... 74

2.14. Westren Blotting (WB) ...... 75-78

2.14.1. Cell Preparation for WB and Protein Extraction ...... 75

2.14.2. Protein Concentration ...... 75-76

2.14.3. Protein Separation...... 76

2.14.4. Protein Transfer ...... 76-77

2.14.5. Protein Detection ...... 77

2.15. Quantitively Assessing the Growth of two Poplulations of Cells in Co-culture by Flow Cytometry ...... 79

2.16. Measuring the Glucose Uptake of two Populations of Cells in Co-culture by Flow Cytometry ...... 79-80

2.17. Studying the Cells Proliferation of two Populations of Cells in Co-culture by Using BrdU and Flow Cytometry ...... 81

2.18. Measuring the Mitochondrial Membrane Potential of two Populations of Cells in Co- culture by Flow Cytometry ...... 81

2.19. Measuring The Mitochondrial Membrane Potential on Live Cell by TMRE Imaging...... 82

2.20. Measuring the Mitochondrial Function and Determine the Metabolic Phenotype of Cells by Using Seahorse Flux Analyzer ...... 82-83

2.21. Measuring the Glycolytic Function of Cells by Using Seahorse Flux Analyzer ..... 83-84

2.22. Metabolomics ...... 84-85

2.22.1. Sample Preparation and Derivatization ...... 84-85

2.22.2. Gas Chromatography Mass Spectrometry (GC-MS) Analysis ...... 85

2.22.3. Data Processing ...... 85

2.23. Citrate Uptake Studies ...... 85-86

2.24. Statistical Analysis ...... 86

CHAPTER-3 (Investigating the Metabolic Phenotype and Studying the Metabolic Regluation of Intracellular Calcium Homeostasis in PDAC vs Fibroblasts vs Stellate Cells vs Pancreatic Ductal Cells).

3.1. Introduction ...... 87-88

3.1.1. Aim ...... 88

3.2. Results ...... 89-119

3.2.1. PDAC Cells Grow Faster than Non-Cancerous Cells ...... 89

3.2.2. Effect of Glucose Restriction on PDAC Cells (MiaPaCa-2) Growth ...... 90

3.2.3. The Growth Rate of PDAC Cells (MiaPaCa-2, PANC-1), hPSCs and BJ Skin Fibroblasts is Unaffected by Glucose Concentration in Long-term Culturing ...... 91

3.2.4. PDAC Cells (MiaPaCa-2) are Highly Glycolytic Compared to hPSCs and HPDE, Since They Exhibit the Warburg Effect ...... 92-93

3.2.5. The Protein Expression of Key Glycolytic Enzymes in PDAC (MiaPaCa-2), hPSCs and HPDE ...... 94-95

3.2.6. The Mitochondrial Function of PDAC Cells (MiaPaCa-2) is the Highest Compared to hPSCs and HPDE ...... 96

3.2.7. The Higher Mitochondrial Function in PDAC is Independent of Mitochondrial Mass/ Number of Mitochondria ...... 97

3.2.8. Inhibition of Glycolysis Induce Pronounced ATP Depletion in Human PDAC cells (MiaPaCa-2, PANC-1) and Human Skin Fibroblasts Regardless of Glucose ...... 102-103

3.2.9. Inhibition of Both Glycolysis and Mitochondrial Metabolism Caused Pronounced ATP Depletion in HPDE cells ...... 104

3.2.10. The Total Cellular ATP Content is Higher in HPDE Compared to PDAC (MiaPaCa-2) and hPSCs ...... 105-106

3.2.11. Inhibition of Glycolysis, but not Mitochondrial Metabolism Caused Irreversible Calcium Overload in Both PDAC Cells and Fibroblasts ...... 107-110

3.2.12. HPDE Cells are Less Sensitive Than PDAC Cells to Glycolytic Inhibitors Induced Calcium Overload ...... 111-112

3.2.13. Inhibition of Glycolysis, Not Mitochondrial Produce Significant Increase in Intracellular Calcium Levels in Pancreatic Stellate Cells ...... 114-115

3.2.14. Comparing the Effect of PFK-15 and IAA in MiaPaCa-2 vs hPSCs vs HPDE on Calcium Overload Studies ...... 116-119

3.3. Discussion ...... 120-122

3.4. Summary ...... 122

CHAPTER-4 (The Cancer-Stromal Interaction in Pancreatic Cancer [the Metabolic Regulation of Cytosolyic Calcium]).

4.1. Introduction ...... 123-124

4.1.1. Aim ...... 124

4.2. Results ...... 125-154

4.2.1. The Expression of Alpha-Smooth Muscle Actin is High in hPSCs Indicating that They are in Their Activated Phenotype ...... 125-126

4.2.2. hPSCs Conditioned Media Increases Mitochondrial Metabolism/Respiration in MiaPaCa-2 cells...... 127-130

4.2.3. hPSCs Conditioned Media Increases Mitochondrial Membrane Potential in MiaPaCa-2 cells ...... 130-134

4.2.4. hPSCs Conditioned Media Reduces the Glycolytic Phenotype in MiaPaCa-2 Cell…...... 135-137

4.2.5. Direct Co-culture of MiaPaCa-2 with hPSCs Has Similar Effect on the Metabolic Phenotype of MiaPaCa-2 Cells...... 138-142

4.2.6. The Expression of Key Glycolytic Enzymes in MiaPaCa-2 and hPSCs Cells in Co- culture ...... 143-145

4.2.7. No Change in Both Calcium Overload and ATP Depletion Response of MiaPaCa-2 Cells in Co-culture Following the Treatment with Different Metabolic Inhibitors ..... 146-149

4.2.8. Co-culturing MiaPaCa-2 with hPSCs Hasn’t Change the Growth Rate of Both Cells ...... 150-154

4.3. Discussion ...... 155-156

4.4. Summary ...... 156

CHAPTER-5 (Investigation of the Mechanism for the Altered PDAC Metabolism when Co- cultured with hPSCs)

5.1. Introduction ...... 157-159

5.1.1. Aim ...... 158-159

5.2. Results ...... 160-197

5.2.1. Conditioned media (CM) Lactate Accumulation and Glucose Depletion ..... 160-161

5.2.2. Reducing Media Glucose Mimics the Effect of Conditioned Media on MiaPaCa-2 Cell Metabolism ...... 162-164

5.2.3. Glucose Replenished hPSCs Conditioned Media Increase Mitochondrial Metabolism and Reduced Glycolysis of MiaPaCa-2 Cells ...... 165-167

5.2.4. The Metabolic Phenotype of MiaPaCa-2 Cells Haven’t Change Following the Treatment with Lactate Regardless of Glucose Concentration ...... 168-174

5.2.5. No Change in the Expression of Lactate Transporter (MCT-1 or MCT-4) in Both PDAC (MiaPaCa-2) and Pancreatic Stellate Cells in Co-culture ...... 175-177

5.2.6. Footprint Metabolomics of Pancreatic Stellate Cells Conditioned Media Show Significant Increase in Citrate; it was excessively elevated in hPSC-CM ...... 178-180

5.2.7. PDAC (MiaPaCa-2) Cells Can Take Up Citrate Following the Treatment with 5mM Citric Acid ...... 181

5.2.8. Citrate Supplemented DMEM Increase Mitochondrial Metabolism and Decrease Glycolysis in MiaPaCa-2 Cells...... 182-190

5.2.9. The Metabolic Phenotype of MiaPaCa-2 Cells Haven’t Change Following the Treatment with Alanine Regardless of the Glucose Concentration ...... 191-197

5.3. Discussion ...... 198

5.4. Summary ...... 199

CHAPTER-6

6. Conclusion ...... 200-202

CHAPTER-7

7. Reference ...... 203-212

CHAPTER-8

8. Supplement ...... 213-267

8.1. Stocks Preparation ...... 213-214

8.2. Calibrating of Resting Intracellular Calcium for al Cell Type ...... 215-216

8.3. Determine the Optimal Calcium Agonist for BJ Skin Fibroblast to Test the Cells Viability Required for Calcium Overload Studies ...... 217-218

8.4. Comparison of Pancreatic Stellate Cells Response in Calcium Overload and ATP Studies When Culture in DMEM/F12 vs 25mM DMEM ...... 219-220

8.5. ATP Standard Curve ...... 221

8.6. Mechanism of Action of Different Metabolic Inhibitors ...... 222

8.7. pDsRed-Monomer-C1 and pEGFP-N1 Vector Information ...... 223

8.8. Summary of the Mito-Stress test ...... 224-225

8.9. Agilent Seahorse XF Cell Energy Phenotype Profile ...... 226

8.10. Seahorse XF Glycolysis Stress Test Profile ...... 227-228

8.11. FCCP Optimization for Different Cell Type for Seahorse Experiment ...... 229

8.12. Florescence Microscope Imaging of MiaPaCa-2-RFP cells ...... 230

8.13. Cell Sorting by Flow Cytometry for Generation of Stable Cell Line (1st Sorting) …...... 231-232

8.14. Cell Sorting by Flow Cytometry for Generation of Stable Cell Line (2nd Sorting) ……...... 233-234

8.15. Cell Count at the End of Conditioned media Collection for Metabolomics ...... 235

8.16. Full List of Metabolites Identified by Using Metabolomic for Citrate Uptake Study...... 236-239

8.17. Full List of Metabolites Identified by Using Metabolomic-Mass Spectrum Footprint...... 240-264

8.18. Full List of ANOVA Test Results of Metabolites Identified by Using Metabolomic Mass Spectrum Footprint...... 265-266

8.19. Summary of Different Co-culture Methods Used ...... 267

Word Count (including footnote and endnotes): 56,720

LIST OF TABELS

CHAPTER 2

Table 2.1. List of cell line ...... 63-64

Table 2.2. List of cell culture media ...... 64

Table 2.3. The transfection conditions used to generate stable cell line ...... 67

Table 2.4. Primary antibodies details ...... 77-78

Table 2.5. Secondary antibodies detail ...... 78

CHAPTER 3

Table 3.1. Summary of the metabolic phenotype finding ...... 101

Table 3.2. Summary of the ATP depletion studies finding...... 105

2+ Table 3.3. Summary of the maximum change in [Ca ]i (nM) following the treatment with glycolytic inhibitors ...... 113

Table 3.4. Summary of calcium overload studies, area under the curve (AUC, µM.s) finding following the treatment with glycolytic inhibitors...... 113

2+ Table 3.5. Summary of the maximum change in [Ca ]i (nM) following the treatment with glycolytic inhibitors ...... 119

Table 3.6. Summary of calcium overload studies, area under the curve (AUC, µM.s) finding following the treatment with glycolytic inhibitors ...... 119

CHAPTER 8

Table 8.1. Stocks preparation...... 213-214

Table 8.18. Full list of metabolites identified by using metabolomic for citrate uptake study… ...... 236

Table 8.17. Full list of metabolites identified by using metabolomic-mass spectrum footprint… ...... 240-264

Table 8.18. Full list of ANOVA test results of metabolites identified by using metabolomic-mass spectrum footprint...... 265-266

LIST OF FIGURES

CHAPTER 1

Figure 1.1. The progression of pancreatic intraepithelial neoplasia (PanINs) into carcinoma. ... 34

Figure 1.2. Pancreatic cancer tumour microenviroment ...... 35

Figure 1.3. Differences between quiescent and activated pancreatic stellate cell...... 37

Figure 1.4. The results of co-injection of pancreatic stellate cells (PSCs) with pancreatic cancer cell line in mouse model ...... 38

Figure 1.5. Comparison between differentiated tissue, proliferative tissue and tumour metabolic preference...... 43

Figure 1.6. Cancer cells reprogram their metabolism to fulfill their need for energy, biosynthesis of essential building blocks and generation of antioxidants...... 44

Figure 1.7. Alteration of key metabolism regulator genes in cancer...... 46

Figure 1.8. The effect of PKM2 enzyme in cancer cell metabolism...... 49

Figure 1.9. The effect of alteration of different metabolizing enzymes on cancer cell metabolism ...... 50

Figure 1.10. Intracellular calcium regulation...... 53

Figure 1.11. The remodelling of key components of the Ca2+ signalling machinery responsible for the hallmark processes of cancer during cancer progression ...... 54

Figure 1.12. Comparison between PMCA activity regulation in normal and cancer cells...... 55

Figure 1.13. The effect of metabolic inhibitors in the glycolytic ATP regulation of PMCA in PDAC cells...... 56

Figure 1.14. The reverse Warburg effects...... 59

CHAPTER 2

Figure 2.1. Optimizing the amount of plasmid DNA for RFP and MiaPaCa-2 cells confluency for transfection ...... 67

Figure 2.2. Optimizing the amount of plasmid DNA for GFP and MiaPaCa-2 cells confluency for transfection ...... 68

Figure 2.3. Fluorescent imaging of MiaPaC-2 cells transfected with RFP and GFP 24 hr Post- treatment with G418...... 68

Figure 2.4. Fluorescent imaging of MiaPaC-2 cells transfected with RFP and GFP 48 hr Post- treatment with G418 ...... 69

Figure 2.5. Florescence microscope images for stable transfected MiaPaCa-2 cell line...... 69

Figure 2.6. Lactate standard curve used to determine lactate concentration...... 73

Figure 2.7. Co-culture of cells in trans-well plate for western blotting studies...... 75

Figure 2.8. BSA standard curve was used to determine the protein concentration...... 76

Figure 2.9. Experimental design to assesses the growth of two population of cells in co-culture using flow cytometry (FACS) ...... 79

Figure 2.10. Quantitatively assessing different function of two population of cells in co-culture by Flow cytometry (FACS) ...... 80

CHAPTER 3

Figure 3.1. Cell proliferation of PDAC, fibroblasts, hPSCs vs HPDE ...... 89

Figure 3.2. MiaPaCa-2 cells growth rate is reduced by acute glucose restriction ...... 90

Figure 3.3. Cell proliferation of PDAC, fibroblasts, hPSCs vs HPDE in low and high glucose media in long-term culturing...... 91

Figure 3.4. PDAC cells (MiaPaCa-2) have higher glycolytic phenotype compared to hPSCs and HPDE ...... 93

Figure 3.5. The expression of key glycolytic enzymes in PDAC (MiaPaCa-2), hPSCs and HPDE ...... 95

Figure 3.6. Metabolic phenotype of PDAC (MiaPaCa-2) vs hPSCs and HPDE cells in response to the mitochondrial stress test ...... 98-99

Figure 3.7. Quantitative determination of mitochondrial membrane potential in PDAC cells (MiaPaCa-2) vs hPSCs and HPDE...... 99

Figure 3.8. Measuring the mitochondrial mass and function in PDAC (MiaPaCa-2), hPSCs and HPDE...... 100

Figure 3.9. Effect of metabolic inhibitors on ATP depletion in PDAC (MiaPaCa-2, PANC-1) and BJ skin fibroblast cell lines ...... 103

Figure 3.10. Effect of metabolic inhibitors on ATP depletion in pancreatic ductal cells ...... 104

Figure 3.11. The ATP depletion following the treatment with different concentration of IAA in PDAC, h-PSCs and HPDE cells ...... 106

Figure 3.12. Effect of metabolic inhibitors on calcium overload in PDAC cells (Mia-PaCa-2) 108

Figure 3.13. Effect of metabolic inhibitors on calcium overload in PDAC cells (PANC-1). .... 109

Figure 3.14. Effect of metabolic inhibitors on calcium overload in skin fibroblast cells...... 110

Figure 3.15. Effect of metabolic inhibitors on calcium overload in HPDE cells ...... 112

Figure 3.16. The effect of metabolic inhibitors on calcium overload in hPSCs...... 115

Figure 3.17. Effect of IAA on calcium overload in PDAC (MiaPaCa-2) vs pancreatic ductal cells (HPDE) vs hPSCs...... 117

Figure 3.18. Effect of PFK-15 on calcium overload in PDAC (MiaPaCa-2) vs pancreatic ductal cells (HPDE) vs hPSCs ...... 118

CHAPTER 4

Figure. 4.1. The protein expression of alpha-smooth muscle actin in hPSCs, MiaPaCa-2 and HPDE ...... 126

Figure. 4.2. Effect of hPSCs conditioned media on the metabolic phenotype of PDAC cells (MiaPaCa-2) ...... 128-129

Figure. 4.3. Effect of MiaPaCa-2 conditioned media on the metabolic phenotype of hPSCs..… ...... 129-130

Figure. 4.4. hPSCs conditioned media increases MiaPaCa-2 mitochondrial membrane potential without effecting mitochondrial mass...... 131-132

Figure. 4.5. MiaPaCa-2 conditioned media has no effect on the hPSCs mitochondrial membrane potential and mitochondrial mass ...... 133-134

Figure. 4.6. The Effect of hPSCs conditioned media on the glycolytic function of PDAC (MiaPaCa-2) cells ...... 136

Figure. 4.7. The Effect of MiaPaCa-2 conditioned media on the glycolytic function of hPSCs...... 137

Figure. 4.8. Changes in mitochondrial functions in PDAC (MiaPaCa-2) and hPSCs cells in co- culture ...... 140

Figure. 4.9. Changes in glucose uptake of PDAC (MiaPaCa-2) and hPSCs in co-culture ...... 141

Figure. 4.10. The expression of glucose transporter of PDAC (MiaPaCa-2) and hPSCs in co- culture...... 142

Figure. 4.11. The expression of different glycolytic enzymes of PDAC (MiaPaCa-2) in co- culture ...... 144

Figure. 4.12. The expression of different glycolytic enzymes of hPSCs in co-culture ...... 145

Figure. 4.13. Effect of metabolic inhibitors on calcium overload and ATP depletion of singly and co-cultured PDAC cells (Mia-PaCa-2)...... 147-148

Figure. 4.14. Effect of metabolic inhibitors on ATP depletion in co-cultured hPSCs ...... 149

Figure. 4.15. The growth rate of both MiaPaCa-2 and hPSCs cells in co-culture ...... 151

Figure. 4.16. Quantitively assessing the growth of PDAC (MiaPaCa-2) and hPSCs in direct co- culture ...... 152

Figure. 4.17. Cell proliferation studies of direct co-culture of PDAC (MiaPaCa-2) and hPSCs by using BrdU and FACS...... 153-154

CHAPTER 5

Figure. 5.1. The metabolic cross talk between pancreatic stellate and PDAC cells...... 159

Figure. 5.2. Lactate and glucose concentration in hPSCs and MiaPaCa-2 cells conditioned media ...... 161

Figure. 5.3. The effect of reducing media glucose on the metabolic phenotype of PDAC (MiaPaCa-2) cells...... 163-164

Figure. 5.4. The effect of reducing media glucse on the glycolytic function of PDAC (MiaPaCa- 2) cells cultured in media with different glucose concentrations ...... 164

Figure. 5.5. Effect of glucose-replenished hPSCs conditioned media on PDAC (MiaPaCa-2) cells metabolism...... 166-167

Figure. 5.6. Effect of glucose-replenished hPSCs conditioned media on PDAC (MiaPaCa-2) cells glycolytic function ...... 167

Figure. 5.7. Effect of lactate supplemented media on mitochondrial function of MiaPaCa-2 cells assessed by Mito stress test in zero glucose ...... 169-170

Figure. 5.8. Effect of lactate supplemented media on mitochondrial function of MiaPaCa-2 cells assessed by Mito stress test in low glucose (5mM)...... 170-171

Figure. 5.9. Effect of lactate supplemented media on mitochondrial function of MiaPaCa-2 cells assessed by Mito stress test in high glucose (25mM) ...... 171-172

Figure. 5.10. Effect of lactate supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in zero glucose ...... 172-173

Figure. 5.11. Effect of lactate supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in low glucose (5mM)...... 173

Figure. 5.12. Effect of lactate supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in high glucose (25mM) ...... 174

Figure. 5.13. The expression of lactate influx transporter (MCT-1) for MiaPaCa-2 and hPSCs cells in co-culture...... 176

Figure. 5.14. The expression of lactate efflux transporter (MCT-4) for MiaPaCa-2 and hPSCs cells in co-culture...... 177

Figure. 5.15. The top 25 significant metabolites in the conditioned media...... 179-180

Figure. 5.16. Citrate uptake studies...... 181

Figure. 5.17. Effect of 1mM citrate supplemented media on mitochondrial function of MiaPaCa- 2 cells assessed by Mito stress test in low glucose (5mM)...... 183-184

Figure. 5.18. Effect of 5mM citrate supplemented media on mitochondrial function of MiaPaCa- 2 cells assessed by Mito stress test in low glucose (5mM) ...... 184-185

Figure. 5.19. Effect of 1mM citrate supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in low glucose (5mM)...... 185

Figure. 5.20. Effect of 5mM citrate supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in low glucose (5mM)...... 186

Figure. 5.21. Studying the mitochondrial function of PDAC (MiaPaCa-2) cells following the treatment with 1mM citric acid in media with high glucose (25mM) ...... 187

Figure. 5.22. Studying the mitochondrial function of PDAC (MiaPaCa-2) cells following the treatment with 5mM citric acid in media with high glucose (25mM) ...... 188

Figure. 5.23. The glycolytic function of PDAC (MiaPaCa-2) cells following the treatment with 1mM citric acid in high glucose media (25mM)...... 189

Figure. 5.24 The glycolytic function of PDAC (MiaPaCa-2) cells following the treatment with 5mM citric acid in high glucose media (25mM) ...... 190

Figure. 5.25. Effect of alanine supplemented media on mitochondrial function of MiaPaCa-2 cells assessed by Mito stress test in zero glucose...... 192-193

Figure. 5.26. Effect of alanine supplemented media on mitochondrial function of MiaPaCa-2 cells assessed by Mito stress test in low glucos (5mM)...... 193-194

Figure. 5.27. Effect of alanine supplemented media on mitochondrial function of MiaPaCa-2 cells assessed by Mito stress test in high glucose (25mM) ...... 194-195

Figure. 5.28. Effect of alanine supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in zero glucose...... 195

Figure. 5.29. Effect of alanine supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in low glucose media (5mM)...... 196

Figure. 5.30. Effect of alanine supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in high glucose (25mM) ...... 197

Figure. 5.31. Citrate is the main energy rich metabolites involve in the metabolic cross talk between pancreatic stellate and cancer cells ...... 199

CHAPTER 8

Figure 8.1. Calcium calibration for MiaPaCa-2, PANC-1, hPSCs, HPDE, BJ skin fibroblast ...... 215-216

Figure 8.2. Calcium imaging of BJ skin fibroblast treated with different calcium agonist……...... 217-218

Figure 8.3. Calcium imaging for hPSCs cultured in DMEM/F12 vs 25mM DMEM…...... 219

Figure 8.4. ATP depletion following the treatment with IAA for hPSCs cultured in DMEM/F12 vs 25mM DMEM ...... 220

Figure 8.5. ATP standard curve...... 221

Figure 8.6. Summary for the mechanism of action of different metabolic inhibitors...... 222

Figure 8.7. Mito stress test summary of the key parameters of mitochondrial respiration ...... 224

Figure 8.8. The mechanism of action of different drugs used in Agilent Seahorse XF cell Mito stress test ...... 224

Figure 8.9. Agilent Seahorse XF cell energy phenotype profile ...... 226

Figure 8.10. Agilent Seahorse XF glycolysis stress test profile of the key parameters of glycolytic function ...... 227

Figure 8.11. Agilent Seahorse XF glycolysis stress test illustation of glycolysis...... 227

Figure 8.12. Optimizing the FCCP for MiaPaCa-2, hPSCs and HPDE cells ...... 229

Figure 8.13. Florescence microscope imaging of MiaPaCa-2 cell-RFP (+) ...... 230

Figure 8.14. Flow cytometry 1st cell sorting ...... 231-232

Figure 8.15. Flow cytometry 2nd cell sorting ...... 233-234

Figure 8.16. Cell count for cells at the end of conditioned media collection ...... 235

Figure 8.17. Citrate uptake studie ...... 237-239

LIST OF ABBREVIATIONS

~ Approximately % Percentage [ATP]i Intracellular ATP [Ca2+]i Intracellular Calcium Ion ° C Degrees Celsius µg/µL Microgram per Milliliter µL Microliter µM Micromolar 2-DG 2-Deoxy-D-Glucose 2-NBDG 2-(N-(7-Nitrobenz-2-oxa-1,3-diazol-4-yl) amino)-2-Deoxyglucose ACLY ATP citrate lyase AKT serine/threonine-specific protein kinase, also known as Protein kinase B (PKB) AM Antimycine AMPK AMP-activated protein kinase ASCT Na+-dependent glutamine transporter ATP Adenosine Triphosphate AU Absorbance unit AUC Area under the curve bFGF Basic fibroblast growth factor BK Bradykinin BM Bone marrow BPE Bovine Pituitary Extract BRCA2 Breast cancer type 2 susceptibility protein; human tumour suppressor gene BrdU Bromodeoxyuridine / 5-bromo-2'-deoxyuridine Brpy Bromopyruvate BSA Bovine serum albumin Ca2+ Calcium Ion CaCl2 Calcium chloride CAFs Cancer Associated fibroblasts CCCP Carbonyl cyanide m-chlorophenyl hydrazone CDKN2A Cyclin Dependent Kinase Inhibitor 2A cm2 Square Centimetre Conc. Concentration COX Cyclooxygenase CPA Cyclopiazonic acid DAPI 4′,6-diamidino-2-phenylindole DMEM Dulbecco’s Modified Eagle’s Medium DMEM/F-12 Dulbecco's Modified Eagle Medium/Nutrient Mixture F-12

DMSO Dimethylsulfoxide DNA Deoxyribonucleic acid DPC4 Deleted in pancreatic cancer locus 4 ECAR Extracellular acidification rate ECL Enhanced Chemiluminescence ECM Extracellular Matrix EDTA Ethylenediaminetetraacetic acid EGF 1-53 Human Recombinant Epidermal Growth Factor EGTA Ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid) EMEM Eagle’s Minimum Essential Media ER Endoplasmic Reticulum ETC Electron Transport Chain

F0 Fluorescence intensity at the beginning of the recording FACS Fluorescence-activated cell sorting FADH2 Reduced form of flavin adenine dinucleotide FBS Fatal Bovine Serum FCCP Carbonilcyanide p-triflouromethoxyphenylhydrazone Fe Fluorescence intensity at the end of the recording FGF Fibroblast Growth Factor FITC Fluorescein Isothiocyanate G418 Neomycin G6P Glucose 6-phosphate GAPDHase Glyceraldehyde-3-Phosphate Dehydrogenase GC-MS Gas Chromatography–Mass Spectrometry GFP Green Fluorescent Protein GLUT Glucose transporter GSH Glutathione H+ Hydrogen ions H2O2 Hydrogen Peroxide HEPES-PSS HEPES-Physiological Saline Solution HIF-1α Hypoxia inducing factor1-alpha HK-II Hexokinase-2 HPDE Human Pancreatic Ductal Epithelial hPSCs Human Pancreatic Stellate Cells IAA Iodoacetate IC50 Half Maximal Inhibitory Concentration IDH Isocitrate dehydrogenase IL-1/6 Interleukin-1/6

IP3 Inositol 1,4,5-trisphosphate

IP3R Inositol 1,4,5-trisphosphate receptors K+ Potassium ion

KCl Potassium chloride Kd Fura-2 binding affinity for Ca2+ kDa kilodalton LDH Lactate dehydrogenase LKB1 Liver kinase-B1 MCT Monocarboxylate transporter MCT-1 Monocarboxylate transporter 1; lactate influx transporter MCT-4 Monocarboxylate transporter 4; lactate efflux transporter MCU Mitochondrial Ca2+ uniporter

MgCl2 Magnesium chloride min. Minute miR Non-coding RNA or MicroRNA miRNA microRNA mm millimeter mM millimolar mMCA mitochondrial membrane Ca2+ATPase MMPs Matrix metalloproteases mNCX Mitochondrial Na+/ Ca2+ exchanger MPT Mitochondrial Permeability Transition Porte MTG MitoTracker Green mTOR Mammalian target of rapamycin MTT Methylthiazolyldiphenyl-tetrazolium bromide Na+ Sodium ion NADH Reduced form of Nicotinamide adenine dinucleotide NaF Sodium Fluoride NCX Na+/ Ca2+ exchanger NH4Cl Ammonium chloride nM Nanomolar NMDA Glutamate-gated N-Methyl D-aspartate receptor OCR Oxygen consumption rate OD Optical Density OM Oligomycin OXPHOS Mitochondrial Oxidation Phosphorylation PanINs Pancreatic Intraepithelial Neoplasia PBS Phosphate Buffered Saline PCA Principle Component Analysis PDAC Pancreatic Ductal Adenocarcinoma PDH Pyruvate dehydrogenase PDK Pyruvate dehydrogenase kinase PEP Phosphoenolpyruvate PFK Phosphofructokinase

PFK-15 1-(4-pyridinyl)-3-(2-quinolinyl)-2-propen-1-one

PFKFB3 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 pH logarithm scale of the reciprocal of the hydrogen ion activity in a solution Extracellular decimal logarithm of the reciprocal of the hydrogen ion activity in a pHe solution Intracellular decimal logarithm of the reciprocal of the hydrogen ion activity in a pHi solution PI Propidium iodide PI3K Phosphoinositide-3-kinase PK Pyruvate kinase PKM2 Pyruvate kinase-M2 PMCA Plasma Membrane Calcium ATPase pmCiC Plasma Membrane Citrate Carrier Transporter PMET Plasma Membrane Electron Transporter PPP Pentose Phosphate Pathway PSCs Pancreatic Stellate Cells PTEN Phosphatase and Tensin Homolog

P2X ATP-gated purinergic receptors QC Quality Control RFP Red Fluorescent Protein Rmax Maximum Calibrated Ratio Rmin Minimum Calibrated Ratio RNA Ribonucleic acid ROS Reactive Oxygen Species rpm Revolutions per minute RT Rotenone RYR Ryanodine receptor Florescence signal generated by second excitatory wavelength 380nM in the presence of Sb 380 saturating [Ca2+]i (the “b” signify fura-2 in the [Ca2+]i- bound state) SCO2 Synthesis Cytochrome-c Oxidase SEM, 1X Keratinocyte-Serum Free, 1X SERCA Sarcoendoplasmic Reticulum Calcium Transport ATPase Florescence signal generated by second excitatory wavelength 380nM in the absence of Sf 380 [Ca2+]i ( “f” signify to the presence of fura-2 in the [Ca2+]i -free state) SLC1A5 Sodium-dependent amino acids transporter SOCE Store-operated Ca2+ entry channels SPCA Secretory pathway calcium ATPase SRB Sulforhodamine B STIM Stromal interacting molecule Sub P Substance P TCA Trichloroacetic acid TCA-cycle Tricarboxylic acid cycle

TGF-b Transforming growth factor-b TME Tumour Microenviroment TMRE Tetramethylrhodamine, ethyl ester TOM-20 Mitochondrial import receptor subunit TRP Transient receptor potential VEGF Vascular endothelial growth factor VOCCS Voltage-operated Ca2+ channels α-KG Alpha-ketoglutarate α-KIC Alpha-ketoisocaproate α-SMA Alpha-smooth muscle actin

ABSTRACT

Institution: The University of Manchester

Name: Ahlam M. Sultan

Degree title: PhD in Pharmacology

Thesis title: The interaction between pancreatic stellate cells and tumor during pancreatic cancer

Date: 2019

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive forms of cancer. It is well known that cancer cell prefers glycolysis as its main metabolic pathway (Warburg effect). In our previous studies, we found that this switch is critical for cancer cells to maintain their intracellular calcium homeostasis because glycolytic ATP is important for fueling PMCA. However, one of the main characteristics of PDAC cells is that it has an abundant stroma. There is an accumulation of evidence suggesting a metabolic cross-talk between cancer cells and their stoma (revere Warburg effect). In this project, we have investigated the effects of cancer-stromal 2+ interaction with a focus on pancreatic cancer cell metabolism and metabolic regulation of [Ca ]i homeostasis by co-culturing PDAC cells (MiaPaCa-2) with pancreatic stellate cells (hPSCs). This has been achieved by testing the relative effect of metabolic inhibitors on cellular ATP and fura-2 Ca2+ overload for both cells in co-culture. Moreover, their metabolic phenotype in co-culture has been determined by using Seahorse flux analyzer. Our results are showing that there was no change in the calcium homeostasis regulation in PDAC cells in co-culture. However, we have found significant changes in their metabolism (↑mitochondrial respiration; ↓glycolysis). We next have identified that citrate is the main metabolite that is secreted by hPSCs to fuel PDAC cells mitochondria and decrease glycolysis by performing a footprint metabolomic analysis of the condition media collected from hPSCs. We have further confirmed that citrate can be taken up and consumed by PDAC cell. More importantly, we have tested the effect of citrate in PDAC cells metabolism and found that it mimics the PDAC cells response in co-culture. These findings elucidate a novel metabolic interaction between PDAC cells and hPSCs where citrate plays a major role in such interaction.

DECLARATION

I declare that no portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.

COPYWRIGHT

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ACKNOWLEDGMENTS

I like to acknowledge the support of my supervisor Dr. Jason Bruce, my adviser Dr. Cathy Tournier, and to the staff and faculty of Biology, Medicine and Health Science at the University of Manchester. Also, I would like to thank Professor Roy Goodacre and Howbeer Muhamad Ali for performing the metabolomics studies for my samples.

Special thanks to my lab members: Pishyaporn Sritangos, Daniel Richardson, Eduardo Pnaalarcon, Rosa Sanchez-Alvarez, Sarah Sugden and Andrew James for their support, constant encouragement and making this experience memorable one.

I would like mostly to thank my family for their endless support, trust, and love, especially my mother, from whom I learned to be patient and forgiving. I would like to dedicate this thesis to the memory of my beloved father my god have mercy in his soul.

CHAPTER 1

1. INTRODUCTION

1.1. THESIS OVERVIEW

Pancreatic ductal adenocarcinoma (PDAC) is one of the most deadly and aggressive types of cancer with poor prognosis and high resistance rate to current therapy. The metabolic shift of PDAC cells towards glycolysis (the Warburg effect), which is one of the hallmarks of cancer, is critical for cancer cells survival (Hanahan and Weinberg, 2011). Our previous studies have shown that inhibition of glycolysis, but not mitochondria, induced ATP depletion, inhibited the ATP-driven Ca2+ pump (PMCA), cytotoxic Ca2+overload and necrosis in PDAC cells (MiaPaCa cells) (James et al., 2013; James et al., 2015). This Suggests, the importance of glycolytic ATP supply to PMCA in maintaining the cytosolic calcium in cancer cells and therefore targeting this pathway might be an effective therapeutic strategy for cancer treatment (James et al., 2013; James et al., 2015). However, one of the main characteristics of PDAC is that it has an abundant dense stroma that facilitates the oncogenic potential of PDAC progression and survival. Recently, there have been several lines of evidence that the interaction between stromal and cancer cells plays a critical role in directing cancer cell metabolism (Bonuccelli et al., 2010; Capparelli et al., 2012; Martinez-Outschoorn et al., 2014; Mercier et al., 2008; Pavlides et al., 2009; Salem et al., 2012). The aim of this thesis was to elucidate the role of pancreatic stellate cells (hPSCs), which usually outnumbered pancreatic cancer cells at the tumor site, in modulation or alteration of numerous cancer hallmarks, the metabolic phenotype and calcium homeostasis in human PDAC cells. This thesis provides new evidence for the stromal-cancer cell interaction in PDAC tumour microenvironment that may offer novel therapeutic strategies for the treatment of PDAC.

This introductory chapter will give a broad overview of the following topics:

• The molecular genetics and pathological changes that occurred during PDAC development and progression.

• Highlight different components of the tumor microenvironment (TME) that can have a profound influence on PDAC cell development and progression.

• The metabolic shift of PDAC cells towards glycolysis (the Warburg effect) as a hallmark of cancer.

• The critical role of the glycolytic-ATP supply to PMCA in maintaining PDAC cells calcium homeostasis.

• The dynamic interaction between tumor microenvironment and PDAC cells that can influence cancer cell metabolism (the reverse Warburg effect).

1.2. PANCREATIC CANCER AN OVERVIEW:

Pancreatic cancer is one of the most aggressive and deadly forms of cancer (Ilic and Ilic, 2016). Although it is uncommon (Raimondi et al, 2009), it has the lowest five-year survival rate which is less than 5% compared with other types of cancer such as breast cancer which has approximately 80% (Ilic and Ilic, 2016). Moreover, any patient diagnosed with metastatic pancreatic cancer will have on average less than 6 months to live (Ferlay et al., 2015; Ghadirian et al., 2003). There are many risk factors associated with pancreatic cancer, such as; old age, tobacco smoking, and pre-existence pancreatic diseases, such as; type II diabetic mellitus and pancreatitis (Maisonneuve and Lowenfels, 2010). Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer, it is usually diagnosed in more than 85% of pancreatic cancer patient (Basturk et al., 2015). PDAC develops in the exocrine portion of the pancreas and the classical view is that it is derived from a ductal lineage (Basturk et al., 2015). However, there is growing evidence that the disease may initiated from acinar cells that undergo cellular injury which triggers dedifferentiate towards a ductal lineage proliferate and then fail to re-differentiate (Basturk et al., 2015). Usually, 60-70% of PDAC develop in the head of the pancreas, while 30-40 % develop in the body and tail. For this reason, PDAC can rapidly metastasize because it developed in the superior part close to the main pancreatic and bile ducts. The treatment options for PDAC are limited. Surgical removal of the tumor is a common clinical practice and the only curative treatment. However, only 20% patients qualify for surgical resection (Raimondi et al, 2009). Gemcitabine is the standard chemotherapeutic agent used in the treatment of PDAC. The use of gemcitabine as monotherapy during PDAC improves patient survival rate; but only by few months (Conroy et al., 2011). Combination therapy with FOLFIRINOX extend survival by a few more months (Conroy et al., 2011). One of the main reasons for the poor survival of PDAC is due to the dense abundant stroma (Neoptolemos et al., 2010), which increases the interstitial tissue pressure in the tumor site which closes blood vessels and therefore reduces the blood supply and drug delivery making it essentially resistant to chemotherapy. Furthermore, PDAC is hard to diagnose because it is essentially symptomless until it reaches an advanced stage and it starts to encroach nerves, blood vessels and biliary system (Maisonneuve and Lowenfels, 2010; Raimondi et al., 2009). Moreover, diagnostic tools for early detection are very limited making it difficult to detect (Ilic and Ilic, 2016). All of this means that more research is needed to elucidate an alternative therapy to such lethal disease.

1.3. MOLECULAR GENETICS AND PATHOLOGY OF PDAC

All types of cancers are caused by an accumulation of genetic mutations and PDAC is no exception (Hidalgo, 2010). Usually, in cancer, the mutation occurs in oncogenes, tumor suppressor genes and/or caretaker genes (Vincent et al, 2011). In addition to genetic mutations, other molecular genetic alterations have been observed in PDAC including epigenetic alterations, telomere dysfunction and abnormal activation of embryonic signaling pathways (Vincent et al, 2011). All these alterations usually occur at a multistep process (Neoptolemos et al, 2010). Close observation of all these changes is essential for proper understanding of PDAC initiation, development and progression.

PDAC starts from a benign non-invasive lesion in the pancreatic tissue that progresses to full-blown PDAC over time (Neoptolemos et al, 2010; Figure 1.1). The most commonly detected precursor lesion observed in PDAC patients is known as pancreatic intraepithelial neoplasia (PanINs) (Neoptolemos et al, 2010). There is much controversy as to whether acinar or ductal cells give rise to PanIN lesion (Habisch et al, 2010). However, accumulating evidence suggests that PanIN lesion arises from acinar cells (De La O et al, 2008). In these studies, models of PDAC were generated by specifically inducing mutant KRAS and notch in either, acinar or ductal cells, using cell-specific promoters (De La O et al, 2008). Their study demonstrates that the co-activation of KRAS and notch in the acinar cell will accelerate its dedifferentiation into ductal like phenotypes and give rise to the PanIN lesions which were identical to the human lesion (De La O et al, 2008). PanINs can be divided into three grade lesions: PanIN-1, PanIN-2 and PanIN-3; PanIN-1 can be further subdivided into PanIN-1A and PanIN-1B (Neoptolemos et al, 2010; Figure 1.1). These histological changes in the pancreatic tissue occur in parallel with the genetic and epigenetic changes (Neoptolemos et al, 2010). In addition, the molecular genetic alteration varies during the multistep progression of the benign lesion into a full-blown tumor (Neoptolemos et al, 2010).

In the early stages of PanIN-1 lesions, several genetic changes are observed. Mutation in KRAS represents the most common genetic alteration at this stage (Vincent et al, 2011; Figure 1.1). KRAS is mutant in more than 90% of PDAC patients (Lu and Zeng, 2017). This oncogenic mutation plays a major role in the initiation, maintenance, and development of PDAC, and is regarded as the major PDAC driver mutation (Neoptolemos et al, 2010). Moreover, telomere length shortening is detected in over 90% of early lesions which causes chromosomal instability (Neoptolemos et al, 2010). During the progression from PanIN-1 to

PanIN-2, mutations in CDKN2A is frequently observed and is associated with the loss of p16 expression thereby enhancing cell proliferation and survival (Neoptolemos et al, 2010; Figure 1.1). CDKN2A is inactivated in around 80-95% of the cases (Bardeesy and DePinho, 2002). The epigenetic dysfunction happens in the form of alteration in the DNA methylation causing silencing or promoting genes, histone modification, and alteration in non-coding RNAs (Vincent et al, 2011). These epigenetic changes occur early but progressively increase from low to high-grade lesion as the tumor progress to full-blown PDAC (Neoptolemos et al, 2010).

In the advanced stages of the lesion and specifically during PanIN-3, a mutation in Tp53, DPC4/Smad4, and BRCA2 genes occur which play a major role in the progression of the precursor lesions into full-blown PDAC (Vincent et al., 2011; Figure 1.1). Tp53 and DPC4/Smad4 are tumor suppressor genes, whereas BRCA2 is a caretaker gene. Tp53 inactivation is detected in 50-75% of PDAC patients, DPC4/Smad4 is observed in 55%, while BRCA2 is detected in only 5-10% (Simeone, 2013; Lu and Zeng, 2017). In normal cells, Tp53 plays a major role in inducing apoptosis if DNA damage occurs (Simeone, 2013). Hence, the inactivation of Tp53 function during PanIN-3 can trigger cancer formation (Simeone, 2013). Also, the loss of Smad4 expression is associated with the activation of TGF-β signaling which will enhance cancer cell proliferation and survival (Simeone, 2013). The BRCA2 gene is normally involved in repairing damaged DNA and minimizing error during DNA replication and loss of this function can facilitate cancer development (Simeone, 2013).

Activation of embryonic signaling pathways such as Hedgehog, Notch, and Wnt are associated with the invasive stage of the lesion (Simeone, 2013). These signaling pathways are usually inactivated in differential pancreatic tissue, however, during PDAC they are re- activated (Neoptolemos et al, 2010). Overexpression of several microRNAs is also observed during the high-grade lesion including miR-21, miR-34, miR-155 and miR-200 (Lee et al., 2007; Vincent et al., 2011). These microRNAs are stable and easily detected in the plasma, making them suitable biomarker candidates for PDAC diagnosis (Vincent et al., 2011).

Molecular genetics of PDAC is complex. The development of PDAC is a multistep process and involve an alteration in both genetic and epigenetic pathways (Neoptolemos et al, 2010). Researchers are trying to use most of these molecular genetic changes as biomarkers (Simeone, 2013). Unfortunately, appropriate tools for the diagnosis of PanINs during the early stages are not available and therefore the early diagnosis of PDAC is still a challenge (Simeone, 2013).

Figure 1.1. The progression of pancreatic intraepithelial neoplasia (PanINs) into carcinoma. The histological modifications occur in parallel with the genetic changes. In the early lesion KRAS and CDKN2A/INK4A mutation occurs, while in the later stage mutation in p53 and SMAD4 will enhance the progression into full blown carcinoma. PDAC: pancreatic ductal adenocarcinoma.

1.4. TUMOUR MICROENVIRONMENT (TME)

In the last decade, the understanding of the role of the genetic mutations in cancer development has been well defined and has influenced new treatments to target different genetic pathways involved in cancer development (Bhowmick et al., 2004). However, cancer cells are present in a complex microenvironment that can play a critical role, too (Bhowmick et al., 2004). PDAC is a highly desmoplastic tumor and contains a very dense stroma. It composed approximately 90% stroma with only 10% of the remaining tumour mass composed of bona fide cancer cells. This suggests that the stroma may play a major role in such type of cancer and that targeting the pancreatic stroma may be an important therapeutic strategy (Apte and Wilson, 2012). The pancreatic cancer stroma is composed of extracellular matrix (ECM), blood vessels and tumor-associated cells, including pancreatic stellate cells (PSC), myofibroblast, pathological enlarge nerve fibers, inflammatory and immune cells, endothelial cells and pericytes (Neoptolemos et al, 2010; Figure 1.2). The TME has a direct impact in all hallmark’s characteristic of tumor cells including, proliferation, resistance, angiogenesis, immune modulation, metabolism, invasion, and metastasis (Vonlaufen et al., 2008; Bachem et al., 2005). Even low-grade PanIN lesions are usually associated with a rich stroma, which means that changing the tumor microenvironment may stop the initiation and progression to full-blown PDAC (Neoptolemos et al, 2010).

Figure 1.2. Pancreatic cancer tumour microenvironment. The pancreatic cancer microenvironment is composed of extracellular matrix and cancer associated cell, such as, pancreatic stellate cell, cancer associated fibroblast, neural cells, inflammatory and immune

cells. The communication between cancer cells and stromal cells is bi-directional through the release of cytokines and growth factors from both cells.

1.4.1. EXTRACELLULAR MATRIX

The extracellular matrix (ECM) is the platform upon which all the communication and interaction between the tumor and adjacent cells takes place (Bae, 2013). In addition, it provides structural support to the tumor or any tissue and acts as a reservoir for different growth factors (Bae, 2013). ECM can influence vital tumor behavior, including survival, motility, and invasion (Bae, 2013). Today there are several studies trying to modulate ECM by targeting hyaluronan, which is a main component of ECM, which is suggested to enhance the drug penetration into the tumor (Bae, 2013). Although such therapy shows a promising result in mice models until now there is no evidence that it could benefit the human disease (Bae, 2013).

1.4.2. PANCREATIC TUMOUR ASSOCIATED CELLS

1.4.2.1. PANCREATIC STELLATE CELLS

Pancreatic stellate cells (PSCs) make up around 4 % of the whole pancreatic cell populations of normal pancreas (Neoptolemos et al, 2010). They are present in the periacinar, perivascular and periductal regions of the healthy pancreas (Neoptolemos et al, 2010). PSCs are normally present in the quiescent state acting as a reservoir for vitamin A and lipid (Farrow et al., 2009). During inflammation, hypoxia, cell injury or cancer of the pancreatic tissue the PSCs switch from the quiescent state to the activated state, losing their fat storing phenotype and acting like myofibroblasts (Apte et al, 2013; Figure 1.3). Moreover, activated PSCs change their morphology to a star-shaped appearance and their proliferation rate increases (Neoptolemos et al, 2010). Activated, but not quiescent, PSCs express alpha-smooth muscle actin (α- SMA) (Apte et al., 2004; Bachem et al., 2005; Figure 1.3). Also, activated PSCs start to release different growth factors and increase the extracellular collagen production (Apte et al., 2004; Bachem et al., 2005).

Interestingly, in pancreatic tumours, PSCs can outnumber cancer cells (Bae, 2013). Many studies have shown that PSCs can increase cancer cell proliferation, metabolism, migration, invasion, and resistance to treatment (Neoptolemos et al, 2010). The origin of PSC is still a matter of debate; however, many studies suggest that they are derived from mesenchymal cells in the bone marrow (BM) (Arcangeli et al, 2014).

Figure 1.3. Differences between quiescent and activated pancreatic stellate cell. During inflammation, hypoxia and cancer PSCs switch from the quiescent to the activated state, changing their shape to star like appearance. Activated PSCs release several cytokines and growth factor and are involve in the production and regulation of the extracellular matrix proteins. ROS: reactive oxygen species; IL: interleukin cytokines; MMP: matrix metalloproteinases; TGF-β1: transforming growth factor beta-1; VEGF: vascular endothelial growth factor; α-SMA: alpha smooth muscle actin.

The communication between pancreatic cancer cells and PSCs is bi-directional (Apte and Wilson, 2012). Usually, pancreatic cancer cells will activate PSCs by secreting growth factors, such as fibroblast growth factor-2 (FGF-2), platelet-derived growth factor (PDGF) and transforming growth factor beta-1 (TGF-β1) (Apte et al., 2004; Bachem et al., 2005). In addition to the direct effect of pancreatic cancer cells on PSCs, hypoxia and inflammatory cells can also activate PSCs (Apte et al, 2013). Inflammatory cells will release growth factors and several cytokines that can activate PSCs similar to cancer cells (Neoptolemos et al, 2010). Once PSC is activated, they will increase collagen production which will form fibrotic capsules that surround pancreatic cancer cells (Apte et al., 2004; Bachem et al., 2005). It has been found that activated PSCs are the main source for ECM protein, such as collagen (type I and II), fibronectin, and laminin, in the tumor (Bachem et al, 1998). Importantly, PSCs not only produce ECM protein but also regulate ECM remodeling by secreting several matrix metalloproteinases (MMP) including MMP-2, MMP-9 and MMP-13 and their inhibitors (Schneiderhan et al., 2007). Formation of such a dense stroma by PSCs will act as a barrier that prevents drug penetration, suggesting that PSCs contribute to chemotherapy resistance (Bae,

2013). Moreover, PSC is also the primary source of vascular endothelial growth factor (VEGF), and for this reason, this process is a primary player in cancer angiogenesis and metastasis ((Cirri and Chiarugi et al, 2011; Figure 1.3).

The cross-talk between PSCs and the pancreatic cancer cell is a matter of debate ((Cirri and Chiarugi et al, 2011; Alkasalias et al, 2018). However, many published studies support the notion that this cancer cell-stroma interaction supports tumor survival and growth (Hwang et al., 2008). Hwang et al (2008) studied the effect of co-injecting PSCs with pancreatic cancer cell lines in orthotopic mouse models of PDAC which shows that PSCs can enhance both the tumor growth and the metastasis compared to PDAC cells alone (Figure, 1.4). Interestingly, PSCs have a stable genotype compared to tumor cells, which makes them more suitable for targeted therapies (Neoptolemos et al, 2010). However, targeting PSCs could be a double- edged sword, since it is still unclear whether the desmoplasia surrounding the tumor produced by PSCs act as a mechanical barrier to prevent tumor spread in addition to preventing drug penetration (Bae, 2013). Therefore, in addition to preventing a nurturing environment to support tumor growth, targeting the stroma may also facilitate invasion and metastasis.

Figure 1.4. The results of co-injection of pancreatic stellate cells (PSCs) with pancreatic cancer cell line in orthotopic mouse model. Panel a: shows that PSC can increase the tumour growth in the mice. Panel b: demonstrate that PSC can enhance the metastasis of tumour. Adapted from (Hwang et al, 2008).

1.4.2.2. MYOFIBROBLASTS

Myofibroblasts also called cancer-associated fibroblasts (CAFs), play a vital role in promoting tumor activity (Hanahan and Weinberg, 2011). They can increase cancer cell proliferation, angiogenesis, invasion, and metastasis (Hanahan and Weinberg, 2011). The exact interaction between cancer cells and CAFs is still a matter of much controversy (Hanahan and Weinberg, 2011). However, many accumulating pieces of evidence from both in-vitro and in- vivo studies show that co-culture of cancer cells with CAFs can support the survival and progression of the cancer cells. Their main function in the tumor is like PSCs. They both play a vital role in the formation of desmoplastic stroma that is seen in solid tumor such as PDAC (Hanahan and Weinberg, 2011). Although PSCs and CAFs share similar phenotype, they express different protein (Erkan et al, 2012). They both express α-SMA protein, but active PSCs also express the glial fibrillary acidic protein (GFAP) and this distinguished them from myofibroblasts (Erkan et al, 2012).

There are two types of fibroblasts that are usually present in tissue that have different biological properties (Hanahan and Weinberg, 2011). The first type is tissue-derived fibroblasts that provide structural support for normal epithelia (Hanahan and Weinberg, 2011). The other one is the myofibroblasts that are recruited from mesenchymal cells of bone marrow (BM) to the wound and chronic inflammation sites (Hanahan and Weinberg, 2011). Normally, their presence should be transient, but in chronic inflammation they become permeant and such presence contributes to the formation of fibrosis that is usually associated with chronic inflammation (Hanahan and Weinberg, 2011).

1.4.2.3. INFLAMMATORY AND IMMUNE CELLS

Like cancer cell-PSCs interaction, the communication between cancer cells and inflammatory/immune cells is also bi-directional (Hanahan and Weinberg, 2011). However, they operate in conflicting ways in cancer; acting as either a tumor promoter or suppressor depending on the context (Hanahan and Weinberg, 2011). The communication is initiated by the cancer cell that secretes several growth factors and chemokines that have a chemotactic effect on inflammatory/immune cells (Hanahan and Weinberg, 2011). On the other hand, the immune and inflammatory cell will be a major source for pro-angiogenic growth factors and pro-invasive matrix-degrading enzymes that support cancer cell progression, metastasis and invasion (Hanahan and Weinberg, 2011).

In the pancreatic tumor microenvironment different inflammatory and immune cells are present, including macrophages, dendritic cell, mast cells, granulocyte, and B and T- lymphocytes (Bae, 2013). However, macrophages and mast cells are the predominant types of cells in PDAC (Bae, 2013). Usually, the presence of a high number of these cells in pancreatic tumors is correlated with worse prognosis (Bae, 2013). Generally, these cells secrete pro- angiogenic factors such as FGF and VEGF that contribute to angiogenesis in the tumor (Bae, 2013). In addition, granulocytes are found in PDAC and play an important role in producing TGF-β that maintains constant PSC activation (Bae, 2013).

1.4.2.4. NEURAL CELLS

The pancreas is a highly innervating organ, it has both extrinsic and intrinsic innervations (Ekin et al, 2015). In PDAC pancreatic nerves undergoes different neuropathic alteration, such as neural hypertrophy, increase in the neural density, neural remodeling, pancreatic neuritis and neural invasion by pancreatic cancer cells (Ekin et al, 2015). In general, there is bidirectional interaction between the pancreatic cancer cell and the nerves which suggest that they may have a role in supporting cancer cell survival (Ekin et al, 2015).

1.4.2.5. ENDOTHELIAL CELL

The endothelial cells play a role in tumor angiogenesis by forming new blood vessels to help nourish cancer cells with nutrients and oxygen (Hanahan and Weinberg, 2011). Tumor cells activate quiescent endothelial cells by releasing VEGF, FGF and other growth factors (Hanahan and Weinberg, 2011). Once activated the endothelial cells start to generate tumor associated vascularity that facilities the tumor growth, metastasis and survival (Hanahan and Weinberg, 2011).

1.4.2.6. PERICYTES

The function of pericyte during cancer is closely related to endothelial cells (Hanahan and Weinberg, 2011); they are both involved in tumor angiogenesis (Hanahan and Weinberg, 2011); and contributed to the synthesis of the vascular basement membrane that helps vessel walls to withstand high pressure produced by blood flow (Hanahan and Weinberg, 2011). Therefore, pericytes are important to support tumor vascularity integrity, function, and stability (Hanahan and Weinberg, 2011).

1.5. CANCER CELL METABOLISM

1.5.1. INTRODUCTION

During tumor progression cancer cells reprogram their metabolism to support the tumor growth (Hanahan and Weinberg, 2011). There are several factors that contribute to such remodelings such as genetic mutations and epigenetic alteration of several oncogenes and tumor suppressor genes that are a key to the regulator of metabolism. These include phosphoinositide-3-kinase (P3K)/AKT/ mammalian target of rapamycin (mTOR), hypoxia inducing factor1-alpha (HIF-1α), MYC, AMP-activated protein kinase (AMPK)/ liver kinase- B1 (LKB1) and Tp53 (Mazurek and Shoshan, 2015). In addition, the modulation of several metabolizing enzymes, e.g., hexokinase (HK), pyruvate kinase-M2 (PKM2), phosphofructokinase (PFK), lactate dehydrogenase (LDH), pyruvate dehydrogenase kinase (PDK), ATP-citrate lyase (ACLY) and isocitrate dehydrogenase (IDH), all play a part in the metabolic remodeling (Mazurek and Shoshan, 2015). Finally, the tumor microenvironment has an important role indirectly controlling cancer cell metabolism by regulating oxygen tension, pH, nutrition supply, autophagy and interaction with surrounding cells (Mazurek and Shoshan, 2015).

1.5.2. CELL METABOLISM

In every human tissue, there are two main energy producing pathways; glycolysis and mitochondrial oxidative phosphorylation (OXPHOS) (Figure 1.5). The metabolism starts with glycolysis by converting glucose to pyruvate (Heiden et al, 2009). In the absence of oxygen, pyruvate will be converted into lactate by LDH which is then excreted from the cells by monocarboxylate transporter (MCTs) (Heiden et al, 2009). This process is called anaerobic glycolysis and generates only 2 adenosine triphosphate (ATP) molecules (Heiden et al, 2009). In contrast, in the presence of oxygen, the pyruvate is taken up by the mitochondria and by the action of pyruvate dehydrogenase enzyme (PDH), it will be converted into acetyl-CoA (Heiden et al, 2009). The latter will be further oxidized at tricarboxylic acid (TCA) cycle and produce NADH, FADH2, and 2 ATP. The NADH and FADH2 are further oxidized by the mitochondrial electron transport chain (ETC) to generate an additional 34 ATP molecules per glucose molecule. This represents the OXPHOS pathway and usually, it produces a total of 38 ATP molecules (2 from glycolysis, 2 from the TCA cycle and 34 from ETC) (Andersen et al,

2014). Because OXPHOS generate higher energy compared with glycolysis, it is the main pathway for energy production in most of the human tissue (Heiden et al, 2009).

1.5.3. CANCER CELL METABOLIC REMODELING

1.5.3.1. THE WARBURG EFFECTS

As discussed previously most of the human tissue rely on OXPHOS as the main source of ATP. However, in a cancer cell, such scenario changes and cancer cells preferentially utilize glycolysis even in the presence of oxygen, and this process is called aerobic glycolysis (Heiden et al, 2009). This phenomenon was discovered by Otto Warburg for which he won the Nobel prize and for this reason, it is called the Warburg-effect (Warburg, 1956). Warburg’s claimed that cancer cells undergo aerobic glycolysis because their mitochondria are impaired (Heiden et al, 2009). However, accumulating evidence demonstrates that cancer cells have functional mitochondria which are at variance to Warburg’s original hypothesis (Heiden et al, 2009). Although cancer cells prefer glycolysis, they also rely on mitochondria for their growth and development (Mazurek and Shoshan, 2015). The mitochondria is not just a source of cellular energy (ATP) but is also important for biosynthesis and cell death. The question remains, why cancer cell which needs high energy to support their growth is biased towards glycolysis that produce lower energy supply compared with the OXPHOS pathway (Heiden et al, 2009)?

The answer to this question comes from close observation and the realization that cancer cells require numerous other factors other than energy to support their growth and proliferation (Cairns et al, 2011; Figure 1.6). They also need to build up a high number of macromolecules, including proteins, lipids, and nucleic acids which provide the essential building blocks to support growth/ cell division (Cairns et al, 2011). Consequently, cancer cells switch to glycolysis because many of the glycolytic intermediates act as a precursor for diverse macromolecular biosynthesis (Cairns et al, 2011). For example, glucose-6-phosphate (G6P) can be directed into the pentose phosphate pathway (PPP) and give rise to ribose-5-phosphate. Dihydroxyacetone phosphate is a precursor for triglyceride and phospholipid synthesis. Moreover, pyruvate is used for alanine and malate synthesis (Kromer and Pouyssegur, 2008). The Warburg effect increase the biosynthesis of building blocks required for cancer cells proliferation, but in the expense of ATP production. Moreover, the lactic acid efflux as a result of glycolysis cause acidification of the tumor microenvironment, this inhibits the immune

surveillance and facilitate the action of matrix metalloproteinases (MMPs) thus promote cancer cells migration and invasion (Fischer et al., 2007; Tannant et al., 2010). Finally, cancer cells need to maintain their intracellular redox status by increasing synthesis of NADPH, which is used to regenerate reduced glutathione from oxidized glutathione (Cairns et al, 2011). This can be achieved by directing G6P entry towards the PPP which generates NADPH (Andersen et al, 2014). Therefore, the switch to glycolysis observed in cancer cells increase antioxidant capacity and improve survival (Cairns et al, 2011). Interestingly, studies have found that differentiated cells prefer OXPHOS as a source of energy, while rapidly proliferating cells, such as those found in embryonic tissue, also exhibit aerobic glycolysis preference (Heiden et al, 2009; figure 5). Because embryonic cells have a similar requirement to cancer cells (Heiden et al, 2009).

Figure 1.5. Comparison between differentiated tissue, proliferative tissue and tumour metabolic preference. Differentiated tissue prefer mitochondrial OXPHOS as a source of energy because it is more efficient as an energy source. Only in the absence of oxygen will these cells undergo anaerobic glycolysis, which is a less efficient source of energy. However, proliferative and tumour cells usually prefer glycolysis even in the presence of oxygen, i.e. aerobic glycolysis or Warburg effect. This is because glycolytic intermediates provide precursors for macromolecular biosynthesis which are the building blocks needed to support cell division. Moreover, the lactate, which is a by-product of glycolysis, is important to support cancer cell invasion and migration. Adapted from (Heiden et al, 2009).

Figure 1.6. Cancer cells reprogram their metabolism to fulfill their need for energy, biosynthesis of essential building blocks and generation of antioxidants. Adapted from (Cairns et al, 2011).

The Warburg effect is one of the hallmarks of cancer (Hanahan and Weinberg, 2011). Cancer cells can rely on glycolysis and undergoes extensive metabolic remodeling (Hanahan and Weinberg, 2011). For example, the expression of glucose transporters (GLUT) increases, thereby, enhancing glucose uptake and utilization by cancer cells (Hanahan and Weinberg, 2011). In addition, cancer cells show high expression of the glycolytic enzymes such as pyruvate dehydrogenase kinases (PDK) (Mazurek and Shoshan, 2015). PDK inhibits pyruvate dehydrogenase (PDH), blocking the conversion of pyruvate to acetyl-CoA in the mitochondria and shifting pyruvate towards alternative macromolecular biosynthesis pathways (Mazurek and Shoshan, 2015). Moreover, cancer cells can increase LDH and enhance plasma membrane electron transporter (PMET), in order to recycle the excess NADH produced during glycolysis and to maintains the intracellular NADH/NAD+ balance (Mazurek and Shoshan, 2015). All of this metabolic remodeling is usually associated with a number of genetic and epigenetic alteration that occurs in a cancer cell (Mazurek and Shoshan, 2015).

1.5.3.2. CANCER CELL METABOLIC FLEXIBILITY

Unfortunately, the Warburg-effect is not the only mechanism by which metabolic pathways are altered in cancer cells in a single tumor, particularly solid tumor, which makes cancer metabolism more complex (Cairns et al, 2011). Tumours are not only a mixture of cell types but even the cancer cells consist of a heterogeneous mixture of cancer cells at different development stages of cell cycle or stages of differentiation, each with their own metabolic (bioenergetic and biosynthetic) requirements (Hanahan and Weinberg, 2011; Mazurek and

Shoshan, 2015). Moreover, there is spatial heterogeneity across the tumour dependent on the specific microenvironment. For example, there is a hypovascular region within tumors, which means that there will be a variation in the oxygen and nutrition supply to different cells within the tumor and this will have differential effects on cancer cells metabolism (Hanahan and Weinberg, 2011). It is very important to understand that cancer cells can easily modulate their metabolism to support the specific requirement and microenvironment of any growing cancer cells (Hanahan and Weinberg, 2011).

For example, in one tumor there might be two subpopulations of cancer cells that behave differently (Hanahan and Weinberg, 2011). One population will exhibit the Warburg effect and produce lactate as a by-product, whereas the other population of cells that are better- oxygenated will take-up and utilize the lactate produced from the hypoxic tumor cells to fuel OXPHOS pathway (Hanahan and Weinberg, 2011). In this way, cancer cells can adapt to the low glucose supply by utilizing lactate as an alternative fuel (Hanahan and Weinberg, 2011).

Moreover, it was found recently that cancer cells do not rely solely on glucose as a fuel and also use other substrates such as glutamine, serine, alanine, proline …. etc (Cairns et al, 2011). Glutamine, which is the main amino acid present in high concentration in the plasma, can enter the cancer cell via the neutral amino acid transporter, SLC1A5 (Andersen et al, 2014). Inside the cell, it is converted to glutamate by the action of glutaminase enzyme, and this process is called glutaminolysis (Cairns et al, 2011). Glutamate has several fates in the cancer cell; it can be converted to alpha-ketoglutarate (α-KG), which is further converted to malate or citrate that can be utilized for amino acid or fatty acid synthesis, respectively (Cairns et al, 2011). In addition, glutamate can be utilized for the biosynthesis of glutathione (GSH), which is an important antioxidant for the cell (Cairns et al, 2011). Finally, glutamine can be used as a starting point for the biosynthesis of nucleotides (Mazurek and Shoshan, 2015). Hence, cancer cells are also equally “addicted” to glutamine similar to glucose to fulfill their anabolic needs, to maintain DNA integrity by increasing antioxidant capacity (glutathione synthesis) and thus reducing intracellular accumulation of reactive oxygen specious (ROS) (Cairns et al, 2011). Importantly, tumors with increased Myc gene expression usually exhibit high glutaminolysis rate (Wise et al., 2008).

Despite the metabolic flexibility of cancer cells, the Warburg phenomena appear to be a dominant mechanism on driving the cancer phenotype (Mazurek and Shoshan, 2015). Indeed,

the most aggressive/ metastatic cancer cells are highly glycolytic (Mazurek and Shoshan, 2015).

1.5.3.3. GENETIC ALTERATION OF CANCER AND METABOLIC REMODELING

There has been an extensive effort to investigate the signaling pathways that are involved in the metabolic phenotype and glycolytic switch observed in cancer cells (Cairns et al, 2011). During cancer several key metabolic regulator genes are altered, such as HIF-1α, PI3K/AKT1, mTOR, AMPK/LKB1, Tp53 and MYC (Cairns et al, 2011; Figure 1.7). The alteration of all these genes contributes to the metabolic remodeling observed in cancer cells (Cairns et al, 2011).

Figure 1.7. Alteration of key metabolism regulator genes in cancer. Phosphoinositide-3- kinase (PI3K); Mammalian target of rapamycin (mTOR); Hypoxia inducing factor (HIF); Lactate dehydrogenase (LDH), pyruvate dehydrogenase kinase (PDK), Synthesis of Cytochrome-c (SCO2). Adapted from (Jang et al, 2013).

1.5.3.3.1. HIF1-ALPHA

The HIF-1 alpha is a key regulator of cell metabolism in response to hypoxia and stress (Cairns et al, 2011). Under hypoxic condition, HIF-1α is stabilized in the cytoplasm which promotes glycolysis (Cairns et al, 2011). In contrast, in normoxic conditions, it is usually

degraded (Cairns et al, 2011). However, some signaling pathways can activate HIF-1α even under normoxic conditions, such as PI3K/AkT1, mTOR (Cairns et al, 2011).

In cancer cells, HIF-1 α is over-expressed and contributes to the metabolic switch to glycolysis by increasing the expression of the glucose transporter (GLUT1), lactate transporter (MCT-4), and several glycolytic enzymes, including; HK, LDH and pyruvate dehydrogenase kinase-1 (PDK1), which inhibit pyruvate dehydrogenase (PDH) (Cairns et al, 2011; Figure 1.7). Which is responsible for the conversion of pyruvate to acetyl-CoA (Cairns et al, 2011). Consequently, this can decrease pyruvate flow to the TCA cycle and eventually will decrease mitochondrial function (Cairns et al, 2011).

1.5.3.3.2. PI3K/AKT1, MTOR

Phosphoinositide-3-kinase (PI3K) is one of the upstream regulators of the metabolic pathway that is overexpressed in cancer cells (Tennant et al, 2010). Its activation in cancer is due to direct mutation of PI3K gene, loss of its inhibitory tumor suppressor gene, such as PTEN, and/or continuous stimulation by receptor tyrosine kinase (Cairns et al, 2011).

PI3K can support tumor growth and survival in several ways (Cairns et al, 2011; Figure 1.7). It can significantly switch cancer cell into glycolysis by stimulating Akt1(Cairns et al, 2011). The latter can regulate glucose uptake and utilization by upregulating glucose transporter (GLUT) expression, increasing the hexokinase (HK) enzyme and enhancing phosphofructokinase (PFK) (Cairns et al, 2011). Moreover, Akt1 can activate mTOR signalling and support cancer cell growth by increasing protein and lipid biosynthesis, enhance mRNA translation and ribosome biogenesis (Cairns et al, 2011). Finally, mTOR can directly activate HIF-1α in normoxic conditions (Cairns et al, 2011). All these effects together can facilitate the metabolic switch to glycolysis and thus the cancer phenotype (Cairns et al, 2011).

1.5.3.3.3. AMPK/ LKB1

AMPK is a sensor of the cell energy status, acting as a metabolic checkpoint (Cairns et al, 2011). It is activated in response to low AMP/ATP ratio, resulting in shifting the cell into catabolism and decreasing the anabolism (Cairns et al, 2011). AMPK stimulation will activate the OXPHOS pathway and inhibit cell proliferation to increase cell bioenergetics (Cairns et al, 2011).

During cancer, AMPK signalling is lost because cancer cells exhibits a mutation in liver kinase B1 (LKB1), which is an upstream regulator of AMPK and required for its activation (Cairns et al, 2011). The loss of AMPK can allow the cancer cell to proliferate and contribute to the glycolytic shift due to suppression of the OXPHOS pathway (Cairns et al, 2011). In addition, mTOR activity will enhance because of AMPK act as an inhibitor to mTOR signalling (Cairns et al, 2011). This is counteracting AkT1 effect (Cairns et al, 2011). To conclude, all these effects contribute to the metabolic remodeling in a cancer cell (Cairns et al, 2011).

1.5.3.3.4. TP53

Tp53 is a tumor suppressor gene that is involved in the regulation of cell growth and metabolism (Cairns et al, 2011). Its activation usually results in the induction of cell apoptosis (Cairns et al, 2011). Tp53 mutation result in the uncontrolled proliferation behavior observed in some types of cancer cell (Cairns et al, 2011).

The loss of Tp53 function can enhance both glycolysis and macromolecular biosynthesis by several mechanisms (Cairns et al, 2011; Figure 1.7). Firstly, it can downregulate TIGAR (Tp53 induced glycolysis and apoptosis regulator), which enhances phosphofructokinase (PFK-1) activity, which is the major rate-limiting enzyme of glycolysis (Jang et al, 2013). In addition, loss of Tp53 will stop its binding with glucose-6-phosphate dehydrogenase (G6PD) which is a rate-limiting step in the pentose phosphate pathway (PPP) (Simeone, 2013). Consequently, G6P will flux towards PPP, and increase NADPH production, which increases antioxidant capacity by facilitating regeneration of reduced glutathione (GSH) from oxidized glutathione, and macromolecular biosynthesis (Simeone, 2013). Furthermore, Tp53 loss can enhance PI3K signalling, due to activation of PTEN, which is an inhibitor for PI3K signalling (Cairns et al, 2011). The loss of Tp53 can decrease OXPHOS pathway, because Tp53 plays a critical role in regulating SCO2 (i.e. synthesis of cytochrome c oxidase 2), which is necessary for cytochrome c oxidase (COX) complex assembly in the electron transport chain (Simeone, 2013).

1.5.3.3.5. MYC

Myc oncogene can control cell growth and regulate cell metabolism (Cairns et al, 2011). It is usually upregulated in cancer cells and plays a role in the metabolic remodeling in several ways (Cairns et al, 2011). Myc can collaborate with HIF-1α in enhancing glycolysis by increasing GLUT, LDH, and PDKs (Cairns et al, 2011). In addition, it can, directly and

indirectly, regulate genes involved in the metabolism of glutamine, such as glutamine transporters and glutaminase enzyme, thereby, increasing macromolecular biosynthesis and NADPH production (Cairns et al, 2011). Interestingly, tumor cells that exhibit high Myc expression are sensitive to glutamine depletion, suggesting that this could be a good target for therapy (Cairns et al, 2011). However, Myc can also activate mitochondrial OXPHOS (Cairns et al, 2011).

1.5.3.4. ALTERATION IN METABOLIZING ENZYME IN CANCER

In cancer, many metabolizing enzymes are altered due to several key signalling pathways (discussed above) that control the expression and activity of hexokinase (HK), phosphofructokinase (PFK), pyruvate kinase-M2 (PKM2), lactate dehydrogenase (LDH), pyruvate dehydrogenase kinase-1 (PDK1), ATP citrate lyase (ACLY), isocitrate dehydrogenase (IDH) and glutaminase leading to the metabolic switch in a cancer cell (Cairns et al, 2011).

Pyruvate kinase (PK) is a rate-limiting enzyme in the ATP generation step of glycolysis, which involves the dephosphorylation of phosphoenolpyruvate (PEP) into pyruvate (Cairns et al, 2011). There are two isoforms of PK enzyme PKM1 and PKM2, however, PKM2 is the one which is expressed in many cancer and self-renewing cells (Cairns et al, 2011). The PKM2 isoform has much lower activity than normal PK isoform. This reduces the production of ATP but allows the build up glycolytic intermediates to enter into different macromolecular biosynthesis pathways, such as PPP and glycerol synthesis (Cairns et al, 2011; Figure 1.8). Therefore, PKM2 support cancer cell survival by favoring macromolecular biosynthesis and NADPH production over ATP generation (Cairns et al, 2011).

Figure 1.8. The effect of PKM2 enzyme in cancer cell metabolism. Pyruvate kinase isoform M2 (PKM2); phosphoenolpyruvate (PEP); pentose phosphate pathway (PPP); nicotinamide

adenine dinucleotide phosphate (NADPH); glucose-6-phosphate (G6P). Adapted from (Cairns et al., 2011).

Figure 1.9. The effect of alteration of different metabolizing enzymes on cancer cell metabolism. Lactate dehydrogenase-A (LDHA); Pyruvate dehydrogenase kinase (PDK); Pyruvate dehydrogenase (PDH). Adapted from (Jang et al., 2013).

1.6. CALCIUM SIGNALLING AND CANCER METABOLISM

1.6.1. INTRODUCTION

The cytosolic calcium (Ca2+) plays a vital role in numerous hallmarks of cancer such as cell proliferation, migration, invasion, metastasis, and angiogenesis. However, up to date, there is no drug that targets Ca2+ signalling machinery in cancer treatment. Recently, an accumulating evidence have shown that a novel Ca2+ channels (e.g. Orai1 and numerous members of the transient receptor potential (TRP) family) and key regulatory protein ((e.g. stromal interacting molecule (STIM) and secretory pathway calcium ATPase (SPCA)) can play an important role in major cancer hallmarks (Monteith et al., 2012). Interestingly, these Ca2+ machinery are localized in the cells surface make them highly accessible to novel drugs or even antibody therapy. However, we need to target Ca2+ machinery that are either uniquely expressed or their expression results in an entirely new function in cancer cells to ensure selectivity and specificity. For example, targeting the plasma membrane SPCA2-mediated regulation of Orai1, which appears to have a unique property in breast cancer cells (Monteith et al., 2012). Moreover, targeting the glycolytic ATP fuelling of the PMCA may also represent a unique phenotype of cancer cells that may prove to be an effective therapeutic strategy (James et al., 2013; James et al., 2015). For this thesis, we will focus on the latter strategy in details.

1.6.2. CALCIUM SIGNALLING AND ITS MACHINERY

Cytosolic Ca2+ is involved in controlling diverse and fundamental physiological process such as proliferation, development, memory, fertilization, learning, contraction and secretion (Berridge, 2000). This is achieved because Ca2+ signals come in different shapes, both in time and space, regulated by diverse Ca2+ channels, transporters, pumps (ATPases) and binding proteins, collectively known as the Ca2+ signalling machinery (Berridge, 2000). They work in concert to generate a complex spatiotemporal pattern of Ca2+ signalling, such as Ca2+ oscillations, Ca2+ waves, spatially restricted Ca2+ spikes and global sustained Ca2+ signals (Berridge, 2000). Each of these spatiotemporal patterns of Ca2+ signalling encoded to diverse physiological process and interestingly different type of Ca2+ signal can activate opposing cellular response within the same cell (Jaggar et al., 2000). For example, in smooth muscle cells, the global Ca2+ waves control the contraction of these cells, whereas localized Ca2+

release can cause relaxation by activating Ca2+-dependent K+ channels causing hyperpolarisation (Jaggar et al., 2000).

The Ca2+ signalling machinery can be broadly categorized into those that elevate or reduce cytosolic Ca2+(Berridge, 2000). Normally, the cell can generate complex Ca2+ signal by 2+ increasing its intracellular calcium [Ca ]i from internal and external source to around 1000 2+ nM concentration (Berridge, 2000). However, at rest, the cell tries to maintain its [Ca ]i at around 100 nM concentration by removing the extra calcium generated from the Ca2+ signal (Berridge, 2000).

2+ Intracellular Ca release channels primarily include inositol 1,4,5-trisphosphate (IP3)- 2+ gated Ca channels or IP3 receptors (IP3Rs) and ryanodine receptors (RyRs), both expressed on the endoplasmic reticulum or sarcoplasmic reticulum. Ca2+ entry channels can be categorized into voltage-operated Ca2+ channels (VOCCs) activated by changes in membrane potential (e.g. L, T, N, and P/Q-type Ca2+ channels), ligand-gated Ca2+ channels (e.g. glutamate-gated N-Methyl D-aspartate (NMDA) receptors, ATP-gated purinergic P2X receptors), store-operated Ca2+ entry channels (SOCE) and transient receptor potential (TRP) family of ion channels. Ca2+ clearance pathways primarily include the ATP-driven Ca2+ pumps found on the plasma membrane (PMCA), sarco/endoplasmic reticulum (SERCA) and golgi apparatus or secretory pathway calcium ATPase (SPCA). Additional Ca2+ clearance pathways include the Na+/Ca2+ exchanger (NCX), driven by membrane potential and mitochondrial Ca2+ uniporter (MCU) responsible for Ca2+ uptake into the mitochondria which drives metabolism.

Figure 1.10. Intracellular calcium regulation. Plasma membrane Ca2+ ATPase (PMCA); Sarcoendoplasmic reticulum calcium transport ATPase (SERCA); Na+/ Ca2+ exchanger (NCX) inositol 1,4,5-trisphosphate receptors (Ins (1,4,5) P3R); Ryanodine receptor (RYR); Mitochondrial permeability transition pore (MPT pore); Mitochondrial Na+/ Ca2+exchanger (MNCX); Mitochondrial membrane Ca2+ATPase (MMCA). Adapted from (Syntichaki and Tavernarakis, 2003).

1.6.3. CALCIUM SIGNALLING AND CANCER

Interestingly, many studies have found that during cancer the expression of most Ca2+ signalling machinery is altered (Monteith et al, 2012; Figure 1.11). This includes channels and pumps (that have been described above) that are involved in the movement of calcium across the plasma membrane and/or across subcellular organelles (Monteith et al, 2012). Although such modulation in Ca2+ is unlikely to contribute to the initiation of cancer, accumulating evidence suggests that it could be involved in the progression to the malignant phenotype, such as uncontrolled proliferation, migration, invasion, and metastasis (Chen et al, 2013). For example, it was found that several types of cancer cells have high expression of PMCA, such as breast cancer and colon cancer cells (Monteith et al, 2012). This overexpression of PMCA in cancer cells contributes to reducing the calcium level in the endoplasmic reticulum (ER) and decrease Ca2+ accumulation in the mitochondria (Monteith et al, 2012). As a result, this protects cancer cell from undergoing apoptosis, because Ca2+ overload is toxic to the cell and can lead

to cell death (Monteith et al, 2012). Importantly, as we discuss previously the two main Ca2+ efflux transporter in cells are PMCA and NCX and in PDAC cells NCX is not functionally expressed making PMCA the main Ca2+ efflux transporter in these cells (James el al., 2013). Therefore, inhibiting PMCA activity could drive pancreatic cancer cell to death and can be a good target for PDAC therapy (Bruce, 2010).

Figure 1.11. The remodelling of key components of the Ca2+ signalling machinery responsible for the hallmark processes of cancer during cancer progression. The major causes and risk factors of cancer induce genomic, epigenetic and adaptive changes in key Ca2+ transporters responsible for the increase in cell proliferation, resistance to apoptosis, cellular migration and invasion and tumour vascularisation.

1.6.4. PMCA AND PDAC CELLS METABOLISM

The Warburg phenomena which are exhibited by many cancer cells is vital for maintaining a highly proliferative, pro-survival and invasive cancer ph

enotype (Hanahan and Weinberg, 2011). Although this metabolic phenotype favours biosynthesis over bioenergetics, ATP production remains essential for many ATP-dependence processes in the cells. Interestingly, ATP plays a critical role in regulating intracellular Ca2+concentration, because many of the Ca2+ homeostatic machinery are ATP dependent, such as PMCA and SERCA. These ATP-dependent Ca2+ pumps are responsible for the maintenance 2+ of a low resting [Ca ]i, and they require a robust supply of ATP. Impairment of either PMCA 2+ or SERCA can results in an inability of the cells to maintain a low resting [Ca ]i, resulting in

2+ [Ca ]i overload and cell death. This suggests that the switch of cancer cells towards glycolysis could have an important implication for Ca2+ signalling. Understanding how the Warburg effect can influence cancer cell Ca2+ homeostasis could be key in discovering new therapeutic targets for cancer.

The PMCA is the main Ca2+ efflux transporter in PDAC cell lines (Bruce, 2010; James et al, 2013). Its activity is ATP dependent and studies suggest that the PMCA has its own localized glycolytic ATP supply (Campanella et al, 2005; Figure 1.12). Previous studies from our lab have shown that depletion of glycolytic ATP from PDAC cells inhibits PMCA activity, causing [Ca2+]i overload and consequent necrosis (James et al., 2013; James et al., 2015; Figure 1.13). Therefore, cutting off glycolytic ATP supply to the PMCA could represent a novel therapeutic strategy in PDAC (Bruce, 2010).

Figure 1.12. Comparison between PMCA activity regulation in normal and cancer cells: Cancer cells prefer glycolysis over mitochondria for their energy supply (the Warburg effect). This switch is critical for maintaining a proliferative and invasive phenotype and is also critical for maintaining the plasma membrane Ca2+ ATPase (PMCA) and low resting cytosolic Ca2+ which is important for cancer cell survival. PDAC cells relay on glycolytic ATP in the regulation of intracellular calcium homeostasis by fuelling PMCA while normal cells relay on mitochondria as the major source of ATP.

Figure 1.13. The effect of metabolic inhibitors in the glycolytic ATP regulation of PMC in PDAC cells: Glycolytic inhibition (iodoacetate, IAA; bromopyruvate, Brpy) induces ATP 2+ depletion (Aii) , PMCA inhibition (Bii), [Ca ]i overload (C) and cell death (D) in PDAC cells (MiaPaCa-2), while mitochondrial inhibition (oligomycin, OM; carbonyl cyanide-3- chlorophenylhydrazone, CCCP) has no effect (Ai, C & D). ATP was measured using GO- ATeam by florescent microscopy, while the metabolic phenotype was measured using Seahorse flux analyzer by measuring the extracellular acidification rate (ECAR, glycolysis) and oxygen consumption rate (OCR, mitochondrial metabolism). PMCA activity was measured following SERCA blockade (cyclopiazonic acid, CPA, 30 µM) and subsequent ER Ca2+ depletion and store-operated Ca2+ entry. Cell death measured using propidium iodide (PI) and normalised to cell number (Hoescht, %) (James et al., 2013).

1.7. TUMOUR MICROENVIROMENT AND CANCER METABOLISM

1.7.1. HYPOXIA

Many cancer cells, particularly solid tumors, have a poor oxygen supply due to the variation and the abnormality of its neovasculature (Hanahan and Weinberg, 2011). Interestingly, hypoxia will stabilize HIF-1α which can direct cancer cells towards glycolysis instead of OXPHOS and this can support cancer cell growth and survival (Hanahan and Weinberg, 2011).

1.7.2. PH

Acidification of the microenvironment is one of the hallmarks of cancer (Hanahan and Weinberg, 2011). There are several reasons for such phenomena to occur in the tumor microenvironment. Cancer cells produce a high amount of lactic acid that can lower the pH of the tumor microenvironment due to the switch to glycolysis (Andersen et al, 2014). Moreover, cancer cells express a high level of LDH which increase the conversion of pyruvate to lactate (Andersen et al, 2014). Also, monocarboxylate transporter (MCT) is overexpressed in cancer cell membranes to cope with the high level of lactate production by excreting it to the extracellular matrix (Andersen et al, 2014). In most cases of MCTs, H+ is co-transported with lactate out of cancer cell (Andersen et al, 2014). Furthermore, the conversion of glutamine by glutaminase enzyme to glutamate generates a high ratio of NH+ that is excreted outside cancer cells (Andersen et al, 2014). All these changes together facilitate the acidification of the cancer microenvironment (Andersen et al, 2014).

Usually, the extracellular pH (pHe) of the cancer cell is around 6-6.5, while the intracellular pH (pHi) is similar or even more alkaline compared with normal cells (Andersen et al, 2014). Cancer cells needs to maintain their pHi in the normal range because many cellular functions are exquisitely sensitive to any pH changes (Andersen et al, 2014).

The acidic pHe supports cancer cell survival. Suppressing the immune surveillance mediate by T-lymphocyte at the tumor site (Fischer et al, 2007). In addition, the extracellular acidification supports cancer cell invasion and metastatic potential by facilitating the action of matrix metallopeptidases (MMPs) and the breakdown of the extracellular matrix, thereby, promoting migration and invasion (Tannant et al, 2010).

1.7.3. AUTOPHAGY

Cancer cells depend on autophagy to support their growth and survival (Mazurek and Shoshan, 2015). Autophagy is a Greek term that literally means self-eating (Mazurek and Shoshan, 2015). It is a catabolic process by which cells can recycle their own components by using the lysosome (Mazurek and Shoshan, 2015). Importantly, cancer cells can undergo autophagy, particularly macroautophagy, to cope with the low nutritional supply and starvation condition (Mazurek and Shoshan, 2015). Particularly, within the dense tumor microenvironment where there is poor nutritional and oxygen supply. Autophagy can provide cancer cells with building blocks for the macromolecular biosynthesis and fuel sources for energy (Mazurek and Shoshan, 2015).

1.7.4. STROMAL CELLS METABOLIC COUPLING WITH THE TUMOUR AND THE REVERSE WARBURGE EFFECT

Recently, there have been numerous studies suggesting a new way of understanding cancer cell metabolism. These studies propose that the Warburg phenomena is limited and does not consider the context of the tumor microenvironment and proposed an alternative or “modified” theory known as “the Reverse Warburg effect” (Bonuccelli et al., 2010). Michael Lisanti’s lab has extensively studied the Revere Warburg effect in breast cancer cells (Bonuccelli et al., 2010; Capparelli et al., 2012; Martinez-Outschoorn et al., 2014; Mercier et al., 2008; Pavlides et al., 2009; Salem et al., 2012). These studies have demonstrated that breast cancer cells can induce oxidative stress in adjacent fibroblast by releasing hydrogen peroxide

(H2O2) (Capparelli, 2012; Figure 1.13). The oxidative stress induced autophagy, particularly mitophagy (i.e. loss of the mitochondria) in neighbouring cancer-associated fibroblasts (Capparelli, 2012; Figure 1.13). This causes the CAFs to exhibit aerobic glycolysis to produce lactate and ketone bodies, which are then taken up by cancer cells and utilized as a fuel for OXPHOS (Capparelli, 2012; Figure 1.13). The Reverse Warburg effect has since been evaluated in other types of cancer, such as osteosarcoma and prostatic cancer (Sotagia et al, 2014; Fiaschi et al., 2012).

According to Sousa et al 2016, PDAC cells can adapt to poor nutrition supply to the tumor site by inducing apoptosis in neighbouring pancreatic stellate cells. This can cause the release of essential amino acid, specifically alanine, from the pancreatic stellate cells and then PDAC cells take up alanine to fuel its TCA cycle used for biosynthetic purposes rather than

bioenergetics purposes. This recent study has highlighted the importance of the metabolic cross-talk between PDAC cells and its stromal cells and in part support the Revere Warburg phenomena observed in other types of cancer.

The reverse Warburg effect seems counterintuitive to our original hypothesis that glycolytic ATP supply to the PMCA is critical for the survival of PDAC cells. However, we think that the overall strategy will remain the same because the stromal-cancer cell interaction is critical for tumor growth and survival. Therefore, targeting the glycolytic-regulation of the PMCA in highly glycolytic fibroblasts may also be predicted to slow the growth or even kill neighbouring cancer cells. This is particularly relevant when one considers that PDAC is one of the most stroma-rich tumors.

Figure 1.14. The reverse Warburg effects. Cancer cells induce oxidative stress in adjacent stromal cells by releasing H2O2. This will cause mitophagy in stromal cell. As a result, stromal cell will undergo glycolysis and produce lactate and ketone bodies that will be uptake by cancer cell as a fuel for mitochondrial OXPHOS. Hydrogen peroxide (H2O2); Oxidative phosphorylation (OXPHOS).

1.8. SUMMARY:

PDAC is a lethal type of cancer with poor prognosis and limited treatment options. Therefore, there an urgent need to find new treatment strategies. One of the hallmarks of cancer is the metabolic switch toward a more glycolysis phenotype (the Warburg effect). Our previous studies show that this switch is important to maintain the cancer cell calcium homeostasis by regulating PMCA. The PMCA has a localized glycolytic ATP supply that is important to maintain calcium levels in PDAC cells. However, one of the main characteristics of PDAC is that it has an abundant stroma. Today there is compelling evidence that suggests the vital role of the tumor microenvironment in cancer progression, proliferation, metabolism (the reverse Warburg effect), metastasis and invasion. The main aim of this thesis is to elucidate the role of hPSCs in PDAC cells metabolism and calcium homeostasis. Understanding and targeting the tumor microenvironment may provide a new way of treating pancreatic cancer cells.

1.9. EXPERIMENTAL AIM:

The main aim of this thesis is to investigate the effects of the interactions between pancreatic ductal adenocarcinoma cells (PDAC) with their stroma with a focus on tumor cell metabolism and cytosolic Ca2+ homeostasis. We propose that human pancreatic stellate cells (hPSCs) can influence PDAC cell metabolism and thus the regulation of cytosolic Ca2+ homeostasis in these cells and we aim to assess whether this affects the sensitivity of pancreatic cancer cells to glycolytic vs mitochondrial inhibitors within the tumour microenvironment. This will be addressed using PDAC-hPSCs co-culture models in attempt to recapitulate the tumour microenvironment. This will be addressed in three separate chapters that will contribute to this overall aim.

1.9.1. SPECIFIC AIM:

1.9.1.1. RESULTS CH3

Specific aim: To compare the metabolic phenotype and growth of non-cancerous cells (i.e. HPDE, hPSCs, and BJ skin fibroblast) with PDAC cells (i.e. MiaPaCa-2 and PANC-1) and to test the effect of different metabolic inhibitors on the glycolytic regulation of PMCA in these cells.

- This will be achieved by observing the proliferation rate of these cells, performing metabolic phenotype test, measuring the mitochondrial mass and comparing the effect of 2+ different metabolic inhibitors on cells cytosolic ATP and resting [Ca ]i.

1.9.1.2. RESULTS CH4

Specific aim: To investigate the relative effect of different metabolic inhibitors on cancer hallmarks (metabolic phenotype; growth) and calcium homeostasis for both PDAC cells and hPSCs using different co-culture model.

- This will be achieved by performing direct and in-direct co-culturing of PDAC cells (i.e. MiaPaCa-2) with hPSCs depend on the type of the study (see supplement 8.19 for more information about different co-culture method used). Responses of PDAC and hPSCs cells in co-culture will be compared to singly cultured PDAC and hPSCs cells.

1.9.1.3. RESULTS CH5

Specific aim: Investigation of the mechanism for the altered PDAC metabolism when co-cultured with hPSCs.

- This will be achieved by: 1. Test the effect of glucose depletion/lactate accumulation in hPSC conditioned media on the metabolic phenotype of MiaPaCa-2 cells. 2. Identify the unique factor/metabolite released from hPSCs that alters the metabolic phenotype using footprint metabolomics.

CHAPTER 2

2. METHODS AND MATERIALS

2.1. Cell culture:

Pancreatic cancer cell lines (MiaPaCa-2 and PANC-1), human pancreatic stellate cells (hPSCs), BJ skin fibroblasts and human pancreatic ductal epithelial cells (HPDE) were all

cultured at 37°C in a humidified cell culture incubator with 5% CO2. MiaPaCa-2 and PANC-1 cells were cultured in either 25 mM or 5 mM glucose DMEM media. BJ skin fibroblasts were cultured in either 25 mM glucose DMEM or 5 mM glucose EMEM. hPSCs were cultured in either 25mM glucose DMEM or DMEM/F-12. Both DMEM and EMEM were supplemented with 10% FBS (fetal bovine serum, S181H-500, biowest®) and 1% of 10,000 units penicillin streptomycin solution/ml (P4333, Sigma®). The HPDE were cultured in keratinocyte- serum free (SFM, 1X) media supplemented with, 25 mg of BPE (bovine pituitary extract, 13028014, GibcoTM), 100 ng of EGF 1-53 (human recombinant epidermal growth factor, PHG0311, GibcoTM) and 1% of penicillin and streptomycin antibiotic solution. Cells were allowed to grow as a monolayer in 25 or 75 cm2 vented tissue culture flasks. The media for all cultured cells were changed every 3-4 days, 0.25% trypsin EDTA solution (T4174-100ml, Sigma®) was used to detach the cells for subculture. See table 2.1 and 2.2 for more details about the cell’s sources and cell culture media, respectively.

Table 2.1. List of cell line:

# Cell line Cell type Passage used Source

1 MiaPaCa-2 cells PDAC cells, Epithelial 8-30 ATCC® CRL-1420™

2 PANC-1 cells PDAC cells, Epithelial 12-30 ATCC® CRL-1469™

3 Human pancreatic Are HPV E6/E7*- 9-30 Gift from Dr. Diane Simeone ductal epithelial immortalized (no p53 or from the University of (HPDE) cells Rb) and are of ductal Michigan origin

4 Human foreskin Fibroblast from normal 3-40 ATCC® CRL-2522TM fibroblast BJ skin tissue

5 Human pancreatic These are primary cells 2-10 Gift from Professor David I. stellate cells (hPSCs) derived from a pancreas Yule from the University of resection from a patient Rochester having a Whipple procedure

*human papillomavirus (HPV) E6 and E7 proteins.

Table 2.2. List of Cell Culture Media:

# Media Abbreviation Glucose Conc. Source

1 Dulbecco’s modified eagle’s DMEM 25mM D6429-500ml, Sigma® medium, high glucose

2 Dulbecco’s modified eagle’s DMEM 5mM D5546-500ml, Sigma® medium, low glucose

3 Eagle’s minimum essential media EMEM 5 mM ATCC® 30-2003TM

4 Dulbecco’s modified eagle’s DMEM Zero 11966025, GibcoTM medium, zero glucose

5 Dulbecco’s modified eagle’s DMEM/F-12 17.5 mM 11320033, GibcoTM medium/nutrient mixture F-12

6 Serum free keratinocyte media SFM, 1X unk** 17005042, GibcoTM

*Conc.: Concentration; **unk.: unknown.

2.2. Preparation of conditioned media with serum:

Both hPSCs or MiaPaCa-2 cells were seeded at density of 20,000 cells/cm2 by using 10% FBS: 25mM DMEM as a media. Next day the cells were washed once with PBS (phosphate buffered saline (D8537, Sigma®) and the media was changed to 10% Nu (Coring Nu-Serum culture supplement Cat# 355500): 25mM DMEM and cells were cultured for 24, 48 or 72hr. Conditioned media were collected at each of these time points, centrifuged at 500 rpm for 10 min at 22 ̊ C to remove any cells debris, and the supernatants were collected, aliquoted and stored in -80 ̊ C until used.

2.3. Preparation of serum free conditioned media with low glucose:

hPSCs cells were seeded at density of 20,000 cells/cm2 by using 10% FBS: 25mM DMEM media. Next day the cells were washed once with PBS and the media was changed to serum-free: 5mM DMEM and cells were cultured for 24, 48 or 72hr. Conditioned media were collected at each of these time points, centrifuged at 500 rpm for 10 min at 22 ̊ C to remove any cells debris, and the supernatants were collected, aliquoted and stored in -80 ̊ C until used.

2.4. Stable transfection of RFP or GFP genes into MiaPaCa-2 cells:

2.4.1. Bacterial transformation, preparation of competence cells:

The pDsRed-Monomer-C1 vector and pEGFP-N1 vector were used; both are consisting of neomycin/kanamycin resistance genes (see supplement 8.7 for more information). To prepare E-coli competent cells a heat-shock transformation was performed by using Stratagene kit (XL 10-Gold Ultracompetent cells, Cat# 200314). Briefly, a mixture of 50ng DNA with 100 µL of competence cell solution was placed on ice for 30 minutes, incubated (in a water bath) at 42 ̊ C for 30 seconds then place on the ice again for 2 minutes. Cells were then allowed to grow in 900 µL of preheated (42 ̊ C) NZY broth (Biochemika, Cat. # 74725) in a shaking incubator at 37 ̊ C for 1 hour. The cell’s suspension (200µL) was spread onto a pre-warmed agar plate containing kanamycin (50µg/mL) with a sterile spreader and cells were allowed to grow overnight in a 37 ̊ C incubator. The following day, one single colony was selected and allowed to grow overnight in LB broth supplemented with 50 µg/mL kanamycin in a 37 ̊ C

shaking incubator. Part of the cell suspension was used to prepare glycerol stocks for long term storage at – 80 ̊ C and the remaining part of the cell suspension was proceeded for amplification. Note all these steps were performed over a Bunsen burner flame to avoid contamination.

2.4.2. Isolation and amplification of DNA:

The Qiagen Plasmid Mini kit (Cat# 12123) was used to isolate and amplify the plasmid DNA from the cell bacterial suspension prepared in the previous step by following the manufacturer's instruction.

2.4.3. DNA quantification:

A NanoDrop 2000 spectrophotometer (Thermo Scientific) was used to quantify the purity and concentration of DNA. It is a small spectrophotometer that can help to get quick and accurate reading of DNA.

2.4.4. Generation of MiaPaCa-2 stably expressing RFP or GFP:

GeneCellin™ DNA transfection reagent, which is a polymer-based reagent, was used to deliver the plasmid DNA into the MiaPaCa-2 cell line, the manufacturer instructions were followed. The amount of plasmid DNA and cell confluency were optimized, see Figure 2.1 and 2.2. The neomycin resistance gene system was performed to generate a stable cell line. Briefly, after 24 hr of transfection, the media of transfected cells was changed to new media supplemented with 750µg/mL of neomycin (G418), the cells were cultured under these conditions for three weeks where the media with G418 was changed every 2 days. The optimum concentration of G418 was empirically determined to be 750µg/mL for MiaPaCa-2 cells. This was previously optimized in our lab by performing a kill curve dose-response experiment. After three weeks, the cells were sorted by flow cytometry and cells expressing RFP or GFP were collected and cultured. See supplement 8.13 and 8.14 for more information about flow cytometry sorting data.

Table 2.3. The Transfection Conditions Used to Generate Stable Cell Line.

DNA Tissue Number of Volume of cell Amount of Volume of Volume of plasmid culture adherent cells culture media DNA (µg) DNA GeneCellin vessel to seed (µL) solution (µL) (µL)

RFP 24 wells 50,000 500 0.25 100 2

GFP 24 wells 50,000 500 0.5 100 2

Figure 2.1. Optimizing the amount of plasmid DNA for RFP and MiaPaCa-2 cell confluency for transfection: The cells were seeded in 24 well plate at a density of 50,000 or 10,000 cells/well and transfected with either 0.25, 0.5 or 1 µg of DNA the following day. Fluorescence microscopy was used to check for transfection efficiency. Scale bar 51µM.

Figure 2.2. Optimizing the amount of plasmid DNA for GFP and MiaPaCa-2 cell confluency for transfection: The cells were seeded in 24 well plate at a density of 50,000 or 10,000 cells/well and transfected with either 0.25, 0.5 or 1 µg of DNA the following day. Fluorescence microscopy was used to check for transfection efficiency. Scale bar 51µM.

Figure 2.3. Fluorescence imaging of MiaPaC-2 cell transfected with RFP and GFP 24 hr Post- treatment with G418. Scale bar 51µM.

Figure 2.4. Fluorescent imaging of MiaPaC-2 cell transfected with RFP and GFP 48 hr Post- treatment with G418. Scale bar 51µM.

Figure 2.5. Florescence microscope images for stable transfected MiaPaCa-2 cell line. Scale bar 25µM.

2.5. Fura-2 imaging and calcium overload assay:

Cells were seeded onto sterile 16mm glass coverslips placed in 6 well plates. The experiment was performed once cells were > 30% confluence. The cells were loaded with 5μM Fura-2 dye in 1ml HEPES-PSS (HEPES-physiological saline solution: 137mM NaCl, 0.56mM

MgCl2, 4.7mM KCl, 10mM HEPES, 5.5mM glucose, 1.28mM CaCl2.2H2O, pH 7.4) for 40 minutes then washed for 20 minutes with HEPES-PSS to allow the non-de-esterified dye to diffuse out of the cells. The coverslip formed the base of a gravity-fed perfusion chamber (Harvard Apparatus). A Nikon TE2000-S microscope fitted with x40 oil immersion objective and Cairn monochromator-based illumination system was used. Cells were excited with light at 340 and 380nm sequentially (100-ms exposure) and emitted light recorded through a dichroic mirror (505 nm DC/510 LP) and cold snap HQ CCD camera. Images of fluorescent cells were acquired using MetaFluor image acquisition and analysis software. A background area was selected and subtracted from the cells field of view and images were acquired every 5 seconds. Each metabolic inhibitor was perfused for 20 or 40 minutes using a gravity-fed perfusion system consisting of automatic valves to allow rapid switching of solutions (Harvard apparatus). The cells were washed with HEPES-PSS for 15 minutes to allow cells to recover, followed by treatment with 100 μM ATP (a purinergic agonist) or 100μM carbachol (a cholinergic agonist) to further allow recoverability and reversibility of responses and thus test cell viability. Responses to metabolic inhibitors were quantitated by measuring the maximum 2+ increase in [Ca ]i, area under the curve (AUC) and % cells responding to ATP or carbachol. The AUC was calculated by using graph pad prism software. All experiments were performed in the dark at room temperature.

2.6. Calibration of resting intracellular calcium:

The cells were prepared and loaded onto the imaging system in a similar way to calcium overload studies. The calibration experiments were performed by applying 10μM ionomycin to the cells for 2 minutes followed by HEPES-PSS supplemented with 1mM EGTA until the minimum ratio achieved (Rmin). Cells were then subsequently perfused with HEPES-PSS supplemented with 20mM of Ca2+ until the maximum ratio (Rmax) was achieved. Calibrated 2+ [Ca ]i was calculated by using the standard equation derived from (Grynkiewicz et al, 1985):

(푹−푹풎풊풏) 푺풇ퟑퟖퟎ [푪풂ퟐ+]i= Kd x x (푹풎풂풙−푹) 푺풃ퟑퟖퟎ

Where Kd is fura-2 binding affinity for Ca2+ (225nM, according to Grynkiewicz et al, 1985); R is the fura-2 fluorescence ratio (F340/F380); Rmin and Rmax are the minimum and maximum ratio, respectively; Sf380 and Sb380 are the fluorescence signals emitted from fura-2 when excited at 380nM in the absence of [Ca2+]i ( “f” signify Ca2+-free state from fura-2); or the presence of saturating Ca2+ (the “b” signified fura-2 in the Ca2+- bound state), respectively. The calibration was repeated at least three times for each cell type and each time up to 20 cells were selected. The Rmax was determined from the cell that had the highest ratio and Rmin from the cell that had the lowest ratio in any given experiment (Supplement 8.2).

2.7. Sulforhodamine-B (SRB) cell proliferation assay:

MiaPaCa-2, PANC-1, skin fibroblasts, HPDE, or hPSCs were seeded into a transparent 96 well plate at 5000 cells/well. The cells were fixed by adding 4° C, 10% trichloroacetic acid (TCA, T13000153-500gm, Fisher chemical) and incubated at 4° C for 1 hour. The cells were then rinsed with tap water and allow to dry at room temperature. Once the cells were dry, they were stained for 30 minutes with 0.057 % sulforhodamine B (SRB, S1402-5gm, Sigma®) in 1% acetic acid. To remove excessive dye 1% acetic acid was used. Once the cells were dry 10 mM Tris (T1503-1KG, Sigma®) was added to dissolve the remaining dye and the protein content was measured as absorbance at 540 nm (absorbance unit, AU) using an absorbance/ colorimetric plate reader (Synergy, HT, BioTek, instruments Inc).

For the co-culture experiments, 5000 cells/well of MiaPaCa-2 or hPSCs were seeded into 96 well plates. The next day the media was removed and 100 µL of conditioned media or fresh media (control) was added to the corresponding cells. SRB cell proliferation assay was then performed as above.

2.8. Luciferase-based ATP assays:

MiaPaCa-2, PANC-1, skin fibroblasts or HPDE were seeded onto a white 96 well plate at a density of 10,000 cells/well and kept overnight in 37 ̊ C incubator with 5% CO2. The next

day cells were treated with different metabolic inhibitors (2mM IAA, 500μM Brpy, 10μM OM, or 0.5μM AM) or with a cocktail of different metabolic inhibitors (2mM IAA, 500μM Brpy, 10μM OM, and 0.5μM AM) and kept under culture condition for 15 minutes. The cells were then lysed and the luminesces counts which represent total cellular ATP were determined by adding the Via-Light Plus kit reagents (LT07-221, lonza®) and using a luminesces plate reader (Synergy, HT, BioTek, instruments Inc). This assay utilized a luciferase enzyme that catalyzes the formation of light from ATP and luciferin. A negative control which represents a well seeded with media only without cells was subtract from all values. This value was then normalized to the untreated control and protein count. The experiment was repeated at least in triplicate for each cell line.

For the dose-response study of IAA, the MiaPaCa-2, hPSCs or HPDE were seeded onto a white 96 well plate at a density of 10,000 cells per well. Next day, the cells were then treated for 15 min or 1hr with different µM concentration of IAA as follow: 3000, 1000, 300, 100, 30, 10, 3, 1. The cells were then lysed, and the ATP count was measured by using Via-Light Plus kit. The ATP cellular count was determined using an ATP calibration curve (supplement 8.5). The results were then normalized to protein count.

For co-culture experiment, MiaPaCa-2 or hPSCs were seeded onto white 96 well plate as a density of 5000 cells/well; 10% FBS: 25mM DMEM was used as a media for both cell lines. Next day the media was removed, and cells were treated with conditioned media and 10% Nu:25mM DMEM for the control. After 48 hr, the cells were lysed, and the ATP count was measured using luminesces plate reader. The values were normalized to untreated control and protein count.

2.9. Measuring the glucose concentration of conditioned media:

After collecting the conditioned media from different cell line its glucose concentration was measured by glucometer (Accu-Chek Performa Nano, Roche®; Accu-Chek Performa Strips, Roche®). The experiment was repeated at least in triplicate for each cell line.

2.10. Measuring the lactate concentration of conditioned media:

The lactate concentration of the conditioned media was measured with L-Lactate Assay kit (ECLC-100) EnzyChrom TM by following the kit instructions. Briefly, this assay is a colorimetric assay, based on lactate dehydrogenase catalyzed the oxidation of lactate, in which the NADH produced from the reaction reduces a formazan (MTT) to a coloured product. The intensity of the coloured product is proportional to the lactate concentration in the sample. The lactate concentration of the samples was determined by reading the optical density at 565 nm using a plate reader (Synergy, HT, BioTek, instruments Inc). The samples and standards optical density readings at time zero (OD0) were subtracted from the optical density after a 20-minute incubation at room temperature (OD20). Then the sample lactate concentration was determined from the standard curve (OD 565 nm vs L-lactate concentration (mM); Figure 2.6). The experiment was repeated at least in triplicate.

Figure 2.6. Lactate standard curve used to determine lactate concentration.

2.11. Measuring the mitochondrial mass and function by immunofluorescence:

MiaPaCa-2, hPSCs or HPDE cells were seeded onto a 16 mm coverslip and the next day the cells were treated with 100nM MitoTracker red FM for 15min in the dark at 37 ̊ C incubator with 5% CO2. Cells were then fixed with 2% formaldehyde methanol free (PN28908 Fisher Scientific®) for 30 min. The cells were then permeabilized with 0.2% BSA; 0.1 %

Triton-x100 in PBS for 10 min. Cells were quenched with freshly prepared 50mM NH4CL for 10 min, rinsed and incubated overnight with TOM-20 antibodies at 4 ̊ C (Table 2.4.). Next day, the samples were fluorescently labeled with secondary antibodies for 30 min at room temperature (Table 2.5.). Finally, one drop of DAPI with antifade was added to each coverslip and samples were analyzed by fluorescence microscopy.

For the co-culture experiment, MiaPaCa-2 or hPSCs were seeded onto a 16mm coverslip and the next day cells were treated with conditioned media or 10% NU:25mM DMEM as a control. After 48hr, the cells were treated with 100nM MitoTracker red FM for

15min in the dark at 37 ̊ C incubator with 5% CO2, fixed, quenched, labeled with TOM-20 antibodies overnight, fluorescently labeled with secondary antibodies, DAPI with antifade was added, analyzed with a confocal microscopy.

2.12. Studying the expression of alpha-smooth muscle actin by immunofluorescence:

hPSCs, HPDE, and MiaPaCa-2 cells were seeded onto a 16mm coverslip and the next day the cells were fixed with 2% formaldehyde methanol free (PN28908 Fisher Scientific®) for 30 min. Cells were then permeabilized with 0.2% BSA; 0.1 % Triton-x100 in PBS for 10 min. Cells were quenched with freshly prepared 50mM NH4CL for 10 min, rinsed and incubated overnight with α-SMA antibodies at 4 ̊ C (Table 2.4.). Next day, the samples were fluorescently labeled with secondary antibodies for 30 min at room temperature (Table 2.5.). Finally, one drop of DAPI with antifade was added to each coverslip and samples were analyzed by fluorescence microscopy.

2.13. Studying the expression of glucose and lactate transporter of cells in co-culture by immunofluorescence:

MiaPaCa-2-RFP (+) and hPSCs were seeded together onto a 16mm coverslips at a 1:5 ratio in favor to hPSCs. After 48hr of co-culture, cells were fixed with 2% formaldehyde methanol free (PN28908 Fisher Scientific®) for 30 min. Cells were then permeabilized with 0.2% BSA; 0.1 % Triton-x100 in PBS for 10 min, quenched with freshly prepared 50mM

NH4CL for 10 min, rinsed and incubated overnight with Glut-1, MCT-1 or MCT-4 antibodies at 4 ̊ C (Table 2.4.). Next day, the samples were fluorescently labeled with secondary antibodies for 30 min at room temperature (Table 2.5.). Finally, one drop of DAPI with antifade was added to each coverslip and samples were analyzed by fluorescence microscopy. The red positive population represent MiaPaCa-2 cells and red-negative were hPSCs.

2.14. Western blotting (WB) 2.14.1. Cell preparation for WB and protein extraction:

For singly cultured HPDE, hPSCs or MiaPaCa-2 cells were seeded onto 100mm dish, and upon reaching 50-60 % confluence were harvested in cold 250µL of RIPA lysis buffer (50mM Trizma (Tris Base); 40mM Na pyrophosphate; 100mM NaF; 150mM NaCl, 10mM EDTA; 10mM EGTA (Na+ salt); 60 µM Vanadate; pH to 7.4; 1% v/v 100xTriton), containing protease and phosphatase inhibitor by scraping the cell with a Corning® cell scraper (Sigma- Aldrich, UK). For co-culture, MiaPaCa-2 cells were seeded onto the bottom of 100mm dish with polyester trans-well inserts 0.4 micrometers (µm) pore size (10630312, Fisher Scientific UK), and hPSCs were seeded onto the top layer of the trans-well insert, and vice versa, see Figure 2.7. After 24, 48 or 72hr of co-culture, the cells in the bottom dish were harvested in cold 250µL of RIPA lysis buffer containing protease and phosphatase inhibitors using a cell scraper. Cell lysates were sonicated with Soniprep 150, kept on ice to rest for 30min and then centrifuged at 13,500 rpm for 25min at 4 ̊ C to remove any debris. The supernatant of clarified protein was transferred into clean prechill Eppendorf tube and stored at -80 ̊ C until used.

Figure 2.7. Co-culture of cells in trans-well plate for western blotting studies. The pore size of the insert is 0.4 µM which allow cell-cells interaction. Cells of each cell type are seeded on the bottom layer and the other cell type onto the top layer of the trans-well insert. Cells on the bottom layer were proceed for western blotting.

2.14.2. Protein concentration:

Protein concentrations were determined using a Bradford protein assay (Sigma-Aldrich Ltd). Briefly, the samples were diluted and placed onto a 96 well plate in triplicate. To

determine the standard curve, different concentration of Bovine Serum Albumin (BSA), (0, 0.05, 0.1, 0.2, 0.3, 0.4 and 0.5 µg/µL) were placed in triplicate in the same 96 well plate. Freshly prepared (1:5, 200 µL) Bradford reagent was added to each well, incubator in the dark at room temperature for 2 minutes and the optical density (OD) was measured at 595 nm using plate reader (Synergy, HT, BioTek, instruments Inc). The OD reader was measured using the Gen5 software. The actual protein concentration for each sample was determined using a BSA standard curve (OD 595 vs BSA concentration) (Figure 2.8.). The volume for 10 µg of protein for each sample was calculated.

Figure 2.8. BSA standard curve was used to determine the protein concentration.

2.14.3. Protein separation:

The protein samples were denatured by adding the appropriate volume of 4x Laemmli with 10% β-mercaptoethanol loading buffer (2.5 mL, 1M Tris PH 6.8; 4mL glycerol, 0.8 gm SDS; 1mL β-mercaptoethanol; 0.01gm of bromophenol blue; complete with MilliQ water to 2.5mL) and incubated at 95 ̊ C for 5min. The samples were then loaded onto the wells of 4- 12% Bis-Tris protein gels,1mm,10 wells (NuPAGE®), together with the precision plus protein standards dual color marker (Cat# 161-0374). The proteins were then separated by electrophoresis in a Min Gel Tank (Invitrogen, Thermo-fisher scientific) in 1x MOPS SDS running buffer (NP0001, Nu PAGE®, Life technology). The samples were run until the separation of the lowest molecular weight protein (i.e. 10 kDa), which represent the last band in the marker.

2.14.4. Protein transfer:

Proteins were transferred to a nitrocellulose blotting membrane (Amersham ™ Protram™, 0.45 µM, Health Care life science, # 10600002). Using a Trans-Blot® Turbo™

(Bio-RAD) transfer system in transfer buffer (1x of Trans-Blot® Turbo™ Transfer buffer, Bio- Rad, #10026938; 10% ethanol).

2.14.5. Protein detection:

The protein membrane was incubated overnight with the appropriate primary antibodies diluted in 5% BSA: 1x TBST buffer (50mM Tris base;150 mM NaCl; PH 7.4; 0.1 % Tween) at 1:1000 dilution, in a shaker at 4 ̊ C. Next day, the membrane was washed three times with TBST in a shaker for 15 min each followed by incubated with the appropriate secondary antibody (1:2000 in 5% milk in 1x TBST solution), for 1 hour at room temperature in a shaker. The membrane was then washed twice with 1x TBST and then one last wash with 1x TBS in a shaker at room temperature. The membrane was then incubated in clarity™ western ECL substrate (1705061, Bio-Rad®) for 5 minutes and imaged by ChemiDOC MP (BioRad) and protein bands were analyzed using Image Lab software.

Table 2.4. Primary antibodies details:

Dilution Catalogue number, Company Primary Host Specificity WB IF Antibody

Mouse Monoclonal 1:1000 1:200 sc-365501, Santa Cruz MCT-1 (H-1)

Mouse Monoclonal 1:1000 1:200 sc-376465, Santa Cruz MCT-4 (G-7)

Rabbit Monoclonal 1:1000 1:200 (D3J3A), Cell signalling Glut-1

Rabbit Monoclonal 1:1000 1:200 (D4K9N) XP®, Cell signalling Alpha-SMA

Rabbit Monoclonal 1:1000 _ (C4B5), Cell signalling LDHA

Rabbit Monoclonal 1:1000 _ (C64G5), Cell signalling HK-II

Rabbit Polyclonal 1:1000 _ (C64G5), Cell signalling PFKFB3

Rabbit Monoclonal 1:1000 _ (D78A4) XP®, Cell signalling PKM2

Rabbit Monoclonal 1:1000 1:200 (D8T4N), Cell signalling TOM-20

Rabbit Polyclonal 1:1000 _ (# 2175), Cell signalling Cyclophillin-A

Rabbit Polyclonal 1:1000 _ (#2144), Cell signalling Alpha tubulin

Mouse Monoclonal 1:1000 _ (8H10D10), Cell signalling Beta-actin

*WB: Western blotting; IF: Immunofluorescent

Table 2.5. Secondary antibodies detail:

Catalogue number and Secondary Antibody Host Specificity Reactivity Dilution Use company

Anti-Mouse IgG HRP-linked Polyclonal Mouse 1:2000 WB 7076, Cell signalling Horse antibody

Anti-Rabbit IgG HRP-linked Goat Polyclonal Rabbit 1:2000 WB 7074S, Cell signalling antibody

Alexa fluor 488 goat anti- Goat Polyclonal Rabbit 4 µg/mL IF A11008, Invitrogen rabbit

1-10 Alexa fluor 488 goat anti- Goat Monoclonal Rabbit IF µg/mL A11029, Invitrogen mouse

*WB: Western blotting; IF: Immunofluorescent.

2.15. Quantitively assessing the growth of two populations of cells in co-culture by flow cytometry:

RFP (+) MiaPaCa-2 cells were co-cultured with hPSCs in 75 cm2 flask at a ratio of 1:5 in favor of hPSCs. The same number of cells seeded in co-culture was used in the control. After 72 hr of co-culture, cells were washed with PBS, harvested and resuspended in 700µL of serum-free media. The cells were then analyzed by flow cytometry using the RFP signal detector with 496/615 nm to detect MiaPaCa-2-RFP (red cells) and hPSCs were the red negative population. The total number of red cells were counted and the same applied to red negative cells. Data were analyzed using BD FACS-Diva software (Figure 2.9).

Figure 2.9. Experimental design to assesses the growth of two population of cells in co- culture using flow cytometry (FACS). The number of each cells in co-culture is the same in the control. After 72hr cells were sent to processed by FACS and subsequent analysis.

2.16. Measuring the glucose uptake of two populations of cells in co-culture by flow cytometry:

The glucose uptake was performed by monitoring the uptake of 2-NBDG, which is a fluorescent glucose analogous with excitation/emission of ~465/540 nm, by using flow cytometry. The RFP (+) MiaPaCa-2 cells were seeded with hPSCs at a ratio of 1:5 in favor of

hPSCs and singly cultured of each of these cells were used as a control. The next day the cells were washed once with PBS and the media was then changed to 10% Nu: DMEM. The cells were kept in co-culture for 48hr. At the day of the experiment, the cells were incubated with 2- NBDG solution, which is 2-NBDG diluted with pre-warmed serum-free media to a final concentration of 200 µM, for 30 minutes at 37 ̊ C tissue culture incubator with 5% CO2. The cells were then washed with PBS, harvested and re-suspended in 500 µL of flow cytometry binding buffer and kept in ice. All these steps were performed in the dark. The cells were then analyzed by flow cytometry, where 2-NBDG was detected by FITC signal with excitation/emission of 488/530 nm and MiaPaCa-2-RFP (Red cells) by RFP signal with excitation/emission of 496/615 nm. The hPSCs cells are the RFP negative population. 10,000 events of each sample were analyzed. The data analysis was performed using BD FACS-Diva software (Figure 2.10).

Figure 2.10. Quantitatively assessing different function of two population of cells in co- culture by flow cytometry (FACS). Summary of the steps used to determine the glucose uptake, mitochondrial function and cell proliferation of two population of cells in co-culture by using flow cytometry.

2.17. Studying the cells proliferation of two populations of cells in co-culture by using BrdU and flow cytometry:

Cell proliferation was performed using a BrdU staining kit for flow cytometry FITC (15581287, Fisher Scientific UK Ltd). BrdU is a pyrimidine analog that incorporate into newly generated DNA, it helps to measure the cells proliferation. Briefly, MiaPaCa-2-RFP was co- cultured with hPSCs at 1:5 ratio in favor of hPSCs and singly culture of each of these cells were used as a control. Next day, the cells were washed, and media was changed to 10%Nu: 25mM DMEM to all the cells. After 2-days cells were washed, and they were treated with10µM of BrdU for 1 hour at 37 ̊ C incubator with 5% CO2. The cells were then harvested, fixed and treated with Anti-BrdU according to the manufacturer instruction. The cells were resuspended in 1 mL of flow cytometry staining buffer (15346785, Fisher Scientific) and kept in ice. The cells were analyzed by flow cytometry, where BrdU-FITC was detected by FITC signal with excitation/emission of 488/530 nm and MiaPaCa-2-RFP (Red cells) by RFP signal with excitation/emission of 496/615 nm. The hPSCs cells are the RFP negative population. 10,000 events of each sample were analyzed. The data analysis was performed using BD FACS-Diva software (Figure 2.10).

2.18. Measuring the mitochondrial membrane potential of two populations of cells in co- culture by flow cytometry:

RFP (+) MiaPaCa-2 cells were co-cultured with hPSCs at a ratio of 1:5 in favor of hPSCs and singly culture of each of these cells were used as a control. The next day the cells were washed once with PBS and media was changed to 10% Nu:25mM DMEM. After 2 days of co-culture cells were treated for 15min with 100 nM of MitoTracker green FM (MTG), which accumulate in the mitochondria by covalently binding with mitochondrial protein thiol group or cysteine residue, and cells were kept at 37 ̊ C incubator with 5% CO2. After that, cells were washed with PBS, harvest and resuspend in 500µL of flow cytometry staining buffer. The cells were then analyzed by flow cytometry using the RFP signal detector with 496/615 nm to detect MiaPaCa-2-RFP (red cells) and GFP signal detector with 488/530 nm to detect MTG. The hPSCs were the red negative population. 10,000 events of each sample were analysed. Data were analyzed using BD FACS-Diva software (Figure 2.10).

2.19. Measuring the mitochondrial membrane potential in live cell by TMRE imaging:

The mitochondrial membrane potential was measured using tetramethylrhodamine ethyl ester (TMRE), a lipophilic cationic dye that accumulates in the mitochondria dependent on the highly negative inner mitochondrial membrane potential. MiaPaCa-2, hPSCs or HPDE cells were grown on coverslips and loaded for 15 min with 100nM TMRE in 1xHEPES-PSS at 37 ̊ C incubator with 5% CO2. The cells were mounted onto the microscope stage and a gravity-fed perfusion chamber was performed. The Nikon TE2000-S microscope fitted with x40 oil immersion objective and Cairn monochromator-based illumination system (Cairn Research, Kent, UK) controlled by MetaFluor image acquisition and analysis software (Molecular Devices, Downingtown, PA) was used. The dye perfusion was maintained during the experiment by adding 100nM of TMRE to all the perfusion solution. Real-time imaging of TMRE-loaded cells was performed, and fluorescence images were recorded every 5 seconds using 545-10 nm excitation light and 5x5 binning. The region of interest was selected within each cell and background subtraction was applied. 4µM of carbonyl cyanide m-chlorophenyl hydrazone (CCCP, which depolarize mitochondria by increasing its permeability to protons) was perfused for 20 min to quantify the change in the mitochondrial membrane potential in each selected region. The relative loss of TMRE fluorescence induced by CCCP represents the mitochondrial membrane potential, where F0, the mean of fluorescence intensities at the beginning of the recording, was subtracted from Fe, the mean fluorescence intensity at the end of the recording was applied.

For cells in co-culture, MiaPaCa-2 or hPSCs were seeded onto coverslips. The next day, media was changed, and cells were treated with conditioned media or 10% Nu: 25mM DMEM for the control. After 48hr, cells were loaded with 100nM TMRE for 15 min at 37 ̊ C incubator with 5% CO2 and all steps explained in the previous paragraph were performed.

2.20. Measuring the mitochondrial function and determine the metabolic phenotype of cells by using Seahorses flux analyzer:

The mitochondrial function and cell metabolic phenotype were determined using XFe96 Extracellular Flux Analyzer (Seahorse Bioscience); Agilent Seahorse XF Cell Mito Stress Test kit (# 103015-100). Briefly, the singly cultured MiaPaCa-2, hPSCs or HPDE were seeded at a density of 25,000 cells/ well in a Seahorse plate and allowed to adhere overnight at

humidified 37 ̊ C incubator with 5% CO2. The next day, media was changed with Agilent Seahorse XF base medium (Cat# 102323-100-100) supplemented with: 2mM Glutamine (200mM, G8540, Sigma®); 10mM Glucose (10%, G8644, Sigma®); 1mM sodium pyruvate (100mM, S8636, Sigma®); adjusted pH 7.4; temperature 37 ̊ C. The cells were then incubated in a non-CO2 incubator at 37 ̊ C for 1 hour. After that, the Mito- stress test was performed following the manufacturer instructions. Briefly the cells were sequentially treated with 1 µM oligomycin (OM), 0.5 µM carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP) (MiaPaCa-2) or 1 µM FCCP (hPSCs or HPDE) and combination of 0.5 µM rotenone/antimycin (RT/AM) in the Seahorse analyzer. The FCCP concentration was optimized for each cell type (Supplement 8.11). Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were recorded throughout the experiment before, during and after each drug injection. The OCR and ECAR data were normalized to the total protein content as assessed using the SRB assay. The normalized values were used to calculate the following metabolic parameters: basal respiration, ATP production, H+ (proton) leak, maximal respiration, spare respiratory capacity, non-mitochondrial respiration, baseline phenotype, stressed phenotype, metabolic potential, ATP rate index and ATP production rate (Supplement 8.8 and 8.9). The data are represented as the mean/AU ± SEM for all replicates. One-way ANOVA was performed for statistical analysis of the data.

For the conditioned media experiments, MiaPaCa-2 or hPSCs were seeded onto a 96- well Seahorse plate at a density of 4500 cells/well and allow to adhere overnight at humidified

37 ̊ C incubator with 5 % CO2. The next day the media was removed, and cells were treated with conditioned media or 10% Nu: 25mM DMEM for the control. After 48hr, the Mito -stress test Seahorse experiments were performed according to the manufacturer instruction.

2.21. Measuring the glycolytic function of cells by using Seahorse flux analyzer:

The glycolytic function was determined using the XFe96 Extracellular Flux Analyzer (Seahorse Bioscience); Agilent Seahorse XF Glycolysis Stress Test (#103020-100). Briefly, the singly cultured MiaPaCa-2, hPSCs or HPDE were seeded at a density of 25,000 cells/ well at 96 well Seahorse plate and allowed to adhere overnight in a humidified 37 ̊ C incubator with

5% CO2. Next day, the media was changed with Agilent Seahorse XF base medium (Cat# 102323-100-100) supplemented with: 1mM Glutamine (200mM, G8540, Sigma®); adjust pH

7.4; temperature 37 ̊ C. Then cell incubated at non-CO2 incubator with 37 ̊ C for 1 hour. After that, the assay was performed following the manufacturer instruction. The cells were sequentially treated with 10mM glucose, 1 µM OM, and 50mM 2-deoxyglucose (2-DG) in the Seahorse analyzer. Extracellular acidification rate (ECAR) was recorded throughout the experiment before, during and after each drug injection. The ECAR data were normalized to the total protein content using the SRB assay. The normalized values were used to determine the following parameters: glycolysis, glycolytic capacity, glycolytic reserve, and nonglycolytic acidification (Supplement 8.10). The data are represented as the mean/AU ± SEM for all replicate. One-way ANOVA was performed for statistical analysis of the data.

For the conditioned media experiments, MiaPaCa-2 or hPSCs were seeded onto a 96- well Seahorse plate at a density of 4500 cells/well and allow to adhere overnight at humidified

37 ̊ C incubator with 5 % CO2. The next day the media was removed, and cells were treated with conditioned media or 10% Nu: 25mM DMEM for the control. After 48hr, the Glyco-stress test was performed according to the manufacturer instruction.

2.22. Metabolomics:

Metabolomics footprint analysis of extracellular metabolites within the conditioned media of MiaPaCa-2 and hPSCs was determined using GC-MS analysis. The conditioned media of MiaPaCa-2 or hPSCs were sent to professor Royston Goodacre lab to perform the analysis. A sample of 5 biological replicates was processed with fresh media as a control. At the end of the conditioned media collection the cells number was counted (Supplement 8.15).

2.22.1. Sample preparation and derivatization:

According to Howbeer Ali [ Quality control (QC) samples were prepared by combining 100 µL from each sample. Samples were aliquoted (300 µL) into new Eppendorf microcentrifuge tubes (Eppendorf Ltd, Cambridge, UK), followed by the addition of 100 µL of the internal standard solution (0.2 mg/mL succinic-d4 acid, and 0.2 mg/mL glycine-d5) and vortexed for 15 second. All samples were lyophilised by speed vacuum concentration at room temperature overnight (HETO VR MAXI vacuum centrifuge attached to a Thermo Svart RVT 4104 refrigerated vapour trap; Thermo Life Sciences, Basingstoke, U.K.). A two-step

derivatization protocol of methoxyamination followed by trimethylsilylation was employed (Wedge, D.C. et al 2011)].

2.22.2. Gas chromatography mass spectrometry (GC-MS) analysis:

According to Howbeer Ali [GC-MS analysis was conducted on a 7890B GC coupled to a 5975 series MSD quadrupole mass spectrometer and equipped with a 7693 autosampler (Agilent, Technologies, UK). The sample (1 μL) was injected onto a VF5-MS column (30 m x 0.25 mm x 0.25 μm; Agilent Technologies) with an inlet temperature of 280 °C and a split ratio of 20:1. Helium was used as the carrier gas with a flow rate of 1 mL/min. The chromatography was programmed to begin at 70 °C with a hold time of 4 min, followed by an increase to 300 °C at a rate of 14 °C/min and a final hold time of 4 min before returning to 70 °C. The total run time for the analysis was 24.43 min. The MS was equipped with an electron impact ion source using 70 eV ionisation and a fixed emission of 35 μA. The mass spectrum was collected for the range 50-550 m/z with a scan speed of 3,125 (N=1). Samples were analysed in a randomised order with the injection of a pooled biological quality control sample after every 6th sample injection].

2.22.3. Data processing:

According to Howbeer Ali [The GC-MS raw files were converted to mzXML and subsequently imported to R. The R package “erah” was employed to de-convolve the GC-MS files. Chromatographic peaks and mass spectra were cross-referenced with the Golm library for putative identification purposes and followed the metabolomics standards initiative (MSI) guidelines for metabolite identification (Summer et. al., 2007). The peak intensities were normalised according to the IS (succinic-d4 acid) before being log10-scaled for further statistical analysis. All pre-processed data were investigated by employing principal component analysis (PCA) (Gromski et. al., 2015)].

(Important note: methods # 2.22.1, 2.22.2, and 2.22.3 was writing and conducted by Howbeer Muhamad Ali from professor Royston Goodacre lab).

2.23. Citrate uptake studies:

MiaPaCa-2 cells were seeded in T-75 cm2 at a density of 300,000 cells/flask and allowed to adhere overnight at 37 ̊ C incubator with 5% CO2. Cells were then treated for zero

and 48 hours with 200µM, 1mM or 5mM of citric acid. The conditioned media was collected at time zero and 48 hr post-treatment, centrifuge at 500 rpm for 10 minutes, 22 ̊ C to remove any cells debris and stored at -80 ̊ C until analysis. The samples together with both blank and control at time zero or at 48hr were sent to professor Royston Goodacre lab for analysis. The uptake of citrate was measured by comparing the amount of citrate at time zero and 48hr post- treatment. A sample of 5 biological replicates was analyzed. All steps mentioned previously in the metabolomics were used to run the samples.

2.24. Statistical analysis:

Graph Pad Prism software was used for data analysis. For all experiment, the mean±SEM were calculated for at least three independence experiment. To compare between more than two groups with one independence variable One-way ANOVA was performed. To compare between more than two groups with two independence variable a two-way ANOVA was used.

CHAPTER 3

3. INVESTIGATING THE METABOLIC PHENOTYPE AND STUDYING THE METABOLIC REGLUATION OF INTRACELLULAR

CALCIUM HOMEOSTASIS IN PDAC VS FIBROBLASTS VS STELLATE

CELLS VS PANCREATIC DUCTAL CELLS

3.1. INTRODUCTION

Our previous studies have shown that glycolytic ATP plays an important role in 2+ regulating intracellular calcium [Ca ]i homeostasis in pancreatic cancer cells through fuelling PMCA that proposed a new way of targeting cancer through this pathway (James et al, 2013; James et al, 2015). These studies have shown that inhibition of glycolysis, but not mitochondria, induced ATP depletion, inhibition of the ATP-driven Ca2+ pump (PMCA), cytotoxic Ca2+ overload and necrosis in PDAC cells grown in high glucose media. These studies were done in pancreatic adenocarcinoma cells (PDAC) (MiaPaCa-2 and PANC-1) in which these cells grown in high glucose were compared to the same cells, and thus same genetic background, grown in zero glucose supplemented with either galactose or ketoisocaproate (KIC). This was done to shift the cells metabolic phenotype to a less glycolytic and more mitochondrial and thus resemble normal cells. However; there have been no controlled studies performed in non-cancerous cells to compare if this phenomenon is a cancer cells-specific or an artifact of cell culture. The current study involves a more rigorous understanding of the 2+ metabolic regulation of [Ca ]i homeostasis in cancerous PDAC cells (MiaPaCa-2 and PANC1) and non-cancerous cell lines (human pancreatic stellate cells (hPSCs), BJ skin fibroblast, and human pancreatic ductal epithelial cells (HPDE)). hPSCs are primary pancreatic stellate cells derived from a pancreatic resection of a patient with Whipple procedure (a surgical procedure to remove PDAC tumours), HPDE are human papillomavirus (HPV) E6 and E7 proteins immortalized cells (no p53 or Rb) of ductal origin and BJ skin fibroblast are immortalized fibroblast from normal human skin. The following chapter will include the different methods we used to address the line of inquiry.

3.1.1. AIM

To compare the metabolic phenotype and growth of normal cells (i.e. HPDE, hPSCs, and BJ skin fibroblast) with PDAC cells (i.e. MiaPaCa-2 and PANC-1) and testing the effect of different metabolic inhibitors on the glycolytic ATP regulation of PMCA which is critical to maintaining the [Ca2+]i homeostasis in PDAC cells. This was achieved by assessing the proliferation rate, metabolic phenotype, mitochondrial mass and testing the effect of different metabolic inhibitors on cytosolic ATP and resting [Ca2+]i on PDAC vs non-cancerous cells.

3.2. RESULTS

3.2.1. PDAC cells grow faster than non-cancerous cells:

To compare the growth rate between PDAC and non-cancerous cells we performed a sulforhodamine-B (SRB) protein stain- based cell proliferation assay. The cells were grown in 96 well plates at a density of 5000 cells/well and SRB assay performed daily on separate plates for 5 sequential days. Cell proliferation was substantially greater in PDAC cells (MiaPaCa-2 and PANC-1) compared to other types of non-cancerous cells (Figure, 3.1.); presumably due to their highly glycolytic nature according to the Warburg effect (James et al., 2013; James et al., 2015). There was no significant difference in the growth rate between non-cancerous hPSCs and HPDE cells; however, BJ skin fibroblasts had the lowest growth rate (Figure 3.1.).

Figure 3.1. Cell proliferation of PDAC, fibroblasts, hPSCs vs HPDE. All cells were cultured in 25mM DMEM except for HPDE that were cultured in keratinocyte serum-free media. The sulforhodamine B assay, a protein-staining colorimetric assay (absorbance unit, AU), was performed to assess cell proliferation rate of the cells in each culture condition. The assay was conducted daily for 5 days. To compare the growth rate of cells a two-way ANOVA followed by Bonferroni's multiple comparisons test was used. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

3.2.2. Effect of glucose restriction on PDAC cells (MiaPaCa-2) growth:

Since previous results show that PDAC cells, especially MiaPaCa-2 cells, had greater proliferation due to their highly glycolytic nature, we next wanted to investigate if the change in glucose concentration can affect the proliferation of these cells. We tested the effect of acutely switching the glucose concentration from high glucose (25mM) to restricted glucose (5mM) on the growth rate of MiaPaCa-2 cells, using the SRB cell proliferation assay. Cells were seeded in a 96 well plate in high glucose media and 24 hours later media was changed to either the same high glucose (25mM) or low glucose (5mM) media. The results suggest that MiaPaCa-2 cell growth rate was affected by the acute change in the glucose concentration in the culture media. Acute switching from high to low glucose concentration caused a significant decreased in the growth rate of MiaPaCa-2 cells (Figure, 3.2).

Figure 3.2. MiaPaCa-2 cells growth rate is reduced by acute glucose restriction. MiaPaCa- 2 cells were cultured in high glucose media, the next day the media was changed to either the same high glucose DMEM (25mM) or low glucose DMEM (5mM). The sulforhodamine-B (SRB)-based proliferation assay, a protein staining colorimetric assay (absorbance units, AU), was performed to assess cell proliferation rate of the cells in each culture condition. The assay was conducted daily for 5 days. To compare the growth rate of cells a two-way ANOVA followed by Bonferroni's multiple comparisons test was used. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

3.2.3. The growth rate of PDAC cells (MiaPaCa-2, PANC-1), hPSCs and BJ skin fibroblasts is unaffected by glucose concentration in long-term culturing:

We next wanted to test the effect of long-term culturing of PDAC and non-cancerous cells in low glucose vs high glucose media on cell growth. Cells were sub-cultured in T25 cm2 flasks over multiple passages over a minimal of 21 days prior to starting the 96 well plate SRB based assay. The cell proliferation studies showed that there was no significant difference in the growth rate of PDAC cells, hPSCs or BJ skin fibroblasts cultured in low glucose (5mM) compared to high glucose (25mM) (Figure, 3.3. (a-d)). These data suggest that cells can adapt to the low glucose in the culture media after long-term culturing.

Figure 3.3. Cell proliferation of PDAC, fibroblasts, hPSCs vs HPDE in low and high glucose media in long-term culturing. PDAC (MiaPaCa-2 (a), PANC-1 (b)), BJ skin fibroblast (c) and hPSCs (d) were cultured in either 5 or 25mM glucose media for a minimum of 21 days. The sulforhodamine B assay, a protein-staining colorimetric assay (absorbance unit, AU), was performed to assess cell proliferation in each culture condition. The assay was conducted daily for 5 days. To compare the growth rate of each cell in different culture condition a two- way ANOVA followed by Bonferroni's multiple comparisons test was used. ns, no change. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

3.2.4. PDAC cells (MiaPaCa-2) are highly glycolytic compared to hPSCs and HPDE, since they exhibit the Warburg effect:

Based on the SRB cell proliferation assays, PDAC cells (MiaPaCa-2), exhibit a much faster growth rate compared to non-cancerous. Moreover, it is clear that MiaPaCa-2 cell growth appears exquisitely sensitive to glucose, suggesting that these cells are highly glycolytic, consistent with our previous studies (James et al., 2013; James et al., 2015) and the Warburg effect (Figure, 3.2). Our data showed that acute switching from high to low glucose concentration caused a significant decreased in the growth rate of MiaPaCa-2 cells (Figure, 3.2.). To further investigate the metabolic phenotype, we performed Glyco and Mito stress tests by using a Seahorse XF analyzer. This works by seeding 25,000 cell/well in 96 well Seahorse plate, the next day the oxygen consumption rate (OCR) and the extracellular acidification rate (ECAR) of the cells were measured during and after specific drugs injection. The Mito stress test involves sequential injection of oligomycin (OM, 1 µM) which inhibit the mitochondrial ATP synthase, carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP, 1 µM (for hPSCs, HPDE), 0.5 µM (for MiaPaCa-2)) which is a protonophores and metabolic uncoupler and a combination of rotenone/Antimycin (RT/AM, 0.5 µM) which inhibit mitochondrial respiratory chain by binding to complex I and III to study the mitochondrial function and determine the metabolic phenotype of the cells (see supplement 8.8 for more details). For this test, we have optimized the concentration of FCCP for each cell line to determine the optimal concentration needed to give the maximal response (see supplement 8.11 for more details). The Glyco stress involves starvation of the cell from glucose for one hour then the injection of glucose (10mM) which fuel glycolysis by converting to pyruvate, oligomycin (OM, 1µM) that will shut down the mitochondria by inhibiting the ATP synthase resulting in total dependency on glycolysis and 2-deoxyglucose (2-DG, 50mM) which is analogue of glucose that shut down glycolysis, to determine the cells glycolysis, glycolytic capacity, and glycolytic reserve (see supplement 8.10 for more details).

The Glyco stress test shows that MiaPaCa-2 cells are highly glycolytic compared to hPSCs and HPDE cells (Figure, 3.4.). Glycolysis was 33±4 mpH/min/AU in MiaPaCa-2, 20±1 mpH/min/AU in HPDE and 15±2 mpH/min/AU in hPSCs (Figure, 3.4.). In addition, the baseline extracellular acidification rate measured by Mito-stress test also support this finding (Figure, 3.6. (g)). The baseline ECAR was 80±10 mpH/min/AU in MiaPaCa-2; 46±3 mpH/min/AU in hPSCs and 15±2 mpH/min/AU in HPDE (Figure, 3.6. (g)).

Figure 3.4. PDAC cells (MiaPaCa-2) have higher glycolytic phenotype compared to hPSCs and HPDE. Cells were analyzed by XFe96 extracellular flux Analyzer by performing the Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2- deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon adapted from Seahorse bioscience summarizing different glycolytic parameters measured during the Glyco-stress test. (c) Mean ±SEM of at least three independent experiment (n=4, MiaPaCa-2) (n=3 for HPDE and hPSCs). All measurements were normalized to the total protein content (absorbance unit, AU) measured by sulforhodamine B assay. All statistical comparisons were performed using one-way ANOVA. *, p<0.05, **, p<0.004.

3.2.5. The protein expression of key glycolytic enzymes in PDAC (MiaPaCa-2), hPSCs and HPDE:

According to the previous finding, MiaPaCa-2 cells were more glycolytic compared to non-cancerous cells (HPDE, hPSCs). Evidence suggests that key glycolytic enzymes are overexpressed in cancer cells which contributes to the Warburg effect. We next wanted to determine whether this was due to altered expression of key glycolytic enzymes or post- translational modification of these enzymes in these cells. This was achieved by western blotting for HK-II, PKM2, LDHA, and PFKFB3. However, to our surprise expression of key glycolytic enzymes was either similar (PFKFB3, HK-II) or significantly lower (PKM2, LDHA) in MiaPaCa-2 cells compared to HPDE (Figure, 3.5). Expression of glycolytic enzymes in hPSCs was very similar to MiaPaCa-2, except PFKFB3 which was significantly lower than both MiaPaCa-2 and HPDE. These data suggest that overexpression of glycolytic enzymes in MiaPaCa-2 cells does not contribute to the higher glycolysis in these cells compared to HPDE and hPSCs. This suggests that some post-translation modification of these key glycolytic enzymes is responsible, whether that be hyperphosphorylation by Akt or tyrosine kinase or subunit assembly (e.g. PKM2).

Figure 3.5. The expression of key glycolytic enzymes in PDAC (MiaPaCa-2), hPSCs and HPDE. Western blots showing the expression of hexokinase-II (HX-II), 6-phosphofructo-2- kinase/fructose-2,6-biphosphatase3 (PKFB3), Pyruvate kinase muscle isozyme M2 (PKM2) and Lactate dehydrogenase A (LDHA) in whole cell lysate of MiaPaCa-2, hPSCs, and HPDE. (a, b) HK-II, (c, d) PFKFB3, (e, f) PKM2, (g, h) LDHA. 10 µg was loaded for each sample. The relative expression of the enzyme is the mean±SEM of four independent experiments. All data were normalized to the expression of control (B-actin for HK-II and PFKFB3; cyclophilin- A for PKM2 and LDHA). One-way ANOVA followed with Tukey's multiple comparisons test was used for statistical analysis. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

3.2.6. The Mitochondrial function of PDAC cells (MiaPaCa-2) is the highest compared to hPSCs and HPDE:

After finding that MiaPaCa-2 cells were highly glycolytic compared to non-cancerous cells, we next wanted to compare the mitochondrial function between these cells. To achieve that we performed several functional assays including TMRE imaging, MitoTracker red FM imaging, and Mito stress test by using Seahorse XFe96 analyzer.

In the Mito stress test, we have measured the oxygen consumption rate (OCR) and the extracellular acidification rate (ECAR) of the cells during and after sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP, 1 µM (for hPSCs, HPDE), 0.5 µM (for MiaPaCa-2)) and a combination of rotenone/Antimycin (RT/AM, 0.5 µM) to study the mitochondrial function of the cells. The result shows that the basal respiration, maximal respiration, and baseline oxygen consumption were higher in MiaPaCa-2 cells compared to HPDE and hPSCs (Figure, 3.6, see supplement 8.8 and 8.9 for more detail and the definition of these parameters). The basal respiration was 127±20 pmol/min/AU in MiaPaCa-2; 45 ±1 pmol/min/AU in hPSCs and 89±9 pmol/min/AU in HPDE. The maximal respiration was 291±45 pmol/min/AU in MiaPaCa-2; 135±3 pmol/min/AU in hPSCs and 150±13 pmol/min/AU in HPDE. The baseline oxygen consumption was 183±28 pmol/min/AU in MiaPaCa-2; 74±6 pmol/min/AU in hPSCs and 94±11 pmol/min/AU in HPDE. The metabolic potential results show that both MiaPaCa-2 and hPSCs prefer to utilize mitochondrial respiration to meet their metabolic demand during stress, while HPDE prefers glycolysis (Figure 3.6, h). In addition, the results show that for both MiaPaCa-2 and hPSCs cells the majority of the total ATP was produced from glycolysis, while in HPDE it was from the mitochondria (Figure 3.6, j).

We next measured the mitochondrial membrane potential by using TMRE imaging. The result shows that MiaPaCa-2 cells exhibit the highest mitochondrial membrane potential (649±71.82 grey levels) compared with HPDE (355.3±36.89 grey levels) and hPSCs (162.2±14.67 grey levels) which corresponded with the MitoTracker Red FM imaging (Figure 3.7 & 3.8). hPSCs appear to exhibit the lowest mitochondrial function compared to MiaPaCa- 2 and HPDE (Figure, 3.6; 3.7; 3.8).

3.2.7. The higher mitochondrial function in PDAC is independent of mitochondrial mass/ number of mitochondria:

Since the previous results have shown that MiaPaCa-2 cells have the highest mitochondrial function compared with non-cancerous cells, we wanted to determine whether this was due to the higher number of mitochondria or mitochondrial mass in these cells. This was achieved using western blotting and immunofluorescence of mitochondrial-specific protein/ marker, Tom-20. However, despite the higher mitochondrial function of MiaPaCa-2 compared to other cells, the Tom-20 expression and localization/ distribution were similar between all the three cell lines (Figure 3.8). This clearly suggests that MiaPaCa-2 cells have more functional mitochondria compared to non-cancerous cells.

Figure 3.6. Metabolic phenotype of PDAC (MiaPaCa-2) vs hPSCs and HPDE cells in response to the mitochondrial stress test. The cells were analyzed by performing a Mito- stress test using Seahorse XFe96 extracellular flux analyzer. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP, 1 µM (for hPSCs, HPDE), 0.5 µM (for MiaPaCa-2)) and a combination of rotenone/antimycin (RT/AM, 0.5 µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) by using the Sulforhodamine-B (SRB) assay. (b) cartoon depicting the different metabolic parameters following treatment with each drug

(adapted from Seahorse Bioscience). (d-j) the mean± SEM of each metabolic parameter normalized to the protein content (AU) was calculated from at least three independent experiments (n=3 for hPSCs & HPDE, n=5 for MiaPaCa-2). (e) cell energy phenotype profile. Statistical analysis was determined by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

Figure 3.7. Quantitative determination of mitochondrial membrane potential in PDAC cells (MiaPaCa-2) vs hPSCs and HPDE. The mitochondrial membrane potential was measured by using tetramethylrhodamine ethyl ester (TMRE) dye. Cells were loaded with TMRE and imaged to measure the intensity of the dye before and after the addition of the mitochondria uncoupler and protonophores carbonyl cyanide-3-chlorophenylhydrazone (CCCP) which completely collapses the mitochondrial membrane potential. Therefore, CCCP- induced loss of the TMRE fluorescence represents the relative measure of mitochondrial membrane potential between different types of cells. (d) The mean ±SEM is calculated from 20-60 cells for seven independent experiments. Statistical analysis was determined using a one- way ANOVA followed by Tukey's multiple comparisons test. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure 3.8. Measuring the mitochondrial mass and function in PDAC (MiaPaCa-2), hPSCs and HPDE. (a) Fluorescence microscope images of different cells showing the cellular distribution of MitoTracker red FM (red), a mitochondrial-specific dye that accumulate on the mitochondria based on the mitochondrial membrane potential or redox potential, and immunofluorescent TOM-20 (green), a mitochondrial import receptor subunit. The nuclei of cells (blue) were counterstained with DAPI. All cells were imaged under identical exposure time and microscope settings (pixel/intensity), scale bar 25µM. (b & c) TOM-20 protein levels were determined in each cell type by western blotting and densitometry values were normalized to the levels of beta-actin loading control. The relative TOM-20 measured to represent the mean of three independent experiments. To compare the three-cell types a one-way ANOVA followed by Tukey's multiple comparisons test was used. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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Table 3.1. Summary of the metabolic phenotype finding:

Metabolic parameter Cells comparison Assay Mitochondrial Membrane Potential MiaPaCa-2>HPDE>hPSCs 1- TMRE imaging, significant different 2- MitoTracker Red FM Imaging Mitochondrial Mass MiaPaCa-2=HPDE=hPSCs 1- TOM-20 immunofluorescent imaging 2- Western Blotting Basal Respiration MiaPaCa-2>HPDE=hPSCs Seahorse XFe96 analyzer, significant different Baseline OCR MiaPaCa-2>HPDE=hPSCs Seahorse XFe96 analyzer, significant different Baseline ECAR MiaPaCa-2>hPSCs=HPDE Seahorse XFe96 analyzer, significant different

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3.2.8. Inhibition of glycolysis induces pronounced ATP depletion in human PDAC cells (MiaPaCa-2, PANC-1) and human skin fibroblasts regardless of glucose:

Since PDAC cells exhibit such high metabolic activity (both mitochondrial and glycolytic rate), we next determine whether this would translate to bioenergetics and thus ATP production. Therefore, we tested the effect of different metabolic inhibitors on ATP depletion. We first tested the effect of glycolytic and mitochondrial inhibitors on the cellular ATP content in both human PDAC cells (MiaPaCa-2 or PANC-1) and non-cancerous human skin fibroblasts cultured separately in either 5- or 25-mM glucose. The cells were treated for 15 minutes with glycolytic or mitochondrial inhibitors followed by measurement of luminescence, which represents total cellular ATP. The glycolytic inhibitors used were iodoacetate (2mM IAA) that inhibits glyceraldehyde-3-phosphate dehydrogenase (GAPDHase)) and bromopyruvate (500μM Brpy) that inhibits hexokinase (HK). The mitochondrial inhibitors used were antimycin (0.5μM AM, that inhibits mitochondrial respiratory chain by binding to cytochrome bc-1 complex II) and oligomycin (10μM OM, that reduces the electron flow through electron transport chain by inhibiting the mitochondrial ATP-synthase), see supplement 8.6 for more details about the drugs mechanisms of action. The results suggested that inhibition of glycolysis induced pronounced ATP depletion, while inhibition of mitochondrial metabolism had minimal effect in PDAC cells as well as fibroblasts (Figure, 3.9).

Treatment with 500μM Brpy for 15-minute caused a dramatic and rapid decrease in ATP to 6 ±1% and 6 ±1% in 25- and 5-mM glucose, respectively, in MiaPaCa-2 (n=5, Figure 3.9, a), p<0.0001). Similar results were observed with PANC-1 and BJ skin fibroblast. In PANC-1, Brpy decrease ATP to 5 ±0% and 6 ±0% in 25- and 5-mM glucose, respectively (n=5, Figure 3.9, b, p<0.0001). In fibroblasts, ATP was decrease to 2 ±0% and 2.2±1% for 25- and 5-mM glucose, respectively (n=3, Figure 3.8, c, p<0.0001). Treatment with 2mM IAA caused similar results (Figure, 3.9, a, b, c).

On the other hand, treatment with 0.5μM AM for 15-minute had minimal effects on ATP depletion compared with glycolytic inhibitors; decrease ATP to 83±5% and 83 ±3% in 25- and 5-mM glucose, respectively in MiaPaCa-2 (n=5, Figure, 3.9 (a), p<0.01). 83±6% and 84±3% in 25- and 5-mM glucose, respectively in PANC-1(n=5, Figure, 3.9, b, p<0.01). In fibroblast, ATP decreased to 83±6% and 95±12% for 25- and 5-mM glucose, respectively (n=3, Figure, 3.9, c).

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Figure 3.9. Effect of metabolic inhibitors on ATP depletion in PDAC (MiaPaCa-2, PANC- 1) and BJ skin fibroblast cell lines. MiaPaCa-2 (a), PANC-1 (b) and BJ skin fibroblast (c) were treated for 15 minutes with either glycolytic inhibitor (2mM IAA; 500μM Brpy), mitochondrial inhibitor (0.5μM AM; 10μM OM) or cocktail of all four inhibitors. Luciferase based luminescence ATP assay was used to determine the total ATP for each condition. The % of control ATP was calculated by normalizing the ATP count for each condition to untreated cells. The mean±SEM is measured from at least five independence experiments. To compare between 25- and 5-mM culture condition a two-way ANOVA was performed followed by Bonferroni’s multiple comparison test. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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3.2.9. Inhibition of both glycolysis and mitochondrial metabolism caused pronounced ATP depletion in HPDE cells:

As with PDAC cells and fibroblasts, we tested the effect of different glycolytic and mitochondrial inhibitors on cytosolic ATP production in HPDE cells. Both glycolytic inhibitors IAA and Brpy together with the mitochondrial inhibitor AM all showed a similar concentration-dependent ATP depletion, whereas the mitochondrial inhibitor OM had no effect on ATP (n=4, Figure 3.9). However, when comparing ATP depletion in HPDE cells to PDAC/fibroblasts there was a lower sensitivity to glycolytic inhibitors, particularly to IAA and more sensitivity to mitochondrial inhibitors AM (Figure, 3.9; 3.10).

Treatment with 2mM IAA decreased ATP to 33±9% in HPDE compared to 19±5% in MiaPaCa-2, 17±6% in PANC-1 and 7±1% in fibroblasts. This supports our previous finding that the majority of the ATP produced in HPDE was from the mitochondria compared with cancer cells, and this makes them less sensitive to glycolytic inhibitors and more sensitive to mitochondrial inhibitors (Figure 3.6, j; 3.9; 3.10).

Figure 3.10. Effect of metabolic inhibitors on ATP depletion in pancreatic ductal cells. HPDE cells were treated for 15 minutes with either glycolytic inhibitors (2mM IAA; 500μM Brpy), mitochondrial inhibitors (0.5μM AM; 10μM OM) or cocktail of all four inhibitors. Luciferase- based luminescence ATP assay was used to determine the total ATP for each condition. The % of control ATP was calculated by normalizing the ATP count for each condition to untreated cells. The mean±SEM is measured from at least four independent experiments. To compare between 25- and 5-mM culture condition a two-way ANOVA was performed followed by Bonferroni’s multiple comparison tests. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Table 3.2. Summary of the ATP depletion studies finding:

Glycolytic inhibitor Mitochondrial inhibitor 2mM IAA 500µM Brpy 0.5µM AM 10 µMOM (%) (%) (%) (%) Glucose conc.** 25mM 5mM 25mM 5mM 25mM 5mM 25mM 5mM MiaPaCa-2 22±5 19±0 6±1 6±1 83±5 83±3 83±2 83±5 PDAC-1 20±6 19±1 5±0 6±1 83±6 84±3 79±7 88±4 BJ skin fibroblast 7±1 8±2 2±0 2±1 83±6 95±12 84±7 86±8 HPDE 33±9 24±9 72±7 104±14 *, HPDE are cultured in keratinocyte serum free media; **, conc.= concentration

3.2.10. The total cellular ATP content is higher in HPDE compared to PDAC (MiaPaCa-2) and hPSCs:

Although there appeared to be subtle differences in the sensitivity of metabolic inhibitors, specifically IAA, in the different cell types, it was not clear whether there were any differences in the basal ATP and to what concentration the ATP decreased. This was because ATP depletion was normalized to untreated control. Therefore, a 50% decrease in ATP in HPDE may be very different to a 50% decrease in MiaPaCa-2. Moreover, the concentration of IAA used may be too high and induce maximal ATP depletion. In order to further explore ATP content of each cell line together with the effect of different concertation of IAA, we performed a dose-response curve and we measured the total cellular ATP by using ATP standard curve and then normalized to SRB. We found that the resting ATP content is higher in HPDE compared to both hPSCs and PDAC (MiaPaCa-2) (Figure, 3.11). The resting ATP appears to be ~4 fold higher in the non-cancerous cells (HPDE) compared to PDAC (Figure, 3.11). This means that PDAC cells prefer glycolysis as their energy source, while HPDE utilized their mitochondria for ATP production. PDAC were more sensitive to IAA ATP depletion compared to HPDE. Following 15min treatment with 100µM IAA, the mean of intracellular ATP/SRB was 937±145 in MiaPaCa-2 which is significantly lower than HPDE where it was 1705±311. The IC50 (µM) for IAA following the treatment for 15 minutes was 129.8, 171.3 and 64.66 in MiaPaCa-2, hPSCs, and HPDE, respectively. Following 1-hour treatment was 41.7, 50.49 and 17.18 in MiaPaCa-2, hPSCs, and HPDE, respectively.

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Figure 3.11. The ATP depletion following the treatment with different concentration of IAA in PDAC, h-PSCs and HPDE cells. Cells were treated for 15 minutes (a) or 1 hour (b) with different concentration of IAA. Luciferase-based luminescence ATP assay was used to determine the total ATP for each condition. The total ATP content for all cells was calculated from the ATP standard curve were luminescent vs different concentration of ATP was used. The total ATP content was normalized to the total protein cellular content for each well-using SRB assay (Absorbance, AU). The mean±SEM of eight independent experiments. The resting ATP appears to be ~4 fold higher in the non-cancerous cells (HPDE) compared to PDAC. Therefore, the degree to which ATP is depleted with IAA might be much higher in the PDAC cells. Statistical analysis was determined using two-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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3.2.11. Inhibition of glycolysis, but not mitochondrial metabolism caused irreversible Ca2+ overload in both PDAC cells and fibroblasts:

We tested the effect of glycolytic and mitochondrial inhibitors on [Ca2+]i in the human PDAC cells and fibroblasts when cultured in either low (5mM) or high (25mM) glucose by using fura-2 fluorescence imaging. The glycolytic and mitochondrial inhibitors were perfused continuously for 20 minutes followed by 15 minutes washout with HEPES-PSS. PDAC cells were then treated with 100μM ATP (to stimulate the purinergic receptor), while fibroblasts with 100μM carbachol (to stimulate cholinergic receptors) to test for recoverability and cell viability.

The results show that treatment with glycolytic inhibitors (2mM IAA or 500μM Brpy), induced an irreversible Ca2+ overload in PDAC (MiaPaCa-2 and PANC-1) and fibroblasts cells (cultured in either 5- or 25-mM glucose), whereas treatment with mitochondrial inhibitors 0.5μM AM or 10μM OM, had no effect (Figure, 3.12; 3.13; 3.14). However, in BJ skin fibroblast cells IAA and Brpy induced significantly greater Ca2+ overload responses in 25mM glucose vs 5mM glucose (Figure, 3.14). In addition, MiaPaCa-2 cells showed glucose dependency to the Ca2+ overload responses, induced by glycolytic inhibitors, whereas in PANC-1 responses were the same regardless of glucose (Figure, 3.12; 3.13).

The Brpy induced increase in Ca2+ in MiaPaCa-2 to 350±43 nM in 5mM glucose and 178±16 nM in 25mM glucose (Figure, 3.12, n=4, p<0.0001). IAA caused increase in Ca2+ in MiaPaCa-2 to 151±26 nM in 5mM glucose and 126±20 nM in 25mM glucose (Figure, 3.12, n=4). Similar results were observed for AUC data (Figure, 3.12, n=4).

These data show that cytotoxic Ca2+ overload induced by glycolytic inhibitors is sensitive to change in glucose concentration in BJ skin fibroblast and MiaPaCa-2 cells, suggesting that these cells are more reliance in glucose compared to PANC-1 (Figure, 3.12, 3.13, 3.14).

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Figure 3.12. Effect of metabolic inhibitors on calcium overload in PDAC cells (Mia-PaCa- 2). Intracellular calcium concentration ([Ca2+]i), was measured in MiaPaCa-2 cells by using fura-2 fluorescence imaging. Panel a-e, are representative traces for each condition following the perfusion of either glycolytic inhibitors (2mM IAA (b)); 500μM Brpy (c)) or mitochondrial inhibitors (10μM OM (d), 0.5μM AM (e)) for 20 minutes, while panel (a) is the time matched control where no drug was perfused. Following the drug perfusion cells were washed and then treated with 100μM ATP to test for cell viability. Data were quantified by measuring AUC (f) 2+ maximum change in [Ca ]i (g) for 35 minute and maximum response to ATP (h). Statistical significance was determined by using one-way ANOVA followed by Tukey's multiple comparisons test. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure 3.13. Effect of metabolic inhibitors on calcium overload in PDAC cells (PANC-1). Intracellular calcium concentration ([Ca2+] i), was measured in PANC-1 cells by using fura-2 fluorescence imaging. Panel b-e, are representative traces for each condition following the perfusion of either glycolytic inhibitors (2mM IAA (b); 500μM Brpy (c)) or mitochondrial inhibitors (10μM OM (d), 0.5μM AM (e)) for 20 minutes, while panel (a) is the control where no drug was perfused. Following the drug perfusion cells were washed and then treated with 100μM ATP to test for cell viability. Responses were quantified by AUC (f), maximum change 2+ in [Ca ]i (g) for 35 minutes and maximum response to ATP (h). Statistical analysis was determined by a one-way ANOVA followed by Tukey's multiple comparisons test. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure 3.14. Effect of metabolic inhibitors on calcium overload in skin fibroblast cells. Intracellular calcium concentration ([Ca2+] i), was measured in fibroblast cells by using Fura- 2 fluorescence imaging. Panel b-e, are representative traces for each condition following the perfusion of either glycolytic inhibitors (2mM IAA (b); 500μM Brpy (c)) or mitochondrial inhibitors (10μM OM (d), 0.5μM AM (e)) for 20 minutes, while panel (a) is the control where no drug was perfused. Following the drug perfusion cells were washed and then treated with 2+ 100μM carbachol to test for cell viability. AUC (f), maximum change in [Ca ]i (g) for 35 minutes, and maximum response to carbachol (h). Statistical analysis was determined by using a one-way ANOVA followed by Tukey's multiple comparisons. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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3.2.12. HPDE cells are less sensitive than PDAC cells to glycolytic inhibitors induced Ca2+ overload:

2+ We next tested the effect of glycolytic and mitochondrial inhibitors on [Ca ]i in the HPDE cells similar to the previous experiment. However, responses to glycolytic inhibitors were much less. The results suggested that HPDE was showing a lower sensitivity to IAA 2+ compared with PDAC cells. Treatment with 2mM IAA increased [Ca ]i in HPDE cells to 56±9 nM compared to 126±20 nM in MiaPaCa-2 and 183±98 nM in PANC-1. In addition, IAA increased AUC to 65±7 µMs in HPDE cells compared to 105±32 µMs in MiaPaCa-2 and 172±62 µMs in PANC-1, (Figure 3.12, 3.13, 3.15).

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Figure 3.15. Effect of metabolic inhibitors on calcium overload in HPDE cells. Intracellular 2+ calcium concentration ([Ca ]i), was measured in HPDE by using fura-2 fluorescence imaging. Panel a-I, are representative traces for each condition following the perfusion of either glycolytic inhibitor (0.5 or 2mM IAA (b,c); 50 or 500μM Brpy (d,e)) or mitochondrial inhibitors (10 or 50μM OM (g), 0.5 or 10μM AM (h,i)) for 20 minutes. Following the drug perfusion cells were washed and then treated with 100μM ATP to test for cell viability. Data 2+ were quantified by measuring AUC (j) and maximum change in [Ca ]i (k) for 35 minute and maximum change of ATP (l). Statistical analysis was determined by a one-way ANOVA followed by Tukey's multiple comparisons test. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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2+ Table 3.3. Summary of the maximum change in [Ca ]i (nM) following the treatment with glycolytic inhibitors:

Glycolytic inhibitors 2mM IAA 500µM Brpy (Mean±SEM) (Mean±SEM) Glucose conc.** 25mM 5mM Sign. *** 25mM 5mM Sign. *** MiaPaCa-2 126±20 151±26 no 178±16 350±43 yes PANC-1 183±98 136±28 no 221±19 241±70 no BJ skin fibroblast 205±54 73±18 no 1048±282 389±302 yes HPDE* 56±9 155±10 *, HPDE are cultured in keratinocyte serum free media.; **, conc. = concentration; ***, sign. = significant difference between high and low glucose.

Table 3.4. Summary of calcium overload studies, area under the curve (AUC, µM.s) finding following the treatment with glycolytic inhibitors:

Glycolytic inhibitors 2mM IAA 500µM Brpy (Mean±SEM) (Mean±SEM) Glucose conc.** 25mM 5mM Sign.*** 25mM 5mM Sign.*** MiaPaCa-2 105±32 146±23 no 225±35 461±89 yes PANC-1 172±62 148±42 no 263±18 265±105 no BJ skin fibroblast 223±57 80±25 yes 937±126 364±167 yes HPDE* 65±7 203±18 *, HPDE are cultured in keratinocyte serum free media.; **, conc. = concentration; ***, sign. = significant difference between high and low glucose.

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3.2.13. Inhibition of glycolysis, not mitochondria produce significant increase in intracellular calcium levels in pancreatic stellate cells:

2+ We next wanted to test the effect of different metabolic inhibitors on the [Ca ]i in pancreatic stellate cells. Results show that glycolytic inhibitors (IAA and PFK-15, PFKFB3 inhibitor) produced an increase in intracellular calcium overload in hPSCs, while mitochondrial inhibitors (OM, AM) had no effect (Figure, 3.16). This suggests that hPSCs behaved similarly to MiaPaCa-2 cells in response to different metabolic inhibitors in Ca2+ overload studies.

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Figure 3.16. The effect of metabolic inhibitors on calcium overload in hPSCs. Intracellular 2+ calcium concentration ([Ca ]i), was measured in hPSCs by using fura-2 fluorescence imaging. Panel b-f, are representative traces for each condition following the perfusion of either glycolytic inhibitors (100µM IAA (b); 10μM PFK-15 (c)) or mitochondrial inhibitors (10μM OM (d), 0.5μM AM (e)) for 20 minutes, while panel (a) is the control were no drug was perfused. Following the drug perfusion cells were washed and then treated with 100μM ATP to test for cell viability. Data were quantified by measuring AUC (f), maximum change in 2+ [Ca ]i (g), and maximum response to ATP (h). Statistical analysis was determined by using a one-way ANOVA followed by Tukey's multiple comparisons test. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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3.2.14. Comparing the Effect of PFK-15 and IAA in MiaPaCa-2 vs hPSCs vs HPDE on calcium overload studies.

We next wanted to find the best glycolytic inhibitors that can selectively induce Ca2+ overload in PDAC cells, only, without or with minimal effect in normal cells. Both IAA and Brpy may give rise to non-specific effects and based on western blotting data the expression of PFKFB3 enzyme was much lower in hPSCs vs MiaPaCa-2 cells vs HPDE. Therefore, we tested the PFKFB3 inhibitor, PFK-15, as a means of selectivity inhibitory glycolysis in MiaPaCa-2 cells vs hPSCs vs HPDE. The results reveal that PFK-15 is more selective to cancer cells in inducing cytotoxic Ca2+ overload compared to other glycolytic inhibitors such as Brpy or IAA (Figure, 3.17, 3.18). The treatment with 10µM PFK-15 increase the intracellular Ca2+ by 412±103 nM in MiaPaCa-2, 121±17 nM in HPDE and 160±41 nM in hPSCs. Whereas, 100µM IAA cause 65±10 nM in MiaPaCa-2, 28±4 nM in HPDE and 110±25 nM in hPSCs.

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Figure 3.17. Effect of IAA on calcium overload in PDAC (MiaPaCa-2) vs pancreatic ductal cells (HPDE) vs hPSCs. Intracellular calcium concentration ([Ca2+] i), was measured in MiaPaCa-2 (a), hPSCs (b) and HPDE (c) cells by using fura-2 fluorescence imaging. Panel (a-c) is representative traces for each condition following the perfusion of 100 µM IAA, a glycolytic inhibitor, for 20 minutes. Following the drug perfusion cells were washed and then treated with 100μM ATP to test for cell viability. Data were quantified by measuring AUC (d), 2+ maximum change in [Ca ]i (e) for 35 minute, and maximum response to ATP (f). Statistical analysis was determined by using a one-way ANOVA followed by Tukey's multiple comparisons test. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure 3.18. Effect of PFK-15 on calcium overload in PDAC (MiaPaCa-2) vs pancreatic ductal cells (HPDE) vs hPSCs. Intracellular calcium concentration ([Ca2+] i), was measured in MiaPaCa-2 (a), hPSCs (b) and HPDE (c) cells by using fura-2 fluorescence imaging. Panel (a-c) is representative traces for each condition following the perfusion of 10 µM PFK-15, a glycolytic inhibitor, for 40 minutes. Following the drug perfusion cells were washed and then treated with 100μM ATP to test for cell viability. Data were quantified by measuring AUC (d), 2+ maximum change in [Ca ]i (e) for 35 minute, and maximum response to ATP (f). Statistical analysis was determined by using a one-way ANOVA followed by Tukey's multiple comparisons test. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Table 3.5. Summary of the maximum change in [Ca2+]i (nM) following the treatment with glycolytic inhibitors:

Glycolytic inhibitors 100µM IAA 10µM PFK-15 (Mean±SEM) (Mean±SEM) MiaPaCa-2 65±10 412±103 hPSCs 110±25 160±41 HPDE 28±4 121±17 - With IAA: (No change) hPSCs vs MiaPaCa-2; (*) hPSCs vs HPDE - With PFK-15: (*) MiaPaCa-2 vs both (hPSCs and HPDE)

Table 3.6. Summary of calcium overload studies, area under the curve (AUC, µM.s) finding following the treatment with glycolytic inhibitors:

Glycolytic inhibitors 100µM IAA 10µM PFK-15 (Mean±SEM) (Mean±SEM) MiaPaCa-2 63±12 309±77 hPSCs 97±27 151±55 HPDE 30±5 192±34 - With IAA: (No change) hPSCs vs MiaPaCa-2; (*) hPSCs vs HPDE

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3.3. DISCUSSION

Our findings revealed that PDAC (MiaPaCa-2) cells have a high glycolytic and growth rate compared to non-cancerous cells (hPSCs and HPDE) which is consistent with the hallmarks of cancer (Hanahan and Weinberg, 2011). These findings support the Warburg effect, which suggests that cancer cells undergo glycolysis as their main metabolic pathway even in the presence of oxygen because they have dysfunctional mitochondria. However, our results also showed that PDAC cells, specifically MiaPaCa-2, have high mitochondrial function according to Seahorse XFe96 analyzer data, mitochondrial membrane potential (TMRE imaging) and MitoTracker red FM imaging results. This is not consistent with the Warburg effect. However, recently there is accumulating evidence suggesting that cancer cells have functional mitochondria and that they play an essential role in tumorigenesis (Corbet and Feron 2017; Jose et al 2011; Iommarini et al., 2017; Solaini et al., 2011). It is important to note that mitochondria are important for numerous other functions in addition to bioenergetics including biosynthesis of macromolecules and regulation of cell death/ apoptosis. The increase in the mitochondrial membrane potential we observed might be important for cancer cells to prevent apoptosis, not for ATP synthesis. This because the loss of mitochondrial membrane potential triggers cytochrome c release and activation/ stabilization of the apoptosome complex important for the activation of executioner caspases 3 and 7 and the ‘’point of no return’’ of apoptosis pathway (Indran et al., 2011). Moreover, the increase we observed in mitochondrial activity might be required for amino and fatty acid biosynthesis which are essential building blocks for cancer cell proliferation (Jose et al 2011). All this proposed that cancer cells have a high glycolytic phenotype to support their growth and as a source for their energy and they still utilized their mitochondria to prevent apoptosis and to support their biosynthesis demand.

The present study supports previous studies from our lab showing that glycolytic ATP 2+ is critical for the regulation of [Ca ]i homeostasis in PDAC cells by fueling PMCA (James et al, 2013). However, the present study had also investigated the effect of culturing PDAC cells in either 5- or 25-mM glucose. Our results show that the behavior of PANC-1 cells was unaffected when cultured in different glucose concentration. Inhibition of glycolysis in both media caused significant ATP depletion and irreversible calcium overload in a similar manner, 2+ while inhibition of mitochondrial metabolism had minimal effect on ATP depletion and [Ca ]i. In contrast, in MiaPaCa-2 cells, although the ATP depletion results were similar for both media the calcium overload studies were markedly different. The Ca2+ overload response induced by

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glycolytic inhibitors was much lower when cells were cultured in high glucose (25 mM) vs low glucose (5 mM), suggesting that MiaPaCa-2 cells are more sensitive to changes in glucose which imply by definition that these cells exhibit a greater reliance on glycolysis as a source of ATP compared to PANC-1 cells.

Also, the present study is the first study to show that skin fibroblasts and hPSCs exhibit similar response to cancer cells in both ATP and calcium overload studies following the treatment with metabolic inhibitors. Although fibroblast and stellate cells have a slow proliferation rate compared to PDAC cells, inhibition of glycolysis in both cells was associated with ATP depletion and irreversible calcium overload, while inhibition of mitochondrial metabolism had no effect. These results were very surprising and cast doubt on the original hypothesis that the glycolytic ATP regulation of the PMCA is specific for cancer cells and is not critical to normal cells. However, targeting glycolytic ATP supply to PMCA in both fibroblast and hPSCs could be a better option for the treatment of cancer cell, because they have more stable genotype make them more suitable for targeted therapy.

Moreover, our results observed that in HPDE cells glycolytic inhibitors, IAA and PFK- 2+ 15 but not Brpy, caused a significant reduction in [Ca ]i overload and ATP depletion compared to PDAC cells. We know that IAA and PFK-15 inhibit glycolysis more selectively compared with Brpy. IAA inhibits glyceraldehyde-3-phosphate dehydrogenase (GAPDHase), which is the enzyme involved in the fifth step of glycolysis. PFK-15 inhibits 6-phosphofructo-2- kinase/fructose-2,6-biphosphatase 3 (PFKFB3), which is involved in the third step of glycolysis, while Brpy inhibits hexokinase (HK-2) enzyme which converts glucose to glucose- 6-phosphate and represents the first step of glycolysis (see supplement 8.6. for more details). However, Brpy is also reported to inhibit mitochondria and induce reactive oxygen species (ROS) production (Ihrlund et al, 2008). This suggests that the Brpy induced Ca2+overload may be due to a mixture of nonspecific effects. In addition, according to the glycolytic enzyme expression studies we performed, we found that HK-II was expressed equally in MiaPaCa-2, hPSCs, and HPDE. This explains the pronounced increase in intracellular calcium following the treatment with Brpy in all cell types.

Importantly, this study found that HPDE cells exhibit less sensitivity to glycolytic inhibitors, such as; IAA and PFK-15, and higher sensitivity to a mitochondrial inhibitor, such as; AM, on ATP depletion and Ca2+ overload, compared with PDAC cells. Our findings support the fact that non-cancer cells such as HPDE have a low proliferation rate and are less glycolytic.

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They rely mainly on their mitochondrial respiration as a source for their energy. For this reason, they are less sensitive to glycolytic inhibitors. They are acting more like α-ketoisocaproate (KIC) cells which are modified PDAC cells that show a shift in their metabolic phenotype towards mitochondrial metabolism (James et al, 2015). These results showed that KIC cells exhibited significantly greater ATP depletion following the treatment with mitochondrial inhibitor and that glycolytic inhibitors were less effective in depleting ATP (James et al, 2015). 2+ They also observed that KIC cells exhibited a dramatic reduction in IAA-induced [Ca ]i overload and inhibition of the PMCA activity (James et al, 2015). For all of this, we can conclude that HPDE cells exhibit a greater reliance on mitochondria as a source of ATP compared to the highly glycolytic PDAC cells like the KIC-cultured PDAC cells. This support 2+ our hypothesis that glycolytic ATP supply to the PMCA is critical for [Ca ]i homeostasis in PDAC cells with no or minimal effect on normal cells such as HPDE. Targeting this pathway could be an effective way for pancreatic cancer treatment because it shows selectivity to cancer cells.

3.4. SUMMARY

PDAC cells have a high metabolic and growth rate compared to a normal cell to cope with both its energy and biosynthesis demand. They are highly glycolytic as explained by the Warburg effect and glycolysis is the main source for their ATP production. However, their mitochondrial function is also essential to support their biosynthesis demand and to prevent apoptosis rather than as an energy supply. In our study, we have determined if whether glycolysis or mitochondrial respiration is critical for fueling the PMCA in both cancerous and non-cancerous cells. Our results have revealed that inhibition of glycolysis cause pronounced 2+ ATP depletion and increase in [Ca ]i in PDAC cells with less sensitivity in non-cancerous cells (HPDE). This because inhibition of glycolysis can inhibit ATP supply to PMCA which is 2+ critical to maintaining [Ca ]i homeostasis in PDAC cells but not in non-cancer cells which rely mainly on its mitochondrial respiration.

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CHAPTER 4

4. THE CANCER-STROMAL INTERACTION IN PANCREATIC CANCER (THE METBAOLIC REGULATION OF CYTOSOLYIC CALCIUM)

4.1. INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and deadly forms of cancer. The metabolic shift of PDAC cells towards glycolysis (the Warburg effect), which is one of the hallmarks of cancer, is critical for cancer cell survival. Our previous studies have shown that inhibition of glycolysis, but not mitochondria, induced ATP depletion, inhibition of the ATP-driven Ca2+ pump (PMCA), cytotoxic Ca2+ overload and necrosis in PDAC cells (MiaPaCa cells). However, one of the main characteristics of PDAC is that it has an abundant stroma. The interaction between stroma and cancer cells plays a critical role in cancer progression, survival, metastasis and even resistance to therapy. Recently, there have been several lines of evidence that the interaction between stromal and cancer cells plays a critical role in directing cancer cell metabolism (Sousa et al., 2016; Bonuccelli et al., 2010; Capparelli et al., 2012; Martinez-Outschoorn et al., 2014; Mercier et al., 2008; Pavlides et al., 2009; Salem et al., 2012). These studies cast doubt on the Warburg effect and introduce a new way of understanding cancer cell metabolism in the context of its microenvironment. They found that there is a metabolic cross-talk between tumor cells with their stromal cells, where cancer cells can induce autophagy in neighboring cells to release metabolite that can be utilized by cancer cells to drive their mitochondria function, and this is called the reverse Warburg effect. In this project, we will investigate the tumor-stromal interactions with a focus on 2+ pancreatic cancer cell metabolism and metabolic regulation of [Ca ]i homeostasis by co- culturing PDAC cells (i.e. MiaPaCa-2) with pancreatic stellate cells (hPSCs). We propose that human pancreatic stellate cells (hPSCs) influence PDAC cell metabolism and thus the 2+ regulation of [Ca ]i homeostasis in these cells. This will be achieved by co-culturing PDAC cells (i.e. MiaPaCa-2) with hPSCs and testing the relative effect of different metabolic 2+ inhibitors on cancer hallmarks such as metabolic phenotype and growth, and on [Ca ]i homeostasis for both PDAC cells and hPSCs in co-culture. Responses of cells in co-culture will be compared to singly cultured cells.

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4.1.1. AIM

To investigate the relative effect of different metabolic inhibitors on cancer hallmarks 2+ such as metabolic phenotype and growth, and on [Ca ]i homeostasis for both PDAC cells and hPSCs in co-culture. This will be achieved by performing direct (both cells grow together in the same dish) or in-direct (conditioned media or trans-well plate) co-culturing of PDAC cells (i.e. MiaPaCa-2) with hPSCs depending on the type of the study (see supplement 8.20 for more information about different co-culture method used). Responses of PDAC and hPSCs cells in co-culture will be compared to singly cultured cells. For the direct co-culture, we will generate MiaPaCa-2 cells that are stably expressing red fluorescent protein (RFP). The RFP (+) MiaPaCa-2 cells can be easily distinguished from hPSCs when cultured in the same dish which allows us to study the behaviour of these two populations of cells in direct co-culture.

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4.2. RESULTS

4.2.1. The expression of α-smooth muscle actin is high in hPSCs indicating that they are in their activated phenotype.

Evidence suggests that the interaction between cancer cells and human pancreatic stellate cells (hPSCs) is bi-directional (Neoptolemos et al., 2010). Cancer cells can release growth factors such as TGF-β, FGF-2, and PDGF that can activate hPSCs to its myofibroblast phenotype that can promote tumorigenesis. Activated hPSCs have a star-like appearance and express alpha-smooth muscle actin (α-SMA) (Apte et al.,2013). Before we study the effect of co-culturing PDAC with hPSCs on cancer hallmark responses, it is necessary to determine whether the hPSCs are activated. This was achieved by studying the expression of α-SMA by immunofluorescent (a, Figure 4.1) or western blotting (b & c, Figure 4.1) in hPSCs. HPDE and MiaPaCa-2 cells were used as a negative control. The expression of α-SMA in hPSCs was significantly highest compared to MiaPaCa-2 and HPDE (Figure 4.1). This suggests that the cells are activated and likely to have a tumor-promoting effect when co-cultured with MiaPaCa-2 cells.

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Figure. 4.1. The protein expression of alpha-smooth muscle actin in hPSCs, MiaPaCa-2 and HPDE. (a) fluorescence microscopy images of different cell lines showing the cellular distribution of alpha-smooth muscle actin (α-SMA) (green), the predominate actin isoform within the vascular smooth muscle cells. The nuclei of cells were counter stained with DAPI (blue). All cells were imaged under identical exposure time and microscope settings, scale bar 25µM. (b & c) α-SMA protein levels were determined in each cell line by western blotting and densitometry values were also normalized to the levels of alpha-tubulin loading control. The relative α-SMA measured represent the mean of three independent experiment. Statistically significant was determined using one-way ANOVA followed by Tukey's multiple comparisons test. *, p<0.05.

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4.2.2. hPSCs conditioned media increases mitochondrial metabolism/respiration in MiaPaCa-2 cells.

To investigate whether PDAC cell mitochondrial metabolism was affected by hPSC cells, PDAC cells were cultured with conditioned media (CM) collected from hPSC cell for 48 hours and then numerous metabolic parameters were assessed using Mito stress test with Seahorse XF analyzer, which simultaneously measures OCR and ECAR (Figure 4.2). The opposite experiment was also performed in which hPSCs cells were cultured with MiaPaCa-2 conditioned media (CM) (Figure 4.3). Results show that culturing MiaPaCa-2 cells with CM collected from hPSCs have caused a significant increase in mitochondrial activity of cancer cells, while there was no change in hPSCs cell response in similar conditions. This suggesting that there was something unique about hPSCs conditioned media that increased MiaPaCa-2 mitochondrial function.

There was a significant increase in the maximal respiration and spare respiratory capacity of MiaPaCa-2 in CM (Figure 4.2.). In normal media, the maximal respiration was 72±2 pmol/min/AU and the spare respiration was 32±1 pmol/min/AU, while in CM, were both significantly increased to 90±3 pmol/min/AU and 43±2 pmol/min/AU, respectively (Figure 4.2.). In addition, MiaPaCa-2 cells in CM exhibited more metabolic flexibility compared to normal media. According to the metabolic potential results, MiaPaCa-2 cells in CM have a greater ability to utilized both mitochondria and glycolysis to meet their energy demand under stress compared to normal media (Figure 4.2, h).

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Figure. 4.2. Effect of hPSCs conditioned media on the metabolic phenotype of PDAC cells (MiaPaCa-2). MiaPaCa-2 cells were cultured for 48hr in a conditioned media collected from hPSCs or control, DMEM media. The cells were analyzed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1µM), carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP, 0.5µM) and a combination of rotenone/antimycin (RT/AM, 0.5µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) by using the Sulforhodamine-B (SRB) assay. (b) cartoon (adapted from Seahorse Bioscience) represents a summary of the mito-stress test and the different metabolic parameters measured by the XFe96 extracellular flux analyzer. (d-j) the mean± SEM of each metabolic parameter was calculated from at least four independent experiments and all measurement were normalized to the protein

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content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

Figure. 4.3. Effect of MiaPaCa-2 conditioned media on the metabolic phenotype of hPSCs cells. hPSCs cells were cultured for 48hr in a conditioned media collected from MiaPaCa-2 or control, DMEM media. The cells were analyzed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP, 1 µM) and a combination of rotenone/antimycin (RT/AM, 0.5µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) by using the Sulforhodamine-B (SRB) assay. (b) cartoon (adapted from Seahorse Bioscience) represent a summary of the mito-

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stress test and the different metabolic parameters measured by the XFe96 extracellular flux analyzer. (d-j) the mean± SEM of each metabolic parameter was calculated from at least three independent experiments and all measurement were normalized to the protein content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

4.2.3. hPSCs conditioned media increases mitochondrial membrane potential in MiaPaCa-2 cells.

To investigate whether PDAC cell mitochondrial membrane potential and mitochondrial mass were affected by hPSC cells, PDAC cells were cultured with conditioned media collected from hPSC cell and after 48 hours we performed TMRE imaging, MitoTracker red FM and TOM-20 imaging (Figure 4.4). The opposite experiment was also performed in which hPSCs cells were cultured with MiaPaCa-2 conditioned media (Figure 4.5). Moreover, to test for the mitochondrial mass of cells in co-culture, the cells were grown in trans-well dish were MiaPaCa-2 cells were grown on the bottom and hPSCs on the top (or vice versa) and after 24, 48 and 72hr the cells in the bottom were collected for western blotting. Results show that culturing MiaPaCa-2 cells with CM collected from hPSCs cause a significant increased in mitochondrial membrane potential of cancer cells, while there was no change in the mitochondrial mass was observed. However, for hPSCs, there was no change in both mitochondrial mass and function in similar conditions. This suggesting that there was something unique about hPSCs conditioned media that increased MiaPaCa-2 mitochondrial membrane potential and such increase was not correlated with the increased in the mitochondrial mass.

According to TMRE imaging experiments, the mitochondrial membrane potential of MiaPaCa-2 cells in CM was significantly higher (229±7 grey levels; Figure 4.4) than in normal media (158±8 grey levels; Figure 4.4.). This means that there is almost 1.5x fold increase in the mitochondrial membrane potential of cancer cells in hPSCs CM. The fluorescence imaging of MitoTracker Red FM and TOM-20 immunofluorescence and the western blotting of TOM- 20 protein expression further support that the mitochondrial function of MiaPaCa-2 cells rather than the mitochondrial mass was increased when co-cultured with hPSCs cells (Figure 4.4, c- e).

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Figure. 4.4. hPSCs conditioned media increases MiaPaCa-2 mitochondrial membrane potential without effecting mitochondrial mass. (a & b) MiaPaCa-2 cells were cultured for 48hr in conditioned media collected from hPSCs or a control. (a & b) The mitochondrial membrane potential was assessed by using TMRE dye. Cells were loaded with TMRE and imaged to measure the intensity of the dye before and after the addition of carbonyl cyanide- 3-chlorophenylhydrazone (CCCP). The loss of the TMRE fluorescence is considered as the relative measure of mitochondrial membrane potential in different types of cells. (b) Th mean ±SEM is calculated from 20-60 cells for at least four independent experiments. (c) For the immunofluorescence studies MiaPaCa-2 cells were cultured for 48hr in a conditioned media collected from hPSCs or control. (c) Confocal microscope images of different condition showing the cellular distribution of MitoTracker red FM (red), a mitochondrial specific dye,

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and TOM-20 (green), a mitochondrial import receptor subunit. The nuclei of cells were counter stained with DAPI (blue). All samples were imaged under identical exposure time and microscope setting by using confocal microscope, scale bar 25µM. (d & e) For western blotting the MiaPaCa-2 cells were co-cultured with hPSCs in trans-well plate, then after 24, 48 or 72hr the cell lysate was collected. TOM-20 protein levels were determined by western blotting and densitometry values were normalized to the levels of alpha-tubulin loading control. The relative TOM-20 measured represents the mean of three independent experiments. Statistical significance was assessed using a one-ways ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure. 4.5. MiaPaCa-2 conditioned media has no effect on the hPSCs mitochondrial membrane potential and mitochondrial mass. (a & b) hPSCs cells were cultured for 48hr in conditioned media collected from MiaPaCa-2 cells or a control. (a & b) The mitochondrial membrane potential was assessed by using TMRE dye. Cells were loaded with TMRE and imaged to measure the intensity of the dye before and after the addition of carbonyl cyanide- 3-chlorophenylhydrazone (CCCP). The loss of the TMRE fluorescence is considered as the relative measure of mitochondrial membrane potential in different types of cells. (b) Th mean ±SEM is calculated from 20-60 cells for four independent experiments. (c) For the immunofluorescence studies hPSCs cells were cultured for 48hr in a conditioned media

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collected from MiaPaCa-2 or control. (c) Confocal microscope images of different condition showing the cellular distribution of MitoTracker red FM (red), a mitochondrial specific dye, and TOM-20 (green), a mitochondrial import receptor subunit. The nuclei of cells were counter stained with DAPI (blue). All samples were imaged under identical exposure time and microscope setting by using confocal microscope, scale bar 25µM. (d & e) For Western blotting the hPSCs cells were co-cultured with MiaPaCa-2 in trans-well plate, then after 24, 48 or 72hr the cell lysate was collected. (d & e) TOM-20 protein levels were determined by western blotting and densitometry values were normalized to the levels of alpha-tubulin loading control. The relative TOM-20 measured represents the mean of three independent experiments. Statistical significance was assessed using a one-ways ANOVA. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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4.2.4. hPSCs conditioned media reduces the glycolytic phenotype in MiaPaCa-2 cells.

To investigate whether PDAC cell glycolytic phenotype was affected by hPSC cells, PDAC cells were cultured with conditioned media collected from hPSC cell for 48 hours and then multiple glycolytic parameters were assessed using Glyco stress test with Seahorse XF analyzer, which measures ECAR following the sequential injection of glucose to give basal glycolysis, OM to inhibit the mitochondria and make the total dependency to glycolysis and 2- DG that will shut down glycolysis (Figure 4.6). The reverse experiment was also performed in which hPSCs cells were cultured with MiaPaCa-2 conditioned media (Figure 4.7). Results show that hPSCs CM caused a significant decreased in the glycolytic function of MiaPaCa-2 cell, while MiaPaCa-2 CM had no effect on the glycolytic phenotype of hPSCs.

hPSCs CM caused a significant reduction in MiaPaCa-2 cells glycolysis (44±4 mpH/min/AU) compared to normal media (61±4 mpH/min/AU) and a significant reduction in their glycolytic capacity (51±1 mpH/min/AU) compared to normal media (62±2 mpH/min/AU) (Figure 4.6).

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Figure. 4.6. The Effect of hPSCs conditioned media on the glycolytic function of PDAC (MiaPaCa-2) cells. MiaPaCa-2 cells were cultured for 48hr with conditioned media collected from hPSCs or control, normal DMEM media. The cells were then analyzed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameter measured during the Glyco-stress test adapted from Seahorse bioscience. (c) Mean ±SEM of at least three independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure. 4.7. The Effect of MiaPaCa-2 conditioned media on the glycolytic function of hPSCs. hPSCs cells were cultured for 48hr with conditioned media collected from MiaPaCa- 2 cells or control, normal DMEM media. The cells were then analysed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameter measured during the Glyco-stress test adapted from Seahorse bioscience. (c) represent the Mean ±SEM of at least three independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA followed by Tukey's multiple comparisons test. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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4.2.5. Direct co-culture of MiaPaCa-2 with hPSCs has similar effect on the metabolic phenotype of MiaPaCa-2 cells.

In the previous studies, the effect of conditioned media collected from one cell type was added to the other cell type and changes in mitochondrial and glycolytic function were assessed as an in-direct measure of both cells in co-culture. However, the depletion of numerous nutrients in the conditioned media together with the accumulation of waste products or metabolites may be confounding factors that interfere with the results. For this reason, direct co-culture of both cells together will circumvent some of these issues and ensures a more intimate physical and functional interaction of these cells that better recapitulates the tumour microenvironment. We have successfully generated stable transfected RFP (+) expressing MiaPaCa-2 cell line that can be grown together with hPSCs that enables the identification of each cell type in co-culture. This allows several experiments to be performed using flow cytometry, such as assessing the mitochondrial function with MitoTracker green (MTG) and glucose uptake using a fluorescent d-glucose analog 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) amino]-2-deoxy-d-glucose (2-NBDG). MTG is a fluorescence probe used to measure the mitochondrial function it accumulates in the mitochondria by covalently binding to mitochondrial protein by reacting with a free thiol group. 2-NBDG is a florescence probe used to measure the glucose uptake.

For both experiments, the RFP (+)-MiaPaCa-2 and hPSCs were cultured in the same dish in a 1:5 ratio in favour of hPSCs. After 48hr of co-culture, the cells were treated with MTG or 2-NBDG, then the cells were collected for flow cytometry sorting. The red cells represent the RFP (+)-MiaPaCa-2 and hPSCs are the RFP (-) negative population. Both MTG and 2- NBDG once taken up by the cells will give a green fluorescence.

The direct co-culture was showing similar results that were observed in the in-direct co-culture where the mitochondrial function of MiaPaCa-2 cells in co-culture was increased together with decreased in glycolysis, while no change in hPSCs metabolic phenotype was observed. The mitochondrial function of MiaPaCa-2 (MTG +ve /RFP +ve population) was significantly increased by 37% in co-culture compared to singly cultured MiaPaCa-2 cells, while, no change was observed in hPSCs cells (MTG +ve /RFP -ve population) in similar conditions (Figure 4.8). Moreover, following 48hr of co-culture, the glycolytic function of MiaPaCa-2 cells (2-NBDG +ve/ RFP +ve population) was significantly decreased by 26 %,

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compared to singly cultured MiaPaCa-2 cells, while no change in the hPSCs cells (2-NBDG +ve/ RFP -ve population) was observed in similar condition (Figure 4.9).

We next wanted to assess the expression of Glut-1 in both cell type in co-culture. Since our previous results showed that MiaPaCa-2 cell glycolysis was reduced in co-culture, we wanted to determine whether this was due to reduced expression of Glut-1 transporter for these cells. To examine that we co-cultured RFP (+) MiaPaCa-2 cells together with hPSCs and after 48hr we performed immunofluorescent study for Glut-1. Unfortunately, the fluorescence images were difficult to analyze because MiaPaCa-2 cells were clustering together in the co- culture samples (Figure 4.10, e-f; see supplement 8.12. for more information). Moreover, we performed western blotting to study the Glut-1 expression in co-culture. Cells were grown in trans-well dish and after 24, 48 or 72hr of co-culture the cell lysates were collected (Figure 2.7.). The results show that Glut-1 transporter expression of both cell types in co-culture did not significantly change compared to cells cultured singly (Figure 4.10, a-d).

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Figure. 4.8. Changes in mitochondrial functions in PDAC (MiaPaCa-2) and hPSCs cells in co-culture: MiaPaCa-2-RFP were co-cultured with hPSCs cells for 48hr, then cells were incubated with MitoTracker green (MTG)/ florescence probe to assess mitochondrial function. Cells were trypsinized and subjected to flow cytometry (FACS) analysis where red cells in co- culture (MiaPaCa-2-RFP) were physically separated from non-florescent hPSCs cells. The intensity of MTG (green) was measured in each cell population. (a-f) are FACS traces of MTG (530/30, Green) vs (610/20, Red) for either singly or co-cultured cells. (g) the mean±SEM was calculated from at least three independent experiments. (h) cartoon summary for the steps of the experiment. Statistical significant was determined by one-way ANOVA followed by Tukey’s multiple comparisons test. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure. 4.9. Changes in glucose uptake of PDAC (MiaPaCa-2) and hPSCs in co-culture: MiaPaCa-2-RFP(+) were co-cultured with hPSCs cells for 48hr, then cells were incubated with fluorescent d-glucose analog 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) amino]-2-deoxy-d- glucose (2-NBDG)/ florescence probe to measure the glucose uptake. Cells where trypsinized and subjected to flow cytometry (FACS) analysis were red cells in co-culture (MiaPaCa-2- RFP) were physically separated from non-flourecent hPSCs cells. The intensity of 2-NBDG (green) was measured in each cell population. (a-g) are FACS traces of 2-NBDG positive cells (530/30, Green) vs (610/20, Red) for either singly or co-cultured cells. (h) The mean±SEM was calculated from at least three independent experiments. (i) cartoon summary for the steps of the experiment. Statistical significant was determined by one-way ANOVA followed by Tukey’s multiple comparisons test. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure. 4.10. The expression of glucose transporter for PDAC (MiaPaCa-2) and hPSCs in co-culture. (a-d) for western blotting both cells were grown in trans-well dish and cell lysate was collected after 24, 48 or 72hr of co-culture. Western blots showing the expression of glucose transporter-1 (Glut-1) in whole cell lysate of MiaPaCa-2 (a & b) and hPSCs (c & d) in co-culture; 10 µg was loaded for each sample. The relative expression of the enzyme is the mean±SEM of four independent experiments. All data were normalized to the expression of cyclophilin A as a control. (e & f) Fluorescence microscope images of direct co-culture of MiaPaCa-2 with hPSCs showing the cellular distribution of glucose transporter-1 (Glut-1) (green). The MiaPaCa-2 cells were stably transfected with red fluorescent protein prior to co- culturing with h-PSC cells, so that they can be identified in the imaging. The nuclei of cells (blue) were counter stained with DAPI. All cells were imaged under identical exposure time and microscope setting, scale bar 25µM. One-way ANOVA was used for statistical analysis. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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4.2.6. The expression of key glycolytic enzymes for MiaPaCa-2 and hPSCs cells in co- culture.

To study the influence of co-culturing PDAC with hPSCs cells on key glycolytic enzymes, both cells were grown in trans-well dish. After 24, 48 or 72hr the cells lysate was collected for western blotting. No change was found in the expression of key glycolytic enzymes, such as HK-II, PFKFB3, PKM2, and LDHA, in both cells in co-culture (Figure 4.11; Figure 4.12).

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Figure. 4.11. The expression of different glycolytic enzymes of PDAC (MiaPaCa-2) in co- culture. MiaPaCa-2 cells were grown with hPSCs by using trans-well dish (i) and cell lysates were collected 24, 48, or 72 hr of co-culture. Western blots showing the expression of hexokinase-II (HX-II), 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase3 (PKFB3), Pyruvate kinase muscle isozyme M2 (PKM2) and Lactate dehydrogenase A (LDHA) in whole cell lysate of co-cultured MiaPaCa-2, (a,b) HK-II, (c,d) PFKFB3, (e,f) PKM2, (g,h) LDHA. 10 µg was loaded for each sample. The relative expression of the enzyme is the mean±SEM of four independent experiments. All data were normalized to the expression of control (B-actin for HK-II and PFKFB3; cyclophilin-A for PKM2 and LDHA). (i) cartoon represent the trans- well dish used for co-culture. One-way ANOVA followed with Tukey’s multiple comparisons test was used for statistical analysis. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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Figure. 4.12. The expression of different glycolytic enzymes of hPSCs in co-culture. hPSCs cells were grown with MiaPaCa-2 by using trans-well dish (i) and cell lysates were collected 24, 48, or 72 hr of co-culture. Western blots showing the expression of hexokinase-II (HX-II), 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase3 (PKFB3), Pyruvate kinase muscle isozyme M2 (PKM2) and Lactate dehydrogenase A (LDHA) in whole cell lysate of co-cultured hPSCs, (a, b) HK-II, (c,d) PFKFB3, (e,f) PKM2, (g,h) LDHA. 10 µg was loaded for each sample. The relative expression of the enzyme is the mean±SEM of four independent experiments. All data were normalized to the expression of control (B-actin for HK-II and PFKFB3; cyclophilin-A for PKM2 and LDHA). (i) cartoon represent the trans-well dish used for co-culture. One-way ANOVA followed with Tukey’s multiple comparisons test was used for statistical analysis. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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4.2.7. No change in both calcium overload and ATP depletion response of MiaPaCa-2 cells in co-culture following the treatment with different metabolic inhibitors.

So far, our results from conditioned media experiments and direct co-culture experiment suggest that hPSCs cells increase PDAC mitochondrial function and decrease PDAC glycolytic metabolism. We next wanted to determine whether this affected PDAC cell bioenergetics; specifically, the glycolytic ATP supply to the PMCA and thus calcium homeostasis. We therefore, tested the effects of glycolytic vs mitochondrial inhibitors on 2+ resting [Ca ]i of RFP (+) MiaPaCa-2 cells in direct co-culture with hPSCs and compared the response to singly cultured MiaPaCa-2 cells. This was made possible using RFP (+) MiaPaCa- 2 cells that are spectrally separated from fura-2 fluorescence that enable identification of RFP (+) cells (MiaPaCa-2) from RFP (-) cells (hPSCs) in the same field of view (Figure 4.13, a).

We co-cultured RFP (+) MiaPaCa-2 cells together with hPSCs and after at least 72hr, calcium imaging was performed by using fura-2 dye. Treatment of MiaPaCa-2 cells in co- 2+ culture with glycolytic inhibitors, such as IAA and PFK-15, caused a pronounced [Ca ]i overload, while mitochondrial inhibitors such as AM and OM had no effect. These responses were very similar to singly culture MiaPaCa-2 cells (Figure 4.13). Treatment with 10 µM PFK- 2+ 15 caused an increase in [Ca ]i to 300±134 nM in co-cultured MiaPaCa-2 and 412±252 nM in singly cultured MiaPaCa-2 cells (Figure 4.13). AM caused 27±7 nM increase in intracellular calcium in co-culture MiaPaCa-2 cell and 21±8 nM in singly culture cells (Figure 4.13). In addition, glycolytic inhibitors (IAA) caused pronounced ATP depletion in both MiaPaCa-2 and hPSCs cell in co-culture, while no change was observed with mitochondrial inhibitors (AM). The response of these cells in co-culture was identical to singly cultured cells (Figure 4.13; 4.14). These data suggest that despite the metabolic changes in MiaPaCa-2 cell in co-culture with hPSCs cells, in term of biogenetics the glycolytic ATP supply to the PMCA remains 2+ critical for maintaining [Ca ]i homeostasis similar to singly cultured MiaPaCa-2 cells.

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Figure. 4.13. Effect of metabolic inhibitors on calcium overload and ATP depletion of singly and co-cultured PDAC cells (Mia-PaCa-2). Panel a, MiaPaCa-2 cell were stably transfected with red fluorescent protein (RFP) prior to co-culturing with h-PSC cells, so that 2+ they can be identified in co-culture during the fura-2 imaging of [Ca ]i. Panel b-e are representative traces for each condition following the perfusion of either glycolytic inhibitor (100 µM IAA (b)); 10 μM PFK-15 (c)) or mitochondrial inhibitor (10μM OM (d), 0.5μM AM (e)). Following the drug perfusion cells were washed and then treated with 100μM ATP to test 2+ cell viability. The AUC (f) and maximum change in [Ca ]i (g) for 35 or 55 minute was calculated to assess the response. For ATP depletion studies MiaPaCa-2 cells were co-cultured for 48hr with conditioned media collected from hPSCs or control. Then cells were treated for 15 minutes with either glycolytic inhibitor (100 µM IAA), mitochondrial inhibitor (0.5μM AM) or control (h). Luciferase based luminescence ATP assay was used to determine the total ATP depletion for each condition. The % of control ATP was calculated by normalizing the ATP luciferases luminesce for each condition to untreated cells. The mean±SEM is measured from at least three independence experiments. (i) A glucometer was used to measure the glucose concentration in the conditioned media collected from both MiaPaCa-2 and hPSCs cells. One- way ANOVA followed with Tukey's multiple comparisons test was used for statistical analysis. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure. 4.14. Effect of metabolic inhibitors on ATP depletion in co-cultured hPSCs. (a) hPSCs were co-cultured for 48hr with conditioned media collected from MiaPaCa-2 or control, DMEM media. Cells were then treated for 15 minutes with either glycolytic inhibitor (100 µM IAA), or mitochondrial inhibitor (0.5μM AM). Luciferase based luminescence ATP assay was used to determine the total ATP for each condition. The % of control ATP was calculated by normalizing the ATP luciferases luminesce for each condition to untreated cells. The mean±SEM is measured from at least three independence experiments. (b) A glucometer was used to measure the glucose concentration in the conditioned media collected from both MiaPaCa-2 and hPSCs cells. One-way ANOVA followed with Tukey's multiple comparisons test was used for statistical analysis. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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4.2.8. Co-culturing MiaPaCa-2 with hPSCs hasn’t change the growth rate of both cells.

To test the effect of co-culturing PDAC cells with hPSCs on the growth rate of both cells, we cultured each cell type with conditioned media collected from the other cell type and we performed direct co-culture of both cells in the same dish, also. Our indirect co-culture results show a decrease in the growth rate of the PDAC cell when cultured in hPSCs conditioned media (CM), while no change in hPSCs growth was observed in the same conditions (Figure 4.15). However, our direct co-culture results show, no change in the growth rate of both MiaPaCa-2 and hPSCs in co-culture (Figure 4.16; Figure 4.17).

For the CM experiment, there were many confounding factors that could interfere with the results, such as the depletion of glucose in the CM, therefore, we tried to perform the experiment in low and high glucose media. Growing of these cells in low or high glucose hasn’t changed the reduction in the growth rate of PDAC cells in hPSCs CM (Figure 4.15). In addition, because the common serum we used (FBS) has high albumin that can bind with different metabolite or growth factors this may effectively buffer any released factor that may influence the growth of MiaPaCa-2 cells, therefore we used low albumin serum (Nu serum) to test this. However, using the Nu serum had no effect and there was a similar reduction in growth rate of PDAC cells in hPSCs CM (Figure 4.15).

As described previously, the CM experiment has many limitations, for this reason, we performed direct co-culture where we grow RFP (+) MiaPaCa-2 cells together with hPSCs in the same dish. Then after 72 hours of co-culture, both the RFP (+) cells population, MiaPaCa- 2, and RFP (-) cells population, hPSCs, were counted by FACS. The results were showing no change in the growth rate of both MiaPaCa-2 and hPSCs cells in co-culture (Figure 4.16). In addition, we have repeated the direct co-culture experiment, but we used BrdU-FITC to measure the cell proliferation rate of both cells in co-culture. RFP (+) MiaPaCa-2 and hPSCs were growing together in the same dish for 48hr then BrdU-FITC, which incorporated into newly proliferating cells to give green florescence, was added after that the cells were collected for FACS sorting. The RFP +ve/ BrdU +ve and RFP -ve/BrdU +ve population of cells in co- culture were compared to singly culture. The Results were similar to our previous finding, there was no change in the growth rate of both cells in co-culture (Figure 4.17).

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Figure. 4.15. The growth rate of both MiaPaCa-2 and hPSCs cells in co-culture. PDAC (MiaPaCa-2) (a, c, e & f) or hPSCs cells (b & d). For (a& b) the cells were cultured in conditioned media (CM) with FBS (fetal bovine serum). For (c & d) the cells were cultured in CM with Nu-serum (substitutions for FBS with low albumin). For (f) the cells were cultured in low glucose (5mM) serum free CM. The cells were treated with pure CM or CM supplemented with fresh media (CM 1:1). The sulforhodamine B assay which estimate the protein content (absorbance unit, AU) was performed to assess cell proliferation rate of the cells in each cultured condition. The assay was conducted daily for 5 days. To compare between the growth rate of MiaPaCa-2 and hPSCs cell in different condition a two-way ANOVA followed by Bonferroni's multiple comparisons test was used. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure. 4.16. Quantitively assessing the growth of PDAC (MiaPaCa-2) and hPSCs in direct co-culture. MiaPaCa-2-RFP cells were co-cultured with hPSCs for three days then cells were tryptinized and collected for FACS analysis where red cells in co-culture (MiaPaCa-2- RFP) are spectrally separated from non-fluorescent hPSCs cells. The number of red and non- red (unlabelled cells) were counted by FACS. (f) no change in the growth rate of MiaPaCa- RFP or hPSCs after three days of co-culture. The mean±SEM is calculated from at least three independent experiments. (g) cartoon summarized the steps for the experiment. For data analysis one-way ANOVA was used followed by Tukey's multiple comparisons test.. SSC (Side SCatter) parameter used to identify the granularity of the cells in flow cytometry. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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Figure. 4.17. Cell proliferation studies of direct co-culture of PDAC (MiaPaCa-2) and hPSCs by using BrdU and FACS. MiaPaCa-2-RFP were co-cultured with hPSCs for 48hr, then cells were incubated with BrdU-FITC, tryptinized and collected for FACS analysis where red cells in co-culture (MiaPaCa-2-RFP) are spectrally separated from unlabelled hPSCs cells. The intensity of the florescence probe (green) is measured in each cell population. (a-f) are FACS traces of BrdU-FITC positive cells (530/30, Green) vs (610/20, Red) for either singly or co-cultured cells. (g) No change in cell proliferation of MiaPaCa-2-RFP or hPSCs after 48hr

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of co-culture. The mean±SEM is calculated from at least three independent experiments. (h) cartoon summarized the steps for the experiment. For data analysis one-way ANOVA was used followed by Tukey's multiple comparisons test. SSC (Side SCatter) parameter used to identify the granularity of the cells in flow cytometry. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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4.3. DISCUSSION

In our previous studies, we found that glycolytic inhibitors cause ATP depletion, PMCA inhibition, calcium overload and cell death in pancreatic cancer cells, such as MiaPaCa-2 and PANC-1 cells, while mitochondrial inhibitors had no effect (James et al., 2013; James et al., 2015). This suggests that the shift in metabolism towards glycolysis (The Warburg effect), which is 2+ commonly observed in cancer cells is critical for maintaining low cytosolic [Ca ]i. This is because 2+ glycolytic ATP supply to the of PMCA is critically important to maintain low cytosolic [Ca ]i in cancer cells and thus survival. Therefore, cutting off the glycolytic ATP supply to PMCA could be a good approach for pancreatic cancer treatment (James et al., 2013: James et al., 2015). However, it is well established that tumour microenvironment can play a critical role in cancer cells survival and progression. Our results show that pancreatic stellate cells alter PDAC cells metabolism by increasing their mitochondrial OXPHOS and decrease glycolysis without affecting the metabolic regulation of intracellular calcium homeostasis. This suggests that hPSCs secrete a factor that is responsible for transforming PDAC cell metabolism. However, despite this change in metabolism, this didn’t affect the sensitivity of PDAC cells to glycolytic vs mitochondrial inhibitors. The response of pancreatic cancer cells in co-culture to different glycolytic inhibitors cause ATP depletion and calcium overload, while mitochondrial inhibitors have no effect. This is a similar response we observe with singly cultured PDAC cells in our previous studies (James et al., 2013; James et al., 2015). All of this suggests that such an increase in PDAC cells mitochondrial function is to prevent apoptosis and to increase biosynthesis without effecting bioenergetics.

As we mentioned early, although there were no changes in the calcium and ATP response following the treatment with different metabolic inhibitors of MiaPaCa-2 cells in co-culture, our results are suggesting that pancreatic stellate cells secrete metabolites that can support pancreatic cancer cells metabolism (↑ mitochondria OXPHOS, ↓ glycolysis). It has been found that lactate and ketone bodies secreted from cancer associated fibroblast could enhance the mitochondrial function of breast cancer cells (the reserve Warburg effect) (Bonuccelli et al., 2010; Capparelli et al., 2012; Martinez-Outschoorn et al., 2014; Mercier et al., 2008; Pavlides et al., 2009; Salem et al., 2012). In addition, Sousa et al recent study has found that alanine secreted from pancreatic stellate cells can support the metabolic function of pancreatic tumour cells (Sousa et al, 2016). There is a direct communication between the stellate cell and pancreatic cancer, where stellate cells secrete metabolite that is consumed by pancreatic cancer cells to fuel the tricarboxylic acid (TCA) cycle. Although our finding supports the above studies, the main mechanism that causes such changes in PDAC cell metabolism in co-culture is still not clear. The above studies are suggesting

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that under starvation cancer cells can induce autophagy to the stromal cell, those cells will then release energy-rich metabolites that can be consumed by cancer cells and this explain the shift of metabolism of cancer cells in co-culture.

Although many studies found that cancer-associated fibroblast such as hPSCs can enhance tumour growth, our studies show a reduction or no change in growth. This is because all our studies are done in-vitro which has many limitations. The interaction between cancer cells and its microenvironment is complex and involve many factors such as hormones, inflammatory and immune cells. Indeed, many factors are involved in the full activation of hPSCs in real tumour such as hypoxia and inflammatory cells together with PDAC cells (Neoptolemos et al, 2010; Apte et al, 2013). Performing in-vivo studies is crucial to elucidate the role of stellate cells in pancreatic tumour growth.

In addition, our current project shows that pancreatic stellate cells maintain their metabolic phenotype and growth rate in co-culture. No change in either metabolism or the growth were observed in in-direct and direct co-culture with pancreatic cancer cells.

4.4. SUMMARY

In summary, pancreatic stellate cells secrete specific metabolite/factor that consumed by pancreatic cancer cells to fuel the mitochondrial metabolism and decrease their glycolytic rate which supports many previous studies that are suggesting the revere Warburg effect. However, we didn’t find any change in response to both glycolytic and mitochondrial inhibitors in the ATP and calcium overload studies. In addition, we didn’t find any change in the growth rate of pancreatic cancer cells when co-cultured with pancreatic stellate cells due to the limitation of in-vitro studies. In pancreatic stellate cells, both their metabolic phenotype and growth rate haven’t changed when co-culture with pancreatic cancer cells. In the following chapter, we will try to identify the key metabolites that is involve the metabolic cross-talk between PDAC and hPSCs cells.

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CHAPTER 5

5. INVESTIGATION OF THE MECHANISM FOR THE ALTERED PANCREATIC CANCER CELLS METABOLISM WHEN CO- CULTURED WITH PANCREATIC STELLATE CELLS

5.1. INTRODUCTION

Pancreatic cancer is an aggressive type of cancer characterizes by abundant stroma. Pancreatic stellate cells are a predominant cell in the pancreatic tumour site that plays a major role in inducing fibrosis by increasing the production of extracellular matrix proteins (Bae, 2013; Neoptolemos et al, 2010). The intense fibrotic stroma causes significant reduction in the blood flow and thus nutrient and oxygen supply to the tumour site and this will cause cancer cell starvation and hypoxia. In order to survive cancer cells, try to adapt by utilizing neighboring cells as a source of nutrition. Many studies have suggested that cancer cells can induce autophagy in neighboring cells, and this will cause the release of metabolites such as ketone bodies, lactate or alanine and this phenomenon called the reverse Warburg effect (Bonuccelli et al., 2010; Capparelli et al., 2012; Martinez-Outschoorn et al., 2014; Mercier et al., 2008; Pavlides et al., 2009; Salem et al., 2012; Sousa et al, 2016). These metabolites are then utilized by cancer cells to fuel their mitochondria metabolism (Sousa et al, 2016). The metabolic cross-talk between the tumor and its stoma is complex (Sousa et al, 2016). Pancreatic stellate cells might play a role in altering cancer cell metabolism (Sousa et al, 2016). In the following chapter, we will try to investigate the mechanism involved in the metabolic cross- talk between pancreatic stellate cells and cancer cells.

One of the major problems of using conditioned media (CM) to dissect the mechanism for the metabolic cross-talk between hPSCs and MiaPaCa-2 cells is that the very nature of the conditioned media introduces numerous confounding factors. The whole nutritional landscape of the media will change dynamically over time in culture as the cells consume the nutrients and release biproduct; therefore, their nutrients become severely depleted in the media over time. Since MiaPaCa-2 cells are cultured in high glucose and are highly glycolytic then glucose depletion over time will be a major confounding factor that may profoundly alter the metabolic phenotype of MiaPaCa-2 cells. Similarly, the accumulation of a plethora of metabolites over

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time may also alter the metabolic phenotype and again due to the highly glycolytic nature of MiaPaCa-2 cells, the accumulation of lactate may also be a major confounding factor altering the metabolic phenotype. Moreover, as the cells become “starved” due to the nutrient depletion their metabolic phenotype may “respond” to their adverse condition accordingly and some cells may undergo autophagy and thus release metabolites that might not necessarily be released in a bona fide co-culture system where there is an exhaustive supply of nutrients or within the tumor microenvironment in-vivo. Conversely, the “starvation” condition of CM may better reflect the tumor microenvironment where there is reduced blood flow and thus nutrient supply and hypoxia. Therefore, the key question is whether this nutrient depletion and metabolite accumulation is an artifact of the experimental design of the CM experiment or whether this could be recapitulated in a genuine co-culture where there is an exhaustive supply of nutrient of the tumor microenvironment. However, it’s important to note that the hPSCs CM had a profound effect on the metabolic phenotype of MiaPaCa-2 cells (↑ mitochondrial respiration and ↓ glycolysis) yet the MiaPaCa-2 CM had no effect on the metabolic phenotype of hPSCs, despite similar nutrient depletion or metabolite accumulation. This suggests that either MiaPaCa-2 cells are particularly sensitive to nutrient depletion/ metabolite accumulation or hPSCs uniquely release/secrete some substance/factor that alters the metabolic phenotype of PDAC cells.

5.1.1. AIM

The overarching aim of this chapter is to dissect the mechanism for the effect of hPSCs- CM on MiaPaCa-2 cell metabolism. This will be achieved by addressing two specific aims:

1- Test the effect of glucose depletion/lactate accumulation on the metabolic phenotype of MiaPaCa-2 cells.

2- Identify the unique factor/metabolite released from hPSCs that alters the metabolic phenotype using footprint metabolomics.

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Figure. 5.1. The metabolic cross talk between pancreatic stellate and PDAC cells. The blood and nutrition supply to pancreatic tumour site is reduced which will cause starvation to cancer cells. The cancer cells try to survive by inducing autophagy in neighboring stellate cells. As a result, stellate cells will secrete energy rich metabolites that can be consumed by cancer cells to fuel its mitochondria for oxidation phosphorylation.

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5.2. RESULTS

5.2.1. Conditioned media (CM) glucose depletion and lactate accumulation.

In the previous chapter, the results show that culturing of PDAC cells in conditioned media (CM) collected from pancreatic stellate cells can increase the mitochondrial function and decrease glycolysis. This seemed to be specific for hPSCs-CM as MiaPaCa-2-CM had no effect on hPSCs metabolism. The most likely explanation for this is nutrient depletion and/or the accumulation of waste products or metabolites. Since MiaPaCa-2 cells are highly glycolytic (James et al., 2013) and glucose is by far the most abundant nutrient in DMEM, glucose depletion and/or lactate accumulation in the media is the most likely explanation for this phenomenon. To test this, we measured glucose and lactate concentration in the conditioned media (CM) at various time points in culture. This was achieved by using a lactate assay kit and glucometer. Results show that there is a profound decrease in glucose and increase in lactate in CM from both hPSCs and MiaPaCa-2 cells. However, there was a greater decrease in glucose concentration in MiaPaCa-2 cells (5±0.2 mmol/L) compared to hPSCs (11±1 mmol/L), which is consistent with highly glycolytic and proliferative cancer cells. Likewise, there was a greater increase in lactate in MiaPaCa-2 (28±4 mM) compared to hPSCs (14±0.3 mM).

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Figure. 5.2. Lactate and glucose concentration in hPSCs and MiaPaCa-2 cells conditioned media. The cells were seeded at density of 20,000 cells/cm2 in 10% FBS: DMEM as a media. Next day the cells were washed once with PBS (phosphate buffered saline) and the media was change to 10% Nu-serum: DMEM and cells were cultured for 24, 48 or 72hr. Conditioned media from each cell type was collected at each of these time points, centrifuged at 500 rpm for 10 min at 22 ̊ C to remove any cells debris, and the supernatants were collected. The glucose level of the CM was measured by glucometer and the lactate levels were measured by L-Lactate Assay kit. The results represent the Mean ±SEM of at least three independent experiment. All statistical comparisons were performed using a one-way ANOVA followed by Dunnett’s multiple comparisons test. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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5.2.2. Reducing media glucose mimics the effect of conditioned media on MiaPaCa-2 cell metabolism.

Since glucose was severely depleted to as little as ~11 mmol/L in hPSCs CM and ~5 mmol/L in MiaPaCa-2 CM, we next wanted to test the effect of culturing cells in “regular” DMEM with reduced glucose. To assesses the effect of reduced glucose on MiaPaCa-2 cells metabolism, we cultured MiaPaCa-2 cells in DMEM containing 25-, 15-, 8- and 5-mM glucose. After 48 hr, a Mito-stress test or Glyco stress test was performed using the XFe96 Seahorse analyzer to determine the metabolic phenotype of MiaPaCa-2 cells grown in different glucose concentrations (Figure, 5.3, 5.4). The results show that some aspects of the metabolic phenotype were affected by glucose concentration. As the glucose concentration decreased the mitochondrial function increased and glycolysis decreased. There was a significant increase in the basal respiration, it was 45±6 pmol/min/AU in 5mM glucose cultured cells vs 28±13 pmol/min/AU in 25mM glucose (Figure, 5.3). For glycolytic function, although the changes were not significant there was a stepwise decrease in the glycolysis as the glucose concentration decreased; in 5mM glucose, the glycolysis was 43±4 mpH/min/AU vs 52±3 mpH/min/AU in 25mM glucose (Figure, 5.4). Collectively, these data suggest that while glucose has some effect on the metabolism it is unlikely to be the sole mechanism. Nevertheless, the next step was to repeat the conditioned media experiment with “replenished” glucose to rule out any effect of glucose depletion.

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Figure. 5.3. The effect of reducing media glucose on the metabolic phenotype of PDAC (MiaPaCa-2) cells. MiaPaCa-2 cells were cultured for 48hr in DMEM media with different glucose concentration. The cells were analysed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP, 0.5µM) and a combination of rotenone/antimycin (RT/AM, 0.5µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured simultaneously in absorbance unit (AU) using the Sulforhodamine-B (SRB) assay. (b) cartoon represents a summary of the mito-stress test and the different metabolic parameters measured by the XFe96 extracellular flux analyzer (adapted

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from Seahorse Bioscience). (d-j) the mean± SEM of each metabolic parameter was calculated from at least four independent experiments and all measurement were normalized to the protein content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

Figure. 5.4. The effect of reducing media glucose on the glycolytic function of PDAC (MiaPaCa-2) cells cultured in media with different glucose concentrations. MiaPaCa-2 cells were cultured for 48hr in DMEM media with different glucose concentration. The cells were then analysed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameter measured during the Glyco-stress test adapted from Seahorse bioscience. (c) Mean ±SEM of at least three independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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5.2.3. Glucose replenished hPSCs conditioned media increase mitochondrial metabolism and reduced glycolysis of MiaPaCa-2 cells:

We next tested the effect of hPSCs-CM that had glucose “replenished”, such that it was the same concentration as standard DMEM. This removes any confounding effects of glucose depletion on hPSCs CM induced changes in metabolism, whereas any “unique” factors produced by hPSCs that might be responsible for the metabolic changes will remain in the CM. This was achieved by measuring glucose in the CM after 48hr (~15 mmol/L) (Figure 5.2.a) and then adding back the glucose (~10 mmol/L) thereby replenishing the glucose to its original DMEM concentration of 25mM DMEM. Results show that despite the glucose repletion hPSCs-CM still increase mitochondrial metabolism and decrease glycolysis (Figure, 5.5; 5.6). The basal respiration was 28±2 pmol/min/AU in the control DMEM, 46±3 pmol/min/AU in pure CM and 48±2 pmol/min/AU in glucose replenished CM (Figure, 5.5). Likewise, the baseline ECAR was 56±1 mpH/min/AU in control DMEM, 36±2 mpH/min/AU in pure CM and was 32±1 mpH/min/AU in glucose replenished CM (Figure, 5.5). Glycolysis was 52±3 mpH/min/AU in the control DMEM, 36±0.5 mpH/min/AU in pure CM and was 33±1 mpH/min/AU in glucose replenished CM (Figure, 5.6). This suggests that glucose depletion of hPSCs-CM is not responsible for the altered metabolic phenotype in MiaPaCa-2 cells (↑ mitochondrial respiration and ↓ glycolysis).

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Figure. 5.5. Effect of glucose-replenished hPSCs conditioned media on PDAC (MiaPaCa- 2) cells metabolism. MiaPaCa-2 cells were cultured for 48hr in a conditioned media collected from hPSCs alone (red trace) or with supplementation with glucose (blue trace). The cells were analyzed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4- (trifluoromethoxy) phenylhydrazone (FCCP, 0.5µM) and a combination of rotenone/antimycin (RT/AM, 0.5µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured

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in absorbance unit (AU) using the Sulforhodamine-B (SRB) assay. (b) cartoon represents a summary of the mito-stress test and the different metabolic parameters measured by the XFe96 extracellular flux analyzer (adapted from Seahorse Bioscience). (d-j) the mean± SEM of each metabolic parameter was calculated from at least four independent experiments and all measurement were normalized to the protein content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

Figure. 5.6. Effect of glucose-replenished hPSCs conditioned media on PDAC (MiaPaCa- 2) cells glycolytic function. MiaPaCa-2 cells were cultured for 48hr with conditioned media collected from hPSCs alone or supplemented with glucose. The cells were then analysed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameter measured during the Glyco-stress test adapted from Seahorse bioscience. (c) Mean ±SEM of at least three independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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5.2.4. The metabolic phenotype of MiaPaCa-2 cells haven’t change following the treatment with lactate regardless of glucose concentration.

After ruling out glucose depletion as a potential mechanism we next wanted to test whether lactate accumulation in hPSCs-CM was responsible for the change in the metabolic phenotype (↑ mitochondrial respiration and ↓ glycolysis) in MiaPaCa-2 cells. This is because lactate was shown to increase to (14±0.3 mM) in hPSCs-CM and previous studies suggest that lactate could be a potential metabolite produced by cancer-associated fibroblast responsible for “reversing” the Warburg effect that increases mitochondrial function and decreasing glycolysis (Martinez-Outschoorn et al., 2014; Martinez-Outschoorn et al., 2012; Martinez-Outschoorn et al., 2010). For this reason, we tested the effect of 5mM and 20mM lactate supplemented in standard DMEM on the metabolic phenotype of PDAC cells by using XFe96 Seahorse analyzer. However, there was no change in any metabolic parameter assessed either using the Mito stress test (Figure 5.7.-5.9) or Glyco stress test (Figure 5.10-5.12) in zero glucose (Figure 5.7., 5.10), low (5mM) glucose (Figure 5.8., 5.11.) or high (25mM) glucose (Figure 5.9., 5.12.). Suffice to say neither mitochondrial nor glycolysis were affected by lactate (5 or 20 mM).

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Figure. 5.7. Effect of lactate supplemented media on mitochondrial function of MiaPaCa- 2 cells assessed by Mito stress test in zero glucose. MiaPaCa-2 cells were cultured for 48hr with 5 mM or 20 mM lactate or control DMEM media. The cells were analysed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP, 0.5 µM) and a combination of rotenone/antimycin (RT/AM, 0.5µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) using the Sulforhodamine-B (SRB) assay. (b) cartoon (adapted from Seahorse Bioscience) represent a summary of the mito-stress test and the different metabolic parameters measured by the XFe96 extracellular flux analyzer. (d-j) the mean± SEM of each metabolic parameter was calculated from at least three independent experiments and all measurement

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were normalized to the protein content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

Figure. 5.8. Effect of lactate supplemented media on mitochondrial function of MiaPaCa- 2 cells assessed by Mito stress test in low glucose (5mM). MiaPaCa-2 cells were cultured for 48hr with 5 mM or 20 mM lactate or control DMEM media. The cells were analysed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP, 0.5 µM) and a combination of rotenone/antimycin (RT/AM, 0.5µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit

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(AU) using the Sulforhodamine-B (SRB) assay. (b) cartoon (adapted from Seahorse Bioscience) represent a summary of the mito-stress test and different metabolic parameters measured by the XFe96 extracellular flux analyzer. (d-j) the mean± SEM of each metabolic parameter was calculated from at least three independent experiments and all measurement were normalized to the protein content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

Figure. 5.9. Effect of lactate supplemented media on mitochondrial function of MiaPaCa- 2 cells assessed by Mito stress test in high glucose (25mM). MiaPaCa-2 cells were cultured for 48hr with 5 mM or 20 mM lactate or control DMEM media. The cells were analysed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4- (trifluoromethoxy) phenylhydrazone (FCCP, 0.5 µM) and a combination of rotenone/antimycin (RT/AM, 0.5µM) to study the mitochondrial function of the cells. (a) The

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oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) using the Sulforhodamine-B (SRB) assay. (b) cartoon (adapted from Seahorse Bioscience) represent a summary of the mito-stress test and different metabolic parameters measured by the XFe96 extracellular flux analyzer. (d-j) the mean± SEM of each metabolic parameter was calculated from at least three independent experiments and all measurement were normalized to the protein content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

Figure. 5.10. Effect of lactate supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in zero glucose. MiaPaCa-2 cells were cultured for 48hr with 5mM or 20mM lactate or control DMEM media. The cells were then analysed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameter measured during the Glyco-stress test adapted from Seahorse bioscience. (c) Mean ±SEM of at least three independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by sulforhodamine B assay. All statistical comparison

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was performed by using one-way ANOVA. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

Figure. 5.11. Effect of lactate supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in low glucose (5mM). MiaPaCa-2 cells were cultured for 48hr with 5mM or 20mM lactate or control DMEM media. The cells were then analysed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameter measured during the Glyco-stress test adapted from Seahorse bioscience. (c) Mean ±SEM of at least three independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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Figure. 5.12. Effect of lactate supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in high glucose (25mM). MiaPaCa-2 cells were cultured for 48hr with 5mM or 20mM lactate or control DMEM media. The cells were then analysed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameter measured during the Glyco-stress test adapted from Seahorse bioscience. (c) Mean ±SEM of at least three independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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5.2.5. No change in the expression of lactate transporter (MCT-1 or MCT-4) in both PDAC (MiaPaCa-2) and pancreatic stellate cells in co-culture.

According to the reverse Warburg effect, cancer cells can utilize lactate secreted from neighboring stromal cells to fuel their mitochondria. To facilitate this process it has been suggested that cancer cell increase the expression of the lactate uptake transporter (MCT-1) and cancer-associated fibroblasts increase the expression of the lactate efflux transporter (MCT-4) (Martinez-Outschoorn et al., 2014; Martinez-Outschoorn et al., 2012; Martinez- Outschoorn et al., 2010). To test this, we performed immunofluorescent imaging together with western blotting to determine the expression of these transporters in both cells in co-culture. The results haven’t shown any changes in the protein expression of lactate transporter (MCT- 1 and MCT-4) for both cells in co-culture (Figure, 5.13; 5.14).

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Figure. 5.13. The expression of lactate influx transporter (MCT-1) for MiaPaCa-2 and hPSCs cells in co-culture. MiaPaCa-2 cells were grown with hPSCs by using trans-well dish and cell lysates were collected 24, 48, or 72 hr of co-culture for western blotting. For immunofluorescent RFP (+)-MiaPaCa-2 cells were grown together with hPSCs and kept in co- culture for 48hr. (a-d) Western blots showing the expression of MCT-1 in whole cell lysate of co-cultured MiaPaCa-2 (a,b) and hPSCs (c,d). 10 µg was loaded for each sample. The relative expression of transporter is the mean±SEM of four independent experiments. All data were normalized to the expression of control which was alpha tubulin. (e & f) Fluorescence microscope images of direct co-culture of MiaPaCa-2 with hPSCs showing the cellular distribution of lactate influx transporter (MCT-1) (green). The MiaPaCa-2 cells were stably transfected with red fluorescent protein prior to co-culturing with h-PSC cells, so that they can be identified in the imaging. The nuclei of cells (blue) were counter stained with DAPI. All cells were imaged under identical exposure time and microscope setting, scale bar 25µM. One- way ANOVA followed with Tukey's multiple comparisons test was used for statistical analysis. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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Figure. 5.14. The expression of lactate efflux transporter (MCT-4) for MiaPaCa-2 and hPSCs cells in co-culture. MiaPaCa-2 cells were grown with hPSCs by using trans-well dish and cell lysates were collected 24, 48, or 72 hr of co-culture for western blotting. For immunofluorescent RFP (+)-MiaPaCa-2 cells were grown together with hPSCs and kept in co- culture for 48hr. (a-d) Western blots showing the expression of MCT-4 in whole cell lysate of co-cultured MiaPaCa-2 (a,b) and hPSCs (c,d). 10 µg was loaded for each sample. The relative expression of transporter is the mean±SEM of four independent experiments. All data were normalized to the expression of control which was alpha tubulin. (e & f) Fluorescence microscope images of direct co-culture of MiaPaCa-2 with hPSCs showing the cellular distribution of lactate efflux transporter (MCT-4) (green). The MiaPaCa-2 cells were stably transfected with red fluorescent protein prior to co-culturing with h-PSC cells, so that they can be identified in the imaging. The nuclei of cells (blue) were counter stained with DAPI. All cells were imaged under identical exposure time and microscope setting, scale bar 25µM. One- way ANOVA followed with Tukey's multiple comparisons test was used for statistical analysis. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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5.2.6. Footprint metabolomics of pancreatic stellate cells conditioned media show significant increase in citrate; it was excessively elevated in hPSC-CM.

So far, our data has shown that hPSC-CM altered the metabolic phenotype of MiaPaCa- 2 cell independent of glucose depletion and lactate accumulation. This suggests that a unique factor must be release/secreted by hPSCs into the CM over time that is responsible for this change in metabolism. Previous studies have shown that numerous metabolites, including lactate and ketone bodies, are produced by neighboring stromal cells that can alter the metabolism of cancer cells (Bounccelli et al., 2010). A more recent study has shown that pancreatic stellate cells can produce alanine that can be consumed by cancer cells to increase their mitochondrial function and decrease glycolysis which is consistent with our own studies (Sousa et al., 2016). Therefore, we next wanted to identify this unique factor using the unbiased approach of footprint metabolomics. We performed a metabolomic analysis for the conditioned media collected from both MiaPaCa-2 and hPSCs cells. Results show that numerous metabolites were found to be significantly elevated and reduced in CM from both hPSCs and MiaPaCa-2 cells. This included lactate, alanine, fumaric acid, glutamine, phenylalanine, malic acid, galic acid, minoxidil, hydroxybutyrate, proline, and ketovaline. However, the main aim of these experiments was to identify any metabolites/factors that were excessively elevated in hPSCs-CM that might be responsible for changing the metabolism of pancreatic cancer cells. The results show that proline, ketovaline, fumaric acid, phenylalanine and citrate were significantly elevated in hPSCs-CM compared with MiaPaCa-2-CM. However, the only metabolite that was extremely elevated was citrate (Figure, 5.15). This suggests that citrate might be the most likely metabolite that is involved in the metabolic cross-talk between hPSCs and MiaPaCa-2 cells. For this reason, we have selected citrate as a potential candidate, and we have done further studies to assess its uptake and effect on MiaPaCa-2 cells metabolism.

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Figure. 5.15. The top 25 significant metabolites in the conditioned media. The metabolites in the conditioned media collected from hPSCs or MP (MiaPaCa-2) cells were compared to control, normal DMEM with no contact to cells. Gas chromatography mass spectrometry were used to identify the metabolites in the conditioned media. The data represent the mean of five biological replicate.

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5.2.7. PDAC (MiaPaCa-2) cells can take up citrate following the treatment with 5mM citrate.

The previous footprint metabolomics experiments suggest that citrate is the only metabolite excessively elevated in hPSCs media that may be involved in the metabolic cross- talk between hPSCs and PDAC cells. To test if MiaPaCa-2 cells are able to take up citrate we treated the cells with media supplemented with different concentration of citrate (0.2-, 1- and 5-mM). After 48hr the conditioned media was collected and analyzed using a footprint metabolomic analysis. However, in this experiment, we were specifically looking for a decrease in relative citrate concentration detected in the media that could indicate that the cells were taking up and consuming the citrate. The citrate concentration at time zero was compared to its concentration after 48hr of treatment. The results show that the only concentration of supplemented citrate that significantly decreases after 48 hours was 5mM. This suggests that MiaPaCa-2 cells actively take up and consume citrate following the treatment with 5mM citrate (Figure, 5.16).

Figure. 5.16. Citrate uptake studies. The citrate concentration at time zero and after 48hr of treatment of MiaPaCa-2 cells with 0.2, 1 or 5 (mM) citrate. Gas chromatography mass spectrometry was used to identify the metabolites. The data represent the mean of five biological replicate.

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5.2.8. Citrate supplemented DMEM increase mitochondrial metabolism and decrease glycolysis in MiaPaCa-2 cells.

According to our previous finding, citrate is the most likely metabolite elevated in hPSCs-CM that may be responsible for the observed metabolic cross-talk between hPSCs and PDAC cells. It was the only metabolite present in extremely high levels in hPSC conditioned media (CM), but not in MiaPaCa-2 CM. Importantly, PDAC cells are able to take up and consuming citrate, making it a very strong candidate involved in this intra-tumoral metabolic cross-talk. We next wanted to investigate the effect of exogenous citrate in altering the metabolic phenotype of MiaPaCa-2 cells. To achieve that we performed XFe96 Seahorse analyzer in PDAC cells following the treatment with low or high citrate cultured in media with low glucose concentration to mimic glucose depleted CM. MiaPaCa-2 cells were cultured in control or citrate supplemented media for 48 hours similar to the previous Seahorse experiment.

Results show that citrate mimics the effect of hPSCs CM on MiaPaCa-2 cells. The mitochondrial function was significantly increased in MiaPaCa-2 cells following the treatment with 1mM citrate in low glucose media. The baseline OCR was 66±3 pmol/min/AU and 80±3 pmol/min/AU in low glucose control and 1mM citric acid treated cells, respectively (Figure, 5.17). The basal respiration was 44±3 pmol/min/AU and 56±3 pmol/min/AU in low glucose control and 1mM citric acid treated cells, respectively (Figure, 5.17). The maximal respiration was 123±5 pmol/min/AU and 143±6 pmol/min/AU in low glucose control and 1mM citric acid treated cells, respectively (Figure, 5.17). In addition, the spare respiration capacity was 78±6 pmol/min/AU and 87±3 pmol/min/AU in low glucose control and 1mM citric acid treated cells, respectively (Figure, 5.17). However, glycolysis was unaffected as assessed by Mito stress and Glyco stress test with 1mM citrate (Figure, 5.17; 5.19).

Although 1mM citrate hadn’t affected glycolysis, the glycolytic rate of MiaPaCa-2 cells was significantly decreased following the treatment with 5mM citrate (Figure, 5.18; 5.20). The baseline ECAR was 25±1 mpH/min/AU and 18±0.6 mpH/min/AU in low glucose control and 5mM citric acid treated cells, respectively (Figure, 5.18). The glycolysis rate was 23±3 mpH/min/AU and 19±0.8 mpH/min/AU in low glucose control and 5mM citric acid treated cells, respectively (Figure, 5.20). Furthermore, the glycolytic capacity was 28±3 mpH/min/AU and 24±1 mpH/min/AU in low glucose control and 5mM citric acid treated cells, respectively (Figure, 5.20).

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Figure. 5.17. Effect of 1mM citrate supplemented media on mitochondrial function of MiaPaCa-2 cells assessed by Mito stress test in low glucose (5mM). MiaPaCa-2 cells were cultured for 48hr with 1mM citric acid or control DMEM media. The cells were then analyzed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4- (trifluoromethoxy) phenylhydrazone (FCCP, 0.5 µM) and a combination of rotenone/antimycin (RT/AM, 0.5 µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) using the Sulforhodamine-B (SRB) assay. (b) cartoon represent a summary of the mito-stress test and showing different metabolic parameters measured by the XFe96 extracellular flux analyzer (adapted from Seahorse Bioscience). (d-j) the mean± SEM of each metabolic parameter was calculated from at least four independent experiments and all measurement were normalized to the protein content (AU). (e) cell energy

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phenotype profile. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

Figure. 5.18. Effect of 5mM citrate supplemented media on mitochondrial function of MiaPaCa-2 cells assessed by Mito stress test in low glucose (5mM). MiaPaCa-2 cells were cultured for 48hr with 5mM citric acid or control DMEM media. The cells were then analyzed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4- (trifluoromethoxy) phenylhydrazone (FCCP, 0.5 µM) and a combination of rotenone/antimycin (RT/AM, 0.5 µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) using the Sulforhodamine-B (SRB) assay. (b) cartoon represent a summary of the mito-stress test and showing different metabolic parameters measured by the XFe96 extracellular flux analyzer (adapted from Seahorse Bioscience). (d-j) the mean± SEM of each metabolic parameter was calculated from at least four independent

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experiments and all measurement were normalized to the protein content (AU). All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

Figure. 5.19. Effect of 1mM citrate supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in low glucose (5mM). MiaPaCa-2 cells were cultured for 48hr with 1mM citric acid or control DMEM media. The cells were then analyzed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameters measured during the Glyco-stress test adapted from Seahorse bioscience. (c) Mean ±SEM of at least four independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by the sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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Figure. 5.20. Effect of 5mM citrate supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in low glucose (5mM). MiaPaCa-2 cells were cultured for 48hr with 5mM citric acid or control. The cells were then analyzed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameters measured during the Glyco-stress test adapted from Seahorse bioscience. (c) Mean ±SEM of at least four independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by the sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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Figure. 5.21. Studying the mitochondrial function of PDAC (MiaPaCa-2) cells following the treatment with 1mM citric acid in media with high glucose (25mM). MiaPaCa-2 cells were cultured for 48hr wit 1mM citric acid or a control. The cells were analyzed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP, 0.5 µM) and a combination of rotenone/Antimycin (RT/AM, 0.5 µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) by using Sulforhodamine-B (SRB) assay. (b) cartoon (adapted from Seahorse Bioscience) represent a summary of the mito-stress test and showing different metabolic parameter measured by the XFe96 extracellular flux analyzer. (d-j) the mean± SEM of each metabolic parameter was calculated from at least four independent experiments and all measurement were normalized to the protein content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure. 5.22. Studying the mitochondrial function of PDAC (MiaPaCa-2) cells following the treatment with 5mM citric acid in media with high glucose (25mM). MiaPaCa-2 cells were cultured for 48hr with 5mM citric acid or a control. The cells were analyzed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP, 0.5 µM) and a combination of rotenone/Antimycin (RT/AM, 0.5 µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) by using Sulforhodamine-B (SRB) assay. (b) cartoon (adapted from Seahorse Bioscience) represent a summary of the mito-stress test and showing different metabolic parameter measured by the XFe96 extracellular flux analyzer. (d-j) the mean± SEM of each metabolic parameter was calculated from at least four independent experiments and all measurement were normalized to the protein content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure. 5.23. The Glycolytic function of PDAC (MiaPaCa-2) cells following the treatment with 1mM citric acid in high glucose media (25mM). MiaPaCa-2 cells were cultured for 48hr with 1mM citric acid or control. Then the cells were analyzed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon adapted from Seahorse bioscience summarizing different glycolytic parameter measured during the Glyco-stress test. (c) Mean ±SEM of at least four independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA followed. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure. 5.24. The Glycolytic function of PDAC (MiaPaCa-2) cells following the treatment with 5mM citric acid in high glucose media (25mM). MiaPaCa-2 cells were cultured for 48hr with 5mM citric acid or control. Then the cells were analyzed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon adapted from Seahorse bioscience summarizing different glycolytic parameter measured during the Glyco-stress test. (c) Mean ±SEM of at least four independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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5.2.9. The metabolic phenotype of MiaPaCa-2 cells haven’t change following the treatment with alanine regardless of the glucose concentration.

It has previously been shown that pancreatic stellate cells secrete alanine that also increased PDAC cells mitochondrial metabolism and decreased glycolysis similar to our observation with conditioned media (CM). According to Sousa et al 2016, alanine was found to contribute in the metabolic interaction between hPSC and PDAC cells. Interestingly, in our metabolomic analysis of hPSCs CM we found that alanine was also elevated although in CM of both cell types. Nevertheless, based on these previous studies we decided to test the effect of alanine on our MiaPaCa-2 cells either in low, high or zero glucose. To investigate this, we performed both Mito and Glyco stress test by using XFe96 Seahorse analyzer to determine the metabolic phenotype of MiaPaCa-2 cell cultured for 48hr with 1mM or 2mM alanine similar to our previous experiments. Our data demonstrate that alanine had no significant effect on any parameter measured during the Mito/Glyco stress test (Figure, 5.25- 5.30.). This suggests that at least using MiaPaCa-2 cells and under the condition of our experiment alanine has no effect on the metabolic phenotype of MiaPaCa-2 cells and is not uniquely elevated in hPSCs CM.

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Figure. 5.25. Effect of alanine supplemented media on mitochondrial function of MiaPaCa-2 cells assessed by Mito stress test in zero glucose. MiaPaCa-2 cells were cultured for 48hr with 1 mM or 2 mM alanine or control DMEM media. The cells were then analysed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4- (trifluoromethoxy) phenylhydrazone (FCCP,0.5 µM) and a combination of rotenone/antimycin (RT/AM, 0.5µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU using the Sulforhodamine-B (SRB) assay. (b) cartoon (adapted from Seahorse Bioscience) represent a summary of the mito-stress test and showing different metabolic parameter measured by the XFe96 extracellular flux analyzer. (d-j) the mean± SEM of each metabolic parameter was calculated from at least three independent experiments and

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all measurement were normalized to the protein content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

Figure. 5.26. Effect of alanine supplemented media on mitochondrial function of MiaPaCa-2 cells assessed by Mito stress test in low glucose (5mM). MiaPaCa-2 cells were cultured for 48hr with 1 mM or 2 mM alanine or control DMEM media. The cells were then analysed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4- (trifluoromethoxy) phenylhydrazone (FCCP, 0.5 µM) and a combination of rotenone/antimycin (RT/AM, 0.5µM) to study the mitochondrial function of the cells. (a) The oxygen consumption rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) using the Sulforhodamine-B (SRB) assay. (b)

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cartoon (adapted from Seahorse Bioscience) represent a summary of the mito-stress test and showing different metabolic parameter measured by the XFe96 extracellular flux analyzer. (d- j) the mean± SEM of each metabolic parameter was calculated from at least three independent experiments and all measurement were normalized to the protein content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

Figure. 5.27. Effect of alanine supplemented media on mitochondrial function of MiaPaCa-2 cells assessed by Mito stress test in high glucose (25mM). MiaPaCa-2 cells were cultured for 48hr with 1 mM or 2 mM alanine or control DMEM media. The cells were then analysed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4- (trifluoromethoxy) phenylhydrazone (FCCP, 0.5µM) and a combination of rotenone/antimycin (RT/AM, 0.5µM) to study the mitochondrial function of the cells. (a) The oxygen consumption

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rate (OCR) and (c) the extracellular acidification rate (ECAR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) using then Sulforhodamine-B (SRB) assay. (b) cartoon (adapted from Seahorse Bioscience) represent a summary of the mito-stress test and showing different metabolic parameter measured by the XFe96 extracellular flux analyzer. (d-j) the mean± SEM of each metabolic parameter was calculated from at least three independent experiments and all measurement were normalized to the protein content (AU). (e) cell energy phenotype profile. All statistical comparison was performed by using one-way ANOVA. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

Figure. 5.28. Effect of alanine supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in zero glucose. MiaPaCa-2 cells were cultured for 48hr with 1mM, 2mM alanine or control DMEM media. The cells were then analysed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameters measured during the Glyco-stress test adapted from Seahorse bioscience. (c) Mean ±SEM of at least three independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by the sulforhodamine B assay. All statistical

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comparison was performed by using one-way ANOVA. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

Figure. 5.29. Effect of alanine supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in low glucose (5mM). MiaPaCa-2 cells were cultured for 48hr with 1mM, 2mM alanine or control DMEM media. The cells were then analysed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameter measured during the Glyco-stress test adapted from Seahorse bioscience. (c) Mean ±SEM of at least three independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by the sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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Figure. 5.30. Effect of alanine supplemented media on glycolytic function of MiaPaCa-2 cells assessed by Glyco stress test in high glucose (25mM). MiaPaCa-2 cells were cultured for 48hr with 1mM, 2mM alanine or control DMEM media. The cells were then analysed by XFe96 extracellular flux Analyzer by performing XF Glyco-stress test. (a) the extracellular acidification rate (ECAR) is measured before and after sequential injection of the following: glucose (10mM), oligomycin (OM, 1µM) and 2-deoxy-glucose (2-DG, 50mM) to determine the glycolytic metabolism of the cells. (b) cartoon summarizing different glycolytic parameter measured during the Glyco-stress test adapted from Seahorse bioscience. (c) Mean ±SEM of at least three independent experiment. All measurements were normalized to the total protein content (absorbance unit, AU) measured by the sulforhodamine B assay. All statistical comparison was performed by using one-way ANOVA. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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5.3. DISCUSSION

Recently there is an accumulation of evidence that cast doubt on the Warburg effect solo understanding of cancer cells metabolism. These studies are proposing a new way of understanding of cancer cell metabolism in the context of its microenvironment (the reverse Warburg effect) (Bonuccelli et al., 2010; Capparelli et al., 2012; Martinez-Outschoorn et al., 2014; Mercier et al., 2008; Pavlides et al., 2009; Salem et al., 2012; Sousa et al, 2016). Our study finding partially support this general hypothesis but provide a novel mechanism by which hPSCs alter PDAC metabolism in which hPSCs derived citrate plays a potentially important role. In this study, we showed that pancreatic stellate cells play a critical role in regulating pancreatic cancer cells metabolism (↑ mitochondrial function, ↓ glycolysis). We first identified that citric acid is the key metabolites released from pancreatic stellate cells into the conditioned media. Next, we confirmed that pancreatic cancer cells can take up and consume citric acid from the media. More importantly, we demonstrate that citrate can mimic the hPSCs conditioned media in pancreatic cancer cells, where the mitochondrial activity is increased together with reducing in glycolysis. Indeed, the take up of citrate by cancer cells can increase fatty acid and amino acid synthesis and fuel the mitochondria for OXPHOS (Figure 5.31) (Lehninger, 1975). Citrate is also known to decrease glycolysis by inhibiting phosphofructokinase-1 (PFK-1) enzyme in PDAC (Figure 5.31) (Mycielska and Geissler, 2018; Lehninger, 1975). All these findings suggest that citric acid is the main metabolites that involve in the metabolic cross-talk between pancreatic stellate and pancreatic cancer cells. However, it is important to highlight that our data shows that low citrate only increased mitochondrial function when glucose is low and high citrate decrease glycolysis but had no effect on mitochondrial metabolism. Therefore, there must be other factors released in the CM that could have an additional effect on our finding such as growth factors or extracellular matrix proteins.

Previous studies claim that lactate and alanine play a major role in the metabolic cross- talk between cancer cells and its stromal cells (Bonuccelli et al., 2010; Capparelli et al., 2012; Martinez-Outschoorn et al., 2014; Mercier et al., 2008; Pavlides et al., 2009; Salem et al., 2012; Sousa et al, 2016). However, lactate or alanine had no effect on any metabolic parameter measured, despite been found to be elevated in the CM of both cell types.

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5.4. SUMMARY

Our results elucidate a novel metabolic interaction between pancreatic stellate cells and cancer cells in which citrate plays a critical role. Our data suggest that pancreatic stellate cells secrete citrate that is then take up by PDAC cell to fuel the mitochondrial Krebs cycle pathway, increase amino acid and fatty acid synthesis, and decrease glycolysis due to inhibition of PFK- 1. The metabolic cross-talk between pancreatic stellate cells and PDAC is important to support cancer cell survival and progression. Although other studies found that lactate and alanine can play an important role in such interaction our data found no clear correlation.

Figure. 5.31. Citrate is the main metabolite involve in the metabolic cross talk between pancreatic stellate and cancer cells. In nutrient deficient environment the glucose supply to the cancer cells decreases. As a result, cancer cells rely on citrate secreted from pancreatic stellate cells as source for energy. Citrate can take up by plasma membrane citrate carrier transporter (pmCiC) by PDAC cells to fuel mitochondrial respiration and increases the fatty acid and amino acid synthesis. It can also, decrease glycolysis by inhibiting Phosphofructokinase-1 (PFK-1) enzyme in PDAC cells.

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CHAPTER 6

6. CONCLUSION

PDAC is a lethal type of cancer with poor prognosis and limited treatment options. There is an urgent need to find a new treatment approach. One of the hallmarks of cancer is the switch in metabolism towards a more glycolysis phenotype (the Warburg effect). Our previous finding has shown that this switch is important to maintain low resting cytosolic calcium homeostasis by fuelling the ATP-dependent PMCA (James et al., 2013; James et al., 2015). The PMCA has a localized glycolytic ATP supply that is important to maintain calcium levels in PDAC cells. However, one of the main characteristics of PDAC is that it has an abundant stroma. Today there is compelling evidence that suggests the vital role of the tumor microenvironment in cancer progression, proliferation, metabolism (the reverse Warburg effect), metastasis and invasion. The main aim of this thesis is to elucidate the role of hPSCs 2+ in PDAC cell metabolism and [Ca ]i homeostasis. Understanding and targeting the tumor microenvironment may provide a new way of treating pancreatic cancer cells.

In this study, we have discovered a novel mechanism that explains the metabolic cross- talk between pancreatic stellate cells and pancreatic cancer cells where citrate plays a critical role in such interaction. We have found that pancreatic cancer cells have metabolic flexibility where it can shift their metabolism to cope with their microenvironment. Once the glucose supply to the pancreatic cancer cells decreased cancer cells can take up citrate released from pancreatic stellate cells to fuel its mitochondria, increase fatty acid and amino acid synthesis, and decrease glycolysis. Interestingly, studies have found that pancreatic cancer cells express high levels of plasma membrane citrate carrier transporter (pmCiC) and that citrate uptake is a common cancer feature and a sign for advanced tumour stages (Mycielska et al., 2018). In those studies, the origin of citrate in cancer cells was unknown and they have suggested that cancer cells can take up citrate from the blood under physiological level (~200µM) (Mycielska et al., 2018). Our results are providing an answer to those studies and suggesting that hPSCs are the main source of citrate for PDAC cells in the pancreatic tumour microenvironment. Interestingly, the pmCiC expression is mainly restricted to cancer cells make it a good target for cancer cells treatment (Mycielska and Geissler, 2018). Although in this thesis we have found that the metabolic phenotype of pancreatic cancer cells following the treatment with citrate mimics the cells in co-culture, further studies are required. For example, repeat the metabolic phenotype test of MiaPaCa-2 cells in both co-culture and citrate with the inhibition

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of pmCiC by gluconate, which is known to inhibit pmCiC (Mycielska et al., 2018). Moreover, we need to study the uptake of exogenous citrate in normal and PDAC cells by using stable isotope tracing experiment. We need to perform these experiments under glucose starvation condition. All these experiments will provide a more definitive conclusion.

Despite prior evidence by Sousa et. al., 2016 and Bonuccelli et. al., 2010 that suggested that alanine and lactate could be involved in the metabolic cross-talk between cancer cells and its stroma, our results couldn’t find any direct correlation. Moreover, our metabolomic studies of the conditioned media (CM) collected from both hPSCs and MiaPaCa-2 cells found that alanine and lactate were high in both cells CM. Which means that we can’t confirm that hPSCs cells are the actual source for these metabolites compared to citrate that was significantly elevated in hPSCs CM only.

One of the more significant findings to emerge from this study is that we found that cutting off the glycolytic ATP supply to the PMCA remains a good therapeutic strategy for selectively killing pancreatic cancer cells in co-culture by inducing a cytotoxic Ca2+ overload. Treatment with glycolytic inhibitors caused profound ATP depletion and cytotoxic Ca2+ overload in PDAC cells in co-culture, while mitochondrial inhibitors have no effect. Consistent with our previous studies in singly cultured PDAC cells (James, et. al., 2013; James, et. al., 2015). This suggests that the shift toward mitochondrial metabolism and decrease glycolysis of PDAC cells in co-culture is unlikely to have any impact on bioenergetics and ATP production, but rather is more important for biosynthesis (amino acid and fatty acid synthesis) and resistance to apoptosis.

Most importantly, in this thesis, we did a comprehensive comparison study of non- cancerous (HPDE and hPSCs) vs PDAC cell metabolism and the glycolytic ATP regulation of PMCA. Our finding consistent with most recent studies showing that PDAC cells have functional mitochondria which is at variance with the Warburg effect (Corbet and Feron 2017; Jose et al 2011; Iommarini et al., 2017; Solaini et al., 2011). PDAC cells have the highest mitochondrial function compared to non-cancerous HPDE and hPSCs. Although they also have the highest glycolytic rate; in other words, they are highly metabolic. However, despite this high mitochondrial function PDAC cells are much more sensitive to glycolytic inhibitors but not to mitochondrial inhibitors with respect to ATP depletion and Ca2+ overload. Therefore, their mitochondrial function is essential to support their biosynthesis demand and to prevent apoptosis rather than as an energy supply. Furthermore, we found that using a more selective

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glycolytic inhibitor, such as PFK-15, which inhibits the oncogenic glycolytic enzyme PFKFB3, we see a greater difference in Ca2+ overload response between non-cancerous hPSCs and HPDE vs PDAC cells. We can conclude that by using more selective glycolytic inhibitors that specifically target overexpressed oncogenic glycolytic enzyme (PFKFB3 or PKM2) we can selectively target cancer cells PMCA activity with no or minimal effect in non-cancerous cells that rely on mitochondrial respiration as their major ATP supply which may be crucial for cancer treatment.

Many published studies have shown that pancreatic stellate cells can facilitate the growth of pancreatic cancer cell. However, our data show a reduction or no effect in the growth of pancreatic cancer cells when co-cultured with pancreatic stellate cells. This might be because we only did in-vitro studies which have many limitations. In the in-direct co-culture studies, we couldn’t control all the confounding factor that involves the depletion and accumulation of numerous metabolites in the conditioned media. Moreover, in the direct co-culture, the only limitation is that the hPSCs weren’t fully activated because it is well known that inflammatory cells and hypoxia can play a role in the activation of hPSCs in an actual tumor. Performing in- vivo studies by using a xenograft nude model could be a better option when investigating any symbiotic relationship between the growth of hPSCs and PDAC cells. Because in-vivo studies mimic the natural extracellular microenvironment providing a platform for cell-cell and cell- matrix interaction, this may provide a more definite answer.

Further research is also needed to perform with other pancreatic cancer cell lines, such as PANC-1, BxPC-3, HPAC, SW1990, HPAF-11, Capan-1, ASPC-1and MDA-PATC53, and other primary pancreatic stellate cells isolated from a different patient with pancreatic cancer. In our studies, we have focused on only one PDAC cell line which is MiaPaCa-2 cells and one primary hPSCs which might limit our finding. It is well recorded that different populations of hPSCs may exhibit functional heterogenicity that can govern their effect on cancer cells (Apte, et. al., 2012). Further translation of key finding in different cells is crucial.

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CHAPTER 7

7. REFERENCE

ALKASALIAS, T., MOYANO-GALCERAN, L., ARSENIAN-HENRIKSSON, M. & LEHTI, K. 2018. Fibroblasts in the Tumor Microenvironment: Shield or Spear? International journal of molecular sciences, 19. ANDERSEN, A. P., PEDERSEN, S. F. & MOREIRA, J. M. A. 2014. Interactions of ion transporters and channels with cancer cell metabolism and the tumour microenvironment. Philosophical Transactions of the Royal Society B: Biological Sciences, 369. ANNETTE, S. V., THAN, T. P., ANANDAROOP, M., HWEE, Y. L., BARRY, H. & KIM, P. W. 2007. Human Skin Keloid Fibroblasts Display Bioenergetics of Cancer Cells. Journal of Investigative Dermatology, 128, 702. APTE, M. & WILSON, J. 2012. Dangerous liaisons: Pancreatic stellate cells and pancreatic cancer cells. Journal Of Gastroenterology And Hepatology, 27, 69-74. APTE, M., WILSON, J., LUGEA, A. & PANDOL, S. J. 2013. A Starring Role for Stellate Cells in the Pancreatic Cancer Microenvironment. Gastroenterology. APTE, M. V., PARK, S., PHILLIPS, P. A., SANTUCCI, N., GOLDSTEIN, D., KUMAR, R. K., RAMM, G. A., BUCHLER, M., FRIESS, H., MCCARROLL, J. A., KEOGH, G., MERRETT, N., PIROLA, R. & WILSON, J. S. 2004. Desmoplastic reaction in pancreatic cancer: role of pancreatic stellate cells. Pancreas, 29, 179. ARCANGELI, A., CROCIANI, O. & BENCINI, L. 2014. Interaction of tumour cells with their microenvironment: Ion channels and cell adhesion molecules. A focus on pancreatic cancer. Philosophical Transactions of the Royal Society B: Biological Sciences, 369. AUGSTEN, M. 2014. Cancer-associated fibroblasts as another polarized cell type of the tumor microenvironment. Frontiers in oncology, 4, 62-62. BACHEM, M. G., SCHNEIDER, E., GROß, H., WEIDENBACH, H., SCHMID, R. M., MENKE, A., SIECH, M., BEGER, H., GRÜNERT, A. & ADLER, G. 1998. Identification, culture, and characterization of pancreatic stellate cells in rats and humans. Gastroenterology, 115, 421-432. BACHEM, M. G., SCHÜNEMANN, M., RAMADANI, M., SIECH, M., BEGER, H., BUCK, A., ZHOU, S., SCHMID-KOTSAS, A. & ADLER, G. 2005. Pancreatic carcinoma cells induce fibrosis by stimulating proliferation and matrix synthesis of stellate cells. Gastroenterology, 128, 907-921. BACHEM, M. G., ZHOU, Z., ZHOU, S. & SIECH, M. 2006. Role of stellate cells in pancreatic fibrogenesis associated with acute and chronic pancreatitis. Journal of Gastroenterology and Hepatology, 21, S92-S96. BAE, Y. H. E. 2013. Cancer Targeted Drug Delivery An Elusive Dream / edited by You Han Bae, Randall J. Mrsny, Kinam Park, New York, NY : Springer New York : Imprint: Springer. BAGGALEY, E. M., ELLIOTT, A. C. & BRUCE, J. I. E. 2008. Oxidant- induced inhibition of the plasma membrane Ca2+- ATPase in pancreatic acinar cells: role of the mitochondria. American journal of physiology. Cell physiology, 295, C1247. BAGLEY, R. G. E. 2010. The Tumor Microenvironment / edited by Rebecca G. Bagley, New York, NY : Springer New York : Imprint: Springer. BARDEESY, N. & DEPINHO, R. 2002. Pancreatic cancer biology and genetics. Nature Reviews Cancer, 2, 897. BASTURK, D. O., HONG, V. S.-M., WOOD, V. L., ADSAY, A. A. N., ALBORES-

204

SAAVEDRA, H. J., BIANKIN, S. A., BROSENS, S. L., FUKUSHIMA, A. N., GOGGINS, A. M., HRUBAN, A. R., KATO, A. Y., KLIMSTRA, A. D., KLÖPPEL, A. G., KRASINSKAS, A. A., LONGNECKER, A. D., MATTHAEI, A. H., OFFERHAUS, A. G. J., SHIMIZU, A. M., TAKAORI, A. K., TERRIS, A. B., YACHIDA, A. S., ESPOSITO, A. I. & FURUKAWA, A. T. 2015. A Revised Classification System and Recommendations From the Baltimore Consensus Meeting for Neoplastic Precursor Lesions in the Pancreas. The American Journal of Surgical Pathology, 39, 1730-1741. BERRIDGE, M. J., LIPP, P. & BOOTMAN, M. D. 2000. The versatility and universality of calcium signalling. Nature Reviews Molecular Cell Biology, 1, 11. BETH, L. 2007. Cell biology: Autophagy and cancer. Nature, 446, 745. BHOWMICK, N., NEILSON, E. & MOSES, H. 2004. Stromal fibroblasts in cancer initiation and progression. Nature, 432, 332-7. BONUCCELLI, G., TSIRIGOS, A., WHITAKER-MENEZES, D., PAVLIDES, S., PESTELL, R. G., CHIAVARINA, B., FRANK, P. G., FLOMENBERG, N., HOWELL, A., MARTINEZ-OUTSCHOORN, U. E., SOTGIA, F. & LISANTI, M. P. 2010. Ketones and lactate "fuel" tumor growth and metastasis: Evidence that epithelial cancer cells use oxidative mitochondrial metabolism. BORAD, M., SIGAL, D., URONIS, H., STEPHENSON, J., BAHARY, N., RARICK, M., DEMARCO, L. C., FINNEGAN, T., CHIOREAN, E., RYAN, D., COHN, A. L., ULRICH, B., HARISH, V., ANDERES, E., MA, W., YU, P. P., SINICROPE, F., ENG, C., SUNKARA, U. K. & REDDY, S. G. 2011. Randomized phase II study of the efficacy and safety of gemcitabine plus TH-302 versus gemcitabine alone in previously untreated patients with advanced pancreatic cancer. Journal Of Clinical Oncology, 29. BRERETON, R. G. 2003. Chemometrics : data analysis for the laboratory and chemical plant, Chichester, Wiley. BRUCE, J. 2010. Plasma membrane calcium pump regulation by metabolic stress. World Journal of Biological Chemistry, 1, 221-228. BRUCE, J. & ELLIOTT, A. 2007. Oxidant- impaired intracellular Ca2+ signaling in pancreatic acinar cells: role of the plasma membrane Ca2+- ATPase. American journal of physiology. Cell physiology, 293, C938-C950. CAIRNS, R., HARRIS, I. & MAK, T. 2011. Regulation of cancer cell metabolism. Nature Reviews Cancer, 11, 85. CAMPANELLA, M. E., CHU, H. & LOW, P. S. 2005. Assembly and regulation of a glycolytic enzyme complex on the human erythrocyte membrane. Proceedings of the National Academy of Sciences of the United States of America, 102, 2402-2407. CAPPARELLI, C., GUIDO, C., WHITAKER-MENEZES, D., BONUCCELLI, G., BALLIET, R., PESTELL, T. G., GOLDBERG, A. F., PESTELL, R. G., HOWELL, A., SNEDDON, S., BIRBE, R., TSIRIGOS, A., MARTINEZ-OUTSCHOORN, U., SOTGIA, F. & LISANTI, M. P. 2012a. Autophagy and senescence in cancer-associated fibroblasts metabolically supports tumor growth and metastasis via glycolysis and ketone production. CAPPARELLI, C., WHITAKER-MENEZES, D., GUIDO, C., BALLIET, R., PESTELL, T. G., HOWELL, A., SNEDDON, S., PESTELL, R. G., MARTINEZ-OUTSCHOORN, U., LISANTI, M. P. & SOTGIA, F. 2012b. CTGF drives autophagy, glycolysis and senescence in cancer-associated fibroblasts via HIF1 activation, metabolically promoting tumor growth. CARAFOLI, E. E. 2007. Calcium Signalling and Disease Molecular Pathology of Calcium / edited by Ernesto Carafoli, Marisa Brini, Dordrecht : Springer Netherlands. CHEN, H., YANG, W.-W., WEN, Q.-T., XU, L. & CHEN, M. 2009. TGF-β-induced fibroblast

205

activation protein expression, fibroblast activation protein expression increases the proliferation, adhesion, and migration of HO-8910PM. Experimental and Molecular Pathology, 87, 189-194. CHEN, Y.-F., CHEN, Y.-T., CHIU, W.-T. & SHEN, M.-R. 2013. Remodeling of calcium signaling in tumor progression. Journal of biomedical science, 20, 23. CHIAVARINA, B., WHITAKER-MENEZES, D., MARTINEZ-OUTSCHOORN, U. E., WITKIEWICZ, A. K., BIRBE, R., HOWELL, A., PESTELL, R. G., SMITH, J., DANIEL, R., SOTGIA, F. & LISANTI, M. P. 2011. Pyruvate kinase expression (PKM1 and PKM2) in cancer-associated fibroblasts drives stromal nutrient production and tumor growth. Cancer biology & therapy, 12, 1101. CIRRI, P. & CHIARUGI, P. 2011. Cancer associated fibroblasts: the dark side of the coin. American Journal Of Cancer Research, 1, 482-497. COLOMBATTI, A., DANUSSI, C., PIVETTA, E. & SPESSOTTO, P. 2013. Cancer stem cells and the microenvironment. CONROY, T., DESSEIGNE, F., YCHOU, M., BOUCHÉ, O., GUIMBAUD, R., BÉCOUARN, Y., ADENIS, A., RAOUL, J.-L., GOURGOU-BOURGADE, S., DE LA FOUCHARDIÈRE, C., BENNOUNA, J., BACHET, J.-B., KHEMISSA-AKOUZ, F., PÉRÉ-VERGÉ, D., DELBALDO, C., ASSENAT, E., CHAUFFERT, B., MICHEL, P., MONTOTO-GRILLOT, C. & DUCREUX, M. 2011. FOLFIRINOX versus Gemcitabine for Metastatic Pancreatic Cancer. The New England Journal of Medicine, 364, 1817-1825. CORBET, C. & FERON, O. 2017. Cancer cell metabolism and mitochondria: Nutrient plasticity for TCA cycle fueling. BBA - Reviews on Cancer, 1868, 7-15. COSTA, A., SCHOLER-DAHIREL, A. & MECHTA-GRIGORIOU, F. 2014. The role of reactive oxygen species and metabolism on cancer cells and their microenvironment. Seminars in Cancer Biology. DE LA O, J.-P., EMERSON, L. L., GOODMAN, J. L., FROEBE, S. C., ILLUM, B. E., CURTIS, A. B. & MURTAUGH, L. C. 2008. Notch and Kras reprogram pancreatic acinar cells to ductal intraepithelial neoplasia. Proceedings of the National Academy of Sciences of the United States of America, 105, 18907. ERKAN, M., ADLER, G., APTE, M., BACHEM, M. G., BUCHHOLZ, M., DETLEFSEN, S., ESPOSITO, I., FRIESS, H., GRESS, T. M., HABISCH, H., HWANG, R., JASTER, R., KLEEFF, J., KLOPPEL, G., KORDES, C., LOGSDON, C. D., MASAMUNE, A., MICHALSKI, C. W., OH, J., PHILLIPS, P. A., PINZANI, M., REISER-ERKAN, C., TSUKAMOTO, H. & WILSON, J. 2012. StellaTUM: current consensus and discussion on pancreatic stellate cell research. Gut, 61, 172-178. ERKAN, M., BROCKS, T. & FRIESS, H. 2013. The role of non-cancerous cells in cancer: Pancreatic ductal adenocarcinoma as a model to understand the impact of tumor microenvironment on epithelial carcinogenesis. FADAKA, A., AJIBOYE, B., OJO, O., ADEWALE, O., OLAYIDE, I. & EMUOWHOCHERE, R. 2017. Biology of glucose metabolization in cancer cells. Journal of Oncological Sciences, 3, 45-51. FARROW, B., ROWLEY, D., DANG, T. & BERGER, D. H. 2009. Characterization of Tumor- Derived Pancreatic Stellate Cells. Journal of Surgical Research, 157, 96-102. FEIG, C., GOPINATHAN, A., NEESSE, A., CHAN, D. S., COOK, N. & TUVESON, D. A. 2012. The pancreas cancer microenvironment. Clinical cancer research : an official journal of the American Association for Cancer Research, 18, 4266. FERLAY, J., SOERJOMATARAM, I., DIKSHIT, R., ESER, S., MATHERS, C., REBELO, M., PARKIN, D. M., FORMAN, D. & BRAY, F. 2015. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. International

206

Journal of Cancer, 136, E359-E386. FIASCHI, T., MARINI, A., GIANNONI, E., TADDEI, M. L., GANDELLINI, P., DE DONATIS, A., LANCIOTTI, M., SERNI, S., CIRRI, P. & CHIARUGI, P. 2012. Reciprocal metabolic reprogramming through lactate shuttle coordinately influences tumor- stroma interplay. Cancer research, 72, 5130. FISCHER, K., HOFFMANN, P., VOELKL, S., MEIDENBAUER, N., AMMER, J., EDINGER, M., GOTTFRIED, E., SCHWARZ, S., ROTHE, G., HOVES, S., RENNER, K., TIMISCHL, B., MACKENSEN, A., KUNZ-SCHUGHART, L., ANDREESEN, R., KRAUSE, S. W. & KREUTZ, M. 2007. Inhibitory effect of tumor cell- derived lactic acid on human T cells. Blood, 109, 3812. FURUKAWA, T. 2008. Molecular targeting therapy for pancreatic cancer: current knowledge and perspectives from bench to bedside. J Gastroenterol, 43, 905-911. GARG, B., GIRI, B., MODI, S., SETHI, V., CASTRO, I., UMLAND, O., BAN, Y., LAVANIA, S., DAWRA, R., BANERJEE, S., VICKERS, S., MERCHANT, N. B., CHEN, S. X., GILBOA, E., RAMAKRISHNAN, S., SALUJA, A. & DUDEJA, V. 2018. NFκB in Pancreatic Stellate Cells Reduces Infiltration of Tumors by Cytotoxic T Cells and Killing of Cancer Cells, via Up-regulation of CXCL12. Gastroenterology, 155, 880-891.e8. GHADIRIAN, P., LYNCH, H. T. & KREWSKI, D. 2003. Epidemiology of pancreatic cancer: an overview. Cancer Detection and Prevention, 27, 87-93. GISKEODEGARD, G., BERTILSSON, H., SELNAES, K., WRIGHT, A., BATHEN, T., VISET, T., HALGUNSET, J., ANGELSEN, A., GRIBBESTAD, I. & TESSEM, M. B. 2013. Spermine and Citrate as Metabolic Biomarkers for Assessing Prostate Cancer Aggressiveness. Plos One, 8. GROMSKI, P. S., MUHAMADALI, H., ELLIS, D. I., XU, Y., CORREA, E., TURNER, M. & GOODACRE, R. 2015. A tutorial review: Metabolomics and partial least squares- discriminant analysis - a marriage of convenience or a shotgun wedding. Analytica Chimica Acta, 879, 10-23. GRYNKIEWICZ, G., POENIE, M. & TSIEN, R. Y. 1985. A new generation of Ca 2+ indicators with greatly improved fluorescence properties. Journal of Biological Chemistry, 260, 3440-3450. GUERINI, D., COLETTO, L. & CARAFOLI, E. 2005. Exporting calcium from cells. Cell Calcium, 38, 281-289. HABISCH, H., ZHOU, S., BACHEM, M. G. & SIECH, M. 2010. Interaction of stellate cells with pancreatic carcinoma cells. Cancers, 2, 1661-1682. HANAHAN, D. & WEINBERG, ROBERT A. 2011. Hallmarks of Cancer: The Next Generation. Cell, 144, 646-674. HAQQ, J., HOWELLS, L. M., GARCEA, G., METCALFE, M. S., STEWARD, W. P. & DENNISON, A. R. 2014. Pancreatic stellate cells and pancreas cancer: Current perspectives and future strategies. European Journal of Cancer, 50, 2570-2582. HARDIN, C. D., RAEYMAEKERS, L. & PAUL, R. J. 1992. Comparison of endogenous and exogenous sources of ATP in fueling Ca2+ uptake in smooth muscle plasma membrane vesicles. The Journal of general physiology, 99, 21. HARDIN, C. D., ZHANG, C., KRANIAS, E. G., STEENAART, N. A., RAEYMAEKERS, L. & PAUL, R. J. 1993. Regulation of glycolytically fueled Ca2+ uptake in smooth muscle plasmalemmal vesicles by phosphorylation. The American journal of physiology, 265, H1326. HARPER, J. & SAINSON, R. C. A. 2014. Regulation of the anti- tumour immune response by cancer associated fibroblasts. Seminars in Cancer Biology. HEIDEN, M., CANTLEY, L. C. & THOMPSON, C. 2009. Understanding the Warburg Effect:

207

The Metabolic Requirements of Cell Proliferation. Science. HEZEL, A., KIMMELMAN, A., STANGER, B., BARDEESY, N. & DEPINHO, R. 2006. Genetics and biology of pancreatic ductal adenocarcinoma. Genes Dev. HIDALGO, M. 2010. Pancreatic Cancer. The New England Journal of Medicine, 362, 1605- 1617. HWANG, R., MOORE, T., ARUMUGAM, T., RAMACHANDRAN, V., AMOS, K., RIVERA, A., JI, B., EVANS, D. & LOGSDON, C. D. 2008. Cancer-associated stroma fibroblasts promote pancreatic tumor progression. Cancer Research, 68, 918-926. ICARD, P., FOURNEL, L., ALIFANO, M. & LINCET, H. 2018. Extracellular Citrate and Cancer Metabolism-Letter. Cancer research, 78, 5176. ICARD, P., POULAIN, L. & LINCET, H. 2012. Understanding the central role of citrate in the metabolism of cancer cells. Biochimica et biophysica acta, 1825, 111-116. IHRLUND, L. S., HERNLUND, E., KHAN, O. & SHOSHAN, M. C. 2008. 3- Bromopyruvate as inhibitor of tumour cell energy metabolism and chemopotentiator of platinum drugs. Molecular Oncology, 2, 94-101. IHSAN EKIN, D., HELMUT, F. & GÜRALP, O. C. 2015. Neural plasticity in pancreatitis and pancreatic cancer. Nature Reviews Gastroenterology & Hepatology. ILIC, M. & ILIC, I. 2016. Epidemiology of pancreatic cancer. World journal of gastroenterology, 22, 9694-9705. INDRAN, I. R., TUFO, G., PERVAIZ, S. & BRENNER, C. 2011. Recent advances in apoptosis, mitochondria and drug resistance in cancer cells. BBA - Bioenergetics, 1807, 735-745. IOMMARINI, L., GHELLI, A., GASPARRE, G. & PORCELLI, A. M. 2017. Mitochondrial metabolism and energy sensing in tumor progression. BBA - Bioenergetics, 1858, 582- 590. JAGGAR, J. H., PORTER, V. A., LEDERER, W. J. & NELSON, M. T. 2000. Calcium sparks in smooth muscle. American journal of physiology. Cell physiology, 278, C235. JAMES, A., CHAN, A., ERICE, O., SIRIWARDENA, A. & BRUCE, J. 2013. Glycolytic ATP Fuels the Plasma Membrane Calcium Pump Critical for Pancreatic Cancer Cell Survival*. Journal of Biological Chemistry, 288, 36007-36019. JAMES, A. D., PATEL, W., BUTT, Z., ADIAMAH, M., DAKHEL, R., LATIF, A., UGGENTI, C., SWANTON, E., IMAMURA, H., SIRIWARDENA, A. K. & BRUCE, J. I. E. 2015a. The Plasma Membrane Calcium Pump in Pancreatic Cancer Cells Exhibiting the Warburg Effect Relies on Glycolytic ATP. The Journal of biological chemistry, 290, 24760. JAMES, E. L., MICHALEK, R. D., PITIYAGE, G. N., DE CASTRO, A. M., VIGNOLA, K. S., JONES, J., MOHNEY, R. P., KAROLY, E. D., PRIME, S. S. & PARKINSON, E. K. 2015b. Senescent human fibroblasts show increased glycolysis and redox homeostasis with extracellular metabolomes that overlap with those of irreparable DNA damage, aging, and disease. Journal of proteome research, 14, 1854-1871. JANG, M., KIM, S. S. & LEE, J. 2013. Cancer cell metabolism: implications for therapeutic targets. Experimental & molecular medicine, 45, e45-e45. JIANG, B. 2017. Aerobic glycolysis and high level of lactate in cancer metabolism and microenvironment. Genes & Diseases, 4, 25-27. JOSE, C., BELLANCE, N. & ROSSIGNOL, R. 2011. Choosing between glycolysis and oxidative phosphorylation: A tumor's dilemma? BBA - Bioenergetics, 1807, 552-561. JUSTUS, C., SANDERLIN, E. & YANG, L. 2015. Molecular Connections between Cancer Cell Metabolism and the Tumor Microenvironment. Basel: MDPI AG. KACZANOWSKI, S. 2016. Apoptosis: its origin, history, maintenance and the medical implications for cancer and aging.

208

KALYANARAMAN, B. 2017. Teaching the basics of cancer metabolism: Developing antitumor strategies by exploiting the differences between normal and cancer cell metabolism. Redox Biology, 12, 833-842. KAWASE, T., YASUI, Y., NISHINA, S., HARA, Y., YANATORI, I., TOMIYAMA, Y., NAKASHIMA, Y., YOSHIDA, K., KISHI, F., NAKAMURA, M. & HINO, K. 2015. Fibroblast activation protein-alpha-expressing fibroblasts promote the progression of pancreatic ductal adenocarcinoma. Bmc Gastroenterology, 15. KINNAIRD, A. & MICHELAKIS, E. 2015. Metabolic modulation of cancer: a new frontier with great translational potential. J Mol Med, 93, 127-142. KOUKOURAKIS, M. I., GIATROMANOLAKI, A., HARRIS, A. L. & SIVRIDIS, E. 2006. Comparison of metabolic pathways between cancer cells and stromal cells in colorectal carcinomas: a metabolic survival role for tumor- associated stroma. Cancer research, 66, 632. KOUMENIS, C. E. 2014. Tumor Microenvironment and Cellular Stress Signaling, Metabolism, Imaging, and Therapeutic Targets / edited by Constantinos Koumenis, Ester Hammond, Amato Giaccia, New York, NY : Springer New York : Imprint: Springer. KROEMER, G. & POUYSSEGUR, J. 2008. Tumor Cell Metabolism: Cancer's Achilles' Heel. Cancer Cell, 13, 472-482. LEE, E. J., GUSEV, Y., JIANG, J., NUOVO, G. J., LERNER, M. R., FRANKEL, W. L., MORGAN, D. L., POSTIER, R. G., BRACKETT, D. J. & SCHMITTGEN, T. D. 2007. Expression profiling identifies microRNA signature in pancreatic cancer. International Journal of Cancer, 120, 1046-1054. LEHNINGER, A. L. 1975. Biochemistry : the molecular basis of cell structure and function, New York, Worth. LOWENFELS, A. B. & MAISONNEUVE, P. 2006. Epidemiology and risk factors for pancreatic cancer. Best Practice & Research Clinical Gastroenterology, 20, 197-209. Lu, L. & Zeng, J. 2017. Evaluation of K-ras and p53 expression in pancreatic adenocarcinoma using the cancer genome atlas. PLoS ONE, 12, e0181532. MAISONNEUVE, P. & LOWENFELS, A. B. 2010. Epidemiology of Pancreatic Cancer: An Update. Digestive Diseases, 28, 645-656. MANKAD, P., JAMES, A., SIRIWARDENA, A. K., ELLIOTT, A. C. & BRUCE, J. I. E. 2012. Insulin protects pancreatic acinar cells from cytosolic calcium overload and inhibition of plasma membrane calcium pump. The Journal of biological chemistry, 287, 1823. MAO, Y., KELLER, E., GARFIELD, D., SHEN, K. & WANG, J. 2013. Stromal cells in tumor microenvironment and breast cancer. Cancer Metastasis Rev, 32, 303-315. MARTINEZ-OUTSCHOORN, U., SOTGIA, F. & LISANTI, M. P. 2014a. Tumor microenvironment and metabolic synergy in breast cancers: critical importance of mitochondrial fuels and function. MARTINEZ-OUTSCHOORN, U. E., BALLIET, R. M., RIVADENEIRA, D. B., CHIAVARINA, B., PAVLIDES, S., WANG, C., WHITAKER-MENEZES, D., DAUMER, K. M., LIN, Z., WITKIEWICZ, A. K., FLOMENBERG, N., HOWELL, A., PESTELL, R. G., KNUDSEN, E. S., SOTGIA, F., LISANTI, M. P. & MARTINEZ- OUTSCHOORN UE, B. R. M. R. D. B. C. B. P. S. W. C. W.-M. D. D. K. M. L. Z. W. A. 2010. Oxidative stress in cancer associated fibroblasts drives tumor-stroma co- evolution: A new paradigm for understanding tumor metabolism, the field effect and genomic instability in cancer cells. MARTINEZ-OUTSCHOORN, U. E., LIN, Z., WHITAKER-MENEZES, D., HOWELL, A., SOTGIA, F. & LISANTI, M. P. 2012. Ketone body utilization drives tumor growth and

209

metastasis. MARTINEZ-OUTSCHOORN, U. E., LISANTI, M. P. & SOTGIA, F. 2014b. Catabolic cancer-associated fibroblasts (CAFs) transfer energy and biomass to anabolic cancer cells, fueling tumor growth. Seminars in Cancer Biology. MARTINEZ-OUTSCHOORN, U. E., PAVLIDES, S., HOWELL, A., PESTELL, R. G., TANOWITZ, H. B., SOTGIA, F. & LISANTI, M. P. 2011. Stromal–epithelial metabolic coupling in cancer: Integrating autophagy and metabolism in the tumor microenvironment. International Journal of Biochemistry and Cell Biology, 43, 1045- 1051. MARTINEZ-OUTSCHOORN UE, T. C. L. Z. W.-M. D. C. B. Z. J. W. C. P. S. M.-C. M. P. C. F. W. A. 2010. Autophagy in cancer associated fibroblasts promotes tumor cell survival: Role of hypoxia, HIF1 induction and NFκB activation in the tumor stromal microenvironment. MASAMUNE, A., HAMADA, S., YOSHIDA, N., NABESHIMA, T. & SHIMOSEGAWA, T. 2018. Pyruvate Kinase Isozyme M2 Plays a Critical Role in the Interactions Between Pancreatic Stellate Cells and Cancer Cells. Digestive Diseases and Sciences, 63, 1868- 1877. MAZUREK, M. P., PRASAD, P. D., GOPAL, E., FRASER, S. P., BOLT, L., RIZANER, N., PALMER, C. P., FOSTER, C. S., PALMIERI, F., GANAPATHY, V., STÜHMER, W., DJAMGOZ, M. B. A. & MYCIELSKA, M. E. 2010. Molecular origin of plasma membrane citrate transporter in human prostate epithelial cells. EMBO reports, 11, 431- 437. MAZUREK, S. E. & SHOSHAN, M. E. 2015. Tumor cell metabolism : pathways, regulation and biology / Sybille Mazurek, Maria Shoshan, editors, Wien : Springer. MERCIER, I., CASIMIRO, M. C., WANG, C., ROSENBERG, A. L., QUONG, J., MINKEU, A., ALLEN, K. G., DANILO, C., SOTGIA, F., BONUCCELLI, G., JASMIN, J.-F., XU, H., BOSCO, E., ARONOW, B., WITKIEWICZ, A., PESTELL, R. G., KNUDSEN, E. S. & LISANTI, M. P. 2008. Human breast cancer- associated fibroblasts (CAFs) show caveolin- 1 downregulation and RB tumor suppressor functional inactivation: Implications for the response to hormonal therapy. Cancer biology & therapy, 7, 1212. MILLER, K. 1977. Calcium and cancer. Medical Hypotheses, 3, 263-264. MIRAN, J., SUNG SOO, K. & JINHWA, L. 2013. Cancer cell metabolism: implications for therapeutic targets. Experimental & Molecular Medicine, 45, e45. MONTEITH, G. R., DAVIS, F. M. & ROBERTS-THOMSON, S. J. 2012. Calcium channels and pumps in cancer: changes and consequences. The Journal of biological chemistry, 287, 31666. MORRIS, J., WANG, S. & HEBROK, M. 2010. KRAS, Hedgehog, Wnt and the twisted developmental biology of pancreatic ductal adenocarcinoma. Nature Reviews Cancer, 10, 683. MUNDY, G. R. 1978. Calcium and cancer. Life Sciences, 23, 1735-1744. MYCIELSKA, M. E., DETTMER, K., RÜMMELE, P., SCHMIDT, K., PREHN, C., MILENKOVIC, V. M., JAGLA, W., MADEJ, G. M., LANTOW, M., SCHLADT, M., CECIL, A., KOEHL, G. E., EGGENHOFER, E., WACHSMUTH, C. J., GANAPATHY, V., SCHLITT, H. J., KUNZELMANN, K., ZIEGLER, C., WETZEL, C. H., GAUMANN, A., LANG, S. A., ADAMSKI, J., OEFNER, P. J. & GEISSLER, E. K. 2018a. Extracellular Citrate Affects Critical Elements of Cancer Cell Metabolism and Supports Cancer Development. Cancer research, 78, 2513. MYCIELSKA, M. E., DETTMER, K., RÜMMELE, P., SCHMIDT, K., PREHN, C., MILENKOVIC, V. M., JAGLA, W., MADEJ, G. M., LANTOW, M., SCHLADT, M.,

210

CECIL, A., KOEHL, G. E., EGGENHOFER, E., WACHSMUTH, C. J., GANAPATHY, V., SCHLITT, H. J., KUNZELMANN, K., ZIEGLER, C., WETZEL, C. H., GAUMANN, A., LANG, S. A., ADAMSKI, J., OEFNER, P. J. & GEISSLER, E. K. 2018b. Extracellular Citrate Affects Critical Elements of Cancer Cell Metabolism and Supports Cancer Development In Vivo. Cancer research, 78, 2513-2523. MYCIELSKA, M. E. & GEISSLER, E. K. 2018. Extracellular Citrate and Cancer Metabolism- Response. Cancer research, 78, 5177. NEOPTOLEMOS, J. P. A. 2010. Pancreatic Cancer / by John P. Neoptolemos, Raul Urrutia, James L. Abbruzzese, Markus W. Büchler, New York, NY : Springer New York. PALSSON-MCDERMOTT, E. M. & O'NEILL, L. A. J. 2013. The Warburg effect then and now: From cancer to inflammatory diseases. BioEssays, 35. PAUL, R. J., HARDIN, C. D., RAEYMAEKERS, L., WUYTACK, F. & CASTEELS, R. 1989. Preferential support of Ca 2+ uptake in smooth muscle plasma membrane vesicles by an endogenous glycolytic cascade. FASEB Journal, 3, 2298-2301. PAVLIDES, S., TSIRIGOS, A., VERA, I., FLOMENBERG, N., FRANK, P. G., CASIMIRO, M. C., WANG, C. G., PESTELL, R. G., MARTINEZ-OUTSCHOORN, U., HOWELL, A., SOTGIA, F. & LISANTI, M. P. 2010. Transcriptional evidence for the " Reverse Warburg Effect" in human breast cancer tumor stroma and metastasis: Similarities with oxidative stress, inflammation, Alzheimer's disease, and "Neuron-Glia Metabolic Coupling". Aging-Us, 2, 185-199. PAVLIDES, S., VERA, I., GANDARA, R., SNEDDON, S., PESTELL, R. G., MERCIER, I., MARTINEZ-OUTSCHOORN, U., WHITAKER-MENEZES, D., HOWELL, A., SOTGIA, F. & LISANTI, M. P. 2012. Warburg Meets Autophagy: Cancer-Associated Fibroblasts Accelerate Tumor Growth and Metastasis via Oxidative Stress, Mitophagy, and Aerobic Glycolysis. Antioxid. Redox Signal. PAVLIDES, S., WHITAKER-MENEZES, D., CASTELLO-CROS, R., FLOMENBERG, N., WITKIEWICZ, A. K., FRANK, P. G., CASIMIRO, M. C., WANG, C., FORTINA, P., ADDYA, S., PESTELL, R. G., MARTINEZ-OUTSCHOORN, U. E., SOTGIA, F., LISANTI, M. P. & PAVLIDES S, W.-M. D. C.-C. R. F. N. W. A. K. F. P. G. C. M. C. W. C. F. P. A. S. 2009. The reverse Warburg effect: aerobic glycolysis in cancer associated fibroblasts and the tumor stroma. PHILIPPE, I. & HUBERT, L. 2016. The reduced concentration of citrate in cancer cells: An indicator of cancer aggressiveness and a possible therapeutic target. Drug Resistance Updates, 29, 47-53. RAIMONDI, S., MAISONNEUVE, P. & LOWENFELS, A. 2009. Epidemiology of pancreatic cancer: an overview. Nature Reviews Gastroenterology & Hepatology, 6, 699. RASANEN, K. & VAHERI, A. 2010. Activation of fibroblasts in cancer stroma. Exp. Cell Res. REN, J.-G., SETH, P., YE, H., GUO, K., HANAI, J.-I., HUSAIN, Z. & SUKHATME, V. 2017. Citrate Suppresses Tumor Growth in Multiple Models through Inhibition of Glycolysis, the Tricarboxylic Acid Cycle and the IGF-1R Pathway. Sci Rep, 7, 4537-4537. RICHARD, A. M., RAOUD, M., ELIZABETH, L. F., KEITH, E. V., LOEZA, S. G. H., KATHERINE, A. H., NAIM, U. R., LINDSAY, A. W., SAMUEL, C. E., ALEXANDER, H. C., JADWIGA, K. S., JUDY, M. A., HONG JIN, K., DAVID, J. B., MARK, S. T., CHRISTINE, A. I.-D., MICHAEL, A. H. & JEN JEN, Y. 2015. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nature Genetics, 47. RICHARDS, K. E., ZELENIAK, A. E., FISHEL, M. L., WU, J., LITTLEPAGE, L. E. & HILL, R. 2016. Cancer-associated fibroblast exosomes regulate survival and proliferation of pancreatic cancer cells. Oncogene, 36. ROBERT, A. G. & ROBERT, J. G. 2004. Why do cancers have high aerobic glycolysis?

211

Nature Reviews Cancer, 4, 891. SALEM, A. F., WHITAKER-MENEZES, D., LIN, Z., MARTINEZ-OUTSCHOORN, U. E., TANOWITZ, H. B., AL-ZOUBI, M. S., HOWELL, A., PESTELL, R. G., SOTGIA, F. & LISANTI, M. P. 2012. Two-compartment tumor metabolism: autophagy in the tumor microenvironment and oxidative mitochondrial metabolism (OXPHOS) in cancer cells. SCHNEIDERHAN, W., DIAZ, F., FUNDEL, M., ZHOU, S., SIECH, M., HASEL, C., MÖLLER, P., GSCHWEND, J. E., SEUFFERLEIN, T., GRESS, T., ADLER, G. & BACHEM, M. G. 2007. Pancreatic stellate cells are an important source of MMP- 2 in human pancreatic cancer and accelerate tumor progression in a murine xenograft model and CAM assay. Journal of cell science, 120, 512. SHIGA, K., HARA, M., NAGASAKI, T., SATO, T., TAKAHASHI, H. & TAKEYAMA, H. 2015. Cancer-Associated Fibroblasts: Their Characteristics and Their Roles in Tumor Growth. Cancers, 7, 2443-2458. SIMEONE, D. M. E. 2013. Molecular Genetics of Pancreatic Cancer / edited by Diane M. Simeone, Anirban Maitra, New York, NY : Springer New York : Imprint: Springer. SOLAINI, G., SGARBI, G. & BARACCA, A. 2011. Oxidative phosphorylation in cancer cells. BBA - Bioenergetics, 1807, 534-542. SOTGIA, F., MARTINEZ-OUTSCHOORN, U. E. & LISANTI, M. P. 2011. Mitochondrial oxidative stress drives tumor progression and metastasis: should we use antioxidants as a key component of cancer treatment and prevention? BMC Medicine, 9, 62-62. SOTGIA, F., MARTINEZ-OUTSCHOORN, U. E. & LISANTI, M. P. 2014. The reverse warburg effect in osteosarcoma. SOTGIA, F., WHITAKER-MENEZES, D., MARTINEZ-OUTSCHOORN, U. E., FLOMENBERG, N., BIRBE, R. C., WITKIEWICZ, A. K., HOWELL, A., PHILP, N. J., PESTELL, R. G. & LISANTI, M. P. 2012. Mitochondrial metabolism in cancer metastasis: visualizing tumor cell mitochondria and the "reverse Warburg effect" in positive lymph node tissue. SOUSA, C. M., BIANCUR, D. E., WANG, X., HALBROOK, C. J., SHERMAN, M. H., ZHANG, L., KREMER, D., HWANG, R. F., WITKIEWICZ, A. K., YING, H., ASARA, J. M., EVANS, R. M., CANTLEY, L. C., LYSSIOTIS, C. A. & KIMMELMAN, A. C. 2016. Pancreatic stellate cells support tumour metabolism through autophagic alanine secretion. Nature, 536, 479-483. STREHLER, E. E. 2013. Plasma membrane calcium ATPases as novel candidates for therapeutic agent development. Journal of pharmacy & pharmaceutical sciences : a publication of the Canadian Society for Pharmaceutical Sciences, Société canadienne des sciences pharmaceutiques, 16, 190. STREHLER, E. E. & ZACHARIAS, D. 2001. Role of Alternative Splicing in Generating Isoform Diversity Among Plasma Membrane Calcium Pumps. In: STREHLER, E. E. (ed.). SUKLABAIDYA, S., DASH, P. & SENAPATI, S. 2017. Pancreatic fibroblast exosomes regulate survival of cancer cells. Oncogene, 36, 3648. SUMNER, L. W., AMBERG, A., BARRETT, D., BEALE, M. H., BEGER, R., DAYKIN, C. A., FAN, T. W. M., FIEHN, O., GOODACRE, R., GRIFFIN, J. L., HANKEMEIER, T., HARDY, N., HARNLY, J., HIGASHI, R., KOPKA, J., LANE, A. N., LINDON, J. C., MARRIOTT, P., NICHOLLS, A. W., REILY, M. D., THADEN, J. J. & VIANT, M. R. 2007. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics : Official journal of the Metabolomic Society, 3, 211-221. SUSHRUT, K., VALERIE, S. L., HIKARU, S., SUJUAN, Y., CAROLINA, F. R., SONIA, A. M., LEE, J. J. & RAGHU, K. 2017. Exosomes facilitate therapeutic targeting of

212

oncogenic KRAS in pancreatic cancer. Nature, 546. SWIETACH, P., VAUGHAN-JONES, R. & HARRIS, A. 2007. Regulation of tumor pH and the role of carbonic anhydrase 9. Cancer Metastasis Rev, 26, 299-310. SYNTICHAKI, P. & TAVERNARAKIS, N. 2003. The biochemistry of neuronal necrosis: rogue biology? Nature Reviews Neuroscience, 4, 672. TAKIKAWA, T., MASAMUNE, A., YOSHIDA, N., HAMADA, S., KOGURE, T. & SHIMOSEGAWA, T. 2017. Exosomes Derived From Pancreatic Stellate Cells: MicroRNA Signature and Effects on Pancreatic Cancer Cells. Pancreas, 46, 19. TANG, D., WANG, D., YUAN, Z., XUE, X., ZHANG, Y., AN, Y., CHEN, J., TU, M., LU, Z., WEI, J., JIANG, K. & MIAO, Y. 2013. Persistent activation of pancreatic stellate cells creates a microenvironment favorable for the malignant behavior of pancreatic ductal adenocarcinoma. Hoboken. TENNANT, D., DURÁN, R. & GOTTLIEB, E. 2010. Targeting metabolic transformation for cancer therapy. Nature Reviews Cancer, 10, 267. THOMAS-TIKHONENKO, A. E. 2010. Cancer Genome and Tumor Microenvironment / edited by Andrei Thomas-Tikhonenko, New York, NY : Springer New York. VINCENT, A., HERMAN, J., SCHULICK, R., HRUBAN, R. H. & GOGGINS, M. 2011. Pancreatic cancer. The Lancet, 378, 607-620. VONLAUFEN, A., JOSHI, S., QU, C., PHILLIPS, P. A., XU, Z., PARKER, N., TOI, C. S., PIROLA, R. C., WILSON, J., GOLDSTEIN, D. & APTE, M. 2008. Pancreatic stellate cells: Partners in crime with pancreatic cancer cells. Cancer Res., 68, 2085-2093. WARBURG, O. 1928. The Chemical Constitution of Respiration Ferment. Science, 68, 437- 443. WARBURG, O. 1956. On the Origin of Cancer Cells. Science, 123, 309-314. WEDGE, D. C., ALLWOOD, J. W., DUNN, W., VAUGHAN, A. A., SIMPSON, K., BROWN, M., PRIEST, L., BLACKHALL, F. H., WHETTON, A. D., DIVE, C. & GOODACRE, R. 2011. Is serum or plasma more appropriate for intersubject comparisons in metabolomic studies? An assessment in patients with small-cell lung cancer. Analytical chemistry, 83, 6689-6697. WISE, D. R., DEBERARDINIS, R. J., MANCUSO, A., SAYED, N., ZHANG, X.-Y., PFEIFFER, H. K., NISSIM, I., DAIKHIN, E., YUDKOFF, M., MCMAHON, S. B. & THOMPSON, C. B. 2008. Myc regulates a transcriptional program that stimulates mitochondrial glutaminolysis and leads to glutamine addiction. Proceedings of the National Academy of Sciences of the United States of America, 105, 18782-18787. ÖZDEMIR, B. C., PENTCHEVA-HOANG, T., CARSTENS, J. L., ZHENG, X., WU, C.-C., SIMPSON, T. R., LAKLAI, H., SUGIMOTO, H., KAHLERT, C., NOVITSKIY, S. V., DE JESUS-ACOSTA, A., SHARMA, P., HEIDARI, P., MAHMOOD, U., CHIN, L., MOSES, H. L., WEAVER, V. M., MAITRA, A., ALLISON, J. P., LEBLEU, V. S. & KALLURI, R. 2015. Depletion of Carcinoma-Associated Fibroblasts and Fibrosis Induces Immunosuppression and Accelerates Pancreas Cancer with Reduced Survival. Cancer Cell, 28, 831-833.

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CHAPTER 8

8. SUPPLEMENT

8.1. Stocks Preparation:

Test Reagent Catalogue #, Company Solvent

Iodoacetate (IAA) D8418-100ml, Sigma® MilliQ water

16490-10gm, Sigma® MilliQ water Bromopyruvate (Brpy)

Oligomycin (OM) 495455-10mg, Calbiochem® (DMSO) dimethylsulfoxide (D8418-100ml, Sigma®)

® Antimycin (AM) A8674-100mg, Sigma 100% Ethanol

® Ionomycin I0634, Sigma DMSO

® Adenosine triphosphate (ATP) A-2383-5gm Sigma MilliQ water

® Carbachol C-4382-10gm, Sigma MilliQ water

® L (+) lactic acid sodium L7022-10gm, Sigma Serum Free DMEM

® L-alanine A7469-25gm, Sigma Serum Free DMEM

® Citric acid C0759-100gm, Sigma Serum Free DMEM, PH 7.4

® 2-Deoxy-D-glucose (2-DG) D6134, Sigma DMSO

1-(4-Pyridinyl)-3-(2- ® quinolinyl)-2-propen-1-one SML-1009-25gm, Sigma DMSO (PFK-15)

Carbonyl cyanide-3- C2759-1gm, Sigma® DMSO chlorophenylhydrazone (Cccp)

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Tetramethylrhodamine Ethyl 87917, Sigma® DMSO Ester (TMRE)

Fluorescent d-glucose analog 2- [N-(7-nitrobenz-2-oxa-1,3- N13195-5mg Invitrogen® DMSO diazol-4-yl) amino]-2-deoxy-d- glucose (2-NBDG)

MitoTracker Red FM M22425 DMSO

MitoTracker Green FM M7514 DMSO

Kanamycin sulfate 11815024, Life Technologies Ltd MilliQ water

® Neomycin (G418) disulfate salt A1720-5gm, Sigma PBS

Fura-2, AM 1:1 mixture of DMSO and Invitrogen®, TEFLabs 0.1% pluronic® F-127 (P2443, Sigma®)

Histamine H7125, Sigma® MilliQ water

Bradykinin B3259, Sigma® 0.1M Acetic acid

Substance P S6883, Sigma® 0.1M Acetic acid

*All stocks were aliquoted and kept in -20°C freezer till use, except for ATP, carbachol, MitoTracker Red FM and MitoTracker Green FM stocks were prepared on the day of the experiment.

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8.2. Calibrating of Resting Intracellular Calcium for All Cell Type:

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Figure 8.1. Calcium calibration for MiaPaCa-2, PANC-1, hPSCs, HPDE, BJ skin 2+ fibroblast. Intracellular calcium concentration ([Ca ]i), was measured by using Fura-2 florescence imaging.

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8.3. Determine the Optimal Calcium Agonist for BJ Skin Fibroblast to Test for Cells Viability Required for Calcium Overload Studies.

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Figure 8.2. Calcium imaging of BJ skin fibroblast treated with different 2+ calcium agonist. Intracellular calcium concentration ([Ca ] i), was measured in BJ skin fibroblast by using Fura-2 florescence imaging. Panel (a-q) are representative traces for each condition following the perfusion of different calcium agonist for 20 minutes. Panel (r) represent the maximal [Ca2+]i response to agonist. Panel (s,t) represent the percentage of cell that has response and no response to calcium agonist. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements. BK, Bradykinin; SubP, Substance P.

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8.4. Comparison of Pancreatic Stellate Cells Response in Calcium Overload and ATP Studies when Culture in DMEM/F12 vs 25mM DMEM.

Figure 8.3. Calcium imaging for hPSCs cultured in DMEM/F12 vs 25mM DMEM. Intracellular calcium concentration ([Ca2+] i), was measured in MiaPaCa-2 cells by using Fura- 2 florescence imaging. Panel b are representative traces following the perfusion of either glycolytic inhibitor (100 µM IAA for 20 minutes, while panel (a) is the time matched control were no drug was perfused. Following the drug perfusion cells were washed and then treated with 100μM ATP to test cell viability. Data were quantified by measuring AUC (c) and 2+ maximum change in [Ca ]i (d) for 35 minute. The maximum response to ATP (e) was calculated, also. For data analysis a one-way ANOVA followed by Tukey's multiple comparisons test was used. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

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Figure 8.4. ATP depletion following the treatment with IAA for hPSCs cultured in DMEM/F12 vs 25mM DMEM. Cells were treated for 15 minutes (a) or 1 hr (b) with different concentration of IAA. Luciferase based luminescence ATP assay was used to determine the total ATP for each condition. The % of control ATP was calculated by normalizing the ATP count for each condition to untreated cells. The absence of stars indicates the absence of statistically significant difference (p>0.05) between the groups of measurements.

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8.5. ATP Standard Curve.

Media DMEM DMEM/F12 SFM,1X Equation Y = 139.1*X - 9205 Y = 165.6*X - 2814 Y = 158.9*X + 3452 R square 0.9995 1 1

Figure 8.5. ATP standard curve. Luciferase based luminescence ATP assay was used where different concentration of ATP was diluted in DMEM, DMEM/F12 or SFM,1x to determine the ATP standard curve.

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8.6. Mechanism of Action of Different Metabolic Inhibitors

Figure 8.6. Summary for the mechanism of action of different metabolic inhibitors: Glycolytic inhibitor involve: Bromopyruvate (Brpy), which inhibit hexokinase enzyme, PFK- 15, a selective 6-phosphofructo-2-kinase (PFK) inhibitor and iodoacetate (IAA), that inhibits glyceraldehyde-3-phosphate (GAPDH). Mitochondrial inhibitors involve: Antimycin (AM), that inhibits mitochondrial respiratory chain by binding to cytochrome bc-1 complex; and Oligomycin (OM), that reduces the electron flow through electron transport chain by inhibiting the mitochondrial ATP-synthase.

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8.7. pDsRed-Monomer-C1 and pEGFP-N1 Vector Information

Plasmid name: pDsRed-Monomer-C1

Antibiotics: Neomycin/Kanamycin resistance gene

Excitation/Emission: 557/585 nm

Source: Clontech Laboratories, Inc., Cat. #632466

Plasmid name: pEGFP-N1

Antibiotics: Neomycin/Kanamycin resistance gene

Excitation/Emission: 488/507 nm

Source: BD Biosciences Clontech, Cat. #6085-1

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8.8. Summary of the Mito-Stress test.

Figure 8.7. Mito Stress Test summary of the key parameters of mitochondrial respiration ( Adapted from Agilent Seahorse XF website).

Figure 8.8. The Mechanism of action of different drugs used in Agilent Seahorse XF Cell Mito Stress Test. oligomycin (OM), inhibit the mitochondrial ATP synthase; carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP), is a protonophores and metabolic uncoupler; rotenone/Antimycin (RT/AM), inhibit mitochondrial respiratory chain by binding to complex I and III ( Adapted from Agilent Seahorse XF website).

Mito stress parameter:

• Basal respiration: It is the mitochondrial function of the cell under baseline conditions.

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• ATP production: It is the amount of basal respiration that contribute to provide for the energy need of the cells. It is measured following the inhibition of the mitochondrial ATP synthase by OM. • H+ (Proton) leak: It is the remaining basal respiration not contribute to ATP production. • Maximal respiration: It is the maximum rate of respiration that a cell can reach. It is measured following the injection of FCCP which stimulate the respiratory chain to work at its maximum capacity to meet energy demand. FCCP will cause rapid consumption of substrate, such as, sugars, fats and amino acids to meet high metabolic demand. • Spare respiratory capacity: It is a measure that indicate the capability of the cell to meet energy demand. • Non-mitochondrial respiration: It is measured following the injection of rotenone and antimycin A and represent the amount of oxygen consumption that persist due to the subset cellular enzymes utilization.

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8.9. Agilent Seahorse XF Cell Energy Phenotype Profile.

Figure 8.9. Agilent Seahorse XF Cell Energy Phenotype Profile. This test allows to determine the energy pathway (Mitochondrial oxidation phosphorylation or glycolysis) utilized by a cell population under baseline and stressed conditions ( Adapted from Agilent Seahorse XF website).

Cell energy phenotype parameter:

• Oxygen consumption rate (OCR): Is the rate of mitochondrial respiration of the cells. • Extracellular acidification rate (ECAR): It is the rate of glycolysis of the cells. • Baseline phenotype: It is the OCR and ECAR of cells at starting of the experiment. • Stressed phenotype: It is the OCR and ECAR of cells under stress. • Metabolic potential: it is the % increase of stressed OCR / baseline OCR, and stressed ECAR/ baseline ECAR. it is the cells ability to meet an energy demand via respiration or glycolysis.

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8.10. Seahorse XF Glycolysis Stress Test Profile.

Figure 8.10. The key parameters of glycolytic function measured by Agilent Seahorse XF Glycolysis Stress Test. The ECAR is measure before, during and after sequential injections of glucose, OM and 2-D. This test allows the measure of glycolysis, glycolytic capacity, glycolytic reserve and nonglycolytic acidification ( Adapted from Agilent Seahorse XF website).

Figure 8.11. Agilent Seahorse XF Glycolysis Stress Test Illustration of Glycolysis. This diagram summaries the mechanism of action of different drugs used in the Glyco stress test. Glucose fuels glycolysis by converting to pyruvate. Oligomycin can shut down the mitochondria by inhibits ATP synthase resulting in total dependency on glycolysis. 2-DG act

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as a glucose analog that can shut down glycolysis ( Adapted from Agilent Seahorse XF website).

Glyco stress parameters:

• Glycolysis: It is the process of converting glucose to pyruvate to produce energy that utilized by the cells. • Glycolytic capacity: It is the maximum ECAR rate that cells can reached following the treatment with oligomycin, which inhibit mitochondrial oxidative phosphorylation, and this make the cells use glycolysis to their maximum capacity. • Glycolytic reserve: It indicate the ability of a cell to respond to a high energetic demand. • Non-glycolytic acidification: It is a measure of the extracellular acidification generated from non-glycolysis sources.

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8.11. FCCP Optimization for Different Cell Type for Seahorse Experiment.

Figure 8.12. Optimizing the FCCP for MiaPaCa-2, hPSCs and HPDE cells. The cells were analysed by using XFe96 extracellular flux analyzer by performing an XF cell Mito-stress test. This test involves sequential injection of oligomycin (OM, 1 µM), carbonyl cyanide-4- (trifluoromethoxy) phenylhydrazone (FCCP,0.125, 0.25, 0.5, 1 or 2 µM) and a combination of rotenone/Antimycin (RT/AM, 0.5 µM) to study the mitochondrial function of the cells. The oxygen consumption rate (OCR) of the cells were measured during and after each drug injection and then normalized to the total protein content, measured in absorbance unit (AU) by using Sulforhodamine-B (SRB) assay.

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8.12. Florescence Microscope Imaging of MiaPaCa-2-RFP cells:

The results showing problem with the cell’s clumping which make it difficult to analysed GLUT-1, MCT-1 and MCT-4 transporter immunofluorescent studies that was performed.

Figure 8.13. Florescence microscope imaging of MiaPaCa-2 cell-RFP (+). The mCherry channel was used to detect the MiaPaCa-2-RFP (+) cells cultured alone (a) or co-cultured with hPSCs (b). 20x images scale bar 51µM. 40x images scale bar 25µM.

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8.13. Cell Sorting by Flow Cytometry for Generation of Stable Cells Line (First Sorting):

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Figure 8.14. Flow cytometry cell sorting.

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8.14. Cell Sorting by Flow Cytometry for Generation of Stable Cells Line (Second Sorting):

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Figure 8.15. Flow cytometry cell sorting.

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8.15. Cell Count at the End of Conditioned media Collection for Metabolomics

Figure 8.16. Cell count for cells at the end of conditioned media collection.

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8.16. Full List of Metabolites Identified by Using Metabolomic for Citrate Uptake Study.

Align ID Metabolite ID Match 204 Citric acid (4TMS) 96.22 202 NA 0.00 67 Phosphoric acid (3TMS) 93.66 198 NA 0.00 199 Fructose, D- (5TMS) 95.93 205 Ornithine (4TMS) 96.61 207 NA 0.00 263 Mannitol (6TMS) 85.21 28 Pyruvic acid (2TMS) 95.98 268 Galactonic acid (6TMS) 86.49 208 Arginine [-NH3] (3TMS) 91.65 35 Isovaleric acid, 2-oxo- (1MEOX) (1TMS) MP 98.73 117 Methionine (2TMS) 94.42 66 Norleucine (2TMS) 99.45 461 beta-Galactopyranosyl-1,3-arabinose, D- (1MEOX) (7TMS) 94.66 209 Arginine [-NH3] (3TMS) 96.09 298 NA 0.00 74 Isoleucine (2TMS) 99.32 111 Pyroglutamic acid (1TMS) 99.02 56 Valine (2TMS) 99.53 90 Alanine (3TMS) 89.42 397 NA 0.00 87 Serine (3TMS) 94.65 149 Phenylalanine (1TMS) 99.14 167 Phenylalanine (2TMS) 96.17 182 Glutamine, DL- (4TMS) 89.85 75 NA 0.00 249 Histidine (3TMS) 97.95 325 Tryptophan (3TMS) 99.50 39 Glycine (2TMS) 99.45 68 NA 0.00 38 Methionine (1TMS) 94.26 148 Creatinine (3TMS) 95.76

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Figure 8.17. Citrate uptake studies. List of different metabolites identified following the treatment with 0.2, 1 or 5 (mM) citrate. Gas chromatography mass spectrometry were used to identify the metabolites. The data represent the mean of five biological replicate.

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8.17. Full list of Metabolites Identified by Using Metabolomic-Mass Spectrum Footprint:

AlignID tmean FoundIn Name.1 MatchFactor.1 DB.Id.1 CAS.1 Formula.1 1 5.6452 22 Indole-3-acetonitrile (1TMS) 75.67 26 771-51-7 C13H16N2Si 2 5.7352 22 Hydroxylamine (3TMS) 66.29 352 01/11/5470 C9H27NOSi3 3 5.8619 22 L-Homoserine (3TMS) 74.28 254 672-15-1 C16H41NO3Si4 5 6.0166 22 Phenylhydrazine (1TMS) 56.78 60 100-63-0 C15H32N2Si3 6 6.1215 22 Dihydroxyacetone 31.19 350 96-26-4 8 6.1583 22 DL-2,3-Diaminopropionic acid (2TMS) 64.06 334 54879-59-5 C18H48N2O2Si5 10 6.1684 22 Linolenic acid (1TMS) 69.94 147 463-40-1 C21H38O2Si 11 6.1724 22 L-Homocitrulline 96.24 381 1190-49-4 12 6.1724 22 L-Homocitrulline 95.45 381 1190-49-4 13 6.441 22 Oxalic acid (2TMS) 48.22 15 6153-56-6 C8H18O4Si2 14 6.4416 22 Oxalic acid (2TMS) 68.2 15 6153-56-6 C8H18O4Si2 17 6.4648 22 Gallic acid 92.89 493 149-91-7 18 6.4866 22 Ketovaline 78.02 274 759-05-7 19 6.9429 22 Minoxidil (xTMS) 28.61 96 38304-91-5 20 7.4931 22 2-Hydroxypyridine 99.65 25 142-08-5 21 7.6596 22 Harmaline (1TMS) 93.16 434 6027-98-1 C16H22N2OSi 22 7.8241 22 L-(+)-Lactic acid 98.16 482 79-33-4 23 7.8297 22 Minoxidil (xTMS) 19.24 96 38304-91-5 24 8.2836 22 Malonic acid (2TMS) 52.83 252 141-82-2 C13H28O2Si 26 8.4225 22 L-Alanine (3TMS) 99.88 335 56-41-7 C12H31NO2Si3 27 8.4379 22 Shikimic acid (4TMS) 96.3 53 138-59-0 C19H42O5Si4 28 8.4779 22 Minoxidil (xTMS) 19.83 96 38304-91-5 29 8.5236 22 Ketovaline 92.51 274 759-05-7 30 8.6036 22 Hydroxylamine (3TMS) 92.26 352 01/11/5470 C9H27NOSi3 31 8.6834 22 Hydrocinnamate 94.1 354 501-52-0 32 8.7042 22 DL-2,3-Diaminopropionic acid (2TMS) 95.67 334 54879-59-5 C18H48N2O2Si5 33 8.7371 22 HydroxyButyrate 99.39 376 600-15-7 36 8.8893 22 Ketovaline 84.28 274 759-05-7

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37 8.9202 22 b-Glutamic acid 77.43 362 1948-48-7 38 8.9562 22 Hydroxylamine (3TMS) 67.38 352 01/11/5470 C9H27NOSi3 39 9.201 22 N-Acetyl-L-Leucine 82.94 65 1188-21-2 40 9.2114 22 Gallic acid 85.48 493 149-91-7 41 9.2107 22 Gallic acid 74.58 493 149-91-7 42 9.2206 22 Octacosanoic acid 31.99 413 506-48-9 43 9.4495 22 3-Methyl-2-oxovaleric acid (3TMS) 92.24 69 66872-74-0 C9H18O3Si 44 9.4827 22 N-Acetyl-L-Leucine 82.14 65 1188-21-2 45 9.4924 22 1,3-Dihydroxyacetone dimer (2TMS) 50.58 388 62147-49-3 46 9.492 22 1,3-Dihydroxyacetone dimer (2TMS) 48.07 388 62147-49-3 49 9.5216 22 Dehydroisoandrosterone-3-sulfate 46.41 426 651-48-9 50 9.6877 22 3-Methyl-2-oxovaleric acid (3TMS) 86.41 69 66872-74-0 C9H18O3Si 51 9.6851 22 4-Methyl-2-oxovaleric acid (3TMS) 46.45 126 816-66-0 C9H18O3Si 52 9.7923 22 Minoxidil (xTMS) 22.09 96 38304-91-5 53 9.8624 22 3-Hydroxyisovaleric acid 97.67 465 625-08-1 55 9.8665 22 4-Methyl-2-oxovaleric acid (3TMS) 74.73 126 816-66-0 C9H18O3Si 56 9.8766 22 L-Norvaline (4TMS) 98.79 127 6600-40-4 C14H35NO2Si3 57 10.2221 22 Urea 78.4 87 57-13-6 58 10.2215 22 L-Homocitrulline 80.17 381 1190-49-4 60 10.3822 22 L-Asparagine (3TMS) 78.53 16 70-47-3 C19H48N2O3Si5 61 10.3821 22 L-Alanine (3TMS) 97.96 335 56-41-7 C12H31NO2Si3 62 10.4855 22 Dopamine 98.63 241 51-61-6 63 10.5032 22 Glycerol (3TMS) 84.77 17 56-81-5 C12H32O3Si3 64 10.4971 22 Glycerol (3TMS) 82.67 17 56-81-5 C12H32O3Si3 65 10.502 22 Pyrophosphate 93.39 310 03/09/2466 66 10.5027 22 Pyrophosphate 76.12 310 03/09/2466 67 10.5125 22 L-Norleucine (2TMS) 99.31 195 327-57-1 C15H37NO2Si3 68 10.6341 22 Psoralen (0TMS) 48.3 448 66-97-7 69 10.6454 22 Minoxidil (xTMS) 21.16 96 38304-91-5 70 10.7575 22 L-Norleucine (2TMS) 98.24 195 327-57-1 C15H37NO2Si3

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71 10.7879 22 L-(+)-Lactic acid 77.22 482 79-33-4 72 10.8176 22 1-Methyl uracil 75.51 212 615-77-0 73 10.8534 22 L-Proline (2TMS) 99.74 42 147-85-3 C11H25NO2Si2 74 10.9064 22 L-Methionine (2TMS) 74.8 31 63-68-3 C14H35NO2SSi3 75 10.9176 22 Dopamine 95.83 241 51-61-6 76 10.969 22 Malonic acid (2TMS) 29.37 252 141-82-2 C13H28O2Si 77 10.9891 22 Succinic acid (2TMS) 80.69 171 110-15-6 C12H18O2Si 78 11.1481 22 Minoxidil (xTMS) 20.34 96 38304-91-5 79 11.2211 22 Mesaconic acid (2TMS) 58.07 161 498-24-8 C11H22O4Si2 80 11.2216 22 Mesaconic acid (2TMS) 67.33 161 498-24-8 C11H22O4Si2 81 11.369 22 Fumaric acid (2TMS) 91.14 34 110-17-8 C10H20O4Si2 82 11.418 22 Shikimic acid (4TMS) 81.59 53 138-59-0 C19H42O5Si4 83 11.467 22 Alanylalanine 90.08 179 1948-31-8 84 11.6399 22 Homoserine lactone 57.99 131 1192-20-7 85 11.6754 22 L-Threonine (3TMS) 80.28 137 72-19-5 C16H41NO3Si4 86 11.8296 22 Dopamine 97.67 241 51-61-6 87 11.8349 22 Maltose (8TMS) 86.97 368 6363-53-7 C36H86O11Si8 89 12.0812 22 Hydrocinnamate 89.47 354 501-52-0 90 12.1211 22 N-Carbamoyl-L-Aspartate 94.78 409 923-37-5 92 12.3222 22 Gallic acid 76.39 493 149-91-7 93 12.3334 22 2,6-Diaminopurine 32.87 428 1904-98-9 94 12.4349 22 Spermine (xTMS) 56.64 62 71-44-3 96 12.5001 22 Cysteinylglycine (nTMS) 80.02 317 19246-18-5 97 12.514 22 DL-Threo-b-HydroxyAspartic acid 87.37 313 4294-45-5 98 12.5256 22 Methyl Dopa 91.51 299 555-30-6 99 12.6496 22 L-(-)-Malic acid (3TMS) 98.29 373 636-61-3 C13H30O5Si3 100 12.7148 22 Indole-3-acetonitrile (1TMS) 40.32 26 771-51-7 C13H16N2Si 101 12.7709 22 16-Hydroxyhexadecanoic acid (2TMS) 41.58 260 506-13-8 C22H48O3Si2 102 12.7719 22 16-Hydroxyhexadecanoic acid (2TMS) 41.24 260 506-13-8 C22H48O3Si2 104 12.7693 22 16-Hydroxyhexadecanoic acid (2TMS) 40.01 260 506-13-8 C22H48O3Si2

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105 12.8951 22 16-Hydroxyhexadecanoic acid (2TMS) 41.65 260 506-13-8 C22H48O3Si2 107 12.782 22 L-Norvaline (4TMS) 74.49 127 6600-40-4 C14H35NO2Si3 108 12.7829 22 L-Mandelic acid 91.72 474 611-71-2 109 12.8372 22 1,10-Phenanthroline 77.37 23 5144-89-8 110 12.898 22 Minoxidil (xTMS) 23.29 96 38304-91-5 111 12.898 22 (+-)-alpha-Tocopherol (1TMS) 19.28 400 10191-41-0 C32H58O2Si 112 12.9412 22 Spermine (xTMS) 57.02 62 71-44-3 113 12.9684 22 Methyl Dopa 92.29 299 555-30-6 114 13.0187 22 L-Methionine (2TMS) 96.85 31 63-68-3 C14H35NO2SSi3 115 13.0773 22 L-Pyroglutamic acid (2TMS) 99.7 228 98-79-3 C11H23NO3Si2 116 13.1092 22 L-Lysine (3TMS) 77.99 395 56-87-1 C21H54N2O2Si5 117 13.1009 22 L-(+)-Tartarate 50.69 86 87-69-4 118 13.2316 22 2-Aminoadipic acid 50.85 145 1118-90-7 120 13.237 22 D-Ribulose 26.29 331 488-84-6 121 13.2819 22 L-Norleucine (2TMS) 74.77 195 327-57-1 C15H37NO2Si3 122 13.3011 22 3,4-Dihydroxy-L-phenylalanine (4TMS) 73.44 47 59-92-7 C24H51NO4Si5 123 13.3772 22 Methyl jasmonate 44.67 27 39924-52-2 125 13.4402 22 L-Tryptophane (3TMS) 70.94 229 73-22-3 C23H44N2O2Si4 126 13.4506 22 alpha-Ketoglutaric acid (2TMS;1MEOX) 83.81 142 305-72-6 127 13.5853 22 L-Proline (2TMS) 77.19 42 147-85-3 C11H25NO2Si2 128 13.6794 22 2-Aminopimelic acid 77.09 378 627-76-9 129 13.769 22 2-Aminopimelic acid 79.9 378 627-76-9 130 13.7107 22 D-Galactosamine (5TMS) 83.69 277 7535-00-4 C24H61NO5Si6 131 13.734 22 D-Galactosamine (5TMS) 71.33 277 7535-00-4 C24H61NO5Si6 133 13.8219 22 Spermine (xTMS) 58.25 62 71-44-3 135 13.8253 22 L-Glutamic acid (3TMS) 89.58 157 56-86-0 C17H41NO4Si4 136 13.8609 22 DL-3,4-Dihydroxymandelic acid 85.32 494 775-01-9 137 13.8406 22 Coniferyl aldehyde 52.71 209 458-36-6 138 13.9782 22 L-Phenylalanine (2TMS) 87.97 407 63-91-2 C18H35NO2Si3 139 13.9838 22 Spermine (xTMS) 52.59 62 71-44-3

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141 14.0706 22 Myo-Inositol (6TMS) 81.3 211 87-89-8 C24H60O6Si6 144 14.1385 22 Indole-3-carboxyaldehyde (1TMS) 39.37 174 487-89-8 C12H15NOSi 146 14.1995 22 Kynurenate 18.57 411 492-27-3 147 14.2492 22 L-Asparagine (3TMS) 91.37 16 70-47-3 C19H48N2O3Si5 148 14.2508 22 N-Carbamoyl-L-Aspartate 57.15 409 923-37-5 149 14.273 22 Inosine (4TMS) 31.6 217 58-63-9 C22H44N4O5Si4 150 14.274 22 D-(+)-Allose 13.12 63 2595-97-3 152 14.5622 22 Kynurenate 30.99 411 492-27-3 153 14.5645 22 Kynurenate 29.75 411 492-27-3 154 14.5765 22 L-Glutamine (3TMS) 49.05 390 56-85-9 C20H50N2O3Si5 156 14.6119 22 DL-Threo-b-HydroxyAspartic acid 70.29 313 4294-45-5 157 14.6287 22 L-(+)-Lactic acid 74 482 79-33-4 159 14.6973 22 Uridine 5'-diphospho-N-acetylglucosamine (nTMS) 69.82 219 91183-98-1 160 14.8645 22 Dopamine 96.29 241 51-61-6 161 14.8834 22 Kynurenate 67.65 411 492-27-3 163 14.9386 22 Mesaconic acid (2TMS) 53.83 161 498-24-8 C11H22O4Si2 164 14.9354 22 Mesaconic acid (2TMS) 53.41 161 498-24-8 C11H22O4Si2 166 14.9182 22 D-Ribulose-5-phosphate 79.41 404 93-87-8 167 14.9719 22 5-Hydoxytryptamine (5TMS) 92.88 78 153-98-0 C22H44N2OSi4 168 15.0227 22 b-Cyano-L-Alanine 94.63 401 6232-19-5 169 15.1052 22 L-Glutamine (3TMS) 97.07 390 56-85-9 C20H50N2O3Si5 170 15.2147 22 1,5-Anhydro-D-glucitol 83.43 114 154-58-5 171 15.1842 22 Myo-Inositol (6TMS) 72.84 211 87-89-8 C24H60O6Si6 172 15.2883 22 1-Kestose 71.71 29 470-69-9 175 15.163 22 Kynurenate 53.71 411 492-27-3 176 15.2397 22 Shikimic acid (4TMS) 92.65 53 138-59-0 C19H42O5Si4 178 15.379 22 Citric acid (4TMS) 89.2 51 77-92-9 C9H13N 179 15.419 22 L-Ornithine (2TMS) 97.76 309 3184-13-2 C20H52N2O2Si5 180 15.4672 22 L-Citrulline 92.6 495 372-75-8 182 15.5667 22 1,5-Anhydro-D-glucitol 83.96 114 154-58-5

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183 15.5183 22 Myo-Inositol (6TMS) 80.35 211 87-89-8 C24H60O6Si6 184 15.5502 22 Myo-Inositol (6TMS) 73.82 211 87-89-8 C24H60O6Si6 187 15.5866 22 L-Glucose 81.41 225 921-60-8 188 15.5917 22 Ribitol (5TMS) 83.64 59 488-81-3 C20H52O5Si5 189 15.7365 22 Psicose 74.57 394 551-68-8 190 15.7789 22 Psicose 91.11 394 551-68-8 191 15.7476 22 D-(+)-Galactose (5TMS) 85.75 311 59-23-4 C21H52O6Si5 192 15.7451 22 L-Lysine (3TMS) 96.72 395 56-87-1 C21H54N2O2Si5 193 15.8801 22 beta-D-(+)-Glucose (5TMS;1MEOX) 83.3 349 492-61-5 194 15.9201 22 D-(+)-Allose 49.46 63 2595-97-3 195 15.9358 22 Shikimic acid (4TMS) 88.44 53 138-59-0 C19H42O5Si4 196 15.9507 22 beta-D-(+)-Glucose (5TMS;1MEOX) 83.15 349 492-61-5 197 15.9515 22 beta-D-(+)-Glucose (5TMS;1MEOX) 83.88 349 492-61-5 198 16.0405 22 L-Mandelic acid 97.59 474 611-71-2 199 16.106 22 beta-D-(+)-Glucose (5TMS;1MEOX) 84.08 349 492-61-5 200 16.2015 22 D-(-)-Sorbitol 83.11 232 50-70-4 201 16.1402 22 Minoxidil (xTMS) 26.37 96 38304-91-5 202 16.1539 22 Octacosanoic acid 32.94 413 506-48-9 203 16.2128 22 alpha-Lactose (8TMS) 82.33 151 63-42-3 C36H86O11Si8 205 16.1619 22 N-a-Acetyl-L-Lysine 76.56 144 1946-82-3 208 16.3697 22 3,4-Dihydroxy-L-phenylalanine (4TMS) 95.96 47 59-92-7 C24H51NO4Si5 209 16.4085 22 1-Kestose 89.82 29 470-69-9 210 16.4605 22 Malonic acid (2TMS) 43.3 252 141-82-2 C13H28O2Si 211 16.481 22 Shikimic acid (4TMS) 89.45 53 138-59-0 C19H42O5Si4 212 16.4853 22 1-Kestose 85.74 29 470-69-9 213 16.5325 22 Shikimic acid (4TMS) 91.87 53 138-59-0 C19H42O5Si4 214 16.614 22 D-(-)-Sorbitol 75.59 232 50-70-4 215 16.8174 22 Galactitol 80.75 464 608-66-2 216 16.8249 22 Myo-Inositol (6TMS) 75.05 211 87-89-8 C24H60O6Si6 217 16.6109 22 Myo-Inositol (6TMS) 75.07 211 87-89-8 C24H60O6Si6

246

221 16.6144 22 D-(+)-Allose 43.65 63 2595-97-3 222 16.6912 22 (+)-pantothenate (nTMS) 88.14 130 137-08-6 223 16.6923 22 5-Hydroxy-L-tryptophan 33.46 451 08/09/4350 224 16.8422 22 Shikimic acid (4TMS) 91.66 53 138-59-0 C19H42O5Si4 225 16.8852 22 Myo-Inositol (6TMS) 72.64 211 87-89-8 C24H60O6Si6 226 17.1096 22 Palmitic acid (1TMS) 79.93 146 57-10-3 C16H34O2Si 227 17.1099 22 Palmitic acid (1TMS) 14.7 146 57-10-3 C16H34O2Si 228 17.1269 22 D-Ribulose 41.26 331 488-84-6 229 17.1683 22 Myo-Inositol (6TMS) 73.05 211 87-89-8 C24H60O6Si6 230 17.3181 22 Shikimic acid (4TMS) 90.84 53 138-59-0 C19H42O5Si4 231 17.3144 22 D-(+)-Raffinose 47.24 55 512-69-6 232 17.3278 22 Myo-Inositol (6TMS) 93.33 211 87-89-8 C24H60O6Si6 234 17.4695 22 1-Kestose 45.02 29 470-69-9 235 17.541 22 Dopamine 97.37 241 51-61-6 236 17.6579 22 D-(+)-Raffinose 24.2 55 512-69-6 237 17.6674 22 Linoleic acid (1TMS) 39.95 24 60-33-3 C21H40O2Si 238 17.7183 22 L-Iditol (6TMS) 77.87 103 488-45-9 C24H62O6Si6 240 17.8383 22 1-Kestose 41.57 29 470-69-9 241 17.848 22 5-Hydoxytryptamine (5TMS) 90.32 78 153-98-0 C22H44N2OSi4 242 17.8657 22 L-(-)-Arabitol (5TMS) 74.86 95 7643-75-6 C20H52O5Si5 245 17.8883 22 1-Kestose 33.21 29 470-69-9 248 18.0647 22 Gluconic acid 75.89 491 526-95-4 249 18.1018 22 1-Kestose 40.19 29 470-69-9 250 18.1221 22 N-Methyl-DL-Alanine (2TMS) 96.32 276 600-21-5 C10H25NO2Si2 252 18.104 22 L-2-Aminobutyric acid 48.31 403 1492-24-6 255 18.16 22 Minoxidil (xTMS) 26.3 96 38304-91-5 257 18.2273 22 Shikimic acid (4TMS) 92.11 53 138-59-0 C19H42O5Si4 258 18.234 22 Shikimic acid (4TMS) 91.71 53 138-59-0 C19H42O5Si4 259 18.3533 22 Shikimic acid (4TMS) 92.81 53 138-59-0 C19H42O5Si4 260 18.2277 22 Shikimic acid (4TMS) 93.52 53 138-59-0 C19H42O5Si4

247

262 18.3466 22 Shikimic acid (4TMS) 90 53 138-59-0 C19H42O5Si4 265 18.2398 22 L-Iditol (6TMS) 75.34 103 488-45-9 C24H62O6Si6 266 18.2867 22 L-Tryptophane (3TMS) 99.21 229 73-22-3 C23H44N2O2Si4 267 18.2827 22 5-Keto-D-Gluconate 14.24 290 21675-47-8 268 18.3233 22 1-Kestose 37.65 29 470-69-9 269 18.3412 22 Spermine (xTMS) 56.55 62 71-44-3 270 18.342 22 Uridine 5'-diphospho-N-acetylglucosamine (nTMS) 57.37 219 91183-98-1 271 18.3895 22 Stearic acid (1TMS) 77.34 90 57-11-4 C21H44O2Si 274 18.4228 22 1-Kestose 36.34 29 470-69-9 275 18.436 22 D-(+)-Raffinose 11.72 55 512-69-6 276 18.5424 22 D-Arabitol 67.35 284 488-82-4 278 18.688 22 Xanthurenate 19.33 314 59-00-7 279 18.6993 22 Galactitol 62.52 464 608-66-2 280 18.7231 22 1-Kestose 24.23 29 470-69-9 281 18.8138 22 5-Sulfosalicylate 36 282 97-05-2 282 18.8787 22 Methylsuccinic acid 35.82 272 498-21-5 283 18.999 22 meso-Erythritol 84.95 479 149-32-6 284 19.0362 22 meso-Erythritol 49.4 479 149-32-6 285 19.0154 22 5-Sulfosalicylate 36.03 282 97-05-2 286 19.0988 22 Sucrose (8TMS) 27.83 71 57-50-1 C36H86O11Si8 287 19.3936 20 L-(-)-Arabitol (5TMS) 13.41 95 7643-75-6 C20H52O5Si5 288 19.527 22 Xanthosine 50.06 481 146-80-5 289 19.5312 22 cis-Aconitic acid (3TMS) 24.56 159 585-84-2 C15H30O6Si3 290 19.6727 22 Psicose 38.17 394 551-68-8 292 19.7726 22 1-Kestose 45.19 29 470-69-9 293 19.8155 22 2-Dehydro-D-gluconate 38.62 201 669-90-9 294 19.8163 22 Allantoin (4TMS) 8.65 271 97-59-6 C19H46N4O3Si5 297 20.5827 22 D-(-)-Mannitol (6TMS) 78.16 3 69-65-8 C24H62O6Si6 298 20.59 22 Psicose 92.96 394 551-68-8 299 20.7473 19 Ribitol (5TMS) 65 59 488-81-3 C20H52O5Si5

248

300 21.1105 22 D-(+)-Trehalose (8TMS) 81.51 140 99-20-7 C36H86O11Si8 302 21.1056 22 alpha-Lactose (8TMS) 95.21 151 63-42-3 C36H86O11Si8

AlignID tmean FoundIn Name.2 MatchFactor.2 DB.Id.2 CAS.2 Formula.2 1 5.6452 22 Succinic acid (2TMS) 58.66 171 110-15-6 C12H18O2Si 2 5.7352 22 N-Acetyl-DL-alanine 53.21 7 1115-69-1 3 5.8619 22 O-Succinyl-L-Homoserine 70.18 268 1492-23-5 5 6.0166 22 Catechol 42.9 43 120-80-9 6 6.1215 22 Phenoxyacetic acid 29.17 168 122-59-8 8 6.1583 22 Maltitol 63.9 427 585-88-6 10 6.1684 22 gamma-Linolenic acid (1TMS) 61.84 72 506-26-3 C21H38O2Si 11 6.1724 22 Urea 63.5 87 57-13-6 12 6.1724 22 Urea 62.27 87 57-13-6 13 6.441 22 Chalcone (1MEOX) 41.98 361 94-41-7 14 6.4416 22 Chalcone (1MEOX) 57.09 361 94-41-7 17 6.4648 22 Pyridoxamine 33.95 440 85-87-0 18 6.4866 22 Paeonol 74.97 186 552-41-0 19 6.9429 22 1,3-Dihydroxyacetone dimer (2TMS) 24.1 388 62147-49-3 20 7.4931 22 4-Hydroxypyridine 97.14 490 626-64-2 21 7.6596 22 Dopamine 92.58 241 51-61-6 22 7.8241 22 Propyleneglycol 91.61 432 57-55-6 23 7.8297 22 Methylmalonic acid (2TMS) 17.86 250 516-05-2 C10H14O2Si 24 8.2836 22 Dihydrouracil (2TMS) 37.19 461 504-07-4 C10H22N2O2Si2 26 8.4225 22 (R)-(-)-Phenylephrine (2TMS) 99.68 322 61-76-7 C18H37NO2Si3 27 8.4379 22 Maltose (8TMS) 79.59 368 6363-53-7 C36H86O11Si8 28 8.4779 22 1,3-Dihydroxyacetone dimer (2TMS) 18.68 388 62147-49-3 29 8.5236 22 3-Methyl-2-oxovaleric acid (3TMS) 78.1 69 66872-74-0 C9H18O3Si 30 8.6036 22 Myricetin 51.27 437 529-44-2 31 8.6834 22 2-Phenylpropionate 39.99 333 492-37-5 32 8.7042 22 Oxamide 78.35 206 471-46-5

249

33 8.7371 22 DL-alpha-Hydroxybutyric acid (2TMS) 99.24 343 103404-58-6 C10H24O3Si2 36 8.8893 22 Indole-3-acetic acid (2TMS) 65.87 124 87-51-4 C16H25NO2Si2 37 8.9202 22 L-Aspartic acid (3TMS) 70.88 121 56-84-8 C16H39NO4Si4 38 8.9562 22 DL-Glyceraldehyde 56.16 298 56-82-6 39 9.201 22 Histamine (3TMS) 75.77 81 51-45-6 C14H33N3Si3 40 9.2114 22 Pyridoxamine 31.87 440 85-87-0 41 9.2107 22 Alanylalanine 39.24 179 1948-31-8 42 9.2206 22 Gallic acid 30.17 493 149-91-7 43 9.4495 22 Ketoisoleucine 91.63 285 1460-34-0 44 9.4827 22 Histamine (3TMS) 75.41 81 51-45-6 C14H33N3Si3 45 9.4924 22 Diethanolamine (3TMS) 35.07 129 111-42-2 C13H35NO2Si3 46 9.492 22 3-Aminopropionitrile fumarate (nTMS) 43.13 46 2079-89-2 49 9.5216 22 Ibuprofen (1TMS) 42.9 5 15687-27-1 C16H26O2Si 50 9.6877 22 Ketoisoleucine 85.84 285 1460-34-0 51 9.6851 22 Ketoisoleucine 40.4 285 1460-34-0 52 9.7923 22 Uracil (2TMS) 17.9 235 66-22-8 C10H20N2O2Si2 53 9.8624 22 2-Hydroxyisobutyric acid (2TMS) 95.82 371 594-61-6 C10H24O3Si2 55 9.8665 22 Ketoisoleucine 60.59 285 1460-34-0 56 9.8766 22 DL-Norvaline 98.69 328 760-78-1 57 10.2221 22 Glyceric acid 57.16 246 473-81-4 58 10.2215 22 Urea 71.43 87 57-13-6 60 10.3822 22 N-Methylethanolamine 74.29 421 109-83-1 61 10.3821 22 N-Methylethanolamine 97.92 421 109-83-1 62 10.4855 22 Harmaline (1TMS) 98.26 434 6027-98-1 C16H22N2OSi 63 10.5032 22 D-(-)-Mannitol (6TMS) 71.84 3 69-65-8 C24H62O6Si6 64 10.4971 22 D-(-)-Mannitol (6TMS) 76.74 3 69-65-8 C24H62O6Si6 65 10.502 22 1-Hexadecanol 61.28 425 36653-82-4 66 10.5027 22 L-Norleucine (2TMS) 54.08 195 327-57-1 C15H37NO2Si3 67 10.5125 22 L-Leucine (2TMS) 89.64 108 61-90-5 C15H37NO2Si3 68 10.6341 22 N-Acetyl-DL-serine (2TMS) 32.17 469 97-14-3

250

69 10.6454 22 Uracil (2TMS) 19.64 235 66-22-8 C10H20N2O2Si2 70 10.7575 22 L-Isoleucine (2TMS) 93.75 222 73-32-5 C15H37NO2Si3 71 10.7879 22 D-(+)-Fucose 76.95 111 2438-80-4 72 10.8176 22 L-Theanine 40.84 360 3081-61-6 73 10.8534 22 L-Prolinamide 99.49 153 7531-52-4 74 10.9064 22 p-Hydroxybenzaldehyde 47.9 119 123-08-0 75 10.9176 22 Harmaline (1TMS) 95.21 434 6027-98-1 C16H22N2OSi 76 10.969 22 Succinic acid (2TMS) 24.9 171 110-15-6 C12H18O2Si 77 10.9891 22 Citramalic acid 69.62 476 08/10/6236 78 11.1481 22 Uracil (2TMS) 18.6 235 66-22-8 C10H20N2O2Si2 79 11.2211 22 N-Acetyl-L-aspartic acid 44.15 45 997-55-7 80 11.2216 22 N-Acetyl-L-aspartic acid 46.98 45 997-55-7 81 11.369 22 Maleic acid 64.44 8 110-16-7 82 11.418 22 L-Serine (3TMS) 76.63 83 56-45-1 C15H39NO3Si4 83 11.467 22 Hypotaurine (3TMS) 64.22 332 300-84-5 C11H31NO2SSi3 84 11.6399 22 2-MethylGlutarate 50.17 248 617-62-9 85 11.6754 22 DL-Threo-b-HydroxyAspartic acid 78.92 313 4294-45-5 86 11.8296 22 5-Hydoxytryptamine (5TMS) 97.22 78 153-98-0 C22H44N2OSi4 87 11.8349 22 Lactitol 86.42 443 585-86-4 89 12.0812 22 2-Phenylpropionate 41.38 333 492-37-5 90 12.1211 22 Ibuprofen (1TMS) 75.56 5 15687-27-1 C16H26O2Si 92 12.3222 22 Pyridoxamine 30.47 440 85-87-0 93 12.3334 22 Myricetin 24.48 437 529-44-2 94 12.4349 22 Minoxidil (xTMS) 42.99 96 38304-91-5 96 12.5001 22 Flavanone 34.83 325 487-26-3 97 12.514 22 3,4-Dihydroxy-L-phenylalanine (4TMS) 82.55 47 59-92-7 C24H51NO4Si5 98 12.5256 22 L-Aspartic acid (3TMS) 80.78 121 56-84-8 C16H39NO4Si4 99 12.6496 22 L-(-)-Malic acid (3TMS) 91.88 377 97-67-6 C13H30O5Si3 100 12.7148 22 Indole-3-Acetaldehyde 33.89 57 2000-57-9 101 12.7709 22 Methylmalonic acid (2TMS) 36.34 250 516-05-2 C10H14O2Si

251

102 12.7719 22 Methylmalonic acid (2TMS) 35.57 250 516-05-2 C10H14O2Si 104 12.7693 22 L-Norvaline (4TMS) 37.01 127 6600-40-4 C14H35NO2Si3 105 12.8951 22 Methylmalonic acid (2TMS) 36.28 250 516-05-2 C10H14O2Si 107 12.782 22 DL-Norvaline 74.35 328 760-78-1 108 12.7829 22 4-Hydroxyphenethyl alcohol 91.16 475 501-94-0 109 12.8372 22 2-Picolinate 74.7 89 98-98-6 110 12.898 22 Uracil (2TMS) 21.09 235 66-22-8 C10H20N2O2Si2 111 12.898 22 Malonic acid (2TMS) 14.89 252 141-82-2 C13H28O2Si 112 12.9412 22 Minoxidil (xTMS) 42.18 96 38304-91-5 113 12.9684 22 L-Aspartic acid (3TMS) 81.91 121 56-84-8 C16H39NO4Si4 114 13.0187 22 p-Hydroxybenzaldehyde 43.88 119 123-08-0 115 13.0773 22 DL-Pyroglutamic acid 99.66 267 149-87-1 116 13.1092 22 N-Acetylputrescine (2TMS) 75.99 386 18233-70-0 C15H38N2OSi3 117 13.1009 22 Gluconic acid 46.5 491 526-95-4 118 13.2316 22 Indole-3-Acetaldehyde 37.81 57 2000-57-9 120 13.237 22 Adenine (2TMS) 21.84 180 73-24-5 C14H29N5Si3 121 13.2819 22 L-Leucine (2TMS) 68.87 108 61-90-5 C15H37NO2Si3 122 13.3011 22 L-Tyrosine (3TMS) 73.32 115 60-18-4 C21H43NO3Si4 123 13.3772 22 3-Pyridylacetic acid 44.33 486 501-81-5 125 13.4402 22 Indole-3-acetic acid (2TMS) 70.71 124 87-51-4 C16H25NO2Si2 126 13.4506 22 alpha-Ketoglutaric acid (2TMS) 83.21 117 328-50-7 C11H22O5Si2 127 13.5853 22 L-Prolinamide 76.2 153 7531-52-4 128 13.6794 22 N-Acetyl-L-aspartic acid 52.51 45 997-55-7 129 13.769 22 N-Acetyl-L-aspartic acid 55.92 45 997-55-7 130 13.7107 22 D-(+)-Glucosamine (6TMS) 79.44 278 66-84-2 C24H61NO5Si6 131 13.734 22 D-(+)-Glucosamine (6TMS) 67.46 278 66-84-2 C24H61NO5Si6 133 13.8219 22 Minoxidil (xTMS) 42.24 96 38304-91-5 135 13.8253 22 DL-threo-beta-Methylaspartic acid (3TMS) 74.13 30 6667-60-3 C17H41NO4Si4 136 13.8609 22 Cyanine 84.2 438 67879-79-2 137 13.8406 22 7-Hydroxy-4-methylcoumarin (1TMS) 44.96 11 90-33-5 C13H16O3Si

252

138 13.9782 22 L-Tyrosine (3TMS) 82.21 115 60-18-4 C21H43NO3Si4 139 13.9838 22 Minoxidil (xTMS) 42.07 96 38304-91-5 141 14.0706 22 Galactitol 77.95 464 608-66-2 144 14.1385 22 L-Glutamic acid (3TMS) 24.49 157 56-86-0 C17H41NO4Si4 146 14.1995 22 5-Hydroxyindoleacetate 17.41 457 54-16-0 147 14.2492 22 L-Alanine (3TMS) 68.46 335 56-41-7 C12H31NO2Si3 148 14.2508 22 Kynurenate 54.82 411 492-27-3 149 14.273 22 Myo-Inositol (6TMS) 31.22 211 87-89-8 C24H60O6Si6 150 14.274 22 D-(+)-Mannose (5TMS) 11.89 307 3458-28-4 C21H52O6Si5 152 14.5622 22 L-(-)-Malic acid (3TMS) 23.94 373 636-61-3 C13H30O5Si3 153 14.5645 22 L-Glutamine (3TMS) 26.12 390 56-85-9 C20H50N2O3Si5 154 14.5765 22 D-Glutamine 48.25 380 5959-95-5 156 14.6119 22 L-Tyrosine (3TMS) 68.71 115 60-18-4 C21H43NO3Si4 157 14.6287 22 D-(+)-Fucose 72.57 111 2438-80-4 159 14.6973 22 N-Acetyl-D-mannosamine (4TMS) 65.03 423 3615-17-6 160 14.8645 22 Harmaline (1TMS) 95.99 434 6027-98-1 C16H22N2OSi 161 14.8834 22 L-Asparagine (3TMS) 30.95 16 70-47-3 C19H48N2O3Si5 163 14.9386 22 N-Acetyl-L-aspartic acid 41.29 45 997-55-7 164 14.9354 22 N-Acetyl-L-aspartic acid 37.78 45 997-55-7 166 14.9182 22 Ribulose-1,5-Bisphosphate 78.09 52 14689-84-0 167 14.9719 22 Harmaline (1TMS) 92.85 434 6027-98-1 C16H22N2OSi 168 15.0227 22 L-Asparagine (3TMS) 35.8 16 70-47-3 C19H48N2O3Si5 169 15.1052 22 D-Glutamine 96.78 380 5959-95-5 170 15.2147 22 1-Kestose 81.74 29 470-69-9 171 15.1842 22 1-Kestose 71.08 29 470-69-9 172 15.2883 22 Myo-Inositol (6TMS) 71.39 211 87-89-8 C24H60O6Si6 175 15.163 22 D-(+)-Turanose 50.14 266 5349-40-6 176 15.2397 22 Maltose (8TMS) 76.58 368 6363-53-7 C36H86O11Si8 178 15.379 22 DL-Isocitric acid (4TMS) 70.72 245 320-77-4 C18H40O7Si4 179 15.419 22 L-Ornithine (2TMS) 96.31 344 70-26-8 C20H52N2O2Si5

253

180 15.4672 22 L-Arginine (3TMS) 90.79 422 74-79-3 C27H70N4O2Si7 182 15.5667 22 1,6-Anhydroglucose 80.9 304 498-07-7 183 15.5183 22 1-Kestose 73.56 29 470-69-9 184 15.5502 22 1-Kestose 71.6 29 470-69-9 187 15.5866 22 D-(+)-Allose 81.36 63 2595-97-3 188 15.5917 22 L-Iditol (6TMS) 82.52 103 488-45-9 C24H62O6Si6 189 15.7365 22 D-Tagatose 74.16 128 87-81-0 190 15.7789 22 D-Tagatose 90.45 128 87-81-0 191 15.7476 22 D-(+)-Galactose 84.82 118 26566-61-0 192 15.7451 22 5-Aminovaleric acid 95.5 194 660-88-8 193 15.8801 22 D-(+)-Allose 81.73 63 2595-97-3 194 15.9201 22 beta-D-(+)-Glucose (5TMS;1MEOX) 47.92 349 492-61-5 195 15.9358 22 Maltose (8TMS) 74.72 368 6363-53-7 C36H86O11Si8 196 15.9507 22 D-(+)-Allose 81.43 63 2595-97-3 197 15.9515 22 D-(+)-Allose 82.41 63 2595-97-3 198 16.0405 22 4-Hydroxyphenethyl alcohol 96.16 475 501-94-0 199 16.106 22 D-(+)-Allose 82.71 63 2595-97-3 200 16.2015 22 D-(-)-Mannitol (6TMS) 78.37 3 69-65-8 C24H62O6Si6 201 16.1402 22 Uracil (2TMS) 24.05 235 66-22-8 C10H20N2O2Si2 202 16.1539 22 Lignoceric acid 28.21 176 557-59-5 203 16.2128 22 Lactitol 80.68 443 585-86-4 205 16.1619 22 L-Lysine (3TMS) 76.31 395 56-87-1 C21H54N2O2Si5 208 16.3697 22 L-Tyrosine (3TMS) 95.5 115 60-18-4 C21H43NO3Si4 209 16.4085 22 D-(+)-Raffinose 82.28 55 512-69-6 210 16.4605 22 Oxalic acid (2TMS) 31.15 15 6153-56-6 C8H18O4Si2 211 16.481 22 Maltose (8TMS) 80.63 368 6363-53-7 C36H86O11Si8 212 16.4853 22 Myo-Inositol (6TMS) 77.46 211 87-89-8 C24H60O6Si6 213 16.5325 22 Maltose (8TMS) 80.43 368 6363-53-7 C36H86O11Si8 214 16.614 22 D-(+)-Allose 74.64 63 2595-97-3 215 16.8174 22 D-(-)-Sorbitol 80.39 232 50-70-4

254

216 16.8249 22 1-Kestose 74.37 29 470-69-9 217 16.6109 22 1-Kestose 73.07 29 470-69-9 221 16.6144 22 beta-D-(+)-Glucose (5TMS;1MEOX) 41.44 349 492-61-5 222 16.6912 22 (+)-Pantothenic acid (xTMS) 84.65 149 79-83-4 223 16.6923 22 (+)-pantothenate (nTMS) 31.99 130 137-08-6 224 16.8422 22 Maltose (8TMS) 82.49 368 6363-53-7 C36H86O11Si8 225 16.8852 22 1-Kestose 71.77 29 470-69-9 226 17.1096 22 Behenic acid 71.17 442 112-85-6 227 17.1099 22 2'-Deoxyribose-5'-Phosphate 6.02 453 7685-50-9 228 17.1269 22 D-Xylulose (4TMS) 34.67 109 551-84-8 C17H42O5Si4 229 17.1683 22 1-Kestose 72.27 29 470-69-9 230 17.3181 22 alpha-Lactose (8TMS) 82 151 63-42-3 C36H86O11Si8 231 17.3144 22 1-Kestose 47.01 29 470-69-9 232 17.3278 22 1,5-Anhydro-D-glucitol 53.38 114 154-58-5 234 17.4695 22 1,5-Anhydro-D-glucitol 43.04 114 154-58-5 235 17.541 22 Harmaline (1TMS) 96.76 434 6027-98-1 C16H22N2OSi 236 17.6579 22 1-Kestose 23.09 29 470-69-9 237 17.6674 22 6-Aminocaproic acid (3TMS) 37.83 231 60-32-2 C15H37NO2Si3 238 17.7183 22 Galactitol 76.81 464 608-66-2 240 17.8383 22 1,5-Anhydro-D-glucitol 38.8 114 154-58-5 241 17.848 22 Harmaline (1TMS) 90.29 434 6027-98-1 C16H22N2OSi 242 17.8657 22 L-(-)-Sorbose 74.58 21 87-79-6 245 17.8883 22 D-(+)-Raffinose 31.33 55 512-69-6 248 18.0647 22 Galactitol 74.69 464 608-66-2 249 18.1018 22 D-(+)-Raffinose 38.02 55 512-69-6 250 18.1221 22 L-2-Aminobutyric acid 96.07 403 1492-24-6 252 18.104 22 N-Methyl-DL-Alanine (2TMS) 48.09 276 600-21-5 C10H25NO2Si2 255 18.16 22 Uracil (2TMS) 23.92 235 66-22-8 C10H20N2O2Si2 257 18.2273 22 Maltose (8TMS) 83.32 368 6363-53-7 C36H86O11Si8 258 18.234 22 Maltose (8TMS) 81.32 368 6363-53-7 C36H86O11Si8

255

259 18.3533 22 Maltose (8TMS) 81.43 368 6363-53-7 C36H86O11Si8 260 18.2277 22 Maltose (8TMS) 80.98 368 6363-53-7 C36H86O11Si8 262 18.3466 22 Maltose (8TMS) 77.83 368 6363-53-7 C36H86O11Si8 265 18.2398 22 D-(-)-Mannitol (6TMS) 74.5 3 69-65-8 C24H62O6Si6 266 18.2867 22 Indole-3-acetic acid (2TMS) 96.72 124 87-51-4 C16H25NO2Si2 267 18.2827 22 D-(+)-Maltose (8TMS) 13.31 293 69-79-4 C36H86O11Si8 268 18.3233 22 D-(+)-Raffinose 34.06 55 512-69-6 269 18.3412 22 Uridine 5'-diphospho-N-acetylglucosamine (nTMS) 44.3 219 91183-98-1 270 18.342 22 D-(-)-Fructose (5TMS) 56.27 166 57-48-7 C21H52O6Si5 271 18.3895 22 Icosanoic acid 70.34 369 506-30-9 274 18.4228 22 D-(+)-Raffinose 33.82 55 512-69-6 275 18.436 22 1-Kestose 8.86 29 470-69-9 276 18.5424 22 Xylitol 67.13 446 87-99-0 278 18.688 22 2'-Deoxyinosine 8.68 19 890-38-0 279 18.6993 22 D-Arabitol 62.41 284 488-82-4 280 18.7231 22 D-(+)-Raffinose 23.61 55 512-69-6 281 18.8138 22 L-(-)-Arabitol (5TMS) 8.38 95 7643-75-6 C20H52O5Si5 282 18.8787 22 N-Acetylneuraminate 35.55 488 131-48-6 283 18.999 22 D-Threitol 84.22 106 2418-52-2 284 19.0362 22 D-Threitol 49.32 106 2418-52-2 285 19.0154 22 Lactitol 24.8 443 585-86-4 286 19.0988 22 2'-Deoxyuridine 27.53 336 951-78-0 287 19.3936 20 Ribitol (5TMS) 13.32 59 488-81-3 C20H52O5Si5 288 19.527 22 Pentachlorophenol (1TMS) 20.71 77 87-86-5 C9H9Cl5OSi 289 19.5312 22 cis-Aconitic acid (3TMS) 22.01 204 209-564-4 C15H30O6Si3 290 19.6727 22 D-Tagatose 38.16 128 87-81-0 292 19.7726 22 1,5-Anhydro-D-glucitol 41.84 114 154-58-5 293 19.8155 22 Psicose 38.13 394 551-68-8 294 19.8163 22 Biotin 7.46 374 58-85-5 297 20.5827 22 D-(-)-Sorbitol 77.45 232 50-70-4

256

298 20.59 22 D-Tagatose 92.79 128 87-81-0 299 20.7473 19 L-(-)-Arabitol (5TMS) 64.99 95 7643-75-6 C20H52O5Si5 300 21.1105 22 D-Melezitose 73.01 288 597-12-6 302 21.1056 22 D-Panose 92.88 185 33401-87-5

AlignID tmean FoundIn Name.3 MatchFactor.3 DB.Id.3 CAS.3 Formula.3 1 5.6452 22 Elaidic acid 54.92 342 112-79-8 2 5.7352 22 L-Cysteine (4TMS) 52.55 94 52-90-4 C15H39NO2SSi4 3 5.8619 22 DL-Homocysteic acid 68.23 439 504-33-6 5 6.0166 22 Shikimic acid (4TMS) 27.62 53 138-59-0 C19H42O5Si4 6 6.1215 22 4-Hydroxyphenylacetic acid (2TMS) 27.44 28 156-38-7 C14H24O3Si2 8 6.1583 22 Oxamide 51.59 206 471-46-5 10 6.1684 22 Oxalic acid (2TMS) 27.76 15 6153-56-6 C8H18O4Si2 11 6.1724 22 6-Amino-1-MethylUracil 58.44 414 2434-53-9 12 6.1724 22 6-Amino-1-MethylUracil 57.11 414 2434-53-9 13 6.441 22 Mesaconic acid (2TMS) 40.16 161 498-24-8 C11H22O4Si2 14 6.4416 22 trans-Cinnamic acid (1TMS) 48.92 44 140-10-3 C12H16O2Si 17 6.4648 22 4-Aminobenzoic acid 33.25 358 150-13-0 18 6.4866 22 Ketoisoleucine 72.53 285 1460-34-0 19 6.9429 22 L-Arginine (3TMS) 23.27 104 1119-34-2 C27H70N4O2Si7 20 7.4931 22 1,10-Phenanthroline (0TMS) 16.66 9 66-71-7 21 7.6596 22 5-Hydoxytryptamine (5TMS) 92.06 78 153-98-0 C22H44N2OSi4 22 7.8241 22 D-(+)-Fucose 91.12 111 2438-80-4 23 7.8297 22 (+-)-alpha-Tocopherol (1TMS) 17.51 400 10191-41-0 C32H58O2Si 24 8.2836 22 Oxalic acid (2TMS) 36.58 15 6153-56-6 C8H18O4Si2 26 8.4225 22 (-)-Epinephrine 99.65 102 51-43-4 27 8.4379 22 Maltotriose (11TMS) 76.41 6 1109-28-0 C51H120O16Si11 28 8.4779 22 (+-)-alpha-Tocopherol (1TMS) 17.59 400 10191-41-0 C32H58O2Si 29 8.5236 22 Paeonol 77.29 186 552-41-0 30 8.6036 22 Taurine (3TMS) 46 20 107-35-7 C11H31NO3SSi3

257

31 8.6834 22 Benzen-1,4-Dicarboxylic acid 34.3 391 100-21-0 32 8.7042 22 Psoralen (0TMS) 52.02 448 66-97-7 33 8.7371 22 2-Hydroxyisobutyric acid (2TMS) 99.08 371 594-61-6 C10H24O3Si2 36 8.8893 22 1-Aminocyclopropane-1-carboxylic acid (2TMS) 63.72 387 22059-21-8 C13H31NO2Si3 37 8.9202 22 D-(+)-Fucose 65.26 111 2438-80-4 38 8.9562 22 Dihydrouracil (2TMS) 53.94 461 504-07-4 C10H22N2O2Si2 39 9.201 22 N-Acetyl-DL-alanine 66.14 7 1115-69-1 40 9.2114 22 4-Aminobenzoic acid 30.45 358 150-13-0 41 9.2107 22 Pyridoxamine 28.25 440 85-87-0 42 9.2206 22 Lignoceric acid 27.33 176 557-59-5 43 9.4495 22 4-Methyl-2-oxovaleric acid (3TMS) 75.81 126 816-66-0 C9H18O3Si 44 9.4827 22 N-Acetyl-DL-alanine 65.48 7 1115-69-1 45 9.4924 22 L-Arginine (3TMS) 33.92 104 1119-34-2 C27H70N4O2Si7 46 9.492 22 N-Acetyl-L-Leucine 39.78 65 1188-21-2 49 9.5216 22 N-Carbamoyl-L-Aspartate 34.75 409 923-37-5 50 9.6877 22 4-Methyl-2-oxovaleric acid (3TMS) 79.46 126 816-66-0 C9H18O3Si 51 9.6851 22 Maltitol 36.57 427 585-88-6 52 9.7923 22 (+-)-alpha-Tocopherol (1TMS) 17.68 400 10191-41-0 C32H58O2Si 53 9.8624 22 HydroxyButyrate 95.24 376 600-15-7 55 9.8665 22 3-Methyl-2-oxovaleric acid (3TMS) 59.29 69 66872-74-0 C9H18O3Si 56 9.8766 22 Spermidine (5TMS) 56.86 356 124-20-9 C22H59N3Si5 57 10.2221 22 L-(+)-Tartarate 56.98 86 87-69-4 58 10.2215 22 6-Amino-1-MethylUracil 66.96 414 2434-53-9 60 10.3822 22 L-Alanine (3TMS) 73.91 335 56-41-7 C12H31NO2Si3 61 10.3821 22 (R)-(-)-Phenylephrine (2TMS) 97.89 322 61-76-7 C18H37NO2Si3 62 10.4855 22 5-Hydoxytryptamine (5TMS) 98.08 78 153-98-0 C22H44N2OSi4 63 10.5032 22 D-Ribulose 70.52 331 488-84-6 64 10.4971 22 D-Ribulose 72.77 331 488-84-6 65 10.502 22 Glycerol 1-phosphate (4TMS) 53.8 347 5746-57-6 C15H41O6PSi4 66 10.5027 22 1-Hexadecanol 51.23 425 36653-82-4

258

67 10.5125 22 L-Isoleucine (2TMS) 87.92 222 73-32-5 C15H37NO2Si3 68 10.6341 22 N-Acetyl-L-Glutamate 31.97 312 1188-37-0 69 10.6454 22 Methylmalonic acid (2TMS) 18.84 250 516-05-2 C10H14O2Si 70 10.7575 22 L-Leucine (2TMS) 88.81 108 61-90-5 C15H37NO2Si3 71 10.7879 22 Propyleneglycol 76.03 432 57-55-6 72 10.8176 22 Citric acid (4TMS) 22.08 51 77-92-9 C9H13N 73 10.8534 22 (S)-(+)-2-(anilinomethyl)pyrrolidine (2TMS) 95.43 287 64030-44-0 C17H32N2Si2 74 10.9064 22 Glycylglycine (4TMS) 31.23 226 556-50-3 C16H40N2O3Si4 75 10.9176 22 5-Hydoxytryptamine (5TMS) 95.06 78 153-98-0 C22H44N2OSi4 76 10.969 22 Glycolic acid (2TMS) 23.27 155 79-14-1 C8H20O3Si2 77 10.9891 22 Methylmalonic acid (2TMS) 56.66 250 516-05-2 C10H14O2Si 78 11.1481 22 (+-)-alpha-Tocopherol (1TMS) 17.67 400 10191-41-0 C32H58O2Si 79 11.2211 22 3-(2-Aminoethyl)indole [Tryptamine] (2TMS) 33.8 165 61-54-1 C19H36N2Si3 80 11.2216 22 Daidzein 31.91 355 486-66-8 81 11.369 22 DL-Isocitric acid (4TMS) 56.76 245 320-77-4 C18H40O7Si4 82 11.418 22 Maltose (8TMS) 69.77 368 6363-53-7 C36H86O11Si8 83 11.467 22 S-Adenosyl-L-methionine (3TMS) 51.3 300 24346-00-7 C33H70N6O5SSi6 84 11.6399 22 3-Methylglutarate 47.25 385 626-51-7 85 11.6754 22 DL-Allothreonine 76.94 337 144-98-9 86 11.8296 22 Harmaline (1TMS) 97.2 434 6027-98-1 C16H22N2OSi 87 11.8349 22 Shikimic acid (4TMS) 86.25 53 138-59-0 C19H42O5Si4 89 12.0812 22 Benzen-1,4-Dicarboxylic acid 36.46 391 100-21-0 90 12.1211 22 D-Glucuronic acid (5TMS) 71.15 80 03/12/6556 C21H50O7Si5 92 12.3222 22 4-Aminobenzoic acid 28.55 358 150-13-0 93 12.3334 22 Guanine 24.17 182 73-40-5 94 12.4349 22 N-Acetylputrescine (2TMS) 38.07 386 18233-70-0 C15H38N2OSi3 96 12.5001 22 Phthalic acid 26.7 462 88-99-3 97 12.514 22 L-Tyrosine (3TMS) 81.66 115 60-18-4 C21H43NO3Si4 98 12.5256 22 6-(gamma,gamma-Dimethylallylamino)purine (1TMS) 77.68 48 2365-40-4 C13H21N5Si 99 12.6496 22 DL-Malic acid 88.95 450 6915-15-7

259

100 12.7148 22 Indole-3-acetaldehyde (1TMS) 30.14 184 20095-27-6 C13H17NOSi 101 12.7709 22 Fumaric acid (2TMS) 34.24 34 110-17-8 C10H20O4Si2 102 12.7719 22 Fumaric acid (2TMS) 32.87 34 110-17-8 C10H20O4Si2 104 12.7693 22 DL-Norvaline 36.61 328 760-78-1 105 12.8951 22 Fumaric acid (2TMS) 33.98 34 110-17-8 C10H20O4Si2 107 12.782 22 Alanylalanine 57.89 179 1948-31-8 108 12.7829 22 4-Hydroxybenzyl alcohol (2TMS) 83.64 247 623-05-2 C13H24O2Si2 109 12.8372 22 1,10-Phenanthroline (0TMS) 74.07 9 66-71-7 110 12.898 22 (+-)-alpha-Tocopherol (1TMS) 17.64 400 10191-41-0 C32H58O2Si 111 12.898 22 Methylmalonic acid (2TMS) 12.83 250 516-05-2 C10H14O2Si 112 12.9412 22 N-Acetylputrescine (2TMS) 37.31 386 18233-70-0 C15H38N2OSi3 113 12.9684 22 6-(gamma,gamma-Dimethylallylamino)purine (1TMS) 78.43 48 2365-40-4 C13H21N5Si 114 13.0187 22 O-Succinyl-L-Homoserine 42.66 268 1492-23-5 115 13.0773 22 L-Glutathione (xTMS) 99.53 173 70-18-8 116 13.1092 22 Cadaverine (4TMS) 62.38 205 462-94-2 C17H46N2Si4 117 13.1009 22 D-Glucarate 35.93 399 87-73-0 118 13.2316 22 Indole-3-acetaldehyde (1TMS) 34.44 184 20095-27-6 C13H17NOSi 120 13.237 22 Ibuprofen (1TMS) 21.51 5 15687-27-1 C16H26O2Si 121 13.2819 22 L-Isoleucine (2TMS) 67.16 222 73-32-5 C15H37NO2Si3 122 13.3011 22 DL-Threo-b-HydroxyAspartic acid 72.44 313 4294-45-5 123 13.3772 22 L-Valine (1TMS) 28.92 257 72-18-4 C14H35NO2Si3 125 13.4402 22 L-Norleucine (2TMS) 65.67 195 327-57-1 C15H37NO2Si3 126 13.4506 22 1-Amino-1-cyclopentanecarboxylic acid (2TMS) 42.79 237 52-52-8 C15H35NO2Si3 127 13.5853 22 (S)-(+)-2-(anilinomethyl)pyrrolidine (2TMS) 73.3 287 64030-44-0 C17H32N2Si2 128 13.6794 22 g-Glutamyl cysteine 29.68 393 636-58-8 129 13.769 22 g-Glutamyl cysteine 32.08 393 636-58-8 130 13.7107 22 D-Galactosamine (5TMS) 74.76 133 1772-03-8 C24H61NO5Si6 131 13.734 22 D-Galactosamine (5TMS) 64.72 133 1772-03-8 C24H61NO5Si6 133 13.8219 22 N-Acetylputrescine (2TMS) 39.13 386 18233-70-0 C15H38N2OSi3 135 13.8253 22 Indole-3-carboxyaldehyde (1TMS) 70.77 174 487-89-8 C12H15NOSi

260

136 13.8609 22 Gentistic acid 82.6 415 490-79-9 137 13.8406 22 beta-Alanine (3TMS) 32.13 49 107-95-9 C12H31NO2Si3 138 13.9782 22 3,4-Dihydroxy-L-phenylalanine (4TMS) 81.77 47 59-92-7 C24H51NO4Si5 139 13.9838 22 Uridine 5'-diphospho-N-acetylglucosamine (nTMS) 38.76 219 91183-98-1 141 14.0706 22 1-Kestose 77.8 29 470-69-9 144 14.1385 22 Podophyllotoxin 23.6 120 518-28-5 146 14.1995 22 5-Hydroxy-L-tryptophan 15.2 451 08/09/4350 147 14.2492 22 N-Methylethanolamine 68.3 421 109-83-1 148 14.2508 22 D-Glucuronic acid (5TMS) 50.83 80 03/12/6556 C21H50O7Si5 149 14.273 22 Galactitol 30.69 464 608-66-2 150 14.274 22 DL-Threo-b-HydroxyAspartic acid 11.85 313 4294-45-5 152 14.5622 22 L-(-)-Malic acid (3TMS) 23.62 377 97-67-6 C13H30O5Si3 153 14.5645 22 D-Glutamine 25.93 380 5959-95-5 154 14.5765 22 N-Acetyl-L-glutamine 43.64 190 2490-97-3 156 14.6119 22 Lanthionine 68.16 375 922-55-4 157 14.6287 22 Propyleneglycol 72.27 432 57-55-6 159 14.6973 22 N-Acetyl-D-glucosamine (4TMS) 64.86 85 7512-17-6 160 14.8645 22 5-Hydoxytryptamine (5TMS) 95.88 78 153-98-0 C22H44N2OSi4 161 14.8834 22 3-Hydroxy-3-Methylglutarate 29.4 215 503-49-1 163 14.9386 22 1,3-Dihydroxyacetone dimer (2TMS) 36.77 388 62147-49-3 164 14.9354 22 1,3-Dihydroxyacetone dimer (2TMS) 37.26 388 62147-49-3 166 14.9182 22 Glycerol 1-phosphate (4TMS) 67.73 347 5746-57-6 C15H41O6PSi4 167 14.9719 22 Dopamine 92.76 241 51-61-6 168 15.0227 22 Adipic acid 32.61 496 124-04-9 169 15.1052 22 DL-Pipecolic acid 86.56 338 535-75-1 170 15.2147 22 Myo-Inositol (6TMS) 79.94 211 87-89-8 C24H60O6Si6 171 15.1842 22 Galactitol 65.21 464 608-66-2 172 15.2883 22 Galactitol 64.85 464 608-66-2 175 15.163 22 meso-Erythritol 49.63 479 149-32-6 176 15.2397 22 Maltotriose (11TMS) 74.7 6 1109-28-0 C51H120O16Si11

261

178 15.379 22 DL-Isocitric acid (4TMS) 61.17 410 1637-73-6 C18H40O7Si4 179 15.419 22 L-Prolinamide 87.03 153 7531-52-4 180 15.4672 22 L-Arginine (3TMS) 90.11 104 1119-34-2 C27H70N4O2Si7 182 15.5667 22 Myo-Inositol (6TMS) 79.77 211 87-89-8 C24H60O6Si6 183 15.5183 22 Galactitol 71.27 464 608-66-2 184 15.5502 22 Galactitol 68.38 464 608-66-2 187 15.5866 22 D-Mannose 81.22 367 31103-86-3 188 15.5917 22 L-(-)-Arabitol (5TMS) 82.31 95 7643-75-6 C20H52O5Si5 189 15.7365 22 L-(-)-Sorbose 73.69 21 87-79-6 190 15.7789 22 L-(-)-Sorbose 89.63 21 87-79-6 191 15.7476 22 D-(-)-Mannitol (6TMS) 82.84 3 69-65-8 C24H62O6Si6 192 15.7451 22 Harmaline (1TMS) 95.31 434 6027-98-1 C16H22N2OSi 193 15.8801 22 D-(+)-Galactose (5TMS) 80.92 311 59-23-4 C21H52O6Si5 194 15.9201 22 D-(+)-Galactose (5TMS) 41.86 311 59-23-4 C21H52O6Si5 195 15.9358 22 Maltotriose (11TMS) 72.74 6 1109-28-0 C51H120O16Si11 196 15.9507 22 D-(+)-Galactose (5TMS) 81.28 311 59-23-4 C21H52O6Si5 197 15.9515 22 D-(+)-Galactose (5TMS) 81.99 311 59-23-4 C21H52O6Si5 198 16.0405 22 4-Hydroxybenzyl alcohol (2TMS) 88.72 247 623-05-2 C13H24O2Si2 199 16.106 22 D-(+)-Galactose (5TMS) 82.31 311 59-23-4 C21H52O6Si5 200 16.2015 22 D-(+)-Allose 78.24 63 2595-97-3 201 16.1402 22 6-Amino-1-MethylUracil 18.55 414 2434-53-9 202 16.1539 22 Behenic acid 25.83 442 112-85-6 203 16.2128 22 alpha-Lactose (8TMS;1MEOX) 77.8 370 5989-81-1 205 16.1619 22 5-Aminovaleric acid 76.07 194 660-88-8 208 16.3697 22 DL-Threo-b-HydroxyAspartic acid 91.07 313 4294-45-5 209 16.4085 22 Galactitol 77 464 608-66-2 210 16.4605 22 Dihydrouracil (2TMS) 29.57 461 504-07-4 C10H22N2O2Si2 211 16.481 22 Maltotriose (11TMS) 79.97 6 1109-28-0 C51H120O16Si11 212 16.4853 22 Galactitol 76.86 464 608-66-2 213 16.5325 22 alpha-Lactose (8TMS) 78.58 151 63-42-3 C36H86O11Si8

262

214 16.614 22 D-(+)-Galactose (5TMS) 73.24 311 59-23-4 C21H52O6Si5 215 16.8174 22 D-(+)-Galactose (5TMS) 75.07 311 59-23-4 C21H52O6Si5 216 16.8249 22 Galactitol 69.72 464 608-66-2 217 16.6109 22 Galactitol 69.61 464 608-66-2 221 16.6144 22 D-(-)-Sorbitol 37.11 232 50-70-4 222 16.6912 22 2-Dehydro-D-gluconate 51.72 201 669-90-9 223 16.6923 22 D-(-)-Penicillamine (3TMS) 28.4 156 52-67-5 C17H43NO2SSi4 224 16.8422 22 alpha-Lactose (8TMS) 82.3 151 63-42-3 C36H86O11Si8 225 16.8852 22 Galactitol 66.72 464 608-66-2 226 17.1096 22 Lignoceric acid 71 176 557-59-5 227 17.1099 22 Homogentisate 5.88 240 451-13-8 228 17.1269 22 Dihydroxyacetone 28.54 350 96-26-4 229 17.1683 22 Galactitol 67.32 464 608-66-2 230 17.3181 22 Maltose (8TMS) 81.75 368 6363-53-7 C36H86O11Si8 231 17.3144 22 Galactitol 45.55 464 608-66-2 232 17.3278 22 Galactitol 52.98 464 608-66-2 234 17.4695 22 D-(+)-Raffinose 41.67 55 512-69-6 235 17.541 22 5-Hydoxytryptamine (5TMS) 96.57 78 153-98-0 C22H44N2OSi4 236 17.6579 22 1,5-Anhydro-D-glucitol 21.31 114 154-58-5 237 17.6674 22 Oleic acid (1TMS) 29.88 436 112-80-1 C21H42O2Si 238 17.7183 22 D-(-)-Mannitol (6TMS) 76.54 3 69-65-8 C24H62O6Si6 240 17.8383 22 D-(+)-Raffinose 38.72 55 512-69-6 241 17.848 22 Dopamine 90.29 241 51-61-6 242 17.8657 22 Psicose 74.42 394 551-68-8 245 17.8883 22 1,5-Anhydro-D-glucitol 30.63 114 154-58-5 248 18.0647 22 D-Arabitol 74.31 284 488-82-4 249 18.1018 22 1,5-Anhydro-D-glucitol 35.52 114 154-58-5 250 18.1221 22 2-Aminoisobutyrate 95.69 33 62-57-7 252 18.104 22 2-Aminoisobutyrate 48 33 62-57-7 255 18.16 22 Methylmalonic acid (2TMS) 18.74 250 516-05-2 C10H14O2Si

263

257 18.2273 22 alpha-Lactose (8TMS) 82.1 151 63-42-3 C36H86O11Si8 258 18.234 22 alpha-Lactose (8TMS) 79.98 151 63-42-3 C36H86O11Si8 259 18.3533 22 Maltotriose (11TMS) 79.96 6 1109-28-0 C51H120O16Si11 260 18.2277 22 Maltotriose (11TMS) 79.04 6 1109-28-0 C51H120O16Si11 262 18.3466 22 Maltotriose (11TMS) 75.1 6 1109-28-0 C51H120O16Si11 265 18.2398 22 Galactitol 73.77 464 608-66-2 266 18.2867 22 Homoserine lactone 63.71 131 1192-20-7 267 18.2827 22 D-(+)-Trehalose (8TMS) 13 140 99-20-7 C36H86O11Si8 268 18.3233 22 Myo-Inositol (6TMS) 32.42 211 87-89-8 C24H60O6Si6 269 18.3412 22 Minoxidil (xTMS) 41.96 96 38304-91-5 270 18.342 22 1,3-Dihydroxyacetone dimer (2TMS) 49.8 388 62147-49-3 271 18.3895 22 Lignoceric acid 70.33 176 557-59-5 274 18.4228 22 1,5-Anhydro-D-glucitol 32.07 114 154-58-5 275 18.436 22 alpha-Methyl-DL-serine (3TMS) 5.94 357 5424-29-3 C16H41NO3Si4 276 18.5424 22 Galactitol 67.1 464 608-66-2 278 18.688 22 g-Glutamyl cysteine 3.45 393 636-58-8 279 18.6993 22 Xylitol 62.19 446 87-99-0 280 18.7231 22 1,5-Anhydro-D-glucitol 21.94 114 154-58-5 281 18.8138 22 D-(-)-Arabinose (4TMS) 8.27 251 10323-20-3 C17H42O5Si4 282 18.8787 22 N-Acetyl-DL-serine (2TMS) 34.7 469 97-14-3 283 18.999 22 L-Iditol (6TMS) 83.41 103 488-45-9 C24H62O6Si6 284 19.0362 22 L-Iditol (6TMS) 47.71 103 488-45-9 C24H62O6Si6 285 19.0154 22 Maltotriose (11TMS) 24.17 6 1109-28-0 C51H120O16Si11 286 19.0988 22 N-Acetylneuraminate 26.35 488 131-48-6 287 19.3936 20 D-Threitol 13.31 106 2418-52-2 288 19.527 22 Taurine (3TMS) 14.42 20 107-35-7 C11H31NO3SSi3 289 19.5312 22 N-Acetyl-DL-serine (2TMS) 16.52 469 97-14-3 290 19.6727 22 L-(-)-Sorbose 38.08 21 87-79-6 292 19.7726 22 Myo-Inositol (6TMS) 41.45 211 87-89-8 C24H60O6Si6 293 19.8155 22 D-Tagatose 38.12 128 87-81-0

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294 19.8163 22 Albiflorin 6.81 132 39011-90-0 297 20.5827 22 L-Iditol (6TMS) 75.74 103 488-45-9 C24H62O6Si6 298 20.59 22 L-(-)-Sorbose 91.98 21 87-79-6 299 20.7473 19 L-Iditol (6TMS) 64.86 103 488-45-9 C24H62O6Si6 300 21.1105 22 D-(+)-Trehalose (8TMS) 71.14 189 6138-23-4 C36H86O11Si8 302 21.1056 22 Lactitol 92.38 443 585-86-4

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8.18. Full list of ANOVA Test Results of Metabolites Identified by Using Metabolomic- Mass Spectrum Footprint:

Peak no p.value Peak no p.value Peak no p.value Peak no p.value 22 1.00E-13 118 4.36E-07 154 0.00011451 265 0.0064263 121 7.47E-13 298 4.58E-07 55 0.00013484 258 0.0064284 73 1.55E-11 129 5.73E-07 209 0.00014686 24 0.0066282 23 1.55E-11 161 8.81E-07 72 0.00014746 176 0.0077753 135 5.46E-11 266 1.06E-06 191 0.00018269 31 0.0078985 92 7.16E-11 144 1.06E-06 126 0.00023024 184 0.008093 18 9.46E-11 11 1.21E-06 89 0.00023384 138 0.0091965 178 2.35E-10 123 1.36E-06 248 0.00024832 299 0.0093551 125 3.00E-10 160 1.40E-06 82 0.00025526 115 0.009477 166 3.46E-10 179 1.55E-06 6 0.00030226 163 0.00976 122 3.47E-10 292 1.55E-06 98 0.00033867 5 0.010608 38 4.84E-10 269 1.83E-06 215 0.00034925 75 0.010813 150 5.19E-10 153 2.16E-06 188 0.00039556 284 0.012225 235 1.16E-09 159 2.55E-06 211 0.00039891 148 0.013523 149 1.21E-09 270 3.07E-06 232 0.00040656 120 0.015064 81 2.13E-09 36 3.36E-06 32 0.00049963 274 0.015414 99 3.35E-09 182 3.80E-06 108 0.00050576 71 0.015454 29 4.03E-09 152 4.08E-06 49 0.00056211 43 0.015549 68 4.11E-09 187 4.36E-06 231 0.00057749 170 0.015722 128 4.57E-09 100 5.24E-06 112 0.00075898 164 0.016617 290 5.93E-09 66 6.74E-06 130 0.00086312 93 0.016808 133 9.32E-09 64 6.86E-06 283 0.00090139 289 0.017181 33 1.24E-08 199 7.47E-06 175 0.00097727 262 0.017182 26 1.25E-08 214 7.81E-06 84 0.0011805 275 0.019494 107 1.38E-08 279 8.53E-06 230 0.0012225 225 0.019784 157 1.41E-08 221 8.80E-06 90 0.0013206 257 0.020197 237 1.50E-08 65 9.87E-06 300 0.0015926 252 0.02714 3 1.72E-08 146 1.10E-05 195 0.0017583 212 0.028474 113 2.72E-08 60 1.19E-05 39 0.0022812 249 0.03222 297 4.34E-08 61 1.19E-05 217 0.0023063 210 0.032257 156 4.43E-08 117 1.20E-05 193 0.002328 62 0.034384 116 7.71E-08 104 1.36E-05 268 0.0026996 141 0.034713 131 8.43E-08 238 1.56E-05 287 0.0027891 96 8.44E-08 137 1.72E-05 171 0.0029268 37 8.99E-08 45 1.74E-05 236 0.0031618 63 1.38E-07 242 1.95E-05 250 0.0034556 294 1.40E-07 41 2.54E-05 44 0.0035092 293 1.63E-07 288 3.07E-05 285 0.0037738 97 1.83E-07 234 3.62E-05 245 0.0037993 224 2.33E-07 8 5.03E-05 281 0.0039257

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Peak no p.value Peak no p.value Peak no p.value Peak no p.value 168 2.62E-07 51 5.56E-05 280 0.0039257 167 2.63E-07 21 6.43E-05 202 0.00477 127 3.10E-07 50 7.37E-05 286 0.005445 276 3.21E-07 30 7.87E-05 101 0.0056685 241 3.23E-07 228 8.18E-05 278 0.0057814 302 3.27E-07 40 0.00010236 180 0.0062703

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8.19. Summary of Different Co-culture Methods Used:

Co-Culture Methods

In-Direct Direct

Trans-well dishes

Conditioned media