Identification of Genes and Pathways Linking Cancer Metabolism to Cell Surface Dynamics Through Protein N-Glycosylation

Identification of genes and pathways linking cancer metabolism to cell surface dynamics through protein N-glycosylation

Xiangyuan Ma

A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Molecular Genetics University of Toronto

Xiangyuan Ma

Master of Science

Department of Molecular Genetics University of Toronto

2014 Abstract

N-glycosylation is a co-translational modification that covalently attaches oligosaccharide to and regulates proper folding, trafficking, and surface residency of secreted proteins. MGAT5 encodes a glycosyltransferase that synthesizes β1,6GlcNAc-branched N-glycans in medial Golgi.

MGAT5 is frequently up-regulated in and associated with the progression of multiple types of human carcinomas. In order to identify Mgat5 interacting genes and signaling pathways, immortalized mouse embryonic fibroblast (MEF) cell lines that are Mgat5 wild-type and null were established on p53 null genetic background. Pooled lentiviral shRNA drop-out screens revealed that mTOR pathway was essential in the wild-type MEFs, whereas signaling dependency shifts to RAS-MEK-ERK pathway was observed in the null cells. Metabolite profiling of the MEFs and human cell lines with MGAT5 knock-down showed drastic changes in tricarboxylic acid cycle metabolites and amino acids upon MGAT5 disruption. Together, these approaches permitted a global survey of genetic and metabolic changes that occur when MGAT5 is disturbed.

Acknowledgements

I would like to express my utmost gratitude to my co-supervisors, Dr. James W. Dennis and Dr. Jason Moffat, for your constant guidance and encouragement over my graduate study. Your passion in pursuing science, patience, and attention to details inspires me, and invigorates me in the future days to come.

I would like to extend my gratitude to my committee members, Dr. Johanna Rommens and Dr. Sean Egan, for your constructive advices and criticisms over my project, as well as your helps when I needed most outside academia life.

I would also like to thank all of the Dr. Moffat Laboratory and Dr. Dennis Laboratory members, as well as people I worked in collaboration with. Without your help, completion of this project would not have been possible.

Last but not least, I am greatly indebted to my parents, Mrs. Xiaoping Chen and Mr. Weijv Ma, for giving my life and your constant love.

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Table of Contents

Abstract ...... ii Acknowledgements ...... iii List of Tables ...... vi List of Figures ...... vii List of Appendices ...... viii Abbreviations ...... ix CHAPTER 1 INTRODUCTION ...... 1 1.1 Protein N-glycosylation and MGAT5 ...... 1 1.2 The N-glycan lattice model ...... 5 1.3 Glucose and glutamine metabolism, the Warburg effect ...... 7 1.4 Hexosamine biosynthesis pathway...... 9 1.5 Ras and PI3K signaling ...... 10 1.6 Summary of the work ...... 12 CHAPTER 2 MATERIALS AND METHODS ...... 13 2.1 Generation and immortalization of the mouse embryonic fibroblast cell lines, PCR genotyping ...... 13 2.2 Cell culture, virus production, and MEF immortalization ...... 14 2.3 L-PHA and PI staining for flow cytometry ...... 14 2.4 Lentiviral shRNA drop-out Screen ...... 15 2.5 Measuring MGAT5 expression by RT-PCR ...... 16 2.6 Metabolite profiling...... 17 2.7 Pathway Enrichment and correlation study...... 18 CHAPTER 3 IDENTIFICATION OF MGAT5 INTERACTING GENES AND SIGNALING PATHWAYS ...... 19 3.1 Summary ...... 19 3.2 Introduction ...... 19 3.3 Generation of the Mgat5 MEF cell lines ...... 21 3.4 Pooled shRNA screens reveal genetic interaction between Mgat5 and mTOR signaling pathway ...... 24 3.5 Discussion ...... 31

CHAPTER 4 MASS SPECTROMETRY ANALYSIS OF MGAT5 INTERACTING METABOLITES ...... 34 4.1 Summary ...... 34 4.2 Introduction ...... 34 4.3 Mgat5+/+ MEF cells contained higher level of glycolysis, TCA cycle, HBP, and PPP intermediates ...... 36 4.4 Human cell lines with Mgat5 knock-down showed higher metabolite levels...... 39 4.5 Multiple metabolites displayed strong correlation with MGAT5 disruption ...... 52 4.6 Discussion ...... 57 CHAPTER 5 DISCUSSION ...... 60 5.1 Mgat5 wt and null MEF cell line displayed different gene essentiality profile ...... 60 5.2 Signaling dependency shift from PI3K pathway in Mgat5 wt to MAPK in Mgat5 null MEFs ………………………………………………………………………………………… 62 5.3 Mgat5 deletion in MEF cell line and MGAT5 knock-down in human cell line caused metabolic reprogramming but of opposite trend ...... 64 5.4 Statistical tests identified key metabolites in glycolysis, TCA cycle, and amino acid metabolism to be differentially regulated in MGAT5 knock-down human cell lines ...... 66 5.5 Conclusions ...... 68 Reference ...... 69 Appendices...... 80

List of Tables

Table 3.1. Gene Set Enrichment Analysis of dGARP score ...... 29 Table 4.1. List of metabolites showing statistically significant difference in at least two of the human cell lines ...... 49 Table 4.2. Pearson’s correlation test of metabolite levels with remaining MGAT5 activity ...... 53

List of Figures

Figure 1.1. N-glycan branching pathway in multicellular organisms...... 2 Figure 1.2. N-glycan-Galectin lattice model...... 6 Figure 3.1. PCR genotyping of the null and the wt MEFs...... 22 Figure 3.2. Serial passage of the null and wt MEFs ...... 23 Figure 3.3. Surface L-PHA staining of the null and wt MEFs ...... 25 Figure 3.4. PI DNA staining of the null and wt MEFs ...... 26 Figure 3.5. zGARP scores plot of Mgat5+/+ and Mgat5-/- screen ...... 28 Figure 3.6. GSEA enrichment of the dGARP score ...... 30 Figure 4.1. Metabolite profiling of the null and wt MEFs ...... 37 Figure 4.2. Selected metabolites in the late passage null and wt MEFs ...... 38 Figure 4.3. RT-PCR analysis of knock-down efficiency by MGAT5 shRNA in human cell lines ...... 40 Figure 4.4. L-PHA binding to HEK 293 cells ...... 41 Figure 4.5. L-PHA binding to BJ hTERT cells ...... 42 Figure 4.6. L-PHA binding to MCF7 cells ...... 43 Figure 4.7. Metabolite profiling in human cells with MGAT5 knock-down...... 44 Figure 4.8. Metabolite profiling in HEK 293 cells with MGAT5 knock-down ...... 45 Figure 4.10. Metabolite profiling in BJ hTERT cells with MGAT5 knock-down ...... 47 Figure 4.11. Venn diagram showing the number of metabolites that was significantly different in MGAT5 knock-down human cell lines...... 50 Figure 4.12. PCA analysis of metabolite profiles of MGAT5 knock-down human cell lines...... 51 Figure 4.13. Pearson’s Correlation test between metabolite levels and remaining MGAT5 acitivty in HEK 293 cells ...... 54 Figure 4.14. Pearson’s Correlation test between metabolite levels and remaining MGAT5 acitivty in BJ hTERT cells ...... 55 Figure 4.15. Pearson’s Correlation test between metabolite levels and remaining MGAT5 acitivty in MCF7 cells...... 56

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List of Appendices

Appendix 1. List of zGARP and dGARP scores in the pooled shRNA drop-out screen ...... 80 Appendix 2. Metabolite level measurements in MEF cells ...... 80 Appendix 3. Metabolite level measurements in HEK 293, BJ hTERT, and MCF7 cells with MGAT5 knock-down ...... 80

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Abbreviations

4E-BP: 4E binding protein ADP: adenosine diphosphate ATP: adenosine triphosphate BSA: bovine serum albumin CoA: coenzyme A CRD: carbohydrate-recognition domain CRISPR: clustered regularly interspaced short palindromic repeat dGARP: differential gene activity ranking profile DMEM: Dulbecco’s modified Eagle’s medium DMSO: dimethyl sulfoxide DNA: deoxyribonucleic acid EDTA: ethylenediaminetetraacetic acid EGF: epidermal growth factor EGFR: epidermal growth factor receptor ER: endoplasmic reticulum ERK: extracellular signal-regulated kinase FACS: fluorescence-activated cell sorting FBS: fetal bovine serum FDR: false detection rate FGFR: fibroblast growth factor receptor GAP: GTPase activating protein GARP: gene activity ranking profile GDP: guanosine diphophate GEF: guanine nucleotide exchange factors GFP: green fluorescent protein GlcNAc: N-acetylglucosamine GlcNAcT: N-acetylglucosaminyltransferase Glc: glucose

GSEA: Gene Set Enrichment Analysis GTP: guanosine triphosphate HBP: hexosamine biosynthesis pathway HIF: hypoxia inducible factor IL3: interleukin-3 L-PHA: phaseolus vulgaris leukocytic phytohemagglutinin LC-MS: liquid chromatography – mass spectrometry LDH: lactate dehydrogenase Man: mannose MAPK: mitogen activated protein kinase MAPKK: mitogen activated protein kinase kinase MAPKKK: mitogen activated protein kinase kinase kinase MEF: mouse embryonic fibroblast MEK: mitogen-activated protein kinase kinase MGAT: mannosyl glycoprotein-N-Acetylglucosaminyltransferase MOI: multiplicity of infection MS: mass spectrometry mTOR: mammalian target of rapamycin mTORC: mammalian target of rapamycin complex NAD: nicotinamide adenine dinucleotide NADP: nicotinamide adenine dinucleotide phosphate NMR: nuclear magnetic resonance OST: oligosaccharyltransferase OXPHOS: oxidative phosphorylation PBS: phosphate buffer saline PC: principal component PCR: polymerase chain reaction PDAC: pancreatic ductal adenocarcinoma PDGFR: platelet-derived growth factor receptor PDH: pyruvate dehydrogenase PFK: phosphofructokinase

PI: propidium iodide PI3K: phosphoinositide 3-kinase

PIP3: phosphatidylinositol 3,4,5-triphosphate PKB: protein kinase B PKM2: pyruvate kinase M2 PPP: pentose phosphate pathway PTEN: phosphatase and tensin homolog PyMT: Polyomavirus middle T RFP: red fluorescent protein RNA: ribonucleic acid RT: reverse transcriptase RTK: receptor tyrosine kinase shARP: short hairpin activity ranking profile shRNA: short hairpin RNA siRNA: small interfering RNA TCA: tricarboxylic acid TGF- β: transforming growth factor beta TIGAR: TP53-inducible glycolysis and apoptosis regulator TOR: target of rapamycin TSC: tuberous sclerosis complex UDP-GlcNAc: uridine diphophate - N-acetylglucosamine UDP: uridine diphosphate UMP: uridine monophosphate UTP: uridine triphosphate zGARP: z-normalized gene activity ranking profile

CHAPTER 1 INTRODUCTION

1.1 Protein N-glycosylation and MGAT5

Protein glycosylation is a post-translational modification that covalently links sugar molecules or oligosaccharides (also called glycans) to proteins translated in the secretory pathway. Protein glycosylation is found in all three domains of life (Helenius and Aebi, 2004). Most protein glycosylation falls into two major categories, N-glycosylation and O-glycosylation. O- glycosylation is the attachment of sugar molecules to the oxygen atom on the side chain of serine, threonine, or tyrosine in the polypeptide. O-glycosylation occurs in the Golgi apparatus, where monosaccharides are added sequentially, and found at high density on mucin glycoproteins. N-glycosylation is the attachment of a glycan to the amide group of asparagine in the consensus peptide sequence Asn-X-Ser/Thr (NXS/T), where X can be any amino acid except for proline (Bause and Legler, 1981; Gavel and Von Heijne, 1990). Unlike O-glycosylation, N- glycosylation occurs co-translationally in the endoplasmic reticulum (ER) in two major steps: 1) the pre-synthesis of oligosaccharide on the dolichyl lipid anchor at the lumen side of the ER, and

2) the en bloc transfer of a 14-mer oligosaccharide (Glc3Man9GlcNAc2) from the dolichyl pyrophosphate anchor to the polypeptide by oligosaccharyltransferase (OST) (Helenius and Aebi, 2004).

Figure 1.1 shows a schematic presentation of the N-glycan branching pathway. After the initial transfer, the glycan undergoes preliminary modification in the ER. α -glucosidase I and II trim the glycan of the glucose, giving rise to Man9GlcNAc2-Asn. One mannose is then removed from some glycans through the action of α -mannosidase I in the ER. In yeast, more mannose residues are then added to Man8GlcNAc2 in the Golgi apparatus, and the glycoproteins mature with large oligo-mannose polymers. In multicellular organisms, the high mannose N-glycan is further trimmed in the cis-Golgi by α -mannosidase I to a minimum of 5 mannose residues (Helenius and Aebi, 2004).

N-glycans are then substrates for glycosyltransferase in the medial-Golgi. A family of Mannosyl- Glycoprotein-N-Acetylglucosaminyltransferases (MGAT), also known as

Figure 1.1. N-glycan branching pathway in multicellular organisms Pre-synthesized oligosaccharide is covalently attached to the asparagine in the NXS/T motif in the ER by oligosaccharide transferase (OST). After removal of terminal glucose residues by glycosidase I (GI) and II (GII), the Mannosyl-Glycoprotein-N- Acetylglucosaminyltransferases initiate the branching by attaching the N-acetylglucosamine in the Golgi Apparatus. Only Mgat1, 2, 4, and 5 are depicted in this diagram.

N-acetylglucosaminyltransferases (GlcNAcT), share the same substrate pool of uridine diphosphate-acetylglucosamine (UDP-GlcNAc), and initiate branching of complex N-glycan structure. The first transferase is MGAT1, which adds GlcNAc to the C-2 position of α1–3 mannose, representing the first branch. The mono-branched N-glycan is recognized by α- mannosidase II, which removes the two terminal mannoses, leaving three mannose residues in the N-glycan. The next transferase in the pathway is MGAT2, which adds the second GlcNAc branch to the C-2 position of the α1–6 mannose. Following this step, the N-glycan can be modified by either MGAT4 (MGAT4A/MGAT4B) or MGAT5, which attaches GlcNAc at the C-4 position of the α1–3 mannose or C-6 position of the α1–6 mannose, respectively, forming a tri-branched N-glycan, or a tetra-branched N-glycan if both modifications occur. Another branch is sometimes added at C-4 of the α1–3 mannose by MGAT6, an enzyme present in birds and some fish but absent in mammals (Helenius and Aebi, 2004). MGAT3 can add GlcNAc at the core linked-mannose. However, MGAT3 modification inhibits further branching by MGAT2, MGAT4, or MGAT5 (Brockhausen, 1988).

Maturation of the branched N-glycans happens in the trans-Golgi. A variety of glycosyltransferases attaches galactose, GlcNAc, GalNAc, and sialic acid to elongate the branches, creating additional structural complexity; until the branches are capped by sialic acid, fucose, and sulfate.

One role for N-linked glycosylation in the ER is to aid in the proper folding of targeted proteins and to facilitate their trafficking to the cell surface (Helenius, 1994). In some cases, N-glycan structure can shield proteins from protease degradation (Rudd et al., 2001). Taking an early example, tunicamycin inhibits dolichol oligosaccharide precursor synthesis and thus inhibits protein N-glycosylation. When used to treat cells infected with either vesicular stomatitis virus or Sindbis virus, tunicamycin cannot stop viral protein translation, but can prevent surface localization of the non-glycosylated proteins. Tunicamycin treatment also causes insolubility of non-glycosylated proteins in mild denaturing conditions, indicating improper protein folding (Leavitt et al., 1977). In rare cases, disruption of N-glycosylation of cell surface proteins can causes conformational changes leading to increases in enzymatic activity or changes in substrate specificity. For example, a point mutation at Asn34 of ribonuclease B abolishes its N- glycosylation, resulting in increased enzymatic activity (Pfeil, 2002). In addition, disruption of glycosylation at Asn384 in lecithin:cholesterol acyltransferase increases its enzymatic activity,

3 while the loss of glycan at Asn272 alters its substrate specificity (Francone et al., 1993; O et al., 1993).

The importance of N-glycan branching enzymes toward the overall fitness of an organism appears proportional to the order in which the enzyme acts in the N-glycan branching pathway. In mice, oocytes with Mgat1 homozygotic deletion show impaired oogenesis, and the embryos suffered severe developmental defects resulting in lethality before E9.5 (Ioffe et al., 1994; Metzler et al., 1994; Shi et al., 2004). In contrast, Mgat2-null mice are viable, but develop gastrointestinal, hematologic and osteogenic abnormalities, resulting in lethality shortly after birth (Wang et al., 2002). In humans, MGAT2 deficiency causes carbohydrate deficient glycoprotein syndrome type II, as well as other developmental defects that are also observed in mice (Tan et al., 1996; Wang et al., 2001). Moreover, Mgat4a-deficient mice are fully viable into adulthood but develop hyperglycemia and diabetes when fed with enriched diet (Ohtsubo et al., 2005). Mgat5-null mice are viable but are leaner and show reduced ability to nurture offspring (Granovsky et al., 2000). In addition, they are resistant to weight gain when put on high-fat diet, and demonstrate anomalies in the immune system such as T-cell hypersensitivity (Granovsky et al., 2000; Cheung et al., 2007; Lee et al., 2007).

Interestingly, although MGAT5 is not essential for survival and development, it has been frequently reported to be up-regulated in breast, liver, pancreatic, and colon carcinoma (Fernandes et al., 1991; Nan et al., 1998; Yao et al., 1998). The product of MGAT5 enzymatic activity are β1,6GlcNAc branched N-glycans, which are directly associated with cancer progression and metastasis (Dennis et al., 1999). Significant effort has been invested in understanding how MGAT5 interacts with oncogenes and tumor suppressor genes. MGAT5 expression is regulated by mitogen activated protein kinase (MAPK) and phosphoinositide 3- kinase (PI3K) signaling pathway (Chen et al., 1998; Guo et al., 2000a; Guo et al., 2000b). Mgat5 knockout desensitizes mouse tumor cells treated with Polyomavirus middle-T antigen (PyMT) towards various cytokine stimuli (Partridge et al., 2004). Mgat5 knock-out also suppressed hyper-activation of the PI3K-Akt signaling induced by PI3K negative regulator phosphatase and tensin homolog (PTEN) haploinsufficiency in mouse embryonic fibroblast (MEF) cells (Cheung and Dennis, 2007). Moreover, MGAT5 promotes cell motility by regulating the N-glycosylation of α5 β1 integrin, N-cadherin, as well as E-cadherin (Guo et al., 2002a; Guo et al., 2002b; Pinho et al., 2009).

1.2 The N-glycan lattice model

The N-glycan branching process follows a linear pathway but with declining enzymatic efficiencies (Grigorian and Demetriou, 2010). The Km value of MGAT1, 2, 4, and 5 are 0.04mM, 0.9mM, 5mM, and 11mM respectively, while the Golgi UDP-GlcNAc concentration is estimated to be around 1.5mM (Waldman and Rudnick, 1990; Sasai et al., 2002; Grigorian and Demetriou, 2010). Since the concentration of UDP-GlcNAc is higher than the Km value of MGAT1 and MGAT2, addition of the first and second N-glycan branches is kinetically favored. However, the Km value for MGAT4 and MGAT5 are much higher than the UDP-GlcNAc concentration, indicating that addition of these two branching forms is variable and sensitive to local UDP-GlcNAc concentration changes (Lau et al., 2007). In addition to branching diversity, the elongation of branches can vary by length and sugar composition. Indeed, mass spectrometry and nuclear magnetic resonance (NMR) identified more than 40 N-glycan forms in the epidermal growth factor receptor (EGFR), while over 80% of them contained at least 2 branches (Stroop et al., 2000).

Galectins are a family of proteins that possess evolutionarily conserved carbohydrate-recognition domains (CRD) for β-galactosides. To date, 15 members have been identified in human, each falling into one of three categories (Hirabayashi and Kasai, 1993). The first category includes galectin-1, -2, -5, -7, -10, -11, -13, -14, and -15, which contains a single CRD and dimerizes upon binding to sugars. The second category includes galectin-4, -6, -8, -9, and -12. These galectins contain two CRDs and also form dimers upon sugar binding. Galectin-3 is the sole member of the third category. Galectin-3 contains only one CRD but forms pentamers instead of dimers (Ahmad et al., 2004). Galectins display different affinity toward different N-glycan branched forms. The weakest interactions are between galectins and mono-branched N-glycan produced by MGAT1, whereas progressively stronger interactions occur with di-, tri-, and tetra- branched structures respectively. The overall avidity of polymerized galectin to glycosylated protein is also dependent on N-glycan multiplicity, that is, the density of N-glycosylation (Lau et al., 2007). Evidence shows that upon polymerization, galectins and N-glycans form higher order lattice-like structure at the cell surface (Ahmad et al., 2004; Nieminen et al., 2007; Cha et al., 2008). Figure 1.2 shows a graphic presentation of the lattice model. The lattice structure forms membrane microdomains that prevent lateral movement of cell surface proteins (Demetriou et al., 2001; Partridge et al., 2004). It also prevents undesired polymerization of

Figure 1.2. N-glycan-Galectin lattice model Secreted proteins and surface proteins with high N-glycosylation density and highly branched N-glycan show increased affinity toward galectins. Galectins polymerize (pentameric galectin-3 in the diagram) upon binding to N-glycans, and forms lattice-like higher order structure. The lattice prevents protein lateral movement and their loss of surface residency through caveolin-1 mediated endocytosis.

6 signaling receptors, and prevents their loss from the surface through caveolin-dependent endocytosis, resulting in increased receptor surface residency (Partridge et al., 2004; Lajoie et al., 2007).

It has been estimated that about 70% of surface protein NXS/T sequons are subject to N- glycosylation (Apweiler et al., 1999). Moreover, the interaction between N-glycans and galectins display low specificificity (Hirabayashi et al., 2002). An important question is how the lattice model generates specificity, and regulates cell surface retention in a targeted fashion. The first stratum of regulation is cellular concentration of UDP-GlcNAc and the interactions between branching enzymes. Notably, Mgat4 and Mgat5 efficiency is highly dependent on Golgi UDP- GlcNAc concentration (Sasai et al., 2002; Grigorian and Demetriou, 2010). Changes in UDP- GlcNAc concentration cause changes in the composition of glycan branched forms, and affinity to the galectin lattice (Lau et al., 2007). Computational modeling predicts another level of regulation based on N-glycan multiplicity. Surface proteins with a high density of N- glycosylation (NXS/T) are more resistant to fluctuations in UDP-GlcNAc concentrations, and maintain high avidity toward the galectin lattice. In contrast, surface proteins with low N-glycan occupancy display sigmoidal response toward increasing substrate concentration, making switch- like adjustments in surface retention possible (Lau et al., 2007). Supporting evidence for this model was provided by studying glutamine uptake in interleukin-3 (IL3) dependent hematopoietic progenitor cells, where GlcNAc supplementation rescued loss of IL3α subunit surface residency induced by glucose starvation, thus restoring glutamine uptake in these cells (Wellen et al., 2010). An examination of NXS/T sites suggests their positions have been under strong selection and evolved extensively with vertebrate radiation. On average, glycoproteins display positive correlation between evolving rate and number of sites (Williams et al., 2014).

1.3 Glucose and glutamine metabolism, the Warburg effect

Glucose is the main carbon source of cells and glucose enters cells through specific transporters. Glucose is phosphorylated upon cellular entry by hexokinases to prevent reverse flow to the extracellular environment. In normal cells, glucose catabolism is carried out by glycolysis through a series of enzymatic reactions. Glycolysis consumes one molecule of glucose and produces 2 adenosine tri-phosphate (ATP) and 2 molecules of pyruvate. The conversion of

7 fructose 6-phosphate to fructose 2,6-diphosphate, the third step, is a key regulatory point for glucose flux through glycolysis. This step is irreversible and is catalyzed by phosphofructokinase-1 (PFK1), which itself is subject to regulation by various post-translational modifications and allosteric controls (Yi et al., 2012). When oxygen levels are adequate, pyruvate is converted to Acetyl-coenzyme A (Acetyl-CoA) by pyruvate dehydrogenase (PDH), and is forwarded to mitochondria, where Acetyl-CoA enters the tri-caboxylic acid (TCA) cycle which yields about 36 molecules of ATP per molecule of glucose entering the TCA cycle. Pyruvate breakdown through the TCA cycle is known as mitochondrial oxidative phosphorylation (OXPHOS), or aerobic respiration. In situations where oxygen supply is limited, pyruvate is converted to lactic acid by lactate dehydrogenase (LDH), an energetically inefficient process that does not yield extra ATP, but replenishes nicotinamide adenine dinucleotide (NAD), which is essential to sustain glycolysis. Metabolizing pyruvate through this pathway is thus known as anaerobic respiration.

Almost a century ago, Otto Warburg discovered that cancer cells and rapidly proliferating cells display metabolic reprogramming (Warburg et al., 1924). Despite of the presence of oxygen, cancer cells block entry of pyruvate into the TCA cycle and glucose catabolism occurs predominately through glycolysis, while lactic acid is secreted as a by-product (Warburg et al., 1924; Warburg, 1956). This shift in metabolism is known as the Warburg effect. To manage for this energetically inefficient process, many cancer cells consume a large amount of glucose through glycolysis and other metabolic pathways. For example, cancer cells actively metabolize glucose through the pentose-phosphate pathway (PPP), which produces precursors for nucleotide synthesis, and Nicotinamide adenine dinucleotide phosphate (NADPH) to sustain redox balance in the cell (Gatenby and Gillies, 2004). By-products of this process (i.e. lactic acid) serve to create a micro environment that favours cancer cells. For example, lactic acid secretion is inhibitory to the activity of activated T-cells, and causes acidification of the extracellular space, which can promote angiogenesis (Fischer et al., 2007; Hunt et al., 2007).

Although cancer cells do not rely on the TCA cycle to metabolize the majority of glucose that enters cells into H2O and CO2, the TCA cycle is still used. Glutamine, through a process called glutaminolysis, can be converted to α-ketoglutaric acid, which can enter the TCA cycle. Intermediates of the TCA cycle, such as citrate and oxaloacetate, are also used as precursors for synthesis of nucleotides, non-essential amino acids, and fatty acids. In addition, glutamine can be

8 converted to lactic acid and reductive nicotinamide adenine dinucleotide phosphate (NADPH). Although glutamine is thought to be the major source of cellular nitrogen, catabolism of non- essential amino acids and ammonia efflux have been found to accompany glutamine catabolism (DeBerardinis et al., 2007). The high rate of glutamine metabolism functions to maintain an ample supply of biosynthetic precursors for the TCA cycle (i.e. anaplerosis) as well as reducing potential in the form of NAD+ (Levine and Puzio-Kuter, 2010).

Cancer cells regulate glycolytic flux by regulating the expression of major glycolytic enzymes including PFK2, which activates PFK1, and the M2 isoform of pyruvate kinase (PKM2), which is a fetal splice variant of pyruvate kinase with much higher enzymatic efficiency (Deprez et al., 1997; Christofk et al., 2008). Many oncogenes, such as MYC, EGFR, AKT, and KRAS, have been shown to promote metabolic reprogramming through the Warburg effect (Levine and Puzio-Kuter, 2010; Ying et al., 2012). The hypoxia inducible factors (HIFs), which are activated during acute hypoxia stress, regulate transcription of glucose transporters and glycolytic enzymes (Semenza, 2003; Macheda et al., 2005). The expression of lactate dehydrogenase A, which metabolizes pyruvate to lactic acid, and pyruvate dehydrogenase kinase, which directs pyruvate to the TCA cycle, are both regulated by HIFs (Levine and Puzio-Kuter, 2010). Moreover, p53 loss of function mutation also promotes the Warburg effect through expression of TIGAR, a negative regulator of glycolysis (Bensaad et al., 2006). p53 also promotes expression of genes involved in mitochondrial respiration as well as glutaminase-2, which performs the first step of glutaminolysis (Matoba et al., 2006; Hu et al., 2010). p53 regulated genes are enriched for amino acid metabolism pathways (Garritano et al., 2013).

1.4 Hexosamine biosynthesis pathway

De novo synthesis of UDP-GlcNAc relies on the hexosamine biosynthesis pathway (HBP). This pathway deviates from glycolysis following production of fructose 6-phosphate, and yields the vital substrate shared by enzymes involved in N-glycan synthesis, O-glycosylation, glycolipids, chitin, hyluronic acid generation, and heparin sulfate proteoglycan synthesis. In addition to glucose, the HBP uses other substrates including glutamine, acetyl CoA, and UTP to generate UDP-GlcNAc. UDP-GlcNAc is an optimal molecule to reflect metabolic status of the cell, since its synthesis requires input from carbon and nitrogen flux, Acetyl CoA which is the central

9 metabolite for the TCA cycle, fatty acid synthesis, and nucleotide biosynthesis through utilization of UTP (Dennis et al., 2009).

It is tempting to hypothesize that metabolic flux and the UDP-GlcNAc pool are altered in the Warburg effect. Indeed, study of metabolites in prostate cancer samples hinted that UDP- GlcNAc levels are increased in samples from tumor compared to benign cells (Sreekumar et al., 2009). Metabolite profiling also revealed that supplementation of cancer cell lines with glucose, glutamine, or GlcNAc increases the cellular UDP-GlcNAc concentration (Abdel Rahmen et al., 2013). Considering the fact that MGAT4 and MGAT5 are both sensitive to UDP-GlcNAc concentration changes, the HBP and N-glycosylation provided a means by which metabolic reprogramming associated with the Warburg effect can affect cell signaling.

1.5 Ras and PI3K signaling

Initially recognized as viral genes, H-Ras and K-Ras were first discovered by studying the mouse sarcoma cells (Harvey, 1964; Kirsten et al., 1970). Later, it was realized that the Ras protein is part of a large family of GTPases that can activate the MAPK signaling cascade (Campbell et al., 1998). Being a GTPase, Ras cycles between two states: the GDP-bound inactive form, and the GTP-bound active form. Guanine nucleotide exchange factors (GEFs) load Ras-GDP with GTP, while GTPase activating proteins (GAPs) assist the hydrolysis of the GTP on Ras (Ras-GTP), thus aiding cycling between both states (Díez et al., 2011).

Ras localizes to the cytoplasmic side of the plasma membrane (Magee and Marshall, 1999). Its activation is induced by a diverse collection of cell surface receptors, such as the receptor tyrosine kinases (RTKs), inducing EGFR and platelet-derived growth factor receptor (PDGFR) (Campbell et al., 1998). Activated Ras has a wide spectrum of targets, among which the Ras- Raf-MEK-ERK pathway is the best characterized effector (Campbell et al., 1998). Raf acts as the MAPK kinase kinase (MAPKKK) of this pathway and phosphorylates MEK1 and MEK2 (MAPKK), which in turn phosphorylates ERK1 and ERK2 (MAPK), leading to their translocation to the cell nucleus. Once inside the nucleus, phosphorylated ERK can further phosphorylate a variety of downstream effectors, such as the transcription factor Elk-1, which can mediate transcription of genes required for cell proliferation, differentiation, migration, cell

10 death, and angiogenesis (Mebratu and Tesfaigzi, 2009). Mutations deregulating this signaling cascade have been found in about 30% of human cancer cases. As a consequence, anti-cancer therapies are being developed to target RAS-ERK pathway (Santarpia et al., 2012).

PI3K enzymes are lipid kinases that target phosphoinositides. These enzymes were first discovered through their association with PyMT antigen (Whitman et al., 1985). PI3K is recruited and activated by RTKs and catalyzes production of phosphatidylinositol 3,4,5- triphosphate (PIP3). PIP3 recruits downstream signaling molecules Akt (also known as protein kinase B or PKB), leading to cell growth, proliferation, and survival (Yuan and Cantley, 2008). PTEN negatively regulates the signal transduction through this pathway by dephosphorylating

PIP3 (Yuan and Cantley, 2008).

A key component downstream of PI3K-Akt signaling is the target of rapamycin (TOR). As indicated by its name, TOR was discovered when a number of studies were attempting to elucidate molecular mechanisms underlying cell cycle arrest induced by rapamycin (Loewith and Hall, 2011). The pathway is highly conserved, and consists of two molecularly and functionally separated branches, namely the TOR complex 1 (TORC1) and complex 2 (TORC2) (Loewith and Hall, 2011).

The mammalian TORC1 (mTORC1) is the rapamycin-sensitive branch of this pathway (Loewith and Hall, 2011). It contains the core subunits mTOR, RAPTOR, and MLST8. In addition to receiving growth signals from the PI3K-Akt signaling pathway, mTORC1 also senses the concentrations of free cellular amino acids through Rag GTPases, (Sancak et al., 2008; Zoncu et al., 2011). Effectors of mTORC1 include ribosomal protein S6 kinase (S6K) and elongation factor 4E binding protein (4E-BP), which mediate protein synthesis, amino acid biosynthesis, and autophagy (Loewith and Hall, 2011; Zoncu et al., 2011). In contrast, mTORC2 controls the rapamycin-insensitive branch of the mTOR pathway. It contains core subunits mTOR, RICTOR, and MLST8, and functions through growth signals including the insulin/PI3K pathway (Zoncu et al., 2011). One function of mTORC2 is to regulate actin, as it’s capable of phosphorylating AKT and controlling cell growth through a positive feedback mechanism (Zoncu et al., 2011).

Although Ras-Raf-MEK-ERK and PI3K-Akt-mTOR signaling pathways are depicted here as separate, cross-talk between these systems have been reported. For example, Ras can directly activate PI3K (Rodriguez-Viciana et al., 1996). Phosphorylated ERK causes tuberous sclerosis

11 complex (TSC) 2 dissociation from the TSC1/TSC2 complex, resulting in activation of mTORC1 (Ma et al., 2005). Activation of the Ras-Raf-MEK-ERK signaling also phosphorylates RAPTOR, which activates mTORC1 (Carrière et al., 2008). In contrast, inhibition of mTORC1 by RAD001 activates the MAPK signaling pathway (Carracedo et al., 2008). Unfortunately, cancer cells can develop resistance against small molecule inhibitors targeting single proteins within this pathway, whereas targeting two or more proteins may be a more effective approach to suppress tumor growth (Wilson et al., 2012).

1.6 Summary of the work

In order to probe for genes regulated by protein N-glycosylation and the N-glycan lattice model, I focused my work around Mgat5 and generated immortalized MEF cell lines that were wild-type (Mgat5+/+) and mutant (Mgat5-/-) on a p53 deficient (Trp53-/-) C57BL/6J background. Using these cell lines, I performed a series of functional genomics assays on each of these cell lines in order to explore their differential states including pooled RNA interference screens (Chapter 3) and metabolomics by mass spectrometry (Chapter 4).

In addition, I also performed metabolite profiling experiments on three human cell lines: BJ hTERT, HEK 293, and MCF7 cells (Chapter 4). Taken together, results from my study revealed genes and pathways suggesting that loss of Mgat5 correlates with reduced dependence on mTOR pathway and gain of dependency on the Ras-MEK-ERK signaling pathway. The loss of Mgat5 also altered metabolic status in tested cell lines, and the results suggest that phenotypic effects may be dependent on genotypic background. In other words, N-glycan branching is an important genotype-to-phenotype modulator.

CHAPTER 2 MATERIALS AND METHODS

2.1 Generation and immortalization of the mouse embryonic fibroblast cell lines, PCR genotyping

Both Trp53 null and Mgat5 null mutations were carried on a C57BL/6J mice background. The Mgat5 mutant mice were generated by previous members of the Dennis laboratory, and the mutant allele contained a LacZ and neomycin resistance cassette replacing a portion of exon 1 (Granovsky et al., 2000). The Trp53 mutant mice were a commercially available strain, and the mutant allele contained a neomycin resistance cassette replacing exon 2 to exon 6 (JAX Mice, B6;129S2-Trp53tm1Tyj/J; Jackson Laboratory). All mouse maintenance and handling were performed according to protocols registered at the Toronto Centre for Phenogenomics (TCP) and approved by the institutional Animal Care Committee at TCP.

Trp53+/- and Mgat5+/- mice were crossed to produce Mgat5+/-; Trp53+/- double heterozygous F1 progeny. 8- to 12-week-old male and female double heterozygous F1 progeny were further crossed to obtain homozygous mutants. Pregnant females were sacrificed by CO2 asphyxiation at day 13.5 post gestation. Embryos were extracted from the female mice in a sterile environment. Internal organs and head were removed from each embryo. Subsequently, the remaining tissue was treated with 0.05% trypsin in phosphate buffer saline (PBS) for 30 minutes and then passed through a 12.5 um syringe vigorously. Suspended tissue was incubated in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) at 37 oC with 5% o CO2 for 2 days. Cells were then frozen at -80 C in 70% DMEM + 20% FBS + 10% dimethyl sulfoxide (DMSO). For later experiments, frozen cells were thawed and plated. Cells were designated as passage 3 upon thawing.

To genotype fibroblast strains, genomic DNA was extracted from embryonic head through proteinase K treatment. Primers for the Mgat5 locus were: 5’-GCC AAG GGA ATG GTA CAT TGC-3’ (wildtype forward), 5’-GTA AGG ACT CAC AGC TGA GG-3’ (wild-type reverse), 5’- TGG TCA AAT GGC GAT TAC CG-3’ (knock-out forward), and 5’-ATG TCT GAC AAT GGC AGA TCC C-3’ (knock-out reverse). Genotyping of the Trp53 locus was performed using

13 the following primers: 5’-CAG CGT GGT GGT ACC TTA T-3’ (forward), 5’-TAT ACT CAG AGC CGG CCT-3’ (forward), and 5’-CTA TCA GGA CAT AGC GTT GG-3’ (reverse).

2.2 Cell culture, virus production, and MEF immortalization

o Cell lines used in this study were maintained in DMEM +10% FBS at 37 C with 5% CO2 unless otherwise specified. Handling of cultured cell lines was performed under sterile conditions following standard tissue culture protocols.

For virus production, HEK 293T cells were seeded in 6-well tissue culture plate at 6.25x105 per well and allowed to settle for 24h. A mixture of 900ng psPAX2, 100 ng pMD2.G, and 1μg hairpin containing pLKO.1 plasmid were used to transfect two wells. Growth media was changed to DMEM +20% bovine serum albumin (BSA) at 18h post transfection. Virus harvest was performed 42h post transfection; culture media was saved as supernatant following brief centrifugation to remove HEK 293T cellular debris before use or frozen at -80 oC for storage.

Human MGAT5 knock-down was achieved using the following short hairpin RNA (shRNA) constructs: sh1: TRCN0000036059, 5’-CCCGAATTTAATCATGCAAAT-3’; sh2: TRCN0000036060, 5’-CCCTCCTTTGACCCTAAGAAT-3’; sh5: TRCN0000036063, 5’- GCTGGAGTCATGACAGCTTAT-3’. Knock-down of the luciferase gene was achieved using the following shRNA construct: shLUC: TRCN0000072246, 5’- CAAATCACAGAATCGTCGTAT-3’.

Extracted MEF cells were immortalized through serial passage (Todaro and Green, 1963). Briefly, for each passage, 3x105 cells were seeded in a new 15cm culture dish and incubated in DMEM +10% FBS for 3 days. Cell numbers were then counted and recorded at harvest.

2.3 L-PHA and PI staining for flow cytometry

For propidium iodide (PI) staining, growth media and a PBS wash were removed from the cell culture and saved. After cells were detached by Acutase, saved media and washing buffer were added back to inactivate the reaction. 500,000 cells were transferred into a new tube, and fixed

14 with 70% ethanol at -20oC for 90min in shaking. Fixed samples were washed with PBS +0.5% BSA once, and then treated in PBS +0.5% BSA with 500ug/ml RNaseA at 37 oC for 50min. Subsequently, samples were kept in PBS+0.5%BSA with 10ug/ml PI until flow cytometry was performed.

For Phaseolus vulgaris leukocytic phytohemagglutinin (L-PHA) staining, MEF cells and human cell lines followed different protocols. MEF cells were detached by Acutase, washed with PBS and fixed with 4% PFA in PBS. Fixed samples were washed with PBS +0.5% BSA three times, and then stained in the dark using PBS +0.5% BSA with 3μg/ml L-PHA for 1h. Human cell lines were detached by 1mM ethylenediaminetetraacetic acid (EDTA) in PBS and immediately stained by PBS with 2μg/ml L-PHA for 30min in darkness.

Flow cytometry was performed on a fluorescence-activated cell sorting (FACS) Calibur flow cytometer (Becton Dickinson, Franklin Lakes, NJ, USA).

2.4 Lentiviral shRNA drop-out Screen

The pooled dropout screen was carried out as described in the literature (Ketela et al., 2011; Blakely et al., 2011; Marcotte et al., 2012). MEF cell lines were in their 18th passage at the time of viral infection with a lentivirus library of ~80,000 unique shRNA elements (80K) targeting ~16,000 mouse genes. Specifically, cells were infected with the pooled mouse 80k shRNA lentiviral library at a multiplicity of infection (MOI, a.k.a. average viral infection per cell) of about 0.35. Fold coverage of the shRNA library was, on average, ~250-fold. 24 hours post viral infection, growth media was changed to DMEM supplemented with 10% FBS and 3ug/mL puromycin. Cells were incubated for a further 48 hours. Following this initial selection period, more than 20 million cells were collected as the T0 sample (corresponding to day 0). Remaining cells were then split into three sub-populations and maintained in DMEM supplemented with 5% FBS, and passaged and collected every three days. Cell samples from T6 (day 6) and T12 (day 12) were chosen as intermediate and end point in my screen. These corresponded to roughly 3 and 7 cell doublings, respectively. Genomic DNA extraction from collected cell pellet and hybridization to microarray chips were performed as described previously (Ketela et al., 2011).

Analysis of the screen results followed the gene-activity-ranking-profile (GARP) method, as previously published (Marcotte et al., 2012). Briefly, under-representation (shRNA drop-outs) of cells carrying a hairpin at T6 and T12 compared to T0 was plotted for each hairpin. A short- hairpin-activity-ranking-profile (shARP) score was calculated based on the rate of drop-out, such that a low shARP score corresponded to potent growth inhibition. Among the multiple hairpins targeting the same gene, the lowest two shARP scores were averaged to give a GARP score for that particular gene. GARP scores were z-score normalized to generate zGARP scores (Vizeacoumar et al., 2013). The differential zGARP (dGARP) score was then calculated by subtracting the zGARP score of a gene in the Mgat5+/+; Trp53-/- screen from that in the Mgat5-/-; Trp53-/- screen.

2.5 Measuring MGAT5 expression by RT-PCR

Total RNA extraction from harvested human cell lines was performed using the Invitrogen RNeasy Mini Kit. Extracted RNA concentration was quantified using the NANOdrop system. 800ng of RNA was used for each cDNA synthesis. Synthesis of cDNA started by degrading contaminating DNA through addition of 1μl of 10x DNase buffer, 1μl of Deoxyribonulease I (300U/μl, Invitrogen), 1μl of RNase Inhibitor Cloned (10U/μl, Invitrogen), and water to make 10μl in total. This was incubated at room temperature for 15min. After that, 1.2μl of 25mM EDTA was added to the reaction mixture and heated to 65 oC for 10min to denature the DNase. The reaction was then split equally into two 5.5μl aliquots. To each aliquot, 4.5μl of Water, 1μl of Random Primer (3μg/μl, Invitrogen), and 1μl of dNTP (10mM) were added. The mixture was heated to 65oC for 5min, and 4μl of 5x First Strand buffer (Invitrogen), 2μl of 0.1M dithiothreitol, and 1μl of water were added. The mixture was incubated first at 25oC for 10min, then at 42oC for 2min. To one of the two aliquots, 1μl of SuperScript II Reverse Transcriptase (200U/μl, Invitrogen) was added, this aliquot contained synthesized cDNA; to the other aliquot, 1μl of water was added, this aliquot was a negative control. Finally, the reaction mixture was incubated at 42oC for 50min, then 70oC for 15min.

Human MGAT5 expression level was measured by performing reverse transcription-polymerase chain reaction (RT-PCR) with the following primers: 5’-GGCAGAAAAGCAGAACCTTG-3’ (forward) and 5’-GCCAGATCGGTTTCCTACAA-3’ (reverse). GAPDH was used as loading

16 control. RT-PCR on GAPDH was performed using the following primers: 5’- AAGGTGAAGGTCGGAGTCAAC-3’ (forward) and 5’-GGGGTCATTGATGGCAACAATA- 3’ (reverse). In each 20μl reaction, 10μl of SyBr Green 2X Master Mix (Applied Biosystems), 0.5μl of each primer (forward and reverse, both 30μM), 9μl of water were mixed. Either 1μl of cDNA or 1μl of negative control from the previous step was then added to the mixture. PCR conditions were as follows: initial denaturation at 94oC for 10min; 40 cycles of denaturation at 94oC for 30s; annealing at 60oC for 30s; elongation at 72oC for 40s; and a final dissociation step of 95oC, 60oC, and 95oC for 15 seconds each. This was performed using an ABI-PRISM 7200 HT. The 2ΔΔCt method was used to calculate the relative expression of MGAT5, as described previously (Yuan et al. 2006).

2.6 Metabolite profiling

Metabolite aqueous extraction was performed on MEF cell lines and human cell lines following published protocols (Abdel Rahman et al., 2013). Each cell line was split into 6 identical samples in 6-well plate and incubated in DMEM +10% FBS for at least 24h. Metabolite extraction was performed from five of the six wells when cultures reached 70% - 90 % confluence, generating 5 technical replicates. The sixth well was used to determine cell number.

During extraction, culturing media was removed, PBS wash was performed before the plate was snap-frozen by submerging it in liquid nitrogen. 1ml of extraction solution, consisting of 40% methanol, 20% water, and 40% acetonitrile, was added to each well. On ice, the cell monolayer was detached using a scrapper. The cell debris was then homogenized in solution, and transferred into an Eppendorf tube. Next, the solution was vigorously shaken on a vortex machine at 4oC for 1h, and then centrifuged at 13,000g on a regular desktop centrifuge at 4oC for 10min. The resulting supernatant was transferred into a fresh tube while the precipitate was discarded. The supernatant was vacuum-dried at room temperature. Dehydrated samples were kept at -80oC for storage if not used immediately. Samples were then reconstituted by adding 200μl of water with 100μg/ml D7 Glucose and 100μg/ml D9 Tyrosine. Reconstituted samples were vortexed thoroughly and centrifuged at maximum speed at 4oC for 10min before installed in mass spectrometry machine.

Liquid chromatography – mass spectrometry (LC-MS) analysis was performed on an ABSciex4000Qtrap mass spectrometer (Toronto, ON, Canada) following a protocol reported in the literature (Abdel Rahman et al., 2013).

2.7 Pathway Enrichment and correlation study

Gene Set Enrichment Analysis (GSEA) is a software that takes ranked list of genes and determines if genes of a pathway are enriched either at the top or the bottom of the ranking (Subramanian et al., 2005). The results from pooled shRNA screens were ranked based on dGARP in ascending order before applied to GSEA. For each gene set (i.e. pathway), the program examines the gene list in a step-wise manner, increasing the enrichment score of that set each time it encounters a gene that belongs to it, while decreasing the enrichment score each time it encounters a gene that is not. The statistical significance of each enrichment score is calculated by comparing the observed enrichment score to that of randomly permutations of the original gene list.

For analysis of the correlation between concentration of each metabolite to MGAT5 activity in human cell lines, Pearson’s Correlation Coefficient was calculated (Rodgers and Nicewander, 1988). MGAT5 activity/expression was determined either by L-PHA staining or by RT-PCR.

CHAPTER 3 IDENTIFICATION OF MGAT5 INTERACTING GENES AND SIGNALING PATHWAYS

3.1 Summary

Previous work in the protein N-glycosylation field suggested that branching enzymes have the potential to globally regulate cell signaling. In order to map the genes and signaling pathways that are affected in a high-throughput manner, I performed pooled shRNA dropout screens with Mgat5 proficient and deficient mouse cell lines (Ketela et al., 2011; Blakely et al., 2011; Marcotte et al., 2012). I started by breeding Mgat5 mutant mice and extracting MEFs from wild- type and homozygous mutant embryos. I then established immortalized MEF cell lines through serial passaging. L-PHA binding and total DNA content was measured in the cell lines to confirm the presence or absence of Mgat5 activity and ploidy of my cell lines, respectively. Through functional screens, I identified genes that displayed differential essentiality in the two lines. In addition, I performed pathway enrichment analysis on the gene hits and found that mTOR signaling was essential in the Mgat5+/+ genotype.

3.2 Introduction

MGAT5 over-expression has been observed in many human cancers and associated with severity of multiple sclerosis (Dennis et al., 2009). Mgat5 has the ability to alter cell signaling by changing surface protein N-glycan branching, as predicted by the lattice model (Lau et al., 2007). However, since N-glycan branching enzymes share the same key substrate, UDP- GlcNAc, disruption of Mgat5 activity can be compensated to various degrees by the action of other branching enzymes. It has been estimated that surface residency of 70% of cell surface proteins are regulated by the N-glycan and galectin lattice, as N-glycan/galectin interaction does not specifically distinguish one protein from another (Apweiler et al., 1999; Lau et al., 2007). Thus, it is important to identify proteins and pathways whose activity is regulated by N- glycosylation and Mgat5. Although previous studies attempted to address this question by

19 controlling the level of UDP-GlcNAc (Lau et al., 2008), a genome-wide approach to identify Mgat5 interacting genes and pathways has not been attempted.

In order identify genes that interact with Mgat5, I performed pooled shRNA screens in Mgat5+/+; Trp53-/- and Mgat5-/-; Trp53-/- immortalized MEF cell lines to find genes that are differentially required for proliferation. Lentiviral-based pooled shRNA screens provide a means to generate stable knock-downs across 16,000 genes in a mixed population of cells where each cell has a different knock-down, whose proliferation can be monitored by barcode genetics (Marcotte et al., 2012; Vizeacoumar et al., 2013). If knock-down of a gene causes differential proliferation in isogenic cell lines that differ only by Mgat5, then that gene can be considered to interact genetically with Mgat5.

In order to identify genes that are differentially essential in Mgat5+/+ and Mgat5-/- cell lines, I generated Mgat5+/+; Trp53-/- and Mgat5-/-; Trp53-/- immortalized MEF cell lines (see Methods in Chapter 2). These cell lines enabled comparison between the wild-type and the Mgat5 null genetic backgrounds. Trp53-/- background was also incorporated in both cell lines for the following reasons. First, freshly extracted MEF cells could only be maintained in cell culture for a limited time period. Starting at about 10 days after extraction, MEF cells enter crisis and their rate of proliferation gradually slow down (Odell et al., 2010). The majority of the cells reach the end point of apoptosis or senescence at about 30 to 40 days after extraction, while a very small percentage of the cells become immortal (Odell et al., 2010). The inclusion of a Trp53 null background rendered MEF lines immortal, which permitted long term culturing of cells in vitro. allowing unlimited proliferation. Second, a Trp53 loss-of-function mutation is the major driving force in sporadic immortalization of fibroblasts (Odell et al., 2010). By including Trp53 null at the beginning, the effect of other genetic mutations was minimized by maintaining heterogeneity in each cell population. Last, Trp53 is a tumor suppressor gene, and its loss-of-function mutation has been seen in many human and murine cancers. Furthermore, Trp53 also plays an important role in regulating cellular glucose and glutamine metabolism (Levine and Puzio-Kuter, 2010).

In this chapter, I will describe how I generated and immortalized Mgat5 MEF cell lines and how I characterized the ploidy and cell surface L-PHA binding in these lines. I will also describe the functional genetic screens that I performed to look for differential growth and proliferation requirements, in Mgat5 positive and negative backgrounds. For the purpose of simplicity, unless

20 otherwise specified, I will use “wt” and “null” to refer to Mgat5+/+; Trp53-/- and Mgat5-/-; Trp53-/- cell lines, respectively, throughout the remainder of my thesis.

3.3 Generation of the Mgat5 MEF cell lines

Mgat5+/- and Trp53+/- mice were crossed to obtain Mgat5+/-; Trp53+/- double heterozygotic F1 progeny. These progeny were then mated, and MEF cells extracted from embryos at 13.5 days post gestation. One embryo of each Mgat5+/+; Trp53-/- and Mgat5-/-; Trp53-/- genotype was obtained from a total of about 60 embryos, as shown by the PCR genotyping (Figure 3.1). Both embryos yielded only a single bright band above the 500bp marker in the genotyping PCR targeting the Trp53 locus, which corresponded to the knock-out allele. The two embryos each showed a single band above and below the 500bp marker in the PCR reactions targeting the Mgat5 locus, corresponding to the wild-type and the knock-out alleles of the gene, respectively. The banding patterns showed no resemblance to that of a double heterozygotic embryo, suggesting that MEF cell lines of the correct genotypes were obtained. Genotypes were confirmed on multiple occasions, including DNA isolated from cultured cell lines just prior to the shRNA screen.

Serial passaging was performed to immortalize the extracted MEF cells according to the 3T3 protocol (Todaro and Green, 1963). In this protocol, cells were transferred into a new culture dish every 3 days, and seeded at 3x105 cells in 15cm dish at every passage. As demonstrated in Figure 3.2, doubling time of MEF cells carrying wild-type Trp53 gradually slowed, and by passage 9, the number of cells surviving the 3-day incubation time was less than the input 3x105 cells. Consequently, I stopped following their proliferation. In contrast, the Trp53-/- cells displayed spontaneous immortality. It is important to note, however, that Trp53-/- MEF culture experienced stress at about passage 5. However, after passage 15, the doubling rate drastically increased with every passage. At late passages beyond passage 16, the wt cell line displayed a slight growth advantage. At passage 23, the cells could reach 100% confluence within the 3-day incubation time, thus the growth curve reached plateau after that.

L-PHA is a plant lectin molecule isolated from Phaseolus vulgaris, which preferentially binds the tri- and tetra- antennary N-glycan products of Mgat5. Surface binding of L-PHA reflects

Figure 3.1. PCR genotyping of the null and the wt MEFs Top panel: Mgat5 primers, 500 bp product is the wt band, < 500 bp is the knock-out band. Bottom panel: Tp53 primers, 450 bp product is the wt band, 615 bp is the knock-out band.

) 6 10 Mgat5-/-; Trp53-/- 8 Mgat5+/+; Trp53-/-

6 Mgat5-/-; Trp53+/+

Cell Count Count Cell (x10 Mgat5+/+; Trp53+/+ 4

P6 P7 P8 P3 P4 P5 P9

P12 P13 P14 P19 P20 P21 P11 P15 P16 P17 P18 P22 P10 Figure 3.2. Serial passage of the null and wt MEFs 5 For each line, 3x10 cells were seeded on a 15cm dish and incubated at 37C with 5% CO2 for three days. DMEM supplemented with 10% FBS was used for all passages. Cells were counted using an automated cytometer.

Mgat5 enzymatic activity, thus can be used to distinguish MEF cells including the wild-type and null genotypes with respect to Mgat5. Figure 3.3 displays the fluorophore-conjugated L-PHA surface binding measured by flow cytometry. At both early and late passage, the difference between the signal from wt and null was small, but consistent. The null cell at early passage showed mean L-PHA binding of 2630 (arbitrary units), while at late passage showed significantly different mean L-PHA of 3389 (p-value < 10-10, Student’s t-test). The difference between wt and null late passage cells was smaller than observed at early passage. The mean L- PHA signal intensity measurement of wt early and late passage cells was 5471 and 5430, respectively.

PI DNA staining was used to measure ploidy of the cell lines. As shown in Figure 3.4, at either early or late passage, the wt line and the null cells displayed a bimodal distribution for PI staining. Different parts of the distribution corresponded to cells at different stages of cell cycle. The left and right peak correspond to G1 and G2 phases, during which each cell possesses one copy or two copies of its genome, respectively. Cells at the valley between both peaks correspond to S phase cells, during which cells synthesize their second copy of chromosomes. The tail to the right of the G2 peak corresponds to M phase cells, namely the cells in the middle of mitosis. As shown in panels C and D, the proportion of G2-phase cells was reduced in late passage cultures compared to early passage. The wt and null cells at passage 18 have twice the amount of DNA content compared to passage 4, indicating tetraploidy and consistent with literature reports (Odell et al., 2010). Because passage 18 cells display stability in DNA content and proliferation rate, the screen was carried out using late passage cells.

3.4 Pooled shRNA screens reveal genetic interaction between Mgat5 and mTOR signaling pathway

The shRNA screens were carried out sequentially beginning with the null cells, followed by wt cells. The null cells were infected with a pooled shRNA library at a MOI of 0.34. The three parallel biological replicates had an average of 3.21 doublings at T6, and an average of 7.08 doublings at T12. For the wt screen, the cells were infected at a MOI of 0.31. The three parallel biological replicates had an average of 3.24 doublings at T6, and an average of 7.24 doublings at T12. The extracted genomic DNA was processed as previously reported (Ketela et al., 2011).

Figure 3.3. Surface L-PHA staining of the null and wt MEFs Both Mgat5+/+; Trp53-/- and the Mgat5-/-; Trp53-/- MEF lines at early and late passage were shown. The surface binding of L-PHA was measured by flow cytometry.

Figure 3.4. PI DNA staining of the null and wt MEFs Both Mgat5+/+; Trp53-/- and the Mgat5-/-; Trp53-/- MEF lines at early and late passage are shown. The PI binding was measured by flow cytometry.

The abundance trend of each hairpin at different timepoints was used to compute the shARP score, while the average of the shARP scores of the top two best performing hairpins of each gene was calculated as the GARP score. Figure 3.5 demonstrated a scatter plot of the zGARP scores (z-normalized GARP) of each gene in the two screens. dGARP score is determined by subtracting the zGARP scores in the wt screen from that of the null screen. The genes were then ranked by their dGARP score in ascending order, thus sorting the list based on their essentiality in the wt and null cell lines. The complete table containing all zGARP and dGARP scores could be found in Appendice 1. At the top of the hit list are more essential genes in the null cell line than wt, or synthetic suppressors of growth in the Mgat5-/- genotype background (Figure 3.5, lower-right data points). These genes included Braf, Calnexin, Stk32a, Entpd5, Met, Mgat4a, Fgfr4, Fgfr1, and Ran. At the bottom of the hit list were genes that were more essential in the wt cell line, or synthetic sick/lethal with Mgat5+/+ cells compared to Mgat5-/- cells (Figure 3.5, upper-left data points). These included Tie1, Tek, Tgfbr1, Aurka, Fbp1, Mgat1, and Pik3ca. In the middle of the list were the genes displaying equal essentiality in both cell lines. This included two groups of genes. The first one was the genes where knock-down showed almost no effect in both lines, which corresponded to the genes localized to the upper-right part of the diagonal in Figure 3.5. This group contained the bulk of the genes screened. The other group was composed of genes that were essential in both cell lines, which corresponded to the genes localized to the lower-left part of Figure 3.5. Knocking-down these genes causes severe growth inhibition in both lines, and these genes were generally referred to as “house-keeping” genes. In either case, genes in these two groups showed no evidence of interaction with Mgat5.

Pathway enrichment was performed on the ranked dGARP score list using GSEA (Subramanian et al., 2005). GSEA is a non-parametric bioinformatic approach that identifies pathways whose components (a.k.a. genes) are enriched at the top and bottom of ranked gene list (Subramanian et al., 2005). Out of 5030 gene sets (a.k.a. pathways) tested, 2722 and 2308 sets were enriched on the wt and null side of the list respectively. There were 186 and 121 pathways that satisfied p- value < 0.05 criteria for the wt and null side of the list, respectively. Table 3.1 lists the top enrichment processes of several example pathways. With a false discovery rate (FDR) less than 25%, only five pathways were identified as essential in the wt cell line, while no pathway was identified as essential in the null line. The five pathways essential in the wt cells included the

Figure 3.5. zGARP scores plot of Mgat5+/+ and Mgat5-/- screen Dash line: diagonal mark. Highlighted in Red: top 400 genes that are more essential in Mgat5- /- cells. Highlighted in Blue: top 400 genes that are more essential in Mgat5+/+ cells.

Table 3.1. Gene Set Enrichment Analysis of dGARP score

A. The top 10 pathways enriched on the null side

Normalized RANK NOM NAME enrichment FDR AT p-value score MAX * Tube development -1.86328 0.0020 1 495 ST GA13 pathway -1.85842 0.0021 0.70 1782 Chiba response to TSA -1.79569 0 1 1732 Reactome SEMA4D in semaphoring signaling -1.77115 0.0020 1 1317 Li Lung Cancer -1.75743 0.0043 0.99 872 CHR15Q13 -1.75288 0.0061 0.87 2827 KEGG epithelial cell signaling in Heliobacter pylori -1.74992 0 0.77 1820 infection Hydrolase activity acting on carbon-nitrogen NOT -1.70912 0.0106 1 688 peptidebondsin linear amides Turashvili breast lobular carcinoma vs. ductal normal DN -1.68864 0 1 1270 Papaspyridonos unstable aterosclerotic plaque up -1.68025 0.0063 1 1455

B. The top 10 pathways enriched on the wt side

Normalized RANK NOM NAME enrichment FDR AT p-value score MAX * Biocarta eIF4 pathway 2.091858 0 0.03 ** 216 Biocarta mTOR pathway 1.968949 0 0.11 216 Biocarta p53 pathway 1.942901 0 0.12 306 Biocarta CXCR4 pathway 1.93753 0 0.10 561 Biocarta NFAT pathway 1.868469 0 0.24 899 Extrinsic to membrane 1.837945 0 0.31 810 Morf thra 1.797562 0.0018 0.45 975 Biocarta creb pathway 1.779513 0.0019 0.49 803 Reactome creb phosphorylation through the activation of 1.77394 0 0.46 Ras 1124 Lipid raft 1.77276 0.0019 0.42 975

* The rank at which the enrichment score reached its highest value.

** Highlighted in Red: false detection rate (FDR) < 25%.

Figure 3.6. GSEA enrichment of the dGARP score GSEA enrichment plots showing enrichment score changes as the program moved along the ranking for (A-E) pathways found to be essential only in wt with FDR <0.25 and (F) a representative example of a pathway found to be essential only in null cells.

EIF4 pathway, mTOR pathway, p53 pathway, CXCR4 pathway, and NFAT pathway. All of these pathways were recorded in detail in the BioCarta Database.

3.5 Discussion

In the mouse genome, the Trp53 and Mgat5 genes are located on separate chromosomes, therefore generating embryos with mutations in both genes was not challenging. The only complication was that neither gene could be stably maintained in a mouse line as homozygotic null. Mice that are null for Trp53 develop lynphomas at an early age (Jacks et al., 1994), while mice that are null for Mgat5 do not nurture offspring well (Granovsky et al., 2000). Thus, mice that were heterozygous for either Mgat5 or Trp53 were crossed to produce double heterozygotes, and double heterozygous F1 offspring were then crossed for the desired genotypes. In each case, the chance of getting Mga5+/+; Trp53-/- or Mgat5-/-; Trp53-/- mice was expected to be 1/16.

The Trp53-/- MEF cell lines grew at a faster rate than their Trp53+/+ counterparts in early passages. Measurement of doubling rates with passaging confirmed that the Trp53-/- genotype promoted rapid immortalization (Figure 3.2). The major difference between these MEF lines was the absence of crisis with the spontaneous immortalization of Trp53-/- cells. Unlike normal MEF cells which generally stopped proliferating by passage 10, Trp53-/- MEF cells briefly experienced stress at about passage 5 with some delay in growth, but no significant cell death was observed at any time point during serial passaging (Figure 3.2). Naturally occurring immortalized MEF cells typically rely on accumulation of random genetic mutations, among which Trp53 loss of function mutation is commonly observed (Odell et al., 2010).

Interestingly, serial passaging increased L-PHA binding of the null cell lines. Nevertheless, late passage null cells still showed lower L-PHA binding than wt cell lines. Previous studies reported that Mgat5-/- MEFs display much lower L-PHA binding (Cheung and Dennis, 2007). The concentration of L-PHA probe is critical for this type of assay, and it may have been higher than optimal in the present study, thus detecting related structures such as the Mgat4 branched N- glycans, which may have increased in late passage MEFs. Treatment of cell lines with drugs blocking the N-glycan branch pathway, such as Swansonine, could provide an accurate measurement of background L-PHA binding (Grigorian and Demetriou, 2010).

According to PI staining results, the proportion of cells in G2 phase in late passage were lower than that of the early passage. This suggests that late passage cells go through G2 more quickly, and with a faster proliferation rate. Serial passaging also resulted in accumulation of tetraploid MEF cells, as expected based on published studies (Odell et al., 2010). The shRNA screening protocol is tuned for entry of one shRNA virus per cell on a statistical basis, such that its effects are sampled in isolation during the period of selection and growth. If the viral infection took place in a mixed population of diploid and tetraploid cells, heterogeneity would likely interfere with the screen. Thus, passage 18 was chosen as the beginning point of the screen, where all cells were tetraploid.

As shown in Figure 3.5, in the zGARP score scatter plot, the majority of genes fell on the diagonal, and roughly centered at the (0, 0) point. This suggested that most of the gene knock- down events exerted almost no impact on the growth of these lines. The top 400 genes showing the highest and lowest dGARP scores, which were colored blue and red in Figure 3.5, respectively, displayed roughly symmetrical distributions on both sides of the diagonal, implying that the null and the wt cell line each had its own set of essential genes. In the previous section, I listed a few examples that were potentially interesting, and pathways that are implicated. In Chapter 5, I will discuss potential mechanisms by which these hits may interact with Mgat5, and propose further studies.

I used pathway enrichment software to sort dGARP scores into groups to facilitate interpretation and hypothesis generation. Pathway enrichment processes may also help filter out false positives by evaluating genes as functional groups. GSEA identified five signaling pathways that were essential in the wt cells. Among them, the EIF4 and mTOR signaling pathways interact functionally and were ranked highest. More importantly, EIF4 signaling pathway is downstream of the mTOR signaling pathway, further strengthening the likelihood these pathways are critical in wt cells. It was quite surprising to find out that GSEA produced no statistically reliable enrichment result for the null cell line. Even if the FDR <25% was not considered, many of the top hits were still gene sets established by individual studies rather than being documented in public pathway databases. Since the human genome annotation is far from complete to date, it is possible that pathway enrichment algorithms are not sophisticated enough to sort out all information generated by my screen. Alternatively, the null cells may have adapted to the partially disrupted surface protein lattice, and thus became less dependent on extracellular

32 signaling pathways. This may explain why the p53 signaling pathway was identified as essential only in the wt cells, while both lines were Trp53-/-. I will revisit pathway enrichment results later in Chapter 5, and propose experimental approaches to move forward with this data.

CHAPTER 4 MASS SPECTROMETRY ANALYSIS OF MGAT5 INTERACTING METABOLITES

4.1 Summary

In addition to identifying the genes that interact with Mgat5, I used metabolite profiling to compare phenotypes of Mgat5 knock-out MEF lines and Mgat5 knock-down human tumor cell lines. Metabolite extraction followed by LC-MS was used to quantify the level of 192 small metabolites in the MEF lines generated in Chapter 3. The wt lines displayed higher levels of glycolysis, tricarboxylic acid cycle, pentose phosphate pathway, and hexosamine biosynthesis pathway metabolites, as well as free amino acids. To investigate whether this observation also applied across species and cell types, I knocked down MGAT5 using shRNA in three human cell lines, namely HEK 293, BJ hTERT, and MCF7. Profiling of 122 metabolites demonstrated that, in contrast to MEFs, MGAT5 knock-down cells caused a global increase in metabolite levels as compared to their respective control strains. In particular, the remaining MGAT5 activity showed strong negative correlation to the level of 2-aminoadipate, acetyl co-enzyme A, ADP, ATP, CMP-sialic acid, and lactate, and strong positive correlation to the level of phosphoethanoloamine.

4.2 Introduction

The Michaelis constant Km of Mgat5 for UDP-GlcNAc has been estimated to be 11mM (Sasai et al., 2002). This is much higher than the normal Golgi concentration of UDP-GlcNAc (~1.5mM), making this enzyme dependent on fluctuations in UDP-GlcNAc and upstream metabolites (Lau et al., 2007; Dennis et al., 2009). Currently, there is very limited literature on how the Warburg effect changes metabolites of the HBP. An increased uptake of glucose and glutamine could increase flux through the HBP (Abdel Rahman et al., 2013). Moreover, metabolites in the HBP increase in prostate cancer samples (Sreekumar et al., 2009). To further characterize Mgat5-/-; Trp53-/- and Mgat5+/+; Trp53-/- MEF cell lines, and to experimentally confirm whether HBP

34 metabolites display differences between the two Mgat5 lines, metabolite profiling using LC-MS was performed. As in the previous chapter, unless otherwise specified, I will use “wt” and “null” to refer to Mgat5+/+; Trp53-/- and Mgat5-/-; Trp53-/- MEF cell lines, respectively.

Briefly, LC-MS is a method for separating a mixture of molecules using LC, and then identifying and quantifying each molecule by MS. After separation by LC, metabolites are ionized into molecular segments and charged. The machine detects molecular fragments by their mass-to- charge ratio. By comparing the mass spectrum of metabolite to those produced by synthesized standard molecules, the identity of each molecule is revealed. The total signal produced by that metabolite is used to quantify its abundance.

The wt and null cell lines displayed distinct metabolic profiles. To be more specific, a number of metabolites in glycolysis, TCA cycle, PPP, and HBP, as well as free amino acids, were reduced in the null late passage cells relative to their wt late passage counterpart. To test whether Mgat5 loss also suppresses metabolite levels in other species and cell types, I generated stable strains of three human cell lines, namely HEK 293, BJ hTERT, and MCF7, with MGAT5 knock-down using shRNA. BJ cells are human fibroblasts immortalized through introduction of human telomerase gene hTERT (Clontech, USA), thus it might share common features with MEF cells. HEK 293 was derived from human embryonic kidney (Graham et al., 1977). It has been used to generate a sub line with a tetracycline inducible MGAT5 (Abdel Rahman et al., 2013). This cell line may be used to fine tune MGAT5 expression levels under different conditions in future experiments. MCF7 is a commonly used human breast cancer cell line (Soule et al., 1973).

Three shRNAs targeting MGAT5 were selected, namely hairpin number 1, 2, and 5, and one shRNA targeting luciferase was used as negative control. For simplicity, for the rest of the thesis, I will use sh1, sh2, sh5, and shLUC respectively to refer to these hairpins or the cell strains treated with these hairpins. Knock-down efficiency was assessed by total mRNA extraction followed by RT-PCR as well as L-PHA staining followed by flow-cytometry. The hairpins reduced endogenous MGAT5 mRNA level by up to ~80%, and caused reduction in L-PHA binding. The knock-down altered cellular metabolite levels in all three human cell lines. Surprisingly, the trend in metabolite changes observed was opposite to that seen in Mgat5 MEF cells. The human MGAT5 knock-down strains displayed higher metabolite levels as compared to their respective luciferase hairpin control strains.

To find out which of the tested metabolites were more likely to be directly influenced by MGAT5 activity, I performed a Pearson’s Correlation Test between the remaining MGAT5 activity and the level of each metabolite in shRNA infected human cells (Rodgers and Nicewander, 1988). 113 out of 120 tested metabolites showed negative average correlation to remaining MGAT5 activity. Among them, 2-aminoadipate, acetyl CoA, ADP, ATP, CMP-sialic acid, and lactate displayed strong negative correlation in all three human cell lines.

4.3 Mgat5+/+ MEF cells contained higher level of glycolysis, TCA cycle, HBP, and PPP intermediates

To examine if Mgat5 knock-down changes the equilibrium of glucose and glutamine uptake and catabolism as well as other metabolic pathways, I extracted metabolites from MEF cells and measured their abundance using LC-MS. The extraction was performed on three independently passaged strains of null and wt lines, in case random mutations generated during the passaging would interfere with the results. I also included samples collected from wt and null cell lines at early passage, to see if they were metabolically different as a result of passaging. Five technical replicates were collected for each strain.

MEF metabolite profiling result can be found in Appendix 2. Figure 4.1 presents an overview of MEF results. The heatmap visualized abundance measurements of 192 metabolites in the 8 above mentioned cell strains. Columns represent the metabolites, while rows represent the samples. Red color corresponds to high values, while green color corresponds to low values. Each row was scaled in order to produce a uniform distribution of color. The area ratios of mass peaks from LC-MS/MS were normalized by cell number in each sample, and were logarithmic transformed in order to prevent biased analysis introduced by high abundance metabolite readings. Missing readings are generally a result of readings below the level of detection, and were replaced by a small value equal to half of the lowest reading of that metabolite. Both columns and rows of the heatmap were rearranged by an unsupervised clustering algorithm based on pattern similarity. The dendrogram to the left of the heatmap represented the similarities between samples.

Figure 4.1. Metabolite profiling of the null and wt MEFs Mass spectrometry profiling of 192 metabolites in Mgat5+/+; p53-/- and Mgat5-/-; p53-/- MEF cell lines at early (P4) and late (P21) passage. Each late cell line had three independently passaged lineages. Each cell line or cell lineage had five technical replications. Clustering was based on the Euclidean distance between each sample.

Figure 4.2. Selected metabolites in the late passage null and wt MEFs Mass spectrometry profiling of free amino acids and metabolites involved in glycolysis, TCA cycle, PPP, and HBP in Mgat5+/+; p53-/- and Mgat5-/-; p53-/- MEF cell lines at late passage (P21). Each cell line had three independently passaged sublines. Each cell line or subline had five technical replications. The clustering was based on the Euclidean distance between each sample.

The dendrogram formed three major clusters: all of the samples harvested from wt cells in late passage formed a cluster on the top; the early passage cells formed a cluster in the middle; while null late passage cells formed a cluster at the bottom. Blocks of green color scattered across the null late passage samples, implying a general lower level of many of the metabolites measured in these samples. Indeed, a close examination of intermediate metabolites from glycolysis, TCA cycle, PPP, and HBP reveals that null cell lines at late passage stored lower amount of these metabolite as compared to wt cells at late passage (Figure 4.2). The null late passage cells also contained less free amino acids.

4.4 Human cell lines with Mgat5 knock-down showed higher metabolite levels

To correlate metabolic changes with Mgat5 disruption, shRNA mediated MGAT5 knock-down was performed in human cell lines. Stable knock-downs were achieved using shRNA delivered by lentivirus in all three human lines, namely HEK 293, BJ hTERT, and MCF7. Three shRNA constructs targeting MGAT5, namely sh1, sh2, and sh5, were used in each case. Residual MGAT5 mRNA level in the knock-down strains were measured using RT-PCR, and are shown in Figure 4.3. sh2 consistently showed the highest knock-down efficiency in all three lines, achieving greater than 45% and as high as 75% knock-down. sh1 knocked down MGAT5 mRNA level by approximately 50%, while sh5 was only effective in HEK 293 cells. MGAT5 enzymatic product was also examined. L-PHA binding was performed on each shRNA treated lines, and flow cytometry was used to measure binding. Figures 4.4, 4.5, and 4.6 present resulting flow cytometry data. Consistent with my RT-PCR results, the L-PHA binding signal intensity shifted leftward in sh2 treated strains of all three cell lines, as compared to their respective shLUC treated control strains. sh1 and sh5 treated HEK 293 and BJ hTERT showed no visible decrease in L-PHA binding. All three hairpins reduced L-PHA binding in MCF7 cells, but with different efficiencies. sh1 was more effective than the sh2 in the MCF7, while the sh5 was less effective than either sh1 or sh2.

LC/MS was then performed on the 12 sublines derived from these human cell lines. A total of 122 metabolites were measured. The results were listed in Appendix 3. Figure 4.7 provides an overview of human metabolite profiling data, while Figures 4.8, 4.9, and 4.10 present the

140

120

100

to shLUC

20 % Expression % Compared 0

Figure 4.3. RT-PCR analysis of knock-down efficiency by MGAT5 shRNA in human cell lines RT-PCR analysis of knock-down efficiency in MGAT5 hairpin 1, 2, and 5 and luciferase hairpin treated HEK 293, BJ hTERT, and MCF7 cell lines. Each bar represents mean and error bars represent standard deviation of two replicates. Expression of MGAT5 was normalized to GAPDH level, and the level of MGAT5 expression in luciferase controls was set as 100%.

Figure 4.4. L-PHA binding to HEK 293 cells HEK 293 surface L-PHA staining was measured by flow cytometry. Cells were detached by EDTA and stained with L-PHA-488.

Figure 4.5. L-PHA binding to BJ hTERT cells BJ hTERT surface L-PHA staining was measured by flow cytometry. Cells were detached by EDTA and stained with L-PHA-488.

Figure 4.6. L-PHA binding to MCF7 cells MCF7 surface L-PHA staining was measured by flow cytometry. Cells were detached by EDTA and stained with L-PHA-488.

Figure 4.7. Metabolite profiling in human cells with MGAT5 knock-down Comparative cellular concentration of 122 metabolites was measured in HEK 293, BJ hTERT, and MCF7 cell lines, stably expressing either MGAT5 hairpin 1, or 2, or 5, or luciferase hairpin. Each cell line or subline had five technical replications. The clustering was based on the Euclidean distance between each sample.

Figure 4.8. Metabolite profiling in HEK 293 cells with MGAT5 knock-down Comparative cellular concentration of 120 metabolites was measured in HEK 293 cells stably expressing either MGAT5 hairpin 1, or 2, or 5, or luciferase hairpin. Each cell line or subline had five technical replications. Clustering was based on the Euclidean distance between each sample.

Figure 4.9. Metabolite profiling in MCF7 cells with MGAT5 knock-down Comparative cellular concentration of 120 metabolites was measured in MCF7 cells stably expressing either MGAT5 hairpin 1, or 2, or 5, or luciferase hairpin. Each cell line or subline had five technical replications. Clustering was based on the Euclidean distance between each sample.

Figure 4.10. Metabolite profiling in BJ hTERT cells with MGAT5 knock-down Comparative cellular concentration of 120 metabolites was measured in BJ hTERT cells stably expressing either MGAT5 hairpin 1, or 2, or 5, or luciferase hairpin. Each cell line or subline had five technical replications. Clustering was based on the Euclidean distance between each sample.

47 profiling of each human cell line. All heatmaps were normalized, processed, and scaled by the same protocol as with MEF metabolite profiling data. In the heatmaps, red color indicates high metabolite measurements, while green color indicates low measurements. Clustering of experimentally related samples together by an unsupervised clustering algorithm indicates pattern similarities between samples. In Figure 4.7, samples from each human cell line formed their own cluster, suggesting that cell lines can be distinguished by their profiles. HEK 293 and BJ hTERT part of the heatmap suggest that HEK 293 cells generally had lower levels of metabolites as compared to BJ hTERT cells. The MCF7 portion of the heatmap displayed patches of intermingled red and green color, suggesting intermediate levels.

When the HEK 293 metabolite profile was analyzed separately, as shown in Figure 4.8, the sh1, sh2, sh5, and shLUC each formed their own cluster in unsupervised clustering, indicating that metabolic changes occurred as a result of MGAT5 shRNA treatment. shLUC cells showed lower metabolite abundance for most of the metabolites as compared to the knock-down strains. MCF7 demonstrated a very similar heatmap pattern as HEK 293, as shown in Figure 4.9, except that one of the sh2 samples was clustered with shLUC. Despite this, the intensity of readings suggest that it could also be clustered with the top group. The misplacement of the sample outside its expected group may have been the consequence of a single outlier metabolite. BJ hTERT showed a different heatmap pattern, as shown in Figure 4.10. The heatmap contained only two major clusters, one corresponded to samples of the BJ sh2 knock-down strain, while the other corresponded to the rest of the samples. Unsupervised clustering could not separate sh1 and sh5 from shLUC in BJ line, probably a consequence of unsuccessful Mgat5 knock-down by sh1 and sh5. Nevertheless, abundance readings on shLUC samples were still in general lower than that of sh2 samples.

I extracted shLUC control data and sh2 knock-down data, since sh2 was the most effective knock-down, from each human metabolite profile and performed Student’s t-test to calculate statistical significance of each metabolite measurement. A Venn diagram was used to present the number of significant metabolites (Figure 4.11), while the exact list of metabolites was tabulated in Table 4.1. After Bonferroni multiple testing correction, a total of 89 metabolites showed statistical significance in at least one human cell line. 24 metabolites were significantly different between sh2 and the respective shLUC in at least two human cell lines. Lactate was the only metabolite significantly different in all three human lines. In addition to the Student’s t-test,

Table 4.1. List of metabolites showing statistically significant difference in at least two of the human cell lines

HEK BJ MCF7 HEK BJ MCF7 Name Fold Fold Fold p-value p-value p-value Change * Change * Change * Lactate 1.59 7.39E-04 ** 1.77 1.51E-02 1.53 1.01E-03 2-Aminoadipate (2-AA) 4.82 2.72E-05 1.73 6.82E-03 0.72 1.71E+01 2'-Deoxy cytidine 2.34 1.11E-03 2.12 7.72E-03 1.19 1.60E+00 diphosphate (dCDP) Carnitine 2.31 2.05E-03 2.85 8.95E-03 1.23 1.92E+00 D,L-Isocitric acid 2 2.11 7.63E-05 2.05 2.84E-03 1.15 1.91E+00 Dihydroxyacetone 3.19 7.36E-04 13.14 7.36E-04 1.48 5.20E+00 phosphate (DHAP) Glutathione oxidized 3.21 4.81E-05 10.69 5.83E-04 2.18 2.26E+00 Glutathione reduced 2.26 1.05E-03 1.83 1.69E-02 1.12 2.31E+01 Glycerol 2.28 1.56E-02 2.25 1.56E-01 1.69 1.65E-02 Glycine 1.74 1.27E-03 1.54 3.16E-01 1.85 1.01E-02 L-Alanine 13.51 6.19E-04 1.30 9.74E-01 1.68 2.28E-02 L-Asparagine 1.59 5.04E-03 1.74 1.84E-01 1.27 4.20E-02 L-Asparagine 2.10 1.62E-02 1.84 4.39E+00 2.10 2.07E-03 L-Aspartic acid 1.71 2.11E-03 1.53 1.07E+00 1.53 2.65E-02 L-glutamine 0.43 1.11E-02 0.81 3.72E+01 1.61 4.16E-03 L-Isoleucine 0.32 8.14E-02 8.56 2.68E-03 2.89 8.17E-06 L-Malic acid 1.29 3.71E-01 1.44 3.66E-02 1.70 2.48E-04 L-Proline 1.25 5.77E+00 5.27 1.67E-04 2.86 6.83E-06 L-Threonine 0.77 3.49E+01 7.24 7.92E-04 2.21 5.33E-03 Ornithine 0.80 1.68E+01 5.59 5.36E-04 1.82 5.75E-05 phosphoethanoloamine 1.19 1.45E+01 7.24 3.54E-03 1.47 6.03E-03 S-(5`-Adenosyl)-L- 1.36 2.42E+00 5.04 1.19E-04 1.59 1.85E-02 homocysteine Taurine 1.18 1.68E+01 7.24 2.06E-03 1.57 4.22E-03 UDP-GlcNAc 1.30 4.17E+00 13.69 7.50E-06 2.48 1.78E-07

* Defined as the ratio between the measurement in the sh2 strain divided by that in the shLUC strain.

** Highlighted in Red: p-value <0.05.

Figure 4.11. Venn diagram showing the number of metabolites that was significantly different in MGAT5 knock-down human cell lines Comparison was performed between MGAT5 hairpin 2 and luciferase shRNA treated HEK 293, BJ hTERT, and MCF7 cells. Results were tested by Student’s t-test and corrected by Bonferroni multiple testing correction. Numbers in the overlapping regions indicates the number of metabolites that was significantly different in both corresponding lines.

Figure 4.12. PCA analysis of metabolite profiles of MGAT5 knock-down human cell lines Comparison was performed between hairpin 2 and luciferase hairpin treated HEK, BJ, and MCF7 cell strains. The X-axis represents principal component 1, which accounted for 96.7% of the variance in the original data. The Y-axis represents principal component 2, which is orthogonal to the first principal component and accounted for 1.8% of the variance in the original data.

I also performed principal component analysis (PCA) on these extracted data. As shown in Figure 4.12, a scatter plot of principal component 1 (PC1) vs. principal component 2 (PC2), data points corresponding to sh2 knock-down strains were localized to the lower right side of their respective shLUC control strains. PC1, a vector that summaries the largest differences, was capable of describing 96.7% of total variances between samples. PC2, whose dimension is perpendicular to PC1 and summaries the second largest differences, was able to describe 1.8% of variances. This implied that the MGAT5 knock-down impacted metabolite profiles with a similar PC1 and PC2 directional trend among all three human cell lines. Indeed, as discussed in the previous paragraph, sh2 knock-down samples always showed an almost global increase in metabolite abundance compared to their shLUC counterparts.

4.5 Multiple metabolites displayed strong correlation with MGAT5 disruption

In order to identify metabolic changes that were likely to be the direct results of MGAT5 perturbation, I performed Pearson correlation testing between the remaining MGAT5 activity and measurement of each metabolite. All four strains from each cell line, namely sh1, sh2, sh5, and shLUC, were included in the study. RT-PCR results measuring MGAT5 mRNA level as well as the L-PHA staining results measuring MGAT5 product were used to reflect remaining MGAT5 activity, so each data set was tested separately. Correlation test results were illustrated in Figure 4.13, 4.14, and 4.15, and metabolites showing highest or lowest average correlation coefficients were listed in Table 4.2. All of the graphs showed correlation coefficient on the x- axis and cumulative distribution on the y-axis. Correlation coefficients ranged from -1 to 1, where -1 indicates absolute negative correlation and 1 indicates absolute positive correlation, and 0 indicates no correlation. The cumulative distribution is simply a ranking of all the correlation coefficients. Each point in the graph represents one metabolite. For example, a negative correlation in the graph indicates inversed proportionality between that metabolite and remaining MGAT5 activity, while a positive correlation indicates direct proportionality.

The graphs in Figure 4.13, 4.14, and 4.15 displayed similar distribution. In all three cell lines, most metabolites displayed a negative correlation with remaining MGAT5 activity, be it measured by RT-PCR or L-PHA staining. In fact, both experimental measures yielded similar

Table 4.2. Pearson’s correlation test of metabolite levels with remaining MGAT5 activity

A. Metabolites with lowest average correlation coefficient

HEK BJ MCF7 HEK BJ MCF7 Name RT- RT- RT- Average L-PHA L-PHA L-PHA PCR PCR PCR Acetyl Co-enzyme A -0.93 -0.86 -0.70 -0.96 -0.93 -0.90 -0.88 Adenosine diphosphate (ADP) -0.88 -0.94 -0.68 -0.87 -0.83 -0.66 -0.81 Guanosine -0.62 -0.60 -0.76 -0.93 -0.94 -0.97 -0.81 2-Aminoadipate -0.69 -0.92 -0.97 -0.51 -0.73 -0.98 -0.80 Lactate -0.99 -0.81 -0.71 -0.86 -0.74 -0.58 -0.78 Adenosine -0.85 -0.87 -0.69 -0.44 -0.84 -0.92 -0.77 CMP-Sialic acid -0.93 -0.59 -0.99 -0.60 -0.55 -0.92 -0.76 Adenosine triphosphate (ATP) -0.86 -0.87 -0.74 -0.87 -0.60 -0.58 -0.76 D-Xylitol -0.99 -0.20 -0.95 -0.77 -0.47 -0.99 -0.73 Glycine -0.86 -0.81 -0.69 -0.75 -0.81 -0.40 -0.72

B. Metabolites with highest average correlation coefficient

HEK BJ MCF7 HEK BJ MCF7 Name RT- RT- RT- Average L-PHA L-PHA L-PHA PCR PCR PCR Uracil 0.97 -0.08 -0.39 0.77 -0.58 -0.75 -0.01 D-sedoheptulose-7-phosphate -0.93 0.65 0.23 -0.61 0.29 0.31 -0.01 Glyoxylic acid 0.49 0.05 -0.42 0.59 0.06 -0.77 0.00 Xylulose -5P 0.03 -0.90 0.95 -0.01 -0.59 0.71 0.03 D-Fructose-6P -0.97 0.08 0.79 -0.88 0.38 0.82 0.04 Lactose 0.57 0.08 -0.80 0.91 0.48 -0.98 0.04 alpha-D-Galactose-6P -0.95 0.55 0.66 -0.83 0.05 0.83 0.05 Pyruvic acid -0.44 -0.29 0.18 0.04 0.43 0.58 0.08 GlcNAc-1P -0.25 -0.74 0.75 0.30 -0.22 0.94 0.13 Phosphoethanoloamine 0.85 0.27 0.40 0.62 0.80 0.25 0.53

Figure 4.13. Pearson’s Correlation test between metabolite levels and remaining MGAT5 acitivty in HEK 293 cells The remaining MGAT5 activity was measured by either (A) MGAT5 RT-PCR or (B) L-PHA binding. Metabolite measurements from all three MGAT5 knock-down strains and the shLUC control strain were used for each correlation test. The x-axis represents the correlation coefficient of each metabolite, and the y-axis represents ranking.

Figure 4.14. Pearson’s Correlation test between metabolite levels and remaining MGAT5 acitivty in BJ hTERT cells The remaining MGAT5 activity was measured by either (A) MGAT5 RT-PCR or (B) L-PHA binding. Metabolite measurements from all three MGAT5 knock-down strains and the shLUC control strain were used for each correlation test. The x-axis represents the correlation coefficient of each metabolite, and the y-axis represents ranking.

Figure 4.15. Pearson’s Correlation test between metabolite levels and remaining MGAT5 acitivty in MCF7 cells The remaining MGAT5 activity was measured by either (A) MGAT5 RT-PCR or (B) L-PHA binding. Metabolite measurements from all three MGAT5 knock-down strains and the shLUC control strain were used for each correlation test. The x-axis represents the correlation coefficient of each metabolite, and the y-axis represents ranking.

56 distribution patterns. For example, in Figure 4.14 Panel A, when the remaining MGAT5 activity was measure by RT-PCR, more than 60% of the metabolites showed less than a -0.5 correlation, while only a handful of metabolites showed > 0 correlation in BJ hTERT. The same was also seen in Figure 4.14 Panel B, where the remaining MGAT5 activity was measure by L-PHA. HEK 293 cells displayed a similar distribution to BJ hTERT, although the effect was weaker (Figure 4.13). MCF7 showed an even weaker effect than HEK 293 and BJ hTERT, as shown in Figure 4.15.

Considering all correlation studies, there were 12 metabolites that qualified for an arbitrary cut- off of average correlation coefficient less than -0.7. They were: acetyl CoA, ADP, guanosine, 2- aminoadipate, lactate, adenosine, CMP-sialic acid, ATP, D-xylitol, glycine, GDP-Mannose, and NAD. There was only one metabolite showing a weak average positive correlation, which was phosphoethanoloamine with an average correlation coefficient of 0.53.

4.6 Discussion

As mentioned in the previous chapter, when I was generating MEF cell lines, the theoretical chance of obtaining the correct genotype was 1/16 when breeding double heterozygotic mice. Less than expected, out of roughly 60 mouse embryos I dissected, I was only able to get one for each of the desired genotypes. This has been a concern, since all phenotypes observed in later experiments could be due to embryo-specific differences instead of Mgat5 gene status. By the same logic, genetic mutations accumulated during serial passage could also interfere with the results of my experiments, although this effect was offset by deploying Trp53-/-. To circumvent this problem, I also profiled early passage MEF lines when I performed metabolite extraction. In addition, late passage MEF lines were represented by three independently passaged strains, meaning that they were split from the same cell population at passage 3, while they went through the immortalization independently. Thus, any metabolite profile differences between the null and the wt at late passage could be traced back. Indeed, based on unsupervised clustering of metabolite profiles in MEFs, the early passage null and wt cells were indistinguishable, implying that they were metabolically identical at the beginning of the immortalization process. Notably, the independently passaged late MEF lines from the same genotype were also extremely similar to each other, while there existed a huge difference between null and wt lines. Clearly, the

57 immortalization process altered the metabolic state of cells, and the effect of Mgat5 was dominating over random mutations.

Despite these internal controls, the experimental setup still suffered a major limitation, which was the lack of biological replicates. By performing metabolite profiling in MEF cells with Mgat5 knock-down was one way to further characterize Mgat5 deficiency. Alternatively, as presented in this chapter, I performed MGAT5 knock-down in human cell lines, which also explored to determine whether the findings were consistent across species.

The two methods used to measure MGAT5 knock-down efficiency yielded similar results. sh2 treated strains always showed the lowest MGAT5 mRNA level, and they always displayed reduced L-PHA binding. In HEK 293 cells, although sh1 and sh5 achieved about 50% MGAT5 mRNA knock-down, they were not able to decrease surface L-PHA staining. The same was observed for sh1 in BJ hTERT cells. It’s possible that reduction of Mgat5 mRNA level in these strains was insufficient to reduce MGAT5 protein level, or that protein level reduction was insufficient to notably reduce MGAT5 activity. A western blot measuring protein level should be performed to probe this issue further. The only inconsistency seen was with sh5 infected MCF7, where RT-PCR data showed no change in mRNA level, but lower surface L-PHA binding was measured by flow cytometry. This was very likely to be due to inaccuracy of RT-PCR, since the PCR process has the potential to amplify very subtle handling errors. Remaining MGAT5 measurements also explained what was observed in metabolite profiles. sh2 always yielded different metabolite profiles than shLUC. sh1 and sh5 treated BJ hTERT cells showed metabolite profiles indistinguishable from that of shLUC treated cells, consistent with their unchanged L- PHA binding measurements as compared to shLUC cells.

I used Pearson Correlation testing to study the relationship between MGAT5 and metabolite abundance. This method had advantage over a Student’s t-test, because it evaluates multiple sublines, thus multiple MGAT5 knock-down states, at the same time. Suppose a metabolite increases whenever MGAT5 is knocked down in reality, it will always show low readings when MGAT5 is present, and vice versa. Therefore, no matter whether the remaining MGAT5 activity was significantly different between the strains, as long as the metabolites always shows low abundance in high MGAT5 expressing strains, and high abundance in low MGAT5 expressing strains, the correlation test will report it as negatively correlated to MGAT5 activity. The Pearson Correlation study showed that most metabolites were in negative correlation with the remaining

MGAT5 activity. It was logical to expect that MGAT5 activity stimulates the use while suppressing the storing of these metabolites, because MGAT5 had been shown to sensitize cell signaling and promote cell proliferation (Patridge et al., 2004; Lau et al., 2007). L-PHA staining and RT-PCR based data were very similar, supporting the idea that these methods were good surrogates for each other, and each reflected an aspect of the remaining MGAT5 activity in the cell.

The most unexpected finding from metabolite profiling was that MEF and human cell lines behaved in an opposite manner. In MEFs, the null line showed lower global cellular metabolite levels, while in human cells, knock-down strains showed higher metabolite abundance. This discrepancy deserves further examination and I will discuss further Chapter 5. Statistical tests identified metabolites that were significantly different between sh2 treated strains and shLUC treated strains, such as lactate. The correlation studies also identified a list of metabolites that were potentially regulated by Mgat5, such as 2-Aminoadipate. I will provide a detailed discussion about them in Chapter 5 as well.

CHAPTER 5 DISCUSSIONS

5.1 Mgat5 wt and null MEF cell line displayed different gene essentiality profile

To gain a better understanding of the signaling pathways that are regulated by protein N- glycosylation and the lattice model, I used Mgat5 knock-out as a way of modulating the N- glycosylation branching pathway, and studied the gene essentiality profiles in Mgat5 wt and Mgat5 null MEF cell lines. The lentiviral shRNA screen scored about 16,000 genes based on their growth inhibitory effect when knocked-down. Comparison between screens revealed genes that showed differential essentiality in null and wt backgrounds. As presented in Chapter 3, the screen identified Braf, Calnexin, Stk32a, Entpd5, Met, Mgat4a, Fgfr4, Fgfr1, and Ran as essential in the null cell line, while Tie1, Tek, Tgfbr1, Aurka, Fbp1, Mgat1, and Pik3ca were essential in the wt cell line.

The fibroblast growth factor receptors 4 and 1 (Fgfr4 and Fgfr1) were essential in the null cell line, while Tie1, Tek, and Tgfbr1 were essential in the wt cells. These genes encode cell surface receptor glycoproteins that participate in various cell-cell signaling pathways. FGFR4, FGFR1, TIE1, and TEK belong to the receptor tyrosine kinase family, while TGFBR1 is a cell surface serine/threonine kinase. FGFR4 and FGFR1 both regulate development, growth, differentiation, migration, and angiogenesis. FGFR1 amplification is often found in squamous cell lung cancer (Weiss et al., 2010). The FGFR4R388 allele has been found in prostate cancer, pituitary cancer, and advanced stages of squamous cell carcinoma of the upper gastrointestinal tract (Wang et al., 2004; Streit et al., 2004; Mete et al., 2012). TIE1 and TEK (also known as TIE2) functions together with angiopoietins to control angiogenesis and lymphatic development (Jeltsch et al., 2013). Binding of Angiopoietin-1 to TIE2 activates PI3K/Akt signaling and promotes cell proliferation (Fukuhara et al., 2008; Saharinen et al., 2008). TGFBR1 is a receptor for the transforming growth factor beta (TGF- β) pathway ligands, and the TGF- β signaling pathway is one of the most frequently altered signaling pathways in human cancer. Depending on cellular context, this pathway can have either negative or positive effects on tumor formation (Akhurst, 2004). It was expected that many of the receptor proteins might be identified in my screen, since

60 disruption of N-glycan branching can change their cell surface retention (Lau et al., 2007). The fact that close gene paralogs (Fgfr1 and Fgfr4, Tie1 and Tek) were identified to be essential in the same cell line further strengthens credibility of this result. My findings also suggested the possibility that the null and the wt cell line developed dependency on different signaling pathways to sustain their growth.

Mgat4a was essential in the null, while Mgat1 was essential in the wt. Both enzymes catalyze steps of the N-glycan branching pathway before Mgat5. This was initially counter-intuitive that genes of the same pathway show different essentiality in different genetic background. However, this observation could be explained by recognizing that all N-glycan branching enzymes share the UDP-GlcNAc substrate pool. When Mgat5 is knocked out, UDP-GlcNAc levels may change and the excess may enhance branching by other enzymes (Lau et al., 2007). Indeed, metabolite profiling in the MEF cells showed lower UDP-GlcNAc levels in the null cells as compared to the wt cells (Figure 4.1 and 4.2). Consequently, the N-glycan branching profile of null cells differs from that of wt cells not only in the absence of the tetra-branched N-glycan form, but also in the mono-, di-, and tri-branched forms. This finding of different Mgat4a and Mgat1 essentialities also raises the possibility of a shift in dependency in these cell lines. Because the null cell line lacks Mgat5 activity, it may have developed other genetic and/or metabolic changes to partly compensate for signaling and protein folding stress. Therefore, the knock-down of Mgat4a, which can generate tri-branched N-glycans, may have made it more difficult for the null cells to compensate. The wt cell line, on the other hand, was still dependent on N-glycan branching pathway as a whole entity. As a consequence, the knock-down of Mgat1, which is the first step of the branching, caused severe growth defects in this N-glycan branching dependent cell line.

To further investigate these results, it will be important to perform secondary screens to validate hits. Because of the high-thoughput nature of the employed screen, false-positive hits and false- negative hits are expected. Indeed, in a similar study in which a pooled shRNA screen was performed to identify negative genetic interaction in isogenic human cell lines, only 24.2% of 826 interactions identified by the primary screen could be confirmed by small interfering RNA (siRNA) transfection mediated gene knock-down (Vizeacoumar et al., 2013). A similar experimental approach using siRNA and measuring growth could be used to validate hits. In one approach, wt and null cell lines could be infected by lentivirus delivering green fluorescent protein (GFP) or red fluorescent protein (RFP), thus permanently labeling the two cell lines

61 green and red respectively. The two cell lines would then be mixed at 1:1 ratio, and reverse transfection of siRNA targeting primary screen hits would be performed on the mixture in a 96- well format. After that, the number of red and green cells would be counted using high throughput microscopy over 7 days. By comparing the red-green ratio of each siRNA treated wells to that of the control siRNA treated wells, concordance with primary screen data would confirm the hits (Vizeacoumar et al., 2013). This approach has the advantages of maintaining the competition aspect of the primary screen within the same well, as well as of tracing the competition over a long time course.

5.2 Signaling dependency shift from PI3K pathway in Mgat5 wt to MAPK in Mgat5 null MEFs

In addition to the gene hits discussed above, my screen revealed Pik3ca to be an essential gene in the wt cell line. This gene codes for the catalytic subunit of PI3K, consistent with our laboratory’s previous finding that Mgat5 deficiency suppressed PI3K activation in Pten +/- MEF cells (Cheung and Dennis, 2007). Downstream of the RTK-PI3K-Akt signaling cascade, mTOR integrates information on intracellular nutrient levels and extracellular growth signal. This pathway directly affects protein translation, proliferation, and autophagy, and is often hijacked to promote cancer (Loewith and Hall, 2011; Zoncu et al., 2011). Indeed, as reported by GSEA analysis, knocking down genes on the mTOR complex pathway selectively inhibited growth of wt cells. Also reassuringly, eIF4 complex, a downstream effector of mTOR signaling, was revealed by the pathway enrichment to be essential in wt cells, making the mTOR signaling worthy of further investigation.

Interestingly, Entpd5 was identified essential in null cells. This gene codes for an ER enzyme that couples ATP hydrolysis cycle with UDP hydrolysis into UMP, producing substrate for UDP- GlcNAc. Entpd5 knock-down has been shown to cause ER protein folding stress and loss of surface receptors in MEF cells (Fang et al., 2010). Entpd5 was also found to enhance glycolysis, and promote activation of the PI3K-Akt signaling pathway (Fang et al., 2010). Therefore, identifying Entpd5 as essential gene in null cells further supports the differential dependency of mTOR signaling in MEF cell lines.

In addition to Pik3ca, GSEA also identified other components of the pathway, such as Pdpk1 and Pik3R1. In follow-up experiments, shRNA targeting of these genes should be tested for knock- down efficiency by RT-PCR, and effects on Akt phosphorylation at Ser473 examined to confirm knock-down dependent inhibition of PI3K signaling. Growth rate and cell size can be measured as the phenotypic readout of mTOR pathway inhibition (Loewith and Hall, 2011). Signaling through mTORC1 or mTORC2 should also be measured. 4E-BP1 and p70S6 kinase phosphorylation can be used to monitor mTORC1 activity (Zoncu et al., 2011), while NDRG1 phosphorylation serves as a read-out for mTORC2 activity (Murray et al., 2004; García-Martínez and Alessi, 2008). The mechanism by which Mgat5 N-glycans interact with the mTOR signaling system, whether by regulating surface retention of nutrient transporters or growth signaling receptors, can also be studied as the next step. Transient expression and shRNA mediated knock- down of Mgat5 in MEF cell lines can be performed followed by measuring mTOR signaling and L-PHA staining as an indicator of Mgat5 activity. Moreover, a collection of PI3K and mTOR chemical inhibitors are now available to modulate pathway activity, such as LY294002 for inhibiting PI3K, and rapamycin for mTORC1, or novel drugs like Torrin (Vlahos et al., 1994; Liu et al., 2010; Benjamin et al., 2011). These molecules should phenocopy shRNA knock-down with the advantage of generating dose response curves. However, a period of adaptation to the drugs may be required to phenocopy null cells.

Although pathway enrichment software could not identify reliable pathway dependencies in null cells, individual gene hits hinted at a pathway worth checking experimentally. Braf showed a low dGARP score, ranking 5th among 16,000 genes, indicating its essentiality in null cells. Braf is a key component of the MAPK pathway involved in cell growth and oncogenic transformation (Prahallad et al., 2012). Stk32a was also essential in null cells. Little is known about Stk32a, but it is required for growth and survival of the HCT116 human colorectal cancer cell line expressing RASG13D (Vizeacoumar et al., 2013).

The RAS-MAPK signaling pathway and the PI3K-Akt signaling pathway are the two major pathways downstream of many RTKs such as EGFR. If mTOR signaling is essential in wt, while Fgfr1, Fgfr4, Braf, and Stk32a are essential in null, perhaps Mgat5 loss reduces dependency of MEFs on PI3K signaling and increases their dependency on RAS signaling. Indeed, mechanisms for cross-talk between these two pathways have been reported (reviewed in Section 1.5). In addition, glucose deprivation in cells reduces mTOR signaling, and has been shown to cause

63 higher mutation rates in Ras (Yun et al., 2009). Inhibition of Braf activity by vemurafenib selects for cells with EGFR activation in several cancer cell lines (Prahallad et al., 2012). PIK3CA and BRAF gain of function mutations can cooperate to increase malignancy of human lung cancer (Trejo et al., 2013).

Provided that validation experiments as described above yield positive results, hair-pins targeting these genes as well as small chemicals could be used to manipulate Ras signaling. Drugs such as AZD6244 targeting MEK or PLX4032 targeting BRAF have been proven extremely effective, although resistance can develop (Yeh et al., 2007; Bollag et al., 2010). Erk phosphorylation and nuclear translocation can be used as a read-out for Ras signaling as previously described (Mendelsohn et al., 2007). The synergistic effect of Ras and mTOR inhibition should also be examined. Mgat5 targeting hair-pins and other N-glycosylation inhibiting chemicals, such as swainsonine, can be used instead of mTOR disruption to see if global disruption of Golgi N- glycan remodeling can be used to alter dependence on specific signaling pathways.

5.3 Mgat5 deletion in MEF cell line and MGAT5 knock-down in human cell line caused metabolic reprogramming but of opposite trend

The wt MEF cell line showed higher levels of glycolysis, TCA cycle, PPP, HBP metabolites, and free amino acids. This finding supported the fact that mTOR was essential in wt cells, as mTOR integrates information on cellular metabolite levels, and is especially sensitive to cellular amino acid concentration (Loewith and Hall, 2011; Zoncu et al., 2011). However, more data is required to determine whether the null phenotype is a result of shifted mTOR dependency or a consequence of disrupting surface glycoprotein receptor profile. Whether increased levels in wt cells implies faster uptake or slower utilization is unclear, metabolite pathway flux experiments with 13C-labeled glucose and glutamine are required to distinguish these possibilities (Crown and Antoniewicz, 2013). With this question answered, metabolite profiling should be performed in cell lines treated with hairpins and drugs as discussed above, to directly addresses the interaction of Mgat5 with mTOR and metabolism. Reactive oxygen species (ROS) levels and reduced/oxidized glutathione ratios may indicate changes in OXPHOS associated with nutrient

64 excess in these MEFs, or hypoxia response pathways in situations of nutrient depletion (Wellen and Thompson, 2010).

The human MGAT5 shRNA sh2 achieved >45% MGAT5 knock-down in HEK 293, BJ hTERT, and MCF7, as supported by several measures. Firstly, sh2 caused reduced levels of MGAT5 mRNA and reduced surface L-PHA binding. Secondly, the metabolite profiles of sh2 treated cells were always assigned to their own clusters when compared against those of the shLUC control cells in unsupervised clustering. Thirdly, reproducible differences in metabolite profiles were seen in sh2 cells as compared to shLUC control. The same trend was observed for all three human cell lines and with replicates, namely that sh2 treated cells showed higher abundance of many metabolites.

My data showed an obvious discrepancy between human and MEF metabolite profiles. While null MEFs showed near global down-regulation of metabolite levels, sh2 treated human cell lines displayed the opposite trend. There were multiple differences between MEFs and human cell lines. The remaining MGAT5 in human cells may contribute to positive feedback through increased UDP-GlcNAc, which fuels activity of the other branching enzymes. To test this hypothesis, knock-down of Mgat5 in wt MEFs can be performed before harvesting for metabolite profile. Alternatively, a complete deletion of MGAT5 in human cell lines could be achieved using the recently developed clustered regularly interspaced short palindromic repeat (CRISPR) method (Cong et al., 2013; Mali et al., 2013). If shown to be correct, this approach could be used to explain why Mgat5 partial knock-down and complete null cells show such a difference. The N-glycan branching structures should be profiled by mass spectrometry to support the model, and signaling pathways mentioned in the previous section should be tested.

Another fundamental difference between MEFs and the human cell lines was p53 status. To aid in the immortalization process, MEF lines were engineered to be Trp53 -/-. In contrast, the three human cell lines contained either wild-type or point-mutant TRP53 gene (Graham et al., 1977; Moore et al., 2001; Wasielewski et al., 2006). p53 negatively regulates glycolysis and TCA cycle, and suppresses mTOR signaling and thus autophagy (Levine and Puzio-Kuter, 2010). Recently, it has been reported that, in a mouse pancreatic ductal adenocarcinoma (PDAC) model, p53 status determined activity of an inhibitory effect on tumor development by suppressing autophagy (Rosenfeldt et al., 2013). In the study, genetic or chemical inhibition of autophagy accelerated PDAC development in mice with p53 deletion, while inhibiting autophagy alone did

65 not cause PDAC development (Rosenfeldt et al., 2013). The difference in p53 status may have caused differential dependency on autophagy between human and mouse cell lines, and autophagy contributes to metabolite pools. Some of the approaches mentioned above could be used to analyze mTOR and autophagy pathway differences between human and mouse cell lines. MGAT5 knock-down could be performed in other TRP53-/- human cell lines and the experiments repeated. Alternatively, complete TRP53 knock-outs could be made using CRISPR technology (Cong et al., 2013; Mali et al., 2013).

5.4 Statistical tests identified key metabolites in glycolysis, TCA cycle, and amino acid metabolism to be differentially regulated in MGAT5 knock-down human cell lines

In order to narrow down to a smaller list of metabolites to work with, I took two approaches. One was the classical Student’s t-test to find metabolites that were significantly changed between the most effective MGAT5 hairpin-treated (sh2) and control hairpin treated cells. The other was a Pearson Correlation test to identify metabolites that are potentially directly regulated by MGAT5. Both tests flagged several metabolites that are worth further investigation. In addition, the correlation test also revealed that L-PHA staining and RT-PCR measuring MGAT5 mRNA level were both effective ways to evaluate MGAT5 knock-down efficiency, and that most metabolites showed a negative correlation with remaining MGAT5 activity.

In fact, the correlation study could be applied to many other phenotypes of the cells. For example, a correlation test could be applied between metabolite levels and doubling times. This specific test could be used to determine whether lower metabolite levels are a result of less uptake or faster utilization. For example, if most metabolites were found to be positively correlating with the doubling time, the interpretation would be that fast proliferating cells tend to have low metabolite levels measured. The only logical conclusion would be that lower metabolite levels are more likely an indication of faster utilization, otherwise the requirement for fast proliferation cannot be fulfilled.

Student’s t-test analysis of sh2 treated and shLUC treated samples suggested that lactic acid was significantly different in all three human lines. Secreting lactic acid as a by-product of glycolysis

66 is a hallmark of the Warburg effect. It was puzzling to find that lactate levels were higher in sh2 treated samples, since loss of Mgat5 in mouse causes reduced proliferation and sensitivity toward growth factor signaling (Granovsky et al., 2000; Patridge et al., 2004; Cheung et al., 2007). Therefore, it’s necessary to first examine signaling status in these cells, such as activation status for mTOR and RAS signaling as discussed in the previous section. To explore if the difference of intracellular lactic acid levels cause acidification of the cellular environment, metabolite extraction can be performed on growth media to quantify secreted lactic acid.

Among the metabolites that were significantly different in at least two human cell lines, many were free amino acids: to name but a few, L-glutamine, L-proline, L-alanine, and L-asparagine. Some metabolites from glucose metabolism were also higher, such as isocitric acid from TCA cycle and DHAP from glycolysis. Both oxidized and reduced forms of glutathione were on the list as well, implying the possibility that these cells suffer an imbalance in reduction-oxidation status. Preliminary pathway enrichment analysis on statistically significant metabolites by hypergeometric test identified changes in amino acid metabolism pathways, such as “Nitrogen Metabolism”, “Alanine, aspartate and glutamate metabolism”, and “Lysine degradation”. However, the 122 metabolites examined in this study were mostly selected from glycolysis, TCA, HBP, and amino acid metabolism pathways, thus strongly biased, which limits interpretation from the enrichment.

Energy currency molecules ADP and ATP were both negatively correlated with MGAT5 level. Lactic acid, Acetyl CoA, and CMP-sialic acid were strong negatively correlated metabolites as well. The identification of lactic acid confirmed the t-test result mentioned above. Acetyl CoA is used as a metabolite in several pathways. In sugar metabolism, the glycolysis product pyruvate is converted into acetyl CoA and then citric acid to be further broken down by the TCA cycle. In fatty acid metabolism, acetyl CoA is used for synthesis of new fatty acid chains, or is produced by β-oxidation of fatty acids. De novo synthesis of CMP-sialic acid utilizes UDP-GlcNAc as substrate. These observations indicated that MGAT5 knock-down cells and control cells were in different metabolic states, although for the same reason as above, it was not possible to conclude which one was more metabolically active.

Unlike the t-test result, glycine was the only amino acid showing average correlation coefficient less than – 0.7. However, 2-aminoadipate was a strong hit elevated by MGAT5 knock-down. 2- aminoadipate participates in the reaction L-2-aminoadipate + alpha ketoglutarate 2-oxoadipate

+ L-glutamate, and is an amino group receptor from glutamine and plays vital roles in the metabolism of lysine and tryptophan (Deshmukh and Mungre, 1989; Okuno et al., 1993). Supporting the hypothesis developed around acetyl CoA, the identification of this molecule as a hit emphasizes potential differences in the TCA cycle between MGAT5 knock-down and wild- type cells. It also provides a link to the mTOR signaling pathway, since mTOR is very sensitive to cellular amino acid concentration changes (Sancak et al., 2008). 15N labeled glutamine can be added in growth media to study the fate and kinetics of glutamine utilization in the cell. The cells should also be challenged with media containing low glutamine to see if signaling is thus altered, and if MGAT5 knock-down cells display a different tolerance to low glutamine conditions.

5.5 Conclusions

This study employed two approaches to define the cell biological functions of Mgat5, namely a pooled shRNA screening approach to identify genetic interactions of Mgat5, and mass spectrometry metabolite profiling approach to analyze metabolic state changes in mouse and human cells when MGAT5 was perturbed. The screen identified gene interactors of Mgat5, where mTOR signaling was found to be essential in wild-type immortalized MEF cells, whereas a shift of signaling dependency towards RAS-MEK-ERK signaling occured in Mgat5 null cells. The metabolite profiling showed that cells with MGAT5 disruption were drastically different in metabolic status as compared to their control counterparts. Analysis of profiles suggested that TCA cycle and amino acid metabolism showed the most significant differences, although the exact effects may depend on remaining MGAT5 activity and interaction with other genes, such as p53.

Overall, these findings further support MGAT5 and the N-glycan branching pathway as a regulator of sensitivity toward growth signals and the metabolic status in the cell. These findings also suggest the possibility of targeting MGAT5 in order to manipulate mTOR and/or RAS signaling and cellular metabolism, which are key pathways for a number of different diseases including cancer. In addition, advancing knowledge of basic biology will strengthen our understanding of MGAT5 genetic interactors and the metabolite profiles defined here. Finally, the cell lines developed in this study will provide ample resources for pursuing new hypotheses about MGAT5 functions.

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Appendices

Appendix 1. List of zGARP and dGARP scores in the pooled shRNA drop-out screen (See attached Excel spreadsheet.)

Appendix 2. Metabolite level measurements in MEF cells (See attached Excel spreadsheet.)

Appendix 3. Metabolite level measurements in HEK 293, BJ hTERT, and MCF7 cells with MGAT5 knock-down (See attached Excel spreadsheet.)