i

METABOLIC REGULATION OF HIF-1α AND mTORC1

SIGNALLING

CHOW AI LEE

(Bachelor of Biomedical Science (Hons), University of

Malaya)

A THESIS SUBMITTED

FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

DEPARTMENT OF BIOCHEMISTRY

NATIONAL UNIVERSITY OF SINGAPORE

2017

Supervisor:

Dr Long Yun Chau

Examiners:

Dr Xu Feng

Dr Ban Hon Kim Kenneth

Professor Anna Krook, Karolinska Institute

ii

DECLARATIONS

I hereby declare that this thesis is my original work and it has been written by

me in its entirety. I have duly acknowledged all the sources of information

which have been used in this thesis.

This thesis has also not been submitted for any degree in any university

previously.

_________________________

Chow Ai Lee

27 July 2017

iii

ACKNOWLEDGEMENTS

Four years ago, if I’d said “No” to NUS and remained part of the allied health

profession, I’d be content helping humankind, one at a time. But, I was still in

search of something, and I quickly realised that as we attended to our patients,

there was always one more question that has no answers to. Still so much that

we don’t know about the science and arts of medicine and dentistry. So, I took

that leap of faith – I said “Yes” and did the PhD. This journey has finally

culminated in this thesis, and I owe it all to the following:

LYC lab: Dr Long Yun Chau, Dr Yu Xinlei, Arthur Sim Yi Loong, Johanne

Shane Tian, Hayden Tan Weng Shiong, Ang Li Fern, Isabel Low, Wan Zhi Yi,

Goh Ying Siu, Cho Yik Lam, Audrey and Jessica Poh;

A/P Thilo Hagen, Dr Jean-Paul Kovalik, A/P Theresa Tan May Chin, and Ms

Sanna Grannas for your wisdom, professional advice and opportunities, and

support throughout these years and what comes after;

Dr Lynette Ng and Dr James Lee Tsung-Lin from TDS (SMG), for enabling the

scientist in me with your insights into academia and R&D, and for showing me

what it means to run a business with compassion;

My brothers- and sisters-in-arm. You guys rocked my world, kept me sane, and

I hope I’ve rocked yours :) Dr Chua Yee Liu, Mohd Sufyan, Mario, Serene

Chua, Chan Shu Ning, Faeze Monji, Zhu Yanan, Anu, Mohd Nadjad, Lee Wai

Yeow (Felix), Fanny, Grace, Desi, Dr Lim Yi Shan, Dr Gireedhar

iv

Venkatachalam (Giree), Dr Tan Hwee Tong, Dr Vidhi Patel, Dr Hong Shin Yee,

Dr Christine Hu, Tan Shi Hua;

Forgive me for the gratuitous name-dropping, but without your genuine interest,

thoughtfulness and openness in discussing the pyruvate-HIF-1α project at

various conferences, I’d never consider all these new perspectives and

shortcomings: William G. Kaelin, Jared Rutter, Uemura Tadashi, Jason

Tennessen, and Bernhard Radlwimmer,

The lurkers and ardent supporters from the Archive since 2015, for being the

yang to the PhD’s yin, for making this the best form of procrastination, and for

persuading me into thinking that cultivating a second career out of a hobby is a

good idea;

Rebecca Helton, God bless, for eight beautiful years of friendship;

To the people who remind me of home: Foo Nian, Yujin, and Jian Zhen;

Mom and Dad, Mei Mei (Dr Chow Wen Lee, yes, it’s official);

My husband, Dr Lee Yin Jen,

Thank you.

Sometimes you just gotta go for it. The more often you can do things you might

get a no for and that you have to put yourself up and face potential ridicule,

the better.

v

PUBLICATION LIST

Wan, Z. Y., Tian, J. S., Tan, H. W., Chow, A. L., Sim, A. Y., Ban, K. H., &

Long, Y. C. (2016). Mechanistic target of rapamycin complex 1 is an essential

mediator of metabolic and mitogenic effects of fibroblast growth factor 19 in

hepatoma cells. Hepatology, 64(4), 1289-1301. doi:10.1002/hep.28639

(Manuscript in preparation) Chow, A.L., Ching, J., Kovalik, J.P. & Long, Y.C.

Pyruvate-mediated HIF-1α stabilisation at normoxia is dependent on α-

ketoglutarate and the implication of lysine catabolism as a metabolic repair

system.

vi

CONTENTS

ACKNOWLEDGEMENTS .............................................................................. iii

PUBLICATION LIST ....................................................................................... v

SUMMARY ...................................................................................................... ix

LIST OF TABLES ............................................................................................ xi

LIST OF FIGURES ......................................................................................... xii

LIST OF ABBREVIATIONS ......................................................................... xiv

CHAPTER 1: GENERAL INTRODUCTION .................................................. 1

1.1 Metabolic dysregulation in cancer ...................................................... 1

1.1.1 The Warburg Effect ..................................................................... 2

1.1.2 Tricarboxylic acid (TCA) cycle ................................................... 5

1.1.3 Metabolism of amidic amino acids .............................................. 8

1.1.4 Hyperammonaemia .................................................................... 13

1.2 Oncogenesis at the signalling level ................................................... 15

1.2.1 Hypoxia inducible factor (HIF)-1α ............................................ 16

1.2.2 Mechanistic target of rapamycin complex 1 (mTORC1) pathway

27

1.3 Metabolic error repair pathways........................................................ 36

1.3.1 DL-2-hydroxyglutarate (DL-2HG) ............................................ 36

1.3.2 Lysine catabolism ...................................................................... 37

1.4 Aims and objectives .......................................................................... 39

CHAPTER 2: MATERIALS AND METHODS ............................................. 42

2.1 Cell culture ........................................................................................ 42

2.2 Western Blot assay ............................................................................ 44

2.3 Quantitative real time-PCR (qPCR) .................................................. 58

2.4 Seahorse Extracellular Flux (XF) Analyser ...................................... 61

2.5 Flow cytometry ................................................................................. 63

2.6 Metabolomics analysis ...................................................................... 64

2.7 Mitochondrial membrane potential (MMP) measurement ................ 65

2.8 Cell fractionation for profiling of compartmentalized metabolites ... 66

2.9 Ammonia quantification .................................................................... 68

2.10 Statistical analysis ............................................................................. 68

vii

CHAPTER 3: Pyruvate-mediated HIF-1α stabilisation at normoxia is

dependent on α-KG and the implication of lysine catabolism as a metabolic

repair mechanism ............................................................................................. 70

3.1 Introduction ....................................................................................... 70

3.2 Results ............................................................................................... 71

3.2.1 Pyruvate-induced HIF-1α stabilisation was restricted to cancer

cell lines 71

3.2.2 Pyruvate increased HIF-1α protein level by downregulating its

rate of degradation .................................................................................... 73

3.2.3 Exhaustion of intracellular oxygen or raised ROS level did not

contribute to HIF-1α stabilisation ............................................................. 76

3.2.4 Organic acids and amino acids profile modulations in response

to pyruvate ................................................................................................ 80

3.2.5 Paradoxical implications of α-KG accumulation in mediating

HIF-1α stabilisation .................................................................................. 83

3.2.6 The significance of malate in regulating cycling of mitochondrial

metabolites ................................................................................................ 86

3.2.7 Lysine catabolism as a metabolic repair mechanism ................. 90

3.2.8 Physiological significance of pyruvate-induced HIF-1α

stabilisation ............................................................................................... 94

3.3 Discussion ......................................................................................... 95

CHAPTER 4: Ammonium ions sensitises mTORC1 to amino acids via the

Src/Akt signalling axis ................................................................................... 102

4.1 Introduction ..................................................................................... 102

4.2 Results ............................................................................................. 103

4.2.1 Pyruvate downregulated mTORC1 signalling likely by

modulating ammonia metabolism........................................................... 103

4.2.2 Ammonium ions activated mTORC1 signalling pathway ....... 108

4.2.3 Ammonium ions activated mTORC1 independently of essential

amino acids, but required glutamine ....................................................... 110

4.2.4 Modulations to amino acids profiling in response to ammonium

ions 113

4.2.5 Implications of Src/Akt pathway in mediating ammonium ion-

induced mTORC1 activation .................................................................. 118

4.2.6 Implications of hyperammonaemia in the liver ....................... 123

4.3 Discussion ....................................................................................... 126

CHAPTER 5: Asparagine regulates mTORC1 signalling as an amino acid . 129

5.1 Introduction ..................................................................................... 129

viii

5.2 Results ............................................................................................. 129

5.2.1 Asparagine did not alter intracellular amino acid profile ........ 129

5.2.2 Intracellular accumulation of asparagine was rate-limiting for

mTORC1 activation ................................................................................ 133

5.2.3 The role of asparagine or glutamine as an amino acid exchange

factor was independent of small GTPase Arf1 ....................................... 137

5.2.4 Asparagine or glutamine mediated mTORC1 in sensing

endogenous amino acids, in an Akt and Arf1-dependent manner .......... 141

5.3 Discussion ....................................................................................... 147

CHAPTER 6: GENERAL DISCUSSION ..................................................... 151

6.1 Clinical significance of metabolite signalling ................................. 151

6.2 Conclusion ....................................................................................... 155

6.3 Future work ..................................................................................... 159

REFERENCES .............................................................................................. 161

ix

SUMMARY

Metabolic dysregulation and hyperactivation of oncogenic pathways are

hallmarks of oncogenesis often described and studied as parallel events.

However, emerging evidences suggest that oncometabolites do crosstalk with

oncogenes, further complicating current understanding of cancer biology. This

thesis focuses on the effects of pyruvate on hypoxia-inducible factor-1α (HIF-

1α) regulation, and the regulatory role of ammonia and amidic amino acids,

glutamine and asparagine on mammalian target of rapamycin complex 1

(mTORC1) signalling.

The expression of HIF-1α is canonically regulated in an oxygen-sensitive

manner. Given that only 50 to 60% of tumours that overexpress HIF-1α exhibit

hypoxic regions, other factors are also likely to contribute to its regulation. This

study provided evidence that pyruvate-induced elevation of HIF-1α was due to

reduced prolyl hydroxylases (PHDs) activities and impaired degradation of

HIF-1α, rather than increased de novo synthesis. Inhibition of pyruvate entry

into the tricarboxylic acid (TCA) cycle relieved HIF-1α accumulation,

suggesting that dysregulated TCA cycle was prerequisite to its stabilisation.

Metabolomics analysis demonstrated robust modulations to TCA cycle

intermediates, particularly that of α-ketoglutarate (α-KG). Increase in α-KG

pool size through pharmacological methods resulted in a concomitant increase

in HIF-1α protein level. On the contrary, relief of α-KG accumulation by malate

or lysine supplementation abrogated HIF-1α stabilisation. This study proposed

x

that localisation of α-KG and maintenance of TCA cycle homeostasis was

important in pyruvate-induced HIF-1α stabilisation at normoxia.

Altered glutamine and asparagine metabolism is associated with tumorigenesis.

Their aggressive catabolism has been linked to hyperammonaemia in tumours’

milieu. This study reports the impact of ammonia ions (NH4+), on the amino

acid sensor mTORC1 signalling pathway. mTORC1 activity was increased in

response to NH4+ in liver cancer cell lines HepG2, HuH7 and H4IIE.

Intriguingly, despite withdrawal of essential amino acids, mTORC1 activity was

preserved in NH4+-treated cells supplemented with glutamine. Untargeted

metabolomics analysis indicated modest upregulation in endogenous essential

amino acids abundance in this system, which was not observed in cells cultured

under nutrient-replete conditions. Upstream regulator Akt was also activated in

NH4+-treated cells, and mTORC1 activity was abolished when Akt and the

upstream non-receptor tyrosine kinase, Src were inhibited.

It is still unknown if NH4+ derived from glutamine or asparagine could regulate

mTORC1. Instead, this thesis reports that the amides’ intracellular accumulation

1) preceded exchange for extracellular essential amino acids that in turn

activated mTORC1, and 2) bridged mTORC1 detection of endogenous amino

acids in an Arf1 and Akt-dependent manner. Thus, this thesis aims to highlight

the otherwise underappreciated significance of “metabolite signalling” in cancer

biology.

(411 words)

xi

LIST OF TABLES

Table 1.1 Hallmarks of cancer metabolism. ...................................................... 2

Table 1.2 Differences between GLS1 and GLS2. ............................................ 10

Table 1.3 Km of PHDs for co-substrates and co-factor. ................................... 21

Table 1.4 Inhibitory constant (Ki) of PHDs for oncometabolites. ................... 22

Table 1.5 Functions of PHDs in proliferation, growth and survival. ............... 23

Table 1.6 Targets of HIF-1α. ........................................................................... 25

Table 1.7 Subunits of the mTORC1 complex. ................................................. 28

Table 2.1 Profiles of various liver cell models. ............................................... 43

Table 2.2 Validation methods for primary antibodies ..................................... 46

Table 3.1 Working concentrations of ETC complex inhibitors. ...................... 78

Table 4.1 Treatment parameters for metabolic profiling. .............................. 113

Table 6.1 Newly identified metabolite biomarkers of diseases using

metabolomics approaches. ............................................................................. 153

xii

LIST OF FIGURES

Fig 1.1 Primary fates of intracellular pyruvate. ................................................. 4

Fig 1.2 Synthesis and degradation of glutamine. ............................................... 9

Fig 1.3 Synthesis and degradation of asparagine. ............................................ 12

Fig 1.4 HIF-α regulation in an oxygen-sensitive manner. ............................... 18

Fig 1.5 Growth factor-mediated mTORC1 activation. .................................... 30

Fig 1.6 Amino acid-mediated mTORC1 activation. ........................................ 31

Fig 1.7 Biosynthesis of DL-2- hydroxyglutarate. ............................................ 37

Fig 1.8 Lysine catabolism – saccharopine arm. ............................................... 38

Fig 1.9 Lysine catabolism – pipecolate arm. ................................................... 39

Fig 2.1 Oxygen consumption profile. .............................................................. 62

Fig 3.1 The effect of pyruvate on HIF-1α expression. ..................................... 73

Fig 3.2 The impact of pyruvate on PHDs hydroxylation activities. ................ 75

Fig 3.3 The effect of pyruvate on ROS generation or consumption of

intracellular O2 levels. ...................................................................................... 80

Fig 3.4 The metabolic impact of pyruvate treatment in HepG2 cells. ............. 83

Fig 3.5 Implications of α-KG on HIF-1a stabilisation at normoxia. ................ 86

Fig 3.6 Hypothetical relief of mitochondrial α-KG accumulation to abrogate

HIF-1a stabilisation. ......................................................................................... 89

Fig 3.7 Lysine catabolism regulates pyruvate-induced HIF-1α stabilisation at

normoxia. ......................................................................................................... 93

Fig 3.8 Physiological impacts of pyruvate-induced metabolic perturbations. . 94

Fig 3.9 A summarised postulation of how pyruvate induced HIF-1α

stabilisation at normoxia and the induction of lysine catabolism as a metabolic

repair mechanism. .......................................................................................... 101

Fig 4.1 The impact of pyruvate metabolism on mTORC1 signalling. ........... 108

Fig 4.2 The impact of NH4+ on mTORC1 signalling in liver cancer cell lines.

........................................................................................................................ 110

Fig 4.3 Glutamine was indispensable in mediating NH4+-induced mTORC1

activation. ....................................................................................................... 113

Fig 4.4 Metabolic profiling in response to NH4+ and amino acids

manipulations. ................................................................................................ 118

Fig 4.5 The role of Src/Akt signalling axis in NH4+-induced mTORC1

activation. ....................................................................................................... 122

Fig 4.6 Physiological relevance of NH4+-induced mTORC1 activation. ..... 125

Fig 4.7 A summarised postulation of how NH4+ sensitise mTORC1 to amino

acids and growth factors. ............................................................................... 128

Fig 5.1 The effect of asparagine supplementation on mTORC1 and amino

acids profile. ................................................................................................... 133

Fig 5.2 The significance of intracellular accumulation of amidic amino acids.

........................................................................................................................ 137

Fig 5.3 The significance of Arf1 in asparagine or glutamine-mediated

mTORC1 activation. ...................................................................................... 140

xiii

Fig 5.4 The implications of Arf1 and Akt in mTORC1 sensing of endogenous

amino acids. ................................................................................................... 146

Fig 5.5 A summarised postulation of how amidic amino acids asparagine and

glutamine signal to mTORC1 via two distinct mechanisms. ......................... 150

Fig 6.1 General workflow to achieve cell functions. ..................................... 151

Fig 6.2 Metabolic regulation of HIF-1α and mTORC1. ................................ 158

xiv

LIST OF ABBREVIATIONS

4E-BP1 Eukaryotic translation initiation factor 4E-binding

protein 1

AMPK AMP-activated protein kinase

BTC 1,2,3-benzenetricarboxylic acid

EAA Essential amino acids

eIF Eukaryotic translation initiation factor

HCC Hepatocellular carcinoma

HIF-1α Hypoxia inducible factor-1α

KMV DL-3-methyl-2-oxovaleric acid sodium salt

mTORC1 Mammalian target of rapamycin complex 1

Na+, K+-ATPase Sodium-potassium pump

NH4+ Ammonium ions

NH4Cl Ammonium chloride

OxPhos Oxidative phosphorylation

PHDs Prolyl hydroxylases

α-KG α-Ketoglutarate

1

CHAPTER 1: GENERAL INTRODUCTION

1.1 Metabolic dysregulation in cancer

A normal cell regulates its physiological functions according to nutrient

availability, as cell expansion involves de novo biosynthesis and assembly of

new cellular structures, an expensive process that commences in the presence

of growth factors. However, mutations that go unrepaired may trigger cells to

transform, and they are induced to proliferate uncontrollably beyond the

restraints of apoptotic signalling. To sustain uncontrolled proliferation, energy

and metabolic demands must be met by some means.

Recently, six hallmarks of cancer metabolism have been proposed to illustrate

how these cells display 1) improved capacities for nutrient uptake, 2)

opportunistic resource allocation to support pro-oncogenic pathways, and 3)

modification of their microenvironment to be more conducive for cancer

propagation (Table 1.1). They highlight an intimate crosstalk between

metabolism and gene regulation to explain the inherent complexity of cancers

(Hanahan & Weinberg, 2011; Pavlova & Thompson, 2016).

2

Table 1.1 Hallmarks of cancer metabolism.

Strategy Mechanism

Metabolite-induced

changes to cell function

Modulate gene regulation

Influence components of tumour

microenvironment

Metabolic

reprogramming

Increase reliance on glycolysis or TCA cycle for

biosynthesis of building blocks

Increase uptake of nitrogenous sources

Oncogene-induced

nutrient uptake

Enhance uptake of glucose and amino acids

Evolve secondary means for nutrient acquisition

1.1.1 The Warburg Effect

In the 1920s, Otto Warburg and colleagues observed that actively proliferating

cells preferentially conduct fermentative glycolysis even in the presence of

oxygen (O2), generating lactate as by-products (Warburg, Wind, & Negelein,

1927). Two metabolic events were subsequently postulated as oncogenic

precursors. One, cells suffered irreparable respiration injury within the

mitochondria (Warburg, 1956a, 1956b). This concept has not been accepted as

a wide range of cancer cells still retain normal mitochondria functions which

are necessary for survival (Jose, Bellance, & Rossignol, 2011; Moreno-Sanchez,

Rodriguez-Enriquez, Marin-Hernandez, & Saavedra, 2007). Two, cells that

grow increasingly dependent on glucose fermentation for energy generation will

survive as they adapt to these respiratory insults. These cells are thus “selected”

for their capacities to thrive on glucose fermentation and become precursors of

3

transformed cells (Warburg et al., 1927). 18-Fluoro-deoxyglucose-based

positron emission tomography imaging demonstrated that most primary and

metastatic cancers exhibit a significant increase in glucose uptake compared to

normal tissues (Gatenby & Gillies, 2004).

Decades later, it remains debatable whether the Warburg effect is a cause or

consequence of tumour formation. A consensus is eventually reached whereby

the fate of intracellular pyruvate, specifically, whether pyruvate is shunted to

fermentative glucose metabolism or oxidative phosphorylation (OxPhos)

dictates whether a cell is committed to proliferation or senescence, respectively

(Kaplon et al., 2013; Olenchock & Vander Heiden, 2013) (Fig 1.1).

4

Fig 1.1 Primary fates of intracellular pyruvate. Under normoxic conditions,

pyruvate is predominantly metabolised in the mitochondria for complete

oxidation via OxPhos, yielding approximately 30 to 36 molecules of adenosine

triphosphate (ATP). The first committed step in this pathway is decarboxylation

of pyruvate to acetyl-coenzyme A (CoA). In the absence of O2 (anaerobic

glycolysis) or reprogrammed metabolism, pyruvate is preferentially converted

to lactate, yielding 2 molecules of ATP (Rich, 2003). Pyruvate can also be

transaminated to alanine, using glutamate as an amino-group donor.

Abbreviations used are as follows: glucose transporter (GLUT), mitochondrial

pyruvate carrier (MPC), alanine transaminase (ALT), lactate dehydrogenase

(LDH), pyruvate dehydrogenase complex (PDH), pyruvate carboxylase (PC),

oxaloacetate (OAA).

Cell reliance on aerobic glycolysis has a major drawback. ATP generation is

rather inefficient at a rate of 2 ATP per molecule of glucose, compared to

OxPhos in the mitochondria at 30 to 36 ATP per molecule of glucose. However,

the conferred advantages are manifold. Intermediates of glycolysis can be

5

channelled into generation of amino acids, nucleic acids, and lipids to sustain

cell proliferation (Duckwall, Murphy, & Young, 2013). Synthesis and excretion

of lactate maintains an acidic extracellular milieu that facilitates evasion of the

host’s immune system, in addition to enhanced self-proliferation and invasion

(Kato et al., 2013). Bypassing OxPhos leads to reduced rate of electron flow

through the electron transport chain (ETC), and thus reduced production of

reactive oxygen species (ROS), deoxyribonucleic acids (DNA) damage and the

triggering of protective mechanisms such as apoptosis (J. Lu, Tan, & Cai, 2015).

1.1.2 Tricarboxylic acid (TCA) cycle

Pyruvate, the end product of glycolysis also serves as a bridge to mitochondrial

respiration. Pyruvate can be decarboxylated to acetyl-CoA by PDH in the

mitochondrial matrix. The mammalian PDH comprises of 132 subunits, and its

activities are regulated by five coenzymes, namely thiamine pyrophosphate,

lipoamide, CoA, flavin adenine dinucleotide (FAD) and nicotinamide adenine

dinucleotide (NAD):

Pyruvate + CoA + NAD+ NADH + acetyl-CoA + CO2

Acetyl-CoA is next condensed with OAA to produce citrate, a reaction which is

catalysed by citrate synthase. Entry of pyruvate into the TCA cycle in this

manner does not contribute net carbon, as the 2 carbons from acetyl-CoA will

be released as carbon dioxide (CO2) in subsequent reactions:

OAA + acetyl-CoA citrate

6

Citrate is isomerised to isocitrate by acotinase, after which it is oxidised and

decarboxylated to α-KG. This is also the first redox reaction that generates of

the first reduced NAD (NADH) and CO2, catalysed by isocitrate dehydrogenase.

Importantly, this enzyme activity is rate-limiting for the TCA cycle:

Citrate isocitrate

Isocitrate + NAD+ α-KG + CO2 + NADH

α-KG is decarboxylated to succinyl-CoA by α-KG dehydrogenase, yielding the

second NADH and CO2. Succinyl-CoA is labile, so it quickly assumes a more

stable form as succinate:

α-KG + NAD+ + CoA succinyl-CoA + CO2 + NADH

Succinyl-CoA + Pi + GDP succinate + CoA + GTP

Subsequent enzymatic reactions are sequential oxidation of succinate to

regenerate oxaloacetate for the next round of TCA cycle. The first of the series

is the isomerisation of succinate to fumarate, catalysed by membrane-localised

succinate dehydrogenase, also known as complex II of the ETC:

Succinate + FAD+ fumarate + FADH2

Fumarate is converted to malate by fumarate hydratase. Malate is then

oxidised to oxaloacetate by malate dehydrogenase in a reaction that produces

the final NADH in the TCA cycle:

7

Fumarate malate

Malate + NAD+ OAA + NADH

The TCA cycle proceeds as described only under aerobic conditions even

though O2 is not a direct substrate of any enzymes. Its continual activity requires

availability of NAD+ and FAD, and these oxidised forms are regenerated by the

transfer of electrons to O2 at complex IV of the ETC. In other word, oxidation

of NADH by complex I is prerequisite for the maintenance of TCA cycle. When

NADH accumulates, PDH and α-KGDH are inhibited by negative feedback,

thus preventing carbon sources from entering the cycle (Berg, Tymoczko, &

Stryer, 2002).

Evidence suggests that coupling of TCA cycle to OxPhos is tumour suppressive

as it promotes senescence (Kaplon et al., 2013; Olenchock & Vander Heiden,

2013). On the contrary, enhanced anaplerosis confers oncogenic properties.

Anaplerosis is the act of replenishing TCA cycle intermediate pool, usually by

consuming pyruvate or glutamine as substrates. This is advantageous to cancer

cells as large abundance of these intermediates sustain synthesis of fatty acids

and cholesterol, nucleotides, and NEAA (Pavlova & Thompson, 2016). As

exemplified in clinical samples, mutations in succinate dehydrogenase have

been observed in paraganglioma, gastric stromal tumours and acute

lymphoblastic leukaemia (ALL) (Rutter, Winge, & Schiffman, 2010). A

8

correlation between upregulated pyruvate carboxylase activities and

malignancy has also been reported in breast, lung and liver cancers (Fan et al.,

2009; Forbes, Meadows, Clark, & Blanch, 2006; K. J. Liu, Kleps, Henderson,

& Nyhus, 1991).

1.1.3 Metabolism of amidic amino acids

1.1.3.1 Glutamine

Glutamine is a non-polar amino acid that forms the largest pool (more than 60%)

of freely circulating amino acid at a concentration of about 0.5 mM (Bergstrom,

Furst, Noree, & Vinnars, 1974; Mayers & Vander Heiden, 2015). The bulk of it

is synthesised in the skeletal muscles, the adipose tissues to a lesser extent

(Curthoys & Watford, 1995), or the lungs when nutrient is scarce (Hulsewe et

al., 2003; Plumley et al., 1990). The liver on the other hand both consume and

synthesise glutamine, depending on zonation, pH and ammonia level (Curthoys

& Watford, 1995; Hensley, Wasti, & DeBerardinis, 2013) (Fig 1.2).

9

Fig 1.2 Synthesis and degradation of glutamine. Synthesis of glutamine from

glutamate in an ATP-dependent reaction that uses ammonia as the amino-group

donor. This reaction is catalysed by glutamine synthetase (GLNS), which is

activated by NH4+ and inhibited by adenosine diphosphate (ADP) (Curthoys &

Watford, 1995). Glutamine can also be serially catabolised to yield glutamate

in the first step by glutaminase (GLS), and α-KG in the next by glutamate

dehydrogenase (GDH) (Curthoys & Watford, 1995).

Glutaminase exists as two isoforms, namely 1) kidney type (GLS1), and 2) liver

type (GLS2). They catalyse the same enzymatic reaction as depicted in Fig 1.2,

but do differ in terms of regulation and localisation, as summarised in Table 1.2

(Mates et al., 2013):

10

Table 1.2 Differences between GLS1 and GLS2.

Glutamine plays a prominent role in systemic ammonia excretion. In peripheral

tissues, glutamine synthetase incorporates NH4+ into glutamine to be circulated

to the kidney. Renal catabolism of a molecule of glutamine releases 2 molecules

of free ammonia, which can be ionised with protons (H+) to generate NH4+. This

process is upregulated during acidosis as more H+ are scavenged to restore

plasma pH (Taylor & Curthoys, 2004). An overreliance on glutamine as fuel, a

phenomenon termed “glutamine addiction” is also a metabolic hallmark of

cancers (Lacey & Wilmore, 1990). Amplification of c-Myc has been shown to

upregulate transporters and enzymes responsible for glutamine uptake and

catabolism (Gao et al., 2009; Qing et al., 2012; Wise et al., 2008), to meet

metabolic demands for cell proliferation by provision of a steady source of

carbon and nitrogen (Krall & Christofk, 2015; J. B. Wang et al., 2010; Wise et

al., 2008).

GLS1 GLS2

Expression in tumours Predominant Less so

Activity in response to NH4+ Unaffected Activated

Activity in response to glutamate Inhibited Unaffected

Expression Ubiquitous Postnatal liver

Localisation Mitochondrial matrix

Km for glutamine Lower Higher

11

1.1.3.2 Asparagine

Asparagine and glutamine are similar in structure, the latter having one

additional methylene bridge in its side chain. Plasma concentration of

asparagine (0.05 – 0.1 mM) is not as high as that of glutamine (Stegink et al.,

1991).

Asparagine synthetase catalyses the biosynthesis of asparagine in an ATP-

dependent manner. There are two bacterial forms of asparagine synthetase, one

which uses ammonia as the nitrogen donor (AS-A), the other uses glutamine

(AS-B). The enzymatic activity of mammalian asparagine synthetase is similar

to AS-B (Balasubramanian, Butterworth, & Kilberg, 2013). Conversely,

asparagine catabolism by asparaginase and aspartate dehydrogenase yields

aspartate and oxaloacetate, respectively (Fig 1.3). Asparaginase exhibits some

glutaminase activity as well, but the Km for this reaction is 100-fold higher

(Muller & Boos, 1998).

12

Fig 1.3 Synthesis and degradation of asparagine. Mammalian asparagine

synthetase (ASNS) uses glutamine instead of free ammonia as the amino-group

donor for asparagine generation. Asparagine can also be serially catabolised to

yield aspartate in the first step by asparaginase, and oxaloacetate in the next by

aspartate dehydrogenase.

The clinical significance of asparagine is best discussed against a background

of childhood ALL, of which asparaginase has been used since 1967

chemotherapeutically (Oettgen et al., 1970). In these cells, asparagine becomes

essential because they lack asparagine synthetase, thus dependent on exogenous

asparagine for survival (Hermanova, Zaliova, Trka, & Starkova, 2012; Hettmer

et al., 2015; Su et al., 2008).

Glutamine and asparagine also function redundantly to a certain extent.

Asparaginase alone induced partial apoptosis, which could be exacerbated upon

13

co-inhibition of glutamine synthetase (Tardito et al., 2007). Depression of TCA

cycle intermediate pools and ETC impairment following glutamine withdrawal

could be restored with supplementation of aspartate, a product of asparagine

deamidation (Birsoy et al., 2015). This highlights the previously neglected role

of asparagine as a promoter of cellular survival.

1.1.4 Hyperammonaemia

Ammonia is commonly described as “waste” of protein catabolism. Up to 1 M

of ammonia is produced on a high-normal intake of 100 g/day. Most of it is

excreted as urea to maintain a plasma level of below 40 μmol/L. At

physiological plasma pH, about 98% of ammonia exists as NH4+, the ionic form

which is impermeable to plasma membrane. Thus, unlike uncharged,

membrane-permeable ammonia, transport of NH4+ requires protein transporters

such as aquaporins (Aqp8 and Aqp9) (Holm et al., 2005), sodium-potassium

pumps (Na+, K+-ATPase) (Post & Jolly, 1957), or sodium proton exchanger

(NHE) (Knepper, Packer, & Good, 1989).

The primary sites of ammonia production are 1) the gut, from bacterial

glutaminolysis (Summerskill & Wolpert, 1970), 2) kidneys, where 30% of renal

NH4+ ions are excreted to buffer H+ in the urine (van de Poll, Soeters, Deutz,

Fearon, & Dejong, 2004), and 3) skeletal muscles, during exercise where

adenosine monophosphates (AMP) and amino acids are catabolised (Meyer,

Dudley, & Terjung, 1980; Wilkinson, Smeeton, & Watt, 2010). The primary

sites responsible for bulk removal of ammonia in the form of urea or glutamine

14

are the liver and skeletal muscles, respectively (Wright, Noiret, Olde Damink,

& Jalan, 2011).

In vitro systems lack sophisticated interorgan transport and disposal of

ammonia, necessitating employment of other means to control ammonia levels.

Two strategies can be considered: 1) reduced dependence on exogenous

glutamine, or 2) filtering of culture media. To achieve the aforementioned, cells

can be genetically modified to overexpress glutamine synthetase, or adapted for

survival in glutamine-free media. Controlled addition of glutamine or

substitution with stable derivatives also prevents spontaneous glutamine

decomposition. On the other hand, ion-exchange membranes and filters, or

electrodialysis can efficiently remove ammonia from culture media

(Capiaumont et al., 1995; Schneider, Marison, & von Stockar, 1996).

In humans, maintenance of plasma ammonia at low basal of 40 μmol/L is

important, and this is predominantly achieved by incorporating excess ammonia

into glutamine and alanine for subsequent processing via hepatic urea cycle. In

the event of increased ammonia production or impaired clearance,

hyperammonaemia sets in. Hyperammonaemia is clinically defined as plasma

ammonia concentration above 50 μmol/L in adults, or above 100 μmol/L in

neonates. Clinical presentations of hyperammonaemia begin at plasma

ammonia >100 mg/dL as appetite loss, nausea, insomnia, agitation and

personality changes. The severity of these symptoms increases at higher

ammonia level where seizures, loss of consciousness, coma and eventually

15

death may occur. Compared to other organs, the brain is most susceptible to

hyperammonaemia, rapidly developing into cerebral oedema (Paprocka &

Jamroz, 2012). Liver dysfunctions, hereditary urea cycle disorders, infections

with urea-splitting organisms, and drug administration (for example,

asparaginase, 5-fluorouracil and carbamazepine) all promote synthesis of

ammonia in vivo (Paprocka & Jamroz, 2012; Walker, 2012).

Episodes of hyperammonaemia are also regularly observed in tumours’ milieu.

Low millimolar concentrations of ammonia have been reported in the interstitial

fluid of human tumour xenografts and tumour-bearing rats (Chance, Cao, Foley-

Nelson, Nelson, & Fischer, 1989; Chance, Cao, Nelson, Foley-Nelson, &

Fischer, 1988; C. H. Eng, Yu, Lucas, White, & Abraham, 2010). How

hyperammonaemia modulates cancer cell functions, if at all, remains to be

investigated.

1.2 Oncogenesis at the signalling level

Cells that are mutated in their genome but escape repair or apoptosis may

transform, and oftentimes this involves translation of proteins with aberrant

functions that allow cells to proliferate uninhibitedly. Six hallmarks of cancers

were first proposed in 2000 (Hanahan & Weinberg, 2000) to characterise

cancers in general, but have since been updated (Hanahan & Weinberg, 2011)

to include two emerging ones. They are 1) constitutive proliferation, 2)

insensitivity to growth inhibitory signals, 3) ability to metastasise, 4) bypassing

senescence checkpoints, 5) expansion of new vasculature network, 6)

16

insensitivity to death signals, 7) dysregulated cellular energetics, and 8) evasion

of host immunity.

To achieve these, cells commonly repurpose cell signalling, assisted by

modified protein effectors encoded by mutated genes. Great effort has been

made to develop therapeutics that intervene such pathways. This year, U.S.

Food & Drugs Administration (FDA) has approved for the first time, treatment

with pembrolizumab for any solid tumour harbouring biomarkers for

microsatellite instability. This is in contrast with the convention of prescribing

drugs according to the tumour’s site of origin (U.S. Food & Drugs

Administration, 2017). Such advancement further underscores the significance

of intervening aberrant signalling pathways to combat oncogenesis. This thesis

focused on the regulation of two key oncogenic pathways, 1) HIF-1α, and 2)

mTORC1 in response to oncometabolites.

1.2.1 Hypoxia inducible factor (HIF)-1α

1.2.1.1 Structural description of HIF

In the early 90’s, the consensus motif now known as hypoxia response element

(HRE) was first identified in the 3’ enhancer region of the gene that encodes for

erythropoietin. Subsequently, HIF-1 was identified as a HRE-binding protein

(Goldberg, Dunning, & Bunn, 1988; Semenza, Nejfelt, Chi, & Antonarakis,

1991). Heterodimerisation of one α- and one β-subunit forms a functional HIF

transcription factor that binds to target promoters at the HRE region. In humans,

there are 3 paralogues of the α-subunit: HIF-1α, HIF-2α and HIF-3α. Either of

17

these can bind with one of the three paralogues of the β-subunit: HIF-1β, HIF-

2β and HIF-3β, also known as aryl hydrocarbon receptor nuclear translocator

(ARNT). The α- and β-subunits are differentially regulated. The β-subunits are

expressed constitutively whereas the α-subunits are regulated in an oxygen-

sensitive manner. For both academic and clinical significance, HIF-1α receives

the most investigative attention as it is 1) ubiquitously expressed, 2) the

preferred substrate for PHD2, the most abundantly and also ubiquitously

expressed paralogue of PHDs, and 3) widespread involvement in oncogenesis

(Semenza, 2003).

The amino terminal contains a basic helix-loop-helix (bHLH) domain that

allows interaction with the DNA. Adjacent to it is the Per-ARNT-Sim (PAS)

domain where heterodimerisation with the β-subunits take place. The oxygen-

dependent degradation domain (ODD) has two proline residues that when

hydroxylated, targets the α-subunits to proteasomal degradation. This allows the

transcription factor to be regulated in an oxygen-sensitive manner. Towards the

carboxy terminal are two transactivation domains – the N- and C-terminal

transactivation domain (N- and C-TAD) for direct interaction with

transcriptional coactivators CREB binding protein (CBP) and p300 family. On

the contrary, the β-subunits harbour bHLH and PAS domains, but not the ODD.

Thus, the regulation of β-subunits expression or stability is insensitive to O2

availability (Smirnova et al., 2012).

18

1.2.1.2 Regulation of HIF-1α expression

The regulation of HIF-1α expression is canonically described to be oxygen-

dependent (Fig 1.4). However, other metabolic intermediates have also been

found to regulate PHDs, and thus HIF-1α stabilisation.

Fig 1.4 HIF-α regulation in an oxygen-sensitive manner. At normoxia, PHDs

use O2 and α-KG as co-substrates to hydroxylate HIF-α within the ODD,

specifically at P402 and P564 of HIF-1α, or P405 and P531 of HIF-2α.

Hydroxylated HIF-α is recognised by the von Hippel Lindau (VHL) factor, an

E3 ubiquitin ligase. This interaction results in the ubiquitination of HIF-α and

clearance via 26S-dependent proteasomal degradation. Under hypoxic

conditions however, low O2 level inhibits PHDs. Non-hydroxylated HIF-α

escapes VHL-mediated degradation, and accumulates in the cytoplasm. This

allows the stabilised α-subunits to dimerise with the β-subunits, forming a

functional transcription factor and translocating to the nucleus (Semenza, 2003).

19

Besides suppression of proteasome clearance during hypoxia, enhanced de novo

synthesis in response to certain stimuli also promotes HIF-1α expression. This

is mediated by the phosphatidylinositol 3-phosphate kinase

(PI3K)/Akt/mTORC1 or mitogen-activated protein kinase (MAPK) pathways

which functions are associated with protein translation (Semenza, 2003).

Constitutive activation of PI3K/Akt signalling in tuberous sclerosis 2 (TSC2)-/-

cells leads to stimulation of mTORC1 and upregulation of HIF-1α synthesis

(Land & Tee, 2007). Likewise, in liver kinase B1 (LKB1) and AMP-activated

protein kinase (AMPK)-deficient mice, a concurrent increase in mTORC1

activities and HIF-1α protein level has been observed (Shackelford et al., 2009).

Growth factors may also act as upstream stimuli of these signalling pathways,

as in the case of hepatocyte growth factor or angiotensin II-induced HIF-1α

expression (Sanchez-Lopez et al., 2005; Tacchini, Dansi, Matteucci, &

Desiderio, 2001).

The influence of mitochondria-derived ROS on HIF-1α stabilisation has been

extensively studied since the proposal of ROS as a direct inhibitor of PHDs.

Superoxides produced at complex III of the ETC is described as an oxidiser of

cofactor ferrous ions (Fe2+). Its oxidation to ferric ions (Fe3+) inactivates PHDs,

resulting in HIF-1α stabilisation (Brunelle et al., 2005; Guzy et al., 2005).

However, counter-evidence has also been presented, in which PHDs are shown

to be non-responsive to redox modulations by hydrogen peroxide (H2O2)

(Masson et al., 2012). Inhibition of ETC is also thought to reduce O2

consumption at complex IV, and allows O2 to be redistributed to other cellular

compartments, especially where PHDs are localised for their reactivation

20

(Hagen, Taylor, Lam, & Moncada, 2003). Mitochondria-derived ROS may even

be dispensable in regulating HIF-1α expression. Rho 0 cells, essentially cells

that have been depleted of mitochondrial DNA, are still capable of stabilising

HIF-1α in response to hypoxia (Schroedl, McClintock, Budinger, & Chandel,

2002).

1.2.1.3 Prolyl hydroxylases (PHDs)

Four paralogues of PHDs have been identified: PHD1, PHD2, PHD3 and P4H-

TM in humans. PHD1 and PHD2 have 407 and 426 amino acids residues

respectively, whereas PHD3 have only 239, due to the absence of N-terminal

domains (harbours only a catalytic domain). The expression of PHD1 and PHD3

is the highest in the testes and cardiac myocytes respectively, whereas PHD2 is

ubiquitously expressed in the highest abundance across tissues. Their cellular

localisations vary as well; PHD1 is mainly nuclear, PHD2 is primarily

cytoplasmic while PHD3 occupies both nuclear and cytoplasmic compartments

(Smirnova et al., 2012). Strikingly, P4H-TM is anchored to the membrane of

the endoplasmic reticulum (ER) with its catalytic domain facing the lumen.

Despite this unusual orientation (catalytic domain faces away from cytoplasmic

and nuclear compartments, which are where HIF-1α usually localise to), it still

hydroxylates HIF-1α and other paralogues (Myllyharju, 2013).

The PHDs are members of 2-oxoglutarate dependent dioxygenases (2-OGDDs)

family of enzymes. They use O2 and α-KG as obligatory co-substrates, in

addition to Fe2+ and ascorbate as cofactors to carry out primarily but not limited

21

to, hydroxylation reactions. A generic enzymatic reaction involves

stoichiometric decarboxylation of α-KG (electron donor) and receipt of one

atom of oxygen, to generate succinate and incorporate a hydroxyl group on a

proline residue of the substrate protein (Smirnova et al., 2012).

Clearly, cellular events that affect endogenous levels of O2 or α-KG are

expected to affect PHDs as well. All paralogues of PHDs have Km values for O2

in the range of 230 – 250 μM, which is slightly above that of dissolved O2 in

aqueous solutions at equilibrium with partial pressure of ambient O2 (Table 1.3)

(Cha et al., 2010). This ensures that a minor decline in O2 levels is sufficient to

inhibit PHDs, provided that other co-substrates and cofactors are not limiting.

Table 1.3 Km of PHDs for co-substrates and co-factor.

Paralogues Km (μM)

O2 α-KG Ascorbate

PHD1 230 60 170

PHD2 250 60 180

PHD3 230 55 140

Competitive inhibition of PHDs by structural analogues of α-KG has been found

to stabilise HIF-1α at normoxia. These “oncometabolites” bind to the catalytic

site of PHDs and displace α-KG from it. Intriguingly, most of them are

intermediates of the TCA cycle (Table 1.4) (Koivunen et al., 2007). Synthetic

22

α-KG “mimic” such as dimethyloxalylglycine (DMOG) and dihydroxybenzoate

are also widely used to stabilise HIF-1α at normoxia. However, as the nature of

inhibition is fundamentally competitive and reversible, high levels of

endogenous α-KG render these inhibitors ineffective (Thirstrup et al., 2011). On

the other hand, deficiencies of α-KG are also expected to impair PHDs. This is

observed when glutamine, an important source of α-KG was withdrawn from

culture media, though HIF-1α was not stabilised (Duran et al., 2013).

Table 1.4 Inhibitory constant (Ki) of PHDs for oncometabolites.

Metabolite Structural formula Ki / μM

α-KG

Not applicable

Citrate

180

Succinate

350 - 460

23

Fumarate

50 - 80

Oxaloacetate

400 - 1000

The biological functions of PHDs are wide-ranged, owing to the various roles

carried out by their substrate proteins (Table 1.5).

Table 1.5 Functions of PHDs in proliferation, growth and survival.

Substrate of

PHDs

Physiological consequence Reference

PKM2 Enhanced binding with HIF-1α binding

to serve as its transcriptional

coactivator

(Luo et al., 2011)

Collagen Generates a stable network in the

extracellular matrix

(Myllyharju, 2003;

Xiong, Deng, Zhu,

Rychahou, & Xu,

2014)

24

IκB kinase b

(IKKb)

Promotes continual association of

inhibitory protein IκB with nuclear

factor kappa-light-chain-enhancer of

activated B cells (NF-κB) to inactivate

the pathway

(Cummins et al.,

2006)

HIF-1α Hyperactivation of PHDs suppress

oncogenic HIF-1α stabilisation, and

vice versa

(Erez et al., 2003;

Lee et al., 2016;

Pientka et al., 2012)

p53 Sustains survival by regulating

effectors of DNA damage and repair

mechanisms.

(Deschoemaeker et

al., 2015; Xie et al.,

2012)

Cyclin D Regulates cyclin D1 expression to

impact cell growth

(Stacey, 2003; Q.

Zhang et al., 2009)

1.2.1.4 Downstream targets of HIF-1α

Once stabilised and translocated into the nucleus, HIF-1α transcription factor

binds to the HRE (denoted as 5’-(A/G)CGTG-3’) of target genes. The HRE

consensus motif occurs at high frequency across the genome, and binding to

HIF transcription factors is highly selective. In actuality, less than 1% is bound

by HIF-1α, in addition to the HIF/HRE-binding being clustered at

approximately 0.5 kb upstream and 1 kb downstream of transcription start site

(Mole et al., 2009). Such binding pattern is synonymous to other transcription

factors, and suggests secondary means to influence their accessibility to the

DNA (Heintzman et al., 2007; Zeller et al., 2006).

25

The vast array of genes regulated by HIF-1α are primarily involved in oxygen

homeostasis and metabolic reprogramming. Luciferase reporter assay,

electrophoretic mobility shift assay and chromatin immunoprecipitation have

been applied to identify targets of HIF-1α (Table 1.6).

Table 1.6 Targets of HIF-1α.

Angiogenesis

Name Gene ID Citation

Nitric oxide synthase NOS (R. Hu, Dai, & Tan, 2002)

Vascular endothelial growth

factor

VEGF (Forsythe et al., 1996; C.

Lin, McGough, Aswad,

Block, & Terek, 2004)

Angiopoietin 2 ANGPT2 (M. P. Simon, Tournaire, &

Pouyssegur, 2008)

Ferrochelatase FECH (Y. L. Liu, Ang, Weigent,

Prchal, & Bloomer, 2004)

Erythropoietin EPO (Varma & Cohen, 1997)

Adrenomedullin ADM (Garayoa et al., 2000)

Endothelin 1 ET1 (Yamashita, Discher, Hu,

Bishopric, & Webster, 2001)

Metabolism

Name Gene ID Citation

26

Hexokinase II HK2 (Mathupala, Rempel, &

Pedersen, 2001; Riddle et al.,

2000)

Glucose transporter 1 GLUT1 (Hayashi et al., 2004)

Glucose transporter 3 GLUT3 (Yu et al., 2012)

Lactate dehydrogenase A LDHA (J. Hu et al., 2011; Semenza

et al., 1996)

Aldolase A ALDA (Semenza et al., 1996)

Enolase 1 ENO1 (Semenza et al., 1996)

Glyceraldehyde 3-phosphate

dehydrogenase

GAPDH (S. Lu, Gu, Hoestje, &

Epner, 2002)

Phosphofructo-3-kinase PFKBF3 (Minchenko et al., 2002)

Monocarboxylase

transporter 4

MCT4 (Ullah, Davies, & Halestrap,

2006)

Pyruvate dehydrogenase

kinase 3

PDK3 (C. W. Lu, Lin, Chen, Lai, &

Tsai, 2008)

Ceruloplasmin CP (Mukhopadhyay,

Mazumder, & Fox, 2000)

Transferrin receptor TFRC (Lok & Ponka, 1999)

Glutathione peroxidase 1 GPX1 (Bierl, Voetsch, Jin, Handy,

& Loscalzo, 2004)

Growth

Name Gene ID Citation

27

BCL2/adenovirus E1B 19

kDa protein-interacting

protein 3

BNIP3 (J. Hu et al., 2011; Kubasiak,

Hernandez, Bishopric, &

Webster, 2002)

1.2.2 Mechanistic target of rapamycin complex 1 (mTORC1) pathway

The discovery of target of rapamycin (TOR) is made possible by the successful

isolation of rapamycin, a macrocyclic lactone from Streptomyces hygroscopicus

(Vezina, Kudelski, & Sehgal, 1975). It was later on found to harbour

immunosuppressive and anticancer properties (C. P. Eng, Sehgal, & Vezina,

1984). About a decade later, genetic screening in yeast model identified two

target genes of rapamycin, TORC1 and TORC2, thus spearheading scientific

interest in the kinase and an entire signalling pathway (Chiu, Katz, & Berlin,

1994; Kunz et al., 1993).

The mammalian orthologue, mechanistic/mammalian target of rapamycin

(mTOR) kinase forms two distinct complexes, mTORC1 and mTORC2. The

latter responds primarily to growth factors to regulate cytoskeleton arrangement,

cell migration, as well as protein synthesis and maturation. These functions are

enabled by engaging key downstream effectors Akt, serum/glucocorticoid

regulated kinase 1 (SGK1) and protein kinase C (PKC). mTORC2 in general

functions distinctly from mTORC1, although they do crosstalk as evidenced by

mTORC2 activating Akt to subsequently enhance mTORC1 activities (Oh &

Jacinto, 2011). Conversely, mTORC1 responds to growth factors, nutrient and

energy signals to regulate cell growth, proliferation and survival (Laplante &

28

Sabatini, 2012). Its regulation in response to nitrogenous metabolites is the

focus of this thesis.

1.2.2.1 Structure of mTORC1

The mTORC1 is a heteromeric complex comprising of subunits detailed in

Table 1.7 (Loewith & Hall, 2011). There are currently two models to describe

how mTORC1 respond to upstream stimuli: 1) presence of amino acids is

conveyed to mTORC1 via Rag GTPases, and 2) growth factors via Rheb

GTPases.

Table 1.7 Subunits of the mTORC1 complex.

Subunit Functions

mTOR kinase A serine/threonine protein kinase of

the phosphatidylinositol kinase related

kinase (PIKK) family

Regulatory associated protein of

mTOR (Raptor)

Substrate recognition and aids

mTORC1 localisation

mTOR associated protein LST8

homolog (mLst8)

Positive regulator of mTORC1

activity and complex stability

Proline-rich Akt substrate of 40

kDa (PRAS40)

Negative regulator of mTORC1

DEP domain-containing mTOR-

interacting protein (DEPTOR)

Negative regulator of mTORC1

29

1.2.2.2 Regulation of mTORC1 by growth factors

Upon binding of growth factors to receptor tyrosine kinase (RTK), these

receptors dimerise, leading to autophosphorylation of tyrosine residues on the

cytoplasmic domain. Then, class I PI3K is activated, either by 1) direct binding

to phosphorylated RTK (Domchek, Auger, Chatterjee, Burke, & Shoelson,

1992), or 2) binding to scaffolding adaptors such as insulin receptor substrate

(IRS) (Myers et al., 1992). Activated PI3K converts phosphatidylinositol 4,5-

bisphosphate (PIP2) to phosphatidylinositol 3,4,5-triphosphate (PIP3) (Bunney

& Katan, 2010), a reaction that is antagonised by the phosphatase and tensin

homolog (PTEN) (Maehama & Dixon, 1998). PIP3 recruits 3-phosphoinositide-

dependent kinase 1 (PDK1) to the plasma membrane (Bunney & Katan, 2010;

Vanhaesebroeck & Alessi, 2000). Here, PDK1 phosphorylates Akt at T308

(Alessi et al., 1997). Activated Akt phosphorylates multiple downstream targets

including PRAS40 (Kovacina et al., 2003) and TSC2 (Inoki, Li, Zhu, Wu, &

Guan, 2002) of the mTORC1 signalling pathway. When PRAS40 is

phosphorylated, it binds to 14-3-3 and dissociates from mTORC1, resulting in

the complex activation (Kovacina et al., 2003). TSC2 is a GTPase-activating

protein (GAP) for Rheb (Dibble et al., 2012). Phosphorylation of TSC2 by Akt

inactivates it, thus allowing Rheb to become guanosine triphosphate (GTP)-

bound and activated. It now directly interacts with mTORC1 and activates it

(Fig 1.5) (Long, Lin, Ortiz-Vega, Yonezawa, & Avruch, 2005).

30

Fig 1.5 Growth factor-mediated mTORC1 activation. Binding of growth

factor (GF) to RTK triggers the PI3K/Akt signalling cascade to inactivate

PRAS40, a negative regulator of mTORC1, and TSC2 that harbours GAP

activities towards Rheb. Collectively, this results in mTORC1 activation.

1.2.2.3 Regulation of mTORC1 by amino acids

The ability of mTORC1 to sense amino acid is mediated by the Rag GTPases

proteins. Mammals express four different Rag proteins, namely RagA, RagB,

RagC and RagD. These proteins form heterodimers in either of these

configurations: RagA/C or RagA/D, or RagB/C or RagB/D (Hirose, Nakashima,

Sekiguchi, & Nishimoto, 1998; Schurmann, Brauers, Massmann, Becker, &

Joost, 1995; Sekiguchi, Hirose, Nakashima, Ii, & Nishimoto, 2001). When

RagA or RagB is bound to GTP, RagC or RagD is bound to guanosine

diphosphate (GDP), and vice versa. The “active” configuration is RagA/B.GTP

– RagC/D.GDP, whereas the “inactive” configuration is RagA/B.GDP –

RagC/D.GTP (Sancak et al., 2008).

31

In the presence of amino acids, RagA/B is loaded with GTP (“active”

configuration) through an unknown mechanism, catalysed by the guanine

exchange factor (GEF) Ragulator (Bar-Peled, Schweitzer, Zoncu, & Sabatini,

2012). Ragulator is a pentameric complex comprising of late

endosomal/lysosomal adaptor, mitogen-activated protein kinase and mTOR

activator (LAMTOR), p14, mitogen-activated protein kinase scaffold protein 1

(MP1), Ctorf59 and hepatitis B virus X-interacting protein (HBXIP) (Bar-Peled

et al., 2012; Nada et al., 2009; Sancak et al., 2010). Ragulator also assists in

targeting Rag GTPases to lysosomal membranes because Rag GTPases do not

express a membrane-targeting sequence (Sancak et al., 2010). The active Rag

heterodimer binds directly to Raptor, a subunit of mTORC1 (Sancak et al.,

2008). The Rag-Raptor/mTORC1-Ragulator complex is tethered to the

lysosome surface via subunit p18 of Ragulator (Sancak et al., 2008).

Fig 1.6 Amino acid-

mediated mTORC1

activation. In the

absence of amino acids,

Ragulator assumes an

inactive conformation.

RagA/B is GDP-loaded

and inactive, and

mTORC1 is scattered

across the cytoplasm.

Accumulation of amino

acids in the lysosomal

lumen activates

Ragulator to promote

GTP loading on

RagA/B. mTORC1 is

recruited to the

lysosomal membrane,

where it is activated by

Rheb.

32

Evidences suggest that amino acids do not all regulate mTORC1 in the same

way, or to the same magnitude. They regulate mTORC1 in a manner that is

distinct from that of growth factors, yet is prerequisite for optimal growth factor

signalling (Hara et al., 1998). How mTORC1 sense amino acids remain a

subject of contention, and evidence so far provide that amino acids (individually

or collectively) (Jewell et al., 2015; Krall, Xu, Graeber, Braas, & Christofk,

2016; Nicklin et al., 2009) as well as their derivative metabolites (Duran et al.,

2013; Duran et al., 2012) can regulate mTORC1. At present, three amino acids

have been identified as potent regulators of mTORC1, namely leucine,

glutamine, and arginine.

Leucine is an essential, branched chain amino acid (BCAA) which is transported

into the cells via system L amino acid transporters. It binds directly to GDH to

promote glutamate deamination to α-KG (Erecinska & Nelson, 1990), the latter

of which drives mTORC1 activation (Duran et al., 2012). In the absence of

leucine, accumulation of uncharged transfer RNA (tRNA) activates general

control nonderepressible 2 (GCN2) to subsequently downregulate mTORC1 in

a eukaryotic translation initiation factor (eIF) 2a-dependent manner (Averous et

al., 2016; Dong, Qiu, Garcia-Barrio, Anderson, & Hinnebusch, 2000). When

leucine is present, the enzyme that ligases leucine to its tRNA, leucyl-tRNA

synthetase (LeuRS) translocates to the lysosome. It next binds to and exerts its

GAP activity on RagD, contributing to the activation of mTORC1 (Bar-Peled et

al., 2013; Han et al., 2012).

33

Unlike leucine, glutamine activates mTORC1 in a separate mechanism that is

mediated by Arf1 GTPases (Jewell et al., 2015; Li et al., 2010). These proteins

are classically components of intracellular vesicle trafficking. Much remains to

be learned about how glutamine regulate GTP/GDP cycling on Arf1, and in turn

how these proteins interact and modulate the activity of mTORC1. Evidence

thus far indicates that constitutive GTP- or GDP-loading on Arf1 significantly

inhibits amino acid-dependent mTORC1 activity, at once highlighting the

importance of normal GTP/GDP cycling of these proteins (Jewell et al., 2015).

Glutamine also activates mTORC1 indirectly by regulating the cellular uptake

of extracellular leucine. It begins with the import of extracellular glutamine via

SLC1A5. Then, SLC7A5 and SLC3A2 in concert exchange accumulated

glutamine for extracellular BCAA such as leucine (Nicklin et al., 2009).

Glutaminolysis also facilitates mTORC1 activities, as sequential deamination

of glutamine yields α-KG, the proximal metabolite responsible for the complex

activation (Curi et al., 2005; Duran et al., 2012).

In comparison, NEAA do not normally regulate mTORC1 at such high

potencies. Chronic withdrawal of NEAA in mouse models is generally well-

tolerated (Maddocks et al., 2013), although under certain conditions can be

made essential or limiting. For example, p53-mediated metabolic

reprogramming confers cancer cells their dependence on serine metabolism

(Maddocks et al., 2013). Transport of small amino acids such as alanine, proline

and glycine by proton-assisted amino acid transporter 1 and 2 (PAT1 and PAT2)

(Boll, Foltz, Rubio-Aliaga, Kottra, & Daniel, 2002; Z. Chen et al., 2003) have

been correlated with mTORC1 activation as well. These “transceptors” are

34

abundantly expressed on the surface of late endosomes and lysosomes, and their

overexpression has been found to inhibit mTORC1 due to drainage of amino

acids from intra-lysosomal lumen (Sagne et al., 2001). Identifying transceptors

or enzymes that directly interact with NEAA would provide additional insight

into their significance in mTORC1 regulation.

1.2.2.4 Significance of mTORC1 signalling pathway

During amino acids limitation, mTORC1 is largely dispersed across the

cytoplasm. No longer in proximity with lysosome-localised Rheb, mTORC1 is

inactivated (Sancak et al., 2010; Sancak et al., 2008). Under these

circumstances, three signalling outcomes are typically observed. One,

dephosphorylation of downstream target eukaryotic translation initiation factor

4E-binding protein 1 (4E-BP1) and p70S6K, and subsequent suppression of

protein synthesis (Chauvin et al., 2014; Hara et al., 1998; X. Wang, Campbell,

Miller, & Proud, 1998). Inactivation of mTORC1 causes hypophosphorylation

of 4E-BP1, and thus locked in a complex with eIF4E and eIF4F (Jankowska-

Anyszka et al., 1998). Hypophosphorylation of p70S6K also inactivates

downstream ribosomal protein S6 and inhibition of its interaction with 40S

ribosomal subunit (Hara et al., 1998). Both events significantly inhibit protein

translation.

Two, Unc-51 like autophagy activating kinase 1 (ULK1) is activated to induce

autophagy, a catabolic process that breaks down superfluous endogenous

protein structures as a means of replenishing amino acid store (J. Kim, Kundu,

35

Viollet, & Guan, 2011). ULK1 activation leads to phosphorylation of Raptor, a

subunit of mTORC1. This post-translational modification prevents Raptor from

recognising substrates of mTORC1, thus abolishes activation of downstream

targets 4E-BP1 and p70S6K (Dunlop, Hunt, Acosta-Jaquez, Fingar, & Tee,

2011). In contrast, in the presence of amino acids, mTORC1 mediates ULK1

phosphorylation and inactivation to suppress autophagy (J. Kim et al., 2011).

Clearly, mTORC1 functions as a molecular regulator on which metabolic cues,

and cell proliferation and growth signalling converge. With respect to stem cell

physiology, mTORC1 can influence a cell’s commitment into self-renewal or

differentiation, depending on nutrient availability. Haematopoietic stem cells

reside in a relatively hypoxic and nutrient-depleted microenvironment in the

bone marrow, thus requiring their physiologies to be regulated accordingly to

the availability of these resources (Hirao & Hoshii, 2013; Suda, Takubo, &

Semenza, 2011). In PTEN-null mice, hyperactivation of mTOR resulted in

propagation and differentiation of marrow stem cells into haematopoietic cells

(Yilmaz et al., 2006; J. Zhang et al., 2006). Modulation of mTORC1 activities

is also relevant to oncogenesis. In hepatocellular carcinoma (HCC), mTOR

activation is commonly associated with aggressive tumour phenotype and

poorer prognosis (Calvisi et al., 2011; Sahin et al., 2004). Owing to pro-growth

and -survival properties that mTORC1 confers once activated, deregulation of

mTORC1 activities has emerged as a key promoter of oncogenesis (Guertin &

Sabatini, 2007). Naturally, abrogating mTORC1 signalling with analogues of

rapamycin (a naturally occurring inhibitor for mTORC1) has been used in the

clinic to treat cancers. The first “rapalog” to be approved by FDA in 2007,

36

temsirolimus is used to treat renal cell carcinoma, and subsequently, sirolimus

and everolimus to treat lymphangioleiomyomatosis or progressive

neuroendocrine tumours of pancreatic origin, respectively (L. C. Kim, Cook, &

Chen, 2017).

1.3 Metabolic error repair pathways

The word “homeostasis” originates from New Latin “homois” which means

similar, and “stasis” which means standing still. In biology, homeostasis is a

cellular property of maintaining parameters within a controlled, acceptable

range at equilibrium which is crucial for survival. Within the narrow context of

metabolism, concentrations of metabolites are kept in check by evolving

corrective mechanisms that are largely enzymatic in nature (Linster, Van

Schaftingen, & Hanson, 2013).

1.3.1 DL-2-hydroxyglutarate (DL-2HG)

DL-2-hydroxyglutarate is a metabolite endogenously found at low levels with

previously unspecified functions. Its generation is mediated by 1) malate

dehydrogenase, 2) DL-2-hydroxyglutarate dehydrogenase (Fig 1.7).

The primary enzymatic activity of MDH involves reversible conversion of

malate to oxaloacetate. It also reduces α-KG to L-2-hydroxyglutarate although

at a much lower efficiency (106-fold lower). This secondary reaction remains

physiologically significant because the pool size of α-KG is greater than that of

oxaloacetate. Up to several g/day of L-2-hydroxyglutarate is formed in this

37

manner (Rzem, Vincent, Van Schaftingen, & Veiga-da-Cunha, 2007). L-2-

hydroxyglutarate is also irreversibly oxidised to α-KG by L-2- hydroxyglutarate

dehydrogenase. In glioma and secondary glioblastomas, mutant isocitrate

dehydrogenase 1/2 gain a preference for converting α-KG to D-2-

hydroxyglutarate, leading to its overabundance and potentiation of cancer

progression (L. Dang et al., 2009; A. P. Lin et al., 2015; Zhao et al., 2009).

Fig 1.7 Biosynthesis of DL-2-

hydroxyglutarate. Interrelationship

between α-KG and DL-2-

hydroxyglutarate under normal

physiological or pathological

conditions. Abbreviations used are

DL-2-hydroxyglutarate (DL-2-HG),

isocitrate dehydrogenase (IDH), DL-

2-hydroxyglutarate dehydrogenase

(DL-2-HGDH), malate

dehydrogenase (MDH).

1.3.2 Lysine catabolism

Lysine is an essential amino acid which is degraded via the pipecolate or

saccharopine pathway. The latter is the preferred means in mammalian

extracerebral tissues, and it takes place in the mitochondria (Fig 1.8).

38

Fig 1.8 Lysine catabolism –

saccharopine arm. The first step is

catalysed by lysine-ketoglutarate

reductase, an irreversible conversion

of substrates lysine and α-KG to

saccharopine. A distal reaction

involving 2-aminoadipate

aminotransferase converts substrates

α-aminoadipate and α-KG to α-

ketoadipate. Essentially, through the

saccharopine arm, degradation of one

unit of lysine consumes two units of α-

KG. This is strategic for scavenging

excess mitochondrial α-KG to

maintain its pool size.

On the other hand, the pipecolate pathway takes place in the cytoplasm and

peroxisomes (Fig 1.9). In mammalian brain tissues, this arm is the predominant

pathway of catabolising lysine. Both saccharopine and pipecolate arms generate

α-ketoadipate as a common downstream metabolite, which is further catabolised

in the mitochondria to maximise energy yield (Hallen, Jamie, & Cooper, 2013).

39

Fig 1.9 Lysine catabolism – pipecolate arm. This pathway is distinct from the

saccharopine arm, localised in different cell compartments and uses different

sets of enzymes. Both arms eventually converge at α-ketoadipate.

1.4 Aims and objectives

The importance of hormone-like properties of metabolites in cancers, a

phenomenon termed “metabolite signalling” have begun to be appreciated

(Husted, Trauelsen, Rudenko, Hjorth, & Schwartz, 2017). Defining the

mechanistic interplay between metabolism and cell signalling would deepen

current understanding of pathologies to benefit therapeutic explorations.

40

In actively proliferating cells in which aerobic glycolysis frequently takes place

(Warburg et al., 1927), the end product pyruvate is generated in high volume.

The subsequent fate of pyruvate, specifically, whether pyruvate is shunted away

from OxPhos to lactate production, or assimilated into the TCA cycle has been

proposed to promote oncogenesis or senescence, respectively (Kaplon et al.,

2013; Olenchock & Vander Heiden, 2013). Crucial to the regulations of these

metabolic events is the oncogenic transcription factor HIF-1α, which functions

as a master regulator of glycolytic pathways (Semenza, 2003, 2007, 2013). It is

thus imperative to determine the mechanistic relationship between the

oncometabolite pyruvate with the oncogene HIF-1α, as explored in the first part

of the thesis. By taking advantage of gas chromatography-mass spectrometry

(GC-MS) platform for metabolomics analyses on whole cell lysates and cell

fractions, this study aims to:

1) Investigate the mechanism of HIF-1α degradation in response to

pyruvate at normoxia

2) Characterize metabolite profiles to explain modulations on HIF-1α

expression

3) Suggest metabolic interventions to reverse pyruvate-induced HIF-1α

expression

Perturbations to metabolic pathways in response to a high influx of pyruvate is

expected to impact other metabolism-associated signalling pathways as well. As

a hub of nutrient and growth factor signalling, the mTORC1 signalling pathway

is subsequently examined in the latter chapters of this thesis. Preliminary data

41

implied that pyruvate-dependent scavenging of ammonia was involved in the

downregulation of mTORC1. Therefore, the study was expanded to examine the

impact of nitrogenous metabolites on mTORC1. This question is particularly

relevant to cancers in which episodic hyperammonaemia is frequently observed

in the surrounding tumour milieu (Chance et al., 1989; Chance et al., 1988; C.

H. Eng & Abraham, 2010). This pathological increase in ammonia synthesis has

been attributed to altered cancers metabolism due to avid consumption of

glutamine (C. H. Eng & Abraham, 2010; Lacey & Wilmore, 1990), or a side-

effect from L-asparaginase therapy in leukemic patients (Heitink-Polle, Prinsen,

de Koning, van Hasselt, & Bierings, 2013; Walker, 2012). Despite the

abundance of associative evidence suggesting modulation of mTORC1 by

nitrogenous metabolites, the exact mechanism has yet to be defined. To this end,

Chapters 4 and 5 aim to:

1) Describe the mechanism of mTORC1 regulation in response to NH4+

2) Assess the contribution of asparagine and glutamine to mTORC1

signalling

3) Investigate alternative means of regulating mTORC1 in response to

asparagine and glutamine

42

CHAPTER 2: MATERIALS AND METHODS

2.1 Cell culture

HCC cell lines, HepG2 and HuH7 were used as the main cell models in this

thesis. These cells were maintained in complete growth media consisting of

Dulbecco’s Modified Eagle Medium (DMEM) (with 4.5 g/L of glucose, 25 mM

of HEPES, and 4 mM of L‐glutamine) supplemented with 10% fetal bovine

serum (FBS), 100 U/ml penicillin and 100 μg/ml streptomycin, and incubated

at 37°C at 5% CO2. The same growth media was used to maintain human

hepatoblastoma cells, HuH6, normal human hepatocytes, L02 and rat hepatoma

cells H4IIE.

Human hepatocytes THLE-2 and THLE-3 expressing phenotypic features of

normal adult liver epithelial cells were maintained in Bronchial Epithelial

Growth Medium (BEGM) supplemented with 70 ng/ml phosphoethanolamine

(Lonza Walkersville, MD, USA), 10% FBS and 100 U/ml penicillin and 100

µg/ml streptomycin. Prior to cell seeding, culture dishes were coated with 0.03

mg/ml bovine collagen type I dissolved in BEGM. Human myoblasts HSMM

were maintained in Skeletal Muscle Growth Media-2 (SkGM-2 Basal Medium),

supplemented with SkGM-2 SingleQuots Kit containing human epidermal

growth factor, fetuin, bovine serum albumin (BSA), dexamethasone, insulin and

gentamicin/amphotericin-B (Lonza Walkersville, MD, USA).

The liver cell lines used in this thesis are derived from organisms of different

genetic and pathological backgrounds (Table 2.1). It is crucial to interrogate

43

hypotheses in a varied array of cell models to ensure that 1) the phenotypes

observed are not atypical and unique to one cell line, and 2) the conclusions

reported are robust and reproducible across different cell contexts.

Table 2.1 Profiles of various liver cell models.

Cell line Species Pathology Age Race

HepG2 Human HCC Adolescent, 15

years old

Caucasian

HuH7 Human HCC Adult, 57 years

old

Japanese

HuH6 Human Hepatoblastoma Infant Japanese

L02 Human None Foetal Undisclosed

H4IIE Rat HCC NA NA

THLE-2 Human None Adult Undisclosed

THLE-3 Human None Adult Undisclosed

For treatment purposes, growth media were replaced with Earle’s Balanced Salt

Solution (EBSS) supplemented with 1X MEM Vitamin Solution (Life

Technologies, CA, USA), 25 mM of HEPES, 0.2% (v/v) of BSA, 0.22% (w/v)

of sodium bicarbonate (NaHCO3), 100 of U/ml penicillin and 100 mg/ml

streptomycin, 25 mM of glucose, 4 mM of L-glutamine and 1X MEM Essential

Amino Acids (Life Technologies, CA, USA). To ensure that the addition of

organic and amino acids into culture media for treatment purposes did not

significantly alter the media’s pH to affect metabolism and cell signalling, the

44

pH of EBSS reconstituted with metabolites of interest (at working

concentrations) were adjusted to physiological 7.0 to 7.2. Media were sterile

filtered and introduced to cells to initiate experiments.

2.2 Western Blot assay

Cells were homogenized on ice in radioimmunoprecipitation assay buffer

(RIPA) buffer supplemented with 5 mM each of β-glycerophosphate, sodium

fluoride, sodium orthovanadate, sodium pyrophosphate and 1X Protease

Inhibitor (Thermo Fisher Scientific). The resultant lysate was sonicated and

centrifuged at 13 000 xg for 10 min at 4°C to pellet down cell debris. Protein

concentration of the supernatant was determined by Pierce BCA protein assay

(Life Technologies, CA, USA). Neat lysate was typically diluted to 1 µg/ml

protein in sample buffer containing 31.25 mM of Tris-HCl, 12.5% (v/v)

glycerol, 1% (w/v) sodium dodecyl sulphate (SDS), 0.005% (v/v) of

bromophenol blue and 0.2 mM of dithiothreitol (DTT). Proteins were denatured

at 65°C for 15 min. A total of typically 30 µg of protein per sample was

separated on a standard 6% or 10%, or 6 to 12% gradient SDS-polyacrylamide

electrophoresis (PAGE) gel. Proteins were then transferred onto polyvinylidene

fluoride (PVDF) membranes and blocked with 5% (w/v) non-fat milk dissolved

in 1X Tris-Buffered Saline and 1% Tween-20 (TBST) for 1 h at room

temperature. Membranes were incubated overnight with relevant primary

antibodies at 4°C. To remove non-specific binding of primary antibodies,

membranes were washed with 1X TBST for 30 min before incubation with

horseradish peroxidase-conjugated secondary antibody for 1 h at room

temperature. After washing off non-specific binding of secondary antibodies

45

with 1X TBST for another 30 min, proteins were visualized by

chemiluminescence. Band intensity was quantified with ImageJ software

(version 1.50i).

To obtain total protein bands, phospho‐ or hydroxylated-protein blots were

stripped with a buffer containing 1.5% (w/v) of glycine pH-ed to 2.2, 1% (v/v)

of stock 10% SDS and 0.1% (v/v) of Tween-20 for 30 min at room temperature.

After thorough washing of membranes with 1X TBST thrice (10 min each) to

remove residual buffer and blocking with 5% (w/v) of non-fat milk, membranes

were re-probed with appropriate primary antibodies targeting total protein-of-

interest. Protein bands were visualised by chemiluminescence and their

intensity quantified as described earlier.

To ensure primary antibodies used are sufficiently characterised for accuracy in

reporting, they are rigorously validated via several methods by the

manufacturing companies. Target proteins are detected via Western Blot or

immunofluorescence 1) in multiple cell lines known to express them, 2) in cells

treated with kinase-specific activators and/or inhibitors, or protein lysates

treated with phosphatases to assess the phosphorylation status of target proteins,

3) in knockout or siRNA-treated cells, and 4) to determine correct subcellular

localisation of target proteins upon their induction of translocation (“CST

Antibody Validation Principle”, 2010; “Cell Signaling Technology”, 2015a – d,

2016a – g, 2017a – f; “Proteintech”, n.d.-a, -b). Details of such validation assays

for each antibody are as follows:

46

Table 2.2 Validation methods for primary antibodies

Target Catalogue

number

Validation method Citation

HIF-1α 610959 Stabilisation of HIF-1α in HeLa cells treated with cobalt chloride, as

determined by Western Blot.

(Proteintech, n.d.-a)

HIF-1α-OH

(P564)

3434 Loss of:

- stabilisation of HIF-1α-OH as determined by Western Blot, and

- nuclear localisation of HIF-1α-OH, as determined by immunofluorescence

after DMOG and MG132 treatment in HeLa cells.

(Cell Signalling

Technology, 2017a)

p-4E-BP1

(T37/46)

2855 Increased phosphorylation of 4E-BP1 after insulin stimulation in 293T cells,

determined by Western Blot.

Loss of phosphorylation of 4E-BP1 after treatment with:

(Cell Signalling

Technology, 2017b)

47

- rapamycin, LY294002 or U0126, determined by immunofluorescence

- phosphatase (in protein lysate), even in the presence of insulin, determined by

immunofluorescence

- shRNA knockout of 4E-BP1

- LY294002 in paraffin-embedded LNCaP cells, determined by

immunohistochemistry

- LY294002, U0126 and wortmannin in Jurkat cells, determined by flow

cytometry

Positive immunohistochemical staining of p-4E-BP1 in human colon

carcinoma, and loss of positive staining in the presence of p-4E-BP1 blocking

peptide.

48

p-Akt (S473) 9271 Increased phosphorylation of Akt after:

- platelet-derived growth factor stimulation in NIH/3T3 cells, determined by

Western Blot; loss of signal upon treatment with wortmannin, LY294002,

rapamycin or PD98059

- insulin stimulation in C2C12 cells, determined by immunofluorescence; loss

of signal upon treatment with LY294002

- platelet-derived growth factor stimulation in NIH/3T3 cells in a time-

dependent manner, determined by Western Blot

Positive detection of p-Akt in 293T cells transfected with HA-tagged Akt (wild

type) or HA-tagged K179A mutant Akt. Loss of detection of p-Akt when

transfected with HA-tagged K179A/S473A mutant Akt, because antibody does

not recognise Akt with an alanine substitution at serine 473.

(Cell Signalling

Technology, 2017c)

49

Loss of Akt phosphorylation in LNCaP cells treated with LY294002,

determined by flow cytometry.

p-AMPKα

(T172)

2535 Increased phosphorylation of AMPKα after:

- oligomycin stimulation in C2C12 cells, determined by Western Blot

- phenformin stimulation in paraffin-embedded NCI-H228 cell pellets,

determined by immunohistochemistry

(Cell Signalling

Technology, 2017d)

p-AS160 (T642) 8881 Increased phosphorylation of AS160 after insulin stimulation in serum-starved

HeLa cells, and loss of signal upon incubation of lysate with phosphatase.

(Cell Signalling

Technology, 2016a)

p-p70S6K (T389) 9234 Increased phosphorylation of p70S6K in the presence of serum in 293,

NIH/3T3 or PC12 cells, determined by Western Blot. Loss of signal in the

absence of serum.

(Cell Signalling

Technology, 2016b)

p-Raptor (S792) 2083 Increased phosphorylation of Raptor after: (Cell Signalling

Technology, 2015a)

50

- AICAR or oligomycin treatment in C2C12 or 293 cells respectively,

determined by Western Blot

- AICAR treatment in MEF cells, and loss of Raptor phosphorylation in

AMPKα1 and AMPKα2 knockout MEFs, determined by Western Blot

p-S6 (S235/236) 4858 Increased phosphorylation of S6 after:

- platelet-derived growth factor or FBS stimulation, determined by Western

Blot

- 20% serum stimulation, determined by immunofluorescence

Loss of S6 phosphorylation after:

- incubation of paraffin-embedded A549 xenografts sections with phosphatase,

determined by immunohistochemistry

(Cell Signalling

Technology, 2016c)

51

- rapamycin treatment in paraffin-embedded LNCaP cells, determined by

immunohistochemistry

- wortmannin, LY294002 or U0126 treatment in Jurkat cells, determined by

flow cytometry

- incubation with p-S6 blocking peptide in paraffin-embedded human breast

carcinoma, determined by immunohistochemistry

p-TSC2 (T1462) 3617 Increased phosphorylation of TSC2 after platelet-derived growth factor

stimulation, and absence of phosphorylation upon incubation of lysate with

phosphatase, determined by Western Blot.

(Cell Signalling

Technology, 2016d)

p-ULK1 (S757) 6888 Loss of ULK1 phosphorylation after treatment with Torin-1 or INK128 in

A172 cells, determined by Western Blot.

(Cell Signalling

Technology, 2015b)

52

Increased phosphorylation of ULK1 after human epidermal growth factor

incubation in A431 cells, determined by Western Blot.

REDD1 10638-1-AP Loss of REDD1 detection in siREDD1 PC-3 cells, determined by Western

Blot.

Detection of REDD1 in K-562 and MCF7 cell lysates.

(Proteintech, n.d.-b)

Total 4E-BP1 9644 Detection of 4E-BP1 in:

- cell lysates of MCF7, HepG2, HeLa, 293, PANC1, RD, A204 and SH-SY5Y,

determined by Western Blot

- paraffin-embedded human lung carcinoma and human hepatocellular

carcinoma, determined by immunohistochemistry

- HeLa cells, determined by immunofluorescence

(Cell Signalling

Technology, 2017e)

53

Loss of 4E-BP1-positive staining in:

- paraffin-embedded human breast carcinoma upon incubation with 4E-BP1

blocking peptide, determined by immunohistochemistry

- paraffin-embedded 4E-BP1 knockout mouse spleen tissue, determined by

immunohistochemistry

- 293 cells expressing shRNA targeting 4E-BP1/2, determined by

immunofluorescence

Total Akt 4691 Detection of:

- Akt1, Akt2 and Akt4 recombinant proteins, and in cell lysates of HeLa,

NIH/3T3, C6 and COS cells, determined by Western Blot

- pan Akt in paraffin-embedded human melanoma, determined by

immunohistochemistry

(Cell Signalling

Technology, 2016e)

54

Enhanced localisation of Akt towards plasma membrane upon insulin

stimulation in C2C12 cells, determined by immunofluorescence. Distribution

of Akt across the cytoplasm upon treatment with LY294002.

Loss of:

- Akt-positive staining in paraffin-embedded human breast carcinoma upon

incubation with Akt blocking peptide, determined by immunohistochemistry

- localisation of Akt towards plasma membrane after treatment with LY294002

in paraffin-embedded LNCaP cells, determined by immunohistochemistry

Total p70S6K 2708 Detection of p70S6K in:

- cell lysates from PC12, NIH/3T3 or SK-N-MC cells, determined by Western

Blot

(Cell Signalling

Technology, 2015c)

55

- paraffin-embedded human colon carcinoma and human lung carcinoma,

determined by immunohistochemistry

Loss of p70S6K-positive staining in paraffin-embedded human breast

carcinoma upon incubation with p70S6K blocking peptide, determined by

immunohistochemistry.

Total S6 2217 Positive staining for S6 in:

- paraffin-embedded human lung carcinoma, human breast carcinoma, human

colon carcinoma, human prostate carcinoma and human Non-Hodgkin’s

lymphoma, determined by immunohistochemistry

- HeLa cells labelled with S6-specific antibody compared to an isotype control,

determined by immunofluorescence

- mouse brain, determined by immunofluorescence

(Cell Signalling

Technology, 2015d)

56

- cell lysates from HeLa, NIH/3T3, PC12 and COS cells, determined by

Western Blot

Total TSC2 4308 Loss of TSC2 detection in TSC2-deficient MEF, determined by

immunofluorescence, Western Blot and flow cytometry.

Detection of TSC2 in:

- SH-SY5Y and MEF cells, determined by immunofluorescence

- cell lysates from SH-SY5Y and rat brain, determined by Western Blot

(Cell Signalling

Technology, 2016f)

Total ULK 8054 Detection of ULK1 from cell lysates of RD, ZR-75-1, RCK8, A172, C2C12

and Vero cells, determined by Western Blot.

Loss of ULK1 detection in ULK1-deficient MEF, determined by Western Blot.

(Cell Signalling

Technology, 2016g)

57

β-actin 8457 Detection of β-actin in:

- cell lysates of HeLa, COS-7, C6, NIH/3T3 or MCF7 cells, determined by

Western Blot

- HeLa cells, determined by immunofluorescence

- NIH/3T3 cells compared to isotype control, determined by flow cytometry

(Cell Signalling

Technology, 2017f)

58

2.3 Quantitative real time-PCR (qPCR)

RNA was extracted using the ReliaPrepTM RNA Cell Miniprep System

(Promega, WI, USA). Snap-frozen cell layers were scrapped on ice in

proprietary BL buffer containing 1% (v/v) of 1-thioglycerol to inactivate

endogenous RNAse. Lysates were pipetted multiple times through a P200 to

shear DNA, before being centrifuged through mini-columns that specifically

bind nucleic acids. After washing of debris, bound DNA were digested by direct

application of RNAse-free DNAse I to the membrane. The mini-columns were

washed again followed by elution of RNA in nuclease-free water. RNA purity

was determined spectrophotometrically, by which a value of A260/A280

between 1.7 to 2.1 was considered of acceptable purity.

Purified RNA was converted to cDNA using SuperScript® III First‐Strand

Synthesis System for RT‐PCR (Life Technologies, CA, USA) with oligo(dT)20.

Typically, 3 µg of RNA sample was added to a mix of oligo(dT)20 primers and

deoxynucleotides to be denatured at 65○C for 5 min and subsequently on ice for

at least 1 min. Next, cDNA synthesis mix comprising of proprietary 1X RT

buffer, 2 U/mL of RNaseOUT, 10 U/uL of SuperScript® III RT, 5 mM of

magnesium chloride (MgCl2) and 10 mM of dithiothreitol was added to initiate

reverse transcription at 50○C for 50 min. Reactions were then terminated at 85○C

for 5 min, and RNase H was added to each reaction to degrade RNA, incubated

at 37○C for 20 min. The resultant cDNA was stored at -20○C.

qPCR was performed using the GoTaq® qPCR Master Mix (Promega, WI,

USA) using the Applied Biosystems 7300 Real‐Time PCR System. A reaction

59

mix of 10 μl containing 1X GoTaq qPCR master mix, 0.4 µM of forward and

reverse primers each, and cDNA was loaded per well in qPCR plates, sealed and

quick-spun to collect reaction components and eliminate air bubbles. The

thermocycler setup was as follows:

Number of cycles Settings

Hot-start activation 1 95○C for 2 minutes

Denaturation 40 95○C for 3 seconds

Annealing/extension 40 60○C for 60 seconds

Dissociation 1 60 to 95○C

A standard curve of cycle threshold value (Ct) against log10 concentration

(expressed in ng DNA) was also generated using serial dilutions of pooled

cDNA. To ensure that the altered fold-changes of mRNA observed is

meaningful and not affected by sample-to-sample variations or irregularities in

sample loading, normalisation to an internal control or a reference gene is

required. Housekeeping genes such as β-actin, RPLP0 or GAPDH are

commonly used as a normaliser because of their constitutive expression at high

abundance across a wide range of tissues and cell types. Selection of which gene

to be used as loading controls should be judiciously performed because their

transcriptions have been found to vary depending on cell density and signalling

modulations (Baddela, Baufeld, Yenuganti, Vanselow, & Singh, 2014; Loseke,

Grage-Griebenow, Wagner, Gehlhar, & Bufe, 2003). Of relevance to this thesis,

GAPDH for example is considered unsuitable due to their functions in cellular

energetics (Baddela et al., 2014; Jose et al., 2011), in addition to being

60

transcriptional targets of HIF-1α (Fukuda et al., 2007; S. Lu et al., 2002). The

possibility for housekeeping genes to be transcriptionally modulated indicates

that normalisation with one housekeeping gene as insufficient. It is preferable

to select multiple reference genes to control for these variations in expression

levels, and to ensure accuracy in data reporting (Vandesompele et al., 2002). To

this end, the expression of each target gene was normalized to the geomean of

β‐actin (ACTB) and 60S acidic ribosomal protein P0 (RPLP0).

The primers used were:

Gene Primer

ACTB F: 5’-TGGATCAGCAAGCAGGAGTATG-3’

R: 5’-GCATTTGCGGTGGACGAT-3’

HIF1A F: 5’-TGCCACATCATCACCATATAGAGA-3’

R: 5’-GACTCCTTTTCCTGCTCTGTTTG-3’

GLUT1 F: 5’-TGCAACGGCTTAGACTTCGA-3’

R:5’-GAGGACACTGATGAGAGGTACGTGTA-3’

BNIP F: 5’-TGCTGGCCATCGGATTG-3’

R: 5’-CAAAAGGTGCTGGTGGAGGTT-3’

PDK1 F: 5’-CCCAGAGTTGCTCAGAATTGG-3’

R: 5’-GGACTTGCCAGCTGATGACA-3’

PFKFB3 F: 5’-GGCTCGCGCAGTTTTCTCT-3’

R: 5’-ACCCCCTACCCACATATTCTGA-3’

PFKFB4 F: 5’-GGAGTTCAATGTTGGCCAGT-3’

R: 5’-TCAGGATCCACACAGATGGA-3’

61

RPLP0 F: 5’-TTGGCTACCCAACTGTTGCA-3’

R: 5’-GGACTCGTTTGTACCCGTTGA-3’

2.4 Seahorse Extracellular Flux (XF) Analyser

Cells were seeded at an optimised density of 0.1 million / well / 250 µl of growth

media overnight to allow adherence to the bottom of the well. Before initiation

of oxygen consumption analyses, cells were equilibrated in fresh 1X EBSS (pH

to 7.0) without NaHCO3, HEPES and serum in a non-CO2 incubator for 1 h, as

presence of any pH buffering materials in the media could alter experimental

readouts.

The following chemicals were prepared fresh, pH was adjusted to 7.0 and

diluted in 1X EBSS:

Name Final concentration

Oligomycin 2 μM

FCCP 4 μM

Antimycin A 0.1 μM

Rotenone 0.1 μM

Pyruvate 10 mM

Malate 5 mM

After equilibration, metabolites (pyruvate or malate) were injected and basal

respiration was measured for 3 h at a 10-min interval. Media were mixed and

62

allowed to stabilize before collection of each data point. Then, cells were

injected with oligomycin to inhibit ATP synthase, and a corresponding decline

in oxygen consumption was observed. Without synthesis of new ATP, oxygen

was no longer required as a final recipient of electrons at complex IV. The

difference between basal oxygen consumption and this decline is the “coupling

efficiency”. Then, FCCP the uncoupler was injected, and oxygen consumption

was raised to the maximum. The difference between basal oxygen consumption

and this value is the “spare respiratory capacity”. Inhibition of complex I and

III with rotenone and antimycin A respectively abolished mitochondrial

respiration, which is associated with reduction of oxygen consumption. The

difference between post-oligomycin and post-rotenone/antimycin A represents

“non-mitochondrial respiration” (Fig 2.1).

Fig 2.1 Oxygen consumption profile. Abbreviations used are as follows:

oligomycin, O; FCCP, F; rotenone and antimycin A, R + A.

63

2.5 Flow cytometry

Flow cytometry was used to determine 1) intracellular ROS, 2) cell cycle, and

3) cell size. Staining protocols and preparations of cell suspension differ for

each purpose.

To measure ROS, maintenance of cell metabolism and speed of sample

processing is crucial, as this assay is performed on viable, metabolically active

cells. Thus, 1 h before termination of experiments, cells were stained with 5 μM

of 2’, 7’-dichlorofluororescin diacetate (DCFDA) at 37°C. Then, treatment

media were collected, and cell layers were treated with 0.25% trypsin-EDTA.

To prevent continuous digestion by trypsin, cells were reconstituted in their

respective media. The advantages of doing this are, 1) trypsin was inactivated

by dilution with a large volume of media containing 0.2% BSA, and 2) treatment

conditions were preserved as cells remain in contact with treatment media. As

a positive control, cells were treated with 1 mM of H2O2 for 15 min. Data

acquisition was carried out on a BD FACSCanto II flow cytometer, analysed

with BD FACSDivaTM software, and post-processed with the Flowing Software.

After gating out cell debris and doublets, cell population of interest was detected

using Ex 488 nm and Em 525 nm.

For analyses of cell cycle and cell size, cells were fixed in 75% ethanol for 15

min at 4°C. Cells were then rinsed once with ice-cold 1X PBS and stained with

FxCycle propidium iodide (PI)/RNAse at room temperature for 45 min in the

dark, after which they were detected on a flow cytometer using Ex 488 nm and

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Em 525 nm. Cell cycle was expressed as a percentage of cells in G0/1 : S : G2/M

phases. Cell size was analysed only on quiescent G0/1-gated cells because cell

size may increase upon DNA duplication, rendering analysis inaccurate.

2.6 Metabolomics analysis

Cells were incubated with 1X EBSS treatment media prepared as described in

section 2.1. At the end of each experiment, cell layers were rinsed thoroughly

with ice cold 1X PBS to remove metabolites found in residual treatment media.

Cells were snapfrozen to immediately terminate experiments and preserve

endogenous metabolites. For untargeted metabolomics, starting material of 6.5

million HepG2 cells per sample were scraped in ice-cold extraction solution

consisting of acetonitrile (LC-MS grade, Sigma Aldrich), isopropanol (GC-MS

grade, Sigma Aldrich) and dH2O in the proportion 3 : 3 : 2. Then, cell lysates

were collected in polypropylene microcentrifuge tubes and delivered on dry ice

to West Coast Metabolomics Centre (UC Davis, CA, USA). Fold change of each

metabolite was obtained by:

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For targeted metabolomics, starting material of 1 million HepG2 cells per

sample were scrapped in ice-cold extraction solution consisting of 0.6% formic

acid (Sigma Aldrich) in dH2O. An aliquot was removed for protein

concentration measurement using Pierce BCA protein assay. Cell lysates were

then topped up to a final volume with acetonitrile, and delivered on dry ice to

Duke-NUS Metabolomics Facility (Duke-NUS, Singapore). Normalised fold

change of each metabolite was obtained by:

2.7 Mitochondrial membrane potential (MMP) measurement

MMP is determined as the fluorescence intensity ratio of red aggregate to green

monomer in cells stained with the lipophilic dye, JC-1. When the mitochondria

are at a “basal”, non-depolarized state, JC-1 is detected at the Em of about 529

nm as green monomers. Upon membrane depolarization, JC-1 migrates across

the membrane to accumulate in the matrix, and is detected at the Em of about

590 nm as red aggregates.

Cells were stained with 2.5 μM of JC-1 at 37°C for 1 h before termination of

experiment. Treatment media were collected, and cell layers were detached by

trypsin treatment. To prevent continuous digestion by trypsin and to preserve

treatment conditions, cells were reconstituted in their respective media. This is

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important because MMP is dynamically regulated and reversible to basal level

once metabolic perturbations are removed. As a negative control, cells were

treated with 10 μM of the uncoupler FCCP for 2 h at 37°C. Uncouplers abolish

potential difference across the mitochondria membranes, thus these cells would

display a low fluorescence intensity ratio of red aggregate to green monomer.

Detection of aggregate and monomer was determined spectrophotometrically,

at Ex 475 nm and Em 590 nm for red aggregate, and Em 530 nm for green

monomer.

2.8 Cell fractionation for profiling of compartmentalized metabolites

A practical protocol for detection of metabolites compartmentalized in different

organelles should achieve the following: 1) preservation of organelle integrity

for clean separation of metabolites to avoid cross-contamination, 2) quickly

performed as some metabolites are labile, and 3) complete inhibition of

endogenous metabolic activities post-experiment (during fractionation). This

necessitated a series of assays to be established and optimised as a “system”.

Cells were rinsed with ice-cold 1X PBS rapidly and thoroughly to remove

residual treatment media. Because cells were processed on ice from this point

onward, detachment with trypsin was unfeasible, and EDTA was used instead.

The concentration of EDTA had to be optimized as EDTA is an effective

calcium ion (Ca2+) chelator, which has direct impact on mitochondrial activities

(Bernardi, Angrilli, & Azzone, 1990; Voccoli, Tonazzini, Signore, Caleo, &

Cecchini, 2014). An optimum concentration of 3 mM of EDTA was chosen as

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cells were detached on ice quickly (in 5 - 7 min, comparable to using trypsin)

without perturbations to MMP.

The resultant cell suspension was collected and centrifuged at 130 xg for 5 min

at 4°C to pellet down the cells. After rinsing of pellet with ice-cold 1X PBS to

remove residual EDTA, cell pellet was reconstituted in ice-cold Medium A

containing 250 mM of sucrose, 20 mM of HEPES, 10 mM of MgCl2 and 12.5

mM of KH2PO4, pH-ed to 7.1. This preserves mitochondria’s integrity during

selective permeabilization of plasma membrane by saponin, a plant glycoside

that interacts with membrane cholesterol to cause perforations, thus is relatively

inert to cholesterol-poor mitochondrial and nuclear membranes (Jamur &

Oliver, 2010). To optimize concentration of saponin, cells were incubated with

a range of concentration at 4°C for 15 min with constant agitation. The extent

of plasma membrane perturbation was determined by Trypan blue exclusion, as

permeabilized cells would appear stained under the microscope. The

concentration of 80 μg/ml of saponin was selected as more than 80% of cell

population was successfully perforated without affecting MMP.

MMP readout was used to approximate mitochondria integrity as it 1) alludes

to preservation of electrochemical gradient across mitochondrial membranes, 2)

can be performed economically, and 3) quickly on a spectrophotometer. Cell

fractions were then stored at -80°C for further analysis.

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2.9 Ammonia quantification

Intracellular ammonia/NH4+ concentration was determined by using Ammonia

Colourimetric Assay Kit (BioVision, CA, USA). To terminate experiment,

treatment media were removed, and cell layers were rinsed with 1X PBS before

snapfreezing. A starting material of 0.8 million HepG2 per sample was

homogenised in proprietary Assay Buffer on ice, then centrifuged at 13 000 xg

for 10 min to remove cell debris. A reaction mix containing Assay Buffer,

OxiRed Probe, Enzyme Mix, Developer and Converting Enzyme was added to

cell lysate and incubated at 37°C for 1 h. A standard curve was generated using

serial dilutions of kit-provided NH4Cl. To control for background, cell lysate

was incubated with all components of reaction mix except converting enzyme.

OD was read at 570 nm on a spectrophotometer, and the concentration of

ammonia/NH4+ was normalised to protein concentration, as determined by

Pierce BCA protein assay.

2.10 Statistical analysis

To assess differences in measured differences 1) between two independent

groups, Student’s T-test was performed, whereby statistical significance was

accepted at p < 0.05, or 2) among groups of three or more, one-way ANOVA

was performed, followed by Tukey’s pairwise comparison whereby statistical

significance was accepted at p < 0.05.

In the case of targeted and untargeted metabolomics analyses, data were

further controlled for false discovery rate (FDR) by using the Benjamini-

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Hochberg procedure. FDR was set between a conservative 0.05 to 0.1 (as

stated in the respective figure captions).

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CHAPTER 3: Pyruvate-mediated HIF-1α stabilisation at normoxia is

dependent on α-KG and the implication of lysine catabolism as a

metabolic repair mechanism

3.1 Introduction

In 2011, Hanahan and Weinberg revised their proposed hallmarks of cancer to

include dysregulation of cellular energetics (Hanahan & Weinberg, 2011),

which has long been described by Warburg in the 1920s, in which he observed

a preference for actively proliferating cells to ferment glucose into lactate even

in the presence of oxygen. The oncogenic potential of glycolytic intermediates,

particularly that of pyruvate have since been questioned.

Pyruvate has been proposed to be the pivoting point of cell fate determination,

specifically whether it is pro-oncogenic or -senescent. Aerobic respiration

drives its mitochondrial decarboxylation to acetyl-CoA, an irreversible step

catalysed by PDH, which then enters the TCA cycle. Diverting pyruvate into

the oxidative arm via PDH has been shown to induce oncogene-induced

senescence against a background of BRAFV600E mutation in melanoma cells

(Kaplon et al., 2013; Olenchock & Vander Heiden, 2013) as a safeguard against

neoplastic transformation. Conversely, during hypoxia or in cancers, pyruvate

is shunted away from the mitochondria for preferential conversion to lactate in

the cytoplasm – the Warburg effect. This metabolic alteration has been shown

to be indispensable in sustaining accelerated proliferation rate (Duckwall et al.,

2013; Kato et al., 2013; J. Lu et al., 2015).

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Another phenomenon prevalent in cancers is HIF-1α stabilisation (Semenza,

2003). As a master regulator of glycolysis, HIF-1α has been implicated to

propagate the Warburg effect and metabolic reprogramming that favours cell

transformation (H. Lu, Forbes, & Verma, 2002; Semenza, 2007, 2013). Multiple

groups have observed pyruvate stabilising HIF-1α at normoxia, and

dysregulated TCA cycle is the underlying contributor across these studies (S. Y.

Kim, Choi, Park, & Jeong, 2010; Koivunen et al., 2007). Understanding the

mechanisms is important because only 50 to 60% of tumours that overexpress

HIF-1α exhibit hypoxic regions (Vaupel & Mayer, 2007), indicating that

oxygen is not the sole regulator of HIF-1α expression.

3.2 Results

3.2.1 Pyruvate-induced HIF-1α stabilisation was restricted to cancer cell

lines

Multiple groups have reported HIF-1α stabilisation at normoxia upon

introduction of exogenous pyruvate (Dalgard, Lu, Mohyeldin, & Verma, 2004;

S. Y. Kim et al., 2010; H. Lu et al., 2005; Ren, Liu, Song, Ma, & Zhai, 2011).

Thus, I first sought to determine if this phenotype was reproducible across

several human liver cancer (predominantly HCC) cell lines because of the

oncogene’s significance in this disease model. HIF-1α overexpression has been

linked to poor prognosis in HCC patients. A meta-analysis across 953 HCC

patients indicated that although HCC do not typically express HIF-1α,

acquisition of HIF-1α overexpression promoted tumour’s aggressiveness and

metastasis (Zheng, Chen, Yin, & Zhang, 2013). In another study across 53 HCC

patients whose livers were resected for treatment purposes, a significantly worse

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5-year disease-free survival rate was reported when HIF-1α expression was high

in the adjacent non-malignant liver tissue, compared to patients with low HIF-

1α expression (F. Simon et al., 2010).

Two different forms of pyruvate were used: 1) sodium pyruvate (NaPyr) which

supplies the conjugate base pyruvate, and 2) membrane permeable methyl-

pyruvate (MePyr). At normoxia, NaPyr and MePyr increased HIF-1α protein

level in a dose- and time-dependent manner in human hepatocarcinoma HepG2

and HuH7, in addition to human hepatoblastoma HuH6 (Fig 3.1A and B), but

not in human hepatocytes, THLE-2 and THLE-3 (Fig 3.1B). The tendency for

tumour cells to stabilise HIF-1α whereas their normal counterparts do not have

also been observed in non-liver samples. Immunohistochemistry staining for

HIF-1α and -2α in 57 patient-derived tumour samples (11 tissue types) reported

a significant sample were tested positive for HIFs’ nuclear localisation. In

contrast, samples identified as normal (24 tissue types) did not express HIFs.

These “normal” tissues were sourced from healthy individuals, or from samples

located adjacent to resected tumours (Talks et al., 2000). Pyruvate-induced HIF-

1α stabilisation was also robustly regulated. Withdrawal of NaPyr from culture

media drastically returned HIF-1α to basal level within 15 min (Fig 3.1C).

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Fig 3.1 The effect of pyruvate on HIF-1α expression. Liver cancer cell lines

A) HepG2, B) HuH7, and HuH6, or normal hepatocytes THLE-2 and THLE-3

were treated with increasing concentrations of NaPyr or MePyr for the indicated

durations, maintained at normoxia (21% O2 level). Treatment media used in this

study were “nutritionally complete” EBSS, containing glutamine, essential

amino acids, glucose and vitamins. C) HepG2 cells were incubated with 10 mM

of NaPyr for 6 h to accumulate HIF-1α. Then, cells were swapped to fresh media

without NaPyr (washout) for the indicated duration.

3.2.2 Pyruvate increased HIF-1α protein level by downregulating its

rate of degradation

The increase in HIF-1α protein level could be due to either 1) an increase in de

novo synthesis, or 2) a decrease in degradation rate. To distinguish which of

these was predominant, the half-life of HIF-1α was determined by introducing

cycloheximide, a global protein translation inhibitor at specific time-points.

Thus, HIF-1α detected on immunoblot thereafter represented peptides regulated

primarily by the degradation pathway. In the presence of NaPyr, the half-life of

HIF-1α was prolonged, suggesting compromised protein clearance (Fig 3.2A

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and B). The mRNA fold change of HIF-1α between NaPyr-treated and control

cells was comparable, confirming that HIF-1α regulation did not occur at the

transcriptional level. Significant increase in the mRNA fold change of

downstream targets GLUT1, PFKFB3, BNIP and PFKFB4 indicated functional

stabilisation of the transcription factor (Fig 3.2C).

One of the best-studied components of HIF-1α degradation machinery is the

PHDs. Their hydroxylation activities can be approximated from the ratio of

hydroxy-HIF-1α (P564) to total HIF-1α. However, detection of hydroxy-HIF-

1α on immunoblot was challenging as it was rapidly ubiquitinated and degraded

via VHL-mediated proteasomal degradation. To circumvent this, proteasome

inhibitor MG132 was added to accumulate hydroxy-HIF-1α. The ratio of

hydroxy-HIF-1α to total HIF-1α declined as concentrations of NaPyr increased

(Fig 3.2D and E), indicating reduced hydroxylation activities of the PHDs. To

summarise, pyruvate promoted HIF-1α accumulation by downregulating the

hydroxylation activities of PHDs and subsequent clearance of HIF-1α.

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Fig 3.2 The impact of pyruvate on PHDs hydroxylation activities. A) Cells

were first treated with 10 mM of NaPyr for 6 h to stabilise HIF-1α. For the

indicated durations, cells were swapped for fresh media containing 10 μM of

C)

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cycloheximide (Chx), in the absence or presence of NaPyr. B) Densitometry

analysis of (A), whereby the gradient of each curve generated is a function of

HIF-1α degradation rate. Data presented as mean ± SD, n = 2. C) Cells were

treated with 0 or 10 mM of NaPyr for 6 h. Data presented as mean ± SD, n = 6.

Statistical significance was determined by Student’s t-test, and accepted at p <

0.05, whereby *p < 0.05, #p < 0.01. D) Cells were pre-treated with 25 μM of

MG132 for 1 h, before incubation with increasing concentrations of NaPyr for

the next 6 h. The ratio of hydroxy HIF-1α to total HIF-1α was depicted as bar

graph, expressed as mean ± SD, n = 4. Statistical significance was determined

by one-way ANOVA followed by Tukey’s pairwise comparison, and accepted

at *p < 0.05. E) Representative Western Blot to assist derivation of the ratio of

hydroxy-HIF-1α to total HIF-1α.

3.2.3 Exhaustion of intracellular oxygen or raised ROS level did not

contribute to HIF-1α stabilisation

As members of the 2-oxoglutarate dependent dioxygenase family, PHDs utilise

O2 as obligatory substrates to carry out their hydroxylation functions. It was

possible that pyruvate promoted respiration to the extent of creating a hypoxic

microenvironment within the cell to inactivate PHDs. Ideally, intracellular

oxygen concentration should be measured directly but in this experiment, it was

extrapolated from oxygen consumption rate (OCR), measured by the Seahorse

XF Analyser. Avid consumption of O2 was presumed to exhaust intracellular

oxygen level. NaPyr-treated cells recorded a modest 10% increase in OCR,

compared to control (Fig 3.3A). Next, 5 mM of malate, a substrate of complex

I, was added to raise mitochondrial oxygen consumption (Ferguson & Williams,

1966; Protti et al., 2007). Theoretically, by promoting further consumption of

O2, endogenous O2 store would be depleted more, further enhancing HIF-1α

accumulation. Intriguingly, malate reduced HIF-1α to near basal level despite a

20% increase in OCR in NaPyr-treated cells (Fig 3.3A and B). These results

provided evidence that 1) promotion of oxygen consumption by pyruvate was

insufficient to stabilise HIF-1α, because 2) further doubling of OCR when

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supplemented with malate did not promote HIF-1α stabilisation; instead, HIF-

1α was reduced to basal level.

ROS are natural by-products of the mitochondrial electron transport chain

(ETC). Considering prior evidences of ROS contributing to HIF-1α stabilisation

at normoxia (Chandel et al., 2000; Moon et al., 2010; Sullivan et al., 2013) and

OCR was raised in response to pyruvate (Fig 3.3A), I sought to assess the

correlations, if any, between ROS and HIF-1α accumulation. Intracellular ROS

was evaluated by flow cytometry analysis of DCFDA-stained cells. The relative

fluorescence unit (RFU) between control and NaPyr-treated samples was

comparable (Fig 3.3C), indicating that pyruvate did not promote significant

ROS production. Supplementation of NaPyr-treated cells with a potent

antioxidant N-acetylcysteine (NAC) did not affect HIF-1α (Fig 3.3D).

Introduction of H2O2 for up to 6 h also did not alter HIF-1α protein content (Fig

3.3E). ROS also takes the form of diverse peroxides of proteins, lipids and

nucleic acids. Non-oxygen-containing free radicals such as reactive carbonyl

species and reactive nitrogen species demonstrate oxidative capacities as well

(Lushchak, 2015). With respect to pyruvate-treated cells however, promotion of

ETC activities were found to decrease its redox potential to incur superoxide

anions production (Murphy, 2009; Starkov & Fiskum, 2003). These are further

processed into H2O2, hydroxyl radical and hydroxyl anion; ROS species that are

detectable by DCFDA and scavenged by NAC (Aruoma, Halliwell, Hoey, &

Butler, 1989; Kristiansen, Jensen, Moller, & Schulz, 2009). Collectively, these

data indicate that ROS did not mediate pyruvate-induced HIF-1α stabilisation.

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Although oxygen exhaustion and ROS were excluded as regulators of pyruvate-

induced HIF-1α stabilisation, it was still unknown if ETC itself was implicated.

To this end, each of the complexes was selectively inhibited as follows:

Table 3.1 Working concentrations of ETC complex inhibitors.

Target Compound Concentration (μM)

Complex 1 Rotenone 0.1

Complex III Antimycin A 0.5

Complex IV Sodium azide 2000

ATP synthase Oligomycin 10

Uncoupling of ETC FCCP 10

Inhibition of complex I, III, IV and ATP synthase at these concentrations

abolished mitochondrial respiration (Fig 3.3A). As expected, cells treated with

NaPyr in the presence of any one of the inhibitors could not accumulate HIF-1α

(Fig 3.3F). Due to lack of O2 consumption, O2 was presumed to be redistributed,

resulting in PHDs-mediated HIF-1α clearance. If this were true, FCCP was

hypothesised to stabilise HIF-1α as respiration uncoupling maximised oxygen

consumption (Fig 3.3A). However, FCCP also abrogated HIF-1α accumulation

(Fig 3.3F). These results indicated that intact, functional mitochondria were

necessary for HIF-1α stabilisation.

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80

Fig 3.3 The effect of pyruvate on ROS generation or consumption of

intracellular O2 levels. A) After equilibration in a non-CO2 chamber for 12

min, cells were injected with 10 mM of NaPyr or 5 mM of malate as indicated,

and OCR was recorded for the next 3 h at a 10-min interval. Inhibitors targeting

specific components of the ETC were added sequentially to obtain the complete

respiratory profile. Data was expressed as mean ± SD, n = 4. B) Cells were

treated with 0 or 10 mM of NaPyr in the presence or absence of 5 mM of malate

for 6 h. C) Cells were treated with increasing concentrations of NaPyr for 6 h,

stained with DCFDA and immediately analysed by flow cytometry to quantify

intracellular ROS. Positive control cells were treated with 1 mM of H2O2 for 15

min before analysis. D) Cells treated with 0 or 10 mM of NaPyr were

supplemented with increasing concentrations of NAC for 6 h. E) Cells were

treated with 50 μM of H2O2 across a range of durations. F) Cells were treated

with 0 or 10 mM of NaPyr in the absence or presence of the indicated ETC

inhibitors for 6 h.

3.2.4 Organic acids and amino acids profile modulations in response to

pyruvate

To understand the metabolic impacts of NaPyr treatment in this system, HepG2

cells were incubated with 0 or 20 mM of NaPyr or MePyr for 6 h and subjected

to untargeted metabolomics on a GC-MS platform. The metabolic profiles

obtained from NaPyr and MePyr-treated cells were comparable, thus 1)

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excluding possibilities that the forms in which pyruvate was supplied (sodium

salt or ester) could affect HIF-1α stabilisation, and 2) confirming that

assimilation of pyruvate itself into cells’ metabolism was important. A

significant increase in TCA cycle intermediates and lysine catabolism was

observed, while amino acids were generally depressed (Fig 3.4A and B).

Several inferences could be made. First, net increase in majority of the TCA

cycle intermediates suggested enhanced anaplerosis. Entry via PDH would not

yield any net carbon, as the two carbon that enter via acetyl-CoA would be

released as CO2 after two cycles. Thus, a net influx of carbon into the TCA cycle

appeared to be a prerequisite for HIF-1α stabilisation at normoxia. Inhibition of

pyruvate carboxylase with phenylacetate impaired HIF-1α stabilisation in a

dose-dependent manner (Fig 3.4C) (Zeczycki, Maurice, & Attwood, 2010),

though TCA cycle intermediates should be re-profiled in this system to confirm

inhibition of anaplerosis. Second, accumulation of citrate, succinate and 2-

hydroxyglutarate, structural analogues of α-KG might competitively inhibit

PHDs (Koivunen et al., 2007). However, this was unlikely as the abundance of

α-KG itself was the most robustly raised (by 7.7- and 9.0-fold, in response to

the treatment of NaPyr and MePyr, respectively). The Km of PHDs for α-KG is

30 - 60 μM, while the Ki for citrate and succinate are 180 and 150 – 460 μM

respectively (Koivunen et al., 2007). Taking into consideration the

overabundance of α-KG and PHDs preference for α-KG as substrate, it was

unlikely that α-KG was “outcompeted” by structural analogues to result in

PHDs inactivation. To summarise, pyruvate perturbed TCA cycle homeostasis

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to impair PHDs activities, resulting in subsequent HIF-1α stabilisation at

normoxia.

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Fig 3.4 The metabolic impact of pyruvate treatment in HepG2 cells.

Untargeted metabolomics analysis of HepG2 cells treated with 0 or 20 mM of

NaPyr or MePyr for 6 h. Data presented as A) bar graph, expressed as mean ±

SD, or B) heatmap, expressed as fold change, n = 4. Differences in measured

variables were determined by one-way ANOVA. False discovery rate (FDR)

was controlled with the Benjamini-Hochberg procedure, whereby FDR was set

at a conservative 0.05. Then, Tukey’s pairwise comparison was performed, and

statistical significance was accepted at p < 0.05, whereby *p < 0.05, #p < 0.01.

C) Cells were treated with 10 mM of NaPyr in the presence of increasing

concentrations of phenylacetate (PAA) for 6 h.

3.2.5 Paradoxical implications of α-KG accumulation in mediating HIF-

1α stabilisation

The marked increase in α-KG abundance in response to pyruvate warranted

further studies. To this end, I raised endogenous α-KG level using two

strategies: 1) inhibiting citrate carrier with 1,2,3-benzenetricarboxylic acid to

impede efflux of mitochondrial citrate into the cytoplasm to presumably favour

downstream α-KG production in the TCA cycle (J. W. Joseph et al., 2006), or

2) inhibiting α-KG dehydrogenase (α-KGDH) with DL-3-methyl-2-oxovaleric

acid sodium salt (KMV) (Oldham, Clish, Yang, & Loscalzo, 2015). Direct

addition of α-KG titred to a concentration that does not uncouple mitochondria

respiration should be considered as well.

Inhibition of citrate carrier further promoted HIF-1α stabilisation in NaPyr-

treated cells in a dose-dependent manner (Fig 3.5A). Inhibition of α-KGDH also

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stabilised HIF-1α in a dose-dependent manner (Fig 3.5B), implying that

accumulation of mitochondrial α-KG was likely to be proximal to HIF-1α

stabilisation at normoxia.

I next questioned how α-KG metabolism might be correlated to HIF-1α

stabilisation. Recently, α-KG and 2-hydroxyglutarate have been shown to be

potent inhibitors of ATP synthase (Chin et al., 2014; Fu et al., 2015). However,

the notion of pyruvate-derived α-KG inhibiting ATP synthase to incur HIF-1α

stabilisation is doubtful, because it implies that a bona fide respiratory substrate

could inhibit ATP synthesis. The increase in O2 consumption in NaPyr-treated

cells was coupled to ATP synthesis (Fig 3.3A), indicating that the functions of

ATP synthase was preserved. Inhibition of ATP synthase with oligomycin also

could not stabilise HIF-1α at normoxia (Fig 3.5C). Taken together, it was

unlikely that compromised ATP synthase mediated HIF-1α stabilisation at

normoxia.

PHDs are another potential target of α-KG. In a counterintuitive experimental

design, cells were treated with 1) NaPyr, 2) dimethyloxalylglycine (DMOG), or

3) deferoxamine (DFO) to stabilise HIF-1α through different mechanisms.

DMOG is a structural analogue of α-KG, and competes with α-KG to bind to

PHDs. DFO chelates co-factor Fe2+ to inactivate PHDs (Smirnova et al., 2012).

As expected, acute introduction of α-KG reversed accumulation of HIF-1α in

DMOG-treated cells, but not in DFO-treated cells. In the former case,

exogenous α-KG displaced DMOG from PHDs to reactivate the enzymes. On

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the contrary, absence of Fe2+ in response to DFO would maintain PHDs’

inactivation even in the excess of α-KG. More importantly, exogenous α-KG

also abrogated NaPyr-induced HIF-1α stabilisation (Fig 3.5D). Thus, despite

elevation of α-KG abundance in NaPyr-treated cells, α-KG somehow still

seemed to be limiting. This likely inhibited PHDs and promoted HIF-1α

accumulation in an oxygen-independent manner.

To summarise, despite the rise in α-KG abundance in NaPyr-treated cells, α-KG

was still somehow limited in their availability as a co-substrate for PHDs. The

enzymes’ inhibition was unlikely due to α-KG inhibitory properties on ATP

synthase.

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Fig 3.5 Implications of α-KG on HIF-1a stabilisation at normoxia. A) Cells

were treated with 0 or 10 mM of NaPyr and increasing concentrations of 1,2,3-

benzenetricarboxylic acid (1,2,3-BTC) for 6 h. B) Cells were treated with

ascending concentrations of KMV for 3 or 6 h. C) Cells were treated with 0 or

10 mM of NaPyr for 6 h, in the presence or absence of 1 μM of oligomycin for

the indicated durations. D) Cells were treated with 0 or 10 mM of NaPyr for 6

h, or 1 mM of DMOG or DFO in the final 2 h to stabilise HIF-1α. In the final 1

h, 5 mM of α-KG was introduced.

3.2.6 The significance of malate in regulating cycling of mitochondrial

metabolites

In a previous experiment, malate supplementation abrogated HIF-1α

stabilisation in NaPyr-treated cells (Fig 3.3B). Malate also abrogated HIF-1α

stabilisation in KMV-treated cells (Fig 3.6A). However, the relief of HIF-1α

stabilisation was not accompanied by a restoration of TCA cycle profile (Fig

3.6B). This indicated that modulations to net abundance of TCA cycle

intermediates, specifically that of α-KG did not completely explain HIF-1α

stabilisation at normoxia.

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Nevertheless, it was imperative to understand how malate relieved HIF-1α

stabilisation. I next postulated that 1) the bulk of α-KG was likely to be

sequestered in the mitochondria, away from the largely cytoplasmic- and

nuclear-localised PHDs, and 2) malate supplementation promoted exchange of

cytoplasmic malate for mitochondrial α-KG via the 2-oxoglutarate carrier which

is a component of the malate-aspartate shuttle. Activation of the shuttle by

malate supplementation was evidenced by an increase in OCR (Fig 3.3A) and

mitochondrial membrane potential (Fig 3.6C and D). Promotion of α-KG efflux

into the cytoplasm would bring it into the proximity of PHDs to enable their

reactivation and facilitation of HIF-1α degradation. To validate this speculation,

the metabolites in cytoplasmic and mitochondrial fractions were profiled to

determine if indeed, α-KG was sequestered in the mitochondria. However,

quantitative GC-MS analysis indicated that the bulk of α-KG was

predominantly cytoplasmic (Fig 3.6E).

There are however, several drawbacks to this protocol. Presence of saponin in

the permeabilization buffer was assumed to sufficiently inactivate endogenous

enzymes. This was not the case as quantitative GC-MS readouts on cell fractions

demonstrated lactate concentration being raised to a spurious level (Fig 3.6E),

which differed significantly from whole lysate analysis (Fig 3.4A and B). Some

troubleshooting was required to quickly inactivate endogenous metabolic

activities for accurate metabolite profiling.

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D)

89

Fig 3.6 Hypothetical relief of mitochondrial α-KG accumulation to abrogate HIF-1a stabilisation. A) Cells were treated with 5 mM of KMV

for 6 h in the presence or absence of 5 mM of malate. B) Cells treated with 0 or 10 mM of NaPyr, with or without 5 mM of malate were profiled

for organic acids via quantitative GC-MS. Data presented as fold change in heatmap, n = 4. C) Cells treated with 0 or 10 mM of NaPyr (NP) in

the presence or absence of 5 mM of malate were stained with 1 μM of JC-1 dye for measurement of mitochondria membrane potential via

spectrophotometer. Data presented as mean ± SD, n = 5. Statistical significance was determined by one-way ANOVA, followed by Tukey’s

pairwise comparison and statistical significance was accepted at p < 0.05, whereby *p < 0.05. D) The malate-aspartate shuttle function:

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cytoplasmic malate dehydrogenase (MDH) catalyses the transfer of electron

from cytoplasmic NADH to oxaloacetate, reducing it to malate. Malate is

exchanged for mitochondrial α-KG via the malate-α-KG transporter. In the

matrix, mitochondrial malate dehydrogenase oxidises malate to oxaloacetate

whereas NAD+ is reduced to NADH. Oxaloacetate is then transaminated to

aspartate, and exchanged for cytoplasmic glutamate via the glutamate-aspartate

antiporter (Nielsen, Stottrup, Lofgren, & Botker, 2011). E) Cells treated with 0

or 10 mM of NaPyr were fractionated for profiling of organic acids in the

cytoplasmic (cyto) and mitochondrial (mito) compartments. Data presented as

mean ± SD, n = 4.

3.2.7 Lysine catabolism as a metabolic repair mechanism

Interestingly, intermediates of lysine catabolism were upregulated in response

to NaPyr and MePyr (Fig 3.4B and 3.7A). It was unknown if these modulations

were implicated in HIF-1α regulation. As catabolism of lysine effectively

consumes α-KG as substrates, its upregulation could thus be a cellular attempt

at restoring metabolic homeostasis (Linster et al., 2013). qGC-MS confirmed

that lysine supplementation in NaPyr-treated cells restored TCA cycle

intermediates to a level comparable to control (Fig 3.7B and C).

I next asked if lysine-induced restoration of TCA cycle homeostasis could also

reverse HIF-1α stabilisation in NaPyr-treated cells. In cells preloaded with 5

mM of lysine prior to NaPyr or KMV incubation, HIF-1α was stabilised to a

lesser extent (Fig 3.7D). It was noted that the exogenously added lysine was in

addition to 0.4 mM of lysine already present in the EBSS treatment media.

Compared to physiological plasma concentration of lysine at approximately

0.19 mM (Stein & Moore, 1954), NaPyr-treated cells were incubated in up to

28-fold higher lysine concentration. Other strategies of upregulating lysine

catabolism could be considered, such as overexpression of key enzymes

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involved in lysine catabolism. The reduction in HIF-1α expression in response

to lysine did not occur at the transcriptional level as mRNA fold change of HIF-

1α remained unchanged (Fig 3.7E). Lysine could not relieve HIF-1α expression

in cells cultured under hypoxic conditions (Fig 3.7F), further underlining the

distinct mechanisms by which HIF-1α is stabilised under the influence of

metabolic perturbations or oxygen availability.

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C)

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Fig 3.7 Lysine catabolism regulates pyruvate-induced HIF-1α stabilisation

at normoxia. A) Untargeted metabolomics analysis of HepG2 cells treated with

0 or 20 mM of NaPyr or MePyr for 6 h. Data presented as mean ± SD, n = 4.

Differences in measured variables were determined by one-way ANOVA. FDR

was controlled with the Benjamini-Hochberg procedure, whereby FDR was set

at a conservative 0.05. Then, Tukey’s pairwise comparison was performed and

statistical significance was accepted at p < 0.05, whereby *p < 0.05, #p < 0.01.

B) Cells were preloaded with 5 mM of lysine (basal EBSS media contained

0.396 mM of L-lysine hydrochloride) for 1 h, before incubation with 10 mM of

NaPyr for the next 6 h and processed immediately for quantitative GC-MS

analysis to profile organic acids. Data presented as heat map in terms of fold

change, or C) bar graphs as mean ± SD, n = 4. D) HepG2 or HuH7 cells were

preloaded with 5 mM of lysine for 1 h, before incubation with 0 or 10 mM of

NaPyr, or 5 mM of KMV for the next 6 h. E) Cells were preloaded with 5 mM

of lysine for 1 h, before incubation with 0 or 10 mM of NaPyr for the next 6 h.

mRNA fold change was expressed as mean ± SD, n = 6. Statistical significance

was determined by one-way ANOVA, and accepted at p < 0.05. No significance

differences were detected in this dataset, thus post-hoc analysis was not

performed. F) Cells were incubated at 21% (normoxia) or 1% (hypoxia) O2 level

for 6 h, in the presence of 5 mM of lysine.

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3.2.8 Physiological significance of pyruvate-induced HIF-1α stabilisation

In this system, HIF-1α stabilisation seemed to manifest as a response to

metabolic stress. Indeed, pyruvate was found to inhibit a key regulator of cell

proliferation and growth, mTORC1 (further discussed in Chapter 4, exemplified

in Fig 4.1B and C). Cell cycle was unaffected in NaPyr- and KMV-treated cells

(Fig 3.8A). Cell size was modestly reduced in NaPyr- and KMV-treated cells,

and could be rescued by lysine supplementation (Fig 3.8B).

Fig 3.8 Physiological

impacts of pyruvate-

induced metabolic

perturbations. Cells

were treated with 0 or 10

mM of NaPyr, or 5 mM of

KMV in the absence or

presence of 5 mM of

lysine for 24 h. A) The

percentage of cells in

G0/1-, S- and G2/M-phases

were presented as mean ±

SD, n = 4. B) Cell size

was determined on PI-

stained, G1-gated cells

and data presented as

representative histogram.

B)

95

3.3 Discussion

In this study, the effect of pyruvate on HIF-1α stabilisation at normoxia was

established by treating human liver cancer cell lines with as high as 10 mM of

pyruvate, a concentration range that is not physiologically relevant and might

allude to off-target stress responses. However, quantification of intracellular

pyruvate showed that the fold change was comparable to that in previously

reported head and neck cancer cells (S. Hu et al., 2015) or when mitochondrial

pyruvate carrier was inhibited (Bricker et al., 2012; Du et al., 2013).

The conduct of all experiments at normoxia, defined as 21% partial pressure of

O2 (pO2) raises doubt on the credibility of data presented. Tissue pO2 typically

ranges between 3 to 10%, defined as physioxia. Oxygenation in tumours is

poorer (between 0.3 to 4.2%) (McKeown, 2014). Thus, the culture conditions at

normoxia is in fact hyperoxic, and does not represent in vivo cellular milieu.

Because this thesis focuses on the metabolic regulation of HIF-1α independent

of O2 contribution, all treatment samples and their respective controls were

subjected to the same 21% pO2 during experiment. Nevertheless, controlling for

hyperoxia-induced errors in this manner is inferior to replicating key

experiments at physioxia. Maintaining cells in a hypoxia chamber can be

considered, in which 95% of gaseous nitrogen and 5% carbon dioxide mixture

is introduced to obtain the desired pO2 at physioxia or normoxia (Carreau, El

Hafny-Rahbi, Matejuk, Grillon, & Kieda, 2011).

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Because pyruvate is the metabolite that links glycolysis to OxPhos, it is

strategically positioned to modulate cell cycle in response to energy and nutrient

availability. Specifically, the channelling of pyruvate to OxPhos is thought to

induce oncogene-induced senescence and reduce oncogenicity, whereas

shunting of pyruvate away from mitochondria, into fermentative glycolysis

(Warburg effect) promotes proliferation (Kaplon et al., 2013; Olenchock &

Vander Heiden, 2013). Inhibition of monocarboxylase transporters to

accumulate intracellular pyruvate has been shown to drive OxPhos and impair

growth (Hong et al., 2016). SLC5A8, a surface transporter of pyruvate is

downregulated across a range of cancer types, suggesting that cancers evolve to

suppress uptake of pyruvate (Ganapathy et al., 2008). However, pyruvate

assimilation into mitochondria metabolism may be prooncogenic as well. In

breast cancer cell lines, growth rate and OxPhos increases at higher

concentrations of NaPyr (up to 10 mM) in culture media (Diers, Broniowska,

Chang, & Hogg, 2012). Anaplerosis via pyruvate carboxylase is often enhanced

due to the enzyme’s overexpression in cancers, which is crucial for cell survival

and proliferation (Christen et al., 2016; Phannasil et al., 2015; Sellers et al.,

2015). Clearly, whether pyruvate is preferentially channelled into or away from

the mitochondria, and whether that in turn incurs growth promotion or arrest

depends on cell context. The cell models used in this thesis showed that pyruvate

increased OxPhos (Fig 3.3A) and anaplerosis (Fig 3.4A and B), without overt

changes to cell cycle and a modest decline in cell size (Fig 3.8A and B). In these

experiments, stabilisation of the glycolytic master regulator HIF-1α was tracked

across a 6 h window, likely representing an early and/or adaptive stress response

to pyruvate-induced oxidative metabolism. Chronic exposure to pyruvate could

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be attempted to determine if, allowance of proper expression of HIF-1α target

genes could reprogram metabolism to favour cell proliferation and growth.

The function of oncometabolites in cancers is also beginning to be appreciated.

In vitro experiments observed that TCA cycle intermediates such as citrate,

succinate, and fumarate could competitively inhibit tumour suppressor PHDs

(S. Y. Kim et al., 2010; Koivunen et al., 2007), which clinical importance is

further exemplified in gliomas harbouring mutations to isocitrate

dehydrogenases. Not only are the mutant IDHs incapable of converting

isocitrate to α-KG, they also convert existing α-KG to 2-hydroxyglutarate, a

structural analogue of α-KG and thus, competitive inhibitor of PHDs (L. Dang

et al., 2009; Ma et al., 2015; Xu et al., 2011; Zhao et al., 2009). Mutant succinate

dehydrogenase in paraganglioma or phaeochromocytoma, or fumarate

hydratase in leiomyoma, leiomyosarcoma and renal cell carcinoma result in

accumulation of succinate and fumarate, respectively to inhibit PHDs and

stabilise HIF-1α (King, Selak, & Gottlieb, 2006; Koivunen et al., 2007; Rutter

et al., 2010). Naturally, high concentrations of endogenous α-KG would render

competitive inhibition of PHDs difficult (Thirstrup et al., 2011). In this study,

α-KG abundance was robustly increased by 7.7- to 9.0-fold in parallel to HIF-

1α accumulation (Fig 3.4A and B), which could be abolished by further addition

of α-KG (Fig 3.5D). This paradox implied that despite the overabundance of α-

KG, it was still a limiting factor.

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A possible explanation to reconcile these findings is that PHDs were unable to

access the pool of α-KG. The idea of metabolite compartmentalisation is

possible because 1) the inner membrane of the mitochondria is largely

impermeable to metabolites, and 2) their cycling across mitochondrial

membrane is regulated in a transmembrane electrochemical gradient-sensitive

manner (Brand & Nicholls, 2011; Greenbaum & Wilson, 1985; Papa &

Paradies, 1974). This concept has also been proposed to regulate insulin

secretion in β-islet cells (Jensen et al., 2008; Odegaard et al., 2010). Of

relevance to this study is the malate-aspartate shuttle. The α-KG-malate

antiporter is a part of the shuttle, and exchanges mitochondrial α-KG for

cytoplasmic malate. Pyruvate- and KMV-induced HIF-1α stabilisation was

abrogated by malate supplementation (Fig 3.3B and 3.6A), and in parallel

promoted cycling of metabolites via the malate-aspartate shuttle without

significantly altering TCA cycle metabolic profile in total cell lysates (Fig 3.3A,

3.6C and D). This demonstrated the likelihood of α-KG relocation, and not total

abundance as a mechanism of regulating HIF-1α expression. There is yet

concrete evidence to confirm these questions: 1) is α-KG indeed

compartmentalised in the mitochondria, and if so, 2) how? An attempt was made

to profile α-KG in cytoplasmic and mitochondrial compartments. Despite

acceptable quality of cell fractionation and maintenance of organelle integrity,

there was difficulty in inactivating endogenous enzymes during sample

processing (compare Fig 3.4A with 3.6E).

Widespread metabolic consequences of pyruvate treatment were also likely to

evoke repair mechanisms to restore homeostasis. Aberrant build-up of

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metabolites is often detrimental to cell survival, and can be rectified

biochemically by shunting these substrates into secondary enzymatic reactions.

In response to excess of α-KG in pyruvate-treated cells, two such metabolic

corrections were observed: 1) conversion of α-KG to 2-hydroxyglutarate, and

2) enhanced lysine catabolism (Fig 3.4A and B). Quantitative GC-MS analysis

indicated a restoration of TCA cycle abundance to near basal level upon lysine

supplementation (Fig 3.7B and C), a phenotype not observed with malate

supplementation (Fig 3.6B), although both lysine and malate could relieve HIF-

1α accumulation (Fig 3.3B and 3.7D). This discrepancy highlighted different

mechanisms by which HIF-1α expression could be regulated, either by 1)

allocation of α-KG pool in different cell compartments, or 2) relief of metabolic

stress by restoring TCA cycle homeostasis.

The physiological significance of HIF-1α stabilisation warrants further

investigation. Owing to HIF-1α function as a master regulator of glycolytic

pathways, it is prudent to assess cellular avidity for glucose uptake and

consumption, and if there is a gradual reduction in OxPhos dependency for

energy generation. The duration of pyruvate incubation should be prolonged to

allow proper expression and functions of HIF-1α transcriptional targets.

Pyruvate-treated cells could be tested if HIF-1α enhances their survival

capacities in typically unfavourable environment, such as a hypoxia versus

normoxia, or high glucose versus low glucose.

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Based on the evidence presented thus far, I concluded that pyruvate promoted

HIF-1α stabilisation by downregulating the hydroxylation activities of PHDs,

enzymes that use O2 and α-KG as obligatory co-substrates to aid HIF-1α

clearance. This was mediated by assimilation of pyruvate into the TCA cycle

via pyruvate carboxylase, thus perturbing TCA cycle homeostasis.

Supplementing cells with malate abrogated pyruvate-induced HIF-1α

stabilisation, presumably by promoting the efflux of α-KG from the

mitochondria into the cytoplasm, via the malate-α-KG antiporter. I thus

hypothesised that in pyruvate-treated cells, the bulk of co-substrate α-KG was

sequestered in the mitochondria, rendering it inaccessible to PHDs that are

largely localised in the cytoplasmic and nuclear compartments. The questions

remain if 1) α-KG was indeed compartmentalised in the mitochondria, as earlier

attempts to profile α-KG in cytoplasmic and mitochondrial compartments were

unsuccessful, and 2) if so, how did the mitochondria achieve such

compartmentalisation. The large influx of pyruvate also triggered lysine

catabolism to restore metabolic homeostasis by scavenging excess α-KG. This

postulation was supported by the ability of lysine supplementation to undo

pyruvate-induced HIF-1α stabilisation at normoxia, and restore TCA cycle

metabolite profile to basal levels (Fig 3.9).

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Fig 3.9 A summarised postulation of how pyruvate induced HIF-1α

stabilisation at normoxia and the induction of lysine catabolism as a

metabolic repair mechanism. The size of ○ denotes the relative pool size (not

drawn to scale) of the indicated TCA cycle metabolites, as determined via

metabolomics analysis and reported in Fig 3.4A. Isocitrate and oxaloacetate

were arbitrarily represented as these metabolites were undetected in the

analysis. → denotes activation of an enzymatic or metabolic activity in the

displayed direction, whereas ┬ denotes inhibitory effects. Abbreviations used

are as follows: pyruvate carboxylase, PC; pyruvate dehydrogenase complex,

PDH, hydroxyl groups, OH.

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CHAPTER 4: Ammonium ions sensitises mTORC1 to amino acids via the

Src/Akt signalling axis

4.1 Introduction

Ammonia is often generated in excess against a tumorigenic background

(Carrascosa, Martinez, & Nunez de Castro, 1982; Chance et al., 1989; Chance

et al., 1988), primarily due to avid consumption of glutamine (Krall & Christofk,

2015). Conventionally perceived as a toxic metabolite, growing evidences are

pointing to its expanded role in cell signalling and metabolism. Glutamine-

derived ammonia has been proposed to stimulate autophagy in an

autocrine/paracrine manner as a means of promoting stress adaptation in

adjacent cells (C. H. Eng & Abraham, 2010). This is important for survival

during short-term nutrient withdrawal, and has been shown to sustain

metabolism in cancer cells under energy stress (Cheong, Lu, Lindsten, &

Thompson, 2012; Villar, Merhi, Djavaheri-Mergny, & Duran, 2015). How

ammonia activate autophagy is likely to be independent of the mTORC1/ULK1

axis or ERK1 (Cheong, Lindsten, Wu, Lu, & Thompson, 2011; Harder,

Bunkenborg, & Andersen, 2014), rather it requires activation of AMPK (Harder

et al., 2014).

Paradoxically, the carbon metabolites of glutaminolysis have been shown to

inhibit autophagy (Villar et al., 2015). Glutamine-derived α-KG activates

mTORC1 to subsequently impede autophagy by 1) enhancing GTP loading on

RagB GTPase (Duran et al., 2012), 2) activating PHDs (Duran et al., 2013), or

3) modulating acetyl-CoA metabolism and thus, protein acetylation (Marino et

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al., 2014). Addressing the differential effects of nitrogen- versus carbon-

metabolites derived from glutaminolysis on autophagy is important. However,

relatively little is known about the impact of ammonia on mTORC1 itself. In

this chapter, I highlighted the role of Akt and Src kinase as key upstream

regulators of mTORC1 in response to NH4+, the ionic form in which 95% of

ammonia exists under physiological plasma pH. The term “ammonia” shall be

used to refer to both molecular ammonia and NH4+, whereas NH4

+ refers

specifically to the ionised species.

4.2 Results

4.2.1 Pyruvate downregulated mTORC1 signalling likely by modulating

ammonia metabolism

In Chapter 3, organic and amino acids profiles were altered in pyruvate-treated

cells (Fig 3.4A, B and 4.1A). These metabolic changes could potentially trigger

cellular nutrient sensors. mTORC1 signalling was observed to be

downregulated in NaPyr-treated cells, as determined by reduced

phosphorylation of downstream targets p70S6K and S6 (Fig 4.B and C). This

could be due to 1) reduced abundance of amino acids, 2) energy stress, 3)

expression of mTORC1-inhibitory proteins such as regulated in development

and DNA damage response 1 (REDD1) secondary to HIF-1α stabilisation, or 4)

scavenging of endogenous ammonia.

Aspartate was the most robustly decreased by 0.05- or 0.14-fold in response to

NaPyr or MePyr, respectively (Fig 4.1A). This could potentially inhibit

104

mTORC1, as maintenance of its basal activities requires amino acids. However,

supplementation of NaPyr-treated cells with aspartate was unable to restore

mTORC1 activities (Fig 4.1B). Supplementation of its amide, asparagine could

on its own activate mTORC1, a phenomenon that was discussed in greater detail

in Chapter 5.

The AMPK pathway is of relevance to this study as it regulates mTORC1 in

response to cell energy status. AMPK is a heterotrimeric complex comprising

of one catalytic α-subunit and two regulatory β- and γ-subunits. It is activated

during hypoxia or nutrient starvation when energy supply is low, leading to a

rise in AMP/ADP : ATP. Subsequently, it phosphorylates TSC2 and Raptor,

leading to inactivation of mTORC1 (Gwinn et al., 2008; Inoki, Zhu, & Guan,

2003; Mihaylova & Shaw, 2011). Immunoblot analysis indicated modest

phosphorylation of Raptor and AMPK in response to pyruvate, suggesting that

energy stress could be responsible for the suppression of mTORC1 activity in

NaPyr-treated cells (Fig 4.1B). Quantification of AMP/ADP : ATP or

assessment of AMPK functions could be conducted to verify this hypothesis.

During hypoxia, HIF-1α inhibits mTORC1 by enhancing the expression of

REDD1 (Shoshani et al., 2002). It displaces TSC2 from binding to 14-3-3, thus

freeing TSC2 to exert its GAP effects on Rheb to inhibit mTORC1 (DeYoung,

Horak, Sofer, Sgroi, & Ellisen, 2008; Dibble et al., 2012). As expected, malate

and lysine supplementation abrogated HIF-1α and REDD1 accumulation in

NaPyr-treated cells, yet mTORC1 activity remained repressed (Fig 4.1B and C),

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suggesting that HIF-1α/REDD1 was unlikely to mediate mTORC1

downregulation.

Pyruvate is also an indirect ammonia scavenger as pyruvate can be

transaminated by alanine transaminase to yield alanine. This reaction allows

alanine to function as a temporary, non-toxic carrier of ammonia. In skeletal

muscles, once incorporated with primarily branched chain amino acids-derived

ammonia, alanine is circulated to the liver for detoxification (Holecek, 2013;

Palmer, Caldecourt, Snell, & Sugden, 1985). Although the overall importance

of alanine as ammonia regulator is weaker compared to glutamine (Forissier &

Baverel, 1981), a modest yet significant reduction in endogenous ammonia

levels (Fig 4.1D), accompanied by a 2.0- or 1.6-fold increase in alanine

abundance was observed in MePyr- or NaPyr-treated cells, respectively (Fig

4.1A). This observation prompted an in-depth investigation into mTORC1

regulation by ammonia, as demonstrated in subsequent sections of this chapter.

A summary of the possible mechanisms of pyruvate-induced mTORC1

inhibition is summarised in Fig 4.1E.

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107

108

Fig 4.1 The impact of pyruvate metabolism on mTORC1 signalling. A) Cells

were treated with 0 or 20 mM of MePyr or NaPyr for 6 h and subjected to

untargeted metabolomics analysis on a GC-MS platform. Data presented as

mean ± SD, n = 4. Differences in measured variables were determined by one-

way ANOVA. FDR was controlled with the Benjamini-Hochberg procedure,

whereby FDR was set at a conservative 0.05. Then, Tukey’s pairwise

comparison was performed and statistical significance was accepted at p < 0.05,

whereby *p < 0.05, #p < 0.01. B) Cells were treated with 0 or 10 mM of NaPyr

in the presence or absence of 5 mM of malate or aspartate for 6 h. C) Cells were

preloaded with 5 mM of lysine for 1 h before incubation with 0 or 10 mM of

NaPyr for the next 6 h. D) Cells were treated with 0 or 10 mM of NaPyr for 6 h

and immediately assayed for ammonia quantification. Data presented as mean

± SD, n = 3. Statistical significance was determined by Student’s t-test,

accepted at p < 0.05 whereby #p < 0.01. E) A summary of hypothetical

mechanisms of pyruvate-induced mTORC1 inhibition, and the conclusions

derived upon investigation.

4.2.2 Ammonium ions activated mTORC1 signalling pathway

To further understand the role of NH4+ on mTORC1 signalling, HepG2 cells

were treated with 0 to 5 mM of NH4+, supplied as ammonium chloride (NH4Cl)

or ammonium sulphate ((NH4)2SO4) for different durations. mTORC1 was

activated in a dose- and time-dependent manner (Fig 4.2A and B). This

concentration range of NH4+ was selected because it is commonly found in

extracellular milieu of solid tumours (Carrascosa, Martinez, & Nunez de Castro,

1984; C. H. Eng & Abraham, 2010). This phenotype was reproducible in HuH7

and rat hepatoma H4IIE (Fig 4.2C). In a separate experiment, endogenous NH4+

scavenged by clinically-approved ammonia scavengers, sodium phenylacetate

and sodium benzoate (Walker, 2012) abrogated mTORC1 activation in HepG2

and HuH7 cells (Fig 4.2D). Taken together, at least in the cell models tested

here, NH4+ was an activator of mTORC1 signalling pathway.

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2 h 4 h 6 h

2 h 4 h 6 h

110

Fig 4.2 The impact of NH4+ on mTORC1 signalling in liver cancer cell lines.

HepG2 cells were treated with increasing concentrations of NH4+ supplied as

A) NH4Cl, or B) (NH4)2SO4 for 2, 4 or 6 h. C) Human and rat hepatoma cell

lines, HuH7 and H4IIE, respectively were treated with increasing

concentrations of NH4Cl for 4 h. D) HepG2 and HuH7 cells were treated with 2

mM of NH4Cl in the presence of ammonia scavengers, 10 mM of sodium

phenylacetate (PAA) or sodium benzoate (B) for 4 h.

4.2.3 Ammonium ions activated mTORC1 independently of essential

amino acids, but required glutamine

Full activation of mTORC1 requires presence of amino acids (Bar-Peled &

Sabatini, 2014). To determine to what extent amino acids could impact NH4+-

induced mTORC1, HepG2 and HuH7 cells were treated with NH4Cl in the

absence or presence of EAA or glutamine. Intriguingly, essential amino acids

were dispensable for NH4Cl to maintain basal mTORC1 activity, but glutamine

111

was not. The upstream kinase of mTORC1, Akt was also consistently activated

in response to NH4Cl (Fig 4.3A). Similarly, although essential amino acids

withdrawal significantly dampened insulin-induced mTORC1 activity,

supplementation with glutamine and NH4Cl was sufficient to restore mTORC1

activation to an optimum (Fig 4.3B). This suggested that NH4+ could sensitise

mTORC1 to amino acids and growth signalling.

The necessity for glutamine, but not essential amino acids in the maintenance

of mTORC1 activity in response to NH4+ was baffling. I first postulated that in

the absence of essential amino acids, glutamine played a compensatory role by

providing carbon metabolites via glutaminolysis. This could potentially

alleviate metabolic stress and sustain mTORC1 signalling. However,

substituting glutamine with α-KG did not sustain mTORC1 in NH4+-treated

cells. AMPKα activation status was determined in parallel to ensure that α-KG

was not supplemented at a dosage high enough to uncouple mitochondrial

respiration (Chin et al., 2014) (Fig 4.3C). Hence, glutamine was not required as

a source of carbon metabolites to sustain basal mTORC1 activities in NH4+-

treated cells, in the absence of essential amino acids.

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113

Fig 4.3 Glutamine was indispensable in mediating NH4+-induced mTORC1

activation. A) HepG2 or HuH7 cells were treated with 0 or 2 mM of NH4Cl for

4 h, in the presence or absence of EAA or 4 mM of glutamine (Gln). B) HepG2

cells were treated with 0 or 2 mM of NH4Cl for 4 h in the absence or presence

of essential amino acids (EAA; 4 mM of Gln was always present) or 20 nM of

rapamycin. In the final 30 min, 10 nM of insulin was added. C) Cells were

treated with 0 or 2 mM of NH4Cl in the absence or presence of EAA or 4 mM

of Gln. In the absence of Gln, cells were supplemented with a range of α-KG

dilutions not known to uncouple mitochondrial respiration, exemplified by the

lack of AMPK activation.

4.2.4 Modulations to amino acids profiling in response to ammonium ions

Nevertheless, it was still crucial to assess the metabolic impacts of NH4+. To

this end, the following treatment groups in HepG2 cells were generated:

Table 4.1 Treatment parameters for metabolic profiling.

EAA Gln NH4Cl

Batch 1 Control + + -

Treatment + + +

Batch 2 Control - + -

Treatment - + +

In Batch 1, the amino acids profile of control and treatment cells were largely

comparable (Fig 4.4A and B), suggesting that metabolism was not drastically

modulated to result in mTORC1 activation.

In Batch 2 however, the relative abundance of essential amino acids was

increased in NH4Cl-treated cells compared to control (Fig 4.4C and D). As

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mammalian cells lack machineries to synthesize essential amino acids, their

surge in abundance was likely attributed to autophagy (Kaur & Debnath, 2015;

Onodera & Ohsumi, 2005). This postulation was further corroborated by prior

reports of NH4+ acting as a stimulator of autophagy (Cheong et al., 2011; C. H.

Eng & Abraham, 2010). Nevertheless, it remained to be confirmed if 1)

autophagy was indeed activated in Batch 2, 2) if so, whether autophagy

induction was due to essential amino acids starvation or NH4+, and 3) if

inhibition of autophagy could abrogate mTORC1 maintenance in this system.

As to why glutamine seemed to play a permissive role in mTORC1-sensing of

endogenous essential amino acids, this is discussed in further detail in Chapter

5.

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116

117

118

Fig 4.4 Metabolic profiling in response to NH4+ and amino acids

manipulations. The relative abundance of endogenous amino acids was

profiled on a GC-MS platform. Data presented as heatmaps as fold change, or

bar graphs as mean ± SD, n = 4. Statistical significance was determined by

Student’s t-test, accepted at p < 0.05, whereby *p < 0.05, #p < 0.01. FDR was

controlled with the Benjamini-Hochberg procedure, whereby FDR was set at

0.1. A and B) HepG2 cells were treated with 0 or 2 mM of NH4Cl for 6 h in the

presence of essential amino acids (EAA) and 4 mM of glutamine (Gln). C and

D) HepG2 cells were treated with 0 or 2 mM of NH4Cl for 6 h in the absence of

EAA, but supplemented with 4 mM of Gln.

4.2.5 Implications of Src/Akt pathway in mediating ammonium ion-

induced mTORC1 activation

Batch 1 registered mTORC1 activation in response to NH4+ without observable

modulations to amino acids profile compared to control (Fig 4.3A and 4.4A).

However, the upstream kinase of mTORC1, Akt was consistently activated in

NH4+-treated cells (Fig 4.3A). MK2206 have been used as a pharmacological

inhibitor of Akt in both in vitro and in vivo systems (Sangai et al., 2012; Yap et

al., 2011). Accordingly, MK2206 abolished NH4+-induced mTORC1 activity,

accompanied by a loss of phosphorylation of Akt at S473, and its downstream

targets, Akt substrate of 160 kDa (AS160) and TSC2 (Fig 4.5A). This indicated

that NH4+-induced mTORC1 activity was sensitive to intact Akt functions.

The next challenge was to identify candidate regulators of Akt that responded

to NH4+. This regulator would likely 1) act upstream of Akt/mTORC1 signalling

axis, 2) be modulated by NH4+ in terms of activity or protein-protein interaction

with Akt/mTORC1 signalling machinery, and 3) abrogate NH4+-induced

mTORC1 activation upon inhibition. Src kinase meets the first two criteria (Dai

et al., 2013; Reinhard, Tidow, Clausen, & Nissen, 2013; Roskoski, 2015;

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Schneider et al., 1996), whereas the third was exemplified in Fig 4.5B.

Inhibition of Src with PP1 or PP2 (Sawyer, Boyce, Dalgarno, & Iuliucci, 2001)

abrogated NH4+-induced Akt phosphorylation, thus placing Src as acting

upstream of Akt. Phosphorylation of downstream targets of mTORC1, p70S6K

and 4E-BP1 was also compromised, indicating that intact Src was required to

mediate NH4+-induced mTORC1 activation. It is still unknown if Src kinase

itself was activated in response to NH4+ in the cell models tested here. Repeating

the experiments reported in Fig 4.5B under essential amino acids starvation

conditions should also be considered. To further corroborate these observations,

usage of other pharmacological inhibitors of Akt and Src that are structurally

dissimilar to MK2209 and PP1/2 respectively, in addition to silencing the

expression of these kinases.

The next challenge was identifying an upstream regulator of Src kinase which

activation was sensitive to NH4+. Na+, K+-ATPase is a likely candidate because

it 1) facilitates import of hydrated NH4+ across the plasma membrane (Dai et al.,

2013; Schneider et al., 1996; Walker, 2014), and 2) serves as a scaffold protein

for signalling pathways Src, MAPK, PI3K and phospholipase C (PLC)-γ

(Reinhard et al., 2013). The glycoside ouabain has been found to inhibit the

enzymatic activity of Na+, K+-ATPase, and is clinically used to manage

complications of heart failure (Blaustein, Juhaszova, & Golovina, 1998;

Wasserstrom & Aistrup, 2005). Preliminary experiment showed that indeed,

ouabain abrogated NH4+-induced mTORC1 activation, although Akt and TSC2

phosphorylation remained unaffected (Fig 4.5C). However, drawing

conclusions from this result was premature, because inhibitory effects of

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ouabain was not robust and only observable within a narrow concentration

range. Thus, other means of inhibiting Na+, K+-ATPase should be considered.

It was unknown if in this system 1) the activities of Na+, K+-ATPase were

altered by low millimolar concentrations of NH4+, and 2) protein-protein

interaction or signalling among Na+, K+-ATPase, Src kinase, Akt and mTORC1

was enhanced by NH4+, as reported previously in astrocytes (Dai et al., 2013).

Surface transporters are required to facilitate import of NH4+ into the cell. Na+,

K+-ATPase transports NH4+ because hydrated NH4

+ has the same ionic radius as

K+, and can compete for K+-binding sites (Schneider et al., 1996). Not only will

this perturb equilibration of K+ across the plasma membrane, it also influences

energy homeostasis, as reestablishment of K+ concentration gradient is ATP-

dependent (Schneider et al., 1996). These consequences are not applicable to

uncharged, membrane-permeable ammonia, or if NH4+ is generated

intracellularly. Thus, results obtained from treating cells with exogenous

addition or intracellular generation of NH4+ should be examined carefully. An

attempt to establish a system in which NH4+ is generated intracellularly was

done by using glucosamine (Cheong et al., 2011). However, the phosphorylation

status of p70S6K and S6 was unchanged in response to glucosamine (Fig 4.5E).

Further calibration to this system should be carried out to confirm if endogenous

NH4+ level was indeed increased in glucosamine-treated cells. Assessing the

effects of exogenously supplied NH4+ should be done in parallel with

endogenously synthesised NH4+.

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122

Fig 4.5 The role of Src/Akt signalling axis in NH4+-induced mTORC1

activation. A) Cells were treated with 0 or 2 mM of NH4Cl in the presence or

absence of essential amino acids (EAA; 4 mM of Gln was always present) for 4

h. Partial inhibition of Akt was achieved with 0.1 μM of MK2206. A more

robust inhibition should be attained by using higher concentrations of MK2206.

B) Cells were treated with 0 or 2 mM of NH4Cl in the presence or absence of

50 μM of Src kinase inhibitors, PP1 or PP2, for 4 h. C) Cells were treated with

0 or 2 mM of NH4Cl in the presence of EAA and 4 mM of glutamine, with or

without 150 nM of ouabain for 4 h. D) Cells were treated with increasing

concentrations of glucosamine in the presence or absence of 25 mM of glucose,

for 4 h. E) Cells were treated with 10 mM of glucosamine, in the presence of 25

mM of glucose for 24 h.

C)

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4.2.6 Implications of hyperammonaemia in the liver

The prescription of L-asparaginase to patients of childhood and adult acute

lymphoblastic leukaemia since 1967 (Oettgen et al., 1970; Silverman et al.,

2001) is particularly effective against transformed lymphoid cells lacking

endogenous asparagine synthetase (Kiriyama et al., 1989). Complications that

frequently arise from L-asparaginase therapy are hyperlipidaemia (which

increases risks for hepatosteatosis and pancreatitis), hypersensitivities

responses, hyperglycaemia and coagulation disorders, likely due to impaired

synthesis of coagulation factors in the liver (Otten et al., 2002) and a concurrent

rise in plasma ammonia levels (Heitink-Polle et al., 2013). These parallel events

might converge on mTORC1, because of this pathway’s pro-lipogenesis

function that is largely mediated by SREBP family of transcription factors

(Duvel et al., 2010; Porstmann et al., 2008), master regulators of lipid

homeostasis (Eberle, Hegarty, Bossard, Ferre, & Foufelle, 2004).

To this end, a preliminary experiment was conducted in which triacylglycerol

accumulation was quantified in NH4+-treated HepG2 cells. However,

triacylglycerol level was not increased in response to NH4+. Rapamycin, a bona

fide inhibitor of mTORC1 used as a negative control to suppress triacylglycerol

biosynthesis (Laplante & Sabatini, 2009; Skop et al., 2012) also did not reduce

triacylglycerol level in this system (Fig 4.6A). Nevertheless, dismissing the lack

of association between hyperammonaemia, mTORC1 activation and hepatic

lipogenesis was premature at this stage. Selection of a different cell model, for

example normal hepatocytes to interrogate the hypothesis should be considered,

in addition to prolongation of NH4+ incubation. Quantitative PCR on SREBP

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family of transcription factors and downstream targets such as fatty acid

synthase and acetyl-CoA carboxylase could also be carried out.

Increased mTORC1 activity promotes cell proliferation and growth (Dibble &

Manning, 2013; Howell, Ricoult, Ben-Sahra, & Manning, 2013). Cell size was

significantly increased in response to NH4+ when cells were maintained under

nutrient-replete conditions. In the absence of essential amino acids, cell size

generally decreased, and was not rescued by NH4+ despite maintenance of basal

mTORC1 activity (Fig 4.6B). This was not unusual, as cell size increment is an

anabolic process that requires supplementation of nutrient.

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Fig 4.6 Physiological relevance of NH4+-induced mTORC1 activation. A) Cells

were treated with 0 or 2 mM of NH4Cl for 24, 48 or 72 h (with daily change of

media) in the presence or absence of 10 mM of sodium phenylacetate (PAA),

sodium benzoate (B) or 20 nM of rapamycin (Rapa). Triacylglycerol (TAG) was

stained with Oil Red O and quantified spectrophotometrically. Data presented

as mean ± SD, n = 3. B) Cells were treated with 0 or 2 mM of NH4Cl for 24 h,

in the presence or absence of essential amino acids (EAA: 4 mM of Gln was

always present). Cell size was determined on propidium iodide-stained, G1-

gated cells. Data presented as mean ± SD, n = 3. Statistical significance was

determined by Student’s t-test, and accepted at p < 0.05, whereby #p < 0.01.

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4.3 Discussion

Across several cancer cell lines tested in this study, NH4+ activated mTORC1

signalling when supplied in millimolar doses in culture media. This is

pathological because 1) plasma ammonia is usually maintained at a low basal

of 40 μM (Walker, 2012), and 2) such concentrations are found in interstitial

fluid surrounding solid tumours (Chance et al., 1989; Chance et al., 1988; C. H.

Eng et al., 2010). Under fed conditions, NH4+ enhanced mTORC1 activity. Even

during essential amino acids starvation (with glutamine supplementation), NH4+

could sustain mTORC1 activity (Fig 4.3A and B). Sensitising mTORC1 to

anabolic stimuli (nutrients and growth factors) could be advantageous in an

oncogenic setting, as it enables growth and survival even under stressed

conditions (Laplante & Sabatini, 2012).

It is still unknown how cells sense NH4+ to influence cell signalling. pH

modulation is thought to be important as salts of NH4+ are weak bases. High

concentrations of NH4Cl have been found to significantly increase lysosomal

pH to the extent of inhibiting lysosomal enzymes, a reason why studies of NH4+

metabolism is often coupled to autophagy induction (Cardelli, Richardson, &

Miears, 1989; Cheong et al., 2011; Y. Hu et al., 2016). Indeed, cells treated with

NH4+ maintained under essential amino acids-starved conditions exhibited

increase in endogenous essential amino acids pool (Fig 4.4C and D). Whether

autophagy had taken place warranted further investigation, by 1) analysing

conversion ratio of LC3-I to LC3-II to measure autophagy flux, 2) quantifying

green fluorescence protein (GFP)-LC3 punctate (Mizushima, Yoshimori, &

Levine, 2010) and 3) inhibiting autophagy with pharmacological agents

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bafilomycin A or chloroquine (Cheong et al., 2012) to assess loss of endogenous

essential amino acids pool replenishment and abrogation of basal mTORC1

activities. If autophagy did take place, it is also prudent to determine whether

NH4Cl or withdrawal of essential amino acids from culture media was the

predominant autophagy inducer. Another target of NH4+ is glutaminase,

specifically GLS2, which activities are upregulated by NH4+ (S. K. Joseph &

McGivan, 1978; Szweda & Atkinson, 1990). Identifying other candidate

proteins that interact with NH4+ is key to understanding how mTORC1 is

subsequently activated. Results collected in this thesis thus far suggested that

they could 1) bind to NH4+ or activity was modulated directly by NH4

+, and 2)

signal to Src/Akt.

Another report presenting similar conclusions was published in parallel (Merhi,

Delree, & Marini, 2017). The authors supplied NH4+ as NH4Cl or ammonium

hydroxide (NH4OH), though at a higher concentration of 5 mM. In addition to

also observing an upregulation in Akt/mTORC1 signalling axis, they further

deduced the role of mTORC2 and Src kinase family members, specifically Yes1

and Fak as upstream regulators of Akt/mTORC1. These findings are in

accordance with evidence presented here 1) as phosphorylation of Akt (S473)

used as a readout in this study is a phosphosite targeted by mTORC2, and 2)

NH4+-induced mTORC1 activation was sensitive to Src kinase inhibitors, PP1

and PP2 (Fig 4.5B). The authors proceeded to suggest NH4+ itself a positive

regulator of mTORC1 and cell growth. This thesis proposed that NH4+ played a

role in sensitising mTORC1 to amino acids- and growth factor-mediated

signalling.

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To summarise, NH4+ was found to promote mTORC1 activation via different

mechanisms depending on amino acids availability. During essential amino

acids starvation, cells typically display significant inhibition of mTORC1.

However, supplementation with NH4+ and glutamine could sustain basal

mTORC1 activities. Autophagy was postulated to have taken place, thus

restoring endogenous essential amino acids pool. This, in conjunction with

NH4+ ability to sensitise mTORC1 to amino acids, resulted in the maintenance

of mTORC1 basal activities. However, these metabolic changes were absent in

NH4+-treated cells that were maintained under a fed state (in the presence of

essential amino acids and glutamine). In this context, NH4+ was found to signal

to mTORC1 in a Src/Akt-dependent manner instead (Fig 4.7).

Fig 4.7 A summarised postulation of how NH4+ sensitise mTORC1 to amino

acids and growth factors. A) Under essential amino acids (EAA) starvation

conditions (but with glutamine supplementation), parameters contained in the

red-dotted box are hypothesised to induce autophagy. This could explain the

observed increase in endogenous EAA in these cells. These EAA were then

sensed by mTORC1, that might have been primed by NH4+ in a Src/Akt-

dependent manner. B) Under EAA replete conditions, Src (dotted in red) was

likely activated in response to NH4+, to subsequently activate downstream

targets Akt and mTORC1.

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CHAPTER 5: Asparagine regulates mTORC1 signalling as an amino acid

exchange factor and mediator of endogenous amino acid sensing

5.1 Introduction

The bulk of ammonia generated in vivo is derived from glutaminolysis

(Schneider et al., 1996). This event is further upregulated in cancers to the extent

of incurring significant weight loss in the hosts (Le, Potter, Busch, Heidelberger,

& Hurlbert, 1952) to provide nitrogen and carbon precursors to sustain

hyperproliferation of cancer cells (C. V. Dang, 2010). However, even under

normal physiological conditions, glutamine is still a key signalling molecule,

although it largely signals to mTORC1 machinery without necessarily

dependent on its catabolism (Jewell et al., 2015; Krall et al., 2016; Nicklin et

al., 2009). Much less is known about the other amidic amino acid, asparagine.

Owing to their structural and biochemistry similarities, it is possible that

asparagine could also modulate mTORC1 in a glutamine-like manner.

5.2 Results

5.2.1 Asparagine did not alter intracellular amino acid profile

I first sought to establish the impact of asparagine metabolism on mTORC1

signalling. When HepG2 cells were treated with increasing concentration of

asparagine, mTORC1 activity was increased in a dose-dependent manner, as

demonstrated by increased phosphorylation of downstream targets p70S6K and

ribosomal protein S6 (Fig 5.1A). To determine if derivative metabolites were

responsible for activating mTORC1, cells were next supplemented with

aspartate, a product of asparagine deamination by asparaginase. Aspartate was

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supplied in two forms, 1) sodium salt of aspartic acid (Na.Asp), or 2) membrane

permeable ester of aspartic acid (P.Asp). Neither sodium salt or ester of

aspartate increased mTORC1 activity (Fig 5.1A), suggesting that mTORC1 was

likely responsive to the amide asparagine itself. To further confirm if

assimilation of asparagine into amino acid metabolism was not required for

mTORC1 activation, cells treated with or without asparagine were subjected to

metabolomics analysis. The general amino acids profile between both

conditions was comparable, except that of asparagine and aspartate (increased

by 28.1- and 5.4-fold, respectively) (Fig 5.1B)

As presence of amino acids is prerequisite for optimal mTORC1 activation, I

next investigated to what extent amino acids could affect asparagine-dependent

mTORC1 regulation. Intriguingly, asparagine could substitute for glutamine in

maintaining basal mTORC1 activity, although to a weaker degree (Fig 5.1C),

likely due to in vitro preconditioning to glutamine metabolism after numerous

passaging in growth media containing 4 mM of glutamine, but none of

asparagine. Asparagine could also substitute glutamine in mediating insulin-

induced mTORC1 activation (Fig 5.1C). To summarise, evidences thus far

indicated that asparagine signalled to mTORC1 independently of metabolic

modulations, and in a manner typically displayed by glutamine.

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132

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Fig 5.1 The effect of asparagine supplementation on mTORC1 and amino

acids profile. A) HepG2 cells were treated with ascending concentrations of

asparagine (Asn), Na.Asp or P.Asp for 3 h. B) HepG2 cells treated with 0 or 10

mM of Asn (in the presence of essential amino acids and 4 mM of glutamine)

were subjected to untargeted metabolomics on a GC-MS platform. Data

presented as mean ± SD in bar graphs. Statistical significance was determined

by Student’s t-test, accepted at p < 0.05, whereby *p < 0.05, #p < 0.01. FDR

was controlled with the Benjamini-Hochberg procedure, whereby FDR was set

at a conservative 0.05. C) Cells were treated with 4 mM of glutamine (Gln) or

Asn for 3 h, in the presence or absence of 20 nM of rapamycin. In the final 30

min, 10 nM of insulin was added into the media.

5.2.2 Intracellular accumulation of asparagine was rate-limiting for

mTORC1 activation

Intracellular accumulation of glutamine has been shown to be rate-limiting and

occur upstream of mTORC1 activation. It involves surface transporters 1)

SLC1A5 that imports glutamine into the cells, and 2) SLC7A5/SLC3A2 that

simultaneously efflux intracellular glutamine while importing extracellular

essential amino acids into the cells (Nicklin et al., 2009). As asparagine and

glutamine share structural and biochemical properties, asparagine could

theoretically be exchanged for extracellular essential amino acids as well.

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To this end, cells were preloaded with asparagine or glutamine (positive control)

in the absence of essential amino acids for 3 h. Upon reintroduction of essential

amino acids, a difference in the kinetics of mTORC1 reactivation was observed

between preloaded and non-preloaded cells. With preloading, mTORC1 was

reactivated within 10 min of essential amino acids repletion, compared to 30

min without preloading (Fig 5.2A). In a separate experiment, essential amino

acids and the amides were introduced in the reverse sequence. Cells preloaded

with essential amino acids did not reactivate mTORC1 upon introduction of

asparagine or glutamine, although co-treatment of cells with asparagine or

glutamine and essential amino acids could (Fig 5.2B). This indicated that the

amide should first be accumulated intracellularly for subsequent mTORC1

reactivation in response to essential amino acids. These observations agree with

the previously reported role of glutamine as an exchange factor for essential

amino acids necessary for mTORC1 activation (Nicklin et al., 2009).

It was still unknown if asparagine or glutamine mediate influx of any specific

essential amino acids that was potent enough to regulate mTORC1. One of the

extracellular essential amino acids that SLC7A5/SLC3A2 could transport is

leucine, a branched chain amino acid known to be a potent stimulator of

mTORC1 activities. Indeed, when preloaded cells were repleted with an

essential amino acids cocktail that did not contain leucine, the extent of

mTORC1 reactivation was severely hampered (Fig 5.2C). Although these

readouts indicated the importance of leucine as the proximal mediator of

mTORC1, it did not discount the significance of other amino acids particularly

serine, arginine and histidine (Krall et al., 2016). To further investigate which,

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if any, essential amino acids could also potently stimulate mTORC1, this

experiment could be repeated by removal of individual essential amino acids

and assessment of mTORC1 impediment.

To summarise, evidences indicated that accumulation of intracellular asparagine

preceded its bidirectional exchange for extracellular essential amino acids. Of

these, leucine in particular was established as a potent proximal mTORC1

activator, although the significance of other essential amino acids had yet to be

determined.

Preload

Preload

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Preload

Preload

*

Preload

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Fig 5.2 The significance of intracellular accumulation of amidic amino

acids. A) Cells were preloaded with 4 mM of Asn or Gln for 3 h and then

supplemented with essential amino acids (EAA) for different durations, or

cotreated with Asn or Gln with EAA for 4 h. B) In reverse, cells were preloaded

with EAA for 3 h and then supplemented with 4 mM or Asn or Gln for different

durations, or cotreated with Asn or Gln with EAA for 4 h. C) Cells were

preloaded with 4 mM of Gln (Q) or Asn (N) for 3 h, and supplemented with *EAA (essential amino acids but without leucine), with or without exogenous

leucine supplementation (Leu, 0.4 mM) for 30 min.

5.2.3 The role of asparagine or glutamine as an amino acid exchange

factor was independent of small GTPase Arf1

In recent years, there is growing interest in understanding how mTORC1 detect

amino acids to subsequently promote anabolism. Glutamine has gained much

attention due to their plasma abundance (Bergstrom et al., 1974) and preference

for cancers to metabolise them as fuel (C. V. Dang, 2010; Wise et al., 2008).

Although the exact mechanisms have not been delineated, Arf1 GTPases have

been implicated in glutamine-dependent mTORC1 activation (Jewell et al.,

2015; Li et al., 2010).

Owing to similarities in structures and biochemistries between asparagine and

glutamine, I next asked if asparagine signal to mTORC1 in an Arf1-dependent

manner as well. To this end, Arf1 was inhibited with a small molecule inhibitor

Brefeldin A. This inhibition is uncompetitive, as Brefeldin A binds to a

transient, cytoplasm-localised Arf1-GDP-Sec7d complex to render Arf1

“stuck” in its GDP-loaded conformation (Robert, Cherfils, Mouawad, &

Perahia, 2004). In both asparagine- or glutamine-treated cells, Brefeldin A

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abolished mTORC1 activity (Fig 5.3A), indicating that asparagine, too could

signal to mTORC1 via Arf1.

It was still unknown if Arf1 inhibition impacted the role of asparagine or

glutamine as an amino acid exchange factor. Theoretically, this would be

unlikely because 1) leucine was shown to be a key proximal stimulator of

mTORC1 activation (Fig 5.2C and 5.3B), and 2) leucine signalling to mTORC1

depends on Rag, not Arf1 (Jewell et al., 2015). To confirm this, cells were either

preloaded with the amides and subsequently replenished with leucine in the

presence of Brefeldin A, or cotreated with the amides, leucine and Brefeldin A.

As expected, two different observations were made. In the cotreatment strategy,

mTORC1 signalling was abolished, indicating that like glutamine, asparagine

too required Arf1 to modulate mTORC1 (Fig 5.3A and B). In contrast,

mTORC1 in preloaded cells remained unaffected in response to Brefeldin A,

upon acute replenishment of leucine (Fig 5.3B). The differential sensitivity of

asparagine-dependent mTORC1 regulation to Arf1 suggested that 1) as an

amino acid exchange factor, Arf1 was not required to sensitise mTORC1 to

leucine, and 2) there was another means by which asparagine regulate mTORC1

in an Arf1-dependent manner.

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140

Fig 5.3 The significance of Arf1 in asparagine or glutamine-mediated

mTORC1 activation. A) Cells were treated with 4 mM of Asn and/or Gln for

3 h in the presence of essential amino acids (EAA) or 1 μM of Brefeldin A (Bref

A). B) Using the preload strategy, cells were first preloaded with 4 mM of Asn

for 3 h in the absence of EAA. *Bref A indicates its addition when applicable.

Then, 0.4 mM of Leu was introduced in the final 1 h, before termination of

experiment. Using the co-treat strategy, cells were deprived of EAA for 3 h, in

the presence or absence of 1 μM of Bref A. After 3 h, 4 mM of Asn and 0.4 mM

141

of Leu were added for 1 h, before termination of experiment. C) Experiments

were carried out as in (B), but terminated 30 min earlier.

5.2.4 Asparagine or glutamine mediated mTORC1 in sensing endogenous

amino acids, in an Akt and Arf1-dependent manner

Results discussed so far describe how 1) mTORC1 was activated by

extracellular essential amino acids that required intracellular accumulation of

asparagine or glutamine beforehand, and 2) Arf1 was dispensable in that

context. However, in a fed state when cells were not dependent on extracellular

essential amino acids for mTORC1 reactivation, the role of Arf1 became more

prominent. It was unknown if Arf1 was required to mediate mTORC1 sensing

of endogenous amino acids. To test the hypothesis, a system had to be first

established in which intracellular amino acids could be 1) manipulated readily,

and 2) quickly replenished without exogenous supplementation. To this end

cycloheximide, an inhibitor of global protein translation was employed. It is a

bacterially-produced toxin that binds to the E-site of 60S ribosomal subunit to

inhibit eukaryotic translation elongation factor (eEF)-2 mediated tRNA

translocation. By inhibiting ribosomal protein translation, amino acids are not

used for peptide synthesis thus increasing their endogenous pool (Watanabe-

Asano, Kuma, & Mizushima, 2014).

In HepG2 cells preloaded with asparagine under essential amino acids-starved

conditions, mTORC1 was reactivated upon cycloheximide introduction. This

was demonstrated by increased phosphorylation of mTORC1 direct targets,

p70S6K (T389), ULK1 (S757) and 4E-BP1 (S37/46) (Hara et al., 1998; J. Kim

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et al., 2011). This was not observed in non-preloaded cells (Fig 5.4A). In this

experiment, it was noteworthy that cells were not exposed to extracellular

essential amino acids at any point in time, thus supporting the postulation that

asparagine or glutamine could also signal to mTORC1 via a second mechanism

that did not require their exchange for extracellular essential amino acids. It

could also be inferred that intracellular asparagine or glutamine aided mTORC1

sensing of endogenous amino acids. These observations were replicable in

HuH7 and human breast cancer cells MCF7. Preloading with glutamine also

achieved similar degree of mTORC1 activation in the presence of

cycloheximide (Fig 5.4B).

However, substituting asparagine and glutamine with aspartate or glutamate,

respectively could not reactivate mTORC1 (Fig 5.4C), demonstrating that only

their amides could mediate this signalling. Reactivation of mTORC1 upon

cycloheximide treatment in preloaded cells was abrogated when Arf1 was

inhibited (Fig 5.4D). This observation supported the hypothesis that Arf1 was

involved in asparagine and glutamine-dependent mTORC1 sensing of

endogenous amino acids.

Besides Arf1, Akt was also likely to play a role as well. Immunoblot analysis

indicated that under essential amino acids-starved conditions, Akt

phosphorylation was sustained in cells preloaded with asparagine or glutamine,

compared to non-preloaded ones (Fig 5.4A). Inhibition of Akt with MK2206

partially downregulated mTORC1 activities in this system (Fig 5.4F). A more

143

robust Akt inhibition should be attempted to confirm this role of Akt in

mediating mTORC1 sensing of endogenous amino acids. Silencing of Akt

expression, or employing other pharmacological inhibitors could be considered.

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145

146

Fig 5.4 The implications of Arf1 and Akt in mTORC1 sensing of

endogenous amino acids. All experiments were performed under essential

amino acids (EAA)-free conditions. A) HepG2, HuH7 and MCF7 cells were

preloaded with 4 mM of Asn for 3 h. Then, 2 μM of cycloheximide (Chx) was

introduced for the indicated durations. B) HepG2, HuH7 and MCF7 cells were

preloaded with 4 mM of Gln for 3 h. Then, 2 μM of Chx was introduced for the

indicated durations. C) HepG2 cells were preloaded with 4 mM of Gln (Q) or

glutamate (E), or 4 mM of Asn (N) or aspartate (E) for 3 hrs, in the absence or

presence of 2 μM of Chx. D) Cells were preloaded with 4 mM of Asn or Gln, in

the presence or absence of 2 μM of Chx or 1 μM of Arf1 inhibitor, Brefeldin A

(Bref A). E) Cells were preloaded with 4 mM of Asn or Gln, in the presence or

absence of 2 μM of Chx or 5 μM of Akt inhibitor, MK2206.

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5.3 Discussion

In this study, addition of exogenous asparagine into culture media enhanced

mTORC1 signalling. It could also substitute glutamine in mediating mTORC1

sensing of endogenous amino acids and growth factor. Considering the largely

unchanged amino acids profiles in asparagine-treated cells compared to control

(Fig 5.1B), it was likely that this manner of regulation was 1) dependent on

signalling pathways upstream of mTORC1, and 2) independent of the amino

acid catabolism.

Glutamine was previously described as an amino acid exchange factor whereby

its intracellular accumulation precedes its exchange for extracellular essential

amino acids to activate mTORC1 (Nicklin et al., 2009). Owing to asparagine’s

structural similarities with glutamine, I postulated that asparagine could be

exchanged for essential amino acids as well. During this investigation, another

report was published in parallel, presenting evidence supportive of this

hypothesis (Krall et al., 2016). The authors showed that cells resistant to

glutamine withdrawal displayed increased dependence on asparagine,

highlighting the amides’ overlapping functions in maintaining cell survival and

growth. This thesis, too observed that asparagine could substitute glutamine in

mediating mTORC1 sensing of amino acids and growth factors (Fig 5.1C). Krall

and colleagues then showed that metabolic functions of asparagine, namely as

a source of carbon and nitrogen, or substrate for peptide synthesis were

unnecessary to sustain cell growth. However, in their cell models, asparagine

seemed to favour the import of serine, arginine and histidine. This study

observed that leucine was a potent proximal activator of mTORC1 (Fig 5.2C),

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though the significance of serine, arginine and histidine has yet to be

investigated.

Glutamine also signals to mTORC1 in an Arf1-dependent manner (Jewell et al.,

2015; Li et al., 2010). However, it has not yet been determined if 1) asparagine

signal to mTORC1 as such, and 2) their function as amino acid exchange factor

is dependent on Arf1. The latter was not impeded by Arf1 inhibition (Fig 5.3B),

although basal mTORC1 in cells constantly supplied with essential amino acids

and the amides was abolished (Fig 5.3A and B). This suggested that while Arf1

did not mediate amides’ function as amino acids exchange factor, it could still

facilitate mTORC1 sensing of endogenous amino acids in a separate

mechanism. To establish a cell system in which amino acids were present only

intracellularly without requiring exogenous supplementation, cells were

maintained in essential amino acids-depleted culture media in the presence of

cycloheximide. Using this strategy, glutamine or asparagine preloading was

shown to be necessary for mTORC1 reactivation upon cycloheximide-induced

replenishment of intracellular amino acid pools (Fig 5.4A to D). These results

indicate that 1) glutamine or asparagine-mediated exchange for essential amino

acids was not the sole mechanism for mTORC1 activation, and 2) the amides

were required to bridge mTORC1 sensing of endogenous amino acids.

Preloading cells with asparagine or glutamine in the absence of essential amino

acids sustained phosphorylation of upstream mTORC1 regulators, Akt (Fig

5.4A). Inhibition of Akt with MK2206 modestly abrogated mTORC1 activities

149

in cycloheximide-treated cells (Fig 5.4F), suggesting that upstream Akt was in

part required for the amides to signal to mTORC1. In summary, further studies

should be conducted to determine if 1) GTP/GDP-loading status on Arf1 was

influenced by glutamine or asparagine, and 2) there was crosstalk between Akt

and Arf1 to regulate mTORC1 in response to the amides.

Of note, Arf1 is a key regulator of intracellular vesicle trafficking involving the

Golgi body and endoplasmic reticulum (Donaldson, Honda, & Weigert, 2005),

whereas Akt is primarily activated in response to growth factors. It is convenient

to postulate that preloading with glutamine or asparagine stimulate secretion of

certain factors in an Arf1-dependent manner, which then activate Akt signalling

to prime mTORC1 for reactivation. Preliminary secretome studies in

conditioned media of asparagine or glutamine-treated cells could be considered.

Another likely candidate of this autocrine/paracrine factor is NH4+, especially

1) since NH4+ was established as an mTORC1 activator in the previous chapter

as well as other studies (Dai et al., 2013; Merhi et al., 2017), and 2) the bulk of

NH4+ source was catabolism of amidic amino acids. Quantification of

intracellular and extracellular NH4+ using commercially available colourimetric

assays could provide insight to this hypothesis.

To summarise, it remains unknown 1) if Arf1 and Akt regulate mTORC1 as two

independent events, or possibly crosstalk with each other, 2) how Arf1

GTP/GDP-loading was altered in response to asparagine or glutamine

preloading, 3) the molecular targets that asparagine and glutamine interact

150

directly with to stimulate mTORC1 via Arf1 and/or Akt, and 4) if NH4+ derived

from asparagine or glutamine was involved in the sensing of endogenous amino

acids. Data so far indicate that asparagine signalled to mTORC1 in a glutamine-

like manner, and this could be achieved by two distinct mechanisms.

Intracellular accumulation of these amides precedes their exchange for

exogenous essential amino acids such as leucine via antiporters, to subsequently

activate mTORC1. This means of regulation was independent of Arf1. In

parallel, an Arf1-dependent was found to facilitate mTORC1 sensing of

endogenous amino acids, which did not require bidirectional exchange of amino

acids (Fig 5.5).

Fig 5.5 A summarised postulation of how amidic amino acids asparagine

and glutamine signal to mTORC1 via two distinct mechanisms. The first

pathway involved a bidirectional exchange of intracellular amides for

extracellular essential amino acids, such as leucine. These imported essential

amino acids were the proximal stimuli for mTORC1 activities. The second

pathway was dependent on intact, functional Arf1 and Akt. It remained

unknown if Arf1 and Akt cross-talked to activate mTORC1. In accordance with

evidence presented in Chapter 4, the product of glutamine and asparagine

catabolism, NH4+ was postulated to modulate mTORC1 as well. The question

mark (?) symbol denotes hypothetical signalling pathways.

151

CHAPTER 6: GENERAL DISCUSSION

6.1 Clinical significance of metabolite signalling

Exhibition of a phenotype requires a cell to tap into its genetic programme,

express it as RNA or proteins, and modulate downstream metabolic pathways

to carry out the encoded cell function. Each level of processes can be examined

for better understanding of cell physiology and pathology (Figure 6.1) (Aboud

& Weiss, 2013; Klupczynska, Derezinski, & Kokot, 2015).

Fig 6.1 General workflow to achieve cell functions. The capacity for a cell to

perform certain functions is encoded in its genome. Expression of these genes

generally yields RNA and proteins, and in the narrow context of enzymology,

execution of enzymatic functions that give rise to a specific metabolic profile.

These metabolites are considered the most “downstream” or proximal to a cell

phenotype. PTM is the abbreviation for post-translational modifications.

The significance of metabolic pathways is beginning to be appreciated in recent

years, largely due to progression in metabolomics that allows examination of

152

the metabolome from a range of biological samples. There are three ways by

which metabolomics can be applied in research: 1) metabolic fingerprinting,

which focuses on rapid profiling of a large range of metabolites for sample

identification while prioritising data reproducibility, 2) untargeted

metabolomics, which intends to identify and quantify a large range of

metabolites that allows study of specific metabolic pathways, and 3) targeted

metabolomics, which aims to monitor a small pool of metabolites of interest,

usually for confirmation of metabolite identities and quantification

(Klupczynska et al., 2015). These methods permit evaluation of novel

biomarkers in diseased states (Table 6.1). This thesis took advantage of the

second and third platforms to elucidate the role of oncometabolites pyruvate,

NH4+ and amidic amino acids, glutamine and asparagine in regulating HIF-1α

and mTORC1 signalling pathways.

Identification of aberrant metabolic pathways might help explain disease

development. Therapies can then be personalised to improve efficacy. For

example, by profiling more than 1126 metabolites across 262 prostate cancer

samples of tissue, urine and plasma origin, sarcosine, an N-methyl derivative of

glycine was identified as a consistent biomarker for metastatic prostate cancer.

Subsequent inhibition of glycine-N-methyl transferase to inhibit sarcosine

synthesis impeded metastasis, whereas elevation of sarcosine by exogenous

supplementation or prevention of its clearance promoted cancer aggressiveness

(Sreekumar et al., 2009). This demonstrated how metabolomics platform could

be used for therapeutic purposes.

153

Table 6.1 Newly identified metabolite biomarkers of diseases using metabolomics approaches.

Pathology Sample Metabolic pathway Potential biomarkers Citation

Prostate cancer Tissue, urine and

plasma

Glycine Sarcosine (Sreekumar et al., 2009)

Rheumatoid

arthritis

Synovial fluid Urea cycle, TCA cycle, fatty

acids, amino acids

20 metabolites including succinate,

citrulline, glutamine, octadecanol,

isopalmitate and glycerol

(S. Kim et al., 2014)

Cancer of the

bladder

Urine Glycerol, amino acids,

glycolysis, nucleic acids

15 metabolites including senecoic acid, 2-

butenedioic acid, ribonic acid, 2,5-

furandicarboxylic acid and melibiose

(Pasikanti et al., 2010)

Colorectal

cancer

Serum Glycolysis, arginine, proline,

fatty acid, oleamide

33 metabolites including pyruvate, lactate,

tyrosine, tryptophan and uridine

(Qiu et al., 2009)

154

HCC Serum, urine Bile acids, free fatty acids,

glycolysis, urea cycle,

methionine

Bile acids, histidine, inosine (T. Chen et al., 2011)

Diabetes Urine, plasma,

tissue

Amino acids (particularly

BCAA)

Isoleucine, leucine, valine, tyrosine and

phenylalanine

(Friedrich, 2012)

155

On its own, metabolomics are largely restricted to 1) distinguishing pathological

states from normal basal, 2) identifying metabolic pathways implicated in

disease development, and 3) being used for designing interventions. To obtain

a more holistic understanding of cell physiology, metabolomics must be read in

parallel with other “-omics”. Although metabolites are frequently perceived as

downstream “consequences”, evidences suggest that they function as upstream

stimuli capable of manipulating cell functions. Histone modifiers such as TET

and lysine demethylase 4C (KDM4C) enzymes belonging to the 2-oxoglutarate

dependent dioxygenase family are amenable to fluctuations to endogenous

abundance of α-KG and its structural analogues, resulting in changes to gene

expression and epigenetics landscape (Harris, 2015; Vissers, Kuiper, & Dachs,

2014). Ammonia derived from glutaminolysis have been shown to stimulate

autophagy to ensure survival under nutrient and energy stress conditions

(Cheong et al., 2011; C. H. Eng & Abraham, 2010). Relying on metabolomics

alone prevents assessment of such crosstalk, although this shortcoming can be

circumvented by integrating the various “-omics”.

6.2 Conclusion

This thesis illustrates how oncometabolites function as signalling molecules to

induce oncogenic signalling pathways. Pyruvate increased HIF-1α protein level

by downregulating the activities of PHDs, a key component of HIF-1α

degradation machinery. This was independent of ROS and intracellular oxygen

deprivation (Fig 3.2). GC-MS analysis showed that abundance of

oncometabolites that are structural analogues of α-KG, in addition to α-KG itself

was robustly upregulated in pyruvate-treated cells (Fig 3.4). Thus, competitive

156

inhibition of PHDs was unlikely. Despite the near 9-fold increase in α-KG level,

further addition of α-KG relieved HIF-1α stabilisation, implying that α-KG was

somehow limiting to the PHDs (Fig 3.5). This paradox may be reconciled should

α-KG be sequestered in the mitochondria, away from the largely cytoplasmic

and nuclear PHDs. However, technical difficulties prevented accurate profiling

of α-KG in mitochondrial and cytoplasmic compartments. Pyruvate-induced

HIF-1α stabilisation could be reversed by supplementing cells with 1) malate,

or 2) lysine. This rescue effect was likely achieved by two distinct mechanisms.

Malate activated the malate-aspartate shuttle and robustly reduced HIF-1α

protein level to low basal without changes to metabolic profiles, further

suggesting that allocation of α-KG, not net abundance of α-KG was important

in regulating PHDs activities in this cell context (Fig 3.6). Lysine on the other

hand restored TCA cycle homeostasis by scavenging excess α-KG, thus

relieving metabolic stress that induced HIF-1α stabilisation (Fig 3.7).

Incubating cells with NH4+ at low millimolar concentrations that are correlated

with pathological states in vivo sensitised mTORC1 to amino acids and growth

factor (Fig 4.2). This catalyst-like property sustained basal mTORC1 activity

even in the absence of essential amino acids (with glutamine supplementation)

(Fig 4.3). In these cells, GC-MS detected partial replenishment of essential

amino acids pool, a possible indication of autophagy induction (Onodera &

Ohsumi, 2005) (Fig 4.4). Whether autophagy was triggered by essential amino

acids withdrawal or NH4+ remained to be investigated. Data also suggested that

a functional upstream Src/Akt signalling axis was required to mediate NH4+-

induced mTORC1 activation (Fig 4.5), although more work should be done to

157

confirm 1) if Src/Akt itself was upregulated in response to NH4+, and 2) the role

of Na+, K+-ATPase in binding extracellular NH4+ to initiate downstream

mTORC1 activation.

Accumulation of intracellular asparagine and glutamine was also found to be

necessary for mTORC1 to “sense” amino acids and growth factors. It was still

unknown if their catabolism and subsequent release of free ammonia was

required for this function. These amides mediated mTORC1 activation by 1)

exchanging with extracellular essential amino acids such as leucine, in an Arf1-

independent manner (Fig 5.2 and 5.3), or 2) promoting sensing of endogenous

amino acids in an Arf1- and Akt-dependent manner (Fig 5.4). The collective

findings are summarised in Fig 6.2.

158

Fig 6.2 Metabolic regulation of

HIF-1α and mTORC1. The

diagram summarises how

metabolites (bolded) pyruvate, NH4+,

glutamine and asparagine signal to

HIF-1α and mTORC1. Parameters

outlined in red indicate hypothetical

events which means of regulation

remain unknown.

159

6.3 Future work

The mechanisms reported in this thesis are proof-of-concept of how carbon and

nitrogen metabolites could influence cell signalling. To fully encapsulate the

impact of these metabolites in a more physiological manner, reproducibility of

these results should be attempted across a range of biological models. Cancer

cell lines known to harbour higher levels of these endogenous metabolites, or

genetically modified to mimic these conditions could be used. For example,

cells engineered to overexpress pyruvate carboxylase, or silencing of α-KGDH

could be used to artificially raise mitochondrial α-KG levels to study its effects

on HIF-1α stabilisation. Certain subtypes of breast, lung and liver cancers are

known to harbour high expression levels of pyruvate carboxylase (Fan et al.,

2009; Forbes et al., 2006; K. J. Liu et al., 1991), thus could also be adopted as

cell models. Tumours addicted to glutaminolysis, addition of asparaginase or

glutaminase in culture media, or genetically modified to overexpress those

enzymes might also be representative of hyperammonaemic, NH4+-treated cells

(Chance et al., 1988; Harder et al., 2014; Heitink-Polle et al., 2013).

These experiments could then be attempted in an in vivo system. This is

important because 1) metabolic pathways are rarely localised or unique to a

specific organ or tissue, 2) metabolites are circulated to distal locations, and 3)

interorgan metabolism is central to proper whole-body function. Thus,

investigating metabolic events in a viable organism might lead to discovery of

previously undetectable phenotypes, compared to when tests were restricted to

a homogenous cell culture environment.

160

Understanding cancer biology from a metabolism angle offers insights into

cotreatment strategies. Currently, very few FDA-approved cancer therapies

target metabolic pathways, due to inadequate understanding of cancer

metabolism and the dangers presented by targeting them, as metabolic

reprogramming also happens in non-transformed but highly proliferative cells

(Galluzzi, Kepp, Vander Heiden, & Kroemer, 2013). It is possible that with

careful optimisation, reduced toxicity and a more targeted approach could be

achieved when used in concert with therapies targeting oncogenic signalling

pathways. Because oncometabolites can influence distant cells in a paracrine or

autocrine manner, impeding these signals might also reduce cancer

aggressiveness.

161

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