Catabolism of Extracellular Protein by Pancreatic Cancer Cells · 2019. 1. 3. · catabolism by...

Catabolism of Extracellular Protein

by Pancreatic Cancer Cells

Michel Ibrahim Nofal

A Dissertation

Presented to the Faculty

of Princeton University

in Candidacy for the Degree

of Doctor of Philosophy

Recommended for Acceptance

by the Department of

Quantitative and Computational Biology

Adviser: Professor Joshua D. Rabinowitz

November 2018

c© Copyright by Michel Ibrahim Nofal, 2018.

All rights reserved.

Abstract

All cells require amino acids to support protein synthesis and cell growth. Until

recently, mammalian cells were thought to depend on monomeric amino acids in the

environment. I showed that pancreatic tumor cells can use extracellular protein as

a source of amino acids. These cells take up intact protein via macropinocytosis

and catabolize it in lysosomes. This process – “protein eating” – enables cultured

pancreatic cancer cells to grow in amino acid-deficient environments. In this thesis, I

present my work on protein eating.

To show that protein eating is capable of fueling growth, I cultured murine pan-

creatic cancer cells in medium lacking leucine (an essential amino acid) and supple-

mented with a physiological concentration of serum albumin. Many cells cells died in

this medium, but some survived and grew to confluence. I passaged these survivors

for months, and they gradually adapted to growth fueled by protein eating. This

proved that protein eating is a viable form of amino acid uptake.

I developed isotope tracer-based methods to quantitatively measure protein eating.

Cells are grown in medium with stable isotope-labeled amino acids and unlabeled

serum protein. Mass spectrometry enables distinction of amino acids taken up as

monomers (labeled) from amino acids taken up as intact protein and catabolized

(unlabeled).

I conducted genome-wide screens to systematically identify genes essential for

growth fueled by protein eating. The most essential gene was GCN2, which suppresses

translation initiation in cells starved for amino acids. I discovered that loss of GCN2

impairs catabolism in amino acid-deprived cells. I propose that GCN2 supports

catabolism by directing amino acids emerging from lysosomes into newly synthesized

proteins that increase the catabolic capacity of the cell – for example, the lysosomal

hydrolase cathepsin L.

iii

Advances in our understanding of protein eating may lead to the development of

better therapies for pancreatic cancer patients. The importance of protein eating as

an amino acid supply route for cells in healthy tissues remains unexplored.

iv

Acknowledgements

I am lucky to be where I am today. I have a lot of people to thank. Before I was

born, my parents immigrated to the Silicon Valley to work as hardware engineers.

They worked hard throughout their lives to make things easy for me. They taught

me to be curious and ambitious. I thank them for their continuous love and support.

Once I have a PhD, I will call more often. I also thank my sister, who had to put up

with me growing up – I was probably more competitive than I even realize now.

My parents spent a lot of money sending me to a private school, Pinewood School,

from kindergarten through twelfth grade. (Pinewood had three campuses: K-2, 3-6,

and 7-12.) Many of my classmates were the children of similarly enterprising parents

who had moved to the area from overseas to work in high tech. Our parents all pushed

us, and we pushed each other. It was easy to succeed in that environment. (Of the

57 students in my graduating class, two went on to Stanford, one to Harvard, one to

Brown, one to Duke, one to Bowdoin, one to Amherst, six to UC Berkeley, two to

UCLA, and so on.) I thank my friends and teachers at Pinewood for preparing me

so well, especially my calculus teacher, Mr. Green. I also thank my basketball coach,

Coach Slayton, and my piano teacher, Mrs. Wang, for toughening me up, preparing

me for grad school.

After Pinewood, I became an undergraduate at Berkeley. For the first two years,

I lived with a high school friend, Tim Wang, who was a year ahead of me in school.

We both majored in bioengineering and joined labs. When the time came for me to

decide what to do after college, I saw Tim applying to PhD programs, and it seemed

like something I would like, so I did it too. I thank Tim for his friendship and his

mentorship over the years.

With my undergraduate resume, I was fortunate to be admitted to exactly one

PhD program: Princeton Quantitative and Computational Biology. Soon after arriv-

ing in Princeton, I joined the Rabinowitz lab and started working on protein eating.

v

(The first experiment I ever did was a growth experiment in leucine-free medium to

see if cells could use extracellular protein to support growth.) Since then, I have

consumed more than my fair share of lab resources while constantly annoying others

peacefully going about their work. I thank everyone that I ever overlapped with in

the Rabinowitz lab for contributing to a great intellectual environment.

In particular, I thank Jurre Kamphorst and Jing Fan for getting me started;

Wenyun Lu and Lin Wang for spending their valuable time helping me maintain

a more-or-less personal mass spectrometer; Sean Hackett and David Robinson for

teaching me R; Tomer Shlomi and Vito Zanotelli for helping me learn metabolic

flux analysis; Ian Lewis, Greg Ducker, Jon Ghergurovich, and Juan Carlos Garcia

Canaveras for putting up with my incessant questions during working hours; Raphael

Morscher and Matt Sonnett for putting up with my incessant questions extremely

late at night; Jun Park for asking me as many questions as I ask him; Lukas Tanner

for a strong friendship in a lonely time; Xiaoyang Su and Rob Marmion for invaluable

cloning help; Sophia Li and Mark Esposito for experiencing graduate school with me

from start to finish; Cholsoon Jang and Gina Lee for sharing valuable papers and

reagents with me; and Lifeng Yang, who is one of my best friends and not a nao-

can. I thank my thesis committee members Yibin Kang, Eileen White, and Martin

Wuhr for providing me with thoughtful advice, as well as Fred Hughson and Alexei

Korennykh for rare high-level discussion of cell biology. I also thank Martin Wuhr

for his generosity with respect to our ongoing collaboration, which I hope does not

end soon. I thank Gary Laevsky and Tina DeCoste for imaging and flow cytometry

support, and the Devenport and Toettcher labs for imaging help. I thank the hard-

working undergraduate researchers I mentored, Kevin Zhang, Aiden Han, Sriram

Cyr, and Agustin Zavala. Most importantly, I thank Josh, who saw potential in me

when few others did. Josh trusted me enough to let me and my research program

drift further and further away from the main interests of his lab over the course of

vi

seven years. Josh taught me biochemistry, how to teach biochemistry, how to think

scientifically, how to write scientifically, and how to see the story through the data.

Also, I recognize that my research program was not cheap.

A short aside: Once we knew protein eating could fuel growth, the two obvious

questions were (i) can we measure protein eating quantitatively, and (ii) can we

identify the genes essential for protein eating? Luckily, I was in exactly the right lab

to answer the first question. The second question, however, called for a genetic screen,

which I could not do in Princeton. At the time, two groups developed the ability to

conduct genome-wide screens in mammalian cells using CRISPR-Cas9 technology;

they published back-to-back papers in Science. One group was led by Feng Zhang,

who may win the Nobel Prize for the development of CRISPR-Cas9 technology. The

other group was led by Tim Wang, whose screening technology was better. The fact

that Tim and his advisors agreed to host me to do CRISPR screens was a stroke of

luck that lifted my career. I thank Tim and his advisors, David Sabatini and Eric

Lander, for their generosity in hosting me.

Many have supported me outside the lab during my tenure in graduate school. I

thank all my friends in Princeton, especially Mark Esposito, Rezma Shrestha, Sara

Forster, Matt Streeter, Jordan Maseng, Sophia Li, Fred Shipley, Benno Mirabelli, and

Jordan Ash. I also thank Max Wilson, who I lived with for two years and grew up

with scientifically. He managed to become a professor at UC Santa Barbara before I

finished graduate school, which qualified him to be my second thesis reader. Finally,

I thank my girlfriend, Gabriela Castro, for her love and patience, and her family, who

regularly welcomes me into their home as if I were one of their own.

vii

To my parents.

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Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

1 Introduction 1

1.1 Foreward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Scientific Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Human Pancreatic Cancer Tumors Are Nutrient Poor and Tumor

Cells Actively Scavenge Extracellular Protein 13

2.1 Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.4 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.5 Results - Metabolomic analysis of human PDAC tumors . . . . . . . 23

2.6 Results - Macropinocytosis in PDAC tumors . . . . . . . . . . . . . . 27

2.7 Results - Support of cultured tumor cell growth by albumin in the

absence of free amino acids . . . . . . . . . . . . . . . . . . . . . . . . 28

2.8 Results - Isotope tracing of serum protein catabolism . . . . . . . . . 30

2.9 Results - Amino acid patterns in cells fed by serum protein

macropinocytosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

ix

2.10 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3 mTOR Inhibition Restores Amino Acid Balance In Cells Dependent

on Catabolism of Extracellular Protein 38

3.1 Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.4 Results - Isotope-Tracer Method Measures Amino Acid Release Due

to Extracellular Protein Catabolism . . . . . . . . . . . . . . . . . . . 43

3.5 Results - Impact of Intracellular Protein Catabolism on Scavenging

Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.6 Results - Excessive mTOR Inhibition Slows Growth on Extracellular

Protein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.7 Results - Amino Acid-Deficiency Induces Protein Scavenging Flux In-

dependently of mTOR . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.8 Results - mTOR Inhibition Induces Punctate DQ-BSA Fluorescence . 52

3.9 Results - mTOR Inhibition Restores Amino Acid Balance and Prevents

Cell Death in Amino Acid-Deprived Cells . . . . . . . . . . . . . . . . 56

3.10 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

3.11 Materials and Methods - Cell lines . . . . . . . . . . . . . . . . . . . 62

3.12 Materials and Methods - Measuring catabolism of extracellular protein 63

3.13 Materials and Methods - Other experimental methods . . . . . . . . . 69

4 A Genome-Wide Screen Identifies The Proteins Behind Protein Eat-

ing: GCN2 and cathepsin L 72

4.1 Proposed Manuscript Title . . . . . . . . . . . . . . . . . . . . . . . . 72

4.2 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

x

4.4 Results - Genome-wide screen systematically identifies genes required

for growth fueled by catabolized extracellular protein . . . . . . . . . 77

4.5 Results - The Three Major Categories of Selectively Essential Genes:

Uptake, Degradation, and Regulation of Translation . . . . . . . . . . 86

4.6 Results - Screen Validation and Proteomics . . . . . . . . . . . . . . . 96

5 Ribosomes on the Night Shift: The universal protein-making ma-

chine becomes a nutrient source between meals 107

6 Conclusion and Future Directions 113

Bibliography 118

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List of Figures

2.1 Cancer Research paper - Figure 1 . . . . . . . . . . . . . . . . . . . . 26





3.1 Molecular Cell paper - Graphical Abstract . . . . . . . . . . . . . . . 40

3.2 Molecular Cell paper - Figure 1 . . . . . . . . . . . . . . . . . . . . . 45







4.1 Genome-wide screen design and summary . . . . . . . . . . . . . . . . 79

4.2 Screen results for Gcn2 and Gcn1 . . . . . . . . . . . . . . . . . . . . 80

4.3 Selectively essential genes were highly expressed . . . . . . . . . . . . 81

4.4 K-Ras and the V-ATPase are essential in all growth conditions . . . . 82

4.5 Selective essentiality of actin capping protein isoforms . . . . . . . . . 83

4.6 Screen results for various protein complexes . . . . . . . . . . . . . . 85

xii

4.7 Selective essentiality of actin-related proteins . . . . . . . . . . . . . . 87

4.8 Selective essentiality of Rab proteins and Rabankyrin-5 . . . . . . . . 90

4.9 Comparison of the selective essentialities of translation regulators . . 95

4.10 Basic validation of selectively essential genes . . . . . . . . . . . . . . 97

4.11 GCN2 supports protein catabolism in amino acid-deprived cells . . . 99

4.12 The effect of GCN2 and amino acid depletion on protein levels . . . . 101

4.13 GCN2 is required to maintain cathepsin L levels in amino acid-deficient

conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.1 NUFIP1 Figure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

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Chapter 1

Introduction

This introduction has two parts: an informal foreward and a formal scientific intro-

duction.

1.1 Foreward

In social settings when I am asked what I research, I find myself reluctant to answer.

I appreciate the curiosity, but I worry that I cannot possibly communicate what it is

that I work on in less than five or ten minutes, which is much longer than the person,

who probably just asked to be polite, is willing to listen. So I give short answers.

Just buzz words, no attempt to communicate real ideas.

“What do you study?”

“Pancreatic cancer.”

“What about pancreatic cancer?”

“I research the response of cultured pancreatic cancer cells to amino acid starva-

tion.”

“Oh, cool!”

Or sometimes: “Oh, sounds complicated...” Either way, I end up deeply unsatis-

fied, because I feel there has been a misunderstanding. To me, it seems the questions

1

I have spent years trying to answer are basic, fundamental, easy for anyone literate in

cell biology to understand. Indeed, the answers to these questions are complicated,

but the questions themselves have always been simple. Nevertheless, they remain in-

accessible to the average reasonable human being, who begins feeling uncomfortable

thirty seconds into any conversations involving words like “pancreatic” and “amino.”

The trouble I have in beginning to describe what I work on is that I dont work

on just one biological thing – a gene, a metabolic pathway, an organelle. I work

on relationships between apparently unrelated things – catabolism of extracellular

protein and regulation of translation, lysosomes and GCN2. Where to start?

Pancreatic tumors are bad news – if you find out you have one, you probably wont

live another year. They emerge from cells of the exocrine pancreas (as opposed to the

other part of the pancreas, the endocrine pancreas). The exocrine pancreas produces

digestive enzymes that are secreted in the form of “pancreatic juice.” These enzymes

travel through the bile duct and into the small intestine, where they digest food. The

endocrine pancreas produces insulin and glucagon, which control blood sugar levels.

Cells in both the exocrine pancreas and the endocrine pancreas have something in

common: their job is to synthesize and secrete proteins. I think it is fair to say that

protein synthesis and protein trafficking are the most important biological processes

in these cells.

Two things seem to be true of cells of the exocrine pancreas that turn into ma-

lignant tumors. First, these cells harbor a genetic mutation that activates the Ras

signaling pathway. Usually, the mutation results in a single amino acid substitution

in the K-Ras protein; commonly, the twelfth amino acid in K-Ras, originally glycine,

becomes aspartate, or valine, or cysteine. Second, the environment in which pancre-

atic tumor-initiating cells reside is inflamed. Inflammation activates other proteins,

including the transcription factor cellular Myc (c-Myc). A viral homolog of c-Myc,

v-Myc, is sufficient to induce tumorigenesis in cells infected by the Avian virus har-

2

boring this gene. In pancreatic cells, however, neither Ras nor Myc alone causes

malignancies; the two “cooperate” to form tumors.

The inflammatory environment of pancreatic tumors elicits responses from tu-

mor cells and non-tumor cells alike. Pancreatic stellate cells, which remain quiescent

(largely inactive) in healthy pancreatic tissue, are activated by inflammation. Acti-

vated stellate cells are induced to secrete extracellular matrix proteins – this is called

“fibrosis.” Fibrosis is noticeable macroscopically: pancreatic tissue is soft, but tu-

mors are hard, like scar tissue. Fibrosis limits the diffusion of circulating nutrients

into the tumor, so cells in the interior of the tumor are deprived of oxygen, glucose,

and other important metabolites. The metabolite most depleted in human pancreatic

tumors, as we report in the first paper included in this thesis, is glutamine. Thus,

pancreatic tumor cells have a problem: they must survive and even grow in an amino

acid-deficient environment. How do they solve this problem?

While the levels of some amino acid monomers are low in pancreatic tumors, the

levels of serum proteins are high. In fact, due to leaky vasculature and deficient

lymphatic drainage, serum protein accumulates in pancreatic tumors. This serum

protein, as well as the extracellular matrix protein secreted by activated pancreatic

stellate cells, is a potential source of amino acids. Tumor cells can take up protein

from the extracellular space and degrade it in lysosomes to produce amino acids to

fuel growth – I refer to the totality of this process as “protein eating.”

The idea that tumor cells might use extracellular protein as a fuel source is rel-

atively new – there were no published papers about it when I started my graduate

career. The biochemical processes underlying protein eating remain unclear. Up-

take is achieved through a process called “macropinocytosis,” in which extracellular

material is taken up non-specifically into big vesicles called “macropinosomes.” The

protein internalized in macropinosomes somehow is delivered to “lysosomes,” where it

is degraded. Degradation yields amino acids, which can be used to synthesize protein

3

required for survival or growth. How exactly are uptake, trafficking, and degradation

achieved? Are any steps in the complicated process of eating extracellular protein

particularly important? Perhaps a pharmacological inhibitor of a protein critical to

this process would thwart pancreatic cancer growth.

These were the questions I started with, but in trying to answer them, another

question emerged: What do amino acid-deprived cells do with the limited amino

acids that they generate through protein eating? These are important decisions for

pancreatic tumor cells residing in the poorly perfused interior of a pancreatic tumor.

Cells that make the wrong decisions die; those that make the right decisions survive

and grow. If enough cells consistently make the right decision, they kill their host, the

patient. Unfortunately, some drugs (mTOR, PI3K inhibitors) that have been tested

in pancreatic cancer clinical trials help cells make the right decision. Big mistakes

have been made because we do not understand this question, and big opportunities

may present themselves once we do.

I have found it useful to approach this question logically. I assume that amino

acid-deprived cells will use the limited amino acids generated by protein eating wisely,

and I try to imagine what a wise cell would do. This is how I generate hypotheses.

A pancreatic tumor cell starved of amino acids would be wise to (i) degrade as much

protein as possible and (ii) use the resulting amino acids to synthesize proteins that

will increase the catabolic capacity of the cell. The cell would then degrade more

proteins, producing more amino acids, enabling further increases in catabolic capacity,

until the cell has enough amino acids. At that point, the amino acids could be used

for growth.

Based on this reasoning, I have had the idea that amino acid-deprived cells upreg-

ulate the synthesis of proteins that help them catabolize more protein. GCN2 is the

protein that directs amino acid-starved cells to use their amino acids wisely. GCN2 is

known to suppress translation initiation when amino acid pools are depleted. My pre-

4

diction is that GCN2 suppresses the translation of most transcripts (GCN2-sensitive

transcripts) to free up amino acids for the synthesis of proteins encoded by other tran-

scripts (GCN2-resistant transcripts). For the past six months, I have been searching

for the key proteins upregulated by GCN2 that induce higher catabolic rates.

Just a few weeks ago, upon analyzing proteomics data from GCN2 wild-type and

GCN2 knockout cells, I developed the following hypothesis. I think that the synthesis

of cathepsin L, a short-lived lysosomal hydrolase that seems to be especially impor-

tant for protein eating, is induced by GCN2. Thus, loss of GCN2 impairs cathepsin

L synthesis in amino acid-deprived cells, and as a result, these cells exhibit lower

catabolic rates. I think that inhibitors of GCN2 and cathepsin L could potentially

block pancreatic tumor growth by impairing the tumor cell response to amino acid

deprivation. I could be wrong.

This thesis contains an introduction, two published papers, a perspective, the

beginnings of a third paper, and a forward-looking conclusion. In the two published

papers, the language and figures are refined, but the findings are crude. (This is a

harsh assessment, but with the benefit of hindsight, I think it is fair.) The opposite is

true for the incomplete third paper and conclusion, which includes a short proposal

to study protein trafficking as a network of fluxes. In other words, the intellectual

contents of the final section have been honed over the course of many years, but

the language and figures are far from honed. I have been busy with experiments,

unwilling to divert time and energy away from the never-ending struggle to get results

that support my ideas. So it goes as a graduate student in biology.

1.2 Scientific Introduction

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease, with a 5-year

overall survival rate of 8% [103]. Existing therapies for PDAC patients have generally

5

been ineffective for the following reasons. First, these patients often do not learn that

they have pancreatic cancer until the tumor has progressed to an advanced stage,

infiltrating surrounding tissues and sometimes metastasizing to distal organs. Only

9% of newly diagnosed pancreatic cancer is localized [103]. Second, despite sustained

efforts over several decades, clinicians and researchers have been unable to identify

specific vulnerabilities of pancreatic tumor cells that can be targeted pharmacologi-

cally. Instead, patients are treated with various combinations of blunt chemothera-

peutic agents that are toxic to pancreatic tumor cells but also cause serious systemic

problems, like thrombocytopenia, anemia, and peripheral neuropathy [120]. Lastly,

characteristic fibrosis by cancer-associated pancreatic stellate cells limits perfusion.

As a result, delivery of therapeutic agents into tumors is a challenge [74].

Pancreatic tumors arise from cells of the exocrine pancreas [41]. (This is not true

for all pancreatic tumors; rarely, pancreatic tumors arise from pancreatic neuroen-

docrine cells. In this thesis, “pancreatic tumors” refer to ductal adenocarcinomas of

the pancreas, and “pancreatic cancer cells” refer to PDAC tumor cells.) The exocrine

pancreas produces and secretes digestive enzymes that are secreted through the pan-

creatic duct, then the bile duct, into the small intestine, where they break down food.

Thus, the cells of the exocrine pancreas exist to synthesize and transport protein.

(This is also true for cells of the endocrine pancreas, which regulates blood glucose

levels through the actions of alpha cells, which produce and export glucagon, and

beta cells, which produce and export insulin.) As a whole, the pancreas is a protein

synthesis machine [33].

There seem to be two preconditions for pancreatic tumorigenesis. The first is that

pancreatic exocrine cells harbor a mutation that activates the Ras signaling pathway.

The second is inflammation in the pancreas, which activates the transcription factor c-

Myc. Activation of only one of these two proteins is insufficient to drive tumor growth;

both must be activated [54]. As mentioned previously, the resulting tumors are fibrotic

6

and poorly perfused; as such, they have long been assumed to be nutrient-poor. In

the second chapter of this thesis (a paper published in Cancer Research in 2015),

we validate this assumption: we report measurements of 127 soluble metabolites in

human pancreatic tumors and paired adjacent pancreatic tissue. The levels of glucose

and glutamine – the most important carbon and nitrogen sources for cultured cells

– were lower in tumors than in healthy adjacent tissue. Thus, we confirmed that

pancreatic tumor cells are starved for basic nutrients [48].

How do pancreatic tumor cells, with limited amounts of these primary nutrients

at their disposal, support survival and growth? This is the central question of pan-

creatic tumor cell metabolism. The ultimate goal of the cancer metabolism research

community is to identify metabolic activities that tumor cells use to overcome the

nutrient scarcity. If we found a metabolic activity uniquely important to pancreatic

tumor cells, for example, it would be an attractive therapeutic target.

Early in my tenure as a graduate student, directed by my advisor Josh Rabinowitz,

I discovered such an activity. To overcome amino acid scarcity, pancreatic tumor cells

take up extracellular protein from the environment and catabolize it in lysosomes. (In

this thesis, I refer to the totality of this process as “protein eating.”) We were not the

first to discover this phenomenon, nor to publish about it; Dafna Bar Sagi and others

were. Over thirty years ago, Bar Sagi had published a paper showing that Ras activity

induces membrane ruffling [4]. For years, the function of membrane ruffling was

not understood, but at some point, it probably became clear to her that membrane

ruffling enabled the uptake (and subsequent degradation) of extracellular protein

via a process called macropinocytosis. Macropinocytosis internalizes the contents of

the extracellular medium non-specifically into large vesicles called macropinosomes,

which enter the large cellular vesicle trafficking network. These contents can then be

trafficked to the degradative compartment, where they are catabolized into nutrients

that can be used for bioenergetics and biosynthesis.

7

In 2013, Bar Sagi and others published a paper showing that macropinocytosis is

an amino acid supply route in Ras-mutant cells and that cultured cells with K-Ras

mutations survived longer in low concentrations of glutamine when cultured with

physiological concentrations of albumin. (Cells without K-Ras mutations did not.)

Imaging experiments showed that serum protein was taken up in macropinosomes

and degraded in lysosomes, establishing macropinocytosis as the primary mode

of uptake of extracellular protein to be degraded. Tumor xenograft experiments

compared xenografted cells from a K-Ras-mutant pancreatic cell line (MIA PaCa-

2) with xenografted cells from a K-Ras-wild-type pancreatic cell line (BxPC-3).

Xenografts were treated with 5-(N-Ethyl-N-isopropyl)-amiloride (EIPA), which in-

hibits macropinocytosis. EIPA targets a plasma membrane Na+-H+ antiporter [51]

and is too toxic to be administered systemically, so it was injected directly into the

tumors. MIA PaCa-2 xenografts were sensitive to EIPA; BxPC-3 xenografts were not.

The authors claim that these results “indicate that a reduction in macropinocytic

capacity may compromise tumor growth” [16]. Given the suspiciousness of both the

tumor models and the inhibitor, I am unconvinced by this particular experiment.

The paper as a whole was important, contributing the idea that cancerous epithelial

cells can use extracellular protein as a fuel source, but evidence that macropinocytosis

is a critical process in pancreatic tumor cells in vivo remained lacking.

In the second chapter of this thesis (the Cancer Research paper), we provide

evidence that freshly resected human pancreatic tumor cells do indeed engage in

macropinocytosis. This evidence came from Cosimo Commisso (a post-doctoral re-

searcher mentored by Bar Sagi at the time and the first author of the 2013 paper

discussed above). He showed that in slices of human pancreatic tumor tissue, tu-

mor cells, moreso than other cells present in the tumor, engage in macropinocytosis

[48]. These experiments provided valuable evidence from human pancreatic tumors,

but the evidence was indirect. They did not confirm that human pancreatic tumor

8

cells catabolize meaningful amounts of extracellular protein – amounts that can fuel

growth.

To directly test whether protein eating can fuel the growth of pancreatic tumors,

I used cultured pancreatic cancer cells derived from autochthonous mouse models of

pancreatic ductal adenocarcinoma (PDAC) [60]. I cultured these cells in a medium

that resembled, in an over-simplified way, the metabolic environment of a pancreatic

tumor. Relative to human serum, typical cell culture medium has very high concen-

trations of amino acids and very low concentrations of serum protein. Instead, I used

medium that lacked leucine, an essential amino acid, to mimic the amino acid-deficient

environment of pancreatic tumors, and I supplemented this leucine-free medium with

a roughly physiological concentration of serum protein (50 g/L bovine serum albu-

min). Since leucine is essential, we reasoned that cells would only grow if protein

eating were a viable alternative to monomeric amino acid uptake. Indeed, while most

cells switched into this leucine-free medium died, some survived and eventually grew

to confluence. I cultured these surviving cells indefinitely, and after several months,

the cells grew robustly in leucine-free medium supplemented with albumin. These

adapted cells even grew in medium without any free amino acids when albumin was

added [48]. This proved that protein eating could provide enough amino acids to fuel

growth.

Also included in the second chapter (the Cancer Research paper) is the first it-

eration of a method that uses isotope tracers to measure the rate of amino acid

release due to catabolism of extracellular protein. Using the method, I showed that

catabolism of extracellular protein can contribute substantially to amino acid pools

[48]. This iteration had its flaws – differences in factors such as cell number and

growth rate were not accounted for – but to my knowledge, this was the first time

that stable isotope tracers were ever used to estimate the rate of a major catabolic

process.

9

Following the publication of the Cancer Research paper, Craig Thompson and

others – including former post-doctoral researcher Wilhelm Palm – discovered that

inhibition of mechanistic target of rapamycin complex 1 (mTORC1) enables robust

growth of cells dependent on protein eating for growth. (They too cultured cells in

leucine-free medium supplemented with serum albumin.) Using fluorescent imaging

experiments, they showed that inhibition of mTORC1 promotes catabolism. These

experiments relied on a fluorescent marker of protein degradation called DQ-BSA.

DQ-BSA is bovine serum albumin (BSA) heavily labeled with the fluorescent dye

BODIPY. BODIPY self-quenches at high concentrations, so intact DQ-BSA does

not fluoresce. Upon degradation, however, DQ-BSA de-quenches. When DQ-BSA

was administered to untreated cells and cells treated with an mTOR inhibitor, the

lysosomes of the mTOR-inhibited cells emitted far more fluorescence – in some ex-

periments, ten-fold more than lysosomes of untreated cells – suggesting that more

degradation is happening in these cells [78].

Degradation, however, is not the only potential cause of increased DQ-BSA fluo-

rescence. In amino acid-starved cells, small lysosomes fuse to create large lysosomes;

once the large lysosomes have degraded their contents, they redistribute into small

lysosomes again. Inhibition of mTORC1 is known to block the reformation of small

lysosomes from large ones [135]. Thus, increased DQ-BSA fluorescence could be the

result of slower fluorophore turnover in mTOR-inhibited cells. In general, imaging

experiments are imperfect substitutes for flux measurements. The authors likely real-

ized these limitations, but at the time, there was no quantitative method to measure

protein eating flux to confirm their DQ-BSA findings.

In the third chapter of this thesis (a paper published in Molecular Cell in 2017),

I present the second iteration of the isotope tracer method that measures the rate of

amino acid release due to catabolism of extracellular protein. This iteration of the

method accounted for differences in cell number and growth rate, producing catabolic

10

rate estimates in terms of amino acids released from lysosomes per µL cell volume per

hour. Using this method, we confirmed that mTOR inhibition induces an increase

in catabolism of extracellular protein. The magnitude of this increase, however, was

much smaller than the imaging experiments suggested. We found that, in amino acid-

replete medium, mTOR-inhibited cells catabolized 50-75% more extracellular protein

than untreated cells. Unexpectedly, in amino acid-deficient media, cells increased

their catabolic rates in mTORC1-independent fashion. (mTORC1 activity persisted

in these cells despite amino acid deprivation.) Moreover, mTOR inhibition did not

substantially increase the rates of catabolism of amino acid-deprived cells. Thus,

we reasoned that mTOR inhibition enhances growth fueled by protein eating by

promoting survival, not by enhancing catabolism [73]. How does mTOR inhibition

promote survival? The answers we provide in this paper – by “restoring amino acid

balance” – are not entirely satisfying.

In the fourth chapter of this thesis (a manuscript in progress), I present the results

of a genome-wide screen designed to systematically identify genes critical for growth

fueled by protein eating. This screen was conducted at MIT in collaboration with

David Sabatini, Eric Lander, and Tim Wang, a graduate student co-mentored by

Sabatini and Lander at the time. We found that the genes critical for growth on

extracellular protein encode proteins that vary widely in function. Some proteins are

required for macropinocytosis, others for degradation of extracellular protein. The

gene most essential for growth fueled by protein eating (and not essential in amino

acid-replete conditions) was GCN2, a regulator of protein synthesis with no known

connection to macropinocytosis or protein degradation. Like mTOR inhibition, GCN2

suppresses translation initiation. Using isotope tracers, we showed that GCN2 activity

was required to increase catabolic rates in amino acid-deprived cells. The biochemical

mechanism underlying this increase remains unknown. My current hypothesis is

the proposed title of the manuscript: GCN2 upregulates translation of cathepsin L-

11

encoding mRNAs, increasing the degradative capacity of amino acid-deprived cells.

This hypothesis remains to be proven. If true, GCN2 and cathepsin L provide two

appealing targets for pancreatic cancer therapy.

In the fifth chapter of this thesis (a perspective published in Science in 2018), I

review the finding, by David Sabatini and others, that NUFIP1 is a ribosome receptor

that delivers ribosomes to lysosomes in nutrient-starved cells [129, 72]. Whereas

protein eating delivers material to lysosomes non-specifically – in principal, anything

in the extracellular space can be taken up and degraded by this process – the NUFIP1

pathway is specific to ribosomes. It remains unclear how many distinct degradative

pathways exist.

To conclude, I summarize the findings in this thesis and discuss future directions.

12

Chapter 2

Human Pancreatic Cancer Tumors

Are Nutrient Poor and Tumor

Cells Actively Scavenge

Extracellular Protein

This chapter is a paper published in Cancer Research in 2015 [48]. Figure numbering

and citation numbers from the original paper are preserved. Supplementary figures

can be found at the Cancer Research website.

2.1 Authors

Jurre J. Kamphorst, Michel Nofal, Cosimo Commisso, Sean R. Hackett, Wenyun Lu,

Elda Grabocka, Matthew G. Vander Heiden, George Miller, Jeffrey A. Drebin, Dafna

Bar-Sagi, Craig B. Thompson, and Joshua D. Rabinowitz

13

2.2 Abstract

Glucose and amino acids are key nutrients supporting cell growth. Amino acids

are imported as monomers, but an alternative route induced by oncogenic KRAS in-

volves uptake of extracellular proteins via macropinocytosis and subsequent lysosomal

degradation of these proteins as a source of amino acids. In this study, we examined

the metabolism of pancreatic ductal adenocarcinoma (PDAC), a poorly vascularized

lethal KRAS-driven malignancy. Metabolomic comparisons of human PDAC and be-

nign adjacent tissue revealed that tumor tissue was low in glucose, upper glycolytic

intermediates, creatine phosphate, and the amino acids glutamine and serine, two

major metabolic substrates. Surprisingly, PDAC accumulated essential amino acids.

Such accumulation could arise from extracellular proteins being degraded through

macropinocytosis in quantities necessary to meet glutamine requirements, which in

turn produces excess of most other amino acids. Consistent with this hypothesis,

active macropinocytosis is observed in primary human PDAC specimens. Moreover,

in the presence of physiologic albumin, we found that cultured murine PDAC cells

grow indefinitely in media lacking single essential amino acids and replicate once in

the absence of free amino acids. Growth under these conditions was characterized by

simultaneous glutamine depletion and essential amino acid accumulation. Overall,

our findings argue that the scavenging of extracellular proteins is an important mode

of nutrient uptake in PDAC.

14

2.3 Introduction

One of the most lethal forms of cancer is pancreatic ductal adenocarcinoma (PDAC)

[39]. Almost all cases of PDAC involve activating KRAS mutations [8]. In addition to

driving growth, KRAS induces metabolic changes including enhanced glucose uptake,

glycolytic flux, and glucose flux into hexosamines and ribose-5-phosphate [132]. In

contrast to other driver oncogenes such as PI3K that broadly increase glucose flux

throughout metabolism [117], oncogenic RAS impairs flux of glucose through pyruvate

dehydrogenase into the tricarboxylic acid (TCA) cycle [26, 27]. RAS-driven cells

instead rely heavily on glutamine as a TCA carbon source, with glutamine catabolism

through the TCA cycle and malic enzyme essential in pancreatic cancer cells [106].

Thus, RAS-driven cancer cells are comparatively less dependent on glucose than other

cancer cells [136].

Generation of significant ATP from substrates other than glucose requires oxygen,

whose availability in tumors is classically limited because of poor perfusion. Indeed,

PDAC tumors, which are characterized by poor vascularization and high interstitial

pressure, are typically hypoxic [23, 53]. Given the high metabolic demands of tumor

growth, poor perfusion may lead to limitation not only for oxygen but also nutrients

including glucose and free amino acids. Given the particular importance of glutamine

as a source of both usable nitrogen and TCA cycle carbon, glutamine can potentially

be a limiting nutrient for tumor growth. Consistent with this, studies in murine

tumor models in the 1940s and 1950s found lower free glutamine in the tumor than

corresponding normal tissue [92, 91].

A potential alternative to traditional uptake of monomeric amino acids via mem-

brane transport proteins is macropinocytosis, a process activated by mutant KRAS

[4, 16]. Macropinocytosis involves bulk uptake of extracellular constituents, includ-

ing proteins that can be subsequently digested in lysosomes into free amino acids.

Intriguingly, in cell culture, feeding of albumin to RAS-driven cells enabled their

15

survival and proliferation in low glutamine, and such survival and proliferation was

dependent upon macropinocytosis [16]. Albumin has been reported to accumulate

in tumors, likely due to a combination of leaky vasculature and lymphatic deficiency

[107]. Thus, it is conceptually possible that plasma protein leakage from tumor vas-

culature provides a nutrient source for cancer cells. The extent to which this actually

occurs in human tumors, however, has not yet been explored. Nor has it been shown

whether such scavenging is sufficient to provide amino acids other than glutamine in

biologically significant quantities.

Here, we investigate protein scavenging in PDAC. Metabolomic analysis of freshly

isolated human PDAC tumor specimens (compared with benign adjacent tissue) re-

vealed that the tumors are low in glucose, upper glycolytic intermediates, glutamine,

and serine. PDAC tumors also accumulated amino acids that are useful primarily

for protein synthesis. Although uptake or synthesis of monomeric amino acids would

be expected to yield each amino acid in quantities balanced with total demand, pro-

tein catabolism instead produces amino acids in proportion to their abundance in

the catabolized protein. Those amino acids that are consumed by multiple anabolic

processes (such as glutamine) would accordingly become depleted relative to those

used solely or primarily for protein synthesis. Thus, the observed pattern of amino

acid depletion and accumulation in human PDAC suggests a reliance on protein scav-

enging. Consistent with this, we find that primary human PDAC specimens display

enhanced macropinocytosis. Moreover, we show that cultured pancreatic cancer cells

can obtain sufficient amino acids via protein scavenging to grow with albumin as the

sole amino acid source, and that this mode of growth is associated with glutamine

depletion and essential amino acid accumulation.

16

2.4 Materials and Methods

Cell culturing and amino acid dropout experiments KRPC cells were kindly

provided by S. Lowe (Memorial Sloan- Kettering Cancer Center, New York, NY)

[60]. These cells were harvested from a murine tumor following orthotopic injection

of murine pancreas progenitor cells with endogenous KRASG12D that were addition-

ally engineered to have MYC expression and silenced (shRNA-mediated) P53. Cell

lines were routinely passaged in DMEM (Mediatech) with 25 mmol/L glucose and

4 mmol/L glutamine and supplemented with 10% (v/v) fetal bovine serum (FBS;

HyClone), 25 IU/mL penicillin, and 25 mg/mL streptomycin (MP Biomedicals), and

split at 80% confluence. For amino acid dropout experiments, pyruvate-free DMEM

was prepared from powder (Cellgro, cat. no. 10-017-CV) by adding glucose (25

mmol/L), salts, vitamins, phenol red, and amino acids, except for the amino acid to

be omitted. For single amino acid dropout experiments, cells were plated in DMEM

at 10% confluence. After 24 hours, cells were switched to dropout DMEM supple-

mented with 5% dialyzed FBS (Thermo). This medium was further supplemented

with 0% or 5% cell culture-grade bovine serum albumin (BSA; Sigma), which was

not fatty acid free. For assaying growth in amino acid-free DMEM, cells passaged in

leucine-free DMEM supplemented with 5% BSA were plated at 20% confluence. After

24 hours, medium was changed to amino acid-free DMEM with 5% BSA. Medium

was replaced as needed.

Imaging and cell counting For imaging, cells were fixed in 10% TCA for 15 min-

utes, and images were obtained using a Nikon Eclipse TE2000-U microscope operated

by Q-Capture Pro software (QImaging). KRPC cells growing in leucine-free medium

supplemented with BSA were seeded at 10% confluence and switched to fresh leucine-

free medium after 24 hours. Cell proliferation was assessed by cell number determina-

17

tion using a Countess Automated Cell Counter (Invitrogen) or by determining total

packed cell volume (PCV) using PCV tubes (Techno Plastic Products).

Stable isotope tracing experiments Medium with fully 13C- and 15N-labeled glu-

cose and amino acids, otherwise equivalent to DMEM, was reconstituted from indi-

vidual components (13C- and 15N-DMEM). This medium contained unlabeled sodium

bicarbonate and vitamins and was supplemented with 5% dialyzed FBS and 1% v/v

penicillin streptomycin (MP Biomedicals). After five doublings in this medium, cells

were seeded at low cell density and switched to specified 13C- and 15N-labeled me-

dia. After 24 hours, metabolites were extracted and analyzed by liquid chromatogra-

phy/mass spectrometry (LC/MS). Growth of KRPC cells in amino acid-free medium

is dependent on pregrowth in leucine-free medium. Thus, cells were grown for five

doublings in 13C- and 15N-DMEM then switched to leucine-free 13C- and 15N-DMEM

with 5% BSA. After two to three doublings in this medium, cells were switched to

(i) complete 13C- and 15N-DMEM with 5% BSA and (ii) amino acid-free 13C- and

15N-DMEM with 5% BSA. Cells in complete medium were grown for 24 hours be-

fore metabolite extraction. Cells in amino acid-free medium were grown for 48 hours

before metabolite extraction, with medium replaced after 24 hours.

Metabolite extraction from cultured cells For analysis of intracellular amino

acids, medium was aspirated and plates were rinsed three times with room tem-

perature PBS. Metabolism was quenched and metabolites extracted in -80◦C 80:20

methanol:water extraction solution. After 15 minutes at -80◦C, plates were scraped

and cell extracts were transferred to 15-mL conical tubes. Cell suspensions were vor-

texed, centrifuged at 3,000 g for 5 minutes, supernatant was kept, and cellular debris

was reextracted with -80◦C 80:20 methanol:water extraction solution. The resulting

suspension was centrifuged and the supernatant was combined with the supernatant

from the first extraction. The resulting solution was dried under nitrogen flow and re-

18

suspended in high-performance liquid chromatography (HPLC)-grade water. Twenty

microliters was added to 80-μL methanol in addition to 10-μL triethylamine and 2-μL

benzyl chloroformate and incubated at room temperature for 30 minutes, to derivatize

and thereby enhance measurement sensitivity of amino acids.

Tissue collection and metabolite extraction procedure Patients undergoing

surgical resection of pancreatic tumors consented to collection and analysis of their

resected tissues. Following partial pancreas resectomy, approximately 100 mg seg-

ments of tumor and adjacent tissue were isolated and immediately frozen in liquid

nitrogen, and the diagnosis of PDAC was confirmed histologically. The samples were

shipped overnight on dry ice to Princeton University (Princeton, NJ) and stored in

liquid nitrogen until analysis.

Tumor and benign adjacent tissues of the same patient were prepared and analyzed

in parallel. The samples were weighed and then pulverized by agitation with stainless

steel balls at liquid nitrogen temperature (CryoMill, Retsch, 25 Hz for 3 minutes). The

pulverized tissue was mixed by vortexing with 2 mL of -80◦C 80:20 methanol:water,

and split into two 1-mL replicate samples, which were set aside to extract for 5 minutes

at -80◦C. Each sample was centrifuged to isolate the soluble extract, and the insoluble

material was extracted twice more with 1 mL 80:20 methanol:water for 5 minutes at

0◦C and the supernatant again isolated by centrifugation. The supernatants from the

three rounds of extraction were combined, dried under nitrogen gas, and reconstituted

in LC/MS grade water (1 mL of water per 25 mg initial tissue weight).

LC/MS analysis Cell culture and tissue samples were analyzed by three separate

LC/MS systems: (i) Stand-alone orbitrap MS (Exactive; Thermo Scientific) oper-

ating in negative full scan mode at 100,000 resolution coupled to C18 ultra per-

formance reversed-phase ion pair LC [62], (ii) triple quadrupole mass spectrometer

(TSQ Quantum Discovery Max; Thermo Scientific) operating in negative multiple

19

reaction monitoring mode coupled to C18 high-performance reversed-phase ion pair

LC, and (iii) triple quadrupole mass spectrometer (TSQ Quantum Ultra; Thermo

Scientific) operating in positive multiple reaction monitoring mode coupled to Hy-

drophilic interaction liquid chromatography (HILIC) chromatography [3]. Metabo-

lites were identified by accurate mass (<5 ppm deviation, Exactive) or characteristic

fragmentation product (triple quads), in combination with retention time match to

validated standards, using in-house software [70]. Linearity of response was verified

by running 2-fold dilutions of most samples and observing 2-fold decreases in peak

intensities. For a subset of tissue samples, amino acid concentrations were determined

using 13C-labeled amino acid standards. For quantification of amino acid pool sizes

in cultured cells, metabolite intensities were normalized by PCV. Cell culture leucine

measurements were obtained using a modified HILIC method [80].

Ex vivo Macropinocytosis assay Fresh PDAC tumor tissue obtained from surgi-

cal resections was cut into slices with an approximate 3-mm cuboidal shape. Tissue

was immersed into serum-free DMEM containing 1 to 2 mg/mL of TMR-dextran and

incubated at 37◦C for 20 to 30 minutes. Tissue was rinsed twice in PBS and imme-

diately frozen in optimal cutting temperature (OCT) compound. Tissue processing

and image analysis was performed as previously described [17].

Data normalization and processing of tissue metabolomic data Ion counts

were normalized to correct for differences in total metabolite abundances across sam-

ples, and for any sample-to-sample drift in the overall instrument response factor. A

normalization factor (γi) was calculated for each LC/MS run i. To calculate γi, every

known metabolite peak Pmi in LC/MS run i was quantified, and compared with the

median value of peak m across all samples, µm. The scaling factor was calculated

according to Eq. 2.1:

γi = median(Pmiµm

)(2.1)

20

and ion counts were corrected accordingly: P ∗mi = Pmi

γi.

The normalized ion count matrix was log2-transformed and averaged over repli-

cates. When a metabolite was measured on multiple instruments, the results were

averaged.

Significance testing of tumor/benign adjacent tissue metabolite differences

To determine whether a subset of metabolites are systematically higher or lower in

cancerous than in benign adjacent pancreatic tissue, P values were computed using a

paired t test with the null distribution generated by bootstrapping [25]. This method

was chosen as a more conservative alternative to determining the test significance

against a t-distribution, because the parametric t-distribution approach makes the

assumption that the log-metabolite abundances are each normally distributed. This

assumption is not valid for many metabolites.

For a given metabolite m, measured in n patients, tumor metabolite abundance

(Cmi) was compared within the same patient to the benign tissue metabolite abun-

dance (Bmi). These abundances, [Bmi, Cmi], were jointly standardized so that they

collectively have a mean of 0 and a standard deviation of 1. The systematic difference

between pairs can be captured by a paired t test statistic (Eq. 2.2).

tm =

∑ni=1

(Bmi−Cmi)n√∑n

i=1(Bmi−Cmi)

2

n−1

n

(2.2)

To generate samples for an empirical null distribution, we need to generate data

where the systematic variation between the benign and cancer samples has been

removed and then use the remaining variation to determine how often we would have

seen such a large systematic difference (tm) by chance. To generate these null data, the

paired difference was removed from the benign abundances (Eq. 2.3a), the modified

abundances were recentered (Eq. 2.3b) and then these residuals were corrected for

21

the 1 degree of freedom eliminated by removing the paired difference (Eq. 2.3c), as

per Efron and Tibshirani [25].

B∗mi = Bmi −

∑ni=1(Bmi − Cmi)

n(2.3a)

Mm = mean[B∗mi, Cmi] (2.3b)

Brmi = (B∗

mi −Mm)

√n

n− 1(2.3c)

Crmi = (C∗

mi −Mm)

√n

n− 1

For each bootstrap sample (500,000 were used), n patients were randomly sampled

(with replacement) and the pairs of null data from these patients (drawn from Brm

and Crm) formed Br

m and Crm. Because there are no systematic differences between

the means of Brm and Cr

m, t statistics of these null pairs (Eq. 2.4) can be used to

approximate the distribution of t statistics expected under the null hypothesis.

tbmr =

∑ni=1

(Bbmi−Cb

mi)

n√∑ni=1

(Bbmi

−Cbmi

)2

n−1

n

(2.4)

A P value for metabolite m can be calculated by determining how often a t

statistic more extreme than the observed statistic (tm) would be expected under the

null hypothesis (Eq. 2.5).

pm = 1−∑R

r |tm| > |tbmr|R

(2.5)

False discovery rate calculation Correction for multiple hypothesis testing fol-

lowed the procedure of Storey and Tibshirani [108]. Briefly, if there are no metabolites

22

that systematically differ between benign tissue and tumors (i.e., all of the metabolites

are true negatives), then the expected distribution of P values across all metabolites

is uniform on [0, 1]. To the extent that some of the metabolites do systematically

differ, the associated P value histogram will be a mixture of true positive P values

(skewed toward zero) and uniform true negatives. This histogram allows us to esti-

mate the fraction of true negatives in the dataset: πo. We can then find a P value

cutoff, q, corresponding to the desired false discovery rate (FDR), by taking the ratio

of the expected number of false positives (πomq) to the number of P values less than

q. Metabolites with P values less than this q-value were treated as discoveries.

2.5 Results - Metabolomic analysis of human

PDAC tumors

Paired PDAC tumor and benign adjacent tissue specimens were acquired by surgical

resection from 49 patients (Fig. 1A). To minimize metabolic changes during the

tissue acquisition process, the preferred technique is freeze-clamping in situ with

liquid nitrogen-cooled Wollenberger tongs [127, 87]. Such in situ freeze-clamping

might compromise clinical outcomes, for example, by precluding proper identification

of tumor margins. Accordingly, we instead relied on excised samples, with the surgical

approach chosen to maintain perfusion until just before excision. Thereafter, tumor

and benign adjacent tissue samples were rapidly quenched in liquid nitrogen and

stored at ≤ −70◦C. Metabolome analysis was conducted at the whole sample level,

without distinguishing between epithelial and stromal tumor components. Paired

samples were extracted in parallel and analyzed by three complementary LC/MS

methods that enabled quantitation of 127 water-soluble metabolites across a majority

of the samples [62].

23

Of the 127 metabolites examined, 57 displayed significantly different levels in

pair-wise analysis of tumor and benign adjacent tissue (Fig. 1B). The most strongly

depleted metabolites in tumors were glutamine, cytidine (whose amino group is de-

rived from glutamine), guanidoacetic acid (a precursor to creatine phosphate, which

was also down), glucose, and several phosphorylated compounds derived from glucose

(glucose-6-phosphate, sedoheptulose-7-phosphate, and glycerol-3-phosphate). The

most strongly increased metabolites in tumors were the DNA base thymine and several

hydrophobic essential amino acids (valine, isoleucine/leucine, and tryptophan). The

tryptophan degradation product kynurenine, which has immunosuppressant bioactiv-

ity [30], was also strongly elevated in the tumors.

Within central carbon metabolism glucose, glucose-6-phosphate, and fructose-6-

phosphate were all decreased in the tumors, as were most TCA cycle compounds.

In contrast, the 3-carbon glycolytic intermediates dihydroxyacetone phosphate and

3-phosphoglycerate were slightly increased, as was lactate. Collectively, these obser-

vations are consistent with increased propensity for aerobic glycolysis but decreased

glucose availability in the tumors.

As a class, the 20 proteogenic amino acids showed particularly strong changes be-

tween the tumor and benign adjacent tissue with some amino acids strongly increased

in the tumors, and others strongly decreased. There was the propensity for “nonessen-

tial” amino acids to be depleted in the tumors, whereas “essential” amino acids ac-

cumulated to higher levels (compare green and red bars in Fig. 1B). This trend,

however, was not absolute. Most importantly, while glutamine was the most depleted

amino acid (with an average depletion of 2.5-fold verified using 13C-labeled internal

standards; Supplementary Table S1), glutamate (which differs from glutamine by a

single amine moiety) was slightly increased. This led us to consider the hypothesis

that the observed patterns of amino acid depletion and accumulation might not re-

flect amino acid essentiality, but rather rates of individual amino acid consumption

24

by anabolic pathways. Specifically, the two most depleted amino acids, glutamine

and serine, play key anabolic roles as amine- and one-carbon donors, respectively. In

addition, serine is a key precursor of lipid head groups, and both CDP-choline and

CDP-ethanolamine levels were increased in tumors. In contrast, levels of amino acids

used primarily for protein synthesis were increased in PDAC.

This pattern of amino acid levels in the tumors is consistent with amino acids

being acquired by protein catabolism to fuel anabolic metabolism (Fig. 1C) [16].

Although uptake or synthesis of monomeric amino acids would be expected to produce

each amino acid in the appropriate amount, protein catabolism instead produces all

amino acids in proportion to their abundance in protein. Those amino acids that

are consumed by multiple anabolic processes (such as glutamine) accordingly become

depleted relative to those used solely or primarily for protein synthesis.

25

Figure 2.1: Cancer Research paper - Figure 1

26

2.6 Results - Macropinocytosis in PDAC tumors

Macropinocytosis mediates the endocytic uptake of extracellular protein in RAS-

driven cancer cells and murine tumors [16]. Therefore, to examine whether this

protein internalization mechanism is active in human PDAC tumor tissue, freshly

acquired human tumor specimens were incubated with high molecular weight

tetramethylrhodamine-conjugated dextran (TMR-dextran), an established marker

of macropinosomes, and intracellular uptake of TMR-dextran was assessed by fluo-

rescent microscopy of tissue sections. TMR-positive macropinosomes were detected

in CK19-positive tumor cells (Fig. 2). Quantitatively lower, but nevertheless sub-

stantial, TMR-dextran staining was also detected in CK19-negative tumor tissue,

which may include both stromal cells and PDAC cells that have undergone epithelial-

mesenchymal transition (thereby losing CK19; Supplementary Fig. S1). In contrast,

few macropinosomes were detected in normal adjacent tissue (Supplementary Fig.

S2). Although some intratumoral variability was observed, stimulated macropinocy-

tosis was evident in each of the five analyzed PDAC tumor samples. These data

indicate that macropinocytosis is an attribute of human pancreatic tumors and that

pancreatic cancer cells have the capacity to take up fluids and their constituents

from the tumor extracellular environment.

27


2.7 Results - Support of cultured tumor cell

growth by albumin in the absence of free

amino acids

Prior work has shown that macropinocytosis enables KRAS-activated cells to prolif-

erate in low glutamine media supplemented with albumin [16]. However, the extent

to which protein scavenging can provide other amino acids in quantities sufficient to

support cellular proliferation remains unknown. To address this question, we incu-

bated KRAS-driven pancreatic cells in medium lacking one or more essential amino

acids and supplemented with physiologic levels of albumin. Murine-derived pancreatic

cancer cells with oncogenic KRASG12D and silenced P53 (KRPC cells) [60], growing

in complete medium were switched to leucine-deficient medium either (i) not sup-

plemented with BSA or (ii) supplemented with the typical circulating concentration

28

of BSA (50 g/L or 5%). As expected, KRPC cells did not survive in leucine-free

medium without added BSA. In contrast, when switched to leucine-free medium sup-

plemented with BSA, leucine removal initially resulted in extensive cell death, but

surviving cells proliferated indefinitely (months) with a doubling time of approxi-

mately 24 hours (Fig. 3A and B and Supplementary Fig. S3). After 24 hours in this

medium, the intracellular leucine concentration in these cells was approximately 12.5

pmol/mL cell volume, roughly 100-fold lower than in the same cells grown in com-

plete medium, but twice as high as in cells cultured in the absence of both leucine and

BSA (Fig. 3C). KRPC cells were also able to proliferate indefinitely in the absence

of lysine or phenylalanine (Supplementary Fig. S4).

One trivial explanation for the growth of KRPC cells in medium without leucine

is trace leucine contamination from serum or other additives in quantities sufficient

to support cell growth. To rule out this possibility, we added 12 mmol/L leucine to

leucine-free medium that was not supplemented with BSA. LC/MS measurements

revealed that, while this leucine-spiked medium contained more free leucine than the

BSA-supplemented leucine-free medium (Supplementary Fig. S5), it did not support

cell growth. Therefore, the continuous proliferation of KRPC cells in leucine-free

medium supplemented with albumin is not due to contaminating leucine.

Given that KRPC cells can use intact protein as their sole source of leucine,

lysine, or phenylalanine, we wondered whether these cells could proliferate in BSA-

supplemented medium without any free amino acids. We switched KRPC cells grow-

ing in leucine-free medium to amino acid-free medium either (i) not supplemented

with BSA or (ii) supplemented with 5% BSA. Although the amino acid-free medium

without BSA supplementation did not support cell survival, cells switched to the

BSA-supplemented amino acid-free medium grew to confluence, doubling once over

a period of 5 days (Fig. 3D and E). Thus, KRAS-driven cancer cells in vitro can

29

grow faster than PDAC tumor cells in vivo solely through scavenging and subsequent

catabolism of extracellular protein.


2.8 Results - Isotope tracing of serum protein

catabolism

The capability of RAS-mutant cells to proliferate in the absence of essential amino

acids suggests that serum protein catabolism might contribute substantially to their

amino acid pools. To quantitatively measure this contribution, we developed an iso-

30

tope tracer-based method enabling separate quantitation of (i) amino acids initially

taken up as monomers and (ii) amino acids acquired via catabolism of serum protein.

We prepared growth medium in which natural glucose and all amino acids were re-

placed by uniformly 13C- and 15N-labeled glucose and amino acids at standard DMEM

concentrations (13C- and 15N-DMEM). KRPC cells were grown in this medium for five

doublings, such that cellular protein in the resulting population was predominantly

labeled. Then, cells were transferred to 13C- and 15N- medium equivalent to DMEM

except that free amino acids were present at 10% of their DMEM concentrations (13C-

and 15N- DMEM, 10% AA). These amino acid concentrations more closely resemble

physiologic conditions [e.g., average human PDAC glutamine concentration is 700

mmol/L (Supplementary Table S1), and 10% of DMEM glutamine concentration is

400 mmol/L]. Use of this medium is important for detecting unlabeled amino acids

coming from protein catabolism, whose concentrations are otherwise overwhelmed by

the high amino acid concentrations in full DMEM. The cells were cultured for 24 hours

in this medium, and metabolites were extracted and analyzed by mass spectrometry

(Fig. 4A).

In cells grown without added albumin, less than 12% of intracellular and less than

5% of extracellular essential amino acids were unlabeled. These observed unlabeled

amino acids are presumably derived primarily from catabolism of the available serum

protein (the cells were cultured in 5% FBS). The addition of albumin dramatically

increased the observed levels of unlabeled (i.e., serum protein-derived) intracellular

amino acids (Fig. 4B). Moreover, substantial concentrations of unlabeled amino acids

were also observed in the medium, suggesting rapid exchange of intracellular and

extracellular amino acid pools (Fig. 4C). Contribution from albumin to intracellular

amino acid pools was also observed in MIA PaCa-2 and E3 human pancreatic cancer

cells (Supplementary Fig. S6). Thus, we were able to directly track amino acids

derived from protein catabolism in cultured PDAC cells.

31

We next sought to validate the hypothesis that catabolism of serum protein oc-

curs in the lysosome by treating KRPC cells with bafilomycin A1, which impairs

lysosomal function by inhibiting the vacuolar-type H+-ATPase [133]. Treatment of

KRPC cells with bafilomycin A1 resulted in a dose-dependent reduction of amino

acids derived from serum protein catabolism (Fig. 4D and E). Treatment with EIPA,

a canonical inhibitor of macropinocytosis [16], also results in a reduction in serum

proteinderived amino acids in KRPC cells (Supplementary Fig. S7). Taken together,

these data demonstrate that, even in the absence of amino acid deprivation, lyso-

somal degradation of extracellular protein contributes substantially to PDAC amino

acid pools.


32

2.9 Results - Amino acid patterns in cells fed by

serum protein macropinocytosis

We next used our isotope tracing strategy to confirm that cells grown in amino acid-

free media supplemented with 5% BSA acquire most of their amino acids from the

unlabeled extracellular protein. With the exception of alanine, serine, and glycine,

which were synthesized from glucose (which, in contrast to the pancreatic cancer mi-

croenvironment, was abundant in the culture media), all amino acids were largely

unlabeled, that is, derived from extracellular protein (Fig. 5A). In addition, amino

acid nitrogen in these cells was predominantly extracellular protein derived (Supple-

mentary Fig. S8).

We next asked how amino acid concentrations differed between cells grown in

amino acid-free medium supplemented with physiologic albumin and cells grown in

standard DMEM. Growth in the albumin-supplemented, amino acid-free medium

resulted in strong depletion of intracellular glutamine after 24 hours in culture (Fig.

5B). However, paradoxically we observed accumulation of essential amino acids to

levels comparable with, and in some cases greater than, those seen in cells grown in

rich medium (Fig. 5C). Moreover, significant concentrations of free amino acids were

excreted into the medium (Fig. 5D and E). As the tumor metabolomic analysis does

not allow for discrimination between cancer cell intracellular and extracellular amino

acid pools, we are unable to determine whether amino acid excretion occurs in vivo. In

addition, perhaps due either to differences in nutrient availability between the actual

tumor and this cell culture model (e.g., of glucose, free amino acids, albumin, and

other proteins) or to the substantial fraction of stroma in the tumor, there was not a

direct correspondence between amino acid concentration changes in the PDAC tumors

and cells grown in albumin-supplemented amino acid-free medium. Nevertheless,

33

these data confirm that growth fed by scavenging of extracellular protein can lead to

glutamine depletion and essential amino acid accumulation.


2.10 Discussion

The definition of cancer is the uncontrolled division and growth of cells. To facilitate

this, cancer cells need nutrients that can be used to generate the necessary energy

and cellular building blocks. It is commonly assumed that cancers primarily rely

on glucose and glutamine as their nutrient sources. The dependence on glucose is

certainly true for many cancers and is perhaps best illustrated by the clinical value of

FDG-PET, which uses a glucose analogue to image and stage tumors [116]. However,

there are exceptions, with a notable example being PDAC: less than 25% of tumors are

markedly FDG-PET positive, and up to 35% do not take up FDG above background

34

[40]. This may be caused by restricted blood perfusion due to the high interstitial

pressure and desmoplasia that are characteristic for PDAC [15]. Despite a limited

availability of free glucose and glutamine, PDAC is notoriously aggressive, suggesting

that other nutrients may play an important role in fueling PDAC growth.

What could these alternative nutrients be? We recently reported the ability of

KRAS-transformed cells to uptake extracellular protein through macropinocytosis

[16], and to use serum lipids to support growth [47]. PDAC cells reuse the amino

acids and fatty acids from these extracellular proteins and lipids to support growth,

much as they also rely on recycling of intracellular materials via autophagy to sur-

vive metabolic stress [131]. Leaky tumor vasculature and lymphatics can result in

accumulation of albumin and other serum proteins in the tumor interstitium. Scav-

enging of proteins and lipids diminishes demand for de novo biosynthesis and thus

the need for glucose and free glutamine-derived carbon, reducing equivalents (NADH

and NADPH), and ATP.

As some key metabolic pathways are oxygen-dependent (TCA cycle and fatty acid

desaturation), bypassing them facilitates growth in hypoxia. Oncogenic RAS, even

in nutrient and oxygen replete conditions, reduces oxygen consumption and increases

macropinocytosis and lipid scavenging [26, 27, 47]. It thus appears that RAS prepares

cells to survive and grow in metabolically harsh conditions, including hypoxia.

Here, we provide an in-depth analysis of the metabolic state of primary human

PDAC tumors. PDAC tumor tissues are heterogeneous, characterized not only by the

presence of tumor cells, but also cancer-associated fibroblasts, immune cells, and ex-

tracellular matrix. Our analyses do not allow us to differentiate between the metabolic

contributions from each of these compartments. Rather, they represent the metabolic

state of the tumor tissue as a whole. Using this approach, we found that relative to the

benign adjacent tissues, PDAC tumors were consistently low in both glucose and glu-

tamine. We further found a pattern of amino acid levels consonant with what would

35

happen if the tumors were largely reliant on protein scavenging (Fig. 1C): a differen-

tial utilization of amino acids for purposes other than protein synthesis (nucleotide

and lipid synthesis etc.) leaves amino acids primarily used for protein synthesis to

build up, whereas amino acids used for other purposes as well (glutamine, serine, and

alanine) deplete.

By culturing cells in the absence of one or all free essential amino acids, we were

able to demonstrate the capacity of extracellular protein scavenging to provide amino

acids to support growth. Intriguingly, cells were able to grow rapidly and indefinitely

by this mode of consumption in the absence of free leucine, lysine, or phenylalanine

(which are all relatively abundant in albumin; Supplementary Fig. S9), and tran-

siently in the absence of all free amino acids. Moreover, in the absence of all amino

acids, BSA scavenging was sufficient to produce elevated intracellular levels of selected

essential amino acids.

An increased scavenging ability allows cells to access vast resources of cellular

building blocks and energy: assuming a total plasma protein concentration of 75 g/L,

the amino acid content of plasma proteins exceeds free amino acids by approximately

200-fold. Thus, while poor perfusion may limit flow of all nutrients through the

tumor, in such flow restriction, plasma protein may increase in relative importance as

an amino acid source. In addition, plasma proteins are also a major potential energy

source, exceeding energy available in glucose by about 75-fold. Similarly, the ability

to scavenge fatty acids from various serum lipids, rather than free fatty acids alone,

increases available fatty acids by at least 4-fold [47].

Although blood flow is often low in PDACs due to the high interstitial pressure,

tumor blood vessels are leaky. In combination with the fact that tumors are lym-

phatic deficient, this may result in plasma protein accumulation [107]. In light of

this, the recent clinical success of a protein-drug conjugate albumin-paclitaxel (nab-

36

paclitaxel, Abraxane) in PDAC is particularly tantalizing [121], and warrants further

investigation into metabolic scavenging and how it can be exploited therapeutically.

37

Chapter 3

mTOR Inhibition Restores Amino

Acid Balance In Cells Dependent

on Catabolism of Extracellular

Protein

This chapter is a paper published in Molecular Cell in 2017 [73]. Figure numbering

from the original paper is preserved. Supplementary methods and figures are available

on-line at the Molecular Cell website.

3.1 Authors

Michel Nofal, Kevin Zhang, Seunghun Han, and Joshua D. Rabinowitz

38

3.2 Abstract

Scavenging of extracellular protein via macropinocytosis is an alternative to

monomeric amino acid uptake. In pancreatic cancer, macropinocytosis is driven

by oncogenic Ras signaling and contributes substantially to amino acid supply.

While Ras signaling promotes scavenging, mTOR signaling suppresses it. Here, we

present an integrated experimental-computational method that enables quantitative

comparison of protein scavenging rates across cell lines and conditions. Using it, we

find that, independently of mTORC1, amino acid scarcity induces protein scavenging

and that under such conditions the impact of mTOR signaling on protein scavenging

rate is minimal. Nevertheless, mTOR inhibition promotes growth of cells reliant

on eating extracellular protein. This growth enhancement depends on mTORC1’s

canonical function in controlling translation rate: mTOR inhibition slows transla-

tion, thereby matching protein synthesis to the limited amino acid supply. Thus,

paradoxically, in amino acid-poor conditions the pro-anabolic effects of mTORC1 are

functionally opposed to growth.

39

Figure 3.1: Molecular Cell paper - Graphical Abstract

40

3.3 Introduction

Amino acids are required substrates for protein synthesis and thus cell growth. While

some organisms can synthesize all proteinogenic amino acids from primitive carbon

and nitrogen sources, mammals cannot. For this reason, mammalian cells have been

thought to strictly depend on the availability of amino acid monomers in their extra-

cellular environment to support growth. Recently, it was shown that Ras signaling

stimulates an alternative route of amino acid acquisition whereby cells take up ex-

tracellular protein via macropinocytosis and catabolize it in lysosomes to yield free

amino acids. This process enables K-Ras-mutant pancreatic ductal adenocarcinoma

(PDAC) cells to survive and proliferate despite amino acid scarcity [16, 19].

The mechanistic target of rapamycin complex 1 (mTORC1) is a master growth

regulator that promotes anabolism [58]. In the presence of amino acids, mTORC1 is

recruited to the cytoplasmic surface of lysosomes, where it can be activated by growth

factor signaling [95]. Upon activation, it phosphorylates multiple targets, which col-

lectively stimulate amino acid uptake and protein synthesis [64] while suppressing

autophagy [46]. Amino acid depletion renders mTORC1 inactive, and protein syn-

thesis rates decline as a result. Meanwhile, cells engage in autophagy – i.e. they

catabolize pre-existing intracellular protein, yielding amino acids necessary to pre-

vent starvation. These amino acids reactivate mTORC1, attenuating autophagy and

restoring protein synthesis [135]. The implications of mTORC1 reactivation in the

context of prolonged starvation remain poorly understood.

Recently, Palm et al. showed that mTORC1 activity inhibits the growth of cancer

cells fed the major serum protein, albumin, in place of free essential amino acids.

Torin1, an ATP-competitive mTOR inhibitor, promoted growth in such conditions.

The authors reasoned that in addition to blocking autophagy, mTORC1 suppresses

the catabolism of extracellular protein [78]. This claim was supported by an assay for

extracellular protein degradation which uses a fluorescently labeled form of bovine

41

serum albumin (BSA) known as DQ-BSA, whose fluorogenic component is initially

hidden – many self-quenching BODIPY molecules are conjugated to each albumin

molecule – and only de-quenches once this albumin has been degraded [88]. Indeed,

Torin1 increases protein scavenging as measured with DQ-BSA. While an elegant tool

for visualizing serum protein catabolism, DQ-BSA fluorescence does not provide an

absolute measure of protein catabolic rate.

To this end, we previously reported a method that, using stable isotope tracers,

distinguishes amino acids derived from the catabolism of extracellular protein from

amino acids imported as monomers. Cells are pre-incubated for multiple generations

in medium containing uniformly 13C-labeled amino acids (13C-AA medium), such

that intracellular amino acids and cellular protein become almost completely labeled.

Cells are then switched to 13C-AA medium supplemented with physiologic levels of

unlabeled BSA. At this point, when cells scavenge and degrade the unlabeled albumin,

they release unlabeled amino acids into otherwise labeled amino acid pools. High

amounts of unlabeled amino acids produced by these cells reflect fast serum protein

uptake and catabolism [48].

In murine pancreatic cancer cells grown in physiological nutrient conditions, we

found that almost half of both intracellular and extracellular amino acid pools were

derived from BSA (i.e. unlabeled), demonstrating that protein catabolism can be

a major contributor to amino acid pools in pancreatic cancer [48]. The measured

unlabeled fractions, however, depend not only on the rate of serum protein catabolism,

but also on the number of cells present in the experiment and on their rate of protein

synthesis. Thus, differences in amino acid labeling patterns between cell lines or

growth conditions do not reliably reflect differences in protein scavenging rates.

Here, we present an integrated experimental-computational method that enables

reliable and quantitative comparison of protein scavenging rates across cell lines and

conditions. We then apply this method to investigate the mechanism by which mTOR

42

inhibition enhances the growth of cells fed by protein scavenging. We find that,

independently of mTORC1, amino acid scarcity strongly turns on protein scavenging,

and that under such conditions the impact of Torin1 on protein scavenging rate is

small. Inhibition of mTOR by Torin1 promotes growth of protein-scavenging cells

instead by decreasing their translation rate and thereby matching protein synthesis

to the limited amino acid supply.

3.4 Results - Isotope-Tracer Method Measures

Amino Acid Release Due to Extracellular

Protein Catabolism

Our general strategy for measuring extracellular protein catabolic rate involves pre-

labeling cells with 13C-AA medium and then feeding them a mixture of 13C-AA

medium and unlabeled albumin. Protein scavenging is then the only source of un-

labeled amino acids, and the rate of appearance of such amino acids can be used to

calculate the protein scavenging rate. The challenge is making such measurements in

a manner that accurately reflects per cell protein scavenging rates.

To this end, as cells grew in 13C-AA medium and unlabeled albumin, we took se-

rial time point measurements of intracellular amino acid labeling, extracellular amino

acid labeling, and total cell volume (Figure 1A). We further developed a simple model

of amino acid metabolism, which includes the following reactions for production and

consumption of intracellular amino acid monomers: (i) import and export from the

cell via amino acid transporters, (ii) incorporation into protein, (iii) catabolism of

extracellular protein, and (iv) catabolism of intracellular protein (i.e. via autophagy)

(Figure 1B) [138, 102]. This model applies exclusively to essential amino acids, which

are not synthesized, and it assumes that catabolism of essential amino acid monomers

43

is negligible. Using this model, the dynamic cell growth data and the extracellular

amino acid labeling data (both unlabeled fractions and absolute amounts) are suffi-

cient to uniquely determine the per cell rate of protein scavenging. Because intracel-

lular amino acid pools mix rapidly with extracellular pools (Supplementary Figure

1), data from intracellular amino acids is not required, making this method relatively

facile (see Methods).

When implementing this method, the rate of production of each essential amino

acid from albumin can be measured independently. Thus, as a first test of the method,

we assessed whether the measurements for different amino acids were in agreement,

focusing on five amino acids that we can easily and accurately measure (Figure 1C,

D). We anticipated that the release rates of different amino acids would reflect their

relative abundances in BSA. Indeed, this was the case: there are 59 lysines and only

17 histidines in BSA, and the measured rate for lysine exceeded that for histidine

by approximately 59:17 fold, with the other amino acids intermediate between these

two (Figure 1D). Dividing the release rate of each amino acid by the number of times

that amino acid appears in BSA yields the protein scavenging rate in units of moles

albumin per cell per unit time (Figure 1E).

As validation of this method, we sought to confirm the effect of constitutive

Ras activation on extracellular protein catabolism. To do so, we compared the

protein scavenging rate of immortalized baby mouse kidney cells (iBMK) with or

without constitutively active Ras or Akt alleles. While Akt activation did not in-

duce any change, constitutive Ras signaling roughly doubled the rate of extracellular

protein catabolism, consistent with the long-standing observation that Ras induces

macropinocytosis (Figure 1F) [4].

44

Figure 3.2: Molecular Cell paper - Figure 1

As further validation, we examined the protein scavenging rate of cells before and

after extended growth in conditions that select for accelerated protein scavenging. For

these experiments, we used KRPC cells, which were originally isolated from sponta-

neously arising, K-Ras-driven murine pancreatic tumors that resemble human PDAC

45

[60]. These cells were grown in leucine-free medium supplemented with 5% BSA [48].

Initial growth was slow, but after prolonged culture (100 generations), the resulting

adapted population (KRPCA cells) doubled approximately every 24 hours despite

the absence of free leucine (Figure 2A). Using the isotope-tracer method, we found

that KRPCA cells have roughly 5-fold higher rates of extracellular protein catabolism

(Figure 2B). Thus, we provide a quantitative method for assessment of the rate of

albumin catabolism by protein-eating cells.


46

3.5 Results - Impact of Intracellular Protein

Catabolism on Scavenging Measurements

An important element of the isotope-tracer method is the extended pre-labeling in

13C-AA medium. Extended pre-labeling ensures that autophagy and other modes of

intracellular protein degradation do not generate unlabeled amino acids and thereby

do not confound measurements of extracellular protein catabolism. To examine the

relative magnitudes of intracellular protein degradation and extracellular protein scav-

enging, we conducted analogous experiments with only 1 h pre-labeling, which is insuf-

ficient to substantially label intracellular protein. These experiments were conducted

in murine embryonic fibroblasts harboring an oncogenic K-RasG12D allele (K-RasG12D

MEFs) and KRPCA cells. In K-RasG12D MEFs, the pre-labeling duration did not

significantly impact the production rates of unlabeled amino acids, suggesting that

extracellular protein scavenging predominates over intracellular protein degradation.

In contrast, in KRPCA cells, we observed a two-fold increase in unlabeled amino acid

production with the brief pre-labeling, indicating similar magnitudes of extracellu-

lar and intracellular protein catabolism (Supplementary Figure 2). To confirm that

the measurements of extracellular protein scavenging do not reflect autophagy in the

murine pancreatic cancer cells, we used a well-established KPC cells line harboring

inducible shRNA against the essential autophagy gene Atg5. With the extended pre-

labeling that results in selective measurement of extracellular protein degradation,

knockdown of Atg5 did not significantly impact the measured scavenging rate (Sup-

plementary Figure 3), validating the selectivity of this isotope-tracer approach for

extracellular protein scavenging.

47

3.6 Results - Excessive mTOR Inhibition Slows

Growth on Extracellular Protein

Recent evidence suggests that Ras-activated cells, even without adaptation, can grow

robustly on extracellular protein if mTORC1 activity is suppressed [78]. We wondered

if KRPCA cells achieve high levels of protein scavenging in amino acid-rich medium

by suppressing mTORC1 signaling. We observed a modest decrease in mTORC1

signaling in adapted KRPC cells, as measured by the phosphorylation of S6 kinase 1,

ribosomal protein S6, and 4E-BP1 (Figure 2C).

Given that mTOR inhibition has been shown to promote protein scavenging and

that mTORC1 remains at least partially active in the adapted KRPC cells, which

have high basal scavenging rates, we tested the impact of the ATP-competitive mTOR

inhibitor Torin1 on KRPC cell growth. These experiments were conducted for a range

of Torin1 doses (100-2000 nM) in both parental KRPC and KRPCA cells, in amino

acid-replete, leucine-free, arginine-free, and glutamine-free medium, all supplemented

with 5% BSA (Figure 3). Among these amino acid-deficient conditions, we anticipated

that leucine deprivation would be most easily overcome by albumin scavenging, as

leucine is the most abundant amino acid in albumin. In contrast, we anticipated that

glutamine deprivation would be hardest to overcome, as glutamine is not particularly

abundant in albumin but required by cells in unusually large amounts. We expected

arginine deprivation to be intermediate. Deprivation of other amino acids was not

examined.

48


As expected, in amino acid-replete conditions, mTOR inhibition slowed the growth

of both parental and adapted KRPC cells. Importantly, however, cells doubled in 24

hours despite high doses of Torin1, indicating that these cells are capable of consid-

erable growth even when mTOR signaling is pharmacologically inhibited. In leucine-

deficient conditions, parental cells grew faster in the presence of Torin1, but optimal

growth was achieved in the middle of the Torin1 dose range, indicating that some

mTOR signaling is beneficial. Strikingly, in KRPCA cells, only the lowest dose of

Torin1 promoted growth; higher doses slowed it. While parental cells struggled to

grow without arginine or glutamine, KRPCA cells were able to grow without these

amino acids, with optimal growth occurring in the middle of the Torin1 dose range.

Collectively, these findings show that for cells fed by protein scavenging, mTOR

inhibition has both growth-promoting and growth-suppressing effects. The relative

strengths of these effects seem to depend on the protein scavenging rate of the treated

cells and the inherent difficulty of overcoming the amino acid deprivation. In more

deprived states (e.g. parental cells, glutamine-free medium), mTOR signaling in-

hibits growth. Conversely, in more favorable states (e.g. adapted cells, leucine-free

medium), mTOR signaling promotes growth.

49

3.7 Results - Amino Acid-Deficiency Induces Pro-

tein Scavenging Flux Independently of mTOR

To further investigate the effects of mTORC1 inhibition, we measured, using the

above isotope-tracer approach, the effect of Torin1 on protein scavenging flux. In both

KRPCA cells and K-RasG12D MEFs cultured in amino acid-replete medium, Torin1

increased protein scavenging in dose-dependent fashion (Figure 4A). The largest in-

crease we observed was less than 2-fold, however, whereas Palm et al. reported that

in K-RasG12D MEFs, Torin1 induced a 10-fold increase in DQ-BSA fluorescence and

a 5-fold increase in growth in leucine-free medium.

We next asked if the effect of Torin1 on protein scavenging rates depends on

amino acid availability. We measured the rates of extracellular protein catabolism

in the same three amino acid drop-out media as above in the presence or absence of

high-dose Torin1 (1000 nM). Amino acid deprivation increased protein catabolism at

least as much as high-dose Torin1 (Figures 4B). Interestingly, the degree to which

scavenging was induced aligned with the severity of amino acid starvation. For exam-

ple, in K-RasG12D MEFs, leucine deprivation, the least severe, increased scavenging

by 60%; glutamine deprivation, the most severe, by 220%. One might expect that

a reduction in mTORC1 activity upon amino acid deprivation accounts for these in-

creases. However, mTORC1 activity persists in these cells (Figure 4C). Thus, amino

acid deprivation turns on scavenging independently of mTOR.

50


We were initially puzzled that mTORC1 was active in amino acid-deficient con-

ditions. Others have demonstrated, however, that in cells deprived of amino acids

for long periods of time, mTORC1 signaling is re-activated once protein catabolic

programs begin to take effect [78, 135]. Indeed, we observed that when K-RasG12D

51

MEFs were switched to media lacking all amino acids, phosphorylation of S6 Kinase 1

immediately declined, but eventually returned, although phosphorylation of another

key substrate of mTORC1, 4E-BP1, was maintained throughout the time course.

Notably, removal of leucine alone resulted in no initial decline in the phosphoryla-

tion of either mTORC1 substrate (Supplementary Figure 4). Thus, at least over 24

h, leucine-, arginine-, and glutamine-deprived cells maintain mTORC1 activity. In

fact, at 24 h, glutamine-deprived cells displayed increased mTORC1 signaling, po-

tentially because glutamine deprivation resulted in accumulation of essential amino

acids within the cell (Supplementary Figure 5).

Given this persistent mTORC1 activity, we studied the impact of mTOR inhi-

bition on protein scavenging in amino acid-deprived cells. In leucine-free medium,

Torin1 increased extracellular protein catabolism by only 14% in KRPCA cells and

by 7% in K-RasG12D MEFs. While these enhancements in scavenging flux may con-

tribute to the pro-growth effects of mTOR inhibition in scavenging cells, they are

not quantitatively commensurate with the substantial enhancements in cell growth

observed upon mTOR inhibition. Thus, the growth-promoting effects of Torin1 in

cells reliant on protein scavenging extend beyond enhancing protein catabolism.

3.8 Results - mTOR Inhibition Induces Punctate

DQ-BSA Fluorescence

We were struck by the difference in magnitude of the effect of Torin1 on protein

scavenging flux measured here via isotope tracing (less than 2x) versus previously

via DQ-BSA fluorescence (roughly 10x). To address this discrepancy, we repeated

the DQ-BSA fluorescence experiment which produced this result, using identical cells

and conditions to Palm et al. Specifically, DQ-BSA was added concurrently with 250

nM Torin1 to K-RasG12D MEFs grown in serum-free DMEM, and cells were imaged

52

after 6 h [78]. As expected, Torin1 induced a visible increase in DQ-BSA fluorescence

(Figure 5A) and a corresponding rightward shift in the histogram of pixel fluorescence

intensity (Figure 5B). Thus, we confirmed that mTOR inhibition induces an increase

in DQ-BSA fluorescence.

We next sought to quantify this induction. When we included all measurable

fluorescence in our calculation, we found that Torin1 increases DQ-BSA fluorescence

per cell by less than two-fold, in line with our isotope tracing results (Figure 5C).

Some fluorescence, however, is inevitably noise. To minimize noise, standard methods

for quantification of fluorescence ignore lower intensity signals, only using pixels that

exceed an arbitrarily chosen fluorescence intensity threshold. We found that the choice

of fluorescence intensity threshold greatly affected the apparent magnitude of the

Torin1 effect: low thresholds produced effects less than 2-fold, while high thresholds

produced effects greater than 5-fold (Figure 5D). To explore this phenomenon further,

we divided the distribution of pixel intensities into five ranges and calculated the sum

of the intensities within each range. This revealed that mTOR inhibition dramatically

increases only very high-intensity fluorescence, which accounts for a minor portion

of the total fluorescent signal while having a modest effect on overall fluorescence

(Figure 5E). The relative contributions of each range of pixel intensities are apparent

in discretized images, which enable simultaneous visualization of all ranges of green

fluorescence (Figure 5F and Supplementary Figure 6).

Comparing our results to those of Palm et al. [78], we note no major differences

in the raw data: analysis of our data using a high fluorescence intensity threshold

yields images and quantitative results comparable to Palm et al. We believe, however,

that lower thresholds are more accurate, as they encompass a substantially greater

amount of total fluorescent signal and give quantitative results in line with the our

isotope-tracer data. In essence, the isotope-tracer data, which was not available to

53

Palm et al., inform the proper choice of threshold for quantitation of the fluorescence

data.

Focusing specifically on the high-intensity fluorescence which was strongly in-

duced by Torin1, we observed this fluorescence in discrete punctae that overlap with

lysosomal staining (Figure 5A). One possible explanation for this strong increase in

lysosomal DQ-BSA signal is that mTORC1 may simultaneously inhibit protein scav-

enging and promote egress of scavenged material from the lysosome. In summary, the

combined isotope tracing and fluorescence results show that mTOR, while profoundly

impacting lysosomal DQ-BSA fluorescence accumulation, has a modest overall impact

on protein scavenging rate.

54


55

3.9 Results - mTOR Inhibition Restores Amino

Acid Balance and Prevents Cell Death in

Amino Acid-Deprived Cells

How does mTOR inhibition promote growth of amino acid-deprived cells on extracel-

lular protein if not by directly increasing their rate of extracellular protein catabolism?

mTORC1 is well-known for its role in regulating protein synthesis, phosphorylating

multiple proteins which collectively activate 5’ cap-dependent translation [64]. We

reasoned that reduction of protein synthesis rates upon Torin1 treatment might pre-

vent cells deprived of free extracellular amino acids from promoting translation to the

point of cellular stress.

As others have shown and we have confirmed, mTORC1 activity persists in amino

acid-deprived cells fed extracellular protein, perhaps because scavenged protein feeds

directly into the lysosomal amino acid pool that is sensed by mTORC1 [78]. Corre-

spondingly, protein synthesis rates are not limited by low mTORC1 activity in these

cells. However, protein scavenging cannot support the high protein synthesis rates

of cells growing in copious free monomeric amino acids. GCN2, which senses amino

acid depletion by binding uncharged tRNAs [7, 124, 22], attenuates global translation

independently of mTORC1, while inducing specific translation of genes involved in

maintaining amino acid homeostasis, including the transcription factor ATF4 [35].

ATF4 induces expression of proteins that collectively promote cell survival during

amino acid deprivation by up-regulating amino acid uptake and enhancing protein

folding capacity. Nevertheless, this cellular response to amino acid starvation (known

as the Integrated Stress Response) fails to prevent apoptosis in cells which are chron-

ically unable to translate mRNAs into properly folded proteins. Moreover, some

proteins induced by the Integrated Stress Response, such as CHOP, promote cell

death if amino acid starvation remains unresolved [34, 139].

56

To test our hypothesis that mTOR inhibition promotes growth on extracellu-

lar protein by reducing translation rates and thereby preventing severe amino acid

starvation, we measured the expression of Integrated Stress Response proteins. In K-

RasG12D MEFs deprived of leucine, we observed a strong induction of both ATF4 and

CHOP, regardless of whether cells were supplemented with 5% BSA. This induction

was suppressed in Torin1-treated cells, suggesting that mTOR inhibition in amino

acid-deprived cells restores amino acid homeostasis (Figure 6A). We next sought to

measure cell death directly, to confirm that mTORC1 activity results in apoptosis.

After 48 hours in leucine-free media, close to 50% of cells grown in leucine-free medium

were either apoptotic or dead. mTOR inhibition prevented cell death. This preven-

tion did not depend on the presence of added BSA. Thus, the effects of mTORC1

on the survival of amino acid-deprived cells do not directly depend on the uptake or

catabolism of extracellular protein (Figure 6B).

To verify that mTOR inhibition prevents cell death by suppressing protein syn-

thesis, we tested the effect of direct inhibition of translation on the viability of

leucine-deprived cells. Low doses of harringtonin, which inhibits translation initi-

ation, prevented apoptosis and cell death to a similar extent as mTOR inhibition

(Figure 6C). We were not, however, able to stimulate cell growth in leucine-free,

BSA-supplemented medium with harringtonin (i.e. to replicate the pro-growth ef-

fects of Torin1), suggesting that the growth-promoting effects of mTOR inhibition

go beyond non-specific translation inhibition. This is consistent with the idea that

mTOR inhibition coordinately suppresses translation of a specific subset of genes and

increases protein catabolism. Collectively, these results show that, in cells reliant on

protein scavenging, excessive translation can result in lethal amino acid depletion.

57


While K-RasG12D MEFs require exogenous translation inhibition to maintain

amino acid balance when using extracellular protein in place of free leucine, KRPCA

cells have adapted to such conditions and can grow robustly without pharmacological

mTOR inhibition. We hypothesized that these cells rely on other translational

regulators to properly tune protein synthesis rates to limited amino acid availabil-

ity. We reasoned that GCN2, which slows translation upon amino acid depletion,

might play such a role. Using CRISPR-Cas9 technology, we generated KRPCA

cell lines deficient in GCN2 activity, as measured by lack of ATF4 induction in

leucine-free medium (Supplementary Figure 7A). We tested the ability of GCN2-

deficient KRPCA cells to grow in leucine-free medium supplemented with 5% BSA.

Remarkably, these GCN2-deficient cells almost completely lost the ability to grow

using extracellular protein in place of free leucine (Supplementary Figure 7B-C). If

this defect is due to excessive translation, it should be rescued by mTOR inhibition.

Indeed, GCN2-deficient KRPCA cells, much like parental KRPC cells, benefited

from high-dose Torin1 treatment (Supplementary Figure 7D). Thus, the ability to

58

turn down translation rates to match limited amino acid availability is essential for

growth via protein scavenging.

3.10 Discussion

All cells require amino acids for cell growth. Classically, mammals maintain a steady

concentration of circulating amino acids, which individual cells import as needed. If

perfusion is impaired, however, cells may struggle to support growth requirements us-

ing only amino acids from the environment. This appears to be the case in pancreatic

tumors, which are marked by dense fibrosis and poor perfusion [66, 85]. Pancreatic

tumor cells, driven by K-Ras mutations, mitigate the shortage of monomeric amino

acids in their immediate environment by taking up and catabolizing extracellular pro-

tein. This process enables these cells to survive and proliferate despite amino acid

deprivation [16, 19, 48].

Recently, mTOR inhibition was shown to promote growth of amino acid-deprived

cells on extracellular protein. To explain this, Palm et al. proposed that mTORC1 ac-

tivity represses extracellular protein catabolism and that mTOR inhibition alleviates

this repression [78]. In the present study, we demonstrate that mTORC1 signal-

ing does not prevent extracellular protein catabolism in amino acid-deprived cells.

Rather, these cells simultaneously maintain mTOR activity and increase protein eat-

ing. Nevertheless, cells relying on extracellular protein for amino acids cannot support

the high rates of translation that are possible in amino acid-replete conditions. Be-

cause mTORC1 remains active, these cells are prone to death by over-translation.

Thus, mTOR inhibition enhances growth on extracellular protein in part by restrict-

ing translation and restoring amino acid balance (Figure 7).

59


We also show that cells deprived of free leucine or glutamine increase extracel-

lular protein catabolism while maintaining mTORC1 signaling. This implicates an

mTOR-independent signaling pathway as an activator of this process during amino

acid deprivation. The other ubiquitous amino acid sensing pathway involves GCN2,

which, upon binding to uncharged tRNA, phosphorylates and inhibits translation

initiation factor eIF2α [7, 124, 22]. While inhibiting translation of most mRNAs,

phosphorylation of eIF2α promotes translation of ATF4 and other transcription fac-

tors which induce genes involved in adaptation to amino acid starvation, including

amino acyl-tRNA synthetases, amino acid transporters, and protein folding chap-

erones [34, 35]. Other proteins expressed upon eIF2α phosphorylation are involved

in diverse cellular processes such as expansion of the endoplasmic reticulum, which

houses a substantial fraction of nascent peptides [34]. It is tempting to speculate that

these proteins might also include unknown activators of protein scavenging.

This study highlights the inability of cancer cells fed extracellular protein to op-

timally adjust levels of mTORC1 signaling to match amino acid availability. These

cells maintain mTORC1 activity even when free leucine or glutamine is absent from

60

the extracellular environment. mTORC1 signaling is insensitive to such amino acid

scarcities in part because multiple amino acids are activators of mTORC1, but even

when no free amino acids are present, mTORC1 signaling, initially suppressed, can

be re-activated by protein catabolism via either autophagy [135] or catabolism of ex-

tracellular protein [78]. Thus, while mTORC1 can sense acute amino acid starvation,

it is insufficient to balance biosynthesis and catabolism in response to chronic amino

acid deprivation in cells with constitutive growth factor signaling. In accordance

with this, proline starvation was recently shown to result in mTORC1 hyperactiva-

tion, unresolved ER stress, and decreased tumorigenesis of multiple cancer cell lines

[94]. In a different context, dysregulated mTORC1 renders cells dependent on an

exogenous supply of unsaturated fatty acids (whose production requires oxygen) in

hypoxia [134]. Thus, excessive mTORC1 signaling can push cells into fatal stress

when biosynthetic substrates are limiting.

These findings have implications for mTOR inhibition in cancer therapy. While

mTOR inhibitors have shown anti-tumor activity in certain cancers, they have unex-

pectedly had limited efficacy in most cases. In assessing the therapeutic potential of

these agents, the deleterious activity of mTORC1 in cells deprived of amino acids may

have been overlooked. We find that moderate mTOR inhibition protects these cells

from cell death by restricting translation. Moreover, if these cells catabolize extra-

cellular protein, mTOR inhibition facilitates robust growth. The pro-survival effects

of mTOR inhibition on amino acid-deprived cells may explain the minimal clinical

activity of mTOR inhibitors on pancreatic tumors [45, 128], which are glutamine-poor

[48]. In accordance with this idea, Palm et al. showed that inhibition of mTORC1

enhances the growth of pancreatic tumors in a murine PDAC model. Specifically,

rapamycin decreased the fraction of proliferating cells in outer, vascularized regions

of these tumors, but increased the proliferation of cells in interior, hypovascularized

regions [78]. The present work suggests that mTOR inhibition promotes the growth

61

of these cells not only by promoting protein scavenging, but also by reducing biosyn-

thetic demands. As a result, cells enduring prolonged nutrient shortages can stably

assimilate biosynthetic substrates for anabolism (e.g. by degradation of extracellu-

lar protein) while simultaneously avoiding the lethal cellular stresses associated with

starvation. More generally, many chemotherapeutics target upregulated biosynthesis

in cancer cells. Our results emphasize the importance of finding new ways to amplify

cellular stresses associated with excessive biosynthesis, rather than focusing solely on

slowing these biosynthetic processes down. Indeed, in tumors poorly supplied with

nutrients, slowing anabolism can paradoxically promote growth.

3.11 Materials and Methods - Cell lines

Cell lines and culture All cell lines used in this study are listed in the Key Re-

sources Table. All cells were propagated in DMEM with 25 mM glucose and 4 mM

glutamine and without pyruvate (Mediatech). DMEM was supplemented with 10%

FBS (HyClone) and 25 IU/mL penicillin and 25 mg/mL streptomycin (MP Biomed-

icals), unless specified otherwise.

Knockout cell lines Oligonucleotides targeting murine Gcn2 (also known as

Eif2ak4) were cloned in lentiCRISPR v2 (Addgene #52961) [96]. Virus was pro-

duced in HEK293FT cells, and KRPCA cells were infected. Infected cells were

selected in puromycin, and clonal knockout cell lines were produced by isolation of

single cells from this infected population. Oligonucleotide sequences are listed in the

Key Resources Table.

62

3.12 Materials and Methods - Measuring catabolism

of extracellular protein

Custom Media Preparation Custom media were prepared using DMEM powder

containing all DMEM salts and vitamins, low glucose, and no amino acids or pyruvate

(US Biologicals). Glucose was added to a final concentration of 25 mM glucose, and

sodium bicarbonate to a final concentration of 3.7 g/L. Pyruvate was not added to

any media. To facilitate custom media preparation, concentrated (20-100X) amino

acid stock solutions were prepared and stored at 4◦C. Such solutions were used to

add all amino acids except glutamine (unstable) and tyrosine (insoluble), which were

added directly in powder form. 13C-AA medium, with or without supplemented

BSA, contained uniformly 13C-labeled histidine, lysine, phenylalanine, threonine, and

valine; all other amino acids were unlabeled.

In 13C-AA medium not supplemented with BSA and in all amino acid-deficient

media, amino acid concentrations were identical to standard DMEM (glutamine: 4

mM; isoleucine, leucine, lysine, threonine, and valine: 0.8 mM; arginine, glycine,

serine, phenylalanine: 0.4 mM; cystine, histidine, methionine, and tyrosine: 0.2 mM;

and tryptophan: 0.078 mM). For 13C-AA medium supplemented with BSA, 13C-

labeled amino acids were added at reduced concentrations to facilitate amino acid

uptake measurements (lysine, threonine, and valine: 0.32 mM; phenylalanine: 0.16

mM; and histidine: 0.08 mM). All BSA-supplemented media contained 5% w/v BSA.

All custom media was adjusted to pH 7.2 immediately before sterile filtration and

was additionally supplemented with 5% dialyzed FBS.

Stable isotope-labeled amino acids (including U-13C6 L-Lysine:2HCl, U-13C9 L-

Phenylalanine, U-13C4 L-Threonine, U-13C5 L-Valine, and U-13C6 L-Histidine) were

from Cambridge Isotope Laboratories. All other components were standard tissue

63

culture-grade reagents (Sigma). Tissue culture-grade BSA, which was not delipidated,

was from Sigma.

Isotope-Tracer Experiments Cells were grown for five doublings in 13C-AA

medium, as described above. After five doublings, cells were seeded at low cell

density in 60 mm tissue culture dishes and switched to 2 mL of 13C-AA medium

supplemented with 5% BSA. After 16 hr and 24 hr (and, where indicated, additional

time points), medium amino acids and total cellular volume were measured as below.

Absolute concentrations were determined by comparison of peak intensities in sam-

ples of interest and samples from fresh medium, in which amino acid concentrations

are known.

Metabolite Extraction and LC-MS Analysis For analysis of intracellular amino

acids, medium was aspirated and plates were rinsed three times with room temper-

ature PBS. Metabolism was quenched and amino acids extracted in ice-cold 80:20

methanol:water extraction solution. Plates were scraped and cell extracts were trans-

ferred to eppendorf tubes, which were vortexed and centrifuged at 16,100 g for 5

min. The resulting supernatant was dried under nitrogen flow and resuspended in

HPLC-grade water. 40 μL of the resulting solution was added to 160 μL HPLC-grade

methanol in a new tube. 10 μL triethylamine and 2 μL benzyl chloroformate were

added sequentially, and the resulting mixture was vortexed and incubated at room

temperature for 30 min to derivatize and thereby enhance measurement sensitivity of

amino acids.

For analysis of amino acids in culture medium, 50 μL of medium was directly

added to 200 microliters of HPLC-grade methanol. This mixture was vortexed then

centrifuged at 16,100 g for 5 min. 200 μL supernatant was transferred to a new tube.

10 μL triethylamine and 2 μL benzyl chloroformate were added sequentially, and the

resulting mixture was vortexed and incubated at room temperature for 30 min.

64

After derivatization, samples were diluted such that amino acids fell within the

linear range of a triple quadrupole mass spectrometer (TSQ Quantum Discovery Max;

Thermo Scientific), operating in negative multiple reaction monitoring mode, coupled

to C18 high-performance reversed-phase ion pair liquid chromatography [63, 61]. Data

were analyzed using open-source software [70].

Extracellular Protein Catabolism Rate Computation To derive an expression

for the rate of amino acid release due to serum protein catabolism, we start with the

following basic relationship: any cellular reaction rate (in units of moles per unit time

per unit cell volume) is equal to the total amount of product being produced by this

reaction in all cells (in units of moles per unit time) divided by the total volume of all

cells. In this case, for a given amino acid, the rate of amino acid release due to serum

protein catabolism is equal to the amount of amino acid being released by all cells

divided by total cell volume. Recalling that amino acids generated by extracellular

protein scavenging are unlabeled:

VAArelease =dAA0/dt

V ol(t)(3.1)

After integrating this equation with respect to time, the rate of amino acid release

is equal to the total amount of amino acid released by all cells over the course of the

experiment divided by the time-integral of total cellular volume:

VAArelease =AA0(T )∫ T0V ol(t)dt

(3.2)

Unlabeled amino acids released by extracellular protein catabolism can meet one

of three fates: they can end up as (i) intracellular amino acid monomers, (ii) amino

acid monomers in the medium, or (iii) amino acids which have been incorporated

into cellular protein. (We assume catabolism of essential amino acid monomers is

negligible.) Thus:

65

VAArelease =AA0

intra(T ) + AA0extra(T ) + AA0

prot(T )∫ T0V ol(t)dt

(3.3)

Because the aggregate volume of cells is very small relative to the volume of the

medium in each dish, the first term in the numerator of Equation (3.3) is negligible.

The second term in the numerator, which represents the molar amount of unlabeled

amino acids in the medium at the end of the experiment, is directly measurable.

The amount of unlabeled amino acids in protein at the end of the experiment was

determined indirectly, assuming recycling of cellular protein is negligible (Figure S2):

AA0prot(T ) = Vsynth

∫ T

0

AA0cyto

AAtotalcyto

(t)× V ol(t)dt (3.4)

We assume metabolic steady state to derive a simple expression for Vsynth:

Vsynth = Vin − Vout + VAArelease (3.5)

Substituting Equations (3.4) and (3.5) into Equation (3.3) gives us an expression

for the rate of amino acid release:

VAArelease =AA0

extra(T ) + (Vin − Vout + VAArelease)∫ T0

AA0cyto

AAtotalcyto

(t)× V ol(t)dt∫ T0V ol(t)dt

(3.6)

Finally, we solve for the rate of amino acid release due to extracellular protein

catabolism:

VAArelease =AA0

extra(T ) + (Vin − Vout)∫ T0

AA0cyto

AAtotalcyto

(t)× V ol(t)dt∫ T0V ol(t)dt−

∫ T0

AA0cyto

AAtotalcyto

(t)× V ol(t)dt(3.7)

Finally, dividing each amino acid release rate by the number of times that amino

acid appears in BSA yields protein scavenging flux estimates:

66

Vserumproteincatabolism =VAAreleaseαAA

(3.8)

To demonstrate how to compute the rate of amino acid release from extracellular

protein catabolism and corresponding protein scavenging flux, we provide an example

in which we compute the rate of lysine release in K-RasG12D MEFs growing in amino

acid-replete medium, using data shown in Figure 1C-E. K-RasG12D MEFs pre-grown

for five doublings in 13C-AA medium were switched to 2 mL of 13C-AA medium

supplemented with 5% BSA. The first term in the numerator of Eq. (3.7) is directly

measurable: after 24 hr, we measured 22,700 pmol unlabeled lysine in the medium:

AA0extra(T = 24h) = 22, 700 pmol (3.9)

The second term in the numerator is equal to the net uptake rate multiplied by

the time-integral of the product of the instantaneous unlabeled amino acid fraction

and the instantaneous total cell volume. Net uptake rate can be measured by tracking

amino acid abundance in the medium over time and normalizing to total cell volume.

In this example, we found that net lysine uptake was 2,900 pmol / μL cell / hr:

Vin − Vout = 2, 900 pmol / μL cell / hr (3.10)

To calculate the product of the instantaneous total cell volume and the instan-

taneous cytosolic unlabeled amino acid fraction integrated with respect to time, we

first fit the dynamic amino acid labeling data and the dynamic total cell volume data,

separately, to exponential functions of the following form:

f(t) = Aekt (3.11)

67

After fitting, the equations describing the unlabeled fraction of lysine in the

medium over time (in hours) and the total cell volume (in μL) over time (in hours)

are the following:

AA0cyto

AAtotalcyto

(t) = 0.014e(0.0756)t (3.12)

V ol(t) = 2.718e(0.0278)t (3.13)

Multiplication of Equation (3.12) by Equation (3.13) gives a single exponential

function under the integral in Eq. (7). Integration with respect to time (from 0 h to

24 hr) yields 4.19 μL cell × hr.

∫ T

0

AA0cyto

AAtotalcyto

(t)× V ol(t)dt = 0.040e(0.0756)t+(0.0278)tdt = 4.19 μL cell× hr (3.14)

The first term in the denominator is equal to the time-integral of total cellular

volume. For this, we can use the fitted exponential function describing cellular growth

from the previous step. We found that this integral was equal to 92.3 μL cell × hr.

∫ T

0

V ol(t)dt = e1.0+(0.028)tdt = 92.3 μL cell× hr (3.15)

The second term in the denominator, which also appears in the numerator and

was calculated above, is equal to 4.19 μL cell × hr. Plugging in Equations (3.9)-(3.15)

into Eq. (3.7):

VAArelease =22, 700 + (2, 930× 4.19 = 12, 300)

92.3− 4.19= 397 pmol / μL cell / hr (3.16)

68

Thus, the release rate of lysine from extracellular protein catabolism is 397 pmol

/ L cell / hr. To compute the corresponding protein scavenging flux, we divide this

number by the number of lysines per BSA molecule (59), as per Eq. (8), to yield the

following protein scavenging flux: 6.73 pmol / uL cell / hr:

Vserumproteincatabolism =VAAreleaseαAA

=397

59= 6.73 pmol / μL cell / hr (3.17)

As a final note, the equation for the rate of amino acid release due to extracellu-

lar protein catabolism includes a term containing the unlabeled amino acid fraction

in the cytoplasm over time. For this, we can either use the intracellular unlabeled

fraction, which contains cellular compartments other than the cytoplasm, or the ex-

tracellular unlabeled fraction. We observed that intracellular amino acid pools rapidly

exchange with amino acids in the medium: when we switched cells growing in stan-

dard unlabeled medium to 13C-AA medium, intracellular amino acid pools became

predominantly labeled (>90%) in roughly 10 min (Figure S1). Given this rapid ex-

change, we use extracellular amino acid labeling to represent cytosolic labeling in our

calculations. This has the benefit of requiring only extracellular, not intracellular,

amino acid measurements.

3.13 Materials and Methods - Other experimental

methods

Proliferation Assays For absolute measurements of proliferation (i.e. using cell

volume, cell number), 500K (parental KRPC) or 200K (adapted KRPC) cells were

seeded in standard 60 mm tissue culture dishes in DMEM supplemented with 5%

FBS. After 24 hr, cells were washed once with PBS and switched to amino acid-

69

deficient medium supplemented with 5% (w/v) BSA. Cell number was measured

using a Countess Automated Cell Counter (Invitrogen), and total cell volume was

measured using Packed Cell Volume tubes (Techno Plastic Products).

For relative measurements of proliferation (i.e. using absorbance of resorufin),

60K (parental KRPC) or 20K (adapted KRPC) cells were seeded in standard 24-well

tissue culture plates in DMEM supplemented with 5% FBS. After 24 hr, cells were

washed once with PBS and switched to amino acid-deficient medium supplemented

with 5% (w/v) BSA. After the indicated time in culture, cells were washed twice with

PBS, and standard DMEM supplemented with 10% FBS and 0.1 mg/mL resazurin,

but without additional BSA, was added. After 2 hr, absorbance was measured.

Western Blotting Cells were washed 3x with PBS, then lysed with ice-cold RIPA

buffer (Cell Signaling) with cOmplete protease inhibitor and PhosSTOP phosphatase

inhibitor cocktails (Roche). Soluble lysate fractions were isolated by centrifugation

at 16,100 g for 10 min. Relative protein content was estimated using total cellular

volume as a surrogate, and equal amounts of protein per sample were analyzed by

SDS-PAGE and Western Blotting.

Fluorescence Microscopy 5,000 cells were seeded in DMEM supplemented with

10% FBS in each well of a fibronectin-coated 8-well Chamber Slide (Nunc Inc). After

48 hr, cells were washed once with serum-free DMEM and switched to medium con-

taining 1 mg/mL DQ Green BSA, 50 nM LysoTracker Red, and 0.5 ug/mL Hoechst.

After 6 hr, cells were washed three times with PBS and fixed in 4% paraformalde-

hyde for 15 minutes. After three more washes to remove fixative, the polystyrene

chamber was removed, mounting medium was applied, and a coverslip was mounted.

The mounting medium was allowed to set overnight, and samples were imaged on a

Nikon A1 Confocal Microscope, with imaging parameters set such that no pixels were

saturated. Images were analyzed in Matlab.

70

Cell Viability Measurements Cell viability was assayed by flow cytometric de-

tection of caspase activity. After 48 hr in the specified condition, medium from each

sample was collected. Cells were washed once with room temperature PBS, which was

added to the collected medium. Cells were then detached with trypsin and added to

the collected medium and saline. The resulting cell suspension was centrifuged for 5

min at 2,000 g, and the pellet was resuspended in DMEM supplemented with 2% FBS,

containing CellEvent Caspase-3/7 Green Detection Reagent (Invitrogen). After 2 hr

at 37◦, SYTOX AADvanced Dead Cell Stain was added, and samples were analyzed

by fluorescence-activated cell sorting using a BD LSRII Multi-Laser Analyzer.

Statistical Analysis For proliferation, fluorescence microscopy, and apoptosis ex-

periments, p-values were calculated using a two-tailed unpaired t-test; for relative

protein scavenging rates, a two-tailed paired t-test. 95% confidence intervals were

calculated as the standard error of the mean multiplied by 1.96.

71

Chapter 4

A Genome-Wide Screen Identifies

The Proteins Behind Protein

Eating: GCN2 and cathepsin L

This chapter includes the beginnings of a manuscript. The hypothesis that has

emerged from the data presented – GCN2 upregulates the synthesis of cathepsin

L in amino acid-deprived cells – has not been confirmed.

4.1 Proposed Manuscript Title

GCN2 Upregulates the Translation of Cathepsin L-Encoding mRNAs, Increasing the

Degradative Capacity of Amino Acid-Deprived Cells

4.2 Abstract

Mammalian cells require amino acids to support growth. In nutrient-poor pancreatic

tumors, cancer cells deprived of free amino acids grow by taking up extracellular

protein and catabolizing it in lysosomes. To decipher the molecular mechanisms

72

underlying this growth, we conducted a genome-wide screen for genes essential for

growth using serum protein as the sole source of leucine. The most essential genes

were GCN2, which canonically represses translation initiation in amino acid-deprived

cells, and its putative binding partner GCN1. Using isotope tracers, we found that

GCN2 supports an increase in protein catabolism in amino acid-deficient conditions.

Proteomics in GCN2 wild-type and knockout cells revealed that GCN2 is required

to maintain levels cathepsin L, the most important lysosomal protease for protein

eating according to the screen. Cathepsin L depletion impairs catabolism. Thus,

GCN2 supports catabolism through regulation of translation, enhancing survival and

growth of cells in amino acid-poor conditions.

4.3 Introduction

All cells require amino acids for growth. Classically, mammalian cells take up

monomeric amino acids through transporters in the plasma membrane. Recently,

however, extracellular protein has emerged as an alternative source of amino acids.

Extracellular protein can be taken up via macropinocytosis, a process by which cells

engulf large amounts of extracellular fluid in bulk. The internalized protein can

then be degraded in lysosomes. This activity protein eating promotes the survival

and growth of cells in amino acid-poor environments [16, 48]. In the context of

whole-organism physiology, the role of protein eating remains poorly understood.

To our knowledge, protein eating has only been examined in one setting: pancreatic

ductal adenocarcinoma (PDAC).

PDAC is a devastating disease, with a median overall survival of less than a year

[103]. Pancreatic tumors are nutrient-poor due to substantial fibrosis that limits

the flow of nutrients into the tumor [74]; glutamine is particularly depleted [48].

By contrast, extracellular protein – for example, fibrotic protein secreted by cancer-

73

associated fibroblasts or serum protein leaked by blood vessels – is present in the

tumor in inexhaustible quantities [5]. Increasing evidence suggests that pancreatic

tumor cells rely on this protein as a source of amino acids, including glutamine.

These cells, nearly universally, express a constitutively active form of K-Ras, and K-

Ras signaling stimulates macropinocytosis. This enables cultured pancreatic cancer

cells to use extracellular protein to grow in culture medium lacking essential amino

acids [48]. In slices of excised tissue from human patients, pancreatic tumor cells

actively engaged in macropinocytosis, and in a murine model of PDAC, isotope tracer

studies have provided direct evidence of extracellular protein catabolism in vivo [19].

Pharmacological inhibition of protein eating reduced tumor growth and decreased

amino acid levels in murine tumors [16, 19]. These experiments relied on intratumoral

injection of the macropinocytosis inhibitor EIPA, which is too toxic to administer

systemically [16]. Indeed, while protein eating appears to be an attractive therapeutic

target, no clinically viable inhibitors of this process exist to date.

Little is known about the biochemical mechanisms underlying protein eating. Be-

sides Ras signaling, several other signaling pathways have been implicated in the

regulation of macropinocytosis: phospho-inositol signaling, Rac signaling, and phos-

pholipase C signaling [77]. Sub-membranous pH has also been implicated in regu-

lation of macropinocytosis. (EIPA, the toxic macropinocytosis inhibitor, blocks a

Na+-H+ plasma membrane antiporter, causing proton accumulation that results in

dissociation of Rac1, an inducer of membrane ruffling, from the plasma membrane

[51].)

While K-Ras signaling is indeed sufficient to stimulate macropinocytosis and im-

prove survival and growth on extracellular protein, K-Ras-mutant pancreatic tumor

cells do not initially grow robustly when cultured in leucine-free medium supple-

mented with physiological concentrations of albumin. Most cells switched into this

medium die after several days, but some cells do survive. As we reported previ-

74

ously, starting with KRPC cells, which are derived from an autochthonous murine

model of PDAC, we cultured these survivors continuously in leucine-free, albumin-

supplemented medium, and after several months, the resulting population grows ro-

bustly in this medium, with low levels of cell death [48]. We called these cells adapted

KRPC cells, or KRPCA cells.

Recently, Palm et al. showed that pharmacological inhibition of mTOR complex

1 (mTORC1) kinase activity also enables robust growth on extracellular protein,

proving that long-term adaptation is not required for efficient growth by protein eat-

ing [78]. mTORC1 is a master growth regulator, stimulating protein synthesis and

suppressing protein catabolism upon stimulation by growth factor signaling. Amino

acid levels also modulate mTORC1 activity; in the absence of amino acids, mTORC1

signaling is suppressed. Eventually, however, catabolism of intracellular or extracel-

lular protein in lysosomes reactivates mTORC1 [78, 135]. Thus, mTORC1 signaling

persists in cells cultured in amino acid-deficient medium supplemented with a physio-

logical amount of albumin. For cells starved of amino acids, this persistent mTORC1

signaling is deleterious, as Torin1, a potent inhibitor of mTORC1, promotes growth

in this condition [78].

Palm et al. also proposed a mechanism by which mTOR inhibition enhances

growth fueled by protein eating. They found that Torin1 treatment did not impact

macropinocytosis. Rather, the authors showed that Torin1 induced the degradation of

extracellular protein. Degradation was measured using a fluorescent tool called DQ-

BSA – bovine serum albumin (BSA) conjugated to the fluorophore BODIPY. DQ-

BSA is formulated such that each BSA molecule is associated with several BODIPY

molecules, which quench each other when in close proximity. Only upon degradation

of the BSA do the molecules de-quench (DQ) and become fluorogenic. Thus, the

authors propose that mTORC1 suppresses the utilization of extracellular protein as

nutrients [78].

75

Using a quantitative isotope tracer-based method, we confirmed that Torin1 treat-

ment increases protein eating, but we also observed that amino acid deprivation in-

duces an increase in protein eating, even as mTORC1 activity persists [73]. This

suggests that (i) cells have another way of sensing amino acid shortage and (ii) this

second amino acid sensor increases protein eating independently of mTORC1. The

only two ubiquitously expressed kinases known to be sensitive to amino acid levels are

mTORC1 and GCN2 [24]. We suspect that GCN2 induces the mTORC1-independent

increase in protein observed in amino acid-deprived cells. GCN2 kinase activity is

regulated by uncharged tRNAs, which accumulate in amino acid-deficient conditions.

Upon binding to uncharged tRNA, GCN2 phosphorylates translation initiation factor

eIF2α, thereby inhibiting translation initiation [124, 22, 7].

Some mRNAs are resistant to this GCN2-mediated inhibition of translation initi-

ation. The Atf4 mRNA is one example of an mRNA whose expression is upregulated

by eIF2α phosphorylation. Through a complex mechanism involving short upstream

open reading frames (ORFs) that, in the absence of GCN2 activity, divert ribosomes

from the protein-coding ORF of Atf4, GCN2 activity increases the synthesis of ATF4

[35]. ATF4, a transcription factor, induces expression of amino acid biosynthesis

genes [34]. Other GCN2-resistant mRNAs have been described, but in general the

fact that different mRNAs may have varying sensitivities to eIF2α phosphorylation

has not been explored.

Here, we exploited the capacity of KRPCA cells to grow in leucine-free medium

to conduct a genome-wide screen. Genome-wide CRISPR-based screens have been

employed effectively to systematically identify the genes required for growth in var-

ious cell lines and conditions [123, 122]. We used this technology to identify genes

essential for growth dependent on protein eating. Our screen data revealed that there

are three major molecular processes required for growth on catabolized protein: (i)

uptake of extracellular protein from the environment via macropinocytosis, (ii) degra-

76

dation of endocytosed extracellular protein, and (iii) regulation of protein synthesis.

The two most important genes were Gcn2 and Gcn1. We validated that Gcn2 is

required for growth fueled by protein eating, and we further showed that, through

regulation of translation, GCN2 signaling also promotes growth by increasing the

catabolic capacity of amino acid-deprived cells. This is achieved through regulation

of translation; specifically, GCN2 blocks translation initiation on most mRNAs, di-

recting limited amino acids to other favored mRNAs. Among the GCN2-resistant

mRNAs was cathepsin L, the most important lysosomal hydrolase identified in the

screen. Thus, through phosphorylation of eIF2α, GCN2 increases catabolic capacity,

promoting the survival and growth of amino acid-deprived cells.

4.4 Results - Genome-wide screen systematically

identifies genes required for growth fueled by

catabolized extracellular protein

We have shown previously that the uptake and catabolism of extracellular protein can

fuel the growth of amino acid-starved cancer cells in pancreatic tumors. This process

enables cultured tumor-derived cells to grow in the absence of essential amino acids

if the culture medium is supplemented with a physiological amount of serum protein

(50 g/L BSA) [48]. Cells cultured in this condition are growth-limited mainly by their

ability to eat extracellular protein and use the resulting amino acids efficiently. Thus,

we expected that these cells would be sensitive to loss of any cellular components

essential for growth fueled by protein eating.

To systematically identify these components, we conducted a genome-wide screen

for genes selectively essential for growth in leucine-free medium supplemented with

serum protein – that is, essential for growth in this medium, not essential for growth

77

in amino acid-replete medium. KRPCA cells, which have been adapted for robust

growth fueled by protein eating, were ideal for this screen. We infected these cells

with an expression cassette with the Cas9 endonuclease and a library of single guide

RNAs (sgRNAs). The sgRNA library targets 18855 genes with 184371 unique guide

RNAs. The guide RNA sequences were stably integrated into host cell genomes, so

the relative frequency of each guide could be measured by high throughput sequencing

of these sequences amplified from genomic DNA.

After infection, we split our cells into three populations to be cultured separately.

One population was grown in leucine-free medium supplemented with serum protein

– protein eating is required for growth in this medium. Another population was

grown in amino acid-replete medium supplemented with serum protein, and the third

population was grown in amino acid-replete medium without serum protein supple-

mentation. We cultured each population for 12 doublings, then extracted genomic

DNA. Guide sequences were amplified and sequenced, and the relative frequencies of

all individual guide sequences were compared across populations.

To determine which genes are essential for growth fueled by protein eating but

not for growth in nutrient-rich conditions, we compared the sgRNA frequencies of the

population grown in leucine-free medium and the populations grown in amino acid-

rich media. For each sgRNA, the log-ratio of sgRNA frequencies between populations

was calculated. For each gene, the “selective essentiality” score is the median of the

log-ratios of all sgRNAs targeting that gene (Figure 4.1). (A high selective essentiality

score indicates that a gene was essential in leucine-free medium but not in amino acid-

replete media.) This screen was performed twice, and the selective essentiality scores

shown are the average of the scores from the two independent screen replicates (Figure

4.1). Hereafter, I refer to the genes with the 100 highest selective essentiality scores

as “selectively essential genes.”

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Figure 4.1: (A) Pooled genome-wide screen design. (B) Selective essentiality forall genes (top) and the top 100 most selectively essential genes (bottom). These top100 genes included several genes involved in three processes: uptake of extracellularprotein, degradation of extracellular protein, and regulation of translation. Selectiveessentiality was calculated for each gene based on the equation in (A). (C) Depictionsof selectively essential genes involved in each process, and scatter plots showing theselectively essentiality scores for these genes. Not all genes plotted are depicted. Theselective essentiality rank of the gene (or the highest ranking gene of the complex) islisted in parenthesis.

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Figure 4.2: Screen results for Gcn2 and Gcn1. Log2-ratios of sgRNA frequenciesin final to initial populations are shown. Data from each screen replicate is shownseparately. Several sgRNAs targeting both Gcn2 and Gcn1 were strongly depleted inthe final populations grown in leucine-free medium, but not in the final populationsgrown in amino acid-replete medium.

The two genes with the highest selective essentiality scores were Gcn2 (also known

as Eif2ak4) and its putative binding partner Gcn1 (also known as Gcn1l1). GCN2,

as discussed previously, is a kinase that is activated by binding to uncharged tRNA

molecules in amino acid-poor conditions and slows translation initiation [124]. GCN1

is required for GCN2 activation in yeast [67]; the role of GCN1 in mammalian cells

is unknown. In the populations grown in amino acid-rich conditions, the frequencies

of the majority of sgRNAs targeting Gcn2 or Gcn1 did not change. In the popu-

lations grown in leucine-free conditions, however, the frequencies of most sgRNAs

targeting these two genes decreased dramatically (Figure 4.2). This indicates that

cells expressing sgRNAs targeting Gcn2 or Gcn1 were unable to grow in leucine-free

medium.

In general, the selectively essential genes were highly expressed (Figure 4.3). This

suggests that the false positive rate was low – the top 100 most selectively essential

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Figure 4.3: Selectively essential genes were highly expressed. (A) Using RNA se-quencing data from KRPCA cells (data not shown), log10 reads per kilobase permillion (RPKM) was plotted for all genes. Selectively essential genes were all highlyexpressed. (B) Non-expressed genes (genes with no reads measured in the RNA se-quencing experiment) were not selectively essential.

genes encode proteins that likely play some role in promoting growth fueled by protein

eating. In support of this notion, no non-expressed genes were in these top 100 genes

(Figure 4.3). Moreover, the members of multi-protein complexes generally scored

similarly. For example, the genes encoding all six members of the HOPS complex

(Vps11, Vps16, Vps18, Vps33, Vps39, and Vps41) scored in the top 100, and the

gene encoding the Rab GTPase that regulates HOPS complex activity (Rab7) was

also selectively essential [105]. The fact that all members of this complex scored

suggests that the false negative rate was low – if a protein is required for growth

by protein eating but is not required for growth in nutrient-rich conditions, the gene

encoding that protein was most likely identified as selectively essential.

Not all proteins with essential roles in protein eating-dependent growth were se-

lectively essential, however. Some genes encoding such proteins are essential in amino

acid-replete medium too. For example, K-Ras was essential in all growth conditions;

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Figure 4.4: K-Ras and the V-ATPase are essential in all growth conditions. For K-Ras, log2-ratios of sgRNA frequencies in final to initial populations are shown. For theV-ATPase, medians of the sgRNA frequency log2-ratios in final to initial populationsfor all subunits are shown. Data from each screen replicate is shown separately.

several vacuolar ATPase components were also essential in all conditions (Figure 4.4).

Other proteins with essential roles in protein eating were not essential in any con-

dition because they have homologs with redundant functions. The subunits of actin

filament capping protein serve as a good example of this. Capping protein is a het-

erodimer composed of CAPZB and either CAPZA1, CAPZA2, or CAPZA3. CAPZB,

which does not have a homolog with redundant function, is selectively essential. Any

CAPZA protein can bind to CAPZB to form a functional complex; because both

CAPZA1 and CAPZA2 are expressed in KRPC cells (data not shown), these pro-

teins are less selectively essential than CAPZB (Figure 4.5). There are undoubtedly

many homologous proteins that perform functions essential to protein eating but are

not selectively essential when knocked out individually and thus escape our attention.

While some complexes include exchangeable proteins like the alpha subunits of

capping protein, other complexes are composed of proteins without co-expressed ho-

mologs. The genes of such complexes had remarkably similar selective essentiality

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Figure 4.5: Screen results for actin capping protein isoforms. Capzb is selectivelyessential, while the Capza proteins, which have redundant function, are not.

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scores. For example, the two genes in the TSC1/TSC2 complex ranked 16th and

19th in selectively essentiality and had remarkably similar essentiality profiles. The

same was true for the genes in the GATOR1 complex, the HOPS complex, and the

BORC complex (Figure 4.6). This enables comparison of essentiality between com-

plexes. For example, the HOPS complex appears to be more essential the BORC

complex. In other cases, genes in the same complex, like the AP1 adaptor complex,

had very different Selective Essentiality scores without apparent reason. Ap1m1 was

by far the most selectively essential subunit, despite expression of Ap1m2. Mean-

while, Ap1b1 was not selectively essential, despite being the only known AP1 beta

subunit in the genome.

In general, complexes as a whole seem to be at least as essential as the most

essential gene in the complex; knockout of the most important gene in the complex

more closely resembles the complete absence of the complex than knockout of the

least important gene. (The essentialities of the least important genes in complexes

provide little information about the essentiality of the complex as a whole, so it does

not make sense to average the selective essentiality scores of all genes in a complex.)

Deriving biochemical mechanism from selective essentiality scores is not straight-

forward for many reasons. The degradation of extracellular protein requires not only

the delivery of this protein to lysosomes but also the delivery of lysosomal components

to lysosomes. Thus, a selectively essential gene with an annotated role in vesicle traf-

ficking may be involved in the macropinosome-to-lysosome route or the endoplasmic

reticulum (ER)-to-lysosome route.

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Figure 4.6: Screen results for various protein complexes. The genes encoding sub-units of a common complex were similarly selectively essential. This enables compar-ison of essentiality between complexes. For example, the HOPS complex appears tobe more essential the BORC complex.

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4.5 Results - The Three Major Categories of Selec-

tively Essential Genes: Uptake, Degradation,

and Regulation of Translation

Many of the selectively essential genes can be grouped into three major categories:

uptake of extracellular protein via macropinocytosis, trafficking and degradation

of extracellular protein, and negative regulation of protein synthesis (Figure 4.1).

Macropinocytosis is driven by mobilization of the actin cytoskeleton, and accord-

ingly, the selectively essential genes encoding proteins involved in macropinocytosis

included several actin cytoskeleton genes. β-actin was one of the three most selectively

essential genes. There are two ubiquitously expressed actins; the other, γ1-actin, was

also selectively essential [115]. We were somewhat surprised that actin was not es-

sential in cells grown in amino acid-rich conditions. We reasoned that perhaps just

one of these two actin monomer proteins is required in amino acid-rich conditions, in

which macropinocytosis is not critical for growth.

Several actin cytoskeletal proteins beyond the actin subunits themselves were also

identified as selectively essential. These included several of the subunits of the actin-

related protein 2/3 (ARP2/3) complex, which catalyzes the nucleation of new fila-

ments and filament branches [65], and Vasp, which encodes a protein that promotes

actin filament elongation, in part by blocking filament capping [93, 6, 56] (Figure

4.7). (Other ARP2/3 subunits were essential even in amino acid-rich conditions, sup-

porting the notion that a partially functional actin cytoskeleton is required for cell

viability.) As mentioned previously, F-actin capping protein, which caps the barbed

(growing) end of actin filaments [68, 69], blocking them from further growth and

stabilizing them, was selectively essential. How is it possible that both Vasp and

capping protein, which have mutually antagonistic functions (anti-capping and cap-

ping), are both essential? The two activities may be required at different points in

86

Figure 4.7: Selective essentiality of actin-related proteins. Log2-ratios of sgRNAfrequencies in final to initial populations are shown. Data from each screen replicateis shown separately.

the multi-step process of taking up extracellular protein. The precise biochemical

mechanisms underlying this process, however, are complicated and remain poorly un-

derstood. The initial step of actin filament polymerization, which creates membrane

protrusions and eventually membrane ruffles, requires actin filament branching and

elongation [111]. The basis for the requirement of actin filament capping is less clear.

It is possible that capping is required to prevent membrane ruffles from turning into

bigger, more permanent structures like filopodia and invadopodia. It is also possi-

ble that without capping protein, the actin cytoskeletal network of a cell collapses,

making the formation of membrane ruffles impossible.

The idea that the actin cytoskeleton plays a role in macropinocytosis is not new –

cytochalasin D, which inhibits actin polymerization, blocks macropinocytosis [9, 12]

– but the following question remains: How is the actin cytoskeleton mobilized and

controlled to specifically produce membrane ruffles and macropinocytosis? Many

signaling pathways have been implicated: PI3K signaling, Rho GTPase signaling,

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phospholipase C signaling, and tyrosine kinase signaling, among others [77, 49]. In-

deed, several selectively essential genes are involved in these pathways. Myo9b has

Rho GTPase activity and has been shown to promote invasiveness in lung cancer cells

[52]. Iqgap1, a target of Rho GTPases Cdc42 and Rac1 [36, 57], was also selectively

essential. Additional evidence comes from genes without which cells in leucine-free

medium grew faster. These genes encode proteins deleterious for growth fueled by

extracellular protein. (To identify such genes, we focused on the ratio of sgRNA fre-

quencies between the final leucine-free population and the initial population, rather

than the final amino acid-rich populations. This analysis avoids genes essential in

amino acid-rich conditions but not leucine-free conditions, because these genes do

not inhibit growth in leucine-free medium.) Cells grew fastest without the PTEN

tumor suppressor, a phosphatase that antagonizes PI3K signaling [119]. Cells also

grew faster in leucine-free medium without Ptpn12, a tyrosine phosphatase that likely

dephosphorylates Vasp [110]. The fact that no single signaling protein was much more

essential for protein eating than the rest reflects the high degree of redundancy among

signaling pathways that activate macropinocytosis.

The second major group of selectively essential genes is involved in the degra-

dation of extracellular protein. Among the genes in this group, only one encodes

a protein directly involved in the breakdown of extracellular protein: cathepsin L

(Ctsl). Cathepsin L is a cysteine protease that degrades protein in lysosomes [50].

Many of the other genes are involved in protein trafficking along two major routes.

The first is the route from macropinosomes, where internalized protein first appears

in the cell, to lysosomes, where this internalized protein is degraded. The second is

the route from the endoplasmic reticulum, where lysosomal hydrolases like cathepsin

L are synthesized, to the lysosome, where they degrade extracellular protein.

Much research investigating the delivery of internalized macromolecules to the

lysosome has focused on low-density lipoprotein (LDL). Upon binding to LDL re-

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ceptor, LDL is taken up into small vesicles called early endosomes. These early

endosomes fuse with one another, growing in size, until they eventually mature into

late endosomes. Late endosomes eventually fuse with lysosomes, at which point their

cargo is degraded [90]. (This classical endocytic pathway seems to be essential for

cell growth fueled by protein eating. LDL receptor is selectively essential, as is Npc1,

which mediates cholesterol efflux from lysosomes. Indeed, cholesterol is required for

macropinocytosis, most likely due its contribution to membrane fluidity [31].)

Compared with LDL uptake, macropinocytosis is poorly understood. The prod-

ucts of macropinocytosis are known as macropinosomes, which are much bigger than

early endosomes. Rather than fusing with one another and increasing in size, these

vesicles appear to decrease in size as they move away from the plasma membrane, to-

ward the center of the cell [112]. The subsequent steps required to deliver the contents

of these macropinosomes to lysosomes are poorly understood. Some of the contents

of macropinosomes are likely sorted into recycling endosomes, which fuse with the

plasma membrane; recycling endosomes dump their cargo back into the extracellular

space and return cell surface receptors back to the plasma membrane. The rest of the

material internalized into macropinosomes, if not recycled, must be degraded. Where

do macropinosomes fit in the early endosome-late endosome-lysosome pathway? The

results of the screen provide some clues.

Membrane trafficking is governed by Rab GTPases. Rab proteins, which are

geranylgeranylated, localize to distinct membrane domains and orchestrate vesicle

docking, membrane fusion, transport along microtubules, and other related activ-

ities [82, 81]. The Rab proteins most essential for growth fueled by protein eat-

ing were Rab35, Rab10, and Rab7. While no single Rab5 homolog was very selec-

tively essential, Rab5c and Rab5a were in the top 10 most selectively essential Rab

genes. (There were 64 total Rab genes that passed through all computational filters.)

Rab5 has been shown to be required for macropinocytosis. Expression of a consti-

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Figure 4.8: Selective essentiality of Rab proteins and Rabankyrin-5. The average se-lective essentialities, across both screen replicates, of the 10 most selectively essentialRab proteins are shown. For Rabankyrin-5, log2-ratios of sgRNA frequencies in finalto initial populations are shown. Data from each screen replicate is shown separately.

tutively active Rab5 mutant increased pinocytosis of horseradish peroxidase (HRP),

whereas expression of dominant negative Rab5 mutant blocked HRP uptake [59].

Rab5 effector Rabankyrin-5 (also known as Ankfy1) was selectively essential (Figure

4.8). Like Rab5, Rabankyrin-5 overexpression caused an increase macropinocytosis,

and Rabankyrin-5 knockdown caused a reduction [98]. Thus, Rab5 and its effector

Rabankyrin-5 seem to play important roles in uptake of extracellular protein. (One

could make the case that Rab5 belongs in the uptake category.)

The role of Rab5, however, is not restricted to regulation of macropinocytosis.

Simultaneous knockdown of all three Rab5 proteins caused a collapse in the en-

dolysosomal system – a reduction in early endosomes, late endosomes, and lyso-

somes [137]. Indeed, Rab5 localizes to endosomes, not the plasma membrane [90].

Thus, it seems likely that Rab5 both induces macropinocytosis and governs the ac-

tivity of macropinosomes once they are formed, and because Rab5 canonically gov-

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erns membrane domains of early endosomes, it seems reasonable to imagine every

macropinosome as a special kind of early endosome.

Early endosomes – and assumedly macropinosomes – undergo a process of matu-

ration into late endosomes that involves loss of Rab5 and gain of Rab7 [90]. Based

on the screen data, the most important Rab7 effector is likely the HOPS complex.

The HOPS complex is a tethering complex that orchestrates both late endosome-

late endosome fusion and late endosome-lysosome fusion [101, 84]. Fusion depends

on SNARE proteins [44], although no individual SNARE protein was selectively es-

sential. Thus, through the sequential action of Rab5 and Rab7 and their effector

proteins, extracellular protein is internalized and delivered to lysosomes.

Besides Rab5 and Rab7, two other Rab proteins were selectively essential: Rab35

and Rab10 (Figure 4.8). Rab10 is responsible for recycling of material from early

endosomes back to the plasma membrane [14, 97]. Recycling is required to maintain

homeostasis; without recycling, levels of both plasma membrane lipids and membrane

proteins would decline until macropinocytosis can no longer occur, with the plasma

membrane taut around the cell. The role of Rab35 is less clear. This protein has been

implicated in cytokinesis, recycling from early endosomes, and exosome secretion

[55, 2, 43]. Intriguingly, activating Rab35 mutations have been found in human

tumors. Expression of one of these mutant Rab35 alleles bestowed fibroblasts with

oncogenic potential. Furthermore, in HEK293 cells expressing mutant Rab35, platelet

derived growth factor (PDGF) receptor was trafficked to lysosomes, suggesting that

Rab35 may play an important role in cells fueled by protein eating [125].

Delivery of extracellular protein to the lysosome cannot yield amino acids if lyso-

somal hydrolases like cathepsin L have not also been delivered there. Lysosomal

hydrolases are synthesized into the endoplasmic reticulum (ER), the production site

for all proteins in the endolysosomal and secretory systems [76]. All proteins syn-

thesized into the ER are modified by the addition of a pre-formed oligosaccharide,

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composed of several N-acetylglucosamine, mannose, and glucose molecules, to an as-

paragine residue of the nascent peptide chain – this is called asparagine (N)-linked

glycosylation. Fully synthesized proteins are next transported from the ER to the

Golgi apparatus. There, lysosomal hydrolases are recognized and modified by the

Golgi resident enzyme GlcNAc-1-phosphotransferase, which adds phosphate groups

to mannose residues of the N-linked oligosaccharide, producing mannose-6-phosphate

(M6P) [18]. (Mechanistically, this phosphorylation is the product of the sequen-

tial addition of GlcNAc-1-phosphate and removal of GlcNAc to a mannose residue.)

GlcNAc-1-phosphotransferase has three subunits: both the α and β subunits are en-

coded by the gene Gnptab, and the γ subunit is encoded by Gnptg. Gnptab, but not

Gnptg, was identified as selectively essential. In humans, mutations in Gnptab cause

mucolipidosis II (or I-cell disease), a fatal lysosome storage disorder [37]. Fibroblasts

from patients with this disease display hypersecretion of lysosomal hydrolases into

the culture medium and accumulation of lysosomes within the cells [75].

Properly M6P-tagged lysosomal hydrolases are recognized by M6P receptors,

which mediate their packaging into clathrin-coated vesicles. The contents of these

vesicles are delivered to early endosomes, the entry point into the endolysosomal sys-

tem. Several genes involved in this process were identified as selectively essential for

growth fueled by protein eating. These included the cation-independent M6P recep-

tor (Igf2r), but not the cation-dependent M6P receptor (M6pr). IGF2R is a receptor

for both insulin-like growth factor and M6P-tagged lysosome hydrolases [71]. Inter-

estingly, Igf1r was also identified as selectively essential. As mentioned previously, the

AP1 adaptor complex, which is required for M6P receptor packaging into clathrin-

coated vesicles destined for early endosomes, is also selectively essential, as is Arf1,

which recruits and activates the AP1 clathrin adaptor complex [89].

Once clathrin-coated vesicles containing lysosomal hydrolases have departed from

the Golgi, they must travel to and fuse with endosomes. This process is not well

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understood. I propose that Vps45, a poorly characterized SM protein, functions

at this step. SM proteins are required for SNARE-mediated vesicle fusion [109].

(Vps33a, a HOPS complex member, is an SM protein.) In yeast, Vps45 mediates

fusion of Golgi-derived vesicles with the pre-vacuolar compartment [10]. Vps45 is

selectively essential. Since no other vesicle fusion steps are required along the two

major vesicle trafficking routes described, it seems likely that Vps45 is required for

delivery of lysosomal hydrolases to the endolysosomal network.

The BORC complex is a multi-protein complex involved in the peripheral position-

ing of lysosomes. Knockout of any of the subunits of this complex caused lysosomes of

cultured cells to collapse toward the center of the cell. This complex is also required

for cell motility, as determined by scratch assay [86]. The BORC complex has not

been studied in the context of catabolism, but it seems that the peripheral positioning

of lysosomes might be particularly important for efficient catabolism of extracellu-

lar protein. One would think that lysosomes close to the plasma membrane reduce

the amount of trafficking required to deliver extracellular protein to the degradative

compartment.

Uptake and degradation of extracellular protein are sub-processes in the larger

process of supplying cells with amino acids. The third major category of selectively

essential genes (regulation of translation) contains genes encoding proteins that regu-

late amino acid consumption, not amino acid supply. While pancreatic tumor cells in

vivo must regulate the consumption of all scarce amino acids, cultured cells grown in

the absence of leucine must only carefully regulate leucine consumption. As leucine

is a strictly proteinogenic amino acid, regulation of leucine consumption means reg-

ulation of protein synthesis.

Besides the olfactory receptors, which are not expressed widely, there are two

amino acid sensing pathways in mammals: the mTORC1 pathway and the GCN2

pathway [24]. Both mTORC1 and GCN2 are kinases that regulate protein synthesis.

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mTORC1 is a positive regulator: when activated by insulin signaling, mTORC1

stimulates protein synthesis [58]. Specifically, mTORC1 induces initiation of cap-

dependent translation, which affects only a subset of transcripts [113]. In the absence

of amino acids, mTORC1 signaling is suppressed. However, mTORC1 can be re-

activated by protein catabolism, which yields lysosomal amino acids. Indeed, after

a few hours, leucine-starved cells cultured in a physiological concentration of serum

protein re-activate mTORC1 [135]. Thus, the mTORC1 pathway fails to respond to

prolonged leucine scarcity in cells fed extracellular protein.

GCN2 is a negative regulator of protein synthesis. When activated by uncharged

tRNA binding, GCN2 phosphorylates eukaryotic translation initiation factor 2

(eIF2α). eIF2α is one of three subunits comprising eIF2, which forms a ternary com-

plex with GTP and the initiator codon iMet-tRNA. This ternary complex initiates

translation. eIF2 GTP-binding is promoted by the guanine exchange factor (GEF)

eIF2B; phospho-eIF2α inhibits the activity of eIF2B, thereby inhibiting formation of

the ternary complex [38, 42]. In this manner, GCN2 inhibits translation initiation

upon amino acid depletion. We postulated that Gcn2 is required to protect cells

from the deleterious effects of amino acid starvation, including ribosome stalling and

protein misfolding. This idea has not been confirmed experimentally, however.

Gcn1 was also selectively essential. While we are unaware of any studies inves-

tigating the function of GCN1 in mammals, the yeast homolog of GCN1 has been

shown to be essential for GCN2 signaling. Yeast lacking Gcn1 exhibit a Gcn2-null

phenotype: no eIF2α phosphorylation upon histidine removal. This phenotype can

be rescued by over-expression of histidine tRNA, much of which presumably remains

uncharged when expressed at high levels. Yeast Gcn1 co-immunoprecipitates with

Gcn2, suggesting that Gcn1 facilitates the binding of uncharged tRNA molecules to

Gcn2. Gcn1 also co-sediments with ribosomes. In fact, Gcn1 is homologous to yeast

translation elongation factor 3 (EF3), suggesting that Gcn1 interacts with the A site

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Figure 4.9: Comparison of the selective essentialities of translation regulators. Theaverage selective essentialities, across both screen replicates, are shown.

of the ribosome where amino acylated (or uncharged) tRNAs enter [67, 118, 28]. (EF3

is not conserved in mammals.) Taken together, this evidence led Alan Hinnebusch,

who discovered Gcn2, to propose that Gcn1 may catalyze the transfer of uncharged

tRNA from the A-site of ribosomes to the uncharged tRNA-binding site of Gcn2,

thereby activating Gcn2 kinase activity [42].

While Gcn2 and Gcn1 were the most essential genes for growth fueled by protein

eating, negative regulators of mTORC1 were less selectively essential (4.9). The idea

that these negative regulators of mTORC1 (TSC and GATOR1) are less important

to amino acid-starved cells than GCN2 and GCN1 is consistent with the idea that the

negative regulators of mTORC1 fail to suppress mTORC1 activity to the extent that

optimizes growth on extracellular protein [73]. The fact that pharmacological inhibi-

tion of mTORC1 promotes robust growth of albumin-fed cells in amino acid-deficient

conditions supports this notion [78]. Thus, in cells dependent on catabolism of extra-

cellular protein, genetic loss of any negative regulator of mTORC1 does not cause,

but rather exacerbates mTORC1 hyperactivity in amino acid-deficient conditions.

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Beyond GCN2 and GCN1 and the negative regulators of mTORC1, there was

one particularly interesting gene involved in translation initiation that was found to

be selectively essential: Dhx29. DHX29 is an RNA helicase which is required for

in vitro translation of mRNAs with structured 5’ UTRs untranslated regions [83].

The precise role of DHX29 in cells, however, remains poorly understood. Parsyan et

al. showed that Dhx29 is broadly required for translation initiation in HeLa cells,

and Dhx29 knockdown impedes HeLa cell growth in culture and in xenografts. The

authors conclude that DHX29 is a “bona fide translation initiation factor,” similar

to eIF4E, and suggest that DHX29 affects “the initiation of translation of mRNAs

with moderately to extensively structured 5’ UTRs,” including “those involved in

controlling cell proliferation and apoptosis” [79]. To our knowledge, the possibility

that DHX29 is selectively essential in specific conditions has never been proposed.

4.6 Results - Screen Validation and Proteomics

To validate the basic findings of the screen, we generated knockout cell lines lacking

selectively essential genes in each of the three categories described above: Gcn2 (regu-

lation of translation), Vasp (uptake of extracellular protein), and Vps39 (degradation

of extracellular protein). Our knockout cell lines each originated from a single cell;

single cells (and the clonal populations that emerge from them) can vary in phenotype

in ways that do not depend on the gene that has been knocked out. To control for

these variations, we expressed either EGFP or the human homolog of the knocked

out gene. We confirmed knockout and re-expression of Gcn2 and Vasp by Western

Blot (Figure 4.10A). (We could not find a reliable Vps39 antibody, so we verified

knockouts by Sanger sequencing (data not shown).)

We measured the growth of these cells in amino acid-replete medium or leucine-

free medium, both supplemented with 50 g/L albumin. In amino acid-replete medium

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Figure 4.10: Basic validation of selectively essential genes. (A) Western blots con-firming knockout and re-expression of Gcn2 and Vasp. We could not find a reliableVps39 antibody, so we verified knockouts by Sanger sequencing (data not shown). (B)Growth of knockout and re-expression cell lines in amino acid-rich and leucine-freemedia. (C) Images of knockout and re-expression cell lines cultured in leucine-freemedium for 48 hours.

97

all three knockout cell lines grew as fast as the re-expression control cell lines, but in

leucine-free medium the knockout cell lines all grew significantly more slowly, with

noticeable cell death (Figure 4.10B-C).

As Gcn2 was the most selectively essential gene, we next asked whether GCN2

impacted the rate of catabolism of extracellular protein. While GCN2-wild-type cells

increased their catabolic rates upon glutamine removal, the catabolic rates of GCN2

knockout cells remained the same (4.11A). The isotope tracer data also revealed

that GCN2 activity does not meaningfully alter the rate of amino acid incorporation

into protein (data not shown). These results suggest a role for GCN2 in upregu-

lating catabolism of extracellular protein in amino acid-deficient conditions. How

does GCN2, with only one known phospho-target (eIF2α), induce increased catabolic

rates? Phosphorylation of eIF2α slows translation initiation – could this somehow

result in increased catabolism? If not, perhaps GCN2 phosphorylates a yet-unknown

lysosome-related protein?

For the following reasons, I hypothesized that GCN2 induces increased catabolic

rates through phosphorylation of eIF2α, not through phosphorylation of any other

protein. (GCN2 is not known to phosphorylate any other protein.) First, the increase

in catabolic rates is gradual, not abrupt. If GCN2 were causing this increase by

phosphorylating a vesicle trafficking component that induced trafficking of endosomal

cargo to the lysosome, for example, the increase in catabolism ought to be relatively

immediate – on the timescale of minutes or hours. Instead, the GCN2-dependent

increase in catabolism occurs gradually over several days (4.11B). Second, inhibition

of mTORC1, like GCN2 activation, induces a similar gradual increase in catabolism.

Might these increases in catabolism – one induced by mTOR inhibition, the other by

GCN2 activation – result from a common mechanism? Both mTOR inhibition and

GCN2 activation slow translation initiation. Perhaps mTORC1 and GCN2 regulate

translation initiation to varying extents depending on the mRNA. If so, mTORC1

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Figure 4.11: GCN2 supports protein catabolism in amino acid-deprived cells. (A)Scavenging flux measurements of GCN2 wild-type and GCN2 knockout cells in aminoacid-rich and glutamine-free media. These measurements were based on 24 hours ofculture in these conditions. (B) Scavenging flux measurements in GCN2 wild-typecells over 72 hours. Scavenging fluxes measured as described previously [73].

inhibition and GCN2 activation might induce catabolism by effectively upregulating

the translation of proteins involved in protein eating.

GCN2 is activated in cells that are starved for amino acids. From an evolutionary

perspective, many of the known downstream consequences of GCN2 activation make

sense as responses to amino acid starvation. GCN2 slows translation initiation; this

prevents ribosome stalling and cell death. GCN2 induces translation of ATF4; ATF4

is a transcription factor that activates expression of amino acid stress response genes

[34]. ATF4 is produced as a result of eIF2α phosphorylation: ATF4 synthesis is

achieved only when the ribosome reads through three decoy start sites in the upstream

untranslated region, then initiates translation at the correct start site [35]. Based on

existing datasets in the literature, it seems unlikely that there are other genes like

ATF4 whose translation is so tightly regulated (completely suppressed unless eIF2α

99

is phosphorylated), but it does seem possible that other genes which are already

translated continue to be translated at relatively high levels. These GCN2-resistant

mRNAs might encode proteins somehow involved in protein catabolism.

The fact that DHX29 is selectively essential supports the notion that translation

of a certain subset of mRNAs may be critical. DHX29 is required for translation

initiation on mRNAs with structured 5’ UTRs [83]. Not all mRNAs have structured

5’ UTRs. Thus, DHX29 facilitates the synthesis of some (unknown) proteins. If

DHX29 is selectively essential because it is required for synthesis of these proteins,

one would expect that some of these proteins are required to support the growth of

cells fueled by protein eating.

To comprehensively assess the effect of GCN2 on cellular protein levels, we mea-

sured the transcriptomes and proteomes of GCN2 wild-type and knockout cells cul-

tured (i) in amino acid-rich medium for 24 hours, (ii)-(iii) in leucine-free medium for

24 hours and 48 hours, and (iv)-(v) in glutamine-free medium for 24 hours and 48

hours. All media were supplemented with 5% dialyzed fetal bovine serum and 5%

bovine serum albumin. Here, I focus on the proteomics data. These data were gen-

erated using isobaric (TMT) tags that enable simultaneous measurement of peptide

abundances across samples. The method (TMT-MS3) quantified the relative levels

of 5738 proteins in Gcn2 WT or KO cells cultured in the five conditions listed above.

Unsupervised clustering of the normalized data revealed that glutamine deprivation

was the factor that altered global protein levels the most, relative to cells grown

in amino acid-rich medium (Figure 4.12A). Interestingly, protein levels in leucine-

deprived GCN2 WT cells were more similar to cells grown in amino acid-rich medium

than leucine-deprived GCN2 KO cells. This suggests that GCN2 is required to main-

tain proteome homeostasis, not to affect proteome remodeling. At present, I do

not understand the details of this process – how exactly GCN2 “maintains proteome

homeostasis.” One general possibility is that GCN2 upregulates the synthesis of short-

100

Figure 4.12: The effect of GCN2 and amino acid depletion on protein levels. UsingTMT-MS3-based proteomics, we quantified the levels of 5738 proteins in GCN2 wild-type and knockout cells cultured in amino acid-rich medium for 24 hours, in leucine-free medium for 24 hours and 48 hours, and in glutamine-free medium for 24 hoursand 48 hours. A heatmap showing the levels of all proteins is on the left. A heatmapshowing the levels of all selectively essential proteins measured is on the right.

lived proteins at the expense of stable proteins, which need not be synthesized unless

cell growth is possible.

We next searched for specific proteins that were upregulated upon amino acid-

deprivation specifically in GCN2-expressing cells, hoping to identify proteins which

might explain the GCN2-dependent increase in catabolism in amino acid-deprived

cells. We reasoned that these unidentified GCN2-regulated proteins that promote

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catabolism should be essential for growth fueled by protein eating. (These proteins

could also be essential for growth in amino acid-rich medium – in other words, they

might not be selectively essential for growth on extracellular protein, but rather just

essential. For example, GCN2 could conceivably induce synthesis of the components

of the V-ATPase to increase catabolism. However, we did not observe any substantial

changes in the levels of V-ATPase components (data not shown).) Thus, we restricted

our initial search for important GCN2-regulated proteins to the genes that scored as

selectively essential in the screen.

Clustering the proteomics data for just the selectively essential genes – of the top

100, 78 were quantified in the proteomics experiment – did not reveal any outstand-

ing proteins upregulated in amino acid-deprived cells in a strictly GCN2-dependent

fashion (Figure 4.12B). To systematically assess which of these genes encode proteins

whose levels are upregulated by GCN2, we first compared protein levels in GCN2 WT

and KO cells in amino acid-rich medium (DMEM). (While GCN2 is thought to be in-

active in amino acid-rich conditions, DMEM lacks several non-essential amino acids,

and inevitably, amino acid pools are periodically depleted.) We found that SLC7A5,

a high-affinity leucine transporter and known GCN2-ATF4 target [34], was the most

GCN2-upregulated protein in amino acid-replete medium. Among the other selec-

tively essential proteins most upregulated by GCN2 in amino acid-replete conditions

were the lysosomal hydrolase Cathepsin L and the mannose-6-phosphate receptor

IGF2R, which delivers Cathepsin L to the lysosome (Figure 4.13A). We next exam-

ined the changes in protein levels upon amino acid deprivation. To do this, we first

calculated the difference in protein levels in amino acid-deficient medium – after 24

hours in glutamine-free medium, for example – relative to the levels in amino acid-

rich medium. This difference was calculated independently for Gcn2 WT and Gcn2

KO cells. We then subtracted this difference in Gcn2 KO cells from the difference

in Gcn2 WT cells, thereby isolating the GCN2-dependent induction in protein level

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changes. We found that GCN2 induced higher Cathepsin L and IGF2R levels upon

either leucine or glutamine deprivation. This was true after both 24 hours and 48

hours of amino acid deprivation (Figure 4.13B-C).

To summarize the effect of GCN2 in amino acid-deprived cells in a single statis-

tic, we compared the average protein levels in all amino acid-deprived conditions –

leucine-deprived for 24 hours and 48 hours, glutamine-deprived for 24 hours and 48

hours – separately in GCN2 WT and GCN2 KO cells. Over all amino acid-deprived

samples, GCN2 upregulated cathepsin L and IGF2R more than any other selectively

essential genes; on average, in amino acid-deficient conditions these proteins were

twice as abundant in cells expressing GCN2 (Figure 4.13D). Other proteins upregu-

lated by GCN2 included the leucine transporter SLC7A5, the lysosomal cholesterol

transporter NPC1, and the actin bundling protein plastin 3. These results suggest a

simple explanation for why GCN2 expressing cells exhibit higher catabolic rates in

amino acid-deficient conditions: GCN2 promotes the synthesis of cathepsin L, which

catalyzes a rate-limiting degradative step in the catabolism of extracellular protein,

and cathepsin L receptor IGF2R, which delivers cathepsin L to the lysosome.

Cathepsin L levels did not increase, however, in cells deprived of amino acids.

Rather, in both Gcn2 WT and Gcn2 KO cells, cathepsin L levels decreased dramati-

cally upon glutamine removal. After 24 hours, cathepsin L levels dropped significantly

– 68% in Gcn2 WT, 75% in Gcn2 KO. This suggests that cathepsin L is a short-lived

protein. There are two obvious reasons cathepsin L might be especially short-lived:

cathepsin L might itself be degraded in the lysosome, or it might be slowly lost due

to secretion into the extracellular space. In any case, if cathepsin L is required for

protein eating, the cell must continuously synthesize it. After 48 hours of glutamine

deprivation, cathepsin L levels in Gcn2 WT cells recover to the levels that were mea-

sured in these cells in rich medium. In Gcn2 KO cells, cathepsin L levels rebound

partially but remain low. Upon leucine deprivation, Gcn2 WT cells maintain cathep-

103

Figure 4.13: Cathepsin L levels are increased in amino acid-deprived cells expressingGCN2. (A-D) Relative protein abundances in GCN2 wild-type and GCN2 knockoutcells cultured in amino acid-replete (A) or amino acid-deficient (D) media. The datafrom all amino acid-deficient samples was averaged for each cell line. (B-C) GCN2-dependent induction in protein levels after leucine or glutamine removal after 24 hours(B) or 48 hours (C). (E) Cathepsin L and IGF2R levels in amino acid-deprived cells.

104

sin L levels after one day and increase cathepsin L levels after two days, whereas

in Gcn2 KO cells cathepsin L levels decline (Figure 4.13E). Meanwhile, cathepsin L

mRNA levels did not change significantly (data not shown). Thus, GCN2 seems to

post-transcriptionally regulate cathepsin L levels.

We have repeatedly observed increases in lysosome volume – as measured by Lyso-

Tracker, a pH-sensitive dye – in cells deprived of amino acids, both Gcn2 WT and

Gcn2 KO (data not shown). Amino acid-deprived cells likely achieve this increase in

lysosome volume by sending various vesicles – possibly endosomes – to fuse with lyso-

somes. In doing so, cells target the material within these endosomes for degradation.

These increases in lysosome volume are not paralleled by increases in the concen-

trations of lysosomal proteins; thus, trafficking of endosomal cargo to the lysosome

does not depend on synthesis of new proteins. However, if key degradative enzymes

like cathepsin L are short-lived, they must be replaced. If not, the cell will lose the

capacity to degrade protein and will become irreparably amino acid-starved.

Interestingly, Ctsl-/- MEFs also exhibit increased lysosome volume, even in amino

acid-replete medium [21]. This suggests that increased lysosome volume can be caused

not only by demand for amino acids in starved cells but also by the failure to de-

grade what was trafficked to the degradative compartment. In light of this idea,

the increased lysosome volumes of glutamine-deprived cells may reflect a combina-

tion of increased trafficking to lysosomes and limited degradative capacity, given that

cathepsin L levels drop upon glutamine removal.

To test this theory, I plan to over-express cathepsin L in Gcn2 WT and Gcn2

KO cells. Naively, I predict that cathepsin L overexpression will increase catabolic

rates, regardless of Gcn2 expression, in amino acid-rich conditions. (This may not

necessarily turn out to be true. For example, if cathepsin L overexpression induces the

cathepsin L-mediated degradation of other important lysosomal enzymes, catabolic

rates may decrease.) More importantly, with cathepsin L levels constitutively high,

105

I predict that Gcn2 WT and Gcn2 KO cells will catabolize extracellular protein at

equal rates upon amino acid removal. Cathepsin L over-expression may also impact

lysosome volume.

106

Chapter 5

Ribosomes on the Night Shift: The

universal protein-making machine

becomes a nutrient source between

meals

(This perspective was published in Science in 2018. The authors are Michel Nofal

and Joshua D. Rabinowitz [72].) From an evolutionary perspective, life involves two

simple goals: survival and reproduction. But these goals are fundamentally at odds.

Reproduction depends on growth, but attempts to grow when nutrients are scarce can

jeopardize survival. In cells, growth is accomplished in large part by ribosomes, huge

RNA-protein machines that translate nucleic acid messages into protein, the main

biochemical constituent of cells. In nutrient-rich conditions, cells can be filled with

ribosomes: they comprise over a third of total biomass in rapidly growing Escherichia

coli [100]. But what happens to ribosomes when nutrient levels decline, as occurs

sporadically in microbes and nightly in sleeping humans? Biosynthesis subsides, and

ribosomes now serve as a reservoir of nutrients. Building on recent progress probing

107

the regulation of protein synthesis and degradation from Gu et al. [32] and Abu-

Remaileh et al. [1], on page 751 of this issue, Wyant et al. [129] elucidate a pathway

in which ribosomes are selectively digested, promoting survival in starved cells.

Eukaryotic cells, from yeast to humans, can gobble up parts of their interior

through a process called autophagy, encapsulating them in double membranes and

forming an enclosed compartment known as an autophagosome. Autophagosomes

then deliver their contents to the degradative compartment (lysosomes) where macro-

molecules are recycled into monomeric nutrients [114].

Autophagy was initially thought to be a non-specific process, but it has become

increasingly clear that cells can pick and choose what to digest in this manner. For

example, defective mitochondria are detected and marked for autophagy through a

system involving the proteins PINK1 (PTEN-induced putative kinase protein 1) and

Parkin [29]. In nutrient-poor conditions, however, mitochondria are valuable – they

provide the most efficient way to generate energy from carbon – whereas ribosomes are

no longer needed in large numbers to fuel biosynthesis. These dispensable ribosomes

can be selectively degraded by ribophagy – autophagy of ribosomes.

How do cells balance growth and survival? The mTOR complex 1 (mTORC1)

kinase has emerged as an important regulator of this balance. When conditions are

favorable for growth, mTORC1 stimulates the synthesis of all major biomaterials in

cells, especially ribosomes, while suppressing autophagy. If growth conditions are poor

– for example, during periods of starvation – mTORC1 is inactive, and autophagy

proceeds.

To decide whether or not growth is appropriate, mTORC1 must sense and inte-

grate a diverse set of environmental cues. One of these cues is amino acid availability.

Cells must be well-stocked with amino acids, which are needed to make protein, in

order to grow. Amino acids within the cytosol promote translocation of mTORC1

to the surface of the lysosome, where its activator, a small guanosine triphosphatase

108

(GTPase) called RHEB (Ras homolog enriched in brain), resides [95, 11]. Numer-

ous specific amino acid sensing proteins have been characterized. These include

the Sestrin and CASTOR (cytosolic arginine sensor for mTORC1) families of pro-

teins, which sense leucine and arginine, respectively [126, 13]. Gu et al. added to

this list when they identified SAMTOR (S-adenosylmethionine sensor upstream of

mTORC1), which indirectly senses the essential amino acid methionine by binding

to S-adenosylmethionine.

Why does the lysosome, which degrades macromolecules, play such a central role

in the regulation of mTORC1, which promotes the construction of macromolecules?

Accumulating evidence suggests that mTORC1 preferentially senses nutrients that

are generated in the lysosome. Perhaps by sensing the products of degradation,

mTORC1 can assess whether catabolic processes have generated enough nutrients.

This reasoning assumes that lysosomes differ from the rest of the cell not only in

acidity and protein content, but also in metabolite content. However, most methods

for lysosomal purification involve ultracentrifugation in sucrose gradients for several

hours, during which time metabolites have likely reacted or escaped, and weakly

associated lysosomal proteins have disassociated. Abu-Remaillah et al. reported a

method for rapid isolation of lysosomes called LysoIP. Cells are genetically engineered

to express a protein tag on lysosomal membranes. Magnetic beads linked to antibodies

specific for the tag are added to lysed cells, and lysosomes are purified magnetically.

This method has enabled systematic analysis of the metabolite content of lysosomes

for the first time.

Using LysoIP, the authors compared the cytosolic and lysosomal concentrations of

numerous metabolites. In cells grown in amino acid-rich conditions, metabolite levels

were generally similar in both compartments, but upon impairment of the vacuolar

ATPase, which acidifies the lysosome, the lumenal concentrations of a large number

of metabolites increased. These metabolites included most nonessential amino acids,

109

which are apparently released from lysosomes in a proton-dependent manner. The

lysosomal levels of most essential amino acids, however, did not change, suggesting

their regulation by another factor.

A series of elegant experiments revealed that mTORC1 promotes the efflux from

lysosomes of most essential amino acids, including leucine. Efflux is mediated by

a lysosomal membrane protein called SLC38A9 (sodium-coupled neutral amino acid

transporter 9). In amino acid-poor conditions, leucine transport out of lysosomes is

required to activate mTORC1 [130]. Taken together, these data suggest a paradox:

mTORC1 activity induces SLC38A9-mediated efflux of leucine out of lysosomes, but

mTORC1 remains inactive until leucine leaves the lysosome.

SLC38A9 has another important function, which may explain this paradox: upon

binding to the non-essential amino acid arginine in the lysosomal lumen, SLC38A9

helps to activate mTORC1. Thus, when amino acid levels accumulate in lysosomes,

mTORC1 may initially become partially activated by SLC38A9. In this scenario,

mTORC1 subsequently induces SLC38A9-mediated amino acid efflux from lysosomes.

Finally, effluxed leucine can augment mTORC1 activation. In other words, SLC38A9

may be at the center of a feed-forward loop whereby nutrients derived from lysosomal

catabolism activate mTORC1 only after accumulating above a threshold.

Why might SLC38A9 sense lysosomal arginine levels? Perhaps mTORC1 evolved

to sense the degradation of proteins rich in arginine. One class of proteins stands

out as arginine-rich: ribosomal proteins. These proteins contain high frequencies of

arginine and lysine, the positive charges of which help to bind the negatively charged

phosphate backbone of ribosomal RNA.

How are ribosomes selectively delivered to lysosomes? Wyant et al. applied quan-

titative proteomics to identify proteins that increase their association with lysosomes

in nutrient-starved cells. NUFIP1 (nuclear fragile X mental retardation-interacting

protein 1), which has a previously annotated role in the nucleus, was found at higher

110

concentrations on lysosomes in cells deprived of glucose and amino acids. The authors

then showed that NUFIP1 binds to ribosomes when mTORC1 is inactive and enables

ribophagy by delivering these ribosomes to autophagosomes for degradation.

Ribosomes are arguably the most important biochemical machine. But the im-

portance of translation has overshadowed the role ribosomes can play as a nutrient

source. By elucidating the function of NUFIP1, Wyant et al. provide a genetic handle

to specifically probe the importance of ribosomes as nutrients. Indeed, genetic loss of

NUFIP1 (that is, the inability to use ribosomes as nutrients) impairs survival of cells

starved of glucose and amino acids.

Although it is easy to induce starvation of mammalian cells experimentally in a

culture dish, cells in vivo are never exposed to glucose-free, amino acid-free environ-

ments. Rather, they are bathed in a steady stream of circulating nutrients. What

prevents this stream from running dry? During extended periods between meals,

macromolecules must be degraded. Proteins are depots of amino acids; glycogen is

a depot of sugar. Ribosomes, uniquely, are depots of amino acids, sugar, and nu-

cleobases, and as such, they can support diverse metabolic activity. A recent report

showed that, in mice, liver size and ribosome content oscillate with the diurnal cycle,

increasing while the animals are awake (and eating at will), then gradually falling

during sleep [104]. Thus, after meals, the liver fills with ribosomes. For a time, these

ribosomes perform their canonical role: using ingested amino acids to make protein.

But as nutrient levels drop, these ribosomes, via ribophagy, are recycled into nutri-

ents for the rest of the body. These findings have not been validated in humans,

but they raise intriguing possibilities. While intact ribosomes are essential for diverse

anabolic functions – protein synthesis is required for long-term memory formation

[20] – degraded ribosomes may maintain nutrient levels as we sleep. Perhaps they

quite literally fuel our dreams. Food for thought.

111

Figure 5.1: (See caption above.)

112

Chapter 6

Conclusion and Future Directions

When I began as a graduate student in Princeton, the idea that any mammalian

epithelial cells might use extracellular protein as an amino acid source was foreign to

the cancer metabolism community. Times have changed. It is now clear that pancre-

atic cancer cells, as well as other cells with active Ras signaling, can use extracellular

protein as an amino acid source. Protein eating does not merely supplement amino

acid pools. I proved that this process can fuel the growth of cultured cells deprived

of essential amino acids. In some cell lines, protein eating can support growth in

medium lacking all amino acids.

Protein eating can be measured in several ways. Imaging methods provide qual-

itative information. For example, DQ-BSA imaging was used to confirm that extra-

cellular protein was degraded in lysosomes [16]. In this thesis, I presented a method

that employs isotope tracers and liquid chromatography-mass spectrometry to mea-

sure protein eating in quantitative terms: extracellular protein-derived amino acids

released per µL cell volume per hour [73]. This method has yet to be used by others,

but I believe it is the only method that provides reliable measurements of protein eat-

ing rate. Using this method, I found that the effect of mTORC1 on protein eating in

amino acid-deprived cells is minimal; thus mTOR inhibition enhances cell growth fu-

113

eled by protein eating by another mechanism. I also showed that amino acid-deprived

cells increase their rate of protein eating in mTOR-independent fashion.

Finally, I presented the results of a genome-wide screen that systematically iden-

tified genes selectively required for growth fueled by protein eating. This screen

revealed the importance of GCN2 signaling in amino acid-deprived cells. GCN2 not

only slows translation initiation to prevent cell death but also increases catabolic

rates. GCN2 may support catabolism by promoting the synthesis of the lysosomal

hydrolase cathepsin L. To demonstrate the importance of cathepsin L in maintain-

ing high catabolic rates, I plan to over-express cathepsin L in GCN2 wild-type and

GCN2 knockout cells. I predict that cathepsin L over-expression will rescue – at least

partially – the protein eating defect of GCN2 knockout cells in amino acid-deficient

conditions.

Upregulation of cathepsin L, however, cannot be the only mechanism by which

amino acid-deprived cells upregulate catabolism. Imaging experiments have shown

that GCN2 wild-type and GCN2 knockout cells alike exhibit an increase in lysosome

volume upon glutamine removal. This suggests that, independently of GCN2, cells

respond to amino acid starvation by mediating fusion of endosomes with lysosomes,

thereby committing additional protein for degradation. Meanwhile, cathepsin L levels

decline – a little bit in GCN2 wild-type cells and a lot in GCN2 knockout cells.

Taken together, these data suggest that cells upregulate catabolism by a translation-

independent mechanism that involves modulation of vesicle trafficking. I propose

that these cells cannot maintain high levels of catabolism solely by trafficking protein

to lysosomes because cathepsin L is short-lived. GCN2 upregulates the synthesis of

cathepsin L and, in doing so, supports increased catabolism in these cells. Without

GCN2, cells direct the contents of endosomes to the degradative compartment, but

the degradative compartment lacks a key enzyme.

114

To prove that GCN2 upregulates the synthesis of cathepsin L, I plan to do ribo-

some profiling of GCN2 wild-type and GCN2 knockout cells in amino acid-rich and

various amino-acid deficient conditions. I predict that there will be an increase in

ribosomes bound to cathepsin L in amino acid-deprived GCN2 wild-type cells and not

in amino acid-deprived GCN2 knockout cells. It will also be interesting to investigate

other GCN2-dependent changes in translational efficiency upon amino acid removal.

Many other questions remain unanswered. How is macropinocytosis regulated?

Why is GCN1 required for GCN2 signaling? Do any cells without Ras mutations eat

protein in vivo? Which mRNAs require DHX29 for translation? Is IGF1R another

mannose-6-phosphate receptor? How does GCN2 suppress the synthesis of some

mRNAs more than others? Why do amino acids liberated from intact protein in

lysosomes have a higher probability of being used for protein synthesis than amino

acids that entered the cell as monomers (data not shown)? What does Rab35 do?

Can the isotope tracer methods I developed to quantify catabolic fluxes be repurposed

to measure protein turnover when coupled with proteomics? Can inhibiting GCN2

be an effective therapy in pancreatic cancer?

How do amino acid-deprived cells upregulate trafficking to the lysosome, as dis-

cussed above? This process occurs in GCN2 knockout cells; thus, GCN2 is not re-

sponsible. In addition, mTORC1 signaling remains active in glutamine-deprived cells;

thus, this process is mTORC1-independent. While mTORC1 may not be the answer

to this question, the relationship between mTORC1 and lysosomes is interesting and

incompletely understood. mTORC1 is required for the reformation of small lysosomes

from large ones once the cargo has been degraded [135]. mTORC1 also interacts with

one of the subunits of the BORC1 complex in ways that are poorly understood.

BORCS6 physically interacts with the Ragulator complex, the lysosomal scaffold to

which many mTORC1-related proteins bind, and disrupts the Rag-Ragulator interac-

tion to inhibit mTORC1 [99]. The consequences of these protein-protein interactions

115

are unclear, but it is tempting to speculate that mTORC1 regulates the reformation

and positioning of lysosomes by modulating the activity of the BORC complex.

In general, the movement of vesicles, including lysosomes, is poorly understood.

Most vesicles transport proteins; lysosomes degrade them, as well as other macro-

molecules. Both transport and degradation cannot be easily measured by measuring

quantities (number of vesicles, DQ-BSA fluorescence). These processes are best de-

scribed in terms of flux. As such, perhaps tools developed to study metabolism can

be applied to the field of vesicle trafficking.

Protein eating – as opposed to any other vesicle trafficking-related activity – en-

ables unprecedented exploration of vesicle trafficking, a fundamental element of cell bi-

ology, in three distinct ways. First, because pancreatic cancer cells cultured in amino

acid-deficient medium rely on this process for survival and growth, proliferation-

based genetic screens can be used to systematically identify the key protein catalysts

of this pathway. Second, using isotope tracers, the rate of protein eating can be

measured quantitatively in terms of amino acid release from the lysosome. Com-

parison of protein-eating rates in wild-type and knockout cell lines revealed that no

single gene is absolutely required for degradation of extracellular protein, with the

possible exception of essential genes such as V-ATPase components. Rather, loss of

selectively essential genes results only in a partial decrease in pathway flux (roughly

50%). Third, because extracellular protein is taken up from the extracellular space

in a receptor-independent manner, endocytic vesicles can easily be labeled with flu-

orogenic substrates. No other vesicle trafficking route can be linked to a growth

phenotype, facilitating genetic screens; quantified in terms of overall pathway flux;

and tracked using fluorescent tracers added to the culture medium.

Protein eating can be considered a metabolic pathway. The intermediates of this

pathway are not metabolites with unique chemical structures but vesicles with unique

sub-cellular localization, membrane composition (both lipids and protein), and cargo.

116

Unlike classical metabolic pathways like glycolysis, the steps of this pathway are not

catalyzed by individual enzymes but by groups of proteins with diverse functions.

Many of these proteins – for example, those involved in transport along microtubules

or vesicle fusion – have been characterized in detail in vitro, but this may not be

enough; decades of in vitro experiments were insufficient to determine the control

points of glycolysis in cells. Rather, isotope tracer studies probing the totality of the

glycolytic pathway in the context of the cellular environment revealed these control

points. Analogous systems-level analyses will likely be required to fully understand

the cellular functions of vesicular trafficking proteins.

The protein-eating pathway is amenable to such systems-level analyses for the

reasons listed above. We can now quantify the overall rate of protein eating, but

little is known about regulates this rate. What are the most rate-limiting steps? To

what extent are the steps reversible? And what are the roles of individual proteins in

the context of the whole pathway? The goal is to develop fluorescence microscopy-

based methodology to enable the quantification of vesicle trafficking fluxes. Such

methodology would enable us to move from a world of crude understanding of isolated

proteins to a systems-level understanding of cellular processes.

117

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