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UC Berkeley UC Berkeley Electronic Theses and Dissertations

Title Using Chemoproteomic Platforms to Discover Therapeutic and Toxicological Mechanisms

Permalink https://escholarship.org/uc/item/43w2z1x9

Author Counihan, Jessica Lynn

Publication Date 2018

Peer reviewed|Thesis/dissertation

eScholarship.org Powered by the California Digital Library University of California Using Chemoproteomic Platforms to Discover Therapeutic and Toxicological Mechanisms

By

Jessica Lynn Counihan

A dissertation submitted in partial satisfaction of the

requirements for the degree of

Doctor of Philosophy

in

Metabolic Biology

in the

Graduate Division

of the

University of California, Berkeley

Committee in charge:

Professor Daniel K. Nomura, Chair Professor James Olzmann Professor Andreas Stahl Professor Roberto Zoncu

Spring 2018

Abstract

Using Chemoproteomic Platforms to Discover Therapeutic and Toxicological Mechanisms

By

Jessica Lynn Counihan

Doctor of Philosophy in Metabolic Biology

University of California, Berkeley

Professor Daniel K. Nomura, Chair

Dysregulation of cancer cell contributes to abnormal cell growth, the biological endpoint of cancer. There have been numerous affected oncogenes and metabolic pathways recorded in cancer, and how they contribute to cancer pathogenesis and malignancy is of great interest; various pharmacological manipulations take advantage of these metabolic abnormalities, and many targeted therapies that have arisen from this research. However, despite the many therapies currently on market or in clinical trials, much work still needs to be done.

Many of these cancer therapeutics include natural products and various small molecules that act through covalent mechanisms. In fact, a large number of pharmaceuticals, as well as endogenous metabolites and environmental chemicals, act through covalent interactions with . Cancer, as well as other diseases such as Alzheimer’s disease and obesity, are often subject to drugs that irreversibly bind and inhibit their respective targets. Endogenous reactive metabolites and environmental chemical exposure, in turn, can also work through covalent interactions with proteins within the body to cause disease. Therefore, understanding what mechanisms can cause disease, and what therapeutic mechanisms can treat disease, are of great importance.

Chemoproteomic technologies have arisen as a powerful strategy that enable the assessment of proteome-wide interactions of these irreversible agents directly in complex biological systems. Using these chemoproteomic strategies has afforded scientists a more thorough understanding of specific protein interactions of irreversibly- acting pharmaceuticals, endogenous metabolites, and environmental electrophiles to reveal novel pharmacological, biological, and toxicological mechanisms. Through these same platforms, researchers have also been able to identify therapeutically active small-molecules and the mechanisms of action underlying these hit compounds.

In this dissertation, I present a thorough review of our current understanding of metabolic pathways and therapies in cancer. I also discuss several of the most-utilized

1 chemoproteomic strategies that have facilitated our understanding of specific protein interactions of irreversibly-acting pharmaceuticals, endogenous metabolites, and environmental electrophiles to reveal novel pharmacological, biological, and toxicological mechanisms. Next, I demonstrate the utility and diversity of chemoproteomic platforms in two separate studies. First, I use two cysteine-reactive covalent ligand libraries to identify hit compounds that impair cell survival and proliferation in non-small cell lung carcinoma, and an activity-based chemoproteomic analysis to identify the protein target of my hit compounds. Finally, I discuss an activity- based proteomics method used to reveal the off-targets of the widely used herbicide, acetochlor, in vivo in mice. Overall, the work reported in this dissertation demonstrates how chemoproteomic platforms can be used to identify direct protein targets of small- molecule covalent ligands and environmental chemicals, and uncover their downstream pathophysiological and biochemical effects.

2 Dedication

This dissertation is dedicated to my son, Ramos Richard.

Ramos, Never have I known a love as strong as mine for you. No amount of success in my academic or professional life will ever surmount my success in my personal life; no job will ever be as rewarding as being your mom. To my amazing son - my “little worm” - thank you for everything that you are and for giving my work, and my life, a greater purpose.

i “The impression sometimes created among the public is that scientists are working away in their labs, and maybe they’re not always thinking about the implications of their work. But we are.”

-Jennifer Doudna

ii Table of Contents

CHAPTER ONE: Cancer Metabolism: Current Understanding and Therapies……..……1

1. INTRODUCTION…………………………………………….………………….…….....…..2 2. ONCOGENES AND TUMOR SUPPRESSORS…...………………………………….….2 2.1. The Warburg Effect: The Link Between Oncogenesis and Metabolism……..2 2.2. Tumor Suppressor Loss…………………………………………………….…….8 3. GLUCOSE METABOLISM………………………………………………………….…..…12 3.1. ...…………………………………………………………………….….12 3.2. Lactate Metabolism……………………………………………………………....15 3.3. The Pyruvate Dehydrogenase Complex……………………………………….16 3.4. The Tricarboxylic Acid Cycle…………………………………………………….17 3.5. The Electron Transport Chain and Oxidative Phosphorylation……………...19 4. AMINO ACID METABOLISM………………...……………………………………………21 4.1. Glutamine…………………….……………………………………………………22 4.2. Asparagine………………………………………………………………...………25 4.3. Serine…………………….………………………………………………...………26 4.4. Tryptophan……………………………………………………………...…………27 5. ADDITIONAL PATHWAYS……………………………………………………...... ….……29 5.1. One-Carbon Metabolism………...……………………………………...….……29 5.2. The Pentose Phosphate Pathway………..…….……...………………….……31 5.3. NADPH………...……………………………………...………………….....…….32 6. FATTY ACID METABOLISM………...…………………...………………...….………….33 6.1. Fatty Acid Anabolism………...………………………..………………...….……33 6.2. Fatty Acid Oxidation………...……………………………………...……….……37 7. CONCLUSION………...……...………………………………………….…...……….……38 8. FIGURES………………………………………………………………………..………..…41

CHAPTER TWO: Mapping Proteome-Wide Interactions of Reactive Chemicals Using Chemoproteomic Platforms………………………………………..…………………………43

1. INTRODUCTION……………………………….………………..…………………………44 2. CHEMOPROTEOMIC PROFILING TO ASSESS SELECTIVITY OF THERAPEUTIC IRREVERSIBLE SMALL-MOLECULE INHIBITORS………………………………………44 3. CHEMOPROTEOMIC PROFILING OF REACTIVE ENVIRONMENTAL CHEMICALS AND ENDOGENOUS REACTIVE METABOLITES TO UNDERSTAND TOXICOLOGICAL MECHANISMS………………………………..……………………...…47 4. CONCLUSION……………………………………..………………………………….……49 5. FIGURES……………………………………..………………………………….……….…51

CHAPTER THREE: Chemoproteomics-Enabled Covalent Ligand Screening Reveals ALDH3A1 as a Lung Cancer Therapy Target……………………………………..….……56

iii 1. INTRODUCTION……………………………………..….…………………………………57 2. RESULTS……………………………………………..….…………………………………57 3. CONCLUSION……………………………………..….……………………………………59 4. METHODS……………………………………..….……………………………..….………60 5. FIGURES……………………………………..….……………………………………….…68

CHAPTER FOUR: Chemoproteomic profiling of acetanilide herbicides reveals their role in inhibiting fatty acid oxidation……………..….……………………………………….……75

1. INTRODUCTION……………..….…………………...……………………………….……76 2. RESULTS…………………..….…………………...………………………………….……76 3. CONCLUSION……………..….…………………...……………………………….………79 4. METHODS……………..….…………………...……………………………….………..…80 5. FIGURES…………………..….…………………...……………………………….……….86

CHAPTER FIVE: Conclusions…………….…………...……………………………….……92

1. CHAPTERS SUMMARIZED……….…………...…………………...……………….……93 2. FINAL REMARKS……….…………...…………………………………….………….……94

References……….…………...……………………………….…………………………….…95

iv List of Abbreviations

1,3-BPG 1,3-bisphosphoglyceric acid 2-AG 2-arachidonylglycerol 2-HG 2-hydroxyglutarate 3-BP 3-bromopyruvate 4E-BP1 4E-binding protein 1 5-FU 5-fluorouracil ABPP activity-based protein profiling AC acetochlor ACC acetyl-CoA carboxylase ACC adrenocortical carcinoma ACL ATP citrate ACP acyl carrier protein ACS acyl-CoA synthetases ACyne acetochlor alkyne AGPS alkylglyceronephosphate synthase AI aromatase inhibitor AKR1B10 aldo-keto reductase family 1 B10 AKT protein B ALDH3A1 aldehyde dehydrogenase 3A1 ALL acute lymphoblastic leukemia AML acute myeloid leukemia AMPK kinase ATP adenosine triphosphate BCH 2-aminobicyclo(2,2,1)-heptane-2- BET bromodomain and extra-terminal BPTES bis-2-[5–phenylacetamido-1, 2, 4-thiadiazol-2-yl] ethyl sulfide BRCA1 breast cancer type 1 susceptibility protein BRD4 bromodomain-containing protein 4 BTK Bruton’s C244 cysteine 244 ccRCC clear-cell renal cell carcinoma CDK cyclin-dependent kinase CES CPT1 carnitine-palmitoyl 1 CTN clorothalonil CuAAC copper-catalyzed alkyne-azide cycloaddition CYP450 cytochrome P450 DAG diacylglycerol DAGL diacylglycerol DCA Dichloroacetate DFMO 2-difluoromethyl ornithine DHAP dihydroxyacetone phosphate DMEM Dulbecco's Modified Eagle's Medium DTT dithiothreitol

v ECAR extracellular acidification rate EGFR epidermal growth factor receptor EMEM Eagle’s Minimum Essential Medium EN enamine ER estrogen receptor ESI electrospray ionization ETC electron transport chain F1,6BP -1,6-bisphosphate F2,6BP fructose 2,6-bisphosphate F6P fructose-6-phosphate FA fatty acid FAAH fatty acid amide FAO fatty acid oxidation FASN fatty acid synthase FCCP trifluoromethoxy carbonylcyanide phenylhydrazone FFA free fatty acid FH Fumarate hydratase G3P glucose-3-phosphate G6P glucose-6-phosphate G6PDH glucose-6-phosphate dehydrogenase GAPDH glyceraldehyde-3-phosphate dehydrogenase GFP green fluorescent protein GIST gastrointestinal stromal tumors GLDH glutamate dehydrogenase GLS glutaminase GLUT glucose transporter GPNA l-γ-glutamyl-p-nitroanilide GPx1 glutathione peroxidase 1 GRB2 growth-factor-receptor-bound protein 2 GSH glutathione GSTP1 glutathione S-transferase Pi GTPase guanosine triphosphatase HAT histone acetyltransferase HDAC histone deacetylase HER2 human epidermal growth factor receptor 2 HIF-α hypoxia induced factor-α HK HLRCC hereditary leiomyomatosis and renal cell carcinoma HNE 4-hydroxy-2-nonenal Hsp90 heat shock protein 90 IA iodoacetamide IAyne iodoacetamide alkyne IDH isocitrate dehydrogenase IDO indoleamine (2,3)-dioxygenase ip intraperitoneal isoTOP-ABPP isotopic tandem orthogonal proteolysis enabled-ABPP

vi JAK janus kinase JNK c-Jun N-terminal kinase -KG -ketoglutarate LDH LOA loss of attachment LPS lysophosphatidylserine MAG monoacylglycerol MAGL MAPK mitogen-activated MAT methionine adenyltransferase MCT monocarboxylate transporter me-TFH 5,10-methylene-THF MMA monomethylarsenous acid mTHF 5-methyltetrahydrofolate MTHFR methylenetetrahydrofolate reductase mTOR mammalian target of rapamycin mTORc mTOR complex MudPIT multidimensional protein identification technology NAD nicotinamide adenine dinucleotide NADP+ nicotinamide adenine dinucleotide phosphate - oxidized NADPH nicotinamide adenine dinucleotide phosphate – reduced Nrf2 nuclear factor erythroid 2-related factor 2 NSCLC non-small cell lung cancer ODC ornithine decarboxylase OCR oxygen consumption rate ONE 4-oxo-2-nonenal OP organophosphorus PA phosphatidic acid PAF platelet activating factor PAFAH platelet activating factor acetylhydrolase PBS phosphate buffered saline PC phosphatidylcholine PDC pyruvate dehydrogenase complex PDK-1 pyruvate dehydrogenase 1 PE phosphatidylethanolamine PFK PHARC polyneuropathy, hearing loss, ataxia, retinitis, and cataracts PHGDH phosphoglycerate dehydrogenase PI phosphatidylinositol PI3K phosphoinositide 3-kinase PIP2 phosphatidylinositol (4,5)-bisphosphate PIP3 phosphatidylinositol (3,4,5)-trisphosphate PK pyruvate PKC protein kinase C PKM PK muscle PLK polo-like kinase

vii PPP pentose phosphate pathway PR progesterone receptor PTEN and tensin homolog pVHL protein VHL R5P ribose-5-phosphate RCC renal cell carcinoma RNAi ribonucleic acid interferance ROS reactive oxygen species SAM S-adenosylmethionine SCC squamous cell carcinoma SCD stearoyl-CoA desaturase SCID severe combined immunodeficiency SCLC small-cell lung cancer SDH succinate dehydrogenase SGOC serine, glycine, one-carbon SHMT serine methyltransferase shRNA short hairpin RNA (RNAi technology) SILAC stable isotope labeling of cells siRNA small interfering RNA (RNAi technology) SIRT1 sirtuin 1 SM sphingomyelin SOS Son of Sevenless SRM single-reaction monitoring TAG triacylglycerol TBST Tween 20 TCA tricarboxylic acid (Krebs or cycle) THF tetrahydrofolate TKTL1 -like protein 1 TNBC triple negative breast cancer TPP triphenylphosphate TSC tuberous sclerosis UQCRH ubiquinol-cytochrome c reductase hinge VDAC voltage-dependent anionic channel VEGF vascular endothelial growth factor VHL von Hippel-Lindau ZAK zipper containing kinase

viii CHAPTER ONE: Cancer Metabolism: Current Understanding and Therapies

1 1. INTRODUCTION

Dysregulation of cancer cell metabolism contributes to abnormal cell growth, the biological endpoint of cancer. Here, in Chapter One, I review numerous affected oncogenes and metabolic pathways common in cancer, and how they contribute to cancer pathogenesis and malignancy. I also discuss various pharmacological manipulations that take advantage of these metabolic abnormalities, and the current targeted therapies that have arisen from this research.

The development of cancer depends on alterations or mutations arising within the cell, driving aberrant behavior that can bypass the typical checkpoints required for normal cell health. Mutations or expression changes in oncogenes and tumor suppressors are known to alter cellular metabolism to fuel cancer pathogenicity1,2. The genomic landscape of cancer is highly complex with a high level of heterogeneity3. It has been determined, however, that many of the mutational or somatic changes in cancer cells impact commonly and fundamentally impact cancer metabolism. In this chapter, I will discuss many aspects of altered cancer cell metabolism and the current therapies developed to target these alterations. I first discuss the major mutated oncogene and tumor suppressors that impact cancer cell metabolism. I then discuss the major alterations in nutrient metabolism and changes of associated proteins within various metabolic pathways. Along the way, I consider current targeted therapies, either in preclinical or clinical trials that are currently being researched and developed to target these metabolic dysregulations.

2. ONCOGENES AND TUMOR SUPPRESSORS

2.1. The Warburg Effect: The Link Between Oncogenesis and Metabolism

One of the classic conundrums in cancer biology is Warburg's observation that cancer cells take up more glucose and produce more than normal tissues4. These observations led Warburg to hypothesize that cancer results from the regression of cells to a more primitive metabolism exhibited by proliferating single cell eukaryotes5. Recent studies have implicated oncogenic activation of glucose uptake as the cause of the ‘Warburg effect’6. The Warburg effect, also more broadly called fermentation or aerobic glycolysis (though it takes place under anaerobic conditions in cancer cells as well), is considered a hallmark of altered metabolism in many types of cancer cells. While resting cells typically rely on mitochondrial oxidation to meet their bioenergetic needs, cancer cells often utilize aerobic glycolysis for both energy and proliferative pathogenesis. Even though mitochondrial oxidation yields much more energy for a cell per glucose, tumor cells are thought to utilize aerobic glycolysis to allow diversion of glycolytic intermediates to biosynthetic pathways to generate lipids, nucleotides, and amino acids, among others, necessary for cell growth and division7. Though this is generally how the Warburg effect is canonically understood, recent studies of sources for biosynthesis have revealed that glucose and glutamine – and their

2 associated increase due to the Warburg effect – are not the main providers of material for mass accumulation8. A full understanding of the Warburg effect’s impact on cancer cell metabolism is yet to be elucidated.

Dysregulated metabolism, as well as altered mitochondrial structure-function, is found across many different cancer cell types. Although fermentation produces almost 20 times less ATP per glucose molecule than oxidative respiration does, ATP is rarely limiting in these rapidly-dividing cells. Further, the production of building blocks needed for proliferation, including the reducing power NADPH, lipid synthesis, nucleotide synthesis, and amino acid synthesis, have increased availability corresponding to increased flux through glycolysis via the Warburg effect. Thus, increasing glycolysis and fermentation, while simultaneously decreasing mitochondrial oxidation, enables cells to balance their energetic requirements with their capacity to divide. Additionally, it has been shown that the production of lactate from glucose occurs 10-100 times faster than the complete oxidation of glucose in the mitochondria. In fact, the amount of ATP synthesized over any given period of time is comparable when either form of glucose metabolism is utilized9,10. In order for cancer cells to utilize the advantageous Warburg effect, they must undergo genetic mutations or possess some form of varying transcriptional profiles11,12. These changes may occur before, as a result of, or in combination with the genetic changes that drive cancer pathogenesis, such as oncogene expression or tumor suppressor loss11,12. Of note, the Warburg effect is thought to be an early event in oncogenesis and does not happen in all cancer cells. For example, it is an immediate consequence of an initial KRAS oncogenic mutation in pancreatic cancer and of BRAF in melanoma9,13,14.

One well-studied link between oncogenesis and glucose metabolism is the serine/threonine kinase AKT/phosphoinositide 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) signaling pathway. Constitutively activated components of PI3K signaling can directly stimulate levels of glucose uptake and metabolism via various mechanisms, such as increased expression and localization of the glucose transporter GLUT1 to the plasma membrane and through increasing the activity of hexokinase (HK), phosphofructokinase-1 (PFK-1), and phosphofructokinase-2 (PFK-2)11,15–17. PI3K also phosphorylates phosphatidylinositol 4,5-bisphosphate (PIP2) to phosphatidylinositol 3,4,5- triphosphate (PIP3), which then activates AKT. AKT plays numerous important biological roles within the cell. For example, AKT regulates cell growth through its effects on the tuberous sclerosis (TSC1/TSC2) complex and mTOR signaling18,19. AKT also contributes to cell proliferation via phosphorylation of the cyclin-dependent kinase (CDK) inhibitors p21 and p27, and is a major mediator of cell survival through direct inhibition of pro-apoptotic proteins like Bad or inhibition of pro-apoptotic signals generated by transcription factors like FoxO19– 21.

3 The serine/threonine kinase mTOR is a downstream effector of AKT/PI3K and is crucial for cell growth and proliferation. mTOR belongs to two separate complexes, mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2), which are structurally similar but functionally different. mTORC1 is the target of rapamycin and rapamycin analogues, such as everolimus, and leads to cell anabolic growth by promoting mRNA translocation and protein synthesis, and also has roles in glucose metabolism and lipid synthesis22,23. mTORC1 positively regulates protein translation through the phosphorylation of its substrates, including the eukaryotic initiation factor 4E-binding protein 1 (4E-BP1). mTORC2 on the other hand, organizes the cellular actin cytoskeleton and regulates AKT phosphorylation24.

The frequency with which dysregulated AKT/PI3K/mTOR signaling contributes to human disease has culminated in the aggressive development of small molecule inhibitors of AKT, PI3K, and mTOR25–27. For example, activation of the AKT/PI3K/mTOR pathway has been estimated to be in as frequent as 70% of breast cancers overall28. Data from a randomized phase II clinical trial, called the FERGI trial (NCT01437566), evaluated the role of adding pictilisib, also called GDC-0941 (Genentech), a PI3K inhibitor, to fulvestrant, a steroidal anti-estrogen hormone therapy drug, to treat patients with ER+, aromatase inhibitor (AI)- resistant advanced or metastatic breast cancer (Table 1-1). In preliminary results, the addition of pictilisib to fulvestrant was associated with a non-statistically significant progression-free survival increased from 5.1 to 6.6 months29. Pictilisib is also currently undergoing trials to treat non-small cell lung cancer as well. BKM120, also called buparlisib (Novartis Pharmaceuticals), is another PI3K inhibitor that is more advanced in clinical development. The BELLE-2 study (NCT01610284) is a phase III trial which randomized 1,148 postmenopausal women with HR+/HER2- advanced breast cancer after progression on AI to fulvestrant and buparlisib or fulvestrant and placebo. Another trial, the BELLE-3 study (NCT01633060), looks at the same treatment combination in patients who have progressed after an AI and mTOR inhibitor.

The first mTOR inhibitor in clinical use was rapamycin, which was used as an immunosuppressant drug given after transplant surgeries. Temsirolimus, a rapamycin derivative, was subsequently developed and is approved for the treatment of renal cell carcinoma (RCC). Everolimus is an oral mTOR inhibitor that has been approved for use in post-menopausal women with HR-positive breast cancer. It is also approved for use in other cancers as well, including RCC, neuroendocrine tumors of the pancreas, and subependymal giant cell astrocytomas26. These agents, called “rapalogues”, exert their effect mainly as allosteric inhibitors of mTORC1. However, since they only inhibit the mTORC1 complex, their use has been associated with negative feedback regulatory mechanisms and other mechanisms of resistance, causing paradoxical activation of AKT and proliferative effects via other downstream targets30.

4 mTOR has been shown to be constitutively activated in some forms of cancer, such as primary acute myeloid leukemia (AML) cells, therefore representing a major target for drug development in this disease. Many mTOR kinase inhibitors fully inhibit 4E-BP1 phosphorylation, suppress protein synthesis, and induce apoptosis31,32. For example, the mTORC1 pathway is rapamycin-sensitive and controls protein translation through the phosphorylation of 4E-BP1 in most models. In AML, however, the translation process is dysregulated and rapamycin-resistant. Furthermore, the activity of PI3K/AKT and mTOR is closely related, as mTORC2 activates the oncogenic kinase AKT. NVP-BEZ235 (Novartis Pharmaceuticals), a dual PI3K/mTOR ATP-competitive inhibitor, was found to inhibit PI3K and mTORC1 signaling, and also mTORC2 activity, in AML31. Furthermore, NVP-BEZ235 fully inhibits the rapamycin-resistant phosphorylation of 4E-BP1, resulting in a marked inhibition of protein translation in AML cells. Hence, NVP-BEZ235 reduces the proliferation rate and induces an important apoptotic response in AML cells without affecting normal CD34(+) survival31. It has additionally been shown that the specific mTOR kinase inhibitor, AZD8055, blocked both mTORC1 and mTORC2 signaling in AML. Significantly, the mTORC1-dependent PI3K/AKT feedback activation was also fully blocked in AZD8055-treated AML cells in situ 32. Although clinical trials are not yet underway in treating AML, AZD8055 is currently being tested in multiple phase I clinical trials for other cancer types, including one to treat recurrent gliomas (NCT01316809), another to treat advanced solid tumors (NCT00731263), and also one to treat cancer (NCT00999882). Due to the critical role of the AKT/PI3K/mTOR signaling pathway in regulating diverse cellular functions, it is an important therapeutic target for the treatment of human disease.

The Warburg effect is positively regulated by oncogenes, such as KRAS and MYC33. Activation of the oncogenic RAS protein contributes significantly to several aspects of cancer cell malignancy, including the dysregulation of tumor- cell growth, programmed cell death and invasiveness, and the ability to induce new blood-vessel formation34. Three canonical members of the RAS family, HRAS, KRAS, and NRAS, have been found to be activated by mutation in human tumors35. The HRAS, KRAS, and NRAS proteins are widely expressed, with KRAS being expressed in almost all cell types. Knockout studies in mice have shown that HRAS and NRAS, either alone or in combination, are not required for normal development, whereas KRAS is essential36.

Following the activation of receptor tyrosine , such as the epidermal- growth-factor receptor (EGFR), the auto-phosphorylated receptor binds to the adaptor protein growth-factor-receptor-bound protein 2 (GRB2). GRB2 is bound to Son of Sevenless (SOS), so activation of the receptor tyrosine kinase results in recruitment of SOS to the plasma membrane, where RAS is also localized as a result of farnesylation. The increased proximity of SOS to RAS results in increased nucleotide exchange on RAS with GDP being replaced with GTP and, thus, activation of RAS. Many other receptor types, including the G-protein- coupled receptors, can activate RAS through stimulation of exchange factors37.

5 GTP-bound RAS is able to bind and activate effector , such as the protein serine/threonine kinase RAF (i.e. c-RAF1, BRAF and ARAF) which promotes cell cycle progression through the mitogen-activated protein kinase (MAPK) pathway38, PI3K which promotes apoptosis evasion through the AKT/PI3K pathway39,40 and the RAS-related RAL proteins which evade cell cycle arrest and apoptosis by inhibiting transcription factors of the FoxO family41. It is through these pathways that RAS controls cell proliferation, survival, and other aspects of cell behavior that can contribute to tumorigenesis. Through the combined action of these RAS-responsive signaling pathways, expression of activated mutant RAS in cells can promote several aspects of malignant transformation. Targeting RAS and its effector pathways could, therefore, impress an impact on several different aspects of malignancy.

Aberrant signaling through RAS pathways mainly occurs as the result of several different classes of mutational damage in cancer cells, often being a mutation of the RAS themselves. More than 20% of human tumors have activating point mutations in RAS, most frequently in KRAS, which comprises about 85% of the total RAS mutations37,42. These mutations all compromise the GTPase activity of RAS, preventing the hydrolysis of GTP on RAS and, therefore, causing RAS to accumulate in the GTP-bound, active form. RAS signaling pathways are also commonly activated in tumors that have overexpressed growth-factor- receptor tyrosine kinases. The most common examples are EGFR and human epidermal growth factor receptor 2 (HER2), which are frequently activated by their overexpression in many types of cancers, including breast, ovarian, and stomach carcinomas 43. Receptor tyrosine kinases activate downstream signal transduction pathways that coordinate tumor cell growth 44. Lapatinib is an FDA- approved tyrosine kinase inhibitor that targets both EGFR and HER2 45.

At least six other small-molecule inhibitors of EGFR tyrosine kinase activity are now in clinical trials. Two of these drugs have shown especially great potential and are at an advanced stage of development, ZD1839, also called Gefitinib or Iressa (AstraZeneca) and OSI-774, also called Tarceva or Erlotinib (OSI Pharmaceuticals/Genentech). ZD1839 has been through several phase I and phase II clinical trials, with the most common toxic side effects being skin rash and diarrhea. Promising single-agent clinical anti-tumor activity has been reported in numerous cancer types, including advanced non-small cell lung cancer (NSCLC) (NCT00259064; NCT00770588; NCT01017679), metastatic squamous cell carcinoma (SCC) (NCT00054691), and adrenocortical carcinoma (ACC) (NCT00215202). Some combinatorial studies have also occurred, including comparing ZD1839 with anastrozole to ZD1839 with fulvestrant in postmenopausal women with metastatic breast cancer (NCT00057941). Phase I, II, and III clinical trials are also currently underway for OSI-774 as a single agent and have produced promising results against NSCLC (NCT00036647; NCT00072631; NCT00137800; NCT00063895), ovarian cancer (NCT00063895), and head and neck cancer (NCT00063895)46. OSI-774 has been studied in combination as well, including OSI-774 plus gemcitabine in patients with locally

6 advanced, unresectable, or metastatic pancreatic cancer (NCT00040183). Other EGFR-directed small-molecule tyrosine kinase inhibitors in early-stage clinical trials include PKI116 (Novartis), GW2016 (GlaxoSmithKline), EKB-569 (Genetics Institute/ Wyeth-Ayerst), and CI-1033 (previously PD183805; Pfizer). Overall, EGFR inhibitors seem to be a promising lead towards the treatment of numerous cancer types.

For RAS proteins to function normally, they must first be post-translationally modified via prenylation, most often by the covalent attachment of a farnesyl isoprenoid group47. The purpose of this is primarily to localize them to the correct subcellular compartment, usually the inner face of the plasma membrane. RAS proteins that are mislocalized at other sites in the cell are inactive37,48. Alternatively, some RAS proteins, including KRAS and NRAS, but not HRAS49–51, can also undergo geranylgeranylation. Since post-translational modification of RAS is required for its biological activity, the enzymes involved in RAS processing are attractive targets for therapeutic intervention. By using high- throughput screening of compound libraries, a large number of highly effective farnesyl transferase inhibitors that are specifically effective against HRAS mutations, which cannot be alternatively modified via geranylgeranylation, have been identified and developed as potential cancer therapies52,53. Tipifarnib, also called Zarnestra (Kura Oncology), is currently being tested in phase II clinical trials, including a trial that targets advanced tumors with HRAS mutations, specifically HRAS-mutated thyroid and squamous head and neck cancer (NCT02383927). For those RAS proteins, such as KRAS, that can gain resistance against farnesyl transferase inhibitors via geranylgeranylation, geranylgeranyl transferase inhibitors are also being developed. For example, co- treatment with FTI-277, a farnesyl transferase inhibitor, and GGTI-298, a geranylgeranyl transferase inhibitor, inhibited KRAS prenylation in multiple human cancer cell lines that were initially resistant to FTI-277 alone54,55.

As mentioned previously, MYC also positively regulates the Warburg effect. MYC plays a crucial role in a variety of cellular processes, including cell proliferation and differentiation, cell cycle progression, metabolism, and apoptosis56,57. MYC is a pleiotropic transcription factor that regulates a variety of functions by promoting activation or repression of genes on a global scale within the cell58,59. MYC expression is tightly regulated under normal circumstances and is increased in response to extracellular stimuli, such as growth factors60,61. Chromosomal translocation, amplification, and mutations in signaling pathways can promote MYC overexpression independently of growth factor stimulation, which leads to unrestrained cell proliferation and tumorigenesis62. When MYC becomes oncogenic within a cancer cell, it is a major driver of cancer cell growth, the cell cycle, metabolism (including promoting glutaminolysis which we will further cover later), and cell survival. Importantly, MYC is dysregulated in approximately 70% of human cancers58, and many studies have observed that MYC inhibition can result in tumor regression and cell differentiation63. Widespread activation of

7 MYC in a range of tumors, and the reversibility of MYC-induced tumorigenesis, have made MYC an appealing target for cancer therapy.

Many efforts have been made to discover compounds that can deliberately target MYC for cancer therapy. This has proved an immense challenge, however, since MYC lacks functional binding pockets that small-molecules may bind and is localized within the nucleus, which makes it inaccessible to any antibody-based therapies. Researchers have, thus, taken advantage of MYC’s heterodimerization with MAX, which is essential for MYC DNA-binding activity, to develop drugs that will disrupt this interaction. 10058-F4, KJ-Pyr-9, and Omomyc compounds all disrupt the MYC/MAX interaction, and have shown efficacy in in vivo experiments64.

Table 1-1. Summary of oncogene-related drugs in clinic or pre-clinic.

2.2. Tumor Suppressor Loss

A well-known and well-researched tumor suppressor loss is that of p53, which plays a role in DNA damage sensing, cell cycle control, and control of apoptosis. Furthermore, p53 has also been shown to be able to oppose the Warburg effect

8 by stimulating respiration and reducing glycolytic flux when present in cells. p53 plays a central role in blocking the formation of tumors and, therefore, when a point mutation or deletion leads to loss of p53 function, cancer cells are better able to evade apoptosis and gain insensitivity to antigrowth signals, leading to immortalization and cancer65–67. Of importance, it has previously been observed that over 50% of human cancers have p53 gene mutations68. Two key strategies have been employed to target cancer cells with p53 mutation; first, developing drugs that restore wild-type p53 activity, and second, developing drugs that deplete mutant p53.

Most p53 mutants lose their ability to bind with p53-specific response elements in DNA, thereby losing transcriptional activity and their tumor suppressive functions69. However, it has previously been shown that sequence-specific p53 transcriptional activity can be restored from mutant p5370. Since the first p53- reactivating compound, CP-31398, was identified69, investigators have made tremendous efforts to identify compounds that restore p53 transcriptional activity. CP-31398, along with another compound, STIMA-1, bind to the cysteine residues in the core domain of mutant p53, leading to stabilization of wild-type p53 conformation and subsequent restoration of p53’s transcriptional activity71 (Table 1-2). Other p53 wild-type restoration compounds include PRIMA-1MET (or APR- 246), MIRA-1, NSC652287, NSC319726/ZMC1, NSC87511, Chetomin, PK7088, and the small-molecule SCH529074 70. Unfortunately, it currently remains unclear if these compounds can reactivate all p53 mutants or just specific mutant types. Furthermore, PRIMA-1MET is the only drug currently undergoing clinical trials, and has been shown thus far to be safe and has a favorable pharmacokinetic profile (NCT02999893; NCT00900614)72. Overall, the development of p53-reactivating compounds are promising, but remains simultaneously challenging.

The second main approach to target oncogenic mutant p53 relies on discovering and developing compounds that specifically deplete mutant p53 while having little effect on wild-type p53. Although the mechanism still remains unclear, several compounds that induce mutant p53 degradation without altering wild-type p53 have been found. For example, 17-AAG and Ganetespib, both Hsp90 inhibitors, have been shown to deplete mutant p53, along with Raf-1 and ErbB2, since Hsp90 has been previously shown to contribute to the accumulation of mutant p5368,73,74. An analog of 17-AAG, IPI-504, has been tested in clinical trials, mostly looking at its use in NSCLC and gastrointestinal stromal tumors (GIST)75,76. A recent phase II clinical trial looking at IPI-504 in NSCLC patients with ALK mutations was terminated due to slow patient accrual (NCT01228435). The GIST trials were also terminated; there were multiple observed mortalities in these patients given IPI-504 (NCT00688766). Ganetespib (Synta Pharmaceuticals Corp.) is also currently under evaluation in clinical trials, including phase II for metastatic breast cancer and phase III for NSCLC77,78. Ganetespib was also granted Fast Track status by the FDA in two clinical trials, GALAXY-1 (NCT01348126) and GALAXY-2 (NCT01798485), which are currently examining

9 the use of ganetespib in combination with Taxotere for treating advanced lung adenocarcinoma, a type of NSCLC. Unfortunately, despite positive results in the phase II GALAXY-1 study, the phase III GALAXY-2 study did not improve overall survival or progression-free survival, and was ultimately terminated after the first interim analysis due to futility79.

Inhibitors of histone deacetylases (HDAC), which prevent Hsp90 from complexing with p53, have also been developed68,80. In fact, one compound, Vorinostat, is already FDA-approved for use in refractory or relapsed cutaneous T cell lymphoma. Other drugs in development to deplete mutant p53 include the natural product Gambogic acid, Spautin-1, YK-3-237, and the small molecule NSC59984 70.

The most significant direct activation of the PI3K pathway in tumors comes from deletion of the tumor suppressor gene phosphatase and tensin homologue (PTEN). PTEN is deleted in approximately 30-40% of human tumors, making it the second most significant tumor-suppressor gene after p5381. The lipid phosphatase activity of PTEN antagonizes the AKT/PI3K/mTOR pathway to repress tumor cell growth and survival. Specifically, this gene encodes a lipid phosphatase that removes the phosphate from the 3' position of phosphatidylinositol (3,4,5)-trisphosphate (PIP3) and phosphatidylinositol (4,5)- bisphosphate (PIP2), thereby reversing the accumulation of these second messengers that is caused by PI3K. PTEN also promotes stability and DNA repair within the nucleus.

Since loss of PTEN lipid phosphatase activity leads to PIP3 accumulation at the plasma membrane, which activates the AKT/PI3K/mTOR pathway to drive cell growth, proliferation, and survival 82, much research is invested in finding therapeutics that target this pathway. For example, it has been shown in mouse models that genetic loss of PTEN is associated with increased sensitivity to Temsirolimus (an allosteric mTORC1 inhibitor), AZD6482 (a PI3K/p110β inhibitor), MK-2206 (an allosteric AKT inhibitor), and 17-AAG (the HSP90 inhibitor that induces degradation of many proteins including p53, HER2, and AKT) 83.

Deletion of the von Hippel-Lindau (VHL) tumor suppressor gene was identified in 1993 as the genetic basis for a rare disorder, VHL disease84. The VHL gene product, protein VHL (pVHL), plays a key role in cellular oxygen sensing by targeting hypoxia-inducible factors (HIF), including HIF-1a, HIF-2a, and HIF-3a, for ubiquitination and proteasomal degradation85–88. Since its discovery, researchers have identified that early loss of VHL function is commonly seen in clear-cell renal cell carcinoma (ccRCC), and patients with VHL disease are at increased risk for developing ccRCC. The discovery of this mechanism has led to the development of drugs that inhibit HIF or its downstream targets to treat RCC. Although suitable inhibitors against HIF itself do not yet exist, a number of agents have been reported to indirectly downregulate HIF. For example, it has been

10 previously shown that the mTOR inhibitor rapamycin can downregulate HIF87,89– 91, as well as heat shock protein 90 (hsp90) inhibitors such as geldenamycin and 17-(allylamino)-17-demethoxygeldanamycin92,93. Alternatively, HIF-responsive gene products, including vascular endothelial growth factor (VEGF), can be targeted. In fact, the VEGF-neutralizing antibody bevacizumab is an approved drug for the treatment of RCC93,94. Bevacizumab when used in combination, often with interferon-a, has produced prolonged progression-free survival in RCC patients93,95. Sorafenib and pazopanib, anti-VEGF tyrosine kinase inhibitors, are also approved ccRCC drugs93,96,97.

11 Table 1-2. Summary of tumor suppressor-related drugs in clinic or pre- clinic.

3. GLUCOSE METABOLISM

3.1. Glycolysis

12 Glycolysis is the universal first step in glucose metabolism, and cells from all domains possess the enzymes of this pathway. Glycolysis involves nine reactions, each of which is catalyzed by a distinct . Of these, three are highly regulated: hexokinase (HK), phosphofructokinase (PFK), and (PK). All three of these enzymes have been shown to be altered in cancer (Figure 1)11. Of these three rate-limiting steps in the glycolytic pathway, one may exert greater control over glycolytic flux depending on the context, such as the presence of the Warburg effect or a distinct lack of precursors98.

Glucose uptake is regulated and facilitated by glucose transporters (GLUT), of which there are four main isoforms, GLUT1-4. GLUT1 is overexpressed in most cancers; though other isoforms are also present in cancer cells, GLUT1 overexpression is correlated with poor prognosis99. Glucose uptake, and associated lactate production, is increased in tumors regardless of hypoxia4. GLUT1 transporters are upregulated by AKT/PI3K, HIF, KRAS, and BRAF in many cancers100,101. Attempts to target these transporters have resulted in identification of the cytotoxic small molecules fasentin and STF-31536, which inhibit GLUT1-mediated glucose transport 102,103 (Table 1-3).

HK catalyzes the first step in glycolysis, the phosphorylation of glucose to glucose-6-phosphate (G6P). G6P feedback inhibits HK. There are five : HK1-4 and HKDC1, in which the latter is poorly characterized104. HK1 is the most ubiquitous, whereas HK2-4 are found in specific tissues. Both HK1 and HK2 binding to the mitochondrial membrane is voltage-dependent anionic channel (VDAC)-dependent104. Though HK1 and HK2 operate in very similar ways, only HK2 is upregulated in cancers; the reason for this is unknown, but its occurrence drives the coupling of glycolysis and oxidative phosphorylation by increasing the glucose flux into a variety of other metabolic pathways104. HK1 and HK2 are allosterically regulated by their product, G6P, and HK1 activity can also be regulated via PI3K. HK2, bound to the outer mitochondrial membrane, has been shown to enhance glycolytic rate and impair glycolytic rate when removed11. Targeting HK2 has been shown to effectively kill various cancer cell types, including hepatocellular carcinoma, as the small molecule 3- bromopyruvate (3BP) causes cell death by covalently binding to HK2, causing its dissociation from the mitochondrial membrane105. Interfering with HK2 activity is a viable option for cancer therapy, as HK2 ablation in vivo inhibited mouse tumor growth without noticeable physiological consequence or an induction in HK1 activity106.

2-Deoxyglucose (2-DG), a glucose analog without the 2-hydroxyl, is taken up into cells and phosphorylated by HK. However, the phosphorylated 2-DG cannot continue through the glycolytic pathway, and acts as a competitive inhibitor against glucose in HK107. 2-DG’s glycolytic blocking abilities have made it a widely pursued clinical candidate. However, its use in clinical trials has shown very little efficacy in solid tumors (NCT00633087; NCT00096707)108–110.

13 PFK catalyzes the third step in glycolysis, the transfer of a phosphate group from ATP to fructose-6-phosphate (F6P) to form fructose-1,6-bisphosphate (F1,6BP). The reaction catalyzed by PFK is extremely thermodynamically favorable, with a large negative DG, and is the practically irreversible “committed step” in glycolysis. Therefore, PFK is the key biochemical valve controlling the flow of substrate to product in glycolysis and is therefore a crucial regulatory point in the glycolytic pathway. ATP allosterically regulates PFK, where a high ATP concentration in the cell will inhibit PFK activity by binding to an allosteric regulatory site. PI3K can regulate PFK-1 and PFK-2 activity, where activation of PI3K increases glycolytic flux. PFK is activated by fructose 2,6-bisphosphate (F2,6BP), whose levels are regulated by bifunctional enzymes with phosphofructo-2-kinase and fructose 2,6-bisphosphatase (PFKFBs) activities. Of these, PFKFB3, which is regulated by the JAK/STAT5 pathway and phosphorylated by various kinases, such as PTEN and HIF-1α111–113, is upregulated in many aggressive cancers, including leukemia114,115.

Various inhibitors of PFKFB3 have been developed based off the scaffold of the weak inhibitor 3PO, with the latest iteration, PFK-158 (Advanced Cancer Therapeutics), having recently undergone phase 1 clinical trials (NCT02044861)111,112,116. PFKFB3 inhibitors are of particular interest in the context of combinatorial therapy, as resistance to targeted cancer therapies can be seen in the MAPK and PI3K/AKT-mediated activation of PFKFB3, restoring survival and proliferative defects induced by drugs such as B-raf inhibitors117. For example, it was found that treatment of ER+ breast cancer with both palbociclib and PFK-158 produced a synergistic effect, causing greater cell death than either drug alone118.

PK catalyzes the last step of glycolysis. Of the four isoforms, PK muscle M2 (PKM2) is the dominant isoform in cancer cells, as well as fetal and proliferating cells. PKM2 activity is relatively low, attenuating the final irreversible glycolytic step and creating a buildup of glycolytic intermediates that can be shuttled elsewhere for use in biosynthetic pathways necessary for proliferation7. PKM2 is implicated in only some cancers, most notably breast and colon cancer119–121. PKM2 is activated by both F1,6BP and serine120. PKM2 is also a co-activator of HIF-1a; HIF-1a is activated by PKM2 hydroxylation, participating in a positive feedback loop that activates its own production122. Though one might anticipate that a loss of PKM2 activity could impair the development of cancer, the absence of PKM2 did not inhibit tumor metabolism119. Rather, it was found that PKM2 exerts its pro-cancerous effect by becoming the dominant isoform over PKM1, which is more constitutively active, in order to control the production of ATP more closely11; this results in the shuttling of glycolytic intermediates into the pentose phosphate pathway (PPP) for the production of NADPH. One approach to cancer treatment focuses on activating PKM2 to make it more similar to PKM1, which has been shown to inhibit tumor growth and sensitize cells to oxidative stress123. Furthermore, PKM2 has been postulated as a potential biomarker in early tumor detection124.

14 F1,6BP can then be converted to dihydroxyacetone phosphate (DHAP) or glucose-3-phosphate (G3P), to continue downstream in toward the production of pyruvate. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) catalyzes the next step in this pathway, converting G3P to 1,3-bisphosphoglyceric acid (1,3- BPG). GAPDH has been reported to be a mechanism of metabolic control in cancer cells exhibiting the Warburg effect via its precursor, glutathione S- transferase Pi 1 (GSTP1)125,126. GSTP1 affects cancer pathogenicity via its control over glycolytic metabolism; a small molecule inhibitor of GSTP1, LAS17, lowers production of lipids and nucleotides, lowers ATP levels, and impairs oncogenic signaling125. Inhibitors of GAPDH itself, such as the natural product koningic acid, also impair cancer cell proliferation in those cancers exhibiting the Warburg effect126. Interestingly, GAPDH has also been shown to exert control over the entirety of glycolytic flux in cells operating under the Warburg effect, given that there is an abundance of precursor materials98

3.2. Lactate Metabolism

In a normal, non-diseased cell, under aerobic conditions, the pyruvate produced in glycolysis enters the pyruvate dehydrogenase complex (PDC) and the tricarboxylic acid (TCA) cycle to be oxidized completely to CO2. The NADH produced in glycolysis and the PDC, as well as the NADH and the FADH2 produced in the TCA cycle, are all re-oxidized in the electron transport chain (ETC), where O2 is the final electron acceptor. In anaerobic conditions, however, electron transport cannot function, and the limited supply of NAD+ becomes entirely converted to NADH, thus inhibiting glycolysis if there is too little NAD+ left in the cell.

Fermentation has evolved to regenerate NAD+ in anaerobic conditions, thereby allowing glycolysis to continue in the absence of O2, which is a defining feature of the Warburg effect. Fermentation uses pyruvate as the acceptor of the high- energy electrons from NADH and, simultaneously, reduces pyruvate to lactate in muscle cells via lactate dehydrogenase (LDH) enzymes, which happens to a much higher percentage of metabolized glucose in tumors. The NAD+ produced by reducing pyruvate is now available for reuse in glycolysis, so more ATP and other various glucose by-products can be produced. In cancerous cells, the oft- increased expression of LDHA produces greater amounts of NAD+, affecting the opposite response and allowing glycolysis to continue127. However, cancer exploits this fermentation step in a variety of ways, with one indication being that the expression of the LDHB isoform is inconsistently up- or down-regulated in various cancers128. Several groups have shown that the knockdown of LDHA is effective in preventing cancer cell proliferation, including limiting anchorage- independent growth as well as in vivo growth of breast tumors129,130. Various inhibitors have been identified; however, selectivity has caused issues for drugs in clinical trials. For example, AT-101 (Ascenta Therapeutics), an enantiomer of the naturally-derived phenol gossypol, is also a BCL2 domain 3 mimetic and has

15 only shown mild effectiveness in phase I and II trials (NCT01285635; NCT00286793; NCT00286806)131–134. Also, a class of compounds, called the 3- hydroxycyclohex-2-enone series (Genentech), are currently being an optimized for inhibition specific to LDHA over the B isoform135.

If lactate builds up to high enough concentrations, however, it can act as a poison to the cell, and lactate production is a contributor to tumor acidity, 136 alongside CO2 production . As cancer cells take in more glucose, more lactate is secreted137. Excess lactate is secreted by monocarboxylate transporters 1-4 (MCT1-4) 104. Extracellular lactate has been shown to inhibit immune responses to tumorigenic cells by both inactivating cytokine release in cyctotoxic T cells and increasing the extracellular pool of lactate to such a great extent that T cell glycolysis is impaired, as the imbalanced concentration gradient does not allow for further excretion of lactate138. Extracellular lactate can also act as a signaling molecule, as several membrane receptors can respond to changes in H+ concentration/extracellular pH to effect changes within the cell. Targeting these membrane receptors holds potential for drug development, and researchers are looking at inhibiting the function of G-protein coupled receptors 4, 65, 68, and 132 via knockdown14,139.

3.3. The Pyruvate Dehydrogenase Complex

The pyruvate produced from glycolysis and not used in lactate production, is transported into the mitochondrial matrix where it is oxidized to CO2. Pyruvate does not enter the Krebs cycle directly, but instead is oxidatively de-carboxylated by PDC. PDC oxidizes pyruvate to release a CO2 and produce NADH, converting pyruvate into acetyl-CoA. This bond between sulfur and the acetyl group in acetyl-CoA is high energy, making it easy for acetyl-CoA to transfer the acetyl fragment into the TCA for further oxidation. PDC also contains a thiamine pyrophosphate prosthetic group at one of its active sites, which is necessary for biological activity and is involved in the catalyzing the oxidative decarboxylation. PDC was recently reported to carry the majority of pyruvate entering the TCA cycle140,141.

Regulation of PDC is crucial; a high ratio of AMP or ADP to ATP will stimulate PDC, thus increasing the rate of entry of pyruvate into the TCA. In addition, PDC is regulated by pyruvate dehydrogenase 1 (PDK-1) expression, which phosphorylates the PDCa subunit and inactivates PDC. Overexpression of pyruvate dehydrogenase kinases, including PDK-1, has been linked to the oncogenic activation of AKT and HIF pathways that are deeply integrated in dysregulated cancer metabolism142. PDK-1 expression can be hijacked by cancer cells and continually overexpressed to inactivate PDC, which contributes to the Warburg-identified glycolytic alterations. Further, it has been shown that knockdown of PDK-1 restores PDC to normal activity levels and reverses these glycolytic effects143.

16 PDK-1 inhibition is an attractive target for cancer therapy. Dichloroacetate (DCA), long used to treat patients with mitochondrial abnormalities and used in topical cosmetics, is a weak pan-PDK inhibitor whose clinical trials for treatment of cancer have not shown conclusive results144,145 (NCT01386632, NCT01029925). Radicicol is also well-documented as a PDK-3 inhibitor, but targeting PDK-3 specifically has not been conclusively shown to effectively kill cancer cells146. However, in combination with cisplatin, an anticancer effect was seen. This further prompted the development of mitaplatin, a DCA-cisplatin combination with 147–149 an IC50 of 14.0 uM . Other small-molecule inhibitors include CPI-613, a lipoate derivative currently in phase I/II clinical trials (NCT01766219; NCT01835041; NCT03435289)150,151. A variety of other efforts are also underway, including recently published structures of two DCA derivatives, N-(3- iodophenyl)-2, 2-dichloroacetamide and Mito-DCA, both with micromolar inhibition of PDK-1 specifically149.

3.4. The Tricarboxylic Acid Cycle

The enzymes of the TCA cycle, as well as the aforementioned PDC, are located in the mitochondrial matrix. The TCA cycle involves a group of reactions that take the 2-C acetyl unit from acetyl-CoA and combine it with a 4-C oxaloacetate molecule to produce citrate, cycles through various reactions that ultimately releases two CO2 molecules, three NADH, one FADH2, and one GTP, and recycles the oxaloacetate molecule for reuse. The two carbons that leave as CO2 during these reactions are not the same ones that entered the cycle as acetate. In this process, reducing power is stored in the high-energy electron carriers NADH and FADH2, which will eventually be re-oxidized in the ETC to store energy as ATP.

Isocitrate is readily converted to citrate, which can be cleaved to produce acetyl- CoA. Under conditions of reductive carboxylation, glutamine becomes the major source of acetyl-CoA for FA synthesis, greatly decreasing the need to produce acetyl-CoA from glucose. Additionally, citrate cleavage produces oxaloacetate, which can then be converted to other 4-carbon intermediates. Therefore, essentially the entire cellular pool of TCA cycle intermediates can be derived from reductive carboxylation 11.

Isocitrate can also be converted to a-ketoglutarate (a-KG), and vise versa, in the mitochondria via isocitrate dehydrogenase 2 (IDH2). Extremely important to driving tumorigenesis in many types of cancer are mutations in IDH2, which results in an enzyme that readily converts a-KG to oncogenic D-2- hydroxyglutarate (2-HG) (Figure 2). 2-HG levels are significantly increased in tumors with IDH2 mutations, and can competitively inhibit the targets of a-KG, which have DNA and histone demethylase activity. Therefore, high levels of 2- HG production can cause changes in gene expression, which researchers have found results in impaired differentiation152–155. Inhibition of the mutated-IDH2 enzyme by AGI-6780 has shown efficacy in vitro in acute myeloid leukemia

17 (AML) cells, in which 2-HG levels normalized, histone and DNA hypermethylation were reversed, and the cells ceased proliferating and differentiated154,156,157. A bromodomain-containing protein 4 (BRD4) inhibitor, JQ1, has also shown promising results in vivo in mice with IDH2-driven AML154. Furthermore, AG-221 (Agios Pharmaceuticals, Inc./Celgene) has shown suppressed 2-HG production and induced cellular differentiation in both in situ primary human IDH2 mutation- positive AML cells and in vivo in xenograft mouse models158. AG-221 is currently in active status in phase I/II clinical trials targeting AML-harboring IDH2 mutations patients (NCT01915498)159. Although this study is still active, the FDA recently approved AG-221 for use in treating AML, and will now be sold as Idhifa. Similar phase I dose escalation studies of AG-221 in patients with IDH2-mutated gliomas, other solid tumors, and angioimmunoblastic T cell lymphoma have recently completed (NCT02273739)159. These results further favored initiation of a phase I/II combination study looking at AG-221 with azacitidine in newly- diagnosed AML patients (NCT02677922).

Some cancer cells contain severe mutations in TCA cycle enzymes (i.e. succinate dehydrogenase and fumarate hydratase) or in components of the ETC that prevent efficient production of oxaloacetate from glutamine11. For example, succinate dehydrogenase (SDH), a known tumor suppressor, converts succinate to fumarate, producing FADH2. SDH mutations lead to familial paraganglioma and familial pheochromocytoma, and no subunit mutation specificity (between B, C, and D) has been identified despite their distinct functions in the complex16,160. SDH also functions as complex II in the ETC. Mutated SDH is inactive, resulting in a buildup of succinate in the mitochondria; excess succinate effectively stabilizes HIF-1a via HIF-1a prolyl hydroxylase inhibition, resulting in the transcription of tumorigenic genes, and leads to hypermethylation of histones and DNA161,162. SDH gene mutations are also observed frequently in several cancer types including RCC, gastrointestinal stromal tumors, colorectal cancer, and ovarian cancer163–167, and are associated with malignancy168 and poor prognosis169. SDHB, one if four subunits if SDH and the subunit with the most frequently detected mutations166,170, has recently been shown to be sensitive to bromodomain and extra-terminal (BET) inhibitors, such as JQ1 and various chemotypes including I-BET151, I-BET762, and PFI-1171. However, these inhibitors are in the beginning stages and effective drug therapies have not yet been established, and the development of such would fulfill an unmet medical need.

Fumarate hydratase (FH), also a tumor suppressor, converts fumarate to malate. FH mutations lead to a buildup of fumarate, which acts as a competitive inhibitor to HIF-1a prolyl hydroxylase and stabilizes HIF-1a6. FH mutations are known to lead to hereditary leiomyomatosis and renal cell carcinoma (HLRCC)16. Some successful in vitro inhibitors have been reported172, but, similar to the SDH inhibitors, development of FH inhibitors remains challenging and is still in very early stages.

18 Reductive carboxylation has previously been observed as a minor source of isocitrate and citrate in a number of non-transformed mammalian tissues173,174. Its importance in cancer cell biology is related to its ability to serve as the minor source of citrate and acetyl-CoA when pathways are inactivated that normally produce these metabolites due to hypoxic conditions or genetic reprogramming of mitochondrial metabolism. For example, the previously mentioned pVHL normally functions in the oxygen-dependent degradation of the alpha subunits of the HIF transcriptional activators. So, in cells expressing pVHL, oxygen facilitates the degradation of HIF-1a and HIF-2a so that HIF target genes are not expressed. In contrast, in malignant cancer cells lacking VHL, HIF target genes are expressed regardless of oxygen availability. These genes, which include glucose transporters and glycolytic enzymes, are part of the metabolic adaptation to hypoxia. Importantly, hypoxia also stimulates the expression of PDK1 to inhibit PDC and, thus, impair the cell’s ability to provide acetyl-coA and citrate from glucose. Cultured cancer cells lacking pVHL produce a substantial amount of their citrate and fatty acids using glutamine-dependent reductive carboxylation11.

3.5. The Electron Transport Chain and Oxidative Phosphorylation

Oxidative phosphorylation is the oxidation of the high-energy electron carriers NADH and FADH2 coupled to the phosphorylation of ADP to produce ATP. The energy released through the oxidation of NADH and FADH2 by the ETC is used to pump protons out of the mitochondrial matrix and into the intermembrane space. This resulting proton gradient is the source of energy used to drive the phosphorylation of ADP to ATP. The ETC is a group of five electron carriers, all of which are bound to the inner mitochondrial membrane. Each member of the chain reduces the next member down the line. Three of these are large protein complexes are cytochromes, thus contain a heme group, while the other two are small mobile electron carriers bound loosely to the inner mitochondrial membrane.

In recent years, analysis of particularly aggressive and drug-resistant cancers has shown that they rely heavily on oxidative phosphorylation rather than glycolysis for survival175. These findings oppose the traditionally accepted, and diminished, role of the mitochondria in cancer cell metabolism176. The importance of the ETC for cancer survival suggests that the current understanding of tumor metabolism is still underdeveloped and lacking in nuance, and underscores the potential effectiveness of therapies targeting oxidative phosphorylation175,176. Developing therapies targeting components of the ETC holds great potential in the goal to effectively drug all cancer types.

The ETC is organized so that the first large carrier, NADH dehydrogenase (also called complex I), receives reducing power, in the form of electrons, from NADH, which is subsequently oxidized to NAD+. Much more interest, however, has been focused on the use of biguanides for NADH dehydrogenase inhibition in cancer, with metformin being the most commonly studied. Metformin, currently approved

19 as an anti-diabetic drug, has been shown to inhibit complex I, though the entirety of its anti-cancer mechanism is yet to be understood177. With initial clinical trials producing promising results, subsequent trials are underway178. Other biguanides, such as phenformin, have also been put forth as potential cancer therapeutics, but similar knowledge gaps exist for these as well. Sensitivity to biguanides varies between cancers; recent work has shed light on cancer response to biguanides – determining low-glucose sensitivity can help identify which cancers would respond to drugs targeting the ETC, allowing for more targeted applications of these biguanides and a more informed expectation of tumor response179. It has also been shown that NADH dehydrogenase polymorphisms correlate with breast cancer incidence, though further research is necessary to investigate its use as a biomarker180,181. In addition, NADH dehydrogenase is inhibited by the small molecules VLX600 and ME-344, which result in cell death upon treatment182–184. Clinical trials with both are currently underway, and results from initial dose escalation studies with ME-344 have been positive (NCT02806817; NCT02222363)185.

NADH dehydrogenase then passes its electrons to one of the small carriers, called coenzyme Q or ubiquinone. Ubiquinone receives electrons from both NADH dehydrogenase as well as directly from FADH2 and any NADH arriving from the cytoplasm produced in glycolysis. Ubiquinone then passes its electrons to the next large protein complex, cytochrome C reductase (complex III), which then passes its electrons to cytochrome C, and finally to the last large bound protein complex called cytochrome C oxidase (complex IV). These electrons are finally passed to O2, reducing it to water, which is the end product of the ETC. Each of the three large, membrane-bound proteins in the ETC pumps protons from the matrix across the inner mitochondrial membrane into the intermembrane space every time electrons flow past. This effort culminates in producing a large proton gradient, with the pH being much higher inside the matrix than the rest of the cell.

Complexes III-IV have also been implicated in cancer pathogenicity. The ubiquinol-cytochrome c reductase hinge protein (UQCRH), a protein within complex III, has been shown to be downregulated in ccRCC as compared to other RCCs, raising its potential as a biomarker186. Cytochrome C plays a part as well, as recent studies have revealed that cancerous cells inhibit cytochrome C- mediated apoptosis by supplying sufficient glutathione to keep cytochrome C in a reduced and inactive state187. In addition, it has been shown that knockdown of complex IV increases cancer aggressiveness, and has specifically been implicated in esophageal tumor progression188.

20 Table 1-3. Summary of glucose metabolism-related drugs in clinic or pre- clinic.

4. AMINO ACID METABOLISM

Cancer cells require the building blocks necessary for cell growth and proliferation, including proteins, lipids, and nucleic acids, as well as the maintenance of cellular redox status. Amino acid metabolism, and the carbon units that they provide, satisfies many of these requirements. Amino acids play many roles in cancer cell growth and survival, including supplying carbons to the TCA cycle, to nucleobase synthesis, and in regulating redox balance to name a few. Much like glucose, amino acids support the energy metabolism and anabolic processes that cancer cells require. Modern cancer therapies are now

21 also focusing on the importance amino acids play in cancer’s survival, and taking advantage of this knowledge to treat it.

4.1. Glutamine

The essential glutamine requirement of proliferating cells was first highlighted by Eagle in the 1950s189. Glutamine is one of the eleven nonessential amino acids in human metabolism, meaning it can be synthesized sufficiently endogenously. In fact, glutamine is the most abundant amino acid in circulation in human plasma. However, under certain circumstances, such as during rapid growth or other stresses, glutamine can become conditionally essential, meaning that the demand for glutamine overwhelms the cell's ability to produce it. In these conditions, the cell must find other means of obtaining glutamine, such as via exogenous means from diet.

Glutamine contains an amine functional group and plays many important biological roles within the cell, including involvement in various anabolic and catabolic processes. In regards to the former, glutamine supplies nitrogen for nucleobase synthesis and carbon for the TCA cycle, lipid synthesis, and nucleotide synthesis. Concerning the latter, once glutamine has been transported into cells through transporters, such as SLC1A5 and SLC7A5, glutamine can be catabolized, called glutaminolysis, and then converted into many important biological metabolites, such as glutamate, aspartate, CO2, pyruvate, lactate, , and citrate. Due to its important, and often essential, role in cancer cell proliferation and survival, the development of small molecule inhibitors to target glutaminolysis for cancer therapy is currently being pursued190,191.

Specific inhibition of SLC1A5, the primary glutamine transporter in many cancer cell types, has been shown to be a promising therapeutic approach for cancer treatment. In fact, benzylserine, l-γ-glutamyl-p-nitroanilide (GPNA), and γ-FBP have all been shown to effectively inhibit SLC1A5 (Table 1-4). Upon inhibition, tumor growth is suppressed in both lung cancer and melanoma192,193. Additionally, it has been shown that 2-aminobicyclo(2,2,1)-heptane-2-carboxylic acid (BCH), an inhibitor of SLC7A5, inhibits mTOR signaling194.

Inhibitors of SLC1A5 and SLC7A5 may also show promise in certain cancer cells as, through secondary means, inhibitors of mTORC1. Glutamine flux is reported to regulate mTOR activation to coordinate cell growth and proliferation194. Substantial evidence supports the role of amino acids in the activation of mTORC1-dependent signaling. Of the essential amino acids, leucine seems to produce the most acute response from mTORC1; however glutamine is also necessary for maximum mTOR activation195–200.

The first step of glutaminolysis is the initial deamination of glutamine into glutamate and ammonia through glutaminase (GLS), of which there are several human . Glutamate can then be oxidized, usually via the enzyme

22 glutamate dehydrogenase (GLDH), into a-KG, NAD(P)H, and ammonia. a-KG can then be used to produce both ATP and anabolic carbons for the synthesis of other amino acids, nucleotides, and various lipid species. Glutamine is also required for nucleotide biosynthesis; it donates its nitrogen to purines and pyrimidines. In addition, glutamine contributes to the biosynthesis of hexosamines and certain other nonessential amino acids201. Therefore, glutamine not only provides a major substrate for respiration, but also for the synthesis of important biological macromolecules. Beyond providing carbon and nitrogen for macromolecule biosynthesis, glutaminolysis also has an important role in regulating redox balance, mTOR signaling, apoptosis, and autophagy191,202–205.

As previously stated, it has been widely shown that glutaminolysis is critical for cancer cell growth and survival, and glutamine is conditionally essential in rapidly growing malignant cells. It has been previously shown that high extracellular glutamine concentrations stimulate tumor growth and survival206–208. Not surprisingly, glutamine utilization is higher in tumor cells, and in rapidly dividing cells in general209. The flux of mitochondrial enzymes involved in glutamine/glutamate oxidation is also elevated in tumor cells210,211. Moreover, many cancer cells depend on glutamine in culture for cell survival, known as glutamine addiction212. For example, human glioma and HeLa cells in culture were found to be completely dependent on glutamine for their survival, and died in its absence even when they had access to glucose-rich media213. The resulting tumor microenvironment of this augmented consumption of glutamine by tumors is one in which T cell/immune cells are deprived of glutamine, which depletes immune cell functions.

In addition to the aforementioned well-established roles that glutamine plays in cell proliferation, recent studies have revealed several additional functions towards regulating cell proliferative events. For instance, cancer cells under hypoxia, or with defective mitochondria, can use glutamine-derived a-KG to produce citrate through reductive carboxylation, which is critical for lipid synthesis in these cells214,215. Metabolic labeling experiments revealed that these cells metabolize glutamine through an unusual pathway characterized by the reversal of IDH enzyme activity. IDH typically acts as an oxidative decarboxylase, converting isocitrate to a-KG and CO2 in the presence of an electron acceptor. Indeed, IDH3, the mitochondrial NAD+-dependent IDH isoform, functions exclusively in this manner. However, the two other mammalian IDH isoforms, IDH1 and IDH2, use NADP+/NADPH as cofactors, and can act either as oxidative decarboxylases or reductive carboxylases. In the latter reaction, a-KG is carboxylated to produce isocitrate, converting NADPH to NADP+. Isocitrate is readily converted to citrate, which can be cleaved to produce acetyl-CoA. Under conditions of reductive carboxylation, glutamine becomes the major source of acetyl-CoA for fatty acid synthesis, greatly decreasing the need to produce acetyl-CoA from glucose. Additionally, citrate cleavage produces oxaloacetate, which can then be converted to other four-carbon intermediates. Therefore,

23 essentially the entire cellular pool of TCA cycle intermediates can be derived from reductive carboxylation11.

In addition to IDH2 inhibitors that were previously discussed, mutant IDH1 inhibitors are also being developed. IDH1, a cytosolic enzyme that converts isocitrate to a-KG, can be mutated, resulting in the conversion of a-KG to oncogenic 2-HG. This, ultimately, interferes with cellular metabolism and epigenetic regulation, contributing to tumorigenesis and lack of cellular differentiation159. Mutated IDH1 additionally is prognostically relevant for measures of overall survival in patients with glioma216. AGI-5198 has been shown to be a selective inhibitor of mutant IDH1 and reduced 2-HG levels and inhibited tumor growth in both in vitro and in vivo glioma models217. Another selective IDH1-mutation inhibitor, AG-120 (Agios Pharmaceuticals, Inc.), is currently undergoing phase 1 clinical trials in advanced hematologic malignancies as well as in advanced solid tumors with an IDH1 mutation, including gliomas, cholangiocarcinomas, and chondrosarcomas (NCT02074839; NCT02073994)159,218. Preliminary results from the ongoing phase I studies demonstrated that AG-120, when administered as a single-agent, had a favorable safety profile and produced an overall response rate of 35 percent from patients with advanced IDH1 mutant positive hematologic malignancies. Other IDH1 inhibitors, such as BAY1436032, FT-2102, and AG-881, are in clinical development as well219–221. Phase I clinical trials are underway for all three drugs; BAY1436032 trials are currently recruiting (NCT02746081; NCT03127735), a FT- 2102 trial is also currently recruiting (NCT02719574), and trials for AG-881 are already running (NCT02492737; NCT02481154).

Glutaminolysis is also involved in many metabolic processes and signaling pathways that inhibit cell death. Cancer cells encounter a variety of stress signals, such as reactive oxygen species (ROS) and various nutrient limitations; consequently, cancer cells must rewire their metabolic pathways under these environmental conditions. Research suggests that cancer cells show elevated levels of ROS compared to normal cells. A moderate increase in ROS promotes cell proliferation and differentiation, whereas excessive amounts of ROS can cause oxidative damage to proteins, lipids, and nucleotides222,223. Glutaminolysis is directly involved in maintaining ROS homeostasis by supporting the production of glutathione (GSH) and NADPH, which are two major reducing species in the cell224,225. Moreover, glutamine-derived fumarate has been shown to be important for the control of oxidative stress through several distinct mechanisms by upregulating the activity of glutathione peroxidase 1 (GPx1), a ROS scavenger enzyme, and through activating nuclear factor erythroid 2-related factor 2 (Nrf2) antioxidant signaling191,226,227. The diverse contributions of glutaminolysis to redox homeostasis highlights its importance in maintaining cancer cell survival.

Interestingly, not all cancer cells exhibit glutamine addiction, and their existence has allowed researchers to probe for oncogenic mutations or alterations that could explain glutamine dependence in some cancers but not others. As alluded

24 to earlier, studies have shown that MYC, for example, is able to increase glutamine metabolism by upregulating GLS expression, which leads to enhanced glutamate production and, eventually, elevated a-KG entry into the TCA cycle224. Metabolomic studies have further confirmed that glutaminolysis is profoundly affected by the induction of MYC in cancer cells228. Not only has MYC been shown to induce the expression of GLS, but it has been revealed that MYC upregulates glutamine transporters as well228. Collectively, these studies indicate that MYC induces a transcriptional program that promotes glutaminolysis in cancer cells.

Silencing GLS gene expression, or inhibiting GLS enzyme activity, has been shown to delay tumor growth in numerous models229–231. For example, inhibition of GLS has significantly suppressed tumor growth in several experimental cancer models, including breast cancer and lymphoma224,231. GLS can be inhibited via several small molecule inhibitors, including bis-2-[5–phenylacetamido-1, 2, 4- thiadiazol-2-yl] ethyl sulfide (BPTES)232, CB-839233, and compound 968231. The orally-bioavailable inhibitor CB-839 (Calithera), for example, was found to be a potent, selective, and reversible inhibitor of GLS in preclinical trials233,234. CB-839 works by allosterically inhibiting the dimer-to-tetramer transition of GLS, which is critical for activation of the enzyme232,235. CB-839 is currently being tested in multiple phase II combination trials, including one trial designed to evaluate the safety and efficacy of CB-839 in combination with everolimus in patients with metastatic ccRCC (NCT03163667) and another phase II clinical trial looking at CB-839 with paclitaxel in patients with TNBC (NCT03057600). Furthermore, it has been shown that CB-839 administered as a single agent has resulted in clinical responses in RCC and AML and clinical benefit in several other tumor types (NCT02071862; NCT02071927).

In addition to GLS inhibitors, it has recently been reported that RNAi-mediated knockdown of GLDH, which is typically upregulated in breast and lung cancer cells, or inhibition via R162, a GLDH-specific inhibitor, in these cells results in significantly decreased a-KG production, decreased anabolic glutamine- dependent RNA biosynthesis, and elevated ROS levels191. Lastly, glutamate- dependent transaminases have also been considered as drug targets for modulating glutaminolysis. For example, the inhibitor aminooxyacetate, which non-specifically inhibits transaminases, has been shown to be effective in inhibiting cell proliferation and tumor growth in several preclinical studies236,237.

4.2. Asparagine

The amino acid asparagine is required for cancer cells to make DNA. L- asparaginase, via Elspar (Merck & Co, Inc.), Erwinase (Speywood Phamaceuticals, Inc.), or Oncospar (Enzon Pharmaceuticals), is a successful therapeutic used to treat pediatric acute lymphoblastic leukemia (ALL), works by hydrolyzing asparagine into aspartic acid and ammonia199,238. ALL cells are incapable of synthesizing asparagine de novo, so this leaves the cells without

25 any asparagine and, thus, unable to make new DNA. Interestingly, asparaginase also possesses significant GLS activity, and is also capable of hydrolyzing glutamine to glutamic acid and ammonia239. Asparaginase significantly depletes glutamine levels, and studies have confirmed that the success of treatment correlates with glutamine depletion239,240.

Unfortunately, clinical trials of asparaginase treatment in adults revealed significant toxicity, including increased rates of thrombosis241,242. Although more research is still needed to clarify the mechanism of asparaginase therapy for the treatment of childhood versus adult glutamine-addicted cancers, researchers have been working on developing less toxic forms of asparaginase that could potentially be used in adult treatment for these cancers as well. For example, asparaginase was modified by covalently conjugating it with monomethoxypolyethylene glycol to increase its half-life and reduce immunogenicity243. Researchers have also developed, and are currently testing in clinical trials, erythrocyte-encapsulated asparaginase, which should also minimize toxicity and improve the delivery of the drug (NCT01523782)242,244,245. Lastly, it is worth noting that, with the proper precautions, asparaginase is a safe and effective agent in the treatment of younger adults with ALL; however, the age cutoff for consideration is not yet clear242.

4.3. Serine

Not only does serine serve as a building block for proteins, but serine and glycine also donate carbon units to the serine, glycine, one-carbon (SGOC) metabolic network246. Through their metabolism across the SGOC network, these amino acids support a wide variety of downstream cellular processes such as nucleotide synthesis, methylation metabolism, sulfur metabolism, polyamine metabolism, lipid and protein synthesis, and redox balance247.

De novo serine metabolism, which is significantly elevated in some cancer cells and feeds into one-carbon metabolism, importantly produces NADPH and glutathione248,249. It has been shown that several cancer types, including breast and colorectal cancer, rely on serine for proliferation and survival, and that hyperactivity in the SGOC network can drive the development of these cancers. Utilization of serine to increase de novo nucleotide synthesis rates in highly proliferative cells has been the main explanation proposed for this observation246. A recent study using 13C serine revealed a flux of serine into nucleotides and glutathione synthesis249. Serine-fueled folate metabolism was shown to have substantial contribution to NADPH production, indicating a potential role of serine metabolism in keeping redox balancing, which could be critical to cell proliferation248,250–253. Furthermore, de novo synthesis of serine from glucose is increasingly seen in serine-deprivation conditions, which indicates the importance of serine to proliferating cancer cells254,255.

26 The first committed step of serine biosynthesis is catalyzed by 3- phosphoglycerate dehydrogenase (PHGDH), which oxidizes 3-phosphoglycerate (diverted from glycolysis) to 3-phosphohydroxypyruvate, a serine precursor256. PHGDH been shown to be upregulated in various cancers, including TNBC and NSCLC257. Cancer cells utilize increased PHGDH activity for proliferative benefit, as the products of its catalyzed reaction not only yield serine but are also used to replenish the TCA cycle’s supply of a-KG256.

Inhibition of PHGDH has been the subject of extended investigation, and multiple inhibitors, including CBR-5884, NCT-502, and NCT-503, are currently undergoing preclinical studies258,259. These inhibitors have been shown to reduce levels of serine in cells that have overexpressed PHGDH, and PHGDH inhibition via NCT-502 or NCT-503 treatment in in vivo tumor xenograft studies resulted in reduced tumor growth as compared to controls260. These results illustrate the potential therapeutic use of developing PHGDH inhibitors for treatment of cancer cells that are addicted to serine.

4.4 Tryptophan

Tryptophan is an essential amino acid and its breakdown proceeds through one of two main pathways in humans leading to the production of either NAD+ or serotonin, with the former being the dominant pathway261,262. Via this dominant pathway, various tryptophan catabolites, especially kynurenine, are active and block effector T cell activation and trigger T cell apoptosis. These T cells are important in mediating the immune system’s ability to destroy pathogens and, therefore, by reducing the number of T cells, upregulated tryptophan catabolites prevent the immune system from effectively destroying cancer cells263–266. The indoleamine (2,3)-dioxygenase (IDO) enzyme, which controls the breakdown of tryptophan to later produce kynurenine267, has also been shown to be broadly expressed in human tumors, including gastrointestinal, lung, gynecological, and breast cancers268,269. In fact, several studies have shown that increased IDO expression is associated with a decrease in the survival of patients including colorectal, small-cell lung cancer (SCLC), and melanoma cancers among others270–273.

The IDO enzyme has two isoforms, IDO1 and IDO2, with IDO1 being the predominantly expressed and much more active isoform274. IDO is a cytoplasmic monomeric, heme-containing enzyme, and its expression can suppress the body’s immune responses to cancer and allow tumors to grow unchecked269. It has been previously shown that overexpression of IDO1 is sufficient to drive immune escape via mediating immunosuppression through preferential sensitivity of T cells to tryptophan deprivation275–278. Furthermore, IDO expression decreases T cell infiltration into tumor microenvironments, promotes inflammation around tumor tissues and the surrounding microenvironment, and causes overall immune tolerance279–284. It is worthy to note that some tumor and surrounding microenvironment cells even express IDO to protect themselves from the

27 immune system269,285. Lastly, kynurenine is a native ligand for the aryl hydrocarbon receptor, a transcription factor that contributes to proinflammatory pathways that promote inflammatory carcinogenesis286.

Recently, there has been great interest in the development of IDO protein inhibitors. It has been previously suggested that overexpression of IDO in tumors is one possible mechanism of tumor drug resistance against chemotherapy287. In support of this, there has been evidence that IDO1 inhibitors boost the effectiveness of current immunotherapies, including epacadostat (Incyte Corporation) combined with anti-PD1 and indoximod (NewLink Genetics) combined with anti-PD1266.

Epacadostat, previously INCB24360, is an orally available tryptophan- competitive IDO1 inhibitor288; epacadostat is currently the most advanced agent in clinical trials with promising early-phase study results, particularly in melanoma (NCT02178722). Epacadostat combined with pembrolizumab, a PD1 inhibitor, to treat advanced melanoma has entered phase III clinical trials (NCT02752074). Similarly, epacadostat combined with nivolumab, another PD1 inhibitor, has also entered clinical trials and has already shown positive results in patients with melanoma (NCT02327078). Further, epacadostat/PD1 inhibitor combination therapies have shown efficacy in other tumor types as well, including squamous cell carcinoma of the head and neck (SCCHN), NSCLC, urothelial cell carcinoma, and RCC (NCT02327078; NCT03085914).

Indoximod (previously D-1-methyl-trytophan and NLG-8189), a well-studied small molecule, acts directly on immune cells to reverse IDO pathway-mediated suppression, most likely through derepressing mTORC1 in T cells266,279,285,289–292. Indoximod also had early-phase clinical trial success in treating melanoma patients when used in combination with PD1 inhibitors, and just recently entered phase III as well (NCT03301636).

Other IDO1 inhibitors in development include BMS-986205 (Bristol-Myers Squibb), NLG-802 (NewLink Genetics), and HTI-1090 (Atridia Pty Ltd)266,293. BMS-986205, which inhibits IDO1, has also shown promising early-phase results across various advanced cancers, including melanoma, NSCLC, cervical, SCCHN, bladder, and other advanced solid tumors266.

28

Table 1-4. Summary of amino acid metabolism-related drugs in clinic or pre-clinic.

5. ADDITIONAL PATHWAYS

5.1. One-Carbon Metabolism

One-carbon metabolism is centered on the chemical reactions of folate compounds, and proceeds in a cyclical nature247,294–296. Folic acid is a B vitamin, so it can only be obtained through diet or via production by the microbiome in the intestines. In cells, folic acid is reduced by a series of enzymes, leading to the generation of tetrahydrofolate (THF). THF participates in a number of metabolic reactions, including being converted to 5,10-methylene-THF (me-THF) by serine hydroxymethyl transferase (SHMT). Me-THF is then either reduced to 5- methyltetrahydrofolate (mTHF) by methylenetetrahydrofolate reductase (MTHFR)

29 or converted to 10-formyltetrahydrofolate through a sequence of steps. mTHF is demethylated to complete the folate cycle. With the demethylation of mTHF, the carbon is donated into the methionine cycle through the methylation of homocysteine by methionine synthase and its vitamin B12, which generates methionine. Methionine is then used to generate S- adenosylmethionine (SAM) via methionine adenyltransferase (MAT). The folate cycle coupled to the methionine cycle are collectively referred to as one-carbon metabolism. The trans-sulphuration pathway is connected to the methionine cycle through homocysteine. Serine can be directly metabolized through trans- sulphuration, eventually resulting in the generation of glutathione, one of the major redox-regulating metabolic systems in cells.

As established earlier, amino acids play an important role in cellular metabolism via providing carbon units involved in one-carbon metabolism. One-carbon metabolism is centered on the chemical reactions of folate compounds, and proceeds in a cyclical nature247,294–296. This cycle functions to provide carbon units to other metabolic pathways, such as nucleotide biosynthesis. Nucleotides are constructed through reactions that involve the folate cycle. Phospholipids can also be generated partly through the methionine cycle. Furthermore, the metabolism of carbon atoms through one-carbon metabolism is linked to changes in redox status247. These changes occur mostly through the reduction of NADPH and the oxidation of NADP+. For example, MTHFR reduces THF, and this reaction consumes one molecule of NADPH for each turn of the folate cycle.

Interestingly, modern cancer therapy partly arose from the hypothesis that antagonists of folates could reduce the proliferation of malignant blood cells297,298. The antagonism of folate metabolism and its downstream effectors has been used in chemotherapy for more than 65 years299–301. Sydney Farber noted in 1947 that folic acid could stimulate the proliferation of ALL cells297. In a landmark study by Farber and colleagues, aminopterin, an intermediate of the chemical synthesis of B vitamins and antagonist of folate, was shown to induce remission in children with ALL298,302 (Table 1-5). Still today, chemical variants of aminopterin, such as methotrexate and pemetrexed, constitute a major class of cancer chemotherapy agents and are used as frontline chemotherapy for a diverse range of cancers, including ALL, breast cancer, bladder cancer, and lymphomas299,303–307. These chemotherapy agents inhibit dihydrofolate reductase and tetrahydrofolate reductase activity in humans, resulting in the disruption of one-carbon metabolism308,309.

Additionally, multiple pathways downstream of one-carbon metabolism are the targets of numerous cytotoxic chemotherapies. 5-fluorouracil (5-FU) is a standard-of-care agent for many cancers, including advanced stage colorectal cancer. 5-FU targets nucleotide metabolism, which relies on metabolites produced from the folate cycle310,311. 5-FU is an analog of the DNA base uracil, and is a potent inhibitor of thymidine synthase, thus blocking the methylation of dUMP to dTMP and disrupting the folate cycle312. The 5-FU and thymidine

30 synthase inhibitor, capecitabine (brand name Xeloda), is also approved for solo and combinatorial use; often used in tandem with docetaxel in breast cancer and oxaliplatin in colorectal cancer313,314. Multiple other combinatorial applications are being tested in clinical trials, including capecitabine plus lapatinib in HER2 amplified breast cancer (NCT02650752). Gemcitabine, which is used to treat various cancers, and cytarabine, used to treat leukemias and lymphomas, are two other inhibitors of nucleotide metabolism315. Gemcitabine, also called Gemzar (Eli Lilly), is FDA-approved to be used alone or in combination to treat pancreatic cancer, ovarian cancer, NSCLC, and metastatic breast cancer. Gemcitabine is, specifically, a nucleoside analogue that interferes with the biosynthesis of cytidine and inhibits ribonucleotide reductase, preventing the formation of deoxynucleotides316,317.

Anticancer drugs are also being developed towards targeting polyamine metabolism, which includes the breakdown of ornithine and the decarboxylation of SAM resulting in the generation of spermidine257. It has been previously established that an increase in polyamines, mainly through upregulation of polyamine biosynthetic enzymes, correlate with increased cell proliferation and tumorigenesis257,318–321. Furthermore, increased levels of polyamines have been associated with breast, colon, prostate, and skin cancers among others322–325.

Ornithine decarboxylase (ODC), the enzyme involved in the rate-limiting step of ornithine catabolism and its regulation, is important for normal cell growth326, is upregulated in many cancers, including non-melanoma skin cancer, breast cancer, and prostate cancer322,324,325,327,328. 2-difluoromethyl ornithine (DFMO), an inhibitor of ODC as well as several enzymes that are competitive inhibitors of SAM decarboxylase, is currently being tested in clinical trials for treatment of patients with neuroblastoma (NCT01586260; NCT02679144; NCT01349881; NCT00003814)329. Other drugs that similarly target polyamine metabolism are also entering clinical trials329.

5.2. The Pentose Phosphate Pathway

The PPP, also called the hexose monophosphate shunt, diverts G6P from glycolysis to biosynthesize NADPH, ribose-5-phosphate (R5P), and various glycolytic intermediates. Like glycolysis, this pathway takes place in the cytosol and consists of an irreversible oxidative phase followed by a non-oxidative phase consisting of a series of reversible reactions. NADPH and R5P are produced in the oxidative phase, while the glycolytic intermediates are made in the non- oxidative phase. Overall, glucose can be shunted out of glycolysis to generate NADPH and R5P when necessary, and the glycolytic intermediates shunted back in.

The first enzyme in the PPP is glucose-6-phosphate dehydrogenase (G6PDH), and it is the primary point of regulation for the PPP. As G6PDH’s substrate, G6P, is converted to 6-phosphogluconate, its product, NADPH, acts via negative

31 feedback to inhibit G6PDH. The conversion of 6-phosphogluconate to R5P, which is the next step of the PPP, also generates NADPH alongside CO2. These two successive oxidations make up the oxidative phase of the PPP, while the remaining non-oxidative reactions generate the glycolytic intermediates that will be shunted back to the glycolysis pathway.

Transketolase-like protein 1 (TKTL1), an enzyme associated with the non- oxidative arm of the PPP, has been shown to be implicated in ROS sensitivity, lactate production, and tumor proliferation330,331. Upregulation of TKTL1 has been correlated to invasive colon and urothelial cancers and poor patient prognosis332. TKTL1 has also been shown to be overexpressed in breast cancer as well, although it has not been yet shown that its overexpression correlates to breast cancer patients’ outcome and survival333. Although it has been shown that inhibited expression of TKTL1- via RNAi- in in vitro experiments significantly inhibited proliferation, successful TKTL1 inhibitors have not yet been developed334.

5.3. NADPH

NADPH is important in cancer metabolism, and its use and generation in cancer has been discussed throughout this review; it will be briefly summarized further here. NADPH is mainly produced from the aforementioned serine-driven one- carbon metabolism and the PPP, and plays a variety of important roles in cancer pathogenesis. NADPH is necessary for reductive biosynthesis, most notably de novo lipid synthesis7,335. In addition, as previously mentioned, NADPH aids in the scavenging and neutralization of ROS, which are increased in most cancer types. The production of NADPH has also been linked to enhanced cancer cell survival and the suppression of apoptosis; NADPH is required for the synthesis of glutathione, which protects cells from redox stress, thus promoting resistance to apoptosis336–338. Moreover, cytosolic NADPH is the substrate for the membrane- associated NADPH oxidases, whose production of hydrogen peroxide inhibits tyrosine , thereby promoting sustained activation of kinases that further promote cell survival as well as mitogenic signaling338.

32

Table 1-5. Summary of additional pathways-related drugs in clinic or pre- clinic.

6. FATTY ACID METABOLISM

Cancers may use a wide variety of substrates and substrate sources to meet their catabolic and anabolic needs, including internally and externally derived fatty acids (FAs). FAs are essential for cellular proliferation, specifically as cellular building blocks for lipid membrane synthesis, for energy storage and production, and also for cellular signaling. It has been shown that a broad increase in endogenous FA synthesis is seen in numerous cancer cell types339. A cell can increase their amount of FAs by increasing biosynthesis (de novo lipogenesis), reducing the breakdown of FAs (fatty acid beta-oxidation (FAO)), increasing their release from storage (lipolysis), and by decreasing their flux towards storage (re-esterification).

6.1. Fatty Acid Anabolism

Cancer cells rely on heightened de novo lipogenesis to produce the necessary FAs for proliferation, particularly at the beginning stages of the disease as compared to more advanced tumors340–342. When tumor cells do not need to rely on heightened de novo lipogenesis, however, they can obtain sufficient levels of FAs from their environment for growth343. For example, it has been reported that proximity to adipocytes influences malignancy and metastasis in ovarian cancers, as the adipocytes prompt metastasis to the omentum and provide cancer cells with fatty acids344. The influence of FAs in the tumor microenvironment is relevant to various cancers, including prostrate, ovarian, breast, and endometrial cancers, and targeting the symbiotic relationship between adipocytes and tumor

33 cells is thought to hold great potential as an effective treatment strategy345–347. Interest has risen in targeting this adipocyte effect for cancer therapies, and work is underway to identify effective modulators of the tumor microenvironment, such as the aforementioned metformin348. Metformin, a drug most known for its standard treatment for type 2 diabetes, has also been shown to exert anti-cancer properties, perhaps through its previously discussed interaction with ETC complex I. Metformin’s attenuation of tumor growth has also been linked to its caloric-restriction-like affect; metformin activates sirtuin 1 (SIRT1) and AMPK while inhibiting AKT and mTOR. Overall, metformin’s mechanism of action in cancer remains contested in the literature349–352.

In addition, interest has risen in targeting signaling lipids for cancer therapy. Platelet-activating factor (PAF) is a phospholipid signaling molecule cleaved by the PAF acetylhydrolases (PAFAHs), PAFAH1B1, PAFAH1B2, and PAFAH1B3. PAFAH1B2 and PAFAH1B3 specifically have been identified as metabolic enzymes upregulated in cancer353. Inhibiting the function of both isoforms with small molecule P11, which blocks PAFAH1B2 and PAFAH1B3 activity (Table 1- 6). Impaired cancer pathogenicity across a broad range of cancer types, including breast and ovarian cancers354.

The synthesis of FAs takes place in the cytoplasm of cells, which allows for easier regulation; the enzymes required for synthesis and breakdown are separated, since FAO occurs in the mitochondria. De novo lipogenesis involves the repeated addition of two-carbon subunits, beginning with acetyl-CoA produced by ATP citrate lyase (ACL). Knocking down ACL has been shown to prevent the formation of FA precursors, resulting in impaired xenograft tumor growth355–357. A small molecule inhibitor, SB-204990, has been shown to provoke the same changes, but the integral role of ACL’s products may render ACL inhibition a poor route for selective cancer therapy 356,358.

Acetyl-CoA is first activated, with the investment of ATP, in a carboxylation reaction to produce the three-carbon malonyl-CoA. This first activation step is the committed step in FA synthesis and is facilitated by the enzyme acetyl-CoA carboxylase (ACC) and regulated by AMPK, as phosphorylation of ACC inactivates it359.

ACC is occasionally found to be overexpressed in several types of cancer, and is even maintained by several tumorigenic mutations359–361. However, its particular role in cancer cell metabolism is not fully understood. While some groups have shown that selective inhibition of ACC activity (via inhibitors such as soraphen A) induces apoptosis in breast and prostate cancer cells, others have shown that inactivation of ACC1 increased lung cell growth362–367. Further study into the differing roles of ACC isoforms will be required to understand these effects; it has been shown that ACC2 can regulate FAO, whereas ACC1 cannot368. For example, the breast cancer type 1 susceptibility protein (BRCA1), a tumor suppressor gene, can prevent the dephosphorylation of ACC, thus stimulating FA

34 synthesis369. Also, aldo-keto reductase family 1 B10 (AKR1B10), a protein often overexpressed in human hepatocellular carcinoma and NSCLC, can protect ACC from degradation, similarly stimulating FA synthesis370. ND-646, an allosteric inhibitor of ACC, was able to suppress FA synthesis in NSCLC in vitro and in vivo, resulting in markedly suppressed lung tumor growth in several mouse models of NSCLC371.

The enzyme that facilitates the synthesis of a FA chain is FA synthase (FASN), a large enzyme with multiple catalytic domains. Acetyl-CoA first binds the acyl carrier protein (ACP) domain, and is then shifted to another domain on the enzyme with a cysteine residue. Malonyl-CoA now binds the ACP, is subsequently condensed with the already-bound acetyl group via the decarboxylation of malonyl, and the ACP domain now holds a four-carbon unit. The four-carbon unit then undergoes two reductions via the oxidation of NADPH, and the resulting saturated unit is shifted to the domain with the cysteine residue. Another malonyl-CoA binds the ACP and the process repeats.

FASN is highly expressed in a variety of cancers, rendering it an attractive target for cancer therapies353,372. Inhibiting FASN has been shown to induce cytotoxicity in cancer cells as well as re-sensitize cells that have become resistant to mainline treatment, such as breast cancer cells to Herceptin366,373–375. Initial inhibitors of FASN, including C-75, orlistat, and GSK837149A, were promising indicators of the effectiveness of targeting FASN for cancer therapy376–378. However, it has taken unexpectedly longer for these inhibitors to make their way into the clinic due to issues with toxicity and off-target effects. For example, C- 75’s reported interaction with CPT1A in the hypothalamus induced hypophagia and body weight loss in mice379. The newest generation of FASN inhibitors, including TVB-2640, GSK2194069, JNJ-54302833, and IPI-9119, attempts to combat these challenges, as they have been built upon previous scaffolds after SAR research380–382. The inhibitor TVB-2640 is the first to reach clinical trials, with phase I studies in solid malignant tumors ongoing (NCT02223247)383.

Another issue plaguing the pursuit of FASN inhibitors is an uncertainty regarding which cancers will be most responsive, or perhaps even resistant, to FASN inhibitor treatment. It has been previously shown that different cancers exhibit varying sensitivities to targeting FASN, unrelated to FASN expression levels384. However, the driving force behind these differences has been shown to rest on a cell’s ability to maintain DAG levels and DAG-PKC signaling amidst FASN inhibition, suggesting that FASN inhibitor resistance can be overcome by combinatorial treatment with FASN and PKC inhibitors; this cellular ability to maintain these levels can be probed by simply investigating the relative fractional incorporation of glucose into complex lipids384. As development of FASN inhibitors advances, it will be critical to understand in which contexts they will be most effective. Results from clinical trials, such as the phase II study of TVB- 2640, will be influential in further developments (NCT03032484).

35 Once a sixteen-carbon long FA is synthesized, additional enzymes aid in further modification of the FA, such as the addition of functional groups and further elongation. These include the oxygen-consuming stearoyl-CoA desaturase (SCD), which catalyzes the rate-limiting step in the production of monounsaturated FA, mainly from stearoyl-CoA. SCD exists in two isoforms in humans, SCD-1 and SCD-5. SCD has been shown to be upregulated in some cancers including colon, esophageal, and liver cancer, and inhibition results in the death of cancer cells, preventing tumor growth without affecting overall body weight385–387. Inhibition of SCD-1 by the small molecule CVT-11127 was reported to impair proliferation in lung cancer cells by activating AMPK and interfering with ACC activity388,389. It has been shown that SCD-1 was successfully inhibited by taking advantage of overexpressed cytochrome P450 (CYP450), which is overexpressed in certain cancers, via developing small molecules that are metabolized by CYP450 into irreversible SCD-1 inhibitors in an effort to avoid toxicity to sebocytes; the reported scaffolds of oxalamides and benzothiazoles are yet to be built upon390. Other attempts to inhibit SCD, such as the small molecule MK-8245, have been undertaken in the context of diabetes and are untested in cancer models391.

Before synthesized FA is bioavailable, a CoA must be appended via acyl-CoA synthetases (ACS). Of the five ACS enzymes (ACSL1, ACSL3, ACSL4, ACSL5, and ACSL6), ACSL4 and ACSL5 have been reported to be upregulated in colon cancer among others, and overexpression of ACSL4 in particular prevents cell apoptosis392. Triacsin C is a reported inhibitor of ACSL1, 3, and 4, and induces cell death in various cancers 392,393. Various thiazolidinedione compounds have also been shown to bind ACSL4, but are not widely studied in the context of cancer treatment394. Further elucidation of each isoform’s role specificity is necessary before therapies can be developed for effective cancer treatment.

As mentioned previously, hypoxia suppresses the production of acetyl-CoA from glucose by stimulating the expression of PDK1, which phosphorylates and inactivates the PDH complex and, thus, impairs the cell’s ability to provide acetyl- CoA from glucose. Under these conditions of reductive carboxylation, glutamine becomes the major source of acetyl-CoA for FA synthesis, greatly decreasing the need to produce acetyl-CoA from glucose. It has been reported that hypoxic cells reduce dependence on de novo lipogenesis altogether, which also reduces the cell’s reliance on SCD1395. This phenomenon can be reproduced with RAS upregulation as well, rendering both hypoxic and RAS-driven cells resistant to SCD1 inhibition as a useful therapy395. As stated previously, silencing of PDK-1 partially reverts metabolism to a phenotype in which citrate and FAs are produced from glucose/PDH.

When F1,6-BP is converted to DHAP, it results in the production of ether lipids, which are present in heightened levels in liver cancer, though this correlation is not fully understood396,397. A critical enzyme in the ether lipid synthetic pathway, alkyl-glycerone phosphate synthase (AGPS), has been shown to be upregulated

36 in a variety of aggressive cancers, such as melanoma and breast cancer398. AGPS catalyzes the conversion of acyl-glycerone-3-phosphate into alkyl- glycerone-3-phosphate, a precursor in the production of ether lipids. It has been shown that inactivation of AGPS results in a reduction of several oncogenic signaling lipids, impairing cancer pathogenicity398. A selective AGPS inhibitor, 1a, caused a reduction in ether lipid levels and impacted cell migration and survival, showing that AGPS is an attractive target for the development of potential therapies 399.

6.2. Fatty Acid Oxidation

When FAs are directed for degradation, FAO cleaves two carbons at a time until acetyl-CoA remains, producing NADH and FADH2 every iteration of the cycle. Although cancer cells are often increasing their amount of lipids, and therefore have decreased FAO, there are times when cancer cells are required to increase FAO. Most often, FAO is increased when there is an augmented need for ATP production; cancer cells that have undergone loss of attachment (LOA) to the extracellular matrix often reactivate FAO to increase their ATP levels and prevent LOA-induced anoikis359,400. FAO has also been shown to be required for cell survival in certain cancer types, such as some lymphomas and leukemias. Although the reason for this is not completely clear, FAO may play a role in BAX- and BAK-dependent mitochondrial permeability transition pore formation or via the anti-apoptotic function of carnitine palmitoyl transferase 1 (CPT1) in these cancers359,401–403.

Directing FAs toward FAO relies on CPT1, which converts FAs to FA carnitines outside the mitochondria404. The role of CPT1 in cancer pathogenicity is complex. Overexpression of CPT1, for example, correlates with tumor progression in many types of cancers, including breast and prostate cancers, and CPT1 plays an integral role in cancer cell apoptosis368. It has been shown that bcl-2-mediated apoptosis decreases CPT1 levels, resulting in a buildup of palmitoyl-CoA and eventual cell death; CPT1 clears palmitoyl-CoA, but when inhibited, the remaining palmitoyl-CoA and other lipid species can be converted to toxic lipids, such as ceramide368. Inhibition of CPT1 also increases flux through aerobic glycolysis368. While increasing CPT1-mediated FAO could be expected to impair cancer cell pathogenicity, survival is instead increased in various lymphomas and leukemias. It has also been shown that blocking FAO reduced tumor growth in certain MYC-driven breast cancers as well405. In addition, chemical inhibition of CPT1 can kill various cancer cells403,406. Multiple CPT1 inhibitors and derivatives are currently in development, including ST1326, ranolazine, and etomoxir368,407,408. Etomoxir showed initial promise, but has encountered some issues in the clinic due to toxicity409,410.

In addition, the specific roles of the various CPT1 isoforms (CPT1A, CPT1B, and CPT1C) are not yet fully understood. CPT1A, which is ubiquitously expressed, is regulated via miR-370 in liver cancer, where miR-370’s downregulation of CPT1A

37 reduced FAO by 40%368. Inhibition of CPT1A also resulted in impaired cancer cell division in AML368. It has also been speculated that theCPT1C isoform is oncogenic; its expression in cancer cells promotes FAO and ATP production, tumor growth, rescue from metabolic stress, and resistance to therapy411. Overall, one can speculate that an increase in FAO provides more ATP to the cell, thus providing energy for continued cancer cell growth and proliferation.

Table 1-6. Summary of fatty acid metabolism-related drugs in clinic or pre- clinic.

7. CONCLUSION

Metabolic dysregulation, an emerging hallmark of cancer, is a clear focus of research today with the goal of developing treatments targeted not only to a specific cancer, but a specific patient. It has been shown that cancers exhibit multifarious alterations to metabolic pathways, as mutational heterogeneity is found even between cancers classified as the same type. From well-understood oncogenes, such as KRAS and MYC, to insufficiently-understood proteins, such as ACSL3 and TKTL1, cancer’s impact on the metabolic landscape is wide- ranging and still poorly understood. Working to understand the variety of metabolic variations in the context of cancer will pave the way for a more comprehensive understanding of the different iterations of the disease and enable the development of more specific therapies with lower toxicities and side effects.

38 A fuller understanding of each metabolic mutation’s implication will allow for more nuanced treatments targeting specific alterations. Some of these steps are already being taken, particularly in the realm of combinatorial therapy. Current efforts focus on designing a therapeutic cocktail targeting mutations specific to the cancer at hand. Such efforts are spurred by the discovery of increased anti- cancer activity upon inhibition of multiple proteins (or isoforms of a protein), such as the anti-cancer effect seen by tandem MCT1 and MCT4 inhibition in colon cancer or the many examples of combinatorial therapies, including ZD1839 and anastrozole, FTI-277 and GGTI-298, CB-839 and everolimus to name a few412– 414. In addition, combinatorial therapies hold promise for those cancers notorious for developing resistance to frontline therapies. By targeting multiple mutations in the cancer, it is hoped that a lower dosage can be used and full drug resistance avoided, and this is a critical goal to meet in the development of cancer therapy414,415.

As our understanding of cancer’s metabolic landscape expands, however, an increasing number of targets arise, and with the aim to effectively drug each of them, it is necessary to reach for all possible avenues in drug discovery. Perhaps one of the most fruitful - yet rather underutilized - approaches is to investigate the natural compounds produced by living organisms. In previous decades, researchers lacked efficient assays for such approaches, but the -omics era has provided viable options for screening natural products and an impetus to do so416. Several natural products mentioned in this review, such as soraphen A and koningic acid, have been integral to our understanding of particular metabolic mutations and are in the process of influencing the development of drugs for those proteins. Rapamycin is also a particularly persuasive example of the power of natural products, as it and several rapalogue derivatives have been approved for use in the clinic. In order to continue bringing metabolism-targeting drugs to market, investigation into natural products and their derivatives must continue to be pursued.

This review serves to underscore the importance of researching cancer’s metabolic alterations; the mutations already identified are plentiful, and the number of metabolic drugs currently in clinical trials emphasizes the potential effectiveness of this strategy. However, we recognize that the current state of knowledge is vastly incomplete – though an enzyme may be implicated in cancer pathogenicity, such as CPT1, simply inhibiting its activity can produce a number of unanticipated physiological effects resulting in toxicity236,237. As the field continues to develop, our understanding of cancer’s metabolic implications is expanding beyond the simplistic, singular cause-and-effect relationships as exemplified by the development of earlier, unsuccessful inhibitors. Though that simplistic relationship is where we must begin our exploration, a more in-depth understanding will be necessary to effectively drug one of society’s most rampant diseases. Innovative metabolic treatments will usher in the availability of highly

39 specific and decreasingly toxic therapies, optimizing clinical outcomes and redefining cancer druggability.

40 8. FIGURES

Figure 1-1. The glycolysis pathway in cancer and its associated dysregulations and inhibitor therapies.

41 Figure 1-2. The Tricarboxylic Acid Cycle and glutamine pathway in cancer: How they are connected, dysregulated, and their relevant inhibitor therapies.

42 CHAPTER TWO: Mapping Proteome-Wide Interactions of Reactive Chemicals Using Chemoproteomic Platforms

43 1. INTRODUCTION

Many of the cancer therapeutics discussed in Chapter One include natural products and various small molecules that act through covalent mechanisms. In fact, a large number of pharmaceuticals, as well as endogenous metabolites and environmental chemicals, act through covalent interactions with proteins. Cancer, as well as other diseases such as Alzheimer’s disease and obesity, are often subject to drugs that irreversibly bind and inhibit their respective protein targets417–421. Endogenous reactive metabolites and environmental chemical exposure, in turn, can also work through covalent interactions with proteins within the body to cause disease. Therefore, understanding what mechanisms can cause disease, and what therapeutic mechanisms can treat disease, are of great importance.

Although a large number of chemicals act through covalent mechanisms with protein targets, many of their specific interactions with the proteome still remain poorly defined (Figure 2-1). Deciphering direct protein targets of reactive small- molecules is critical in understanding their biological action, off-target effects, potential toxicological liabilities, and development of safer and more selective agents. Chemoproteomic technologies have arisen as a powerful strategy that enable the assessment of proteome-wide interactions of these irreversible agents directly in complex biological systems. In this chapter, I discuss several chemoproteomic strategies that have facilitated our understanding of specific protein interactions of irreversibly-acting pharmaceuticals, endogenous metabolites, and environmental electrophiles to reveal novel pharmacological, biological, and toxicological mechanisms (Figures 2-2 and 2-3).

2. CHEMOPROTEOMIC PROFILING TO ASSESS SELECTIVITY OF THERAPEUTIC IRREVERSIBLE SMALL-MOLECULE INHIBITORS

Pharmaceutical companies have historically shied away from pursuing covalent inhibitors due to risks of haptenization and immunologic reactions that may occur through non-specific covalent modification of small-molecules with protein targets422. Nonetheless, many irreversible or pseudo-irreversible inhibitors have been successfully developed as well-tolerated drugs in the clinic. Examples include the anti-inflammatory drug aspirin, the broad class of antibacterial beta- lactam antibiotics such as penicillin, drugs that require metabolic bioactivation including the proton pump inhibitor omeprazole, the Alzheimer’s drug rivastigmine that inhibits , the cancer therapy bortezomib (Velcade) that targets the proteosome, and the anti-obesity drug tetrahydrolipstatin (Orlistat) that inhibits gastric lipase417–419,421. In recent years, there has been resurgence in developing covalent and irreversible inhibitors, including several acrylamide-based inhibitors that act through Michael addition with a cysteine in the ATP binding pocket of oncogenic kinases for cancer therapy. Some examples include PCI-32765 (ibrutinib), a Bruton’s tyrosine kinase (BTK) inhibitor now FDA approved for mantle cell lymphoma and chronic

44 lymophoblastic leukemia; BIBW-2992 (afatinib) and HKI-272 (neratinib) that dually inhibit HER2 and EGFR, both of which are approved or in development for NSCLC and breast cancer, respectively; and CO1686 (Rociletinib) that specifically inhibits the mutant T790M form of EGFR and is also currently in development for NSCLC420.

Although it may be counterintuitive to develop selective inhibitors through reactive and covalent mechanisms, irreversible inhibitors as therapeutics in the modern era of drug discovery and chemical biology affords many benefits. First, covalent inhibitors can provide extended target engagement without the need to maintain high levels of drug. Second, the electrophilicity of the inhibitor can be fine-tuned with the affinity of the small-molecule for a particular binding pocket of a specific protein target, such that the reaction occurs selectively with minimal off-target liabilities. Third, various modern chemoproteomic approaches can be utilized to confirm target engagement and proteome-wide selectivity of covalent inhibitors in situ and in vivo, which can, in-turn, inform further medicinal chemistry efforts to optimize inhibitor properties or to confirm the safety and specificity of lead molecules. We will discuss several examples showcasing the utility of chemoproteomic platforms to define the selectivity of irreversible small-molecule inhibitors and drugs.

One chemoproteomic platform that has been successfully used to develop selective inhibitors against many protein targets is activity-based protein profiling (ABPP). ABPP uses activity-based chemical probes that directly bind to the active sites of large numbers of enzymes, thus providing a functional readout of enzyme activities en masse directly in complex proteomes423,424. Because these activity-based probes bind to the active-sites of enzymes, small-molecule inhibitors can be competed against probe-binding, therefore enabling the development of small-molecules for both characterized and uncharacterized enzymes. Since the activity-based probes evaluate enzyme activities across an entire enzyme class, the proteome-wide selectivity of the small-molecule inhibitor can be assessed within that particular enzyme class. While the ABPP platform has been used to develop selective reversible and irreversible inhibitors of enzymes425–432, this approach has been particularly useful for testing the efficacy and selectivity of irreversible inhibitors. Target engagement and proteome-wide selectivity can be confirmed for irreversible inhibitors by comparing ex vivo labeling of vehicle and inhibitor-treated proteomes425,428. The ABPP platform has also been adapted to be compatible with modern quantitative proteomic approaches through stable isotope labeling of cells (SILAC), in which vehicle- treated “light” cells and inhibitor-treated “heavy” cells are combined after labeling with activity-based probes and, subsequently, analyzed for their selectivity by SILAC ratios431.

Several highly potent, selective, and in vivo active irreversible small-molecule inhibitors that show potential therapeutic benefit have been developed using the ABPP platform. These include monoacylglycerol lipase (MAGL) and

45 (DAGL) inhibitors that hydrolyze or generate, respectively, the endocannabinoid signaling lipid 2-arachidonoylglycerol (2-AG), and also control arachidonic acid release for pro-inflammatory prostaglandin synthesis433,434. The development of selective and in vivo efficacious irreversible MAGL inhibitors, such as JZL184, KML29, and MJN110, have led to the discovery that MAGL blockade leads to heightened 2-AG levels, cannabinoid receptor stimulation, and lower arachidonic acid and pro-inflammatory prostaglandin levels in the brain, thus providing antinociceptive, anti- inflammatory, anxiolytic, and neuroprotective effects428,435,436. Hsu et al. developed DAGL inhibitors, such as KT172 and KT109, which have been used to show that DAGL blockade leads to depletion of 2-AG, arachidonic acid, and pro- inflammatory prostaglandin levels to suppress inflammatory cytokine release from macrophages433.

Hoover et al. used ABPP platforms to show that the obesity drug tetrahydrolipstatin inhibits multiple metabolic enzyme targets in brain, including ABHD12, TPP2, BAT5, and PLA2G7419. Inloes et al. recently discovered that the previously uncharacterized enzyme DDHD2, which is linked to the genetic disorder hereditary spastic paraplegia, was the primary triacylglycerol hydrolase in brain. Using ABPP platforms, the authors developed a selective, in vivo efficacious and irreversible DDHD2 inhibitor, KLH45437. Using this inhibitor alongside genetic DDHD2 knockout mouse models, the authors showed that DDHD2 blockade led to striking accumulations in triacylglycerol levels in the brains of these mice, potentially explaining the metabolic mechanisms underlying the associated neurological disorder. Using ABPP platforms, Kamat et al. developed a highly selective inhibitor, KC01, for a previously uncharacterized enzyme, ABHD16A. They then used this inhibitor to characterize ABHD16A as a phosphatidylserine hydrolase that generates lysophosphatidylserine (LPS) that, in-turn, fuels a neuroinflammatory response438. Previous studies showed that another formerly uncharacterized hydrolase ABHD12 is mutationally inactivated in a neurodegenerative disease known as Polyneuropathy, Hearing loss, Ataxia, Retinitis Pigmentosa, and Cataracts (PHARC), leading to accumulation of brain LPS and neuroinflammation439. Kamat et al. showed that KC01 lowers the high levels of LPS found in ABHD12-deficient macrophages, leading to suppression of inflammatory cytokine release, indicating that ABHD16A inhibitors may act as anti-inflammatory agents through modulating LPS signaling438. Thus, ABPP has been used successfully to develop irreversible small-molecule inhibitors against both characterized and uncharacterized enzymes to further our understanding of the biological and potential therapeutic functions of these enzymes.

Many studies have also developed “clickable” analogs of lead small-molecule therapies or inhibitors of therapeutic targets bearing either alkyne or azide handles for chemoproteomic profiling to confirm the small-molecule’s selectivity or identify any off-targets. Lanning et al. used alkyne-bearing analogs of cysteine-reactive irreversible kinase inhibitors, ibrutinib and PF-6274484, that target BTK and EGFR, respectively, to assess their selectivity in cancer cells

46 using click-chemistry-based chemoproteomic approaches440. Cheng et al. developed an alkyne-bearing analog of the widely used inhibitor, C75, of the cancer therapy target FASN, and showed that it possessed many off-targets including CPT1A, GAPDH, and 13 other enzymes beyond FASN, which may explain the high level of toxicity associated with C75441. Bateman et al. developed an aspirin-alkyne probe and coupled the labeling of this probe with chemoproteomic profiling to identify 120 protein targets of aspirin, 112 of which had not been previously reported to be acetylated by aspirin in cellular or in vivo contexts442. The authors showed that aspirin-alkyne modified core histone proteins, thus implicating aspirin as a potential chemical-regulator of transcription442. The proteome-wide selectivity of many of the inhibitors that have been developed using ABPP platforms have also been confirmed for their selectivity outside the serine hydrolase family through the development of alkyne-bearing analogs. Examples include the FAAH inhibitor PF-3845 and PF-04457845, the PME inhibitor ABL127, and the MAGL inhibitor MJN110435,443–445.

Chemoproteomics has also been used to identify the targets of various natural products through the synthesis of reporter-bearing analogs. Using a Wortmannin analog bearing a tetramethylrhodamine, the conjugate AX7503 was shown to not only bind PI3K and PI3K-related kinases, but also PLK1446. Stephan Sieber’s group synthesized a series of alkyne-bearing b-lactam antibiotic analogs of penicillin, aztreonam, and cephalosporin to label diverse penicillin binding proteins447. The authors also synthesized a series of additional b-lactam probes, which labeled and inhibited a selection of penicillin-binding proteins as well as unrelated bacterial targets, including the virulence-associated enzyme ClpP and resistance-associated b-lactamase447. Yang et al. synthesized an alkyne-bearing analog of tetrahydrolipstatin (Orlistat), an FDA-approved anti-obesity drug with potential antitumor activities, and identified 8 novel targets of orlistat beyond FASN, including Hsp90AB1, GAPDH, Annexin A2, RPL7a, and RPS9448. In another example, Abegg et al. used an ethynyl benziodoxolones cysteine- reactive probe, JW-RF-010, to identify biological targets of the potential anti- cancer therapy curcumin. The authors identified 42 additional targets of curcumin, only one of which was previously known449.

3. CHEMOPROTEOMIC PROFILING OF REACTIVE ENVIRONMENTAL CHEMICALS AND ENDOGENOUS REACTIVE METABOLITES TO UNDERSTAND TOXICOLOGICAL MECHANISMS

We are exposed to countless chemicals, many of which have been linked to adverse health effects, and most of which have not been characterized in terms of their toxicological potential or mechanisms. Of particular concern among chemicals in our environment are reactive electrophiles that we are directly exposed to or those that form through bioactivation, which have the potential to covalently and cumulatively react with nucleophilic amino acid hotspots within the proteome, leading to potential protein dysfunction and pathophysiological effects. Understanding the direct chemical-protein

47 interactions of these reactive agents informs our understanding of downstream molecular, metabolic, and pathophysiological effects that may arise from chemical exposure, and provides a more direct approach towards identifying toxicological drivers of human disease. We will discuss several chemoproteomic approaches that have been successfully applied to understand unique and novel toxicological mechanisms for both environmental chemicals and endogenous reactive metabolites.

ABPP platforms have been used to identify off-targets of widely used organophosphorus (OP) and carbamate pesticides in vivo. These pesticides act as insecticides through inhibiting acetylcholinesterase, but there have been toxicological effects associated with exposure to these agents that cannot be explained by acetylcholinesterase inhibition alone, indicating possible off-targets. Using the serine hydrolase-directed activity-based probe, Nomura et al. and Medina-Cleghorn et al. identified many in vivo off-targets of these pesticides that are inhibited in mice, leading to downstream biochemical effects. For example, several studies have shown that OP pesticides inhibit MAGL and fatty acid amide hydrolase (FAAH) in mouse brain causing elevations in endocannabinoid signaling lipids, 2-AG, and anandamide, all of which lead to downstream cannabinoid-like behavioral effects450–452.

Wang et al. used an elegant quantitative chemoproteomic strategy, termed isotopic tandem orthogonal proteolysis activity-based protein profiling (isoTOP- ABPP), for mapping cysteine reactivity to investigate direct targets and site-of- modifications of lipid aldehydes generated during lipid peroxidation through competition of lipid aldehydes against the cysteine-reactive iodoacetamide- alkyne (IAyne) reactivity-based probe453,454. Probe-labeled control and treated proteomes were appended to a biotin-azide analytical handle bearing a light or heavy valine and TEV protease cleavage site using click-chemistry, control and probe labeled proteomes were combined, and probe-labeled tryptic peptides were subsequently enriched and analyzed by quantitative proteomic platforms. Surprisingly, the authors showed that 4-hydroxy-2-nonenal (HNE) interacts with a select set of proteins that constitute hotspots for modifications by various lipid- derived electrophiles, rather than non-specifically reacting with cysteines. For example, they showed that HNE specifically reacts with an active-site proximal cysteine on sterile alpha motif and leucine zipper containing kinase (ZAK), leading to enzyme inhibition creating a negative feedback mechanism that can suppress the activation of c-Jun N-terminal kinase (JNK) pathways induced by oxidative stress453.

The toxic mechanisms of alkylation by lipid aldehydes were also explored with alkyne-bearing analogs of HNE and 4-oxo-2-nonenal (ONE) coupled with chemoproteomic approaches. The authors showed that HNE and ONE show particular susceptibility towards alkylating protein targets mapping to networks involved in cytoskeletal regulation with low susceptibility towards proteins involved in protein synthesis and turnover. The authors then postulated that the

48 differential sensitivity of protein targets to lipid aldehyde alkylation may protect cells from cytotoxicity as a result of moderate levels of lipid aldehydes455.

Morris et al. used chemoproteomic profiling approaches to comprehensively identify the biological targets of the widely-used flame retardant chemical triphenylphosphate (TPP) by using an alkyne-bearing TPP analog. The authors showed that specific liver carboxylesterases (CES), in particular CES1G, were inhibited by TPP leading to heightened DAG levels and protein kinase C stimulation in liver and serum hypertriglyceridemia456.

Medina-Cleghorn et al. recently used ABPP approaches to map direct biological targets of several reactive environmental chemicals, including the fungicide chlorothalonil (CTN), the environmental contaminant monomethylarsenous acid (MMA), and a broad-spectrum insecticide chloropicrin457. The authors performed in vitro competition of these agents against the cysteine-reactive IAyne reactivity- based probe directly in mouse liver proteomes and found that CTN, MMA, and chloropicrin commonly inhibit several metabolic enzymes involved in fatty acid metabolism and energetic enzymes. The authors further delved into the mechanisms underlying previously reported -specific toxicity associated with CTN through in vivo profiling of CTN targets, and subsequent ex vivo labeling with an alkyne-bearing CTN analog for chemoproteomic discovery of in vivo CTN targets in kidney. The authors showed that CTN inhibits fatty acid transport proteins, fatty acid oxidation enzymes, and glycolytic enzymes in vivo, leading to alterations in kidney lipid metabolism, thus revealing a novel mechanism of toxicity underlying this major fungicide458.

In another study, which will be discussed in detail in Chapter Four, Counihan et al. used similar ABPP platforms to identify the biological targets of the common acetanilide herbicide, acetochlor. The authors showed that acetochlor directly reacts with >20 protein targets in vivo in mouse liver, including the catalytic cysteines of several thiolase enzymes involved in mitochondrial and peroxisomal fatty acid oxidation. They further showed that the fatty acids that are not oxidized, due to impaired FAO, are instead diverted into other lipid pathways, resulting in heightened free FAs, triglycerides, cholesteryl , and other lipid species in the liver. These findings further show the utility of chemoproteomic approaches for identifying novel mechanisms of toxicity associated with environmental chemicals459.

4. CONCLUSION

Discussed in this chapter were several examples of chemoproteomic platforms and their applications to assess the selectivity or off-target profiles of tool compounds, therapeutics, and environmental chemicals that act through irreversible mechanisms. Historically, small-molecule agents that act through covalent mechanisms have been feared to cause non-specific adducts on proteins, which, in-turn, may lead to non-specific toxicities and potential

49 haptenization or other types of idiosyncratic toxicities. Certainly, there have been historical examples of highly reactive agents or reactive metabolites that have caused these types of toxicities422. However, modern chemoproteomic technologies have provided a more precise and deeper understanding of how reactive chemicals interact with the proteome.

There are indeed reactive chemicals that show large numbers of off-targets. However, chemoproteomic studies have shown that even highly reactive chemicals, such as lipid aldehydes, show relatively selective interactions with specific subsets of targets over others and that these interactions occur oftentimes at hyper-reactive and functional sites on protein targets, rather than non-specific alkylation events. Chemoproteomic profiling of covalently-acting and clinically approved drugs, such as ibrutinib, have revealed potentially large numbers of off-targets at high concentrations. However, studies have shown that these off-target liabilities can be greatly minimized upon even slight chemical modifications of a drug. There have also been a substantial number of highly selective irreversibly-acting small-molecule inhibitors that have been developed in conjunction with technologies such as activity-based proteomics or click- chemistry based chemoproteomic approaches.

Thus, while many pharmaceutical and environmental electrophilic chemicals show potential off-target liabilities, a high degree of selectivity and specificity can still be achieved with irreversible compounds through medicinal chemistry efforts, especially when optimization efforts are coupled with chemoproteomic profiling. Irreversible inhibitors coupled with chemoproteomic platforms also affords substantial advantages for confirming target engagement and in vivo selectivity profiling, which is oftentimes difficult with reversible inhibitors. Thus, the aim of this chapter was not to disparage the development of irreversible inhibitors, but instead to promote the development of irreversible inhibitors coupled with the application of chemoproteomic platforms to facilitate the development of highly selective and covalent therapeutics or even agrochemicals.

50 5. FIGURES

Figure 2-1. Examples of irreversibly-acting drugs, tool compounds, environmental chemicals, and endogenous electrophiles.

51 Figure 2-2. Chemoproteomic platforms for assessing proteome-wide targets of irreversibly-acting chemicals. (A) SILAC-ABPP uses active-site directed chemical probes to assess the functional state of large numbers of enzymes directly in complex proteomes. Small-molecule inhibitors can be competed against the binding of activity-based probes to enzymes to assess enzyme class-wide selectivity. Cells can be labeled with light or heavy isotopic amino acids for quantitative proteomic analysis. (B) Analogs of these inhibitors bearing a bioorthogonal handle (e.g. alkyne) can be used to assess proteome- wide selectivity of small-molecule inhibitors using chemoproteomic approaches. (C) Isotopic Tandem Orthogonal Proteolysis-ABPP (isoTOP-ABPP) can be used to map hyper-reactive and functional sites across the proteome using reactivity- based chemical probes bearing bioorthogonal handles (e.g. alkyne). Reactive electrophiles can be competed against probe binding to hyper-reactive sites to map protein targets of these reactive agents. Probe-labeled peptides can be identified through subsequent appending of a biotin-azide analytical handle

52 bearing a TEV protease recognition sequence and heavy or light isotopic valine tag using copper-catalyzed click chemistry. Upon mixing control and treated proteomes, probe-labeled proteins can be avidin-enriched, tryptically digested, and probe-labeled peptides can be subsequently enriched and released by TEV protease for subsequent quantitative proteomic analysis.

53 Figure 2-3. Biological insights gained from using chemoproteomic platforms. (A) ABPP has been successfully used to develop selective small- molecule inhibitors against enzymes involved the synthesis and degradation of the endocannabinoid 2-AG. Selective DAGL inhibitors KT109 and KT172 and selective MAGL inhibitors JZL184, KML29, and MJN110 have been used to not only identify that DAGL and MAGL regulate endocannabioid metabolism and signaling, but also to show that this pathway controls arachidonic metabolism that fuels pro-inflammatory prostaglandin synthesis. (B) ABPP was used to develop the selective DDHD2 inhibitor KLH45, which was used to show that the previously uncharacterized enzyme DDHD2 was the primary TAG hydrolase in the brain, and that inhibiting this enzyme led to accumulation in brain triacylglycerol levels and accumulation of lipid droplets. (C) ABPP was used to develop the selective ABHD16A inhibitor KC01 to determine that ABHD16A was the primary PS hydrolase that generates the pro-inflammatory signaling lipid lyso-PS, which is in-turn hydrolyzed by ABHD12. Previous studies had shown that ABHD12 inactivation caused a neurodegenerative disease known as PHARC. ABHD16A inhibition protected mice from the neurotoxicological markers associated with PHARC. (D) Lipid aldehydes such as HNE were competed

54 against the cysteine-reactive IAyne probe and coupled to the isoTOP-ABPP platform to map the direct protein targets of HNE. HNE showed selective interactions with certain sites such as the active-site proximal cysteine of ZAK, leading to ZAK inhibition and JAK inactivation. (E) Reactive environmental chemicals, such as the fungicide CTN were competed against IAyne to map direct protein targets of these chemicals, leading to the discovery that CTN binds to and inhibits multiple enzymes involved in FAO.

55 CHAPTER THREE: Chemoproteomics-Enabled Covalent Ligand Screening Reveals ALDH3A1 as a Lung Cancer Therapy Target

56 1. INTRODUCTION

Chemical genetics is a powerful approach for identifying therapeutically active small-molecules, but identifying the mechanisms of action underlying hit compounds remains challenging460. Chemoproteomic platforms, many of which were described in Chapter Two, have arisen to tackle this challenge and enable rapid mechanistic deconvolution of small-molecule screening hits461,462. As previously discussed, these approaches are particularly useful with covalently- acting small-molecules where covalent ligands can be competed against broad reactivity-based probes to facilitate rapid target identification using ABPP chemoproteomic platforms, without the need for incorporating photoaffinity and enrichment handles into the lead compound structure463–466. In recent years, ABPP platforms using reactivity-based chemical probes have led to the expansion in our understanding of proteome-wide druggable hotspots, as well as the ligandability of these sites with covalent ligands, for potential drug discovery applications463–466. These platforms have also been successfully used in combination with phenotypic or biochemical screening efforts to rapidly discover new therapeutic agents, targets, and druggable hotspots6,8–11.

2. RESULTS

Here, we have coupled the phenotypic screening of a cysteine-reactive covalent ligand library with ABPP-based chemoproteomic approaches to rapidly identify hit compounds and targets that impair lung cancer pathogenicity (Figure 3-1A). To discover novel anti-cancer compounds and targets for lung adenocarcinomas, we screened our fragment-based cysteine-reactive covalent ligand library in A549 lung cancer cells to identify compounds that impaired A549 serum- containing proliferation or serum-free survival (Figure 3-1B). We then counter- screened any hits that impaired A549 survival and proliferation by >80 % in BEAS2B primary human bronchial epithelial cells to identify compounds that showed <25 % impairments in proliferation and survival of this line to eliminate generally cytotoxic compounds for further follow-up. Using this filtering criteria, we identified DKM 3-42 as our hit compound (Figures 3-1C and 3-1D). Treatment of DKM 3-42 (50 mg/kg ip) once per day in established A549 tumor xenografts in mice led to significantly attenuated tumor growth in vivo with no apparent outward toxicity or body weight loss (Figures 3-1E and 3-1F).

We next performed competitive isotopic tandem orthogonal proteolysis-enabled ABPP (isoTOP-ABPP)454,463,465 to determine the druggable hotspots targeted by DKM 3-42. We competed DKM 3-42 binding against the broad cysteine-reactive iodoacetamide-alkyne (IA-alkyne) probe directly in A549 lung cancer cell proteomes, after which an isotopically light or heavy cleavable enrichment handle was appended onto probe-modified control or DKM 3-42-treated proteomes, respectively, using copper-catalyzed azide-alkyne cycloaddition (CuAAC). Probe- modified proteins were enriched, tryptically digested, and probe-modified tryptic peptides were then re-enriched and enzymatically eluted for proteomic analysis.

57 Only those probe-modified peptides evident in two out of three biological replicates were interpreted. Of those, the probe-modified peptides that showed an isotopically light versus heavy ratio of >5 were deemed targets of DKM 3-42. The primary target of DKM 3-42 was identified as the catalytic cysteine of aldehyde dehydrogenase 3A1 (ALDH3A1), cysteine 244 (C244) (Figure 3-2A). We observed many other ALDH enzymes in the isoTOP-ABPP data but none of these enzymes showed >5 ratios, indicating that DKM 3-42 had preferred reactivity with ALDH3A1 compared to other ALDH enzymes. We confirmed this interaction of DKM 3-42 with ALDH3A1 using gel-based ABPP approaches, where we show competition of DKM 3-42 against IA-rhodamine labeling of pure human ALDH3A1 protein (Figure 3-2A). Consistent with targeting the catalytic site of the enzyme, DKM 3-42 inhibits ALDH3A1 activity with pure protein (Figure 3-2B). We also show that DKM 3-42 inhibits total aldehyde dehydrogenase activity in A549 cell lysate, indicating that ALDH3A1 is the primary aldehyde dehydrogenase enzyme (Figure 3-2B). We further genetically validated the importance of ALDH3A1 showing that ALDH3A1 knockdown recapitulates the effects observed with DKM 3-42, including inhibition of total A549 cell lysate aldehyde dehydrogenase activity, reduced cell survival and proliferation, and impaired in vivo tumor xenograft growth in A549 cells (Figures 3-2C through 3- 2F).

While DKM 3-42 showed promising data both in cells and in vivo, the chemical scaffold was not ideal for optimization and medicinal chemistry efforts. To identify alternate chemical scaffolds that may serve as ALDH3A1 inhibitors, we performed a covalent ligand screen with a complementary cysteine-reactive library directly against pure human ALDH3A1 protein using gel-based ABPP methods (Figure 3-3A). In this screen, we competed cysteine-reactive covalent ligands against iodoacetamide (IA)-rhodamine labeling of ALDH3A1, and then subjected to SDS/PAGE and analysis of in-gel fluorescence. From this screen, we identified EN40 as the top hit (Figure 3-3B). Using gel-based ABPP, we further confirmed that EN40 competed against IA-rhodamine labeling of ALDH3A1 with comparable potency to that of DKM 3-42 (Figure 3-3C). We further showed that EN40 also inhibits ALDH3A1 activity and impairs A549 cell survival (Figures 3-3D and 3-3E). We performed isoTOP-ABPP analysis of EN40 on A549 proteomes and showed that C244 of ALDH3A1 was targeted rather selectively, with C13 of RPLP0 as an off-target (Figure 3-3F). Daily treatment with EN40 also exerted strong anti-tumorigenic effects in established A549 tumor xenografts (Figure 3-3G) and showed good tolerability with no body weight loss in mice (Figure 3-3H). We next performed ex vivo isoTOP-ABPP analysis on the tumors from these mice and showed that C244 on ALDH3A1 was the primary site targeted by EN40 in vivo in the tumors with an isotopically light to heavy ratio of 5, confirming relatively selective target engagement (Figure 3-3I).

We next wanted to understand whether ALDH3A1 inhibition would more broadly affect lung cancer cell survival across multiple lung cancer cell lines and what parameters would dictate sensitivity to ALDH3A1 inhibitors. We profiled

58 ALDH3A1 expression across BEAS2B primary human bronchial epithelial cells, A549 lung adenocarcinoma cells, and 5 other lung cancer cell lines and found that ALDH3A1 expression was high in A549, NCI-H460, and NCI-H332 cells, but was not expressed in BEAS2B, Calu6, NCI-H23, and NCI-H661 cells (Figure 3- 4A). Consistent with this expression profile, we observed serum-free survival impairments with EN40 treatment in the lung cancer cell lines expressing ALDH3A1, but not in the cell lines that do not express ALDH3A1 (Figure 3-4B). These results indicate that ALDH3A1 expression may serve as an indicator of responsiveness to ALDH3A1 inhibitors in impairing lung cancer pathogenicity. These data showing that only ALDH3A1-positive lung cancer cell lines respond to EN40 further support the specificity of EN40 as an ALDH3A1 inhibitor in cancer cells. We also tested an EN40 analog EN A, which did not bind to ALDH3A1 by gel-based ABPP analysis against pure ALDH3A1 protein nor did it show cell survival impairments in A549 cells (Figure 3-5). To further confirm on- target activity of DKM 3-42 and EN40, we stably overexpressed ALDH3A1 in A549 lung cancer cells and showed that the anti-survival effects conferred by DKM 3-42 and EN40 were significantly attenuated by ALDH3A1 overexpression (Figures 3-4C through 3-4E).

3. CONCLUSION

Overall, ALDH3A1 may represent a novel lung cancer therapeutic target and that ALDH3A1 inhibitors impair lung cancer pathogenicity in cells that express high levels of ALDH3A1. We put forth two scaffolds for selective and in vivo active ALDH3A1 inhibitors, DKM 3-42 and EN40, which were identified by coupling phenotypic screening of cysteine-reactive covalent ligands with chemoproteomics and through direct covalent ligand screening against ALDH3A1, respectively. ALDH3A1 has been previously reported to play important roles in cancer chemoresistance471–474. This enzyme has also been shown to be overexpressed in subsets of various types of cancers, including lung cancers, hepatocellular carcinomas, gastric cancers, and prostate cancers475–478. Previous studies have reported selective ALDH3A1 inhibitors, which confer sensitivity to chemotherapy agents such as oxazaphosphorine drugs471,479, but have not shown their direct efficacy as single therapy agents. While our study shows that ALDH3A1 inhibition is a promising therapeutic strategy for impairing lung cancer pathogenicity for those cancer cells that express high levels of ALDH3A1, we do not yet understand the mechanism underlying our observed effects. While ALDH3A1, like other aldehyde dehydrogenase enzymes, acts on aldehyde substrates, the physiological substrates of ALDH3A1 in cancer cells is poorly understood. Our data, with both ALDH3A1 inhibitors and ALDH3A1 knockdown, show that, at least in A549 cells, ALDH3A1 represents the predominant aldehyde dehydrogenase activity with the artificial benzaldehyde substrate. Future metabolomics endeavors aimed at uncovering the various aldehyde substrates of ALDH3A1 will likely reveal important mechanistic insights into how ALDH3A1 drives cancer pathogenicity in certain subsets of cancers.

59 Taken more broadly, Chapter Three shows how the phenotypic screening of covalent ligand libraries can be coupled with ABPP-based chemoproteomic platforms to rapidly identify anti-cancer lead compounds, targets, and druggable hotspots. Chapter Three also demonstrate how higher-throughput ABPP approaches with covalent ligands against specific protein targets can rapidly yield new inhibitor scaffolds for future therapeutic development.

4. METHODS

Chemicals. Synthesis and characterization of covalent ligand libraries were either described previously465,466,469, or purchased from Enamine LLC (compounds starting with “EN”), or described below. IA-alkyne (N-hex-5-ynyl-2- iodo-acetamide) was purchased from Chess GmbH (product number 3187) and IA-rhodamine (tetramethylrhodamine-5-iodoacetamide dihydrochloride) was purchased from Thermo Fisher Scientific (catalog number T6006).

General synthetic methods Chemicals and reagents were purchased from major commercial suppliers and used without further purification. Reactions were performed under a nitrogen atmosphere unless otherwise noted. Silica gel flash column chromatography was performed using EMD or Sigma Aldrich silica gel 60 (230-400 mesh). Proton and carbon nuclear magnetic resonance (1H NMR and 13C NMR) data was acquired on a Bruker AVB 400, AVQ 400, or AV 600 spectrometer at the University of California, Berkeley. High resolution mass spectrum were obtained from the QB3 mass spectrometry facility at the University of California, Berkeley using positive or negative electrospray ionization (+ESI or -ESI). Yields are reported as a single run.

General Procedure A The amine (1 eq.) was dissolved in DCM (5 mL/mmol) and cooled to 0oC. To the solution was added acryloyl chloride (1.2 eq.) followed by triethylamine (1.2 eq.). The solution was warmed to room temperature and stirred overnight. The solution was then washed with brine and the crude product was purified by silica gel chromatography (and recrystallization if necessary) to afford the corresponding acrylamide.

General Procedure B The amine (1 eq.) was dissolved in DCM (5 mL/mmol) and cooled to 0oC. To the solution was added chloroacetyl chloride (1.2 eq.) followed by triethylamine (1.2 eq.). The solution was warmed to room temperature and stirred overnight. The solution was then washed with brine and the crude product was purified by silica gel chromatography (and recrystallization if necessary) to afford the corresponding chloroacetamide.

60 N-(naphthalene-1-yl)acrylamide (TRH 1-57). To a solution of 1-naphthylamine (294 mg, 2.0 mmol) in dichloromethane (10 mL) was added acryloyl chloride (0.20 mL, 2.4 mmol) followed by triethylamine (248 mg, 2.4 mmol) at 0˚ C under N2 atmosphere. The reaction mixture was allowed to warm to room temperature and was stirred for 16 hours. The solution was washed twice with brine, and the resulting crude was purified by silica gel chromatography (30% to 40% ethyl acetate in hexanes) and recrystallized from toluene to yield 173 mg of white solid (44% yield). 1H NMR (400MHz, MeOD): δ 7.96-7.94 (m, 1H), 7.88-7.86 (m, 1H), 7.76 (d, J = 8.1 Hz, 1H), 7.64 (d, J = 7.3 Hz, 1H), 7.52-7.44 (m, 3H), 6.43 (dd, J = 16.9, 10.4 Hz, 1H), 6.41 (dd, J = 16.9, 1.5 Hz, 1H), 5.82 (dd, J = 10.1, 1.0 Hz, 1H). 13C NMR (100MHz, MeOD): δ 167.3, 135.7, 133.9, 132.1, 129.9, 129.4, 128.2, 127.7, 127.3, 127.2, 126.4, 123.8, 123.3. HRMS (+ESI): Calculated: 198.0913 (C13H12NO). Observed: 198.0912.

N-(2,3-dihydro-1H-inden-1-yl)acrylamide (TRH 1-58). To a solution of 1- aminoindan (274 mg, 2.0 mmol) in dichloromethane (10 mL) was added acryloyl chloride (0.20 mL, 2.4 mmol) followed by triethylamine (276 mg, 2.4 mmol) at 0˚ C under N2 atmosphere. After stirring for 20 minutes, the reaction mixture was allowed to warm to room temperature and was stirred for 28 hours. The solution was washed twice with brine, and the resulting crude was purified by silica gel chromatography (20%-40% ethyl acetate in hexanes) to yield 238 mg of white solid (62% yield). 1 H NMR (400MHz, CDCl3): δ 7.22-7.11 (m, 4H), 6.76 (d, J = 7.8 Hz, 1H), 6.23- 6.13 (m, 2H), 5.56 (dd, J = 3.5, 8.1 Hz, 1H), 5.40 (q, J = 7.9 Hz, 1H), 2.95-2.88 (m, 1H), 2.84-2.76 (m, 1H), 2.51-2.43 (m, 1H), 1.83-1.74 (m, 1H). 13 C NMR (100MHz, CDCl3): δ 165.5, 143.2, 143.1, 130.9, 127.8,126.6, 126.3, 124.6, 124.0, 54.5, 33.7, 30.2. HRMS (+ESI): Calculated: 188.1070 (C12H14NO). Observed: 188.1068.

61 N-(2,3-dihydro-1H-inden-5-yl)acrylamide (TRH 1-59). To a solution of 5- aminoindan (269 mg, 2.0 mmol) in dichloromethane (10 mL) was added acryloyl chloride (0.20 mL, 2.4 mmol) followed by triethylamine (270 mg, 2.4 mmol) at 0˚ C under N2 atmosphere. The reaction mixture was allowed to warm to room temperature and was stirred for 20 hours. The solution was washed twice with brine, and the resulting crude was purified by silica gel chromatography (40% ethyl acetate in hexanes) to yield 129 mg of white solid (34% yield). 1 H NMR (400MHz, CDCl3): δ 8.87 (s, 1H), 7.54 (s, 1H), 7.31 (d, J = 7.6 Hz, 1H), 7.11 (d, J = 7.8 Hz, 1H), 6.40 (d, J = 5.6 Hz, 2H), 5.66 (t, J = 5.6 Hz, 1H), 2.87- 2.80 (m, 4H), 2.04 (t, J = 7.2 Hz, 2H). 13 C NMR (100MHz, CDCl3): δ 164.4, 144.9, 140.4, 136.0, 131.6, 127.0, 124.3, 118.8, 117.0, 32.9, 32.4, 25.6. HRMS (+ESI): Calculated: 188.1070 (C12H14NO). Observed: 188.1068.

N-(naphthalene-2-yl)acrylamide (TRH 1-60). To a solution of 2-naphthylamine (289 mg, 2.0 mmol) in dichloromethane (10 mL) was added acryloyl chloride (0.20 mL, 2.4 mmol) followed by triethylamine (269 mg, 2.4 mmol) at 0˚ C under N2 atmosphere. After 15 minutes, the reaction mixture was allowed to warm to room temperature and was stirred for 16 hours. The solution was washed twice with 5% citric acid and once with brine, and the resulting crude was purified by silica gel chromatography (30% ethyl acetate in hexanes) to yield 266 mg of an off-white solid (67% yield). 1H NMR (400MHz, MeOD): δ 8.25 (d, J = 1.8 Hz, 1H), 7.73-7.69 (m, 3H), 5.54 (dd, J = 2.1, 8.8 Hz, 1H), 7.39-7.34 (m, 1H), 7.33-7.29 (m, 1H), 6.44 (dd, J = 9.7, 17.0 Hz, 1H), 6.36 (dd, J = 2.2, 17.0 Hz, 1H), 5.72 (dd, J = 2.2, 9.7 Hz, 1H). 13C NMR (100MHz, MeOD): δ 166.2, 137.1, 135.1, 132.4, 132.1, 129.5, 128.6, 128.5, 127.9, 127.4, 126.1, 121.1, 118.1. HRMS (+ESI): Calculated: 198.0913 (C13H12NO). Observed: 198.0912.

N-(7-phenyl-2,3-dihydro-1H-inden-4-yl)acrylamide (TRH 1-68). To a solution of N-(7-bromo-2,3-dihydro-1H-inden-4yl)acrylamide (TRH 1-65, 56 mg, 0.2 mmol) in a solution of dioxane and water (4:1 dioxane:water, 2.1 mL) was added sequentially phenylboronic acid (55 mg, 0.4 mmol), potassium carbonate (78 mg, 0.5 mmol), and tetrakis(triphenylphosphine)palladium(0) (26 mg, 10 mol%). The

62 reaction mixture was heated to a reflux and was stirred overnight. The reaction was diluted with water (20 mL) and extracted with DCM (3x20 mL). The combined organics were evaporated and the resulting crude was purified by silica gel chromatography (10% to 50% ethyl acetate in hexanes) then recrystallized from toluene to give 11 mg of white solid (20% yield).

1H NMR (400MHz, MeOD): δ 7.48 (d, J = 8.2 Hz, 1H), 7.40-7.39 (m, 4H), 7.33- 7.27 (m, 1H), 7.16 (d, J = 8.2 Hz, 1H), 6.53 (dd, J = 10.2, 17.0 Hz, 1H), 6.36 (dd, J = 1.7, 17.0 Hz, 1H), 5.77 (dd, J =1.7, 10.2 Hz, 1H), 2.96 (t, J= 7.3 Hz, 2H), 2.91 (t, J = 7.3 Hz, 2H), 2.04 (quint, J = 7.3 Hz, 2H).

13C NMR (100MHz, MeOD): δ 166.3, 144.2, 142.4, 139.2, 133.8, 132.2, 129., 129.3, 128.2, 127.9, 127.8, 123.0, 34.3, 32.1, 26.6.

HRMS (+ESI): Calculated: 264.1383 (C18H18NO). Observed: 264.1381.

N-(7-(4-(hydroxymethyl)phenyl)-2,3-dihydro-1H-4-yl)acrylamide (TRH 1-70). To a solution of N-(7-bromo-2,3-dihydro-1H-inden-4yl)acrylamide (TRH 1-65, 56 mg, 0.2 mmol) in a solution of dioxane and water (4:1 dioxane:water, 2.1 mL) under nitrogen atmosphere was added sequentially 4- (hydroxymethyl)phenylboronic acid (66 mg, 0.4 mmol), potassium carbonate (78 mg, 0.5 mmol), and tetrakis(triphenylphosphine)palladium(0) (26 mg, 10 mol%). The reaction mixture was heated to a reflux and stirred overnight. The reaction was diluted with water (20 mL) and extracted with DCM (3x20 mL). The combined organics were dried with magnesium sulfate, filtered, and evaporated, and the resulting crude was purified by silica gel chromatography (20% to 50% ethyl acetate in hexanes) to give 16 mg of white solid (26% yield).

1H NMR (400MHz, MeOD): δ 7.47 (d, J = 8.1 Hz, 1H), 7.41-7.38 (m, 4H), 7.15 (d, J = 8.2 Hz, 1H), 6.52 (dd, J = 10.2, 16.9 Hz, 1H), 6.36 (d, J = 17.0 Hz, 1H), 5.77 (d, J = 10.5 Hz, 1H), 4.63 (s, 2H), 2.95 (t, J = 7.1 Hz, 2H), 2.90 (t, J = 7.2 Hz, 2H), 2.03 (t, J = 7.3 Hz, 2H).

13C NMR (100MHz, MeOD): δ 166.3, 144.1, 141.4, 139.2, 136.9, 133.8, 132.2, 129.5, 128.5,128.2, 128.0, 127.9, 123.0, 65.0, 34.3, 32.0, 26.6.

HRMS (+ESI): Calculated: 294.1489 (C19H20NO2). Observed: 294.1486.

63 N-(2,3-dihydro-1H-inden-2-yl)acrylamide (TRH 1-74). To a solution of 2- aminoindan (253 mg, 2.0 mmol) in dichloromethane (10 mL) was added acryloyl chloride (0.19 mL, 2.4 mmol) followed by triethylamine (222 mg, 2.4 mmol) at 0˚ C under N2 atmosphere. The reaction mixture was allowed to warm to room temperature after 15 minutes and was stirred for 23 hours. The solution was washed twice with brine, and the resulting crude was purified by silica gel chromatography (40% ethyl acetate in hexanes) to yield 126 mg of an off-white solid (35% yield).

1 H NMR (400MHz, CDCl3): δ 7.23-7.16 (m, 4H), 6.27 (dd, J = 1.3, 17.0 Hz, 1H), 6.10 (s, 1H), 6.04 (dd, J = 10.3, 17.0 Hz, 1H), 5.60 (dd, J = 1.3, 10.3 Hz, 1H), 4.82-4.75 (m, 1H), 3.32 (dd, J = 7.1, 16.2 Hz, 2H), 2.84 (dd, J = 4.4, 16.1 Hz, 2H).

13 C NMR (100MHz, CDCl3): δ 165.4 ,140.9, 130.9, 126.8, 126.5, 124.9, 50.7, 40.1.

HRMS (+ESI): Calculated: 188.1070 (C12H14NO). Observed: 188.1068.

Cell Culture. All cells were maintained at 37°C with 5% CO2, and all medium contained 10% FBS and 1% glutamine. A549 cells (ATCC) were cultured in F12K medium. NCI-H322 (Sigma-Aldrich), NCI-H460 (ATCC), NCI-H23 (ATCC), and NCI-H661 cells (ATCC) were cultured in RPMI medium. Calu-6 cells (ATCC) were cultured in EMEM medium. HEK293T/17 cells (ATCC) were cultured in DMEM medium. Finally, BEAS2B cells (ATCC) were cultured in flasks in BEBM media with all BEGM additives except gentamycin-amphotericin B mix (Lonza/Clonetics Corporation; Kit CC-3170).

Cellular Phenotype Studies. Cell survival and proliferation studies were performed as described previously465,480. Briefly, cells were seeded at 2 x 104 and 4 x 104 cells/well, respectively, in serum-containing or serum-free media in 96-well plates overnight. Cells were treated with DMSO vehicle- or compound- containing media for 24 or 48 h before fixation and staining with 10% formalin and Hoechst 33342 (Invitrogen) according to manufacturer’s protocol.

Gel-Based ABPP analysis. Gel-based ABPP analyses were performed as previously described480. Recombinant active ALDH3A1 pure human proteins were purchased from Sigma Aldrich. Pure protein (0.3 µg) were pre-treated with DMSO or a covalent ligand compound for 30 min at 37°C in 50 µL, and were then treated with IA-rhodamine (100 nM final concentration). The proteins were

64 incubated for 60 min at RT. The samples were then separated by SDS/PAGE and scanned using a ChemiDoc MP (Bio-Rad Laboratories, Inc.), and gels were analyzed for their in-gel fluorescence. isoTOP-ABPP analysis. IsoTOP-ABPP analyses were performed. A549 cell lysates were preincubated with DMSO vehicle or DKM 3-42 (50 µM) or EN40 (50 µM) for 30 min at 37°C and then labeled with IA-alkyne (100 µM) for 1 h at RT. The lysates were subsequently treated with isotopically light (control) or heavy (treated) TEV-biotin (100 µM), and click chemistry was performed. Proteins were precipitated and pelleted by centrifugation. Proteins were washed 3 times with cold methanol, then denatured and resolubilized by heating 1.2% SDS/PBS to 85°C for 5 min. Insoluble components were precipitated by centrifugation at 6500 g, and the soluble proteome was diluted in 5 mL of PBS, for a final concentration of 0.2% SDS. Labeled proteins were bound to avidin-agarose beads (170 µL beads; Thermo Pierce) while rotating overnight at 4°C. Bead-linked proteins were then washed 3 times each in PBS and water, resuspended in 6 M urea/PBS, reduced in dithiothreitol (DTT) (1 mM), alkylated with IA (18 mM), washed and resuspended in 2 M urea/PBS with 1 mM calcium chloride, and trypsinized overnight (0.5 µg/µL sequencing grade trypsin; Promega). Tryptic peptides were discarded, and beads were washed 3 times in PBS and water, then washed with TEV buffer containing DTT (1 µM). TEV-biotin tag was digested overnight in TEV buffer containing DTT (1 µM) and Ac-TEV protease (5 µL) at 29°C. Peptides were diluted in water and acidified with final concentration of 5% formic acid.

Peptides from all proteomic experiments were pressure-loaded onto a 250 µm silica capillary tubing packed with 4 cm of Aqua C18 reverse-phase resin (Phenomenex #04A-4299), which was previously equilibrated. The peptides on this capillary were then attached to using a MicroTee PEEK 360 µm fitting (Thermo Fisher Scientific #p-888) to a 13 cm laser pulled column packed with 10 cm Aqua C18 reverse-phase resin and 3 cm of strong-cation exchange resin. Samples were analyzed using a Q Exactive Plus mass spectrometer (Thermo Fisher Scientific) using a Multidimensional Protein Identification Technology (MudPIT) program. Data were collected in data-dependent acquisition mode with dynamic exclusion enabled (60 s). One full MS (MS1) scan (400-1800 m/z) was followed by 15 MS2 scans of the most abundant ions. Heated capillary temperature was set to 200°C, and the nanospray voltage was set to 2.75 kV.

Peptides were searched with a static modification for cysteine carboxyaminomethylation (+57.02146) and up to two differential modifications for either the light or heavy TEV tags (+464.28596 or +470.29977, respectively). Peptides were required to have at least one tryptic end and to contain the TEV modification. ProLUCID data were filtered through DTASelect to achieve a peptide false-positive rate below 1%.

65 ALDH3A1 Activity Assays. The ALDH3A1 activity assay methods were adapted from an assay previously published481. The ALDH activity was found with a spectrometer (VERSAmax, Molecular Devices) by detecting the NADPH production at 340 nm at 25°C. Recombinant active pure ALDH3A1 protein (Sigma-Aldrich, 0.325 µg per well) and either DMSO (control) or inhibitor (100 µM final concentration) were combined in a 96-well plate in sodium pyrophosphate buffer (100 mM, pH 8.0) with 150 µL final volume and pre-incubated for 15 min at 25°C. A549 lysate (50 µg per well) and shALDH3A1 or shControl lysates (100 µg per well) were treated with either DMSO (control) or inhibitor (1 mM and 100 µM final concentration) in a 96-well plate sodium in pyrophosphate buffer (100 mM, pH 8.0) with 100 µL final volume, and pre-incubated for 15 min at 25°C. NADP+ (2.5 mM) and benzaldehyde (5 mM) were subsequently added to initiate the reaction and absorbance at 340 nm was detected every 12 s for 1 h. The slope of a linear portion (between 0.8 min and 5 min) was used with an NADPH standard curve to calculate enzyme activity.

Gene Expression by qPCR. RNA was extracted from cells with Trizol. Replicates were normalized by concentration and converted to cDNA. qPCR was performed according to the manufacturer’s protocol for Fisher Maxima SYBR Green with 10 µM primer concentrations. Primers were purchased from Sigma- Aldrich. PCR primers were purchased from Sigma.

Knockdown of ALDH3A1 in A549 Cells. Stable ALDH3A1 knockdown was achieved by using two independent short-hairpin oligonucleotides to knock down the expression of ALDH3A1 in lentiviral plasmids in the pLKO.1 backbone continuing shRNA (Sigma-Aldrich) against human ALDH3A1, and were transfected into HEK293T/17 cells using Lipofectamine 2000. Lentivirus was collected from filtered cultured medium and used to infect the target A549 cells with polybrene. Target cells were selected over 3 days with 1 µg/mL puromycin. Knockdown was confirmed by qPCR. shRNA oligonucleotides used to knockdown ALDH3A1 were:

shALDH3A1-1: GATCCCGGTGTCCAGCAGTTGCTTGAAATCTCGAGATTTCAAGCAAC TGCTGGACATTTTTTG

shALDH3A1-2: GATCCCGGAGATACTCAGGGCGTTGTTAACTCGAGTTAACAACGCCC TGAGTATCTTTTTTTG

Overexpression of ALDH3A1 in A549 Cells. Stable ALDH3A1 overexpression was achieved by subcloning the ALDH3A1 gene from human cDNA into a pLenti CMV puro vector (Cyagen Biosciences), and were transfected into HEK293T/17 cells using Lipofectamine 2000. Lentivirus was collected from filtered cultured

66 medium and used to infect the target A549 cells with polybrene. Target cells were selected over 3 days with 1 µg/mL puromycin. Overexpression was confirmed by qPCR.

Tumor Xenograft Studies. All experimental studies were approved by the Animal Care and Use Committee of the University of California, Berkeley. Human tumor xenografts were established by subcutaneously injecting cancer cells into the flank of C.B17 severe combined immunodeficiency (SCID) mice (6-8 weeks old; Taconic Farms). Briefly, cells were washed twice with PBS, trypsinized, and harvested in serum-containing medium. The harvested cells were then washed with serum-free medium, resuspended, and injected (2,000,000 cells). Tumors were measured every 7 days by caliper measurements. For the small-molecule studies, once tumors were established, the mice were exposed by intraperitoneal (ip) injection with either vehicle (18:1:1 PBS/ethanol/PEG40) or 50 mg/kg of DKM 3-42 or EN40 once per day, every day for the duration of the study.

Western Blots. Vinculin antibody was obtained through Abcam. ALDH3A1 antibody was obtained from OriGene. Cells were lysed in CST lysis buffer containing 20 mM Tris pH 7.5, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% Triton X-100, 2.5 mM pyrophosphate, 50 mM NaF, 5 mM β-glycero-phosphate, 1 mM Na3VO4, 50 nM calyculin A (EMD Millipore), and protease inhibitors (Roche). Lysate was incubated on a rotator at 4°C for 30 min, and insoluble residue was subsequently removed with a centrifugation at 14,000 rpm for 10 min. Protein samples were normalized to a single concentration between 1 and 2 mg/mL. Proteins were separated by SDS/PAGE and transferred to nitrocellulose membranes with the iBlot system (Invitrogen). Blots were blocked with 5% BSA in Tris-buffered saline containing Tween 20 (TBST) solution for 1 h at RT and then washed with TBST. The blots were probed overnight at 4°C with primary antibodies diluted in 5% BSA in TBST according to manufacturer’s instructions. Following washes with TBST, the blots were incubated in the dark for 1 h at RT with secondary antibodies (Rockland). Blots were visualized using a ChemiDoc MP (Bio-Rad Laboratories, Inc.).

67 5. FIGURES

Figure 3-1. Cysteine-reactive covalent ligand screen to identify lead compounds that impair lung cancer pathogenicity. (A) Schematic of workflow for the study. We screened our library of cysteine-reactive covalent ligands (examples shown) in A549 lung cancer cells to identify ligands that impair lung

68 cancer cell survival or proliferation. Upon identifying hits, we used the competitive isoTOP-ABPP platforms to map the druggable hotspots targeted by the hit compound in A549 proteomes. (B) Cysteine-reactive covalent ligands were screened in A549 lung cancer cells for impairments in serum-free survival or serum-containing proliferation at 50 µM for 48 h compared to DMSO vehicle- treated controls, assessed by Hoechst stain. (C) Hits that impaired A549 survival or proliferation by >75% were counterscreened against BEAS2B primary human bronchial epithelial cells (50 µM) for survival and proliferation for 48 h compared to DMSO vehicle-treated controls, assessed by Hoechst stain. (D) Structure of hit compound DKM 3-42 that impaired survival and proliferation by >75% in A549 cells but <25% in BEAS2B cells. Cysteine-reactive acrylamide warhead is highlighted in red in the DKM 3-42 structure. DKM 3-42 is highlighted in red in the screening data in (B) and (C). (E) A549 tumor xenograft growth in immune- deficient SCID mice treated with vehicle (18:1:1 saline:ethanol:PEG40) or DKM 3-42 (50 mg/kg ip, once per day) started 7 days after injection of A549 cells into the flank of mice, and continued to the end of the study. (F) Weight of mice at the end of the tumor xenograft study described in (E). Data shown in (B, C, E, F) are average ± sem, n=3-7/group. Significance in (E) is expressed as *p<0.05 compared to vehicle-treated controls.

69 Figure 3-2. isoTOP-ABPP analysis of DKM 3-42 reveals ALDH3A1 as primary target. (A) isoTOP-ABPP analysis of DKM 3-42 targets in A549 lung cancer cell proteomes. A549 proteomes were pre-treated with DMSO vehicle or DKM 3-42 (50 µM) for 30 min prior to IA-alkyne labeling (100 µM) for 1 h, followed by isoTOP-ABPP analysis. Primary target with highest isotopically light (control) to heavy (DKM3-42-treated) probe-modified tryptic peptide ratio was C244 of ALDH3A1. IsoTOP-ABPP data is from n=3. In the inset of (A) is a gel- based ABPP analysis of DKM 3-42 dose-response against IA-rhodamine labeling of pure human ALDH3A1 protein. (B) ALDH3A1 activity assay. DMSO vehicle or DKM 3-42 (50 µM) was pre-incubated with either pure ALDH3A1 protein or A549 cell lysate for 30 min prior to addition of benzaldehyde (5 mM) substrate for 1 h. (C) ALDH3A1 knockdown. ALDH3A1 was stably knocked down using two independent short-hairpin RNA (shRNA) oligonucleotides - shALDH3A1 and shALDH3A2, and ALDH3A1 expression was ascertained by qPCR. (D) ALDH3A1 activity assay in shControl and shALDH3A1 A549 cell lysates. (E) A549 cell survival and proliferation in shControl and shALDH3A1 cells, assessed by Hoechst stain. (F) Tumor xenograft growth in immune-deficient SCID mice of A549 shControl and shALDH3A1 cells. Data shown in (B-F) are average ± sem, n=3-8/group. Significance in (E) is expressed as *p<0.05 compared to vehicle- treated control or shControl groups.

70 Figure 3-3. Covalent ligand discovery against ALDH3A1 reveals EN40 as an additional ALDH3A1 inhibitor scaffold. (A) Gel-based ABPP screen of additional cysteine-reactive ligands against ALDH3A1. Pure human ALDH3A1 was pre-incubated with DMSO vehicle or covalent ligand (100 µM) for 30 min prior to IA-rhodamine (100 nM) labeling for 1 h and proteins were separated by

71 SDS/PAGE and in-gel fluorescence was analyzed. Hits were deemed as compounds that displaced IA-rhodamine labeling and thus resulted in lower in- gel fluorescence compared to vehicle-treated controls. EN40 was the top hit from this screen in red. (B) Structure of top hit EN40 is shown. (C) Gel-based ABPP confirmation of EN40 interactions with pure human ALDH3A1. DMSO vehicle or EN40 at various concentrations were pre-incubated for 30 min prior to IA- rhodamine (10 µM) labeling for 1 h followed by SDS/PAGE and in-gel fluorescence analysis. (D) ALDH3A1 activity assay. DMSO vehicle or EN40 (50 µM) was pre-incubated with pure ALDH3A1 protein for 30 min prior to addition of benzaldehyde (5 mM) substrate for 1 h. (E) A549 48 h cell survival in cells treated with DMSO vehicle or EN40 (50 µM), assessed by Hoechst stain. (F) isoTOP-ABPP analysis of EN40 targets in A549 proteomes. A549 proteomes were pre-treated with DMSO vehicle or EN40 (50 µM) for 30 min prior to IA- alkyne labeling (100 µM) for 1 h, followed by isoTOP-ABPP analysis. Primary target with highest isotopically light (control) to heavy (EN40-treated) probe- modified tryptic peptide ratio was C244 of ALDH3A1. IsoTOP-ABPP data is from n=4. (G) A549 tumor xenograft growth in immune-deficient SCID mice treated with vehicle (18:1:1 saline:ethanol:PEG40) or EN40 (50 mg/kg ip, once per day) started 14 days after injection of A549 cells into the flank of mice, and continued to the end of the study. (H) Weight of mice at the end of the tumor xenograft study described in (G). (I) isoTOP-ABPP analysis of A549 tumor xenografts from mice treated with vehicle or EN40 from tumors taken from the end of the study described in (G). Primary target with highest isotopically light (control) to heavy (EN40-treated) probe-modified tryptic peptide ratio was C244 of ALDH3A1. isoTOP-ABPP data is from n=3. Data shown in (D, E, G, H) are average ± sem, n=5-8/group. Significance in (D, E, G) is expressed as *p<0.05 compared to vehicle-treated controls.

72 Figure 3-4. ALDH3A1 sensitivity across lung cancer cell lines. (A) Expression of ALDH3A1 across BEAS2B cells and multiple lung cancer cell lines. Vinculin is used as a loading control. Shown is a representative gel of n=3. (B) Cell survival (24 h) of EN40 (50 µM) assessed by Hoechst stain. (C) ALDH3A1 was stably overexpressed in A549 cells. ALDH3A1 expression was confirmed by qPCR compared to GFP-infected control cells. (D, E) Effects of DMSO vehicle or DKM 3-42 (100 µM) (D) or EN40 (100 µM) (E) on cell survival (24 h) in GFP- expressing control or ALDH3A1 overexpressing (O/E) A549 cells. Data shown in (B, C, D, E) are average ± sem, n=3-5/group. Significance in (B, C, D, E) is expressed as *p<0.05 compared to vehicle-treated controls, #p<0.05 (in D, E) compared to DKM 3-42 or EN40-treated GFP-infected control cells.

73 Figure 3-5. Negative control compound for ALDH3A1. (A) Gel-based ABPP analysis of EN40 and an analog EN A (structure shown). EN40 and EN A (100 µM) were pre-incubated with pure human ALDH3A1 protein for 30 min prior to labeling with IA-rhodamine (100 nM) labeling for 30 min, followed by SDS/PAGE, and in-gel fluorescence analysis. Shown is a representative gel of n=3. (B) A549 cell survival. A549 cells were treated with DMSO vehicle or EN A (50 µM) for 24 h and cell survival was assessed by Hoechst stain. There is no significant difference in cell survival between EN A and vehicle-treated groups. Data in (B) are shown as average ± sem, n=5/group.

74 CHAPTER FOUR: Chemoproteomic profiling of acetanilide herbicides reveals their role in inhibiting fatty acid oxidation

75 1. INTRODUCTION

Current toxicological testing paradigms for pesticides include testing chemicals for various pre-defined phenotypic endpoints such as carcinogenicity, usually through treatment of pesticides at maximum tolerated doses under acute to chronic exposures. While these studies may reveal whether chemicals cause pre-defined toxicological endpoints such as cancer, they may miss more insidious toxicological mechanisms or pathologies, since only a handful of phenotypic endpoints are tested. Another approach towards better understanding the toxicological mechanisms of chemicals is to map their direct on and off-target activity directly in complex biological systems and test for toxicological phenotypes based on the biology known about these targets to provide a more direct route for testing how pesticides may affect health. Chemoproteomic platforms have arisen to tackle this challenge by enabling an approach for identifying direct protein targets of chemicals. In Chapter Four, I will discuss my study on using chemoproteomic platforms to identify the direct targets of acetanilide herbicides, towards understanding potential novel mechanisms of toxicity.

Acetanilide herbicides including metolachlor, alachlor, and acetochlor (AC), are among the most widely used pesticides in the US, with collective usage of nearly >60 million pounds per year, and are primarily used for agricultural weed control482 (Figure 4-1A). Exposure to these agents has been associated with various adverse health effects in rodent models including cancer, developmental and reproductive abnormalities, and dysregulation of thyroid and liver function483, but the mechanisms underlying these or other toxicological effects are poorly understood.

Of particular concern with acetanilide herbicides are their shared electrophilic chloroacetamide scaffolds that may adduct with cysteines within proteins to cause protein dysfunction. Cysteines play important roles in protein function, including enzyme , post-translational regulation, redox balance, metal binding, and protein-protein interactions484. The reactivity of AC with specific cysteines within certain protein targets may thus affect protein function and cause downstream pathologies, but the proteome-wide reactivity of acetanilide herbicides remains unknown.

2. RESULTS

Here, we have used activity-based protein profiling (ABPP) to map the proteome- wide reactivity of acetanilide herbicides in vitro and in vivo in mice, towards understanding novel biological and toxicological mechanisms associated with exposure to these agents. ABPP is a chemoproteomic platform that uses activity- or reactivity-based chemical probes to map proteome-wide reactivity, functionality, and ligandability directly in complex proteomes. When used in a competitive manner, electrophilic environmental chemicals like AC can be competed against the binding of reactivity-based probes to ligandable protein

76 hotspots to map their proteome-wide reactivity and direct protein targets462,485 (Figures 4-1B and 4-1C). To discover the direct targets of acetanilide herbicides, we utilized two chemical probes for this study: 1) a broad cysteine-reactive iodoacetamide probe bearing a biorthogonal alkyne handle (iodoacetamide- alkyne or IAyne) and 2) an analog of AC also bearing an alkyne handle (AC- alkyne or ACyne) (Figure 4-1B).

To get an initial portrait of whether acetanilide herbicides might disrupt cysteine reactivity, we performed gel-based ABPP studies in which we competed metolachlor, alachlor, or AC against IAyne or ACyne labeling of mouse liver proteome in vitro, followed by appendage of a rhodamine-azide tag by copper- catalyzed click chemistry and visualization of probe-labeled proteins by in-gel fluorescence (Figure 4-1C). While metolachlor appears to be inactive, we observed significant inhibition of IAyne and ACyne probe labeling with alachlor and even more so with AC, indicating that these acetanilide herbicides reacted with specific cysteines on specific protein targets in vitro in mouse liver proteomes (Figure 4-1D). Based on the in vitro data, we decided to focus on AC for follow-up in vivo studies, since it showed the highest degree of reactivity.

We next investigated AC reactivity in vivo in mouse liver. We acutely treated mice with AC and subsequently labeled liver proteomes ex vivo with IAyne or ACyne. We show that AC treatment inhibits IAyne and ACyne labeling ex vivo, demonstrating that AC shows protein reactivity in vivo in mouse liver (Figure 4- 1E). To identify these AC protein targets, we next performed ABPP- Multidimensional Protein Identification Technology (ABPP-MudPIT) studies, in which we labeled liver proteomes from vehicle and AC-treated mice with IAyne followed by appendage of biotin-azide by click-chemistry for enrichment and proteomic analysis of probe-labeled proteins (Figure 4-1C). Out of 96 protein targets enriched by IAyne labeling, we show 28 distinct targets that exhibit >2- fold inhibition of IAyne labeling ex vivo with AC treatment (Figure 4-2A). We also performed enrichment and proteomic analysis on ACyne treated mouse liver proteomes and identified 28 distinct enriched protein targets (Figure 4-2B). We plotted the overlap between these two proteomic experiments and observed 6 common targets of AC, as these targets are likely to be direct targets of AC itself rather than a potential bioactivated metabolite of AC (Figure 4-2C). While these bioactivated metabolites that show cysteine reactivity may be of interest, we wanted to focus our efforts for this study on targets that could be more easily validated with the parent pesticide AC. Interestingly, 5 of these 6 targets are involved in fatty acid metabolism, including Scp2, Acaa2, Acaa1b, Acat1, and Acsf2, of which Scp2, Acaa2, Acaa1b, and Acat1 are all part of the same thiolase family of enzymes and Acsf2 is a fatty acyl CoA synthetase486.We decided to follow-up on these 5 specific targets of AC.

To identify the specific sites of labeling of AC on these 5 protein targets, we also complemented our ABPP protein pull-down experiments with isotopic tandem orthogonal proteolysis-enabled ABPP (isoTOP-ABPP), in which we labeled

77 mouse liver proteomes from vehicle or AC-treated mice with IAyne, appended a biotin-azide tag bearing an isotopically light (for vehicle-treated) or heavy (for AC- treated) valine and a TEV protease recognition sequence by click-chemistry, avidin-enriched and tryptically digested probe labeled proteins, released probe- modified peptides by TEV protease, and analyzed light to heavy ratios of probe- labeled proteins by quantitative proteomics (Figures 4-1C and 4-2D). Across all 5 targets, we identified specific IAyne-labeled cysteines that showed >1.5 light-to- heavy (L/H) ratios indicating that these sites were bound by AC. Of particular interest were Acaa2 C92, Acaa1b C123, and Scp2 C94 that showed L/H ratios of 2.7, 1.8, and 2.0, respectively (Figures 4-2D and 4-2E). These particular cysteines are the catalytic cysteines of these enzymes, suggesting that AC treatment in vivo inhibited the activity of these enzymes. We further validated these findings by showing competition of AC against IAyne labeling of pure Acaa2, Acaa1b, and Scp2 protein by gel-based ABPP and showing inhibition of Acaa1 and Acaa2 thiolase activity by AC (Figures 4-2F and 4-2G).

Acaa1b, Acaa2, and Scp2 are all degradative thiolases and are critical enzymes in the fatty acid oxidation pathways in both mitochondria (Acaa2) and in long- chain and branched-chain fatty acid oxidation in peroxisomes (Acaa1, Scp2). Genetic deficiencies in these thiolases or of peroxisomal or mitochondrial fatty acid oxidation pathways have been shown to cause liver dysfunction and lipid dysregulation in the form of elevated triacylglycerols, ceramides, and sterols, as well as hepatic steatosis, likely because the fatty acids that are not oxidized are diverted into other lipid metabolism pathways487–491.

We, thus, postulated that AC treatment, through inhibiting mitochondrial and peroxisomal fatty acid oxidation enzymes, may also cause lipid dysregulation due to the diversion of fatty acids away from oxidation and towards other lipid pools. Consistent with this premise, using targeted metabolomic platforms, we show that daily subacute treatment of mice with AC leads to heightened levels of fatty acids, neutral lipids, phospholipids, sphingolipids, and sterols in mouse liver normalized to mouse liver weight (Figure 4-3A). We further show that these changes in triglycerides and cholesteryl esters are particularly exacerbated upon AC treatment and feeding mice with a high-fat diet (Figure 4-3B).

While our steady-state in vivo lipidomic data suggested that fatty acids may be diverted towards lipid storage in the liver due to inhibition of fatty acid oxidation enzymes, we further tested this hypothesis by tracing isotopically labeled [U- 13C]palmitate in HepG2 hepatocyte cell lines. We demonstrate that AC treatment in HepG2 cells leads to significantly heightened isotopic fatty acid incorporation into fatty acyl carnitines, ceramides, and triacylglycerols, compared to vehicle- treated controls (Figure 4-3C).

To further show that the oxidation of fatty acids is inhibited upon AC exposure, we measured the oxygen consumption rate (OCR), a measure of cellular oxygen consumption, mitochondrial respiration, and energy production, in HepG2

78 hepatocytes. We show that basal OCR is heightened upon exogenous palmitate treatment, but significantly lowered upon AC treatment compared with vehicle- treated controls, to comparable levels as those observed with treatment with a fatty acid oxidation carnitine palmitoyltransferase-1 (CPT-1) inhibitor etomoxir (Figure 4-3D). We also inhibited mitochondrial ATP production using oligomycin, and show that both AC and etomoxir treatment impaired OCR, reflecting an impairment of ATP synthesis. We also find impaired OCR in AC and etomoxir- treated cells compared to vehicle-treated controls upon mitochondrial uncoupling with FCCP, which induces maximal mitochondrial respiration. To determine whether AC also impaired OCR from non-mitochondrial sources, such as the peroxisome, HepG2 cells were also treated with mitochondrial respiratory chain inhibitors rotenone and antimycin A. Consistent with the hypothesis that AC exposure leads to inhibition of both mitochondrial and perioxisomal fatty acid oxidation, OCR impairment was also observed with AC, but not with etomoxir, treatment upon addition of mitochondrial respiratory chain inhibitors. Taken together, our data demonstrate that AC inhibits fatty acid oxidation in hepatocytes (Figure 4-3D).

3. CONCLUSION

Collectively, our data suggest that AC, through inhibiting the catalytic cysteines of key thiolase enzymes Acaa1b, Acaa2, and Scp2 involved in mitochondrial and peroxisomal fatty acid oxidation, leads to an accumulation of fatty acids that are diverted towards synthesis of various other lipid species, including neutral lipids, sphingolipids, triacylglycerols, and cholesteryl esters (Figure 4-3E). Our data point to a unique and novel toxicological mechanism associated with exposure to AC, a very highly used herbicide, in which chronic exposure to this agent may exert potential dyslipidemic effects, such as hepatic steatosis. We acknowledge that the doses and routes of exposure used in this study do not necessarily represent the doses and exposure routes encountered in agricultural or home settings. However, AC covalently modifies its targets and chronic and low dose exposure may lead to accumulating inhibition of these targets over time, since protein half-life rather than the half-life of AC in vivo will likely dictate the length of inhibition of these targets. It will be of future interest to investigate whether these targets are inhibited in vivo from low-dose and chronic AC exposure in drinking water or through inhalation exposures. Furthermore, ABPP as it is used in this study is primarily useful for identifying irreversible and covalent targets of chemicals, but there may be reversible targets of acetochlor that we missed in our profiling efforts.

From the viewpoint of pesticide toxicological testing, pesticides are oftentimes tested at the maximum tolerated doses for whether they cause toxicity. Here, we used a rather high dose, but far lower than doses that would cause overt toxicity and far lower than the maximum tolerated doses. We envision in the future that chemoproteomic platforms such as ABPP would be integrated into early-stage toxicity testing, rather that applied on chemicals that are already in our

79 environment, towards identifying direct toxicological targets and using these information to fine-tune toxicological endpoints that may be tested for in long- term toxicological testing studies.

Nonetheless, our data here underscore the utility of using chemoproteomic platforms like ABPP to map the direct proteome-wide targets of environmental chemicals towards understanding unique and novel mechanisms of toxicity, particularly for those chemicals that are potentially reactive or can be transformed to reactive metabolites. While testing environmental chemicals for mutagenicity, carcinogenicity, or endocrine disruption are standard toxicological practices, testing whether a chemical inhibits fatty acid oxidation enzymes, and thus whether it alters fatty acid and lipid dynamics, is not part of standard testing and would likely be missed as a toxicological endpoint. By using ABPP platforms, we show here that direct off-targets of environmental chemicals can be identified in vivo, enabling more in-depth and broader assessments of toxicities and toxicological mechanisms. We put forth the chemoproteomic strategies described here as a generalizable approach towards testing the toxicity and elucidating toxicological mechanisms of environmental, industrial, and pharmaceutical chemicals.

4. METHODS

Mice. Male C57BL/6 mice (6-8 weeks old) were acutely (4 hours) or sub-acutely (6 days) exposed by intraperitoneal (ip) injection to 85 mg/kg AC (Sigma #33379) in a vehicle of 18:1:1 PBS:PEG40:ethanol (300 µl per mouse). Mice used in metabolic studies were in a vehicle of 19:1 PBS:PEG40 (300 µl per mouse). Following exposure, mice were sacrificed by cervical dislocation, the liver was immediately removed, and subsequently flash frozen in liquid nitrogen. All animal experiments were conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee of the University of California, Berkeley (AUP R342).

Processing of Mouse Liver Proteomes. Tissues were homogenized in phosphate buffered saline (PBS) followed by a 1000 x g centrifugation of the homogenate. The resulting supernatant was collected and used for subsequent assays. Protein concentrations were determined by BCA protein assay (Pierce #23225).

Cell Culture, Recombinant Overexpression, and in situ [13C]Palmitic Acid Tracing. HepG2 (ATCC HB-8065) cells were cultured in Eagle’s Minimum Essential Medium (EMEM) containing 10% fetal bovine serum (Corning). Substrate-limited Dulbecco's Modified Eagle's Medium (DMEM) containing 0.5 mM glucose (Sigma-Aldrich #G7021), 1 mM GlutaMAX (Life Technologies #35050061), 0.5 mM carnitine (Sigma-Aldrich #C0283), and 1% fetal bovine serum was used for the HepG2 Seahorse studies. FAO Assay Medium for the HepG2 Seahorse studies contained 111 mM NaCl, 4.7 mM KCl, 1.25 mM CaCl2,

80 2 mM MgSO4, 1.2 mM Na2HPO4, and supplemented on the day of the assay with 2.5 mM glucose, 0.5 mM carnitine, and 5 mM HEPES. Both Seahorse mediums were pH adjusted to 7.4. HEK 293T/17 (ATCC CRL-11268) cells were cultured in DMEM containing 10% fetal bovine serum and 2 mM L-glutamine (Life Technologies) and maintained at 37°C with 5% CO2. Recombinant cDNA construct containing Scp2 in the SPORT6 vector was purchased from GE Healthcare Dharmacon Inc. and transiently transfected into HEK 293T/17 cells using Lipofectamine 2000 (Life Technologies #11668019). HepG2 cells were treated with 1 mM AC or vehicle for 1 h in serum-free EMEM and then treated for 6 h with 10 µM [12C] or [13C]palmitic acid (Cambridge Isotope Laboratories, Inc.).

Synthesis of ACyne (2-ethyl-N-(hex-5-yn-1-yl)-6-methylaniline). Chloroacetyl chloride (70.0 mg, 0.625 mmol) was added to a solution of 2-ethyl-N-(hex-5-yn-1- yl)-6-methylaniline (39.5 mg, 0.184 mmol) in dichloromethane. The reaction was stirred, and diisopropylethylamine (300 µL, 1.72 mmol) was added via syringe. The color of the solution quickly turned black and a white vapor arose from the reaction mixture. The reaction was stirred at room temperature for 16 hours. It was then quenched by addition of aqueous sodium bicarbonate, and extracted into ethyl acetate. The organic solvent was removed by rotary evaporation to give the crude product. Purification by silica gel chromatography (20% to 30% ethyl acetate in hexanes) gave the product in 78% yield (39.5 mg)

Characterization of ACyne (2-ethyl-N-(hex-5-yn-1-yl)-6-methylaniline). 1 H NMR (500 MHz, CDCl3): d 7.25 (m, 1H), 7.20 (d, J = 6.5 Hz, 1H), 7.13 (d, J = 7.5 Hz, 1H), 3.64 (s, 2H), 3.62-3.52 (m, 2H), 2.59-2.50 (m, 2H), 2.23 (s, 3H), 2.19 (dt, J = 2.5, 7.0 Hz, 2H), 1.89 (t, J = 2.5 Hz, 1H), 1.71-1.64 (m, 2H), 1.55- 1.46 (m, 2H), 1.23 (t, J = 7.5 Hz, 3H). 13 C NMR (500 MHz, CDCl3): d 167.0, 142.1, 136.3, 129.6, 129.3, 127.7, 83.9, 69.1, 50.2, 42.2, 27.0, 26.5, 24.0, 18.9, 18.6, 14.5. HRMS (ESI+): Expected 292.1463 (M+H), C17H23ClNO. Found 292.1465

Gel-based ABPP. Proteome samples diluted in PBS (50 µg in 50 µl PBS) or pure proteins (5 µg in 50 µl PBS, OriGene Technologies, Inc.) were subjected to vehicle or AC treatment for 30 min at 37ºC. Then, IAyne (10 µM, CHESS Gmbh #3187) or ACyne labeling was performed for 30 min at 37ºC. Copper-catalyzed azide-alkyne cycloaddition “click chemistry” was performed with the IAyne or ACyne probe using previously described methods454,457. Fluorescent detection was performed by running a 16 cm Protean II xi 10% resolving SDS-PAGE gel system (Bio-Rad) and scanned using a ChemiDoc MP (Bio-Rad Laboratories, Inc.). Inhibition of target labeling was assessed by densitometry using ImageLab software 5.2.1 (Bio-Rad Laboratories, Inc.) and regressions were calculated by Prism (GraphPad Software).

ABPP-MudPIT. Proteome samples from control- or AC-treated mice were diluted (1 mg in 500 µL PBS), and then labeled with IAyne or ACyne for 1 h at 37°C. Click chemistry was performed by sequential addition of tris(2-

81 carboxyethyl)phosphine (1 mM, Sigma-Aldrich), copper (II) sulfate (1 mM, Sigma- Aldrich), tris[(1-benzyl-1H-1,2,3-triazol-4-yl)methyl]amine (34 µM, Sigma-Aldrich) using previously described methods454,457. After the click reactions, proteomes were precipitated by centrifugation at 6500 x g, washed twice in ice-cold methanol, then denatured and re-solubilized by heating in 1.2% SDS/PBS to 85ºC for 5 minutes. Insoluble components were precipitated by centrifugation at 6500 x g and the soluble proteome was diluted in 5 ml PBS, for a final concentration of 0.2% SDS. Labeled proteins bound to avidin-agarose beads (170 µL re-suspended beads/sample, Thermo Pierce) while rotating overnight at 4ºC. Bead-linked proteins were enriched by washing three times each in PBS and water, re-suspended in 6 M urea/PBS (Sigma-Aldrich), reduced in dithiothreitol (DTT) (1 mM, Sigma-Aldrich), alkylated with iodoacetamide (18 mM, Sigma-Aldrich), then washed and re-suspended in 2 M urea and trypsinized overnight at 37°C while being agitated with 0.5 µg/µl sequencing grade trypsin (Promega). The resulting tryptic peptides were diluted in water and acidified with a final concentration of 5% formic acid (1.2 M, Spectrum). The entire volume was pressure-loaded onto a 250 mm i.d. fused silica capillary tubing packed with 4 cm of Aqua C18 reverse-phase resin (phenomenex #04A-4299), which was previously equilibrated on an Agilent 600 series HPLC using gradient from 100% buffer A to 100% buffer B over 10 min, followed by a 5 min wash with 100% buffer B and a 5 min wash with 100% buffer A. The sample was then attached using a MicroTee PEEK 360 um fitting (Thermo Fisher Scientific #p-888) to a 10 cm laser pulled column of 100 mm fused silica capillary packed with 10 cm Aqua C18 reverse-phase resin, which was previously equilibrated using the same conditions as above. Tryptic peptide samples were analyzed by attaching the loaded sample to an Orbitrap Q Exactive Plus mass spectrometer (Thermo Fisher Scientific) set to run full scan in the range 400-1800 MW, and 15 data- dependent scans at a temperature of 200ºC and a spray voltage of 2.75kV. Samples were run using a two-hour gradient from 5% to 80% acetonitrile with 0.1% formic acid at 100 nl/min. Data was extracted in the form of MS2 files using Raw Extractor 1.9.9.2 (Scripps Research Institute) and searched against the Uniprot mouse database using ProLuCID search methodology in IP2 v.3 (Integrated Proteomics Applications, Inc.).

IsoTOP-ABPP. For isoTOP-ABPP, liver proteomes from vehicle and AC-treated mice were labeled with IAyne (100 µM) for 1 h at room temperature. Proteomes were subsequently treated with isotopically light (control) or heavy (treated) TEV- biotin (100 µM) and copper-catalyzed alkyne-azide cycloaddition (CuAAC) was performed as previously described 454,463. TEV-biotin was synthesized in our lab per methods previously described 454,492. Proteins were precipitated over one hour and pelleted by centrifugation at 6500 x g. Proteins were washed 3 times with cold methanol then denatured and resolubilized by heating in 1.2% SDS/PBS to 85º C for 5 min. Insoluble components were precipitated by centrifugation at 6500 x g and soluble proteome was diluted in 5 ml PBS, for a final concentration of 0.2% SDS. Labeled proteins were bound to avidin-agarose beads (170 µL resuspended beads/sample, Thermo Pierce) while rotating

82 overnight at 4ºC. Bead-linked proteins were enriched by washing three times each in PBS and water, then resuspended in 6 M urea/PBS (Sigma-Aldrich) and reduced in dithiothreitol (1 mM, Sigma-Aldrich), alkylated with iodoacetamide (18 mM, Sigma-Aldrich), then washed and resuspended in 2 M urea/PBS with 1 mM calcium chloride and trypsinized overnight with 0.5 µg/µl sequencing grade trypsin (Promega). Tryptic peptides were discarded and beads were washed three times each in PBS and water, then washed with one wash of TEV buffer containing 1 µM DTT. TEV-biotin tag was digested overnight in TEV buffer containing 1 µM DTT and 5 µL Ac-TEV protease at 29ºC. Peptides were diluted in water and acidified with final concentration of 5% formic acid (1.2 M, Spectrum).

MS Analysis. Peptides from proteomic experiments were pressure-loaded onto a 250 mm inner diameter fused silica capillary tubing packed with 4 cm of Aqua C18 reverse-phase resin (Phenomenex # 04A-4299) which was previously equilibrated on an Agilent 600 series HPLC using gradient from 100% buffer A to 100% buffer B over 10 min, followed by a 5 min wash with 100% buffer B and a 5 min wash with 100% buffer A. The samples were then attached using a MicroTee PEEK 360 µm fitting (Thermo Fisher Scientific #p-888) to a 13 cm laser pulled column packed with 10 cm Aqua C18 reverse-phase resin and 3 cm of strong-cation exchange resin for isoTOP-ABPP studies. Samples were analyzed using an Q Exactive Plus mass spectrometer (Thermo Fisher Scientific) using a 5-step Multidimensional Protein Identification Technology (MudPIT) program, using 0 %, 25 %, 50 %, 80 %, and 100 % salt bumps of 500 mM aqeous ammonium acetate and using a gradient of 5-55 % buffer B in buffer A (buffer A: 95:5 water:acetonitrile, 0.1 % formic acid; buffer B 80:20 acetonitrile:water, 0.1 % formic acid). Data was collected in data-dependent acquisition mode with dynamic exclusion enabled (60 s). One full MS (MS1) scan (400-1800 m/z) was followed by 15 MS2 scans (ITMS) of the nth most abundant ions. Heated capillary temperature was set to 200º C and the nanospray voltage was set to 2.75 kV.

Data was extracted in the form of MS1 and MS2 files using Raw Extractor 1.9.9.2 (Scripps Research Institute) and searched against the Uniprot mouse database using ProLuCID search methodology in IP2 v.3 (Integrated Proteomics Applications, Inc) 493. Cysteine residues were searched with a static modification for carboxyaminomethylation (+57.02146) and up to two differential modifications for methionine oxidation and either the light or heavy TEV tags (+464.28596 or +470.29977, respectively). Peptides were required to have at least one tryptic end and to contain the TEV modification. ProLUCID data was filtered through DTASelect to achieve a peptide false-positive rate below 1%.

Thiolase activity assay. Enzymatic activity of Acaa1b and Acaa2 was performed using the Fluorometric Acetyltransferase Activity Assay Kit (ABCAM ab204536). The assay was performed per the protocol with 4 µg pure protein and

83 100 nM acetoacetyl-CoA (Sigma A1625), fluorescence was measured at 380/520 ex/em on a SpectraMax i3x detection platform.

Metabolomic profiling. Lipidomic profiling was performed as previously described457,494. Nonpolar lipid metabolites from the liver of in vivo-treated mice or HepG2 cells were extracted in 3 ml of 2:1 chloroform:methanol and 1 ml of PBS with inclusion of internal standards dodecylglycerol (10 nmol, Santa Cruz Biotechnology) and pentadecanoic acid (10 nmol, Sigma-Aldrich). Organic and aqueous layers were separated by centrifugation at 1000xg for 5 min and the organic layer was collected, dried under a stream of nitrogen and dissolved in 120 µl chloroform. Metabolites were separated by liquid chromatography as previously described494,495. Metabolomes were separated using reverse-phase chromatography with a Luna C5 column (50 mm x 4.6 mm with 5 µm diameter particles, Phenomenex). Mobile phase A consisted of 95:5 ratio of water/methanol and mobile phase B consisted of 2-propanol, methanol, and water in a 60:35:5 ratio. Solvent modifiers 0.1 % formic acid with 5 mM ammonium formate and 0.1 % ammonium hydroxide were used to assist ion formation as well as to improve the LC resolution in both positive and negative ionization modes, respectively. The flow rate for each run started at 0.1 ml/min for 5 min to alleviate backpressure associated with injecting chloroform. The gradient started at 0 % B and increased linearly to 100 % B over the course of 45 min with a flow rate of 0.4 ml/min, followed by an isocratic gradient of 100 % B for 17 min at 0.5 ml/min before equilibrating for 8 min at 0 % B with a flow rate of 0.5 ml/min.

MS analysis was performed with an electrospray ionization (ESI) source on an Agilent 6430 QQQ LC-MS/MS (Agilent Technologies). The capillary voltage was set to 3.0 kV, and the fragmentor voltage was set to 100 V. The drying gas temperature was 350°C, the drying gas flow rate was 10 l/min, and the nebulizer pressure was 35 psi. Metabolites were identified by SRM of the transition from precursor to product ions at associated optimized collision energies and retention times as previously described 494,495. Metabolites were quantified by integrating the area under the curve, and then normalized to internal standard values and for , tissue weight in mg. Metabolite levels are expressed as relative abundances as compared to controls.

HepG2 FAO Seahorse studies. HepG2 cells were plated into XF24 cell culture plates (50,000 cells per well, 500 µl substrate-limited DMEM per well, Seahorse Biosciences) overnight. 120 min before starting the assay, 1 mM AC or DMSO were added to their respective wells. The culture medium was removed from each well and replaced with FAO medium (375 µl) containing 1 mM AC or DMSO 45 min before starting the assay. 15 min prior to the start of the assay, etomoxir (40 µM final, Abcam, Inc. #144763) was added to its respective wells. Cells were incubated in a CO2-free incubator at 37 °C for 45 min. Prior to the rate measurements, Palmitate:BSA or BSA control (87.5 µl per well, Seahorse Biosciences #102720-100) were added to all of the wells. The XF Instrument

84 (Seahorse Biosciences) gently mixed the assay media in each well for 10 min to allow the oxygen partial pressure to reach equilibrium. The oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured simultaneously three times to establish a baseline rate. For each measurement, with a total of 12 measurements, there was a 3 min mix followed by 3 min wait time to restore normal oxygen tension and pH in the microenvironment surrounding the cells. Drug injection was performed throughout the assay. Oligomycin (2.5 µg/ml final) was injected after measurement 3, FCCP (0.5 µM final) was injected after measurement 6, and rotenone/antimycin A (2 and 4 µM final, respectively) after measurement 9.

85 5. FIGURES

Figure 4-1. Assessing acetanilide herbicide reactivity using ABPP platforms. (A) Structures of acetanilide herbicides. (B) Structures of the broad cysteine-reactive probe iodoacetamide-alkyne (IAyne) and acetochlor-alkyne (ACyne). (C) ABPP platforms used in this study. Acetanilide herbicides were

86 treated either in vitro or in vivo followed by treatment of liver proteomes with either IAyne or ACyne probes. IAyne and ACyne reactivity and acetanilide herbicide targets were subsequently determined either by fluorescence using gel- based ABPP, by protein pull-down by ABPP-MudPIT, or by mapping site-of- modification using isoTOP-ABPP. (D) Acetanilide herbicide reactivity in vitro in mouse liver proteome using gel-based ABPP. Proteomes were pre-treated in vitro (30 min) with metolochlor, alachlor, or AC, and then labeled with IAyne labeling (10 µM, 60 min) or ACyne labeling (1 µM, 60 min) followed by conjugation of rhodamine-azide by click-chemistry, and visualization of IAyne or ACyne reactivity by SDS/PAGE and in-gel fluorescence. (E) AC reactivity in vivo in mouse liver proteome using gel-based ABPP. Mice were pre-treated in vivo with AC (85 mg/kg ip, 4 h), and mouse liver proteomes were subsequently labeled ex vivo with IAyne (10 µM, 60 min) or ACyne (1 µM, 60 min), followed by rhodamine conjugation, SDS/PAGE, and in-gel fluorescence. Gels in (D) and (E) are representative gels from n=3.

87 Figure 4-2. Mapping in vivo targets of AC in mouse liver using ABPP platforms. (A) ABPP-MudPIT analysis of AC targets in vivo. Mice were treated in vivo with vehicle or AC (85 mg/kg ip, 4 h) and mouse liver proteomes were labeled with IAyne ex vivo (10 µM, 30 min) or DMSO (no-probe control), followed by conjugation of biotin-azide by click-chemistry, avidin-enrichment and MudPIT analysis. Shown are targets that showed significant (p<0.05) and >50 % reduction in IAyne labeling in AC-treated groups compared to vehicle-treated controls that also showed >2-fold IAyne enrichment in vehicle-treated compared to no-probe controls. (B) ACyne targets in mouse liver proteome. Mouse liver proteomes were labeled with ACyne (1 µM, 30 min) or DMSO (no-probe control), and subsequently conjugated to biotin-zide, avidin-enriched, and analyzed by MudPIT. Shown are targets that showed significant (p<0.05) and >2-fold ACyne enrichment compared to no-probe controls. (C) Experiments in (A) and (B) each

88 yielded 28 targets of AC, of which 6 were overlapping. (D) isoTOP-ABPP analysis of in vivo AC targets. In vivo vehicle or AC (85 mg/kg ip, 4h)-treated mouse liver proteomes were labeled with IAyne, followed by conjugation of biotin-azide bearing an isotopically light (vehicle-treated) or heavy (AC-treated) tag and TEV protease recognition sequence, vehicle and treated groups were combined in a 1:1 ratio, avidin-enriched, tryptically digested, and probe-modified tryptic peptides were avidin-enriched again and released by TEV protease digestion. Probe-modified light or heavy tryptic peptides were analyzed by MudPIT. A larger light to heavy ratio indicates that AC bound to a particular cysteine on that peptide. (E) Specific cysteines on Acaa2, Acaa1b, Scp2, Acat2, and Acsf2 labeled by IAyne and their light to heavy ratios determined from isoTOP-ABPP studies in (D). Highlighted in red are catalytic cysteines. (F) Validation of AC binding to thiolase enzymes. AC was pre-incubated for 30 min with either overexpressed mouse protein in HEK293T cells or pure mouse protein and labeled with IAyne (10 µM, 30 min), followed by conjugation of rhodamine-azide, SDS/PAGE, and in-gel fluorescence. Gels are representative of n=3. (G) Thiolase activity assays. DMSO or AC (1 mM, 30 min) was incubated with pure mouse Acaa1 and Acaa2 protein and thiolase activity was measured using a substrate assay. Data in (A, B, G) are presented as mean ± sem, n=3. Significance in (G) is presented as *p<0.05 compared to DMSO-treated control.

89

Figure 4-3. AC inhibits fatty acid oxidation in mouse liver leading to lipid dysregulation. (A, B) Liver lipid levels in mice treated with AC. Mice were treated with vehicle or AC (85 mg/kg ip, once per day for 6 days) on a chow diet (A) or vehicle or AC (85 mg/kg ip once every other day for 6 weeks) on a high-fat diet (B). High-fat diet was initiated 4 weeks before initiation of AC treatments. Single-reaction monitoring (SRM)-based targeted LC-MS/MS metabolomic

90 profiling was performed on livers. Shown are representative metabolites that were significantly (*p < 0.05) altered in livers from mice treated with AC compared to vehicle-treated controls. Abbreviations: FFA, free fatty acid; TAG, triacylglycerols; DAG, diacylglycerol; MAG, monoacylglycerol; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PI, phosphatidyl inositol; PA, phosphatdic acid; SM, sphingomyelin. “L” denotes lyso. (C) Isotopic fatty acid tracing in HepG2 hepatocyte cells treated with AC. HepG2 cells were pre- treated with DMSO vehicle or AC (1 mM, 1 h) before labeling cells with [U- 13C]palmitic acid ([13C]C16:0 free fatty acid (FFA)) (10 µM, 6 h). Isotopic incorporation of [13C]C16:0 FFA into complex lipids was measured by SRM- based LC-MS/MS. (D) Oxygen consumption rate (OCR) in HepG2 cells pre- treated with AC (1 mM, 2 h). Etomoxir (40 µM) was added 15 min prior to the start of the experiment to ensure that CPT-1 was inhibited. BSA control or BSA;Palmitate (87.5 µL) were added immediately before the start of the experiment. Oligomycin (2.5 ug/ml) was added between measurements 3 and 4, FCCP (0.5 µM) was added between 6 and 7, and rotenone and antimycin A (2 and 4 µM, respectively) were added between 9 and 10, with 12 measurements total. (E) AC inhibits fatty acid oxidation enzymes, leading to impaired fatty acid oxidation and diversion of fatty acids into other lipid pathways. Data in (A-D) are presented as mean ± sem, n=5 per group for (A-C) and n=4 for (D). Significance is presented as *p<0.05 compared to vehicle-treated controls in (A-C). Significance in (D) is presented as *p<0.05 comparing BSA groups to AC + BSA groups and #p<0.05 comparing BSA+palmitate to AC + palmitate.

91 CHAPTER FIVE: Conclusions

92 1. CHAPTERS SUMMARIZED

Dysregulation of cancer cell metabolism contributes to abnormal cell growth, the biological endpoint of cancer. In Chapter One, I reviewed numerous affected oncogenes and metabolic pathways common in cancer, and how they contribute to cancer pathogenesis and malignancy. Chapter One also discusses various pharmacological manipulations that take advantage of these metabolic abnormalities, and the current targeted therapies that have arisen from this research.

A large number of the pharmaceuticals presented in Chapter One, as well as various endogenous metabolites and environmental chemicals, act through covalent mechanisms with protein targets. Yet, their specific interactions with the proteome still remain poorly defined for most of these reactive chemicals. Deciphering direct protein targets of reactive small-molecules is critical in understanding their biological action, off-target effects, potential toxicological liabilities, and development of safer and more selective agents. Chemoproteomic technologies have arisen as a powerful strategy that enable the assessment of proteome-wide interactions of these irreversible agents directly in complex biological systems. In Chapter Two I reviewed several chemoproteomic strategies that have facilitated our understanding of specific protein interactions of irreversibly-acting pharmaceuticals, endogenous metabolites, and environmental electrophiles to reveal novel pharmacological, biological, and toxicological mechanisms.

In Chapter Three, I used some of the chemoproteomic platforms discussed in Chapter Two to reveal a novel protein target of NSCLC, ALDH3A1. Here I screened a cysteine-reactive covalent ligand library to identify hit compounds that impair cell survival and proliferation in NSCLC cells, but not in primary human bronchial epithelial cells. Through this screen, I identified a covalent ligand hit, DKM 3-42 which impaired both in situ and in vivo lung cancer pathogenicity. ABPP-based chemoproteomic analysis revealed the primary target of DKM 3-42 as the catalytic cysteine in aldehyde dehydrogenase 3A1 (ALDH3A1). I performed further chemoproteomics-enabled covalent ligand screening directly against ALDH3A1, and identified a more potent and selective lead covalent ligand, EN40, which inhibits ALDH3A1 activity and impairs lung cancer pathogenicity. Here I presented ALDH3A1 as a potentially novel therapeutic target for lung cancers that express ALDH3A1 and identified two selective and active ALDH3A1 inhibitors. Overall, Chapter Three aims to show the utility of combining chemical genetics screening of covalent ligand libraries with chemoproteomic approaches to rapidly identify anti-cancer leads and targets.

Finally, in Chapter Four, I show further utilization of the chemoproteomic approaches I presented in Chapter Two when I applied a competitive, activity- based ABPP strategy to map the proteome-wide cysteine reactivity of acetochlor, the most widely used acetanilide herbicide, in vivo in mice. I showed here that

93 acetochlor directly reacts with >20 protein targets in vivo in mouse liver, including the catalytic cysteines of several thiolase enzymes involved in mitochondrial and peroxisomal fatty acid oxidation. I further showed that the fatty acids that are not oxidized, due to impaired fatty acid oxidation, are instead diverted into other lipid pathways, resulting in heightened free fatty acids, triglycerides, cholesteryl esters, and other lipid species in the liver. Overall, the findings presented in Chapter Four show the utility of chemoproteomic approaches for identifying novel mechanisms of toxicity associated with environmental chemicals like acetanilide herbicides.

The immediate future challenges concerning discovering therapeutics for cancer metabolism entail identifying metabolic pathways and nodes that are necessary for cancer cell survival, and further identifying which proteins within these pathways can be manipulated to inhibit cancer cell proliferation and survival. As mentioned in this dissertation, many therapeutic proteins that have already been discovered or will be discovered in the future are currently thought to be undruggable; most proteins do not possess known binding pockets or druggable 'hotspots' that small-molecules can bind to modulate protein function. The research presented in this dissertation used chemoproteomic platforms to discover and pharmacologically target ALDH3A1, a novel druggable protein in NSCLC. All in all, applying and advancing these platforms and similar technologies to find more druggable proteins will play a significant role in paving the way for future drug discoveries and disease therapies.

2. FINAL REMARKS

The ultimate aim of this dissertation was to present a current understanding of cancer metabolism and chemoproteomic platforms, and apply this knowledge to further discover new druggable proteins in cancer and pharmacologically target these with small molecules. One of the largest challenges remaining in developing new therapies is that most of the proteome is considered “undruggable”; most proteins do not have known functional binding pockets that drugs can bind and pharmacologically modulate to treat disease. Here, I have shown how, by coupling chemoproteomic strategies with covalent ligand libraries, we can pharmacologically target proteins that were once considered undruggable, including ALDH3A1, to treat human disease.

Using similar chemoproteomic approaches, I further demonstrated the utility and importance of these strategies when I looked at the direct protein targets of the environmental chemical acetochlor and its downstream biochemical and pathophysiological effects. Here, I was able to identify acetochlor’s chemical- protein interactions in complex biological systems and, in-turn, a novel toxicological mechanism of this environmental chemical. Overall, in this dissertation, I used chemoproteomic platforms to 1) identify anticancer targets and relevant small molecule therapeutics and to 2) map proteome-wide toxicological targets of environmental chemicals.

94 References

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