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Computational Approaches for Understanding One-Carbon

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Computational Approaches for Understanding One-Carbon Computational approaches for understanding one-carbon metabolism in cancer A Dissertation Presented to the Faculty of the Graduate School of Cornell University In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Mahya Mehrmohamadi January 2017 © 2017 Mahya Mehrmohamadi ABSTRACT COMPUTATIONAL APPROACHES FOR UNDERSTANDING ONE- CARBON METABOLISM IN CANCER Mahya Mehrmohamadi, Ph. D. Cornell University 2017 Cancer metabolism is an emerging research area in cancer biology and therapeutics. One of the major metabolic pathways known to play important roles in the pathogenesis of cancer is one-carbon (1-C) metabolism. 1-C metabolism integrates the status of many dietary nutrients as inputs, and in turn regulates a variety of cellular processes including de novo nucleotide synthesis, lipid metabolism, protein biosynthesis, redox metabolism, transsulfuration, and epigenetics. As the regulation of these cellular processes is critical to cells, the tuning of the activity of 1-C metabolism plays important roles in cancer. Previous studies have established implications of genetic and dietary perturbations of multiple components of 1-C metabolism in human cancers. However, the heterogeneity among cancer types and subtypes with respect to the usage and flux distribution of 1-C metabolism has not been systematically quantified. There remain great potentials in deciphering how 1-C metabolism plays different roles in different human cancers, especially since this metabolic pathway is targeted by a number of the existing antimetabolite chemotherapeutic agents. In this dissertation, I quantitatively characterize various aspects of 1-C metabolism across human cancers. I first investigate the between-cancer-type variation in the usage of serine by 1-C metabolism using flux distribution analyses and find substantial heterogeneity. I also show that a common feature across cancers is correlated activation of nucleotide and redox metabolism. Next I assess the link between 1-C metabolism and DNA methylation using computational modeling and machine-learning. I find significant contribution from particular enzymes within 1-C metabolism— such as methionine adenosyltransferases— in explaining the within- cancer-type (inter-individual) variation in DNA methylation. My results provide evidence that misregulation of 1-C metabolism is at least in part responsible for disrupted DNA methylation profiles in tumors leading to epigenetic instability and higher malignancy. Given evidence for the role of 1-C metabolism and the methionine cycle in methylation dynamics, I next evaluate the potential for dietary intervention using the amino acid methionine. To this end, I model human serum methionine levels and quantify the contribution of various factors in determining the concentration of methionine. I discover that dietary factors could together explain nearly 30% of overall variation in methionine concentrations, and also provide evidence that the relationship between 1-C metabolism and methylation exists at physiological concentrations of methionine. Finally, I use a novel approach to identify gene expression markers of tumor response to 5-FU and Gemcitabine —two of the commonly used antimetabolite chemotherapies that target enzymes in 1-C metabolism. I discover that response to these agents is to a large degree determined by the metabolic state of tumors and the expression levels of specific target pathways of each of these agents. Together, my findings provide quantitative information about the heterogeneity among tumors with respect to the usage of 1-C metabolism, and delineate some of the ways this information can be translated into clinical decision- making. BIOGRAPHICAL SKETCH Mahya Mehrmohamadi was born in Los Angeles, California on March 1st, 1987. When she was very young, she moved with her family to Iran where they are originally from. Her parents are both professors in academia in Tehran, Iran. When Mahya was in high school, she took a liking for solving problem sets in human genetics. She grew fond of the interdisciplinary nature of this field and how math— which she had been good at since elementary school— can be applied to human health and disease. Also, as a teenager she was very active in community service outside of school. Together with a group of her friends, she worked as a volunteer in a few charity centers including a pediatric cancer hospital in Tehran. Those experiences drew her attention to cancer as a disease that remains difficult and perplexing to clinicians. After finishing high school, like all other high school graduates in Iran, Mahya participated in a national exam for entrance to college. She was ranked 8th nation-wide, which gave her the opportunity to study in any field and at any institution of her choosing in Iran. She chose to pursue her passion for genetics and interdisciplinary science. Mahya got her B.S and M.S in Biotechnology from the University of Tehran in a 5-year combined program. She also worked as a research assistant at Royan stem cell research institute and clinic in Tehran for two years before she decided to continue her education in Genetics and Genomics at Cornell University in 2012. At Cornell, she became very interested in computational biology and decided to switch to more computationally focused research. Mahya joined the lab of Dr. Jason Locasale where she worked on a thesis mainly focused on modeling cancer metabolism using genomics and epigenomics tumor data. She plans to continue i working as a computational cancer scientist and become an independent research investigator in the future. ii Dedicated to my grandparents, Hassan Mehrmohammadi, Mohammad Jafar Pourazar, Sedigheh Giahi, Fatemeh Katouzian And to my little nephew, Mohammad Safabakhsh iii ACKNOWLEDGMENTS I would first like to express my gratitude to my advisor Dr. Jason Locasale for his continuous support. You have been a great mentor to me, and your advice on both my research and career has been priceless. I owe my progress in scientific writing, research, and communication skills to your patience and guidance. You taught me how to learn from my failures and rise above them, lessons that will help me for the rest of my life. I also want to sincerely thank my co-advisor Prof. Andrew Clark for his encouragement support, not only on matters related to research, but also on other important decisions throughout my PhD. I am especially grateful to you for kindly welcoming me in your lab when the Locasale lab moved to Duke University. It has been an amazing experience and I have learned a great deal from you and the talented members of your diverse research group during this time. I would also like to thank my thesis committee as well as the faculty at Cornell’s MBG department for the great environment they have made that provides students with invaluable resources, helping them develop their careers while in graduate school. I especially like to thank my third committee member Dr. Frank Schroder for all of his support throughout my PhD; and Dr. Jason Mezey for the valuable concepts I learned from his Quantitative Genetics and Genomics course, which I also had the opportunity to TA last year. Finally, I want to thank the faculty at Cornell’s Statistics department, especially Drs. James Booth, Martin Wells, and Jacob Bien for useful discussions and collaborations. I also like to thank the team of assistants at Statistical consulting unit and also at Cornell’s Cloud Computing center, especially Dr. Brandon Barker for his help with our Red Cloud remote computing account. iv I want to thank my all of my collaborators. I am especially grateful to Xiaojing Liu for teaching me experimental metabolomics, Alex Shestov for the flux balance analysis (FBA) work, Lucas Mentch for teaching me about Random Forests, Stephen Salerno for our collaboration on RRmix, and Samantha Mentch for our collaboration on the methionine project. I would also like to thank all current and former Locasale and Clark lab members for their support and useful comments on my research throughout my PhD. I am especially thankful to my husband Abolhassan Vaezi for his incredible patience and whole-hearted love and support during the past five years. Thank you for always being there for me and reassuring me at times of disappointment and hopelessness! I cannot imagine how I would have been able to survive graduate school without you by my side. You helped me make important decisions when I felt lost and confused, kept me going when I had no idea what I was doing, and helped me keep things in perspective at challenging times. I am truly lucky and grateful for having you. I would also like to thank my amazing parents Mahmoud Mehrmohamadi and Roya Pourazar for their unconditional love and support. You are truly the best parents I could ask for, and my role models in personal and academic life. Making you proud has always been my biggest motivation for moving toward my dreams. Dad, thank you for always believing in me, even way beyond my potentials! You taught me to aim high, be strong and think big. Mom, thank you for all of the sacrifices you made to keep our home the most peaceful place on Earth! You taught me it is possible to pursue my career goals while building and caring for a family. And special thanks for the amazing packages that you occasionally sent to Ithaca, full of gifts and goodies from home that you and my dear aunt Raheleh prepared for me with love. Seeing v those big yellow Iran post office boxes at our apartment’s door always put a big smile on my face! I also want to thank my family and friends who have contributed appreciably to my happiness and sanity while I was going through grad school. I would first like to thank my sisters. Boshra, thank you for always having helped me see the bigger picture and not get too distracted by the ups and downs of everyday life.
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