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Debugging Parasite Genomes: Using Metabolic Modeling to Accelerate Antiparasitic Drug Development

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Debugging Parasite Genomes: Using Metabolic Modeling to Accelerate Antiparasitic Drug Development Debugging parasite genomes: Using metabolic modeling to accelerate antiparasitic drug development Maureen A. Carey Charlottesville, Virginia Bachelors of Science, Lafayette College 2014 A Dissertation presented to the Graduate Faculty of the University of Virginia in Candidacy for the Degree of Doctor of Philosophy Department of Microbiology, Immunology, and Cancer Biology University of Virginia September, 2018 i M. A. Carey ii Abstract: Eukaryotic parasites, like the casual agent of malaria, kill over one million people around the world annually. Developing novel antiparasitic drugs is a pressing need because there are few available therapeutics and the parasites have developed drug resistance. However, novel drug targets are challenging to identify due to poor genome annotation and experimental challenges associated with growing these parasites. Here, we focus on computational and experimental approaches that generate high-confidence hypotheses to accelerate labor-intensive experimental work and leverage existing experimental data to generate new drug targets. We generate genome-scale metabolic models for over 100 species to develop a parasite knowledgebase and apply these models to contextualize experimental data and to generate candidate drug targets. M. A. Carey iii Figure 0.1: Image from blog.wellcome.ac.uk/2010/06/15/of-parasitology-and-comics/. Preamble: Eukaryotic single-celled parasites cause diseases, such as malaria, African sleeping sickness, diarrheal disease, and leishmaniasis, with diverse clinical presenta- tions and large global impacts. These infections result in over one million preventable deaths annually and contribute to a significant reduction in disability-adjusted life years. This global health burden makes parasitic diseases a top priority of many economic development and health advocacy groups. However, effective prevention and treatment strategies are lacking. Like most infectious disease problems, social, eco- nomic, and biological challenges converge, amplifying the disease burden and slowing the development of sustainable solutions. The work described here focuses on only one facet of this complex problem: the biology of the parasite, specifically its metabolism, during infection. However, this work broadly aims to increase our understanding of these parasites directly by studying disease-relevant phenotypes and indirectly by developing computational tools to study these organisms. Moreover, the tool development presented in this dissertation aims to increase accessibility and usability of computational biology tools and foster solutions sensitive to and compatible with the social and economic environment in which these diseases are most serious. M. A. Carey iv Acknowledgements: Document was generated using R (R Core Team 2017), Rmarkdown (Allaire et al. 2018), and R packages: kableExtra, knitr, readxl, dplyr, ggpubr, tidyverse, and reshape2 (Zhu 2018; Xie 2014; Wickham and Bryan 2017; Wickham et al. 2015; Kassambara 2017; Wickham 2017, 2012), in addition to analytic and visualization software mentioned throughout the document. The research presented in this document was also dependent on the EuPathDB community for the database services and support. I use one of the EuPathDB databases nearly everyday and would be far less productive without the EuPathDB community’s vision and hard work. The EuPathDB services inspired the knowledgebase project (Ch. 3), as I am sure they will inspire many more. Thank you to my coauthors and the Guler, Papin, and Petri lab groups for their contributions to my work and evolution as a scientist. The Guler and Papin labs were my research homes and their thoughtful feedback and discussion was invaluable to the publications presented here and the work I will continue. The weekly Petri group meeting and friendships found there stretched my research in new and interesting directions. I am so looking forward to joining this group full time! Thank you to my thesis committee, Drs. Hervé Agaisse, Alison Criss, Young Hahn, and Norbert Leitinger, for your time and feedback. I am so grateful that my committee meetings were always fun, interesting, and productive - what science should be! An extra thank you to Dr. Alison Criss for her support. On a personal note: • To all of my (science and nonscience) advocates and mentors over the years - Thank you all for believing in me and for all of the lessons I will carry with me. In particular, thank you to Mr. O for teaching me how to teach myself and for being my archetype of a mentor. Drs. Kurt and Ho - thank you for helping me discover science! • To my family and friends - Thank you for your support and for keeping me grounded in what matters most. Thank you Ron, Cami, Vicki, Scott, Brad, Donnie, Granddad, Aunt Debbie, Uncle Ed, Jenn, Nicole, Aunt Karen, Lela, Carson, Christina, Caitlin, Rusty, Mahmoud, Ali, Mom and Dad - I love you all! An extra thank you to the original Drs. Carey - Thank you for teaching me to be pleasantly persistent and showing me what leadership looks like. To my "twin" - I still hope I am as cool/kind/fun/impressive as you are when I grow up. Thank you for being my role model and leading me to where I am now. • To my best friend and collaborator, Gregory - Without you, the sun would shine a bit less bright and my ideas would be half-baked. Thank you for everything. None of this work would be possible without my team, and I thank you all! M. A. Carey v Contents 1 Introduction 2 2 Background 6 2.1 Plasmodium ................................ 6 2.1.1 Malaria . 6 2.1.2 Transmission and Pathogenesis . 6 2.1.3 Prevention and treatment . 8 2.1.4 Parasite biology and previous research . 9 2.1.5 Challenges . 16 2.2 Toxoplasma gondii ............................ 16 2.2.1 Toxoplasmosis . 16 2.2.2 Transmission and Pathogenesis . 18 2.2.3 Prevention and treatment . 18 2.2.4 Parasite biology and previous research . 19 2.2.5 Challenges . 20 2.3 Cryptosporidium ............................. 20 2.3.1 Cryptosporidiosis . 20 2.3.2 Transmission and Pathogenesis . 20 2.3.3 Prevention and treatment . 21 2.3.4 Parasite biology and previous research . 21 2.3.5 Challenges . 22 2.4 Project motivation and shared challenges . 23 3 Building a parasite knowledgebase 25 3.1 Synopsis . 25 3.2 Background ................................ 25 3.3 Methods.................................. 26 3.4 Results................................... 34 3.5 Discussion . 47 3.6 Conclusions . 50 4 Curating a high quality reconstruction of parasite metabolism 53 4.1 Synopsis . 53 4.2 Methods.................................. 53 4.3 Results................................... 56 4.4 Discussion . 97 4.5 Conclusions . 100 5 Applying metabolic models to better understand drug resistance 102 M. A. Carey vi 5.1 Artemisinin . 102 5.1.1 Synopsis . 102 5.1.2 Background . 102 5.1.3 Methods . 104 5.1.4 Results . 107 5.1.5 Discussion . 126 5.1.6 Conclusions . 132 5.1.7 Experimental interrogation . 132 5.2 Chloroquine . 136 5.2.1 Synopsis . 136 5.2.2 Background . 136 5.2.3 Methods . 137 5.2.4 Results and Discussion . 137 5.2.5 Conclusions . 141 6 Extending modeling with metabolomics 143 6.1 Synopsis . 146 6.2 Metabolomics as model constraints . 146 6.2.1 Background . 146 6.2.2 Methods . 147 6.2.3 Results and Discussion . 148 6.2.4 Conclusions . 149 6.3 Intracellular pathogen metabolomics methodology . 150 6.3.1 Background . 150 6.3.2 Methods . 151 6.3.3 Results . 157 6.3.4 Discussion . 168 6.4 Conclusions . 172 7 Becoming a computational biologist 174 7.1 Learning to program . 174 7.1.1 Ten Simple Rules for biologists learning to program . 174 7.2 Contributioning to open source software . 185 7.2.1 Gapfilling . 185 7.2.2 Renaming genes . 186 7.2.3 Additional examples . 188 8 Reflections and Future Directions 190 8.1 Parasite phenotyping with host in mind . 190 8.2 Moving from malaria to more neglected tropical diseases . 191 M. A. Carey vii 8.3 Making modeling accessible . 191 8.4 Using computational biology to ‘level the playing field’ . 193 9 Conclusions 196 10 Appendix 201 10.1 Supplementary Tables . 201 10.2 Code and analyses . 201 References 202 M. A. Carey viii List of Figures 0.1 Image from blog.wellcome.ac.uk/2010/06/15/of-parasitology-and-comics/. iii 2.1 Malaria parasites require host and vector . 7 2.2 Plasmodium falciparum .......................... 8 2.3 Compartmentalization in the malaria parasite . 12 2.4 Trends in malaria incidence . 17 2.5 Drug resistance has emerged to every antimalarial on the market . 18 3.1 Metabolic modeling . 27 3.2 Parasite Database for Genome-Scale Metabolic Models (Paradigm) . 30 3.3 Sequence similarity . 38 3.4 Genome reannotation by species . 39 3.5 Genome reannotation by database . 40 3.6 Draft reconstructions for 162 genomes . 41 3.7 Draft reconstructions reveal unique functions in all organisms . 42 3.8 Network similarity . 43 3.9 Genetically-supported transporters . 47 4.1 iPfal17 model curation is broad and comprehensive . 57 5.1 Ring-stage parasites are genotypically and phenotypically distinct . 105 5.2 Computational pipeline . 106 5.3 Distribution of genome-wide expression data . 108 5.4 Artemisinin resistance is better predicted by metadata classifier than expression classifier . 109 5.5 Functional differences in data-driven sensitive and resistant models . 111 5.6 Artemisinin sensitive and resistant parasites utilize different metabolic genesandpathways............................ 125 5.7 Artemisinin resistant and sensitive parasites have unique metabolite transport capabilities . 128 5.8 Artemisinin resistance displays unique metabolic weaknesses . 130 5.9 Putrescine scavenging and synthesis in Plasmodium falciparum .... 133 5.10 Artemisinin sensitive and resistant parasites are sensitive to DFMO treatment ................................. 134 5.11 Putrescine does not affect sensitivity to artemisinin . 135 5.12 Resistant parasites respond to chloroquine by downregulating expression138 5.13 Chloroquine-treated resistant parasites have new metabolic weaknesses 139 5.14 Flux shifts in isoprenoids metabolism and folate biosynthesis in response to chloroquine .
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