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Comparative Systems Analysis of Opportunistic Gram-Negative Pathogens

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Comparative Systems Analysis of Opportunistic Gram-Negative Pathogens Comparative systems analysis of opportunistic Gram-negative pathogens A Dissertation presented to the faculty of the School of Engineering and Applied Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Jennifer Anne Gardner Bartell May 2015 Department of Biomedical Engineering ii APPROVAL SHEET This dissertation is in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Engineering Jennifer Anne Gardner Bartell Author This dissertation has been read and approved by the examining committee: Jason Papin, Ph.D. Dissertation Advisor Department of Biomedical Engineering Shayn Peirce-Cottler, Ph.D. Committee Chair Department of Biomedical Engineering Erik Hewlett, M.D. Committee Member, Division of Infectious Diseases and International Health Joanna Goldberg, Ph.D. Committee Member Department of Pediatrics, Emory University Jeff Saucerman, Ph.D. Committee Member Department of Biomedical Engineering Accepted for the School of Engineering and Applied Science: Jaymes H. Aylor, Ph.D. Dean, School of Engineering and Applied Science May 2015 Dedication To my committee and secondary advisors – thank you so much for your steady guidance and support. It has been so helpful to know that you have all been rooting for me to succeed while you helped to shape my path forward. I appreciate your reliability in always being willing to meet with me at short notice, answer questions, and have critical but supportive discussions about my projects. Your advice, additional training and unique perspectives have been invaluable over the years. To my labmates past and present – thanks for playing your role as both colleagues working in partnership towards the same goals and reliable friends so well. You’ve made my graduate years so much more positive and enjoyable than they would be without you. Thanks for all the work chats, the coffee breaks, the happy hours, and the evenings out sprinkled with both jokes and camaraderie as fellow students and nerdy conversations about project details. I will miss working with all of you. To my friends, my second family – I have tried to convey how much your friendship has meant to me as I’ve gotten older and learned the value of reliable and supportive relationships, but I think I have still failed in expressing it as well as I would like. Thanks for the listening, the advice, the easy hang outs, the shared rants, the growth together rather than apart, the ridiculous dance parties, and the wine Skypes so essential to continued close friendships even as we spread across the world. To my advisor Jason – you have been an incredible mentor; I am so lucky that a position opened in your lab nearly five years ago that you thought I could fill. You have let me grow at my own pace, start too many projects and analyses at one time, and then guided me back towards the promising ones, and I have nearly always left your office feeling more encouraged and positive than when I went in. You have been a kind and incredibly supportive listener through both professional and personal struggles, and allowed me to address each in turn as I needed. Your optimism has balanced my pessimism, and you have shown me that it is possible to handle the crazy demands of professorship with grace, humor, balance, and a large grain of salt. Thank you so much for your mentorship and friendship. To my family – thank you for your unflagging support over the years that I have pursued this dream. You know more than anyone what it means to me to finally be at the end of my time at UVa with a successful PhD behind me and excitedly planning the next steps of my career. You were the ones to never have a shred of doubt in my abilities from the very beginning and always encourage my next steps with full belief in my future success. You have always, always been my staunchest, loudest, and most faithful and unquestioning fans and perpetually available listeners in this long process, and I thank you for that and love you very much. Abstract Persistent bacterial infections are a rapidly growing concern worldwide; combatting these drug-resistant infections is hampered by a limited understanding of multifactorial pathogen evolution. Pathogens must adapt to novel nutrient restrictions, stresses induced by the host environment, competition with other microbes, and therapeutic interventions such as antibiotic treatment. The lung infections of cystic fibrosis (CF) patients are an excellent model of long-term pathogen evolution. Pseudomonas aeruginosa and species of the Burkholderia cepacia complex (Bcc) are considered the most problematic CF pathogens, known for their dominance over other pathogens, ability to induce infections lasting decades, and resistance to antibiotics (factors which also contribute to their role in serious hospital-acquired infections). With the goal of providing novel insights into potential new therapeutic targets to combat rising drug resistance, I investigate the metabolic flexibility, virulence capability, and adaptive metabolic rewiring of these pathogens using comparative systems analyses via the framework of genome-scale metabolic models. I have built two new genome-scale metabolic reconstructions of Bcc species, updated and reconciled an existing model of P. aeruginosa PAO1 and also created a new model of P. aeruginosa PA14. I couple these models with constraint- based analysis techniques and experimental phenotype screening to validate predictions and pursue model-generated hypotheses. My work has resulted in the prediction of specific mechanistic causes for differential antibiotic resistance and capacity for virulence between B. cenocepacia and B. multivorans via quantitative examinations of genetic redundancy and predicted activity of secondary metabolite production pathways. I extended my comparative analysis approaches to the study of decades-long evolution in P. aeruginosa clinical isolates from chronic CF infections, using a novel integration of single nucleotide polymorphism- and expression-based constraints to create isolate-specific metabolic models representing early and late stage adaptation. I identified network rewiring of redox metabolism as a potentially important contributor to the successful persistence of the late stage strains. The P. aeruginosa models have also been used to evaluate genes and enzymes essential to the production of known virulence factors and tradeoffs between virulence factor production and bacterial growth, providing a parallel avenue of treatment to enhance current antibiotic approaches. My analysis of these predictions identifies novel therapeutic targets for inhibiting virulence factor production alone or in addition to growth, of which a subset are experimentally evaluated using in vitro gene knockouts. In summary, I use an integrated computational and experimental framework to conduct a comparative systems analysis of important drug- resistant pathogens, contributing novel insights into their adaptive metabolic capabilities and proposing new therapeutic approaches. 1 Table of Contents Abstract ...................................................................................................... 1 Table of Contents ........................................................................................ 2 List of Figures and Tables ........................................................................... 5 Chapter 1: Clinical applications of microbial metabolic reconstructions ...... 7 Systems biology in biomedical research ................................................... 8 Systems analysis via metabolic modeling ................................................. 9 Construction & analysis approaches ....................................................... 10 Growth of clinical applications ............................................................... 12 Drug targeting ........................................................................................ 14 Comparative genomics ........................................................................... 15 Adaptive evolution ................................................................................. 16 Engineering the host environment ......................................................... 17 Interactions within the microbiome ....................................................... 18 Applications in infections by opportunistic pathogens............................ 19 Metabolic adaptation .............................................................................. 21 Virulence factors .................................................................................... 22 Comparing growth and virulence-linked adaptation ............................... 24 Chapter 2: Whole-genome metabolic network reconstruction ................... 27 and constraint-based modeling ................................................................. 27 SYNOPSIS .............................................................................................. 28 Introduction ........................................................................................... 29 Metabolic Network Reconstruction ......................................................... 30 Genome annotation ................................................................................ 31 Automated network reconstruction ........................................................... 33 Network refinement ................................................................................ 34 In vitro experimentation
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