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Systems Biology Approaches to Probe Gene Regulation in Bacteria The Texas Medical Center Library DigitalCommons@TMC The University of Texas MD Anderson Cancer Center UTHealth Graduate School of The University of Texas MD Anderson Cancer Biomedical Sciences Dissertations and Theses Center UTHealth Graduate School of (Open Access) Biomedical Sciences 8-2012 Systems Biology Approaches to Probe Gene Regulation in Bacteria Diogo F. Troggian Veiga Follow this and additional works at: https://digitalcommons.library.tmc.edu/utgsbs_dissertations Part of the Bioinformatics Commons, Computational Biology Commons, Microbiology Commons, and the Systems Biology Commons Recommended Citation Troggian Veiga, Diogo F., "Systems Biology Approaches to Probe Gene Regulation in Bacteria" (2012). The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access). 278. https://digitalcommons.library.tmc.edu/utgsbs_dissertations/278 This Dissertation (PhD) is brought to you for free and open access by the The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences at DigitalCommons@TMC. It has been accepted for inclusion in The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access) by an authorized administrator of DigitalCommons@TMC. For more information, please contact [email protected]. SYSTEMS BIOLOGY APPROACHES TO PROBE GENE REGULATION IN BACTERIA By Diogo Fernando Troggian Veiga, M.Sc. APPROVED ___________________________ Gábor Balázsi, PhD ___________________________ Tim Cooper, Ph.D ___________________________ Ju-Seog Lee, Ph.D ___________________________ John N. Weinstein, M.D, Ph.D ___________________________ Ambro van Hoof, Ph.D APPROVED ___________________________ Dean, The University of Texas Health Science Center at Houston Graduate School of Biomedical Sciences SYSTEMS BIOLOGY APPROACHES TO PROBE GENE REGULATION IN BACTERIA A DISSERTATION Presented to the Faculty of The University of Texas Health Science Center at Houston and The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY by Diogo Fernando Troggian Veiga, M.Sc. Houston, TX August 2012 Dedication I dedicate this work to my first mentor, Luismar Marques Porto, who gave me the opportunity to grow in a fruitful scientific environment, and taught me the basics of scientific research. His vision and ideas inspired me to become a computational biologist, and to pursue scientific questions by multidisciplinary approaches. I also dedicate this dissertation to my mother, Zenaide Salete Veiga, for her continuous support and unconditional trust during this period of my life. My mother is one of the most courageous persons I have ever known, and her courage and determination serve as a model to my own endeavors. iii Acknowledgments I would like to express my deep gratitude to my mentor, Gábor Balázsi, who accepted me in his lab, trusted on my ideas and gave me the freedom to pursue them. Gábor was an excellent mentor, always available when I needed, and the lab microenvinroment that he created provided the ideal conditions for my doctoral studies. I thank all my colleagues for their invaluable help, suggestions and “hard time” during lab meetings, which were vital to improve my research. In special Dmitry Nevozhay, who taught me most of the molecular biology experiments performed in this dissertation. As a foreign student staying for a long period abroad, I met many wonderful people going through similar experiences. Each of them enriched my life with their friendship. Many of these friends already went back to their countries, but they will always be remembered. In special, I would like to thank Tiago Paixão, my friend and roomate who shared with me much of this period. Tiago was the older brother that I never had, helping me through difficult moments. Of course, he also taught me to cook many excellent Portuguese dishes, as well as introduced me to very good music. Finally, I was lucky to meet the love of my life, Manuela during this period. Meeting her was certainly the greatest gift I had while living in Houston, and her love and support were essential during this endeavor. iv Abstract Mechanisms that allow pathogens to colonize the host are not the product of isolated genes, but instead emerge from the concerted operation of regulatory networks. Therefore, identifying components and the systemic behavior of networks is necessary to a better understanding of gene regulation and pathogenesis. To this end, I have developed systems biology approaches to study transcriptional and post-transcriptional gene regulation in bacteria, with an emphasis in the human pathogen Mycobacterium tuberculosis (Mtb). First, I developed a network response method to identify parts of the Mtb global transcriptional regulatory network utilized by the pathogen to counteract phagosomal stresses and survive within resting macrophages. As a result, the method unveiled transcriptional regulators and associated regulons utilized by Mtb to establish a successful infection of macrophages throughout the first 14 days of infection. Additionally, this network-based analysis identified the production of Fe-S proteins coupled to lipid metabolism through the alkane hydroxylase complex as a possible strategy employed by Mtb to survive in the host. Second, I developed a network inference method to infer the small non-coding RNA (sRNA) regulatory network in Mtb. The method identifies sRNA-mRNA interactions by integrating a priori knowledge of possible binding sites with structure- driven identification of binding sites. The reconstructed network was useful to predict functional roles for the multitude of sRNAs recently discovered in the pathogen, being that several sRNAs were postulated to be involved in virulence-related processes. v Finally, I applied a combined experimental and computational approach to study post-transcriptional repression mediated by small non-coding RNAs in bacteria. Specifically, a probabilistic ranking methodology termed rank-conciliation was developed to infer sRNA-mRNA interactions based on multiple types of data. The method was shown to improve target prediction in Escherichia coli, and therefore is useful to prioritize candidate targets for experimental validation. vi Table of Contents Dedication..........................................................................................................................iii Acknowledgments.............................................................................................................iv Abstract...............................................................................................................................v Table of Contents.............................................................................................................vii List of Illustrations.............................................................................................................x List of Tables...................................................................................................................xiii List of Abbreviations......................................................................................................xiv Chapter 1. General Introduction – The Systems Biology Workflow.............................1 Chapter 2. Transcriptional Regulatory Network Response during Mycobacterial Long-term Infection of Macrophages..............................................................................6 2.1 Overview of the tuberculosis infection.....................................................................6 2.2 Transcriptional profiling of Mtb during prolonged infection of macrophages.........9 2.3 Assembling a large-scale Mtb TRN........................................................................13 2.4 Regulon response identification using NetReSFun.................................................16 2.5 Transcriptional Regulatory Network (TRN) response to phagosome cues.............21 2.6 Assigning a role to rubredoxins synthesis during infection....................................24 2.7 Discussion...............................................................................................................26 Chapter 3. Inferring the sRNA-mediated Regulatory Network in Mycobacterium tuberculosis........................................................................................................................29 3.1 Small non-coding RNAs carry out post-transcriptional regulation in bacteria.......29 vii 3.2 Experimental identification of mycobacterial sRNAs............................................32 3.3 Current approaches for computational prediction of sRNA regulons.....................34 3.4 Inferring targets for the novel trans-encoded M. tuberculosis sRNAs....................36 3.5 Functional enrichment of sRNA sub-networks.......................................................40 3.6 sRNA-mediated post-transcriptional regulatory network ......................................41 3.7 Functional analysis of M. tuberculosis genes regulated by antisense sRNAs........45 3.8 Discussion ..............................................................................................................48 Chapter 4. Data Integration by Rank-Conciliation Applied to Small RNA Target Inference...........................................................................................................................50
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