Computational Investigation of Biological Networks And
Total Page：16
File Type：pdf, Size：1020Kb
COMPUTATIONAL INVESTIGATION OF BIOLOGICAL NETWORKS AND PROGESTERONE SIGNALING DYNAMICS IN PRETERM BIRTH by DOUGLAS K. BRUBAKER Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Systems Biology and Bioinformatics CASE WESTERN RESERVE UNIVERSITY May 2016 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the thesis/dissertation of Douglas K. Brubaker candidate for the degree of Doctor of Philosophy Committee Member Jill Barnholtz-Sloan, PhD Committee Member Mark R. Chance, PhD Committee Member Sam Mesiano, PhD Committee Member Alethea Barbaro, PhD Date of Defense February 10, 2016 *We also certify that written approval has been obtained for any proprietary material contained therein 1 Table of Contents List of Tables………………………………………………………………………...…..3 List of Figures…………………………………………………………………..............5 Acknowledgements……………………………………………………………………..7 Abstract…………………………………………………………………………………..8 Chapter 1. Spontaneous Preterm Birth Without Inflammatory Pathway Activation: Functional Genomics Investigation of Myometrial Gene Expression Data Characterizes Three Parturition Subtypes. …………………………………………………………....……10 Chapter 2. Finding Lost Genes in GWAS via Integrative-omics Analysis Reveals Novel Sub-networks Associated with Preterm Birth……………………………………………………………..….…..…..26 Chapter 3. A Dynamical Systems Model of Progesterone Receptor B Interactions with Inflammation in Human Pregnancy…………..……..55 Discussion………………………………………………………………………………88 References……………………………………………………………………………..93 2 List of Tables Table 1-1 Myometrial Transcriptome Datasets For Meta-analysis……………….14 Table 1-2 Confirmation of Studies of Term Labor Differential Gene Expression…………………………………………………………………18 Table 1-3 Confirmation of Studies of HCA-indicated Preterm Birth Differential Gene Expression……………………………..……….19 Table 1-4 Confirmation of Studies of Spontaneous Preterm Birth Differential Gene Expression………………………………..…….19 Table 2-1 Single Gene qRT-PCR Results for Term Myometrium Subnetworks………………………………………………………….…...46 Table 2-2 Single Gene qRT-PCR Results for Preterm Myometrium Subnetworks…………………………………………………………..…..47 Table 2-3 Term and Preterm Myometrium Subnetwork Significant Testing Results………………………………………………48 Table 3-1 Performance of Single Gene and Model Classifiers………………..….68 3 Table 3-2 Characterization of Stability of Equilibrium Points of Dynamical Systems Model…………………………………………………………....73 4 List of Figures Figure 1-1 Pipeline for Meta-analysis of Myometrium Transcriptome Data………………………………………………………13 Figure 1-2 Differentially Expressed Genes and Enriched Pathways in Term Labor, Preterm Labor with HCA, and Spontaneous Preterm Labor……………………………………….20 Figure 1-3 Pathways Active in Both Term Labor and Preterm Labor with HCA……………………………………………………………21 Figure 1-4 Pathways Active in Spontaneous Preterm Labor………...……...……21 Figure 2-1 Pipeline for Integrative Analysis of GWAS, PPI Network, and Myometrium Transcriptome Data……………………….34 Figure 2-2 Preterm Birth SNP Enriched PPI Network………………………..……40 Figure 2-3 Coordinately Regulated Subnetworks in Term Laboring Myometrium…………………………………………………….42 5 Figure 2-4 Coordinately Regulated Subnetworks in Preterm Laboring Myometrium…………………………………………………….44 Figure 2-5 Validated Term Labor Myometrium Subnetworks Regulated by MEF2C…………………………………………………….48 Figure 3-1 Illustration of the Basin of Attraction of the Laboring Equilibrium Point……………………………………………….63 Figure 3-2 Two-Gene Model Classifiers of Laboring Phenotype…………………66 Figure 3-3 Single-Gene Inflammatory Classifiers of Laboring Phenotype…………………………………………………………………67 Figure 3-4 Single Gene PR-B Surrogate Classifiers of Laboring Phenotype…………………………………………………………………67 Figure 3-5 Phase Space Transition of Labor Bifurcation………………………….74 Figure 4-1 Summary of Thematic Areas and Findings……………………………88 6 Acknowledgements This dissertation would not have been possible without the exemplary mentoring of my adviser, Dr. Mark R. Chance. He stimulated my scientific development by consistently challenging me and by teaching me how to effectively communicate and argue for my findings. He has played a formative role in my development as a scientist by honing my sense of how to effectively conceptualize and attack problems in systems biology. These skills will stay with me my entire career and owe him immeasurably for cultivating them. Dr. Jill Barnholtz-Sloan taught me early on to seek out effective and fruitful collaborators. Because of this, I was able to initiate and benefit from the collaboration and mentoring of Dr. Alethea Barbaro. Their council in research and in navigating the world of academia has kept me on track through the program. The direction and creative elements of this work owe a great debt to Dr. Sam Mesiano for always keeping his door open to talk and always being wiling to try something new. I benefitted greatly from working on projects beyond this dissertation with Dr. Gurkan Bebek and Elena Svenson. Junye Wang deserves recognition for his experimental contributions to this dissertation. Dr. Lindsay Stetson has been my sounding board for many projects and a good friend throughout my studies. Perhaps most importantly, I thank my mother Carol, brother David, sister Sara and girlfriend Stephanie Doran for keeping me grounded throughout my studies. Most of all, I thank my grandfather Robert K. Koehler for whom this and all my future work is dedicated. 7 Computational Investigation of Biological Networks and Progesterone Signaling Dynamics in Preterm Birth Abstract by DOUGLAS K. BRUBAKER Preterm birth (PTB) is a major public health issue that is the leading cause of infant mortality worldwide. To identify dysregulation and therapeutic opportunities in PTB a better understanding of healthy term labor is required. Further understanding of how the interaction of progesterone signaling with inflammatory pathways maintains quiescence is essential to assessing the therapeutic potential of progesterone for PTB. It has also been observed that PTB has a strong heritability from mother to daughter. This has motivated several genome wide association studies (GWAS) to try to identify single nucleotide polymorphisms (SNP) with genome wide significance that predispose a woman to PTB. To date, no SNPs have been identified and replicated with genome wide significance raising concerns about the effectiveness of GWAS in identifying the genetic predisposition of PTB. The myometrium, uterine smooth muscle tissue, undergoes a dramatic phenotypic transition from quiescent to forcefully contracting to deliver the conceptus. Understanding the biological signaling networks driving this transition, how genetic factors may modulate it, and the role 8 of progesterone signaling in labor are essential factors to addressing the challenge of PTB. This dissertation addresses each of these issues to better characterize PTB. A meta-analysis approach is used to characterize the signaling events governing the quiescent to laboring transition of the myometrium. We show that while inflammatory pathways are crucial to term labor and inflammation indicated PTB, spontaneous PTB has a unique set of signaling pathways governing the myometrium’s transition. By organizing insignificant PTB-GWAS SNPs in a protein-protein interaction (PPI) network context, groups of modest effect SNPs were tested for combined effects on modules of a PPI network. Module function was assessed with term and preterm labor myometrium transcriptome data to identify modules dysregulated with labor onset. A module characterized by myocyte enhancer factor -2C (MEF2C) and 9 PTB-SNPs was implicated in term labor. Finally, we modeled progesterone signaling with inflammation using a dynamical systems model and used the model to precisely predict laboring phenotypes. Progesterone signaling dynamics are well characterized by this competitive interaction model, but the lack of inflammatory pathways in spontaneous PTB suggest limited effectiveness of progesterone modulation as a PTB therapy in that context. This dissertation illustrates how understanding a complex disorder like PTB requires a systems level approach. Such an approach is only possible when high dimensional data is carefully modeled, assessed, and the dimensionality reduced using appropriate and diverse computational approaches. 9 Chapter 1: Spontaneous Preterm Birth Without Inflammatory Pathway Activation: Functional Genomics Investigation of Myometrial Gene Expression Data Characterizes Three Parturition Subtypes. 10 Background One strategy for preventing preterm birth is to better understand the signaling pathways of term labor in hopes of identifying dysregulation and therapeutic opportunities in preterm birth (PTB). For most of pregnancy the myometrium (uterine smooth muscle) is maintained in a relaxed and quiescent state. Labor is characterized by a drastic transformation of the myometrium from quiescent and hypertrophied to contractile. It is well accepted that inflammatory pathways become active in the myometrium with the onset of labor and that these are regulated by and interact with the relaxatory actions of the hormone progesterone [1-3]. While some preterm labor occurs spontaneously, there is sometimes an indication of inflammation in preterm labor. The clinical indication for this is usually inflammation of the choriodecidua, referred to as histologic chorioamnionitis (HCA). This inflammatory event is a major risk factor for PTB and the resulting signaling cascade of labor is enriched with inflammatory pathways