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Data-Driven and Knowledge-Driven Computational Models of Angiogenesis in Application to Peripheral Arterial Disease

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Data-Driven and Knowledge-Driven Computational Models of Angiogenesis in Application to Peripheral Arterial Disease DATA-DRIVEN AND KNOWLEDGE-DRIVEN COMPUTATIONAL MODELS OF ANGIOGENESIS IN APPLICATION TO PERIPHERAL ARTERIAL DISEASE by Liang-Hui Chu A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland March, 2015 © 2015 Liang-Hui Chu All Rights Reserved Abstract Angiogenesis, the formation of new blood vessels from pre-existing vessels, is involved in both physiological conditions (e.g. development, wound healing and exercise) and diseases (e.g. cancer, age-related macular degeneration, and ischemic diseases such as coronary artery disease and peripheral arterial disease). Peripheral arterial disease (PAD) affects approximately 8 to 12 million people in United States, especially those over the age of 50 and its prevalence is now comparable to that of coronary artery disease. To date, all clinical trials that includes stimulation of VEGF (vascular endothelial growth factor) and FGF (fibroblast growth factor) have failed. There is an unmet need to find novel genes and drug targets and predict potential therapeutics in PAD. We use the data-driven bioinformatic approach to identify angiogenesis-associated genes and predict new targets and repositioned drugs in PAD. We also formulate a mechanistic three- compartment model that includes the anti-angiogenic isoform VEGF165b. The thesis can serve as a framework for computational and experimental validations of novel drug targets and drugs in PAD. ii Acknowledgements I appreciate my advisor Dr. Aleksander S. Popel to guide my PhD studies for the five years at Johns Hopkins University. I also appreciate several professors on my thesis committee, Dr. Joel S. Bader, Dr. Feilim Mac Gabhann and Dr. Brian H. Annex, for the guidance and constructive suggestions in my thesis proposal and dissertation. I especially thank many great scientists and post-docs in our labs or other labs with whom I have collaborated: Dr. Corban Rivera, Dr. Stacey Finley, Dr. Kerri-Ann Norton, Dr. Niranjan Pandey, Dr. Esak Lee, Dr. Hojjat Bazzazi, Dr. Jacob Koskimaki, Dr. Vijay Ganta and Dr. Yongjin Park. I also thank undergraduate and master students George Chen, Conan Chen and Chen Zhou, who worked with me closely. I also thank for Dr. Reza Shadmehr and Dr. David Yue who helped with my courses, rotations, annual BME retreats and talent shows. I must thank Hong Lan for her great help during my PhD studies at JHU. It has been enjoyable to study in our BME program at Hopkins. Finally, I thank my family, previous advisors and Taiwanese friends who encouraged me to pursue the PhD degree in the United States. iii Intended to be blank iv Table of Contents Abstract ................................................................................................................................ii Acknowledgements ............................................................................................................. iii List of Tables ..................................................................................................................... viii List of Figures ...................................................................................................................... x I. INTRODUCTION ...................................................................................................... 1 1.1 Angiogenesis and diseases .............................................................................. 2 1.2 Bioinformatics approaches in angiogenesis .................................................... 3 1.3 Biological networks of angiogenesis .............................................................. 4 1.4 Microarray data on endothelial cells ............................................................... 7 1.5 Peripheral arterial disease (PAD) .................................................................... 8 1.6 Drug repositioning in PAD ............................................................................. 9 1.7 Anti-angiogenic isoform VEGF165b .............................................................. 10 1.8 Mechanistic three-compartment model of VEGF ......................................... 12 II. CONSTRUCTING THE ANGIOME ....................................................................... 13 2.1 Methods......................................................................................................... 13 2.1.1 GeneHits: integration of heterogeneous data .................................... 13 2.1.2 Construction and analysis of angiome .............................................. 14 2.1.3 Functional enrichment of proteins in the network ............................ 14 2.1.4 Analysis of microarray on endothelial cells...................................... 15 2.2 Results ........................................................................................................... 15 2.2.1 The set of angiogenesis-annotated genes .......................................... 15 2.2.2 Angiome, global protein-protein interaction network of angiogenesis 16 2.2.3 Structure and topological properties of angiome .............................. 17 2.2.4 Functional Enrichment of proteins in angiome ................................. 18 2.3 Discussion ..................................................................................................... 19 2.3.1 Regulators of angiogenesis ............................................................... 19 2.3.2 Extension of angiome in the following chapters ............................... 21 III. DYNAMIC ANGIOGENESIS INTERACTOME ............................................ 22 3.1 Methods......................................................................................................... 22 3.1.1 Proteins annotated as positive and negative regulation of angiogenesis ...................................................................................................... 22 3.1.2 Temporal activation pattern of proteins in dynamic angiogenesis interactome ........................................................................................................ 23 v 3.2 Results ........................................................................................................... 25 3.2.1 Constructing the networks of positive and negative regulation of angiogenesis ...................................................................................................... 25 3.2.2 Temporal gene expression pattern on endothelial cells .................... 25 3.2.3 Activation patterns of the receptor protein tyrosine kinase .............. 27 IV. CONSTRUCTING THE PADPIN .................................................................... 30 4.1 Methods......................................................................................................... 30 4.1.1 Construction of PIN of immune response and arteriogenesis ........... 30 4.1.2 Microarray data in PAD mouse models ............................................ 31 4.1.3 Human microarray dataset in PAD ................................................... 32 4.2 Results ........................................................................................................... 33 4.2.1 Construction of the immunome and arteriome ................................. 33 4.2.2 Differentially expressed genes in mouse PAD model ....................... 34 4.2.3 Visualization of PADPIN .................................................................. 43 4.2.4 Differentially expressed genes between two inbred mouse strains... 45 4.3 Discussion ..................................................................................................... 46 4.3.1 Prediction of potential drug targets in PAD ...................................... 46 4.3.2 Comparisons of gene expression between the two mouse strains .... 50 V. COMPUTATIONAL DRUG REPOSITIONING IN PAD ....................................... 51 5.1 Methods......................................................................................................... 51 5.1.1 Resources of drug-targets relations ................................................... 51 5.1.2 List of anti-angiogenic and pro-inflammatory genes ........................ 52 5.2 Results ........................................................................................................... 53 5.2.1 Drug-targets relations in PADPIN .................................................... 53 5.2.2 Inhibition of anti-angiogenic and pro-inflammatory genes .............. 54 5.2.3 Drug-Target Network ........................................................................ 57 5.3 Discussion ..................................................................................................... 58 VI. COMPARTMENTAL MODEL OF VEGF165b .................................................. 59 6.1 Methods......................................................................................................... 59 6.1.1 Three-compartment models .............................................................. 59 6.1.2 Secretion rate and secretion ratio of VEGF165b to total VEGF ......... 60 6.1.3 Molecular Interactions and kinetic equations of VEGF isoforms..... 60 6.1.4 Geometric parameters ....................................................................... 62 6.1.5 Transport parameters ......................................................................... 62 6.1.6 Sensitivity analysis............................................................................ 64 6.2 Results ..........................................................................................................
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