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An Investigation of Gene Networks Influenced by Low Dose Ionizing Radiation Using Statistical and Graph Theoretical Algorithms

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An Investigation of Gene Networks Influenced by Low Dose Ionizing Radiation Using Statistical and Graph Theoretical Algorithms University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 12-2012 An Investigation Of Gene Networks Influenced By Low Dose Ionizing Radiation Using Statistical And Graph Theoretical Algorithms Sudhir Naswa [email protected] Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Bioinformatics Commons, Biology Commons, and the Computational Biology Commons Recommended Citation Naswa, Sudhir, "An Investigation Of Gene Networks Influenced By Low Dose Ionizing Radiation Using Statistical And Graph Theoretical Algorithms. " PhD diss., University of Tennessee, 2012. https://trace.tennessee.edu/utk_graddiss/1548 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Sudhir Naswa entitled "An Investigation Of Gene Networks Influenced By Low Dose Ionizing Radiation Using Statistical And Graph Theoretical Algorithms." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Life Sciences. Michael A. Langston, Major Professor We have read this dissertation and recommend its acceptance: Brynn H. Voy, Arnold M. Saxton, Hamparsum Bozdogan, Kurt H. Lamour Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) To the Graduate Council: I am submitting herewith a dissertation written by Sudhir Naswa entitled “An investigation of gene networks influenced by low dose ionizing radiation using statistical and graph theoretical algorithms”. I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Life Sciences. _______________________________ Michael A. Langston, Co-Chair _______________________________ Brynn H. Voy , Co-Chair We have read this dissertation and recommend its acceptance: Arnold M. Saxton Hamparsum Bozdogan Kurt H. Lamour Accepted for the Council ______________________________ Carolyn R. Hodges Vice Provost and Dean of the Graduate School i An Investigation Of Gene Networks Influenced By Low Dose Ionizing Radiation Using Statistical And Graph Theoretical Algorithms A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville omggggn Sudhir Naswa December 2012 i Copyright © 2012 by Sudhir Naswa All rights reserved. ii DEDICATIONS This dissertation is dedicated to my parents (Mrs. Santosh Naswa and Mr. N. R. Naswa) and my wife Babita for always being there to support and encourage me. It is also dedicated to my cheerful daughter Shachi for motivating and inspiring me. iii ACKNOWLEDGEMENTS I would like to thank my advisor Dr. Michael A. Langston and co-advisor Dr. Brynn H. Voy for guiding, encouraging and supporting me throughout my graduate education. I was fortunate to have them as my mentors since I could gain knowledge in computer science as well as biology from them that would be invaluable for me in pursuit of a scientific career in bioinformatics. I would also like to thank Dr. Arnold M. Saxton for training me in biostatistics and for always providing me valuable advice on biosatistical issues relating to my research. I am thankful to Dr. Hamparsum Bozdogan for mentoring me for the Master's degree in statistics and for teaching me mathematical principles of statistics. I would also like thank Dr. Kurt Lamour for providing me with opportunity to learn laboratory skills in his lab that have always helped me think computational biology problems from the perspective of a wet bench scientist. I would like to thank GST program at University of Tennessee for giving me an opportunity to pursue graduate studies and to Low dose radiation research program of Department of Energy for supporting my research. iv ABSTRACT Increased application of radiation in health and security sectors has raised concerns about its deleterious effects. Ionizing radiation (IR) less than 10cGys is considered low dose ionizing radiation (LDIR) by the National Research Committee to assess health risks from exposure to low levels of IR. It is hard to extract the effects of mild stimulus such as LDIR on gene expression profiles using simple differential expression. We hypothesized that differential correlation instead would capture the effects of LDIR on mutual relationships between genes. We tested this hypothesis on expression profiles from five inbred strains of mice treated with LDIR. Whereas ANOVA detected little effect of LDIR on gene expression, a differential correlation graph generated by a two stage statistical filter revealed gene networks enriched with genes implicated in radiation response, DNA damage repair, apoptosis, cancer and immune system. To mimic the effects of radiation on human populations, we profiled baseline expression of recombinant inbred strains of BXD mice derived from a cross between C57BL/6J and DBA/2J standard inbred strains. To establish a threshold for extraction of gene networks from the baseline expression profiles, we compared gene enrichment in paracliques obtained at different absolute Pearson correlations (APC) using graph algorithms. Gene networks extracted at statistically significant APC (r≈0.41) exhibited even better enrichment of genes participating in common biological processes than networks extracted at higher APCs from 0.6 to 0.875. Since immune response is influenced by LDIR, we investigated the effects of genetic background on variability of immune system in a population of BXD mice. Considering immune response as a complex trait, we identified significant QTLs explaining the ratio of CD8+ and CD4+ T-cells. Multiple regression modeling of genes neighboring statistically significant QTLs identified three candidate genes (Ptprk,Acp1 and Lamb1-1) explaining 61% variance of ratio of CD4+ and CD8+ T cells. Expression profiling of parental strains of BXD mice also revealed effects of LDIR and LDIR*strain on expression of genes related to immune response. Thus using an integrated approach involving transcriptomic, SNP and immunological data, we have developed novel methods to pinpoint candidate gene networks putatively influenced by LDIR. v TABLE OF CONTENTS CHAPTER 1 : INTRODUCTION AND LITERATURE REVIEW ...................................... 1 IONIZING RADIATION AND ITS EFFECTS .............................................................................. 2 Sources of ionizing radiation .................................................................................... 3 Effects of Ionizing radiation ...................................................................................... 7 Linear No Threshold Model (LNT model) ................................................................. 7 Hormesis and Adaptive Response ........................................................................... 8 Low dose Hypersensitivity ...................................................................................... 11 Influence of radiation beyond the targeted cells ..................................................... 12 Bystander Effect ................................................................................................. 12 Genomic instability .............................................................................................. 13 Radiation and Cancer ............................................................................................. 15 Other effects of radiation ........................................................................................ 17 Reactive Oxygen Species ...................................................................................... 17 DNA Damage Repair .............................................................................................. 19 Base Excision Repair (BER) ............................................................................... 19 Single Strand Break Repair (SSB repair) ............................................................ 20 Double Strand Break Repair (DSB repair) .......................................................... 20 P53 dependent apoptosis ................................................................................... 24 P53 independent apoptosis ................................................................................ 25 Antiapoptosis ...................................................................................................... 25 GENE EXPRESSION PROFILING USING MICROARRAYS ...................................................... 26 One color microarrays ............................................................................................ 26 Preprocessing of Microarray Data .......................................................................... 28 Transformation .................................................................................................... 28 Normalization ...................................................................................................... 29 Extraction
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