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Gravitropic Signal Transduction: a Systems Approach to Gene Discovery

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Gravitropic Signal Transduction: a Systems Approach to Gene Discovery Gravitropic Signal Transduction: A Systems Approach to Gene Discovery A dissertation presented to the faculty of the College of Arts and Sciences of Ohio University In partial fulfillment of the requirements for the degree Doctor of Philosophy Kaiyu Shen May 2013 © 2013 Kaiyu Shen. All Rights Reserved. 2 This dissertation titled Gravitropic Signal Transduction: A Systems Approach to Gene Discovery by KAIYU SHEN has been approved for the Program of Molecular and Cellular Biology and the College of Arts and Sciences by Sarah E. Wyatt Professor of Environmental and Plant Biology Robert Frank Dean, College of Arts and Science 3 ABSTRACT SHEN, KAIYU, Ph.D., May 2013, Molecular and Cellular Biology Gravitropic Signal Transduction: A Systems Approach to Gene Discovery Director of Dissertation: Sarah E. Wyatt Gravity is an important stimulus for plants. Gravitropism, the plants’ response to gravity, can be divided into three phases: gravity perception, signal transduction and response. Various theories have been proposed to explain the process of gravitropism, yet more genes are needed to elucidate the mechanism of gravitropic signal transduction. A transcriptome analysis, in combination with the Gravity Persistent Signal treatment, was performed to specifically study the genes involved in signal transduction. Analysis generated a list of 318 transcripts that were differentially expressed in plants that were reoriented with respect to gravity as compared to vertical controls. Based on the expression profiles and gene function annotations, five transcription factors, WRKY18, WRKY26, WRKY33, BT2 and ATAIB, were selected for further study. In addition to the standard analysis of differentially expressed genes, a systems approach was adopted to uncover more gravity related genes. A semi-supervised learning method was developed to find additional novel genes. This learning method took a set of 32 known gravity genes from the literature as well as a collection of heterogeneous annotation features, such as existing protein-protein interactions, and co-expression profiles. The learning classifier predicted a list of 50 genes that are functionally related to gravity signal transduction. Based on this list of genes, an interaction network was predicted 4 based two complementary approaches: a dynamic Bayesian network and a time-lagged correlation coefficient. To increase confidence in the predication, genes/interactions that appeared in both networks were selected. This ‘intersected’ network provided 20 hub and bottleneck genes, fourteen of which had not been previously identified as involved in gravitropism. Such an approach provides a framework to extend current research in a more comprehensive manner, and serves a complementary to the traditional mutant/gene discovery model. 5 DEDICATION To my family. 6 ACKNOWLEDGMENTS I express my deep sense of gratitude to my advisor, Dr. Sarah Wyatt who provided me with moral and intellectual guidance throughout my Ph.D. research. I would also like to thank Dr. Bunescu Razvan for the whole hearted help on my machine learning project and research. I also greatly appreciate for the efforts and time of my Ph.D. committee members: Dr. Lonnie Welch, Dr. Frank Horodyski and Dr. Allan Showalter. I also thank Vijay Nadella, the director of Ohio University Genomics Facility, who has given me wonderful opportunities to gain experience on analyzing meta-scale biological data. Finally I thank all the previous and current Wyatt lab members for supporting my research and dissertation. Finally, I would thank for my family who have always been the strongest support for me. 7 TABLE OF CONTENTS Page Abstract......................................................................................................................................................... 3 Dedication .................................................................................................................................................... 5 Acknowledgments ..................................................................................................................................... 6 List of Tables .............................................................................................................................................. 9 List of Figures ........................................................................................................................................... 11 Abbreviations ............................................................................................................................................ 13 Chapter 1: Introduction .......................................................................................................................... 14 Gravitropism .................................................................................................................. 14 The perception of gravity and the starch statolith hypothesis.................................... 15 Signal transduction..................................................................................................... 19 Gravity response ........................................................................................................ 24 Arabidopsis mutants ...................................................................................................... 26 The gravity persistent signal (gps) mutants ............................................................... 28 Chapter 2: Transcriptome analysis oF gravitropic signal transduction .................................. 31 Introduction ................................................................................................................... 31 Methods ......................................................................................................................... 33 Plant preparation ........................................................................................................ 33 GPS treatment ............................................................................................................ 34 Microarray experiment design ................................................................................... 35 RNA extraction .......................................................................................................... 36 Microarray data analysis ............................................................................................ 39 Gene annotation ......................................................................................................... 42 qRT-PCR experiment and selection of housekeeping genes ..................................... 42 cDNA preparation and primer design ........................................................................ 44 Optimization of primer concentrations ...................................................................... 47 Results ........................................................................................................................... 48 Microarray data analysis ............................................................................................ 48 Enrichment analysis of the annotations ..................................................................... 49 Genes selected for future studies ............................................................................... 53 Transcription factors .................................................................................................. 54 qRT-PCR results ........................................................................................................ 59 Discussion ..................................................................................................................... 62 Chapter 3: Mining functionally related genes using Semi-supervised learning .................... 66 Introduction ................................................................................................................... 67 Methods ......................................................................................................................... 70 8 Information sources and feature engineering ............................................................. 70 Feature vector............................................................................................................. 73 Feature selection and filtering .................................................................................... 74 Learning methods....................................................................................................... 75 Selection of unlabeled data for training ..................................................................... 79 Results ........................................................................................................................... 80 Benchmark data ......................................................................................................... 80 Feature collection ....................................................................................................... 81 Algorithm implementation and comparison .............................................................. 82 Time dependent evaluation ........................................................................................ 85 Varied composition of unlabeled genes ..................................................................... 87 Different set of negative genes .................................................................................. 88 Comparison with
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