Georgia State University ScholarWorks @ Georgia State University Biology Dissertations Department of Biology 8-8-2017 Computational Interrogation of Transcriptional and Post- Transcriptional Mechanisms Regulating Dendritic Development Surajit Bhattacharya Follow this and additional works at: https://scholarworks.gsu.edu/biology_diss Recommended Citation Bhattacharya, Surajit, "Computational Interrogation of Transcriptional and Post-Transcriptional Mechanisms Regulating Dendritic Development." Dissertation, Georgia State University, 2017. https://scholarworks.gsu.edu/biology_diss/190 This Dissertation is brought to you for free and open access by the Department of Biology at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Biology Dissertations by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. COMPUTATIONAL INTERROGATION OF TRANSCRIPTIONAL AND POST- TRANSCRIPTIONAL MECHANISMS REGULATING DENDRITIC DEVELOPMENT by SURAJIT BHATTACHARYA Under the Direction of Daniel N. Cox, PhD ABSTRACT The specification and modulation of cell-type specific dendritic morphologies plays a pivotal role in nervous system development, connectivity, structural plasticity, and function. Regulation of gene expression is controlled by a wide variety of cellular and molecular mechanisms, of which two major types are transcription factors (TFs) and microRNAs (miRNAs). In Drosophila, dendritic complexity of dendritic arborization (da) sensory neurons of the peripheral nervous system are known to be regulated by two transcription factors Cut and Knot, although much remains unknown about the molecular mechanisms and regulatory networks via which they regulate the final arbor shape through spatio-temporal modulation of dendritic development and dynamics. Here we use bioinformatics analysis of transcriptomic data to identify putative genomic targets of these TFs with a particular emphasis on those that effect neuronal cytoskeletal architecture. We use transcriptomic, as well as data from various genomic and protein interaction databases, to build a weighted functional gene regulatory network for Knot, to identify the biological pathways and downstream genes that this TF regulates. To corroborate bioinformatics network predictions, knot putative targets, which classify into neuronal and cytoskeletal functional groups, have been experimentally validated by in vivo genetic perturbations to elucidate their role in Knot-mediated Class IV (CIV) dendritogenesis. MicroRNAs (miRNAs) have emerged as key post-transcriptional regulators of gene expression, however identification of biologically-relevant target genes for this epigenetic regulatory mechanism remains a significant challenge. To address this knowledge gap, we have developed a novel R based tool, IntramiR-ExploreR, that facilitates integrated discovery of miRNA targets by incorporating target databases and novel target prediction algorithm to arrive at high confidence intragenic miRNA target predictions. We have explored the efficacy of this tool using D.melanogaster as a model organism for bioinformatics analyses and functional validation, and identified targets for 83 intragenic miRNAs. Predicted targets were validated, using in vivo genetic perturbation. Moreover, we are constructing interaction maps of intragenic miRNAs focusing on neural tissues to uncover regulatory codes via which these molecules regulate gene expression to direct cellular development. INDEX WORDS: gene regulatory networks, microRNA target predictions, dendrite development, bioinformatics, transcription factors. COMPUTATIONAL INTERROGATION OF TRANSCRIPTIONAL AND POST- TRANSCRIPTIONAL MECHANISMS REGULATING DENDRITIC DEVELOPMENT by SURAJIT BHATTACHARYA Committee Chair: Daniel N. Cox Committee: Deborah Baro Yi Jiang Electronic Version Approved: Office of Graduate Studies College of Arts and Sciences Georgia State University August 2017 iv DEDICATION I dedicate this dissertation to my Grandmother and Jules Verne, who taught me the power of Imagination and taught me nothing in the world is impossible or unachievable, if you have the correct dose of imagination, determination and hard work. v ACKNOWLEDGEMENTS I would like to thank my mentor Dr. Daniel N. Cox, for guiding me through my doctoral training. Being trained in computational aspect of biology, he helped in understanding and appreciating the biological aspects of the problems and how to better utilize my skills in solving them. I also thank, Dr. Deborah Baro and Dr. Yi Jiang for providing me with valuable inputs on my dissertation research. I thank all the Cox Lab members, especially Atit A. Patel, Shatabdi Bhattacharjee, Ravi Das and Jamin Letcher for helping me biologically validate my hypothesis. I also thank Noah Yasarturk, my undergraduate researcher, who helped me build the user interface for the tools. I would also like to thank Cox Lab alumni Srividya Chandramouli Iyer to frame my hypothesis for miRNA target prediction algorithm and Dan Veltri and Jaimin Patel for providing me with their inputs in the computational aspect of the project. I also thank High Performance Computing group, especially Neranjan Edirisinghe and Semir Sarajlic, for their help in providing a smooth and effective computing experience. I also would like to thank my parents, my sister, my wife, friends and relatives, for believing in me and being there during this whole process. vi TABLE OF CONTENTS ACKNOWLEDGEMENTS ............................................................................................ V LIST OF FIGURES ...................................................................................................... XII LIST OF ABBREVIATIONS ..................................................................................... XIV 1 INTRODUCTION................................................................................................... 15 1.1 Regulation of gene expression-an overview ........................................................ 15 1.2 Understanding gene expression: A Bioinformatics approach ........................... 16 1.3 Neuronal Architecture and its Biomedical Significance .................................... 20 1.4 Drosophila da neurons: The model of study ....................................................... 24 1.5 Transcription factor roles in da neuron dendritic architechture ..................... 25 1.6 Post Transcriptional Regulation: microRNA ..................................................... 29 1.7 Summary ................................................................................................................ 31 2 CHARACTERIZATION OF TRANSCRIPTIONAL REGULATORY NETWORKS FOR TWO KEY TRANSCRIPTION FACTORS, CUT AND KNOT, WHICH FUNCTION TO DIFFERENTIALLY REGULATE THE DENDRITIC CYTOSKELETON IN SPECIFYING CELL-TYPE SPECIFIC NEURONAL ARCHITECTURES. ................................................................................................................... 33 2.1 Scientific Premise ............................................................................................ 33 2.2 Material and Methods .................................................................................... 34 2.2.1 Drosophila strains ............................................................................................ 34 2.2.2 Drosophila CRISPR-mediated conditional mutagenesis ............................... 35 vii 2.2.3 Cell isolation, purification, and microarray expression profiling ................. 35 2.2.4 Neurogenomic analyses ................................................................................... 36 2.2.5 Phenotypic screening and live image confocal microscopy ........................... 37 2.2.6 Neurometric quantification ............................................................................. 38 2.2.7 Co-expression analysis .................................................................................... 39 2.2.8 Putative Targets Gene Ontology Classification .............................................. 40 2.2.9 Gene and Protein Interaction datasets............................................................ 41 2.2.10 Gene Regulatory Network ............................................................................. 42 2.2.11 Statistics and Visualization............................................................................ 42 2.2.12 Contributions ................................................................................................. 43 2.3 Results .............................................................................................................. 43 2.3.1 Putative targets of Cut and Knot from bioinformatics analysis ..................... 43 2.3.2 RNAi screen of the Putative targets ................................................................ 48 2.3.3 Validation of the putative Cut/Knot Targets ................................................... 56 2.3.4 Knot directly regulates genes in the 3rd instar larval stage compared to the other stages ....................................................................................................................... 59 2.3.5 Knot targets genes with varied functionality .................................................. 59 2.3.6 Knot targets genes form regulatory network in each functional class .......... 64 2.3.7 Knot targets effect dendritic arborization in Class IV da neurons ................ 67 2.3 Discussion........................................................................................................