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Dynamics of Mrna and Microrna Expression in the Estrogen Response of Breast Cancer Cells

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Dynamics of Mrna and Microrna Expression in the Estrogen Response of Breast Cancer Cells DYNAMICS OF MRNA AND MICRORNA EXPRESSION IN THE ESTROGEN RESPONSE OF BREAST CANCER CELLS Jeanette Baran-Gale A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Curriculum in Bioinformatics and Computational Biology. Chapel Hill 2016 Approved by: Praveen Sethupathy Jeremy Purvis Terry Furey Jan Prins Alain Laederach Shawn Gomez © 2016 Jeanette Baran-Gale ALL RIGHTS RESERVED ii ABSTRACT Jeanette Baran-Gale: Dynamics of mRNA and microRNA expression in the estrogen response of breast cancer cells (Under the direction of Praveen Sethupathy and Jeremy Purvis) Cellular signaling leads to broad changes in gene expression that reprogram the cell and alter cell state. Signaling often begins with cellular receptors binding a ligand and initiating a transcriptional response. One example of this is the estrogen receptor, which binds the ligand estrogen and translocates to the nucleus where it binds to estrogen response elements and regulates the expression numerous target RNAs. The regulatory network of both messenger RNAs (mRNAs) and microRNAs (miRNAs) responding to estrogen stimulation is a complex, dynamic and multilayered program that is critical to the etiology of breast cancer. Estrogen receptor α (ERα) is an important biomarker of breast cancer severity and a common therapeutic target. Recent studies have demonstrated that in addition to its role in promoting proliferation, ERα also protects tumors against metastatic transformation. Current therapeutic strategies inhibit estrogen stimulated signaling and interfere with both beneficial and detrimental signaling pathways regulated by ERα. Additionally, ERα cyclically binds estrogen response elements and induces bursts of transcriptional activity. Together these observations suggest that ERα regulated genes and miRNAs may exhibit temporal variation in expression. Furthermore, it remains unclear if estrogen stimulated pathways exhibit the same temporal expression patterns, or if different pathways exhibit different temporal expression patterns. By combining both RNA-sequencing and small RNA-sequencing of cells responding to estrogen, we uncover the dynamics of both mRNA and miRNA expression in response to iii estrogen stimulation. Furthermore, we identify a regulatory circuit with potential therapeutic relevance to breast cancer that more specifically inhibits ERα-stimulated growth and survival pathways without interfering with its protective features. In response to estrogen stimulation, MCF7 cells (an estrogen receptor positive model cell line) exhibit induction of miR-503, and repression of the oncogene ZNF217. miR-503 inhibits proliferation in MCF7 cells, in part through its inhibition of the oncogene ZNF217 and the cell-cycle gene CCND1. While numerous regulatory interactions can be mined from this temporal profile of estrogen responsive mRNAs and miRNAs, the induction of the anti-proliferative microRNA, miR-503, both highlights the protective aspects of estrogen signaling and indicates that miR-503 holds promise as a therapeutic for breast cancer. iv ACKNOWLEDGEMENTS My project would not have been possible without the support and guidance of members of the UNC Chapel Hill community. I would like to thank the members of both the Sethupathy and Purvis labs. I am grateful for the daily help and guidance of my mentors Praveen Sethupathy and Jeremy Purvis, without which I would not be the scientist I am today. Their support and guidance has helped me to grow as a scientist, and thanks to that guidance I am a much better writer, presenter and experimentalist today than I was at the beginning of this journey. I would also like to thank Lisa Kurtz, for her extensive help in teaching me the laboratory techniques used throughout this work. Also, I would like to thank Sam Wolff, Bailey Peck, Denise Davis and Phil Coryell for additional guidance on experimental techniques. I also want to extend my thanks to the rest of my thesis committee: Terry Furey, Jan Prins, Alain Laederach and Shawn Gomez. Each of my committee members has provided invaluable feedback, constructive criticism, and guidance both during our official committee meetings and in one-on-one conversations. I am especially grateful to Terry Furey for (1) agreeing to be my committee chair, (2) keeping us all on track during committee meetings and (3) always having time to joke around with me. I am truly lucky to have the support of these amazing faculty members. I am truly grateful for all the support I have received from the entire community at UNC Chapel Hill. Interactions with my fellow Bioinformatics and Computational Biology graduate students, weekly Genetics department seminars, and numerous other scientific and professional development opportunities have all contributed to my scientific development. In particular, I v would like to thank Jessica Harrell and Bailey Peck for giving me the opportunity to co-develop and co-teach a Bioinformatics module to scholars from the UNC Postbaccalaureate Research Education Program. Lastly, I would like to thank my husband for his infinite patience, unending support and unswerving confidence in my eventual success. vi TABLE OF CONTENTS LIST OF TABLES ......................................................................................................................... x LIST OF FIGURES ...................................................................................................................... xi LIST OF ABBREVIATIONS ...................................................................................................... xiii CHAPTER 1 ................................................................................................................................. 2 Introduction ........................................................................................................................... 2 1.1 Dynamic RNA expression ........................................................................................... 2 1.2 Post-transcriptional repression by microRNAs ........................................................ 3 1.2.1 microRNA introduction ............................................................................................. 3 1.2.2 Considerations for small RNA-sequencing ............................................................ 4 1.3 Breast cancer and the estrogen response ................................................................ 6 1.3.1 miRNAs in breast cancer ....................................................................................... 7 1.3.2 Estrogen responsive coding RNAs ....................................................................... 8 1.4 Dynamic expression of coding and non-coding RNAs in breast cancer ......................................................................................................................... 8 CHAPTER 2: QUANTIFICATION OF MIRNAS AND THEIR ISOMIRS FROM HIGH THROUGHPUT SEQUENCING ........................................................................... 10 2.1 Overview ..................................................................................................................... 10 2.2 Introduction ................................................................................................................ 11 2.3 Results ........................................................................................................................ 15 2.3.1 A pipeline for detailed quantification of miRNAs and their isomiRs .................... 15 2.3.2 Comparing the miRNA and 5’-isomiR profiles of MIN6, human beta cell, and human islet ................................................................................... 17 2.3.3 Characterization of beta cell 5’-shifted isomiRs .................................................. 18 2.4 Discussion .................................................................................................................. 22 vii 2.5 Materials and methods .............................................................................................. 22 CHAPTER 3: ADDRESSING BIAS IN SMALL RNA LIBRARY PREPARATION FOR SEQUENCING: A NEW PROTOCOL RECOVERS MICRORNAS THAT EVADE CAPTURE BY CURRENT METHODS ................................................... 24 3.1 Overview ..................................................................................................................... 24 3.2 Introduction ................................................................................................................ 25 3.3 Results ........................................................................................................................ 29 3.4 Discussion .................................................................................................................. 36 3.5 Materials and Methods .............................................................................................. 39 CHAPTER 4: IDENTIFYING MIRNA “MASTER REGULATORS” THROUGH TARGET SITE ENRICHMENT ....................................................................................... 43 4.1 Overview ..................................................................................................................... 43 4.2 Introduction ................................................................................................................ 44 4.3 Results .......................................................................................................................
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