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Microrna Identification and Target Prediction in the Whitefly (Bemisia Tabaci)

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Microrna Identification and Target Prediction in the Whitefly (Bemisia Tabaci) The University of Southern Mississippi The Aquila Digital Community Honors Theses Honors College Summer 8-2019 microRNA Identification and arT get Prediction in the Whitefly (Bemisia tabaci) Alexis Aleman University of Southern Mississippi Follow this and additional works at: https://aquila.usm.edu/honors_theses Part of the Molecular Biology Commons Recommended Citation Aleman, Alexis, "microRNA Identification and arT get Prediction in the Whitefly (Bemisia tabaci)" (2019). Honors Theses. 686. https://aquila.usm.edu/honors_theses/686 This Honors College Thesis is brought to you for free and open access by the Honors College at The Aquila Digital Community. It has been accepted for inclusion in Honors Theses by an authorized administrator of The Aquila Digital Community. For more information, please contact [email protected]. The University of Southern Mississippi microRNA Identification and Target Prediction in the Whitefly (Bemisia tabaci) by Alexis Aleman A Thesis Submitted to the Honors College of The University of Southern Mississippi in Partial Fulfillment of Honors Requirements July 2019 ii Approved by Alex Flynt, Ph.D., Thesis Adviser School of Biological, Environmental, and Earth Sciences Jake Schaefer, Ph.D., Interim Director School of Biological, Environmental, and Earth Sciences Ellen Weinauer, Ph.D., Dean Honors College iii Abstract RNA interference, referred to as RNAi, is a biological phenomenon whereby knock-down of gene expression can be achieved through the use of RNA molecules, including small interfering RNAs (siRNAs) and microRNAs (miRNAs). The use of miRNAs is an endogenous pathway that results in the degradation of the miRNA strand’s complementary messenger RNA (mRNA), preventing the translation of the mRNA and therefore the production of the proteins necessary for gene expression. While RNAi is a biological phenomenon that occurs naturally, it is also a method that can be used and manipulated in the laboratory to try to control the expression of genes associated with various issues. One such problem that has been studied using RNAi is pest control. One of the most destructive pests to crops globally is the whitefly, Bemisia tabaci. RNAi has produced the best results in B. tabaci when double-stranded RNAs (dsRNAs) are fed to the fly. In order to better understand miRNA evolution and to examine the process of miRNA-mediated mRNA degradation via RNAi in the whitefly, a miRDeep analysis was performed on the genome of Bemisia tabaci to identify miRNAs. Doing so allowed for the identification of 41 novel miRNAs and a total of 177 confidently assigned miRNAs in the whitefly. Of the novel miRNAs discovered, 83% were monocistronic, and the 177 confidently assigned miRNAs were predicted to regulate 51% of the mRNAs in the whitefly, consistent with the findings of previous studies. The ten mRNAs that are targeted the most by miRNAs in the whitefly play serious roles in medicine, including coding for a brain tumor protein, pointing to the clinical significance of further investigating the RNAi pathways in these genes. In addition, of the 51% of the whitefly’s mRNAs that were predicted to be regulated by miRNAs, 4% were discovered to be iv targeted by 100 or more miRNAs, indicating numerous RNAi pathways that can be further studied and analyzed in order to better control the expression of these genes. Key words: Whitefly, Bemisia tabaci, RNAi, miRNA, mRNA, dsRNA v Dedication To my husband, Jonathan Stockman, who has supported me through the many obstacles and trials I faced during this process. Without your unwavering belief in me as a scientist and researcher, I do not know where I would be today. To my family members, especially Mom, Mawmaw, and Pawpaw, for never ceasing to believe in me and for encouraging me when I lost hope. To my best friends, Savannah and Cory, for constantly telling me that I am a phenomenal scientist and that I can achieve anything with the right amount of determination and resilience. If not for your love, friendship, and support, I would not have accomplished this great achievement. vi Acknowledgments I would like to thank my thesis advisor, Dr. Alex Flynt, for his mentorship during the process of completing this study. You have taught me so much and have continually shown me patience and understanding. This work would not have been possible had it not been for your guidance and support. Thank you for all you have seen me through, and for continuing to believe in the completion of this project. Additionally, I want to specially thank the faculty of the Honors College. Throughout my four years at The University of Southern Mississippi, their support, resources, and guidance have molded me into the scholar and woman that I am today. I also would like to thank Dr. Mac Alford of The University of Southern Mississippi for his assistance in creating Fig. 4, a phylogenetic tree of the shared miRNAs between the whitefly and its relatives, by allowing me to use his WinClada software. Your aid came after I had become overwhelmed trying to map so many miRNAs by hand and I greatly appreciate your help. vii Table of Contents List of Tables ..................................................................................................................... ix List of Figures ......................................................................................................................x List of Abbreviations ......................................................................................................... xi Chapter 1: Introduction ........................................................................................................1 Chapter 2: Methodology .....................................................................................................3 2.1 miRDeep Analysis .............................................................................................4 2.2 Classification as Polycistronic or Monocistronic. .............................................6 2.3 Discovering Shared miRNAs with Related Species… ......................................6 2.4 Target Prediction ................................................................................................6 2.5 dsRNA Feeding and DEseq ...............................................................................7 2.6 Calculating the Percent of Whitefly mRNAs that Are Regulated by miRNAs .7 Chapter 3: Results ................................................................................................................8 Chapter 4: Conclusions. .....................................................................................................18 References ..........................................................................................................................20 viii List of Tables Table 1. Classification of Potential miRNAs from miRDeep Analysis...............................8 Table 2. Net Number of miRNAs Shared Between Related Orders and the Whitefly ......12 Table 3. Top Ten Genes Targeted by Whitefly miRNAs ..................................................16 ix List of Figures Figure 1. Scoring Criteria for Evaluating Potential miRNA Candidates .............................5 Figure 2. Arrangement of Novel miRNAs in the Bemisia tabaci Genome ........................9 Figure 3. The Seven Bemisia tabaci Polycistronic miRNAs .............................................10 Figure 4. Phylogenetic Tree of Bemisia tabaci and Relatives with Shared miRNAs .......11 Figure 5. Target Prediction of Known miRNAs ................................................................13 Figure 6. Target Prediction of Novel miRNAs .................................................................14 Figure 7. DEseq After dsRNA Feeding .............................................................................15 Figure 8. Distribution of Whitefly mRNA Target Site Values ..........................................16 Figure 9. Percent of mRNAs Regulated by miRNAs ........................................................17 Figure 10. Percent of mRNAs Targeted by 100 or More miRNAs ..................................17 x List of Abbreviations AGO Argonaute family protein Bps base pairs dsRNA double-stranded RNA miR-# micro RNA (followed by number, e.g., miR-1120) miRNA micro RNA mRNA messenger RNA pre-miRNA precursor miRNA pri-miRNA primary miRNA RISC RNA-induced silencing complex RNAi RNA interference RNase ribonuclease siRNA small interfering RNA UTR untranslated region xi Chapter 1: Introduction MicroRNAs are small, non-coding, highly conserved ribonucleic acids with a length of about 22 nucleotides that have been found to play a variety of significant roles in molecular biology. For example, many microRNAs (miRNAs) have been linked to development. Bantam, a gene in Drosophila melanogaster, encodes a 21-nucleotide miRNA that triggers cell proliferation and inhibits apoptosis (Brennecke, Hipfner, Stark, Russell, and Cohen, 2003). Various miRNAs have also been tied to homeostasis and cartilage development in mammals, including miR-140, whose loss resulted in the development of osteoarthritis in mice (Miyaki et al., 2010). Still other miRNAs have been found to play roles in the development of larvae and neurons and the differentiation of hematopoietic stem cells (He and Hannon, 2004). In addition, miRNAs play a crucial role in the cell’s stress response, such as when p53 triggers the transcription of various miRNAs that halt growth and induce apoptosis when the cell’s DNA is damaged (Leung and Sharp, 2010). Due
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