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Malvika Sharan Bio-computational identification and characterization of RNA-binding proteins in bacteria German Title: Bioinformatische Identifikation und Charakterisierung von RNA-bindenden Proteinen in Bakterien Doctoral thesis for a doctoral degree at the Graduate School of Life Sciences, Julius-Maximilians-Universität Würzburg, Section: Infection and immunity submitted by Malvika Sharan from Ranchi, India Würzburg, 2017 Submitted on: Members of the Doctoral Thesis Committee: Chairperson: Prof. Jörg Schultz Primary Supervisor: Prof. Jörg Vogel Supervisor (Second): Prof. Thomas Dandekar Supervisor (Third): Dr. Ana Eulalio Supervisor (Fourth): Dr. Cynthia Sharma Date of Public Defence: Date of Receipt of Certificates: II III Summary RNA-binding proteins (RBPs) have been extensively studied in eukaryotes, where they post-transcriptionally regulate many cellular events including RNA transport, translation, and stability. Experimental techniques, such as cross-linking and co-purification followed by either mass spectrometry or RNA sequencing has enabled the identification and characterization of RBPs, their conserved RNA-binding domains (RBDs), and the regulatory roles of these proteins on a genome-wide scale. These developments in quantitative, high- resolution, and high-throughput screening techniques have greatly expanded our understanding of RBPs in human and yeast cells. In contrast, our knowledge of number and potential diversity of RBPs in bacteria is comparatively poor, in part due to the technical challenges associated with existing global screening approaches developed in eukaryotes. Genome- and proteome-wide screening approaches performed in silico may circumvent these technical issues to obtain a broad picture of the RNA interactome of bacteria and identify strong RBP candidates for more detailed experimental study. Here, I report APRICOT (“Analyzing Protein RNA Interaction by Combined Output Technique”), a computational pipeline for the sequence-based identification and characterization of candidate RNA-binding proteins encoded in the genomes of all domains of life using RBDs known from experimental studies. The pipeline identifies functional motifs in protein sequences of an input proteome using position-specific scoring matrices and hidden Markov models of all conserved domains available in the databases and then statistically score them based on a series of sequence- based features. Subsequently, APRICOT identifies putative RBPs and characterizes them according to functionally relevant structural properties. APRICOT performed better than other existing tools for the sequence-based prediction on the known RBP data sets. The applications and adaptability of the software was demonstrated on several large bacterial RBP data sets including the complete proteome of Salmonella Typhimurium strain SL1344. APRICOT reported 1068 Salmonella proteins as RBP candidates, which were subsequently categorized using the RBDs that have been reported in both eukaryotic and bacterial proteins. A set of 131 strong RBP candidates was selected for experimental confirmation and characterization of RNA-binding activity using RNA co-immunoprecipitation followed by high-throughput sequencing (RIP-Seq) experiments. Based on the relative abundance of transcripts across the RIP-Seq libraries, a catalogue of enriched genes was established for each candidate, which shows the RNA-binding potential of 90% of these proteins. Furthermore, the direct targets of few of these putative RBPs were validated by means of cross-linking and co-immunoprecipitation (CLIP) experiments. This thesis presents the computational pipeline APRICOT for the global screening of protein primary sequences for potential RBPs in bacteria using RBD information from all kingdoms of life. Furthermore, it provides the first bio-computational resource of putative RBPs in Salmonella, which could now be further studied for their biological and regulatory roles. The command line tool and its documentation are available at https://malvikasharan.github.io/APRICOT/. Zusammenfassung RNA-bindende Proteine (RBPs) wurden umfangreich in Eukaryoten erforscht, in denen sie viele Prozesse wie RNA-Transport, -Translation und -Stabilität post-transkriptionell regulieren. Experimentelle Methoden wie Cross-linking and Koimmunpräzipitation mit nachfolgedener Massenspektromentrie / RNA-Sequenzierung ermöglichten eine weitreichende Charakterisierung von RBPs, RNA-bindenden Domänen (RBDs) und deren regulatorischen Rollen in eukaryotischen Spezies wie Mensch und Hefe. Weitere Entwicklungen im Bereich der hochdurchsatzbasierten Screeningverfahren konnten das Verständnis von RBPs in Eukaryoten enorm erweitern. Im Gegensatz dazu ist das Wissen über die Anzahl und die potenzielle Vielfalt von RBPs in Bakterien dürftig. In der vorliegenden Arbeit präsentiere ich APRICOT, eine bioinformatische Pipeline zur sequenzbasierten Identifikation und Charakterisierung von Proteinen aller Domänen des Lebens, die auf RBD-Informationen aus experimentellen Studien aufbaut. Die Pipeline nutzt Position Specific Scoring Matrices und Hidden-MarkovModelle konservierter Domänen, um funktionelle Motive in Proteinsequenzen zu identifizieren und diese anhand von sequenzbasierter Eigenschaften statistisch zu bewerten. Anschließend identifiziert APRICOT mögliche RBPs und charakterisiert auf Basis ihrer biologischeren Eigenschaften. In Vergleichen mit ähnlichen Werkzeugen übertraf APRICOT andere Programme zur sequenzbasierten Vorhersage von RBPs. Die Anwendungsöglichkeiten und die Flexibilität der Software wird am Beispiel einiger großer RBP-Kollektionen, die auch das komplette Proteom von Salmonella Typhimurium SL1344 beinhalten, dargelegt. APRICOT identifiziert 1068 Proteine von Salmonella als RBP-Kandidaten, die anschließend unter Nutzung der bereits bekannten bakteriellen und eukaryotischen RBDs klassifiziert wurden. 131 der RBP- Kandidaten wurden zur Charakterisierung durch RNA co-immunoprecipitation followed by high-throughput sequencing (RIP-seq) ausgewählt. Basierend auf der relativen Menge an Transkripten in den RIP-seq-Bibliotheken wurde ein Katalog von angereicherten Genen erstellt, der auf eine potentielle RNA-bindende Funktion in 90% dieser Proteine hindeutet. Weiterhin wurden die Bindungstellen einiger dieser möglichen RBPs mit Cross-linking and Co-immunoprecipitation (CLIP) bestimmt. Diese Doktorarbeit beschreibt die bioinformatische Pipeline APRICOT, die ein globales Screening von RBPs in Bakterien anhand von Informationen bekannter RBDs ermöglicht. Zudem enthält sie eine Zusammenstellung aller potentieller RPS in Salmonella, die nun auf ihre biologsche Funktion hin untersucht werden können. Das Kommondozeilen-Programm und seine Dokumentation sind auf https://malvikasharan.github.io/APRICOT/ verfügbar. ii iii Table of Contents Summary ............................................................................................................................. I List of figures ..................................................................................................................... vi List of tables .................................................................................................................... viii Abbreviation index ............................................................................................................ ix Introduction ....................................................................................................................... 1 1.1 Overview ............................................................................................................................... 1 1.2 RNA-binding proteins and RNA-binding domains ................................................................... 2 1.3 Regulatory roles of RNA-protein interactions ......................................................................... 9 1.4 Biological features of RBPs in eukaryotes and bacteria ........................................................ 13 1.4.1 Overview of eukaryotic RBPs ............................................................................................... 14 1.4.2 Overview of bacterial RBPs .................................................................................................. 18 1.5 Bioinformatic approaches for RBP prediction ....................................................................... 20 1.5.1 Prediction of RNA-binding residues in proteins ................................................................... 21 1.5.2 Prediction of RNA-binding proteins ..................................................................................... 22 1.6 Aim of the study .................................................................................................................. 23 Computational identification and characterization of RBPs using APRICOT ....................... 25 2.1 Overview of APRICOT pipeline for RBP identification ........................................................... 26 2.1.1 Program input ...................................................................................................................... 26 2.1.2 Modules for domain prediction and annotations ................................................................ 29 2.1.3 Program output ................................................................................................................... 36 2.2 Data sets used in this study ................................................................................................. 38 2.2.1 Training sets ........................................................................................................................
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