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Systems and Chemical Biology Approaches to Study Cell Function and Response to Toxins

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Systems and Chemical Biology Approaches to Study Cell Function and Response to Toxins Dissertation submitted to the Combined Faculties for the Natural Sciences and for Mathematics of the Ruperto-Carola University of Heidelberg, Germany for the degree of Doctor of Natural Sciences Presented by MSc. Yingying Jiang born in Shandong, China Oral-examination: Systems and chemical biology approaches to study cell function and response to toxins Referees: Prof. Dr. Rob Russell Prof. Dr. Stefan Wölfl CONTRIBUTIONS The chapter III of this thesis was submitted for publishing under the title “Drug mechanism predominates over toxicity mechanisms in drug induced gene expression” by Yingying Jiang, Tobias C. Fuchs, Kristina Erdeljan, Bojana Lazerevic, Philip Hewitt, Gordana Apic & Robert B. Russell. For chapter III, text phrases, selected tables, figures are based on this submitted manuscript that has been originally written by myself. i ABSTRACT Toxicity is one of the main causes of failure during drug discovery, and of withdrawal once drugs reached the market. Prediction of potential toxicities in the early stage of drug development has thus become of great interest to reduce such costly failures. Since toxicity results from chemical perturbation of biological systems, we combined biological and chemical strategies to help understand and ultimately predict drug toxicities. First, we proposed a systematic strategy to predict and understand the mechanistic interpretation of drug toxicities based on chemical fragments. Fragments frequently found in chemicals with certain toxicities were defined as structural alerts for use in prediction. Some of the predictions were supported with mechanistic interpretation by integrating fragment- chemical, chemical-protein, protein-protein interactions and gene expression data. Next, we systematically deciphered the mechanisms of drug actions and toxicities by analyzing the associations of drugs’ chemical features, biological features and their gene expression profiles from the TG-GATEs database. We found that in vivo (rat liver) and in vitro (rat hepatocyte) gene expression patterns were poorly overlapped and gene expression responses in different species (rat and human) and different tissues (liver and kidney) varied widely. Eventually, for further understanding of individual differences in drug responses, we reviewed how genetic polymorphisms influence the individual's susceptibility to drug toxicity by deriving chemical-protein interactions and SNP variations from Mechismo database. Such a study is also essential for personalized medicine. Overall, this study showed that, integrating chemical and biological in addition to genetic data can help assess and predict drug toxicity at system and population levels. Keywords: mechanism of drug-action, toxicogenomics, biological features, chemical features, 1000 Genomes iii ZUSAMMENFASSUNG Toxizitäten von Arzneimitteln sind eine der Hauptursachen des Scheiterns während des Wirkstoff-Entdeckungsprozesses - und der Vom-Markt-Nahme, sollten diese bereits den Markt erreicht haben. Daher ist die Vorhersage potentieller Toxizitäten im frühen Stadium der Arzneimittelentwicklung von großem Interesse geworden, um diesen langen und teuren Prozess zu verbessern. Da Toxizitäten aus der Störung biologischer Systeme durch Chemikalien resultieren, kombinierten wir biologische und chemische Strategien, um vollständige Vorhersagemodelle für Arzneimitteltoxizitäten bereitzustellen. Zuerst haben wir eine systematische Strategie vorgeschlagen, um die mechanistische Interpretation von Wirkstofftoxizitäten auf der Basis chemischer Fragmente vorherzusagen und zu verstehen. Fragmente, die in hohem Maße mit bestimmten Toxizitäten zusammenhängen, wurden als Strukturalarmhinweise für die Verwendung in der Toxizitätsvorhersage definiert. Einige der Vorhersagen wurden mittels mechanistischer Interpretation durch Integration von Fragment-Chemie-, Chemie-Protein-, Protein-Protein- Interaktionen und Genexpressionsdaten unterstützt. Als nächstes haben wir systematisch die Mechanismen von Arzneimittelwirkungen und - toxizitäten durch Analyse der Assoziationen zwischen chemischen Merkmalen und biologischen Merkmalen von Arzneimitteln und ihren Genexpressionsprofilen aus der TG- GATE-Datenbank entschlüsselt. In der Zwischenzeit fanden wir, dass sich in vivo (Rattenleber) und in vitro (Rattenhepatozyten) Genexpressionsmuster nur selten ähneln und - reaktionen bei verschiedenen Spezies (Ratte und Mensch), verschiedenen Geweben (Leber und Niere) weit variieren. Zum besseren Verständnis der individuellen Unterschiede in den Medikamentenreaktionen untersuchten wir, wie genetische Polymorphismen die Anfälligkeit des Individuums gegenüber der Wirkstofftoxizität beeinflussen, indem wir chemische Protein-Interaktionen und SNP-Variationen aus der Mechismo-Datenbank abgeleitet haben. Eine solche Studie ist auch wichtig für die personalisierte Medizin. Insgesamt zeigte diese Studie, dass die Integration von chemischen Merkmalen (z. B. chemische Fragmente, chemische Struktur), biologische Merkmale - einschließlich Genexpression und genetische Daten - die Toxizität des Wirkstoffs auf System- und Populationsniveau einschätzen und voraussagen können. v Schlüsselwörter: Mechanismus der Arzneimittelwirkung, Toxikogenomik, biologische Merkmale, chemische Eigenschaften, 1000 Genome ACKNOWLEDGMENTS This thesis is a milestone in my four years of work at Heidelberg University and specifically within the Professor Russell's laboratory. My experience in Heidelberg has been nothing short of amazing. Since high school, I started to dream of studying in Heidelberg. Until 2012, I have been given unique opportunities, starting as a PhD research assistant. Throughout these years in the lab, I have learned a lot from the whole team. Here, I would like to express my gratitude to all those who helped me during the writing of this thesis. My deepest gratitude goes first and foremost to Professor Rob Russell, my supervisor, for his constant encouragement and guidance. He has walked me through all the stages of the writing of this thesis. I appreciate his continuous support and his promotion of my scientific work. This thesis is also the result of many experiences I have encountered at Russell's group from dozens of remarkable individuals who I also wish to acknowledge, Gordana, Yvonne, Matthew, Qianhao, Oliver, Francesco, Juan-Carlos, Gurdeep and Chu-Harn. Then, I would like to express my gratitude to Professor Wölfl, who gave me valuable advice on the thesis. I am also greatly indebted to the professors and co-workers at the Cell Networks, BioQuant, Biochemie Zentrum Heidelberg (BZH), Cambridge Cell Networks Ltd, Merck KgaA who have instructed and helped me a lot in the past four years. I would also like to thank PhD programs in Institute of Pharmacy and Molecular Biotechnology and BZH for providing lectures, a great network of researchers, and academic training. Finally, I want to say thanks to my beloved family for their loving considerations in China, who have been doing the utmost to support me. I sincere thank Norbert and Rodica for sharing their caring thoughts and taking care of me. I also owe my gratitude to all my friends who gave me their help and concern in helping me work out my problems during the difficult course of the living in Germany. vii TABLE OF CONTENTS Chapter Page CONTRIBUTIONS .................................................................................................................. i ABSTRACT ........................................................................................................................... iii ZUSAMMENFASSUNG ........................................................................................................ v ACKNOWLEDGMENTS ..................................................................................................... vii TABLE OF CONTENTS ....................................................................................................... ix ABBREVIATIONS .............................................................................................................. xiii CHAPTER I: Introduction ....................................................................................................... 1 1.1 Cheminformatics-based predictions in toxicology ........................................................ 1 1.2 Bioinformatics-based predictions in toxicology ............................................................ 3 1.3 Integrated chemical and biological modeling-based predictions in toxicology ............ 6 1.4 Predictive toxicology at human population level .......................................................... 8 1.5 Dissertation outline ...................................................................................................... 10 CHAPTER II: Chemical fragments as foundations for understanding the toxic effects of chemicals on biological systems ........................................................................................... 13 2.1 Abstract ........................................................................................................................ 13 2.2 Introduction ................................................................................................................. 13 2.3 Methods ....................................................................................................................... 17 2.3.1 Data preparation ................................................................................................... 17 2.3.2 Extracting toxicity information ...........................................................................
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