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Systems Toxicology: Beyond Animal Models

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Systems Toxicology: Beyond Animal Models Systems Toxicology: Beyond Animal Models by Alexandra Maertens A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland September, 2014 © Copyright by Alexandra Maertens 2014 All Rights Reserved ACKNOWLEDGMENTS The path to my Ph.D. has been longer than most and I have accumulated a correspondingly large group of people to thank. Dr. Peter Ranslow and Dr. Elizabeth Becker both helped me get back on my feet and back into my field, and I will always be thankful they gave me the time and support necessary to finish my Ph.D. My first advisor, Dr. Joseph Bressler, started with me when I was a scientist who could barely pipette and showed no great talent for tissue culture, yet he endured with me through thick and thin and taught me not only how to be a good scientist, but also how to be a good teacher. Dr. James Yager was not only instrumental in helping me back on track after a personal setback, but was unfailing in his encouragement and unstinting with his time. Dr. Vanessa sa’Rocha’s calm perseverance was both an inspiration and a model, and Thomas Luechtfeld’s keen coding ability kept our Integrated Testing Strategy on track. Dr. Andre Kleensang has always been a good sparring partner and a good friend, and most importantly, he always kept the statistics honest. I will be forever grateful to Dr. Thomas Hartung’s extraordinary generosity in giving me an opportunity to finish my Ph.D. It was my great fortune to have landed in one of the most intellectually stimulating and welcoming environments possible at CAAT. Finally, this is dedicated to my son, Mikhail Maertens, who cannot remember a time when his mother wasn’t trying to finish her Ph.D. ii ABSTRACT Toxicology – much like the rest of biology – is undergoing a profound change as new technologies begin to offer a more systems oriented view of cellular physiology. For toxicology in particular, this means moving away from black-box animal models that provide limited information about mechanisms of toxicity towards the use of in vitro approaches which can both expedite hazard assessment while at the same time providing a more data – rich insight into toxic effects at the molecular level. One motivator of this shift is Green Toxciology, which seeks to support the Green Chemistry movement. In order for this approach to succeed, it will require two separate but parallel efforts. The first is an Integrated Testing Strategy which seeks to use machine learning and data mining techniques to combine QSARs and in vitro tests in the most efficient way possible to accurately estimate hazard, which is discussed both theoretically and demonstrated practically with the example of skin sensitization. Secondly, toxicology will require new approaches that exploit the insights of network biology to look at toxic mechanisms from a systems perspective. The theoretical concept of a Pathway of Toxicity is outlined, and an example of how to extract a suggested Pathway of Toxicity is given, using a Weighted Gene Correlation Network Analysis of a small microarray study of MPTP toxicity combined with text- mining and other high-throughput data to suggest novel candidate transcription factors and proteins. In conclusion, it discusses some of the current limitations of another promising –omics technology, metabolomics. September, 2014 ALEXANDRA MAERTENS, BS Directed by: Professor Thomas Hartung, M.D. iii TABLE OF CONTENTS CHAPTER I – Introduction: Green Toxicology as a Motivator for Systems Toxicology .........................................................................................................1 CHAPTER II – Probabilistic Hazard Assessment for Skin Sensitization Potency using Machine Learning to Design Integrated Testing Strategies ..........................................................................................................8 1. Introduction ............................................................................................................................ 9 2. Material and methods ..................................................................................................... 13 3. Results .................................................................................................................................... 18 4. Discussion ............................................................................................................................. 27 5. Acknowledgements .......................................................................................................... 28 CHAPTER III – MPTP’s Pathway of Toxicity Indicates Central Role of Transcription Factor SP1 .......................................................................... 30 1. Introduction ......................................................................................................................... 30 2. Materials and Methods ................................................................................................... 34 3. Results .................................................................................................................................... 36 4. Discussion ............................................................................................................................. 56 CHAPTER IV – Pathways of Toxicity and Metabolomics ........................................... 61 1. Introduction: Metabolomics—The Promise and the Pitfalls .......................... 61 2. Materials and Methods ................................................................................................... 70 3. Results and Discussion ................................................................................................... 72 CHAPTER V – Conclusion ................................................................................................. 104 APPENDIX I Green Toxicology ....................................................................................... 117 APPENDIX II Integrated Testing Strategy .................................................................. 133 APPENDIX III Pathways of Toxicity Workshop Report .......................................... 169 1. Introduction ....................................................................................................................... 173 2. What are the benefits of mapping PoT? ................................................................ 176 3. What gap could a PoT database fill that is not met by existing databases? .......................................................................................................................... 179 4. What is a Pathway of Toxicity; how many PoT are there and is the number finite? .................................................................................................................. 181 5. How to identify and validate a PoT? ....................................................................... 183 6. Future challenges and directions; creation of a PoT consortium ............... 187 iv REFERENCES: ....................................................................................................................... 190 CURRICULUM VITAE: ......................................................................................................... 199 v LIST OF TABLES Chapter II, Table 1: Overview on dataset 1 to 3 as described the section dataset. ................................................................................................................................... 14 Chapter III, Table 2: Confusion matrix of predicted chemical’s sensitizing potency vs. LLNA Reference classification for datasets 1 to 3 including balanced accuracy and balanced error. .............................................. 26 Chapter III, Table 1: Modules Correlated with Time. ........................................................... 39 Chapter III, Table 2: Modules annotated by DAVID. ............................................................. 40 Chapter III, Table 3: Overlap of the modules with Alzheimer’s genes, from MSigDB C2 gene sets. Alzheimer’s genes are based on the Blalock dataset (Blalock et al., 2004). ....................................................................................... 42 Chapter III, Table 4: Candidate transcription factors associated with Parkinson’s and MPTP via text-mining; p-values based on enrichment in MSigDB C3 gene sets. .................................................................................................. 44 Chapter III, Table 5: Addition of Predicted Transcription Factors Substantially Increased Connected Component. ............................................................................. 46 Chapter IV, Table 1: From Consensus and Conflict Database, data taken from http://www.molgenis.org/c2cards/molgenis.do ............................................... 65 Chapter IV, Table 2: Estrogen From Consensus and Conflict Database ....................... 66 Chapter IV, Table 3: Estrone, From Consensus and Conflict Database ......................... 67 Chapter IV, Table 4: Reaction of estrone -> estrogen-sulfate, From Consensus and Conflict Database ...................................................................................................... 68 Chapter IV, Table 5: Experiment 1, QEA 24-Hour Dose–Response Curve. .................. 80 Chapter IV, Table 6: Pathways Returned by IMPaLA for All Metabolites Identified in Sample ........................................................................................................
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