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Integrative Systems Biology Applied to Toxicology

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Integrative Systems Biology Applied to Toxicology Integrative Systems Biology Applied to Toxicology Kristine Grønning Kongsbak PhD Thesis January 2015 Integrative Systems Biology Applied to Toxicology Kristine Grønning Kongsbak Søborg 2015 FOOD-PHD-2015 PhD Thesis 2015 Supervisors Professor Anne Marie Vinggaard Senior Scientist Niels Hadrup Division of Toxicology and Risk Assessment National Food Institute Technical University of Denmark Associate Professor Aron Charles Eklund Center for Biological Sequence Analysis Department for Systems Biology Technical University of Denmark Associate Professor Karine Audouze Mol´ecules Th´erapeutiques In Silico Paris Diderot University Funding This project was supported financially by the Ministry of Food, Agriculture and Fisheries of Denmark and the Technical University of Denmark. ©Kristine Grønning Kongsbak FOOD-PHD: ISBN 978-87-93109-30-8 Division of Toxicology and Risk Assessment National Food Institute Technical University of Denmark DK-2860 Søborg, Denmark www.food.dtu.dk 4 Summary Humans are exposed to various chemical agents through food, cosmetics, pharma- ceuticals and other sources. Exposure to chemicals is suspected of playing a main role in the development of some adverse health effects in humans. Additionally, European regulatory authorities have recognized the risk associated with combined exposure to multiple chemicals. Testing all possible combinations of the tens of thousands environmental chemicals is impractical. This PhD project was launched to apply existing computational systems biology methods to toxicological research. In this thesis, I present in three projects three different approaches to using com- putational toxicology to aid classical toxicological investigations. In project I, we predicted human health effects of five pesticides using publicly available data. We obtained a grouping of the chemical according to their potential human health ef- fects that were in concordance with their effects in experimental animals. In project II, I profiled the effects on rat liver gene expression levels following exposure to a 14- chemical mixture ± the presence of an endocrine disrupting chemical. This project helped us shed light on the mechanism of action of the 14-chemical mixture and the endocrine disrupting chemical. In project III, I modeled a predictive signature for an in vivo endpoint that is sensitive to endocrine disruption. I used publicly available data generated for the purpose of modeling predictive signatures for vari- ous in vivo endpoints. From this modeling effort, I have suggested a mechanism of action for a subset of the chemicals that has not previously been associated with endocrine disruption. ii The use of computational methods in toxicology can aid the classical toxicological tests by suggesting interactions between separate components of a system thereby suggesting new ways of thinking specific toxicological endpoints. Furthermore, computational methods can serve as valuable input for the hypothesis generating phase of the preparations of a research project. Resume Mennesker bliver eksponeret for adskillige kemiske stoffer gennem fødevarer, kos- metik, lægemidler og andre kilder. Eksponering for kemikalier er mistænkt for at spille en vigtig rolle i udviklingen af nogle uønskede sundhedseffecter hos mennesker. Derudover har de europæiske tilsynsmyndigheder anerkendt risikoen forbundet med kombineret eksponering for flere kemikalier. Test af samtlige mulige kombinationer af de titusinder tilgængelige miljø- kemikalier er upraktisk. Dette Ph.d.-projekt blev iværksat for at anvende eksisterende systembiologiske computermetoder i toksikol- ogisk forskning. I denne afhandling præsenterer jeg i tre projekter tre forskellige tilgange til at bruge computaterbaseret toksikologi til at støtte klassiske toksikologiske undersøgelser. I projekt I, forudsagde vi sundhedsvirkningerne af fem pesticider ved brug af of- fentligt tilgængelige data. Vi opn˚aede en gruppering af kemikalierne i henhold til deres potentielle indvirkninger p˚a menneskers sundhed, der var i overensstemmelse med deres effekter i forsøgsdyr. I projekt II, profilerede jeg effekterne af en kemisk blanding med 14 kemikalier med og uden tilstedeværelse af et hormonforstyrrende stof p˚a genekspressionsniveauerne i rottelevere. Dette projekt hjalp os med at belyse virkningsmekanismen af den kemiske blanding og det hormonforstyrrende stof. I projektet III, modellerede jeg en prædiktiv signatur for et in vivo effektm˚al, der er følsomt over for hormonforstyrrende stoffer. Jeg brugte offentligt tilgæn- gelige data, der er genereret med henblik p˚a modellering af prædiktive signaturer til forskellige in vivo effektm˚al. Fra denne modelleringsindsats, har jeg foresl˚aet en virkningsmekanisme for en gruppe af de undersøgte kemikalier, som ikke tidligere iv har været forbundet med hormonforstyrrende stoffer. Brugen af computerbaserede metoder i toksikologi kan støtte de klassiske tok- sikologiske undersøgelser ved at foresl˚a interaktioner mellem separate komponen- ter i et system og dermed foresl˚a nye m˚ade at opfatte særlige toksikologiske ef- fektm˚al. Desuden kan computerbaserede metoder give værdifuldt input til den hypotesegenererende fase af forberedelsen af et forskningsprojekt. Preface This thesis was prepared at the National Food Institute and Center for Biological Sequence Analysis, Department of Systems Biology, the Technical University of Denmark in partial fulfillment of the requirements for acquiring the PhD degree in toxicology. During my work I was supervised by Anne Marie Vinggaard and Niels Hadrup from the National Food Institute, and Karine Audouze and Aron Eklund from the Center for Biological Sequence Analysis. The thesis deals with different aspects of computational toxicology using various methods and data sources. Collectively, my work has been focused on integrative data analyses using either existing data from the literature or in vivo and in vitro data generated for my and other studies. The thesis consists of a summary report and a collection of four research pa- pers/manuscripts written during the period 2011–2014, and published elsewhere along with preliminary data for a modeling project. Søborg, October 2014 Kristine Grønning Kongsbak vi Acknowledgments All my supervisors are greatly acknowledged for their patience and supervision during my studies. With their varied skill sets they have all contributed to my development towards becoming an independent and critically thinking scientist. They have all greeted me with an open-door policy, supported me on the rainiest days, and celebrated the successes with me. I would like to express my greatest gratitude to my colleagues at the National Food Institute, especially my fellow PhD students for making a very supportive and caring working environment. Anna, in particular, has been my go-to person and my ”partner in crime”. Dr. Richard Judson is thanked for accepting me as as his mentee at the National Center for Computational Toxicology at the U.S. Environmental Protection Agency. He and his colleagues made my six months in North Carolina very memorable. They all welcomed me to their professional yet warm place to enhance/improve my professional as well as personal skills. I would like to thank my friends, especially Sabine, Maria, Vicki, and Jill for always being there. Lastly, I would like to thank my family and partner for creating an escape for my thoughts to regenerate. viii Papers and manuscripts included in the thesis Manuscript 1 Kongsbak K, Hadrup N, Audouze K, Vinggaard AM. Applicability of Com- putational Systems Biology in Toxicology. BCPT. 2014;115(1):45–9. Manuscript 2 Kongsbak K, Vinggaard AM, Hadrup N, Audouze K. A computational ap- proach to mechanistic and predictive toxicology of pesticides. ALTEX. 2013;1–17. Manuscript 3 Hadrup N, Pedersen M, Skov K, Hansen NL, Berthelsen LO, Kongsbak K, Boberg J, Dybdahl M, Hass U, Frandsen H, Vinggaard AM. Perfluo- rononanoic acid in combination with 14 chemicals exerts low dose mixture effects in rats. Arch Toxicol. doi: 10.1007/s00204-015-1452-6 Manuscript 4 Skov K, Kongsbak K1, Hadrup N, Frandsen H, Smedsgaard J, Audouze K, Eklund A, Vinggaard AM. Metabolite and gene expression profiling of rats exposed to perfluorononanoic acid combined with a low-dose mixture of 14 human relevant compounds. Submitted 1Skov K and Kongsbak K contributed equally to the work. x Acronyms AA aggregated assay set value. AC50 half maximal active concentration. ACToR Aggregated Computational Toxicology Resource. AGD anogenital distance. AR androgen receptor. BA balanced accuracy. CAR constitutive androstane receptor. cDNA complementary strand deoxyribonucleic acid (DNA). CTD Comparative Toxicogenomics Database. CYP cytochrome P450. DNA deoxyribonucleic acid. DTU Technical University of Denmark. EDC endocrine disrupting chemical. xii Acronyms ER estrogen receptor. EU European Union. FDR false discovery rate. GEO Gene Expression Omnibus. H295 human adrenocortical carcinoma cell line. HTS high-throughput screening. iCSS interactive Chemical Safety for Sustainability. KEGG Kyoto Encyclopedia of Genes and Genomes. LDA linear discriminant analysis. LOAEL lowest observed adverse effect level. LXR liver X receptor. mg/kg BW/day mg/kg body weight/day. MGR multi-generational reproductive study. Mix 14-compound mixture. mRNA messenger-ribonucleic acid (RNA). NOAEL no observed adverse effect level. OMIM Online Mendelian Inheritance in Man. PBR peripheral benzodiazepine receptor. PFC perfluorinated compound. PFHxS perfluorohexanoic acid. PFNA perfluorononanoic acid. PFOA perfluorooctanoic
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