Abstract
When evaluating compound similarity, addressing multiple sources of information to reach conclusions about common pharmaceutical and/or toxicological mechanisms of action is a crucial strategy. In this chapter, we describe a systems biology approach that incorporates analyses of hepatotoxicant data for 33 compounds from three different sources: a chemical structure similarity analysis based on the 3D Tanimoto coefficient, a chemical structure-based protein target prediction analysis, and a cross-study/cross-platform meta-analysis of in vitro and in vivo human and rat transcriptomics data derived from public resources (i.e., the diXa data warehouse). Hierarchical clustering of the outcome scores of the separate analyses did not result in a satisfactory grouping of compounds considering their known toxic mechanism as described in literature. However, a combined analysis of multiple data types may hypothetically compensate for missing or unreliable information in any of the single data types. We therefore performed an integrated clustering analysis of all three data sets using the R-based tool iClusterPlus. This indeed improved the grouping results. The compound clusters that were formed by means of iClusterPlus represent groups that show similar gene expression while simultaneously integrating a similarity in structure and protein targets, which corresponds much better with the known mechanism of action of these toxicants. Using an integrative systems biology approach may thus overcome the limitations of the separate analyses when grouping liver toxicants sharing a similar mechanism of toxicity.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D (2015) Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet 16:85–97
Holzinger ER, Ritchie MD (2012) Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies. Pharmacogenomics 13:213–222
Reif DM, White BC, Moore JH (2004) Integrated analysis of genetic, genomic and proteomic data. Expert Rev Proteomics 1:67–75
Hamid JS, Hu P, Roslin NM, Ling V, Greenwood CM, Beyene J (2009) Data integration in genetics and genomics: methods and challenges. Hum Genom Proteomics. DOI: 10.4061/2009/869093
Hawkins RD, Hon GC, Ren B (2010) Next-generation genomics: an integrative approach. Nat Rev Genet 11:476–486
Shon J, Abernethy DR (2014) Application of systems pharmacology to explore mechanisms of hepatotoxicity. Clin Pharmacol Ther 96:536–537
Howell BA, Siler SQ, Watkins PB (2014) Use of a systems model of drug-induced liver injury (DILIsym((R))) to elucidate the mechanistic differences between acetaminophen and its less-toxic isomer, AMAP, in mice. Toxicol Lett 226:163–172
Bhattacharya S, Shoda LK, Zhang Q, Woods CG, Howell BA, Siler SQ, Woodhead JL, Yang Y, McMullen P, Watkins PB, Andersen ME (2012) Modeling drug- and chemical-induced hepatotoxicity with systems biology approaches. Front Physiol 3:462
Chen M, Vijay V, Shi Q, Liu Z, Fang H, Tong W (2011) FDA-approved drug labeling for the study of drug-induced liver injury. Drug Discov Today 16:697–703
Cui Y, Paules RS (2010) Use of transcriptomics in understanding mechanisms of drug-induced toxicity. Pharmacogenomics 11:573–585
Giuliano KA, Gough AH, Taylor DL, Vernetti LA, Johnston PA (2010) Early safety assessment using cellular systems biology yields insights into mechanisms of action. J Biomol Screen 15:783–797
DiMasi JA, Hansen RW, Grabowski HG (2003) The price of innovation: new estimates of drug development costs. J Health Econ 22:151–185
Senior JR (2008) What is idiosyncratic hepatotoxicity? What is it not? Hepatology 47:1813–1815
Holmes AM, Creton S, Chapman K (2010) Working in partnership to advance the 3Rs in toxicity testing. Toxicology 267:14–19
Soldatow VY, Lecluyse EL, Griffith LG, Rusyn I (2013) In vitro models for liver toxicity testing. Toxicol Res (Camb) 2:23–39
Hendrickx DM, Aerts HJ, Caiment F, Clark D, Ebbels TM, Evelo CT, Gmuender H, Hebels DG, Herwig R, Hescheler J, Jennen DG, Jetten MJ, Kanterakis S, Keun HC, Matser V, Overington JP, Pilicheva E, Sarkans U, Segura-Lepe MP, Sotiriadou I, Wittenberger T, Wittwehr C, Zanzi A, Kleinjans JC (2015) diXa: a data infrastructure for chemical safety assessment. Bioinformatics 31:1505–1507
Bolton EE, Chen J, Kim S, Han L, He S, Shi W, Simonyan V, Sun Y, Thiessen PA, Wang J, Yu B, Zhang J, Bryant SH (2011) PubChem3D: a new resource for scientists. J Cheminform 3:32
Kim S, Bolton EE, Bryant SH (2012) Effects of multiple conformers per compound upon 3-D similarity search and bioassay data analysis. J Cheminform 4:28
Kim S, Bolton EE, Bryant SH (2011) PubChem3D: biologically relevant 3-D similarity. J Cheminformatics 3:26
Jenkins JL, Bender A, Davies JW (2006) In silico target fishing: predicting biological targets from chemical structure. Drug Discov Today Tech 3:413–421
Nidhi, Glick M, Davies JW, Jenkins JL (2006) Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases. J Chem Inf Model 46:1124–1133
Southan C, Sitzmann M, Muresan S (2013) Comparing the chemical structure and protein content of ChEMBL, DrugBank, human metabolome database and the therapeutic target database. Mol Informat 32:881–897
Mugumbate G, Abrahams KA, Cox JA, Papadatos G, van Westen G, Lelievre J, Calus ST, Loman NJ, Ballell L, Barros D, Overington JP, Besra GS (2015) Mycobacterial dihydrofolate reductase inhibitors identified using chemogenomic methods and in vitro validation. PLoS One 10:e0121492
Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107
ChEMBL Team (2013) ChEMBL release 17. DOI: 10.6019/CHEMBL.database.17
Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754
Kreyszig E (1979) Applied mathematics. Wiley Press, New York
Caiment F, Tsamou M, Jennen D, Kleinjans J (2014) Assessing compound carcinogenicity in vitro using connectivity mapping. Carcinogenesis 35:201–207
Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM (2004) Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci U S A 101:9309–9314
Vilardell M, Rasche A, Thormann A, Maschke-Dutz E, Perez-Jurado LA, Lehrach H, Herwig R (2011) Meta-analysis of heterogeneous down syndrome data reveals consistent genome-wide dosage effects related to neurological processes. BMC Genomics 12:229
Rasche A, Al-Hasani H, Herwig R (2008) Meta-analysis approach identifies candidate genes and associated molecular networks for type-2 diabetes mellitus. BMC Genomics 9:310
Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185–193
Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31:e15
Rasche A, Yildirimman R, Herwig R (2009) Integrative analysis of microarray data: a path for systems toxicology, General, applied and systems toxicology. Wiley, Hoboken, NJ
Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akil H, Watson SJ, Meng F (2005) Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res 33:e175
Guo L, Fang H, Collins J, Fan XH, Dial S, Wong A, Mehta K, Blann E, Shi L, Tong W, Dragan YP (2006) Differential gene expression in mouse primary hepatocytes exposed to the peroxisome proliferator-activated receptor alpha agonists. BMC Bioinformatics 7(Suppl 2):S18
Ogata M, Tsujita M, Hossain MA, Akita N, Gonzalez FJ, Staels B, Suzuki S, Fukutomi T, Kimura G, Yokoyama S (2009) On the mechanism for PPAR agonists to enhance ABCA1 gene expression. Atherosclerosis 205:413–419
Hanke T, Dehm F, Liening S, Popella SD, Maczewsky J, Pillong M, Kunze J, Weinigel C, Barz D, Kaiser A, Wurglics M, Lammerhofer M, Schneider G, Sautebin L, Schubert-Zsilavecz M, Werz O (2013) Aminothiazole-featured pirinixic acid derivatives as dual 5-lipoxygenase and microsomal prostaglandin E2 synthase-1 inhibitors with improved potency and efficiency in vivo. J Med Chem 56:9031–9044
Seo M, Inoue I, Ikeda M, Nakano T, Takahashi S, Katayama S, Komoda T (2008) Statins activate human PPARalpha promoter and increase PPARalpha mRNA expression and activation in HepG2 cells. PPAR Res 2008:316306
Paumelle R, Blanquart C, Briand O, Barbier O, Duhem C, Woerly G, Percevault F, Fruchart JC, Dombrowicz D, Glineur C, Staels B (2006) Acute antiinflammatory properties of statins involve peroxisome proliferator-activated receptor-alpha via inhibition of the protein kinase C signaling pathway. Circ Res 98:361–369
Wierzbicki AS, Mikhailidis DP, Wray R, Schacter M, Cramb R, Simpson WG, Byrne CB (2003) Statin-fibrate combination: therapy for hyperlipidemia: a review. Curr Med Res Opin 19:155–168
Barnett J, Viljoen A, Wierzbicki AS (2013) The need for combination drug therapies in patients with complex dyslipidemia. Curr Cardiol Rep 15:391
Chateauvieux S, Morceau F, Dicato M, Diederich M (2010) Molecular and therapeutic potential and toxicity of valproic acid. J Biomed Biotechnol. http://www.ncbi.nlm.nih.gov/pubmed/20798865
Lampen A, Carlberg C, Nau H (2001) Peroxisome proliferator-activated receptor delta is a specific sensor for teratogenic valproic acid derivatives. Eur J Pharmacol 431:25–33
Ren H, Aleksunes LM, Wood C, Vallanat B, George MH, Klaassen CD, Corton JC (2010) Characterization of peroxisome proliferator-activated receptor alpha – independent effects of PPARalpha activators in the rodent liver: di-(2-ethylhexyl) phthalate also activates the constitutive-activated receptor. Toxicol Sci 113:45–59
Kliewer SA, Xu HE, Lambert MH, Willson TM (2001) Peroxisome proliferator-activated receptors: from genes to physiology. Recent Prog Horm Res 56:239–263
Jia R, Cao LP, Du JL, Wang JH, Liu YJ, Jeney G, Xu P, Yin GJ (2014) Effects of carbon tetrachloride on oxidative stress, inflammatory response and hepatocyte apoptosis in common carp (Cyprinus carpio). Aquat Toxicol 152:11–19
Jimenez-Lopez JM, Cederbaum AI (2005) CYP2E1-dependent oxidative stress and toxicity: role in ethanol-induced liver injury. Expert Opin Drug Metab Toxicol 1:671–685
Jaeschke H, Gores GJ, Cederbaum AI, Hinson JA, Pessayre D, Lemasters JJ (2002) Mechanisms of hepatotoxicity. Toxicol Sci 65:166–176
Yang JW, Shin JS, Lee JJ, Chang HI, Kim CW (2001) In vitro model using mouse hepatocytes for study of alcohol stress. Biosci Biotechnol Biochem 65:1528–1533
Kwolek-Mirek, M., R. Zadrag-Tecza, S. Bednarska and G. Bartosz (2014) Acrolein-Induced Oxidative Stress and Cell Death Exhibiting Features of Apoptosis in the Yeast Saccharomyces cerevisiae Deficient in SOD1. Cell Biochem Biophys 71:1525–1536
Kujawska M, Ignatowicz E, Murias M, Ewertowska M, Mikolajczyk K, Jodynis-Liebert J (2009) Protective effect of red beetroot against carbon tetrachloride- and N-nitrosodiethylamine-induced oxidative stress in rats. J Agric Food Chem 57:2570–2575
Mo Q, Wang S, Seshan VE, Olshen AB, Schultz N, Sander C, Powers RS, Ladanyi M, Shen R (2013) Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc Natl Acad Sci U S A 110:4245–4250
Zhu B, Bai R, Kennett MJ, Kang BH, Gonzalez FJ, Peters JM (2010) Chemoprevention of chemically induced skin tumorigenesis by ligand activation of peroxisome proliferator-activated receptor-beta/delta and inhibition of cyclooxygenase 2. Mol Cancer Ther 9:3267–3277
Dhir A, Naidu PS, Kulkarni SK (2007) Neuroprotective effect of nimesulide, a preferential COX-2 inhibitor, against pentylenetetrazol (PTZ)-induced chemical kindling and associated biochemical parameters in mice. Seizure 16:691–697
Ghio L, Cervetti A, Respino M, Belvederi Murri M, Amore M (2014) Management and treatment of gamma butyrolactone withdrawal syndrome: a case report and review. J Psychiatr Pract 20:294–300
Acknowledgments
This work was supported by the European Commission under its 7th Framework Programme with the Grant “diXa” (283775) and by the European Chemical Industry Council - Long-Range Research Initiative (CEFIC-LRI) with the Grant “DECO2” (AIMT4-UM).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media New York
About this protocol
Cite this protocol
Hebels, D.G.A.J., Rasche, A., Herwig, R., van Westen, G.J.P., Jennen, D.G.J., Kleinjans, J.C.S. (2016). A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure. In: Benfenati, E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 1425. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3609-0_15
Download citation
DOI: https://doi.org/10.1007/978-1-4939-3609-0_15
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3607-6
Online ISBN: 978-1-4939-3609-0
eBook Packages: Springer Protocols