2008 Progress Report: Carolina Center for Computational Toxicology

EPA Grant Number: R833825
Center: Carolina Center for Computational Toxicology
Center Director: Rusyn, Ivan
Title: Carolina Center for Computational Toxicology
Investigators: Rusyn, Ivan , Wright, Fred A. , Yeatts, Karin B. , Tropsha, Alex , Gomez, Shawn , Elston, Timothy
Current Investigators: Rusyn, Ivan , Wright, Fred A. , Tropsha, Alex , Nobel, Andrew , Gomez, Shawn , Elston, Timothy , Sun, Wei
Institution: University of North Carolina at Chapel Hill
Current Institution: University of North Carolina at Chapel Hill , North Carolina State University
EPA Project Officer: Hahn, Intaek
Project Period: April 1, 2008 through March 31, 2012
Project Period Covered by this Report: April 1, 2008 through March 31,2009
Project Amount: $3,400,000
RFA: Computational Toxicology Centers: Development Of Predictive Environmental And Biomedical Computer-Based Simulations And Models (2007) RFA Text |  Recipients Lists
Research Category: Computational Toxicology , Human Health , Safer Chemicals

Objective:

The objectives of the Carolina Center for Computational Toxicology are (i) to develop innovative methods and tools for dramatically increasing throughput of chemical screening and precision of hazard identification; (ii) to improve existing methodologies for chemical hazard testing in silico and in vitro with novel strategies that take into account the genetic diversity of the population; and (iii) to improve linkages in the source-to-outcome paradigm and quantitative risk assessment by translating and applying the scientific findings of the Center into easy-to-use high-throughput computational models of chemical-perturbed signaling networks.

Progress Summary:

The Center is arranged into three Research projects and the Administrative Core. Project 1, Biomedical modeling of chemical-perturbed networks (PIs Gomez and Elston), is focusing on the fine-scale predictive simulations of the protein-protein/-chemical interactions in nuclear receptor networks, mapping of chemical-perturbed networks and development of modeling tools that can predict the pathobiology of the test compounds based on a limited set of biological data. This project has been actively exploring ToxCast Phase I data in combination with additional external data sets describing known functional networks with the goal to potentially reduce the dimension of the biological variables to a set that is both statistically predictive as well as biologically relevant. A recently developed Bayesian data integration approach is being applied with the goal of linking assays into an integrated representation of biological relationships and associated perturbations. Project 2, Toxico-genetic modeling (PIs Wright and Rusyn), is collecting data and building tools that will enable toxicologists to understand the role of genetic diversity between individuals in responses to toxicants. This project has already produced software, FastMap, which allows fast association mapping in homozygous populations. In addition, our continued work on analyzing transcription factor binding will help integrate data from eQTL studies with data about transcription regulation, eventually providing a much more complete view of transcriptional regulation in toxicity response. Finally, toxicity phenotype (cell viability and apoptosis) data was collected on 14 EPA-relevant chemicals in 87 HapMap human lymphoblastoid cell lines and appropriate cell culture conditions are being established for murine hepatocytes from a large panel of inbred strains. Project 3, Chem-informatics (PI Tropsha), is developing unbiased discovery-driven prediction of adverse chronic in vivo outcomes based on statistical modeling of chemical structures, high-throughput screening and the genetic makeup of the organism. Progress was made in further development of Quantitative Structure-Activity Relationship (QSAR) methodology and its applications to chemical toxicity datasets. We have been working with ToxCast Phase I data and other EPA-relevant datasets to establish relationships and predictive signatures in datasets where chemical structures, in vivo toxicity endpoints and high throughput in vitro screening data are available. The Administrative Core provided administrative support to the entire Center and was responsible for ensuring that Center objectives and goals were being met. The Core provided oversight for each of the Projects and coordinated communications with EPA, monthly meetings of the Center personnel, and the annual External Advisory Board meeting. The Public Outreach function was achieved via establishing a content-rich center website and ensuring public release of the software and publications through the website.

Future Activities:

In Project 1, in depth analysis of ToxCast Phase I data will be the primary activity of the subsequent reporting period. Significant emphasis will be placed on the development of methods for the integration of different data types. In addition, the challenge of understanding results across species needs to be addressed and we plan on initiating investigations of this using the ToxCast data. In addition, based on our prior work developing a mechanistic model of metabolism, focused on glycolysis/gluconeogenesis that includes the liver, muscle, fat and blood as communicating compartments, we plan on investigating the applicability of this model as a tool for the prediction of the possible effects of chemical perturbation of metabolic pathways. Interaction with Project 2 will include integration of the eQTL analyses/approaches with the network-focused methodologies. Similarly, interactions with Project 3 will emphasize establishment of the network context in which a chemical or class of chemicals are likely to demonstrate an effect. Interactions and collaboration with EPA investigators at NCCT (Dr. Jason Pirone) will also be increased.

In Project 2, we will continue extending FastMap by applying the software to the human lymphoblast toxicity profiling studies. We will investigate the possibility of incorporating our geometric p-value approach into FastMap. For our integrated genomic approaches, we will incorporate the eQTL data, nucleosome occupancy, and transcription regulation information, possibly including microRNA expression to construct larger transcription regulation networks in the Bayesian network framework. We would be interested in the hub genes in the constructed network, especially in their response to toxic exposures, an area of collaboration with Project 1. In our in vitro toxico-genomic experiments we will complete characterization of the mouse hepatocyte cultures and perform experiments with several key toxicants for which multi-strain panel in vivo data is available. In the experiments with human lymphoblasts, we will complete GWAS analyses of the cell viability and apoptosis data and will correlate the toxicity endpoints with basal gene expression profiles collected from these cell lines. We will continue interactions with Project 3 and EPA/NCCT on the analysis of the ToxCast Phase I data.

In Project 3, we will primarily focus on the analysis of ToxCast data, but will also continue to explore other datasets that provide both in vivo and in vitro data for chemicals. For ToxCast data analysis our primary goal is to build models that could be used by EPA to prioritize the selection of ToxCast Phase 2 compounds. In addition, we will also continue making progress in both methodology development and applied studies using additional datasets. We will integrate novel data analytical approaches into our QSAR modeling workflow. We also intend to complete the second round of studies into structure – in vitro cytotoxicity – in vivo carcinogenicity relationships using extended dataset generated in collaboration with Projects 1 and 2 and EPA, National Toxicology Program, and National Chemical Genomic Center at NIH.


Journal Articles: 63 Displayed | Download in RIS Format

Other center views: All 98 publications 65 publications in selected types All 63 journal articles
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Journal Article Abell AN, Jordan NV, Huang W, Prat A, Midland AA, Johnson NL, Granger DA, Mieczkowski PA, Perou CM, Gomez SM, Li L, Johnson GL. MAP3K4/CBP-regulated H2B acetylation controls epithelial-mesenchymal transition in trophoblast stem cells. Cell Stem Cell 2011;8(5):525-537.
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  • Journal Article Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicology and Applied Pharmacology 2015;284(2):262-272.
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  • Journal Article Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization. Toxicology and Applied Pharmacology 2015; 284(2):273-280.
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  • Journal Article Artemenko AG, Muratov EN, Kuz'min VE, Muratov NN, Varlamova EV, Kuz'mina AV, Gorb LG, Golius A, Hill FC, Leszczynski J, Tropsha A. QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action. SAR and QSAR in Environmental Research 2011;22(5-6):575-601.
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  • Journal Article Berginski ME, Vitriol EA, Hahn KM, Gomez SM. High-resolution quantification of focal adhesion spatiotemporal dynamics in living cells. PLoS ONE 2011;6(7):e22025 (13 pp.).
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  • Journal Article Borysov P, Hannig J, Marron JS, Muratov E, Fourches D, Tropsha A. Activity prediction and identification of mis-annotated chemical compounds using extreme descriptors. Journal of Chemometrics 2016;30(3):99-108.
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  • Journal Article Bradford BU, Lock EF, Kosyk O, Kim S, Uehara T, Harbourt D, DeSimone M, Threadgill DW, Tryndyak V, Pogribny IP, Bleyle L, Koop DR, Rusyn I. Interstrain differences in the liver effects of trichloroethylene in a multistrain panel of inbred mice. Toxicological Sciences 2011;120(1):206-217.
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  • Journal Article Chaki SP, Barhoumi R, Berginksi ME, Sreenivasappa H, Trache A, Gomez SM, Rivera GM. Nck enables directional cell migration through the coordination of polarized membrane protrusion with adhesion dynamics. Journal of Cell Science 2013;126(Pt 7):1637-1649.
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  • Journal Article Chen Z, Lessey E, Berginski ME, Cao L, Li J, Trepat X, Itano M, Gomez SM, Kapustina M, Huang C, Burridge K, Truskey G, Jacobson K. Gleevec, an Abl family inhibitor, produces a profound change in cell shape and migration. PLoS ONE 2013;8(1):e52233 (14 pp.).
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  • Journal Article Choi K, Gomez SM. Comparison of phylogenetic trees through alignment of embedded evolutionary distances. BMC Bioinformatics 2009;10:423.
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  • Journal Article Doolittle JM, Gomez SM. Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens. Virology Journal 2010;7:82 (15 pp.).
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  • Journal Article Doolittle JM, Gomez SM. Mapping protein interactions between Dengue virus and its human and insect hosts. PLoS Neglected Tropical Diseases 2011;5(2):e954 (15 pp.).
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  • Journal Article Duncan JS, Whittle MC, Nakamura K, Abell AN, Midland AA, Zawistowski JS, Johnson NL, Granger DA, Jordan NV, Darr DB, Usary J, Kuan P-F, Smalley DM, Major B, He X, Hoadley KA, Zhou B, Sharpless NE, Perou CM, Kim WY, Gomez SM, Chen X, Jin J, Frye SV, Earp HS, Graves LM, Johnson GL. Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple negative breast cancer. Cell 2012;149(2):307-321.
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  • Journal Article Fourches D, Barnes JC, Day NC, Bradley P, Reed JZ, Tropsha A. Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. Chemical Research in Toxicology 2010;23(1):171-183.
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  • Journal Article Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of Chemical Information and Modeling 2010;50(7):1189-1204.
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  • Journal Article Fourches D, Muratov E, Ding F, Dokholyan NV, Tropsha A. Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches. Journal of Chemical Information and Modeling 2013;53(8):1915-1922.
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  • Journal Article Gatti DM, Shabalin AA, Lam T-C, Wright FA, Rusyn I, Nobel AB. FastMap: fast eQTL mapping in homozygous populations. Bioinformatics 2009;25(4):482-489.
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  • Journal Article Gatti DM, Sypa M, Rusyn I, Wright FA, Barry WT. SAFEGUI: resampling-based tests of categorical significance in gene expression data made easy. Bioinformatics 2009;25(4):541-542.
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  • Journal Article Gatti DM, Harrill AH, Wright FA, Threadgill DW, Rusyn I. Replication and narrowing of gene expression quantitative trait loci using inbred mice. Mammalian Genome 2009;20(7):437-446.
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  • Journal Article Gatti DM, Zhao N, Chesler EJ, Bradford BU, Shabalin AA, Yordanova R, Lu L, Rusyn I. Sex-specific gene expression in the BXD mouse liver. Physiological Genomics 2010;42(3):456-468.
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  • Journal Article Gatti DM, Barry WT, Nobel AB, Rusyn I, Wright FA. Heading down the wrong pathway: on the influence of correlation within gene sets. BMC Genomics 2010;11:574.
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  • Journal Article Gatti DM, Lu L, Williams RW, Sun W, Wright FA, Threadgill DW, Rusyn I. MicroRNA expression in the livers of inbred mice. Mutation Research 2011;714(1-2):126-133.
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  • Journal Article Golbraikh A, Muratov E, Fourches D, Tropsha A. Data set modelability by QSAR. Journal of Chemical Information and Modeling 2014;54(1):1-4.
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  • Journal Article Ha MJ, Sun W. Partial correlation matrix estimation using ridge penalty followed by thresholding and re-estimation. Biometrics 2014;70(3):765-773.
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  • Journal Article Harrill AH, Ross PK, Gatti DM, Threadgill DW, Rusyn I. Population-based discovery of toxicogenomics biomarkers for hepatotoxicity using a laboratory strain diversity panel. Toxicological Sciences 2009;110(1):235-243.
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  • Journal Article Herschlag G, Garcia GJ, Button B, Tarran R, Lindley B, Reinhardt B, Elston TC, Forest MG. A mechanochemical model for auto-regulation of lung airway surface layer volume. Journal of Theoretical Biology 2013;325:42-51.
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  • Journal Article Kesseler KJ, Blinov ML, Elston TC, Kaufmann WK, Simpson DA. A predictive mathematical model of the DNA damage G2 checkpoint. Journal of Theoretical Biology 2013;320:159-169.
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  • Journal Article Lee S, Wright FA, Zou F. Control of population stratification by correlation-selected principal components. Biometrics 2011;67(3):967-974.
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  • Journal Article Lock EF, Abdo N, Huang R, Xia M, Kosyk O, O'Shea SH, Zhou YH, Sedykh A, Tropsha A, Austin CP, Tice RR, Wright FA, Rusyn I. Quantitative high-throughput screening for chemical toxicity in a population-based in vitro model. Toxicological Sciences 2012;126(2):578-588.
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  • Journal Article Low YS, Sedykh AY, Rusyn I, Tropsha A. Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Current Topics in Medicinal Chemistry 2014;14(11):1356-1364.
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  • Journal Article Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Kuz’min V, Fourches D, Zhu H, Rusyn I, Tropsha A. Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chemical Research in Toxicology 2011;24(8):1251-1262.
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  • Journal Article Martinez SM, Bradford BU, Soldatow VY, Kosyk O, Sandot A, Witek R, Kaiser R, Stewart T, Amaral K, Freeman K, Black C, LeCluyse EL, Ferguson SS, Rusyn I. Evaluation of an in vitro toxicogenetic mouse model for hepatotoxicity. Toxicology and Applied Pharmacology 2010;249(3):208-216.
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  • Journal Article Muratov EN, Varlamova EV, Artemenko AG, Khristova T, Kuz'min VE, Makarov VA, Riabova OB, Wutzler P, Schmidtke M. QSAR analysis of [(biphenyloxy)propyl] isoxazoles: agents against coxsackievirus B3. Future Medicinal Chemistry 2011;3(1):15-27.
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  • Journal Article Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Kuz'min VE. Existing and developing approaches for QSAR analysis of mixtures. Molecular Informatics 2012;31(3-4):202-221.
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  • Journal Article Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Nikolaeva-Glomb L, Galabov AS, Kuz'min VE. QSAR analysis of poliovirus inhibition by dual combinations of antivirals. Structural Chemistry 2013;24(5):1665-1679.
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  • Journal Article O'Shea SH, Schwarz J, Kosyk O, Ross PK, Ha MJ, Wright FA, Rusyn I. In vitro screening for population variability in chemical toxicity. Toxicological Sciences 2011;119(2):398-407.
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  • Journal Article Politi R, Rusyn I, Tropsha A. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods. Toxicology and Applied Pharmacology 2014;280(1):177-189.
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  • Journal Article Rusyn I, Daston GP. Computational toxicology: realizing the promise of the toxicity testing in the 21st century. Environmental Health Perspectives 2010;118(8):1047-1050.
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  • Journal Article Rusyn I, Gatti DM, Wiltshire T, Kleeberger SR, Threadgill DW. Toxicogenetics: population-based testing of drug and chemical safety in mouse models. Pharmacogenomics 2010;11(8):1127-1136.
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  • Journal Article Rusyn I, Sedykh A, Low Y, Guyton KZ, Tropsha A. Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data. Toxicological Sciences 2012;127(1):1-9.
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  • Journal Article Sedykh A, Zhu H, Tang H, Zhang L, Richard A, Rusyn I, Tropsha A. Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity. Environmental Health Perspectives 2011;119(3):364-370.
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  • Journal Article Soldatow VY, LeCluyse EL, Griffith LG, Rusyn I. In vitro models for liver toxicity testing. Toxicology Research 2013;2(1):23-39.
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  • Journal Article Staab JM, O’Connell TM, Gomez SM. Enhancing metabolomic data analysis with Progressive Consensus Alignment of NMR Spectra (PCANS). BMC Bioinformatics 2010;11:123.
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  • Journal Article Strychalski W, Adalsteinsson D, Elston TC. A cut-cell method for simulating spatial models of biochemical reaction networks in arbitrary geometries. Communications in Applied Mathematics and Computational Science 2010;5(1):31-53.
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  • Journal Article Sun W, Buck MJ, Patel M, Davis IJ. Improved ChIP-chip analysis by a mixture model approach. BMC Bioinformatics 2009;10:173 (13 pp.).
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    R833825 (2008)
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  • Journal Article Sun W, Xie W, Xu F, Grunstein M, Li K-C. Dissecting nucleosome free regions by a segmental semi-Markov model. PLoS One 2009;4(3):e4721 (10 pp.).
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    R833825 (2008)
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  • Journal Article Sun W, Wright FA, Tang Z, Nordgard SH, Van Loo P, Yu T, Kristensen VN, Perou CM. Integrated study of copy number states and genotype calls using high-density SNP arrays. Nucleic Acids Research 2009;37(16):5365-5377.
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  • Journal Article Sun W, Wright FA. A geometric interpretation of the permutation p-value and its application in eQTL studies. Annals of Applied Statistics 2010;4(2):1014-1033.
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  • Journal Article Sun W. A statistical framework for eQTL mapping using RNA-seq data. Biometrics 2012;68(1):1-11.
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  • Journal Article Sun W, Li L. Multiple loci mapping via model-free variable selection. Biometrics 2012;68(1):12-22.
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  • Journal Article Sun W, Hu Y. eQTL mapping using RNA-seq data. Statistics in Biosciences 2013;5(1):198-219.
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  • Journal Article Sushko I, Novotarskyi S, Korner R, Pandey AK, Cherkasov A, Li J, Gramatica P, Hansen K, Schroeter T, Muller KR, Xi L, Liu H, Yao X, Oberg T, Hormozdiari F, Dao P, Sahinalp C, Todeschini R, Polishchuk P, Artemenko A, Kuz’min V, Martin TM, Young DM, Fourches D, Muratov E, Tropsha A, Baskin I, Horvath D, Marcou G, Muller C, Varnek A, Prokopenko VV, Tetko IV. Applicability domains for classification problems: benchmarking of distance to models for Ames mutagenicity set. Journal of Chemical Information and Modeling 2010;50(12):2094-2111.
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  • Journal Article Wright FA, Shabalin AA, Rusyn I. Computational tools for discovery and interpretation of expression quantitative trait loci. Pharmacogenomics 2012;13(3):343-352.
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    R833825 (Final)
    R835166 (Final)
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  • Abstract: Future Medicine-Abstract
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  • Journal Article Xia K, Shabalin AA, Huang S, Madar V, Zhou YH, Wang W, Zou F, Sun W, Sullivan PF, Wright FA. seeQTL: a searchable database for human eQTLs. Bioinformatics 2012;28(3):451-452.
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  • Journal Article Xu K, Morgan KT, Todd Gehris A, Elston TC, Gomez SM. A whole-body model for glycogen regulation reveals a critical role for substrate cycling in maintaining blood glucose homeostasis. PLoS Computational Biology 2011;7(12):e1002272 (13 pp.).
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  • Journal Article Zhang L, Sedykh A, Tripathi A, Zhu H, Afantitis A, Mouchlis VD, Melagraki G, Rusyn I, Tropsha A. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches. Toxicology and Applied Pharmacology 2013;272(1):67-76.
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    R835166 (2013)
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  • Journal Article Zhang X, Huang S, Sun W, Wang W. Rapid and robust resampling-based multiple-testing correction with application in a genome-wide expression quantitative trait loci study. Genetics 2012;190(4):1511-1520.
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  • Journal Article Zhou Y-H, Xia K, Wright FA. A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics 2011;27(19):2672-2678.
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  • Journal Article Zhou Y-H, Barry WT, Wright FA. Empirical pathway analysis, without permutation. Biostatistics 2013;14(3):573-585.
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  • Journal Article Zhu H, Rusyn I, Richard A, Tropsha A. Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity. Environmental Health Perspectives 2008;116(4):506-513.
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    R833825 (2008)
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    R832720 (2007)
    R832720 (2008)
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  • Journal Article Zhu H, Ye L, Richard A, Golbraikh A, Wright FA, Rusyn I, Tropsha A. A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. Environmental Health Perspectives 2009;117(8):1257-1264.
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    R833825 (2008)
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    R832720 (2009)
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  • Journal Article Zhu H, Martin TM, Ye L, Sedykh A, Young DM, Tropsha A. Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. Chemical Research in Toxicology 2009;22(12):1913-1921.
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    R832720 (2009)
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  • Journal Article Zou F, Lee S, Knowles MR, Wright FA. Quantification of population structure using correlated SNPs by shrinkage principal components. Human Heredity 2010;70(1):9-22.
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  • Supplemental Keywords:

    bioinformatics, biostatistics, computational toxicology, QSAR, ToxCast, high throughput screening

    Progress and Final Reports:

    Original Abstract
  • 2009
  • 2010
  • Final Report