A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion' - concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.

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10.1038/ncomms15932

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Kohonen, P, Parkkinen, J A, Willighagen, E L, Ceder, R, Wennerberg, K, Kaski, S & Grafström, R C 2017, ' A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury ', Nature Communications, vol. 8, 15932, pp. 1-15 . https://doi.org/10.1038/ncomms15932

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https://urn.fi/URN:NBN:fi:aalto-201708036345

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[article-cris] Perustieteiden korkeakoulu / SCI

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A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

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