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

Author

Listed:
  • Pekka Kohonen

    (Institute of Environmental Medicine, Karolinska Institutet)

  • Juuso A. Parkkinen

    (Helsinki Institute for Information Technology HIIT, Aalto University)

  • Egon L. Willighagen

    (Institute of Environmental Medicine, Karolinska Institutet
    Maastricht University)

  • Rebecca Ceder

    (Institute of Environmental Medicine, Karolinska Institutet)

  • Krister Wennerberg

    (Institute for Molecular Medicine Finland, FIMM, University of Helsinki)

  • Samuel Kaski

    (Helsinki Institute for Information Technology HIIT, Aalto University
    Helsinki Institute for Information Technology HIIT, University of Helsinki)

  • Roland C. Grafström

    (Institute of Environmental Medicine, Karolinska Institutet)

Abstract

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.

Suggested Citation

  • Pekka Kohonen & Juuso A. Parkkinen & Egon L. Willighagen & Rebecca Ceder & Krister Wennerberg & Samuel Kaski & Roland C. Grafström, 2017. "A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury," Nature Communications, Nature, vol. 8(1), pages 1-15, August.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15932
    DOI: 10.1038/ncomms15932
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