OA Paper: A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury
July 3rd, 2017 by denise.slenter
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.
Read the full paper (which is published as an open access paper).
(a) ‘Eye diagram’ showing the associations between the genes associated with the 14 PTGS components (middle, colour) and the top 5 CMap instances (left) and overrepresented toxicological functions (right). Line widths indicate association strengths. The components have been sorted according to similarity, as shown in Supplementary Fig. 3b; data in Supplementary Data 4. (b) Biological and toxicological complexity of the PTGS components defined as the proportion of results (above a set statistical threshold) in each analysis category ascribed to the component gene set. Numbers above bars denote the numbers of genes in each component. Details of the data are found in Supplementary Data 3–7. (c) Frequency plot of the upstream regulator enrichments for the PTGS components depicting multiple transcriptional regulators associated with stress responses, inflammation and with cell division. For data and further related analyses, see Supplementary Fig. 6b and Supplementary Data 5.