Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)

Abstract

Adverse event pathogenesis is often a complex process which compromises multiple events ranging from the molecular to the phenotypic level. Adverse Outcome Pathways (AOPs) aim to formalize this as temporal sequences of events, in which event relationships should be supported by causal evidence according to the tailored Bradford-Hill criteria. One of the criteria is whether events are consistently observed in a certain temporal order and, in this work, we study this time concordance between gene expression- and histopathology-derived events as data-driven means to generate hypotheses on potentially causal mechanisms. As a case study, we analysed liver data from repeat-dose studies in rats from the TG-GATEs database which comprises measurements across eight timepoints, ranging from 3 hours to 4 weeks post-treatment. We identified time concordant pathway- and transcription factor (TF)- level events preceding adverse histopathology, which serves as surrogate readout for Drug-Induced Liver Injury (DILI). Among known events in DILI, we found some to change strongly before adverse histopathology, e.g. fatty acid beta oxidation, while others were more confident, e.g. bile acid recycling, or frequent, e.g. ATF4-mediated stress response, further characterizing their mechanistic roles. Moreover, we used the temporal order of TF expression and regulon activity to separate induced TFs, such as Cebpa, from post-transcriptionally activated ones, e.g. Srebf2, and subsequently combined this with known functional interactions (TF-target or protein-protein) to derive detailed gene-regulatory mechanisms, such as Hnf4a-dependent Cebpa expression. We additionally evaluate which time concordant events show sustained or increasing activation over time, as this time dependence is favourable for biomarker development, and identify pathways indicating dyslipidaemia, and a decrease in Hnf1a and Hnf4a indicating deteriorating liver function. At the same, time also potentially novel events are identified such as Sox13 which shows a more significant time dependence and -concordance than many known TFs in liver injury. Overall, we demonstrate how time-resolved transcriptomics can derive and support mechanistic hypotheses by quantifying time concordance and how this can be combined with prior causal knowledge, with the aim of both understanding mechanisms of toxicity, as well as potential applications to the AOP framework. We make our results available in the form of a Shiny app (https://github.com/anikaliu/DILICascades_App), which allows users to query events of interest in more detail.