Addressing systematic inconsistencies between in vitro and in vivo transcriptomic mode of action signatures. (August 2019) - McMullen, Patrick D.; Pendse, Salil N.; Black, Michael B.; Mansouri, Kamel; Haider, Saad; Andersen, Melvin E.; Clewell, Rebecca A.
Abstract: Because of their broad biological coverage and increasing affordability transcriptomic technologies have increased our ability to evaluate cellular response to chemical stressors, providing a potential means of evaluating chemical response while decreasing dependence on apical endpoints derived from traditional long-term animal studies. It has recently been suggested that dose-response modeling of transcriptomic data may be incorporated into risk assessment frameworks as a means of approximating chemical hazard. However, identification of mode of action from transcriptomics lacks a similar systematic framework. To this end, we developed a web-based interactive browser—MoAviz—that allows visualization of perturbed pathways. We populated this browser with expression data from a large public toxicogenomic database (TG-GATEs). We evaluated the extent to which gene expression changes from in-life exposures could be associated with mode of action by developing a novel similarity index—the Modified Jaccard Index (MJI)—that provides a quantitative description of genomic pathway similarity (rather than gene level comparison). While typical compound-compound similarity is low (median MJI = 0.026), clustering of the TG-GATES compounds identifies groups of similar chemistries. Some clusters aggregated compounds with known similar modes of action, including PPARa agonists (median MJI = 0.315) and NSAIDs (median MJI = 0.322). Analysis of paired in vitro (hepatocyte)-in vivo (liver) experiments revealed systematic patterns in the responses of model systems to chemical stress. Accounting for these model-specific, but chemical-independent, differences improved pathway concordance by 36% between in vivo and in vitro models. Highlights: We introduce the MoAviz online tool for exploration of biological pathways. We introduce a metric (MJI) for quantitative description of pathway similarity. MJI can be used to identify compounds with similar modes of action. Correcting MJI for bias improves overlap between experimental models.