Leveraging ontologies to predict biological activity of chemicals across genes

TL;DR

This paper introduces DART, a Bayesian model leveraging gene ontologies and chemical structures to predict chemical-gene activity, aiding toxicology assessments amid sparse high-throughput screening data.

Contribution

The paper presents a novel Bayesian factor model, DART, that integrates gene ontologies and chemical structures to predict chemical activity across genes, addressing data sparsity in HTS.

Findings

DART accurately predicts chemical-gene activity in simulations.

Application to PFAS data reveals actionable insights for chemical prioritization.

DART uncovers latent biological mechanisms influencing dose-response behavior.

Abstract

High-throughput screening (HTS) is useful for evaluating chemicals for potential human health risks. However, given the extraordinarily large number of genes, assay endpoints, and chemicals of interest, available data are sparse, with dose-response curves missing for the vast majority of chemical-gene pairs. Although gene ontologies characterize similarity among genes with respect to known cellular functions and biological pathways, the sensitivity of various pathways to environmental contaminants remains unclear. We propose a novel Dose-Activity Response Tracking (DART) approach to predict the biological activity of chemicals across genes using information on chemical structural properties and gene ontologies within a Bayesian factor model. Designed to provide toxicologists with a flexible tool applicable across diverse HTS assay platforms, DART reveals the latent processes driving dose-response behavior and predicts new activity profiles for chemical-gene pairs lacking experimental data. We demonstrate the performance of DART through simulation studies and an application to a vast new multi-experiment data set consisting of dose-response observations generated by the exposure of HepG2 cells to per- and polyfluoroalkyl substances (PFAS), where it provides actionable guidance for chemical prioritization and inference on the structural and functional mechanisms underlying assay activation.