Chemical read-across is commonly evaluated without specific knowledge of the biological mechanisms leading to observed adverse outcomes in vivo. Integrating data that indicate shared modes of action in humans will strengthen read-across cases. Here we studied transcriptomic responses of primary human hepatocytes (PHH) to a large panel of carboxylic acids to include detailed mode-of-action data as a proof-of-concept for read-across in risk assessment. In rodents, some carboxylic acids, including valproic acid (VPA), are known to cause hepatic steatosis, whereas others do not. We investigated transcriptomics responses of PHHs exposed for 24 h to 18 structurally different VPA analogues in a concentration range to determine biological similarity in relation to in vivo steatotic potential. Using a targeted high-throughput screening assay, we assessed the differential expression of ~3,000 genes covering relevant biological pathways. Differentially expressed gene analysis revealed differences in potency of carboxylic acids, and expression patterns were highly similar for structurally similar compounds. Strong clustering occurred for steatosis-positive versus steatosis-negative carboxylic acids. To quantitatively define biological read-across, we combined pathway analysis and weighted gene co-expression network analysis. Active carboxylic acids displayed high similarity in gene network modulation. Importantly, free fatty acid synthesis modulation and stress pathway responses are affected by active carboxylic acids, providing coherent mechanistic underpinning for our findings. Our work shows that transcriptomic analysis of cultured human hepatocytes can reinforce the prediction of liver injury outcome based on quantitative and mechanistic biological data and support its application in read-across.
Keywords: TempO-Seq; Toxicogenomics-MAPr; gene network analysis; liver toxicity; risk assessment.