ArticleToxicological sciences : an official journal of the Society of Toxicology2025
Quantifying liver-toxic responses from dose-dependent chemical exposures using a rat genome-scale metabolic model.
Article in Toxicological sciences : an official journal of the Society of Toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A systems-level integration of liver-kidney transcriptomics and genome-scale metabolic models reveals organ-specific injury mechanisms.Frontiers in toxicology · 2026Article
- A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.npj drug discovery · 2026Article
- Article
- Investigating Lipid and Energy Dyshomeostasis Induced by Per- and Polyfluoroalkyl Substances (PFAS) Congeners in Mouse Model Using Systems Biology Approaches.Metabolites · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
Because the liver plays a vital role in the clearance of exogenous chemical compounds, it is susceptible to chemical-induced toxicity. Animal-based testing is routinely used to assess the hepatotoxic potential of chemicals. Although large-scale high-throughput sequencing data can indicate the genes affected by chemical exposures, we need system-level approaches to interpret these changes. To this end, we developed an updated rat genome-scale metabolic model to integrate large-scale transcriptomics data and utilized a chemical structure similarity-based ToxProfiler tool to identify chemicals that bind to specific toxicity targets to understand the mechanisms of toxicity. We used high-throughput transcriptomics data from a 5-day in vivo study where rats were exposed to different non-toxic and hepatotoxic chemicals at increasing concentrations and investigated how liver metabolism was differentially altered between the non-toxic and hepatotoxic chemical exposures. Our analysis indicated that the genes identified via toxicity target analysis and those mapped to the metabolic model showed a distinct gene expression pattern, with the majority showing upregulation for hepatotoxicants compared with non-toxic chemicals. Similarly, when we mapped the metabolic genes at the pathway level, we identified several pathways in carbohydrate, amino acid, and lipid metabolism that were significantly upregulated for hepatotoxic chemicals. Furthermore, using our system-level integration of gene expression data with the rat metabolic model, we could differentiate metabolites in these pathways that were systematically elevated or suppressed due to hepatotoxic versus non-toxic chemicals. Thus, using our combined approach, we were able to identify a set of potential gene signatures that clearly differentiated liver toxic responses from non-toxic chemicals, which helped us identify potential metabolic pathways and metabolites that are systematically associated with the toxicant exposure.
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