DOI: 10.5061/dryad.k3j9kd5hz
title: Data from: Quantifying liver-toxic responses from dose-dependent chemical exposures using a rat genome-scale metabolic model

description: 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. While 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 to 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.
description: Supplementary files information for the manuscript titled:
 "Quantifying liver-toxic responses from dose-dependent chemical
 exposures using a rat genome-scale metabolic model" Files The
 processed datasets used as input to generate the figures in the main text
 and can be used for reproducibility Processed files included: 1\) Table
 S1: Chemical structure information (Sup_Table_S1_ToxPro_input.csv) *
 Chemical structure information gathered from PubChem in SMILES format -
 Used as input to ToxProfiler webtool
 ([https://toxpro.bhsai.org](https://toxpro.bhsai.org)) to identify liver
 toxicity targets 2\) Table S2: Genome-scale model reaction identifiers
 (Sup_TableS2_rat-GEM_reactions.tsv) * List of reaction identifiers that
 are common between rat genome-scale models (iRno model and ratGEM models)
 * Used to compare reactions between the each model to identify common and
 unique reactions between the models * Used as input to generate iRno_ver4
 model 3\) Table S3: List of new reactions added into iRno_ver4 model
 (Sup_TableS3_Updates_iRno_ver3.xlsx) * Information on reactions matched
 between the two models * Information on list of reactions for which charge
 balance updated * List of new reactions added into the model * List of new
 metabolites added into the model 4\) Table S4: Chemical structure-based
 toxicity target results (Sup_TableS4_ToxPro_Output.xlsx) * A heatmap of
 identified toxicity targets using ToxProfiler * List of transcription
 factors and the corresponding downstream genes regulated by them 5\) Table
 S5: Alteration in expression pattern of toxicity target driven genes
 (Sup_TableS5_ToxPro.xlsx) * Dose-dependent alterations in gene expression
 across 18 chemicals used in the study * The logarithmic fold change values
 at the highest dose for each chemical is used as input to generate Figure
 2 6\) Table S6: Alteration in expression pattern of iRno model mapped
 genes (Supplementary_TableS6_MetGenes.xlsx) * Dose-dependent alterations
 in gene expression across 18 chemicals used in the study for model mapped
 genes * The logarithmic fold change values at the highest dose for each
 chemical is used as input to generate Figure 3 7\) Table S7:  Genome-scale
 prediction of metabolite alterations (Sup_TableS7_Timbr_Scores_All.xlsx) *
 Dose-dependent metabolite TIMBR production scores for the 18 chemicals
 used in the study (Used as input to Figure 5) * Metabolite predictions
 classified into amino acid metabolism (Used as input to Figure 6) *
 Metabolite predictions classified into lipid metabolism (Used as input to
 Figure 7) 8\) The updated versions of rat genome-scale metabolic model
 (Supplementary_Models.zip) * Rat genome-scale model version 3 in MATLAB
 and SBML format (iRno_*ver3.mat and iRno*_ver3.xml) * Rat genome-scale
 model version 4 in MATLAB and SBML format (iRno*ver4.mat and
 iRno*_ver4.xml) * The MATLAB (.mat) and SBML (.xml) files are generated
 using the latest version of MATLAB (R2024b)