Data from: Quantifying liver-toxic responses from dose-dependent chemical exposures using a rat genome-scale metabolic model
Data files
Jan 26, 2025 version files 13.61 MB
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README.md
2.96 KB
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Sup_Table_S1_ToxPro_input.csv
831 B
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Sup_TableS2_rat-GEM_reactions.tsv
314.63 KB
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Sup_TableS3_Updates_iRno_v3.xlsx
290.47 KB
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Sup_TableS4_ToxPro_Output.xlsx
36.64 KB
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Sup_TableS7_Timbr_Scores_All.xlsx
3.51 MB
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Supplementary_Models.zip
6.83 MB
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Supplementary_TableS5_ToxPro.xlsx
176.46 KB
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Supplementary_TableS6_MetGenes.xlsx
2.45 MB
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. 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.
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)
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Chemical structure information gathered from PubChem in SMILES format
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Used as input to ToxProfiler webtool (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 (iRnover4.mat and iRno_ver4.xml)
- The MATLAB (.mat) and SBML (.xml) files are generated using the latest version of MATLAB (R2024b)