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Advancing transcriptomics-based mechanistic assessment of nephrotoxicity in vitro using the human RPTEC/TERT1
Hugo W van Kessel1, Steven J Kunnen1, Giulia Callegaro1
1Division of Cell Systems and Drug Safety, Leiden Academic Centre for Drug Research, Leiden University, 2300 RA Leiden, The Netherlands.
Summary
This study introduces a novel human kidney gene co-expression network to improve chemical safety testing. This approach enhances the prediction of toxic effects, moving beyond traditional animal models for better human health risk assessment.
Area of Science:
- Toxicology
- Genomics
- Computational Biology
Background:
- Traditional animal testing for chemical safety has limitations in predicting human toxicity.
- New Approach Methodologies (NAMs), particularly in vitro transcriptomics, are crucial for understanding toxicity mechanisms.
- There is a need for human-relevant in vitro models for chemical risk assessment.
Purpose of the Study:
- To develop the first human kidney in vitro toxicogenomic co-expression network.
- To identify and analyze gene co-expression modules related to nephrotoxicity.
- To create an interactive platform for interpreting these networks in chemical safety assessment.
Main Methods:
- Utilized transcriptomic profiles from human renal proximal tubule epithelial cells (RPTEC/TERT1) exposed to nephrotoxicants.
- Applied weighted correlation network analysis to identify gene co-expression modules.
- Developed the R Shiny platform, RPTEC/TERT1 TXG-MAPr, for network visualization and analysis.
Main Results:
- Identified distinct gene co-expression modules associated with specific nephrotoxicant mechanisms of action.
- Demonstrated that module-based analysis can differentiate toxicological pathways.
- Successfully linked transcriptional modules to Key Events (KE) within the nephrotoxicity Adverse Outcome Pathway (AOP) network.
Conclusions:
- Gene co-expression network analysis provides a powerful tool for mechanism-based chemical risk assessment.
- The developed TXG-MAPr platform facilitates the interpretation of complex toxicogenomic data.
- This approach supports the advancement of next-generation, human-relevant chemical safety evaluations.
