Transcriptomics hit the target: Monitoring of ligand-activated and stress response pathways for chemical testing

Alice Limonciel1, Konrad Moenks2, Sven Stanzel3

  • 1Department of Physiology, Medical University of Innsbruck, Innsbruck, Austria.

Insights

This study links high-content omics data with transcription factor (TF) activity to understand chemical toxicity. We developed TF target gene signatures to monitor cellular responses in toxicology, aiding chemical hazard identification.

Area of Science:

  • Toxicology
  • Molecular Biology
  • Genomics

Background:

  • High-content omics methods offer deep cellular insights but lack mechanistic understanding for interpreting biological changes.
  • Transcriptomics (TCX) can reveal transcription factor (TF) activity modulation, a key molecular effect of chemical exposure.
  • Publicly available ChIP-seq data enables the creation of TF target gene lists for toxicological relevance.

Purpose of the Study:

  • To generate target gene signatures for key transcription factors (Nrf2, ATF4, XBP1, p53, HIF1a, AhR, PPAR gamma).
  • To track TF modulation in a large dataset of in vitro transcriptomics data from renal and hepatic cell models.
  • To establish a mechanistically based tool for chemical hazard identification.

Main Methods:

  • Generation of TF target gene signatures using ChIP-seq data.
  • Analysis of in vitro transcriptomics (TCX) datasets from renal and hepatic cell models.
  • Exposure of cell models to clinical nephro- and hepato-toxins.

Main Results:

  • Successfully generated target gene signatures for seven key transcription factors.
  • Tracked TF modulation across a comprehensive collection of in vitro TCX datasets.
  • Demonstrated the potential for global monitoring of TF modulation in response to chemical exposure.

Conclusions:

  • The developed TF target gene signatures provide a mechanistically based approach to interpret transcriptomic changes.
  • Global monitoring of TF modulation shows promise as a tool for chemical hazard identification.
  • This approach enhances the interpretation of high-content omics data in toxicology.