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Network-Based Assessment of Environmental Neurotoxicants Using High-Throughput Transcriptomics in Zebrafish Embryos.

Pu Xia1, Pingping Wang2, Shengnan Liu3

  • 1Centre for Environmental Research and Justice, and School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K.

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Summary
This summary is machine-generated.

This study introduces an Adverse Outcome Pathway (AOP) network framework to link molecular data from zebrafish transcriptomics to neurobehavioral outcomes. This approach successfully predicts neurotoxicant effects, advancing the use of omics in new approach methodologies (NAMs).

Keywords:
network-based screeningnext-generation chemical risk assessmentpredictive toxicologysystem toxicology

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Area of Science:

  • Environmental Toxicology
  • Computational Biology
  • Neuroscience

Background:

  • Evaluating neurotoxicants using omics data is difficult due to challenges in translating molecular signals into observable phenotypic effects.
  • Existing methods struggle to bridge the gap between molecular responses and organism-level toxicity.
  • There is a need for robust frameworks to interpret complex omics data for neurotoxicity assessment.

Purpose of the Study:

  • To develop and validate an Adverse Outcome Pathway (AOP) network-based framework for interpreting concentration-dependent transcriptomics in zebrafish embryos.
  • To correlate molecular signals with behavioral outcomes for quantitative neurotoxicity assessment.
  • To advance the application of omics data within new approach methodologies (NAMs) for chemical safety evaluation.

Main Methods:

  • Curated an annotation of key events (KEs) to zebrafish molecular pathways for mapping transcriptomics to an AOP network.
  • Analyzed concentration-dependent transcriptomics of 24 environmental chemicals to identify concentration-responsive KEs (CRKEs).
  • Utilized partial least-squares regression integrating CRKEs and time-responsive KEs (TRKEs) to estimate apical POD (aPOD) and performed external validation.

Main Results:

  • Identified CRKEs that effectively differentiated neurotoxic from non-neurotoxic compounds.
  • Demonstrated a significant correlation between transcriptomic points of departure (tPOD) and behavioral aPOD (R² = 0.539, P < 0.01).
  • Achieved high performance in estimating aPOD using integrated omics data (slope = 0.70, R² = 0.64, P = 1.30 × 10⁻⁵) and validated findings with external data.

Conclusions:

  • The AOP network-guided omics analysis framework is a promising approach for quantitative neurotoxicity assessment.
  • This method facilitates the prediction of chemical-induced neurotoxicity by linking molecular responses to behavioral outcomes.
  • The study supports the integration of omics data into NAMs for more efficient and accurate chemical risk assessment.