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

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.