Multi-Omics Characterization of Fish Responses to Emerging Environmental Contaminants: Implications for Aquatic Toxicology and Sustainable Fisheries Management

Abstract

Emerging environmental contaminants (EECs), including microplastics, pharmaceutical residues, personal care products, endocrine-disrupting chemicals, nanomaterials, and per- and polyfluoroalkyl substances (PFAS), have become major threats to aquatic ecosystems worldwide. These contaminants may induce oxidative stress, metabolic dysfunction, endocrine disruption, immunotoxicity, and reproductive impairment in fish populations, thereby threatening fisheries sustainability and ecosystem resilience. Recent advances in multi-omics technologies provide unprecedented opportunities to investigate biological responses to contaminant exposure at molecular, cellular, physiological, and ecological levels. The present study was designed as a predictive multi-omics framework to characterize anticipated fish responses to environmentally relevant contaminant mixtures. Importantly, no live fish experiments, contaminant exposure trials, laboratory analyses, or biological sample collections were performed. Instead, the study integrates current scientific knowledge, published toxicological evidence, systems biology principles, and artificial intelligence-assisted modeling to generate biologically plausible response scenarios and methodological templates for future experimental validation. The proposed framework integrates transcriptomics, proteomics, metabolomics, epigenomics, gut microbiome profiling, histopathological assessment, and machine-learning-based toxicity prediction. Simulated outcomes suggest that exposure to contaminant mixtures may significantly alter oxidative stress pathways, inflammatory responses, energy metabolism, endocrine signaling, and microbial community composition. Multi-omics integration identifies NRF2, CYP1A, HSP70, TNF-α, IL-1β, and mitochondrial dysfunction pathways as central hubs associated with contaminant-induced toxicity. Artificial intelligence models further demonstrate the potential for predicting ecological risk and biomarker responses with high accuracy. This framework provides a comprehensive template for future aquatic toxicology investigations and highlights the importance of integrating multi-omics approaches with predictive analytics to support sustainable fisheries management and environmental monitoring programs.