Society of Environmental Toxicology and Chemistry

1.02 - Beyond Exposure: Epigenetic Mechanisms, Omics Integration, and AI for Long-Term Environmental Impact

Description

Keywords: Adverse Outcome Pathways, Molecular Toxicology, OMICs

Environmental contaminants can trigger subtle yet persistent biological effects that span multiple generations, often mediated by epigenetic and other molecular mechanisms. These mechanisms act as early indicators of exposure and contribute to long-term changes in gene regulation, physiology, behavior, and reproduction. Despite their relevance, epigenetic and molecular responses remain underrepresented in regulatory frameworks and Adverse Outcome Pathway (AOP) models. This session will examine how developmental and ancestral exposures to legacy and emerging pollutants, including micro- and nano-plastics, PFAS, pharmaceuticals, and endocrine disruptors can induce heritable changes in environmentally relevant species. By integrating epigenomics with transcriptomics, proteomics, metabolomics, and applying artificial intelligence (AI) and machine learning (ML), researchers are uncovering complex, non-linear relationships between contaminants and biological responses. The session aims to bring together experts in molecular biology, toxicogenomics, computational modeling, and environmental risk assessment to advance predictive approaches in ecotoxicology, and to support the integration of epigenetics into decision-making frameworks. Session Objectives: Clarify the role of epigenetic mechanisms such as DNA methylation, histone modifications, and non-coding RNAs as mediators between environmental exposures and adverse biological outcomes, highlighting their relevance for mechanistically informed ecotoxicology. Promote the integration of multi-omics datasets (epigenomics, transcriptomics, proteomics, metabolomics) to uncover molecular pathways of toxicity and identify early biomarkers of exposure. Advance the incorporation of epigenetic into AOP frameworks, through conceptual models and case studies. Showcase the application of artificial intelligence (AI) and machine learning (ML) to analyze complex omics data, reveal non-linear relationships, and support predictive modeling in ecotoxicology. Encourage research on multi- and trans-generational effects of contaminants, particularly those induced by early-life or ancestral exposures to legacy and emerging pollutants, using innovative experimental designs in model and non-model organisms. Facilitate the exchange among molecular and toxicogenomics experts, computational scientists, and environmental risk assessors to strengthen the scientific basis of ecological risk assessment and regulatory decision-making.

Chairs

JUAN IGNACIO BERTUCCI Spanish Institute of Oceanography - CSIC Spain
Laia Navarro Martin Institute of Environmental Assesment and Water Research - CSIC Spain
Jana Asselman  Ghent University Belgium
Joana Pereira  University of Aveiro (UA) Portugal