HORIZON-HLTH-2027-01-ENVHLTH-02.Mechanistic resolution of the climate-driven human exposome through proteome-wide interaction mapping

We bring extensive experience in European collaborative research as both coordinator and partner, with a strong track record in environmental proteomics, exposomics, and multi-omics integration. Our work focuses on identifying early molecular interactions between environmental exposures and biological systems, enabling mechanistic interpretation of exposure–health relationships.

For this topic, we address a key limitation in current exposome research: while large-scale studies increasingly capture complex exposure patterns, including climate-related stressors, they remain largely associative, with limited ability to resolve underlying biological mechanisms . This gap constrains the translation of exposome data into actionable health insights.

We propose to integrate a mechanistic molecular layer into exposome research by linking climate-driven exposures to protein-level perturbations, biological pathways, and early health risk signals. Our approach is based on high-throughput proteome-wide interaction profiling (PISA), which enables the identification of molecular initiating events from complex and interacting exposure mixtures.

Specifically, we will:

• Map exposure–protein interaction landscapes across conditions

• Identify molecular targets and affected pathways

• Detect early biological perturbations preceding clinical outcomes

• Support the discovery of mechanistically grounded biomarkers

This approach enables the transition from exposure–outcome associations to exposure–mechanism–risk frameworks, improving interpretation, prediction, and relevance for health risk assessment under climate change.

We aim to contribute as partner and potential Work Package leader, focusing on:

• Mechanistic interpretation of exposome data

• Biomarker discovery and validation

• Integration of omics with exposure and health datasets

Our role is to complement, not duplicate, cohort studies, epidemiology, and exposure modelling by providing the causal and mechanistic layer required to make exposome data interpretable and actionable.

By bridging environmental exposures and biological function, this contribution strengthens the scientific basis for climate-resilient health strategies, supporting prevention, policy, and improved understanding of environmentally driven disease risk.