Adopting mechanistic molecular biology approaches in exposome research for causal understanding

Abstract

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

The contents of this paper arose from a collaborative workshop run in 2020 on “Connecting molecular biology and big data in humans to study the exposome” funded and coordinated by the Human Ecosystems Transversal Theme (European Molecular Biology Laboratory) and Inserm with support provided by HP and GC. Thanks go to participants Ewan Birney, Remy Slama, Naomi Allen, Marc Chadeau, Andreas Kortenkamp, William Bourguet, Samuli Ripatti, Marc Chadeau-Hyam, and Xavier Basagańa. S.B. acknowledges support from the Novo Nordisk Foundation (grants NNF17OC0027594 and NNF14CC0001). E.L.S. acknowledges funding support from the Luxembourg National Research Fund (FNR) for project A18/BM/12341006. E.J.P., H.H., J.K., and K.H. acknowledge the research infrastructure RECETOX RI (LM2023069), H2020 CETOCOEN Excellence 857560, and OP RDE CZ.02.1.01/0.0/0.0/17_043/0009632. E.L.S. acknowledges funding support from the Luxembourg National Research Fund (FNR) for project A18/BM/12341006. This publication reflects only the authors’ views, and the European Commission is not responsible for any use that may be made of the information it contains.