Exposure to chemicals and other environmental stressors (including silica) is a major (occupational) health risk linked to respiratory diseases and autoimmune disorders. However, the underlying molecular mechanisms responsible for the variable health outcomes observed among exposed individuals remain poorly understood. The hypothesis is that volatile organic chemicals (VOCs), organic and inorganic dust (including silica) exposure induces distinct molecular alterations elucidating the differential susceptibility among exposed individuals. This project aims to explore these alterations by integrating multi-omics data to identify molecular pathways and biomarkers associated with silica-induced autoimmune diseases. Using the large-scale UK Biobank dataset we will include comprehensive analyses of genomic, transcriptomic, proteomic, and metabolomic data, to uncover the molecular alterations induced by these exposures. In a second phase a multi-omics integration will be conducted. By integrating these data layers, the contribution of different molecular interactions to individual susceptibility to autoimmune diseases will be examined. Lastly, using machine learning techniques, models will be developed that can predict health outcomes based on multi-omics data. Overall, the project will lead to the identification of predictive biomarkers for disease risk, and will thus offer insights into early detection and personalized prevention strategies.
