E.PAGE: A curated database and enrichment tool to predict gene modules associated with gene-environment interactions

Background The purpose of this study was to manually and semi-automatically curate a database and develop an R package that will provide a comprehensive resource to uncover associations between biological processes and environmental factors in health and disease.

We followed a two-step process to achieve the objectives of this study. First, we conducted a systematic review of existing gene expression datasets to identify those with integrated genomic and environmental factors. This enabled us to curate a comprehensive genomic-environmental database for four key environmental factors (smoking, diet, infections and toxic chemicals) associated with various autoimmune and chronic conditions. Second, we developed a statistical analysis package that allows users to interrogate the relationships between differentially expressed genes and environmental factors under different disease conditions.

Results The initial database search run on the Gene Expression Omnibus (GEO) and the Molecular Signature Database (MSigDB) retrieved a total of 90,018 articles. After title and abstract screening against pre-set criteria, a total of 186 studies were selected. From those, 243 individual sets of genes, or “gene modules”, were obtained. We then curated a database containing four environmental factors, namely cigarette smoking, diet, infections and toxic chemicals, along with a total of 25789 genes that had an association with one or more of these gene modules. In six case studies, the database and statistical analysis package were then tested with lists of differentially expressed genes obtained from the published literature related to type 1 diabetes, rheumatoid arthritis, small cell lung cancer, COVID-19, cobalt exposure and smoking. On testing, we uncovered statistically enriched biological processes, which revealed pathways associated with environmental factors and the genes.

Conclusions A novel curated database and software tool is provided as an R Package. Users can enter a list of genes to discover associated environmental factors under various disease conditions.