Disease-gene Immune cell Expression (DIME)

Disease-gene Immune cell Expression (DIME) integrates immunome transcriptome profiles, disease-gene networks, and drug-gene interactions to identify disease-associated cells and genes and to prioritize drug repurposing candidates in immune-mediated inflammatory diseases.

Key Features:

• Integration of Immunome Data: Utilizes transcriptome profiles from 40 immune cell types comprising a curated immunome dataset for analyses of disease-associated genes (DAGs) across immune cells.
• Identification of Top Disease-Associated Cells and Genes: Applies unsupervised machine learning together with disease-gene networks to identify top disease-associated cells (DACs) and DAGs across 12 phenotypically distinct IMIDs.
• Pathway Analysis Across Diseases: Compares DIME networks across different IMIDs to uncover common pathways and shared cellular mechanisms.
• Drug Repurposing Mapping: Maps identified pathways and DAGs to publicly available drug-gene networks to nominate drug repurposing candidates, including lifitegrast for Crohn’s disease and related conditions.

Scientific Applications:

• Understanding Cellular Mechanisms: Pinpoints top DACs such as CD4+Treg, CD4+Th1, NK cells, neutrophils, granulocytes, and BDCA1+CD14+ cells in diseases including ankylosing spondylitis, psoriatic arthritis, rheumatoid arthritis, systemic lupus erythematosus, systemic scleroderma, and inflammatory bowel diseases to elucidate cellular contributors to IMIDs.
• Exploring Therapeutic Strategies: Identifies common pathways in HLA-B27–type diseases and primary-joint-inflammation-based inflammatory arthritis to focus therapeutic hypotheses on lymphoid cells and associated pathways.
• Facilitating Pre-Clinical Research: Integrates immunome and disease-gene data to prioritize targets and drug candidates for pre-clinical studies and potential clinical translation.

Methodology:

Integrates transcriptome profiles from 40 immune cell types with disease-gene networks, applies unsupervised machine learning to identify DACs and DAGs across 12 IMIDs, performs comparative network/pathway analysis across IMIDs, and maps results to publicly available drug-gene networks for repurposing.