InTiCAR: Network-based identification of significant inter-tissue communicators for autoimmune diseases

Area of Science:

• Biomedical informatics
• Systems biology
• Network medicine

Background:

• Inter-tissue communicators (ITCs) are crucial for maintaining homeostatic equilibrium.
• Dysregulation of tissue communication underlies various disorders, including autoimmune diseases (AIDs).
• AIDs are challenging to diagnose and treat due to their complex and diverse pathologies.

Purpose of the Study:

• To develop a novel computational method for identifying disease-specific ITCs.
• To evaluate the diagnostic potential of ITCs for autoimmune diseases.
• To explore ITCs as potential therapeutic targets for AIDs.

Main Methods:

• Introduction of InTiCAR (Inter-Tissue Communicators for Autoimmune diseases by Random walk with restart), a network exploration-based analysis method.
• Utilizing prior knowledge of disease genes without external expression data.
• Validation using the UK Biobank plasma proteome dataset and the CMap LINCS dataset.

Main Results:

• InTiCAR successfully identifies distinct ITC profiles for various diseases.
• Disease-specific ITCs demonstrate superior diagnostic performance compared to disease genes for AIDs in the UK Biobank dataset.
• High perturbation on AIDs genes is observed through disease-specific ITCs in the CMap LINCS dataset.

Conclusions:

• InTiCAR provides a powerful tool for identifying disease-specific ITCs.
• ITCs represent promising biomarkers for diagnosing autoimmune diseases.
• Focusing on extracellular communication entities offers unique perspectives for biological network analysis and therapeutic strategies.