Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease...

Area of Science:

• Bioinformatics
• Systems Biology
• Genetics

Background:

• Discovering disease-associated genes (DG) is crucial for understanding disease mechanisms.
• Diseases with similar phenotypes often share DGs or have closely interacting DGs within protein networks.

Purpose of the Study:

• To develop and validate a novel computational method, Specific Betweenness (S2B), for identifying disease-associated genes.
• To explore shared molecular mechanisms between related diseases using network analysis.

Main Methods:

• Developed Specific Betweenness (S2B) to prioritize genes in shortest paths between disease modules in protein interaction networks.
• Validated S2B using simulated network modules with up to 80% accuracy, even with noisy or incomplete data.
• Applied S2B to Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) gene sets.

Main Results:

• S2B successfully identified genes within simulated disease module overlaps.
• Applied to ALS and SMA, S2B identified candidate genes enriched in known motor neuron degeneration pathways.
• Identified closely interacting gene cliques among S2B candidates, suggesting shared molecular underpinnings for ALS and SMA.

Conclusions:

• S2B is an effective tool for predicting disease-associated genes and uncovering shared pathological mechanisms.
• The S2B method can be generalized to infer overlaps between various biological network modules.
• An R package for S2B is available for public use.