Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks

Area of Science:

• Genomics
• Systems Biology
• Computational Biology

Background:

• Heart disease development involves dynamic changes in gene pathway activity and connectivity.
• Current computational methods lack the ability to analyze multiple differential gene networks simultaneously.
• Understanding these dynamics is crucial for elucidating pathogenic mechanisms and developing effective treatments.

Purpose of the Study:

• To introduce the iMDM algorithm for identifying unique and shared gene modules across multiple differential co-expression networks (M-DMs).
• To analyze gene pathway dynamics in the context of heart failure progression using a murine model.
• To correlate gene module dynamics with disease phenotype dynamics.

Main Methods:

• Development and application of the iMDM algorithm.
• Analysis of a time-course RNA-Seq dataset from a murine heart failure model across two genotypes.
• Comparison of iMDM accuracy against single and multiple co-expression network analyses.

Main Results:

• iMDM demonstrated higher accuracy in inferring gene modules compared to existing methods.
• Condition-specific M-DMs showed differential activities, mediated distinct biological processes, and were enriched for cardiovascular genes.
• Analysis of M-DMs across conditions revealed dynamic changes in pathway activity and connectivity during heart failure.
• Gene module dynamics correlated with the dynamics of disease phenotypes.

Conclusions:

• Pathway dynamics is a powerful metric for understanding disease pathogenesis.
• The iMDM algorithm offers a principled approach to dissect gene pathway dynamics and their relationship to disease phenotypes.
• iMDM can generate systems-level insights into disease progression from omics data.