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PLoS Comput Biol. 2015 Jun 17;11(6):e1004332. doi: 10.1371/journal.pcbi.1004332. eCollection 2015 Jun.

Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks.

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Department of Internal Medicine, University of Iowa, Iowa City, Iowa, United States of America.
Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States of America.
Department of Anesthesiology and Pain Medicine, Mitochondria and Metabolism Center, University of Washington School of Medicine, Seattle, Washington, United States of America.


Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the iMDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (multiple differential modules). We applied iMDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that iMDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. iMDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.

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