Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks

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.Author Summary: Recent advances in systems biology have revealed that changes in the structure and activity of gene network play a critical role in the disease progression. Heart failure is a complex disease involving multiple molecular pathways. Yet little is known regarding the dynamic changes in the gene network of heart cells during heart failure development. We have combined experimental and computational approaches to address this question. We developed a computational method to analyze multiple gene networks, each of which exhibits differential activity compared to the network of the healthy condition. In doing so, we are able to identify both unique and shared gene pathways across multiple differential networks. By applying our algorithm to our time-course transcriptome data of heart failure, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that pathway dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Our approach provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype.