Integrative network analysis interweaves the missing links in cardiomyopathy diseasome

Abstract

Background: Cardiomyopathies are genetic disorders resulting in an abnormal heart phenotype due to intrinsic cardiac muscle malfunction, which leads to heart failure. These are chronic disorders contributing more than 36% of heart failure in the patients. Cardiomyopathies also show co-morbidity with other diseases that frequently affect the elderly. Apart from this, many drugs used for treating other unrelated diseases cause cardio-toxicity and lead to cardiomyopathies. Unravelling the cross-talk between them is essential for useful characterization, subtyping, and better treatment of the cardiomyopathies. Results: In this work, we applied a systems biology-based analysis to explore the associations between cardiomyopathies and other morbidities. For this, we built a cardiomyopathy disease-disease network based on the combine biological data relevant to cardiomyopathies. For this, we make use of publicly available genetic, non-coding, drug and PPI datasets related to cardiomyopathies and network science tools to identify new modifiers genes that may have a role in cardiomyopathies. These modifier genes are further enriched using mouse phenotype data. Lastly, we highlight key modifier genes that may be potentially associated with cardiomyopathies. Many predicted genes link cardiomyopathies to neoplasms, metabolic and neurological disorders; some of these have been explained as case studies in this analysis. Conclusions: Traditional one drug-one target-one disease outlook alone in drug discovery is inefficient. We hereby develop an integrative approach based on primary cardiomyopathy target genes to discover new targets that provide clues in drug re-purposing, possible off-targets and hidden genetic overlap between cardiomyopathies and other morbidities. We report potential modifier genes like NOTCH4, SIRT1 that may be investigated for role in cardiomyopathy and other diseases.

Transcriptome Data Analysis of Primary Cardiomyopathies Reveals Perturbations in Arachidonic Acid Metabolism

“…For human interactome data, PPI data (HPRD, MINT, IntAct) along with protein complex and kinase substrate data (CORUM, Phosphositeplus) were obtained from our previous study (43). Then, the AA metabolism genes and cell type marker genes were mapped to the human interactome, and a subnetwork consisting of these genes was constructed.…”