National Centre for Biological Sciences (Tata Institute of Fundamental Research), GKVK Campus, Bangalore, Karnataka, 560065, India.
Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.
Sci Rep. 2022 Nov 16;12(1):19670. doi: 10.1038/s41598-022-24246-x.
Cardiomyopathies are progressive disease conditions that give rise to an abnormal heart phenotype and are a leading cause of heart failures in the general population. These are complex diseases that show co-morbidity with other diseases. The molecular interaction network in the localised disease neighbourhood is an important step toward deciphering molecular mechanisms underlying these complex conditions. In this pursuit, we employed network medicine techniques to systematically investigate cardiomyopathy's genetic interplay with other diseases and uncover the molecular players underlying these associations. We predicted a set of candidate genes in cardiomyopathy by exploring the DIAMOnD algorithm on the human interactome. We next revealed how these candidate genes form association across different diseases and highlighted the predominant association with brain, cancer and metabolic diseases. Through integrative systems analysis of molecular pathways, heart-specific mouse knockout data and disease tissue-specific transcriptomic data, we screened and ascertained prominent candidates that show abnormal heart phenotype, including NOS3, MMP2 and SIRT1. Our computational analysis broadens the understanding of the genetic associations of cardiomyopathies with other diseases and holds great potential in cardiomyopathy research.
心肌病是一种进行性疾病，会导致心脏表型异常，是普通人群中心力衰竭的主要原因。这些疾病较为复杂，常与其他疾病合并发生。局部疾病区域的分子相互作用网络是解析这些复杂疾病潜在分子机制的重要步骤。在这一探索中，我们运用网络医学技术，系统地研究了心肌病与其他疾病的遗传相互作用，并揭示了这些关联背后的分子参与者。我们通过在人类相互作用组上探索 DIAMOnD 算法，预测了一组心肌病的候选基因。接下来，我们揭示了这些候选基因如何在不同疾病中形成关联，并突出了与大脑、癌症和代谢疾病的主要关联。通过对分子通路、心脏特异性小鼠敲除数据和疾病组织特异性转录组数据的综合系统分析，我们筛选并确定了表现出异常心脏表型的显著候选基因，包括 NOS3、MMP2 和 SIRT1。我们的计算分析拓宽了对心肌病与其他疾病遗传关联的理解，在心肌病研究中具有巨大潜力。
