Panday Namuna, Sigdel Dibakar, Adam Irsyad, Ramirez Joseph, Verma Aarushi, Eranki Anirudh N, Wang Wei, Wang Ding, Ping Peipei
Department of Physiology, School of Medicine, University of California, Los Angeles, CA 90095, USA.
NHLBI Integrated Cardiovascular Data Science Training Program (iDISCOVER), University of California, Los Angeles, CA 90095, USA.
Antioxidants (Basel). 2024 Nov 20;13(11):1420. doi: 10.3390/antiox13111420.
A growing body of biomedical literature suggests a bidirectional regulatory relationship between cardiac calcium (Ca)-regulating proteins and reactive oxygen species (ROS) that is integral to the pathogenesis of various cardiac disorders via oxidative stress (OS) signaling. To address the challenge of finding hidden connections within the growing volume of biomedical research, we developed a data science pipeline for efficient data extraction, transformation, and loading. Employing the CaseOLAP (Context-Aware Semantic Analytic Processing) algorithm, our pipeline quantifies interactions between 128 human cardiomyocyte Ca-regulating proteins and eight cardiovascular disease (CVD) categories. Our machine-learning analysis of CaseOLAP scores reveals that the molecular interfaces of Ca-regulating proteins uniquely associate with cardiac arrhythmias and diseases of the cardiac conduction system, distinguishing them from other CVDs. Additionally, a knowledge graph analysis identified 59 of the 128 Ca-regulating proteins as involved in OS-related cardiac diseases, with cardiomyopathy emerging as the predominant category. By leveraging a link prediction algorithm, our research illuminates the interactions between Ca-regulating proteins, OS, and CVDs. The insights gained from our study provide a deeper understanding of the molecular interplay between cardiac ROS and Ca-regulating proteins in the context of CVDs. Such an understanding is essential for the innovation and development of targeted therapeutic strategies.
越来越多的生物医学文献表明,心脏钙(Ca)调节蛋白与活性氧(ROS)之间存在双向调节关系,这通过氧化应激(OS)信号传导在各种心脏疾病的发病机制中起着不可或缺的作用。为了应对在日益增长的生物医学研究中寻找隐藏联系的挑战,我们开发了一种用于高效数据提取、转换和加载的数据科学管道。利用CaseOLAP(上下文感知语义分析处理)算法,我们的管道量化了128种人类心肌细胞钙调节蛋白与八种心血管疾病(CVD)类别之间的相互作用。我们对CaseOLAP分数的机器学习分析表明,钙调节蛋白的分子界面与心律失常和心脏传导系统疾病具有独特的关联,将它们与其他心血管疾病区分开来。此外,知识图谱分析确定128种钙调节蛋白中有59种与OS相关的心脏疾病有关,其中心肌病是主要类别。通过利用链接预测算法,我们的研究阐明了钙调节蛋白、OS和心血管疾病之间的相互作用。我们研究中获得的见解为在心血管疾病背景下深入了解心脏ROS与钙调节蛋白之间的分子相互作用提供了依据。这种理解对于靶向治疗策略的创新和发展至关重要。