Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA.
Molecular, Cellular and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA, 90095, USA.
Curr Atheroscler Rep. 2023 Dec;25(12):1013-1023. doi: 10.1007/s11883-023-01170-7. Epub 2023 Nov 27.
Coronary artery disease is a complex disorder and the leading cause of mortality worldwide. As technologies for the generation of high-throughput multiomics data have advanced, gene regulatory network modeling has become an increasingly powerful tool in understanding coronary artery disease. This review summarizes recent and novel gene regulatory network tools for bulk tissue and single cell data, existing databases for network construction, and applications of gene regulatory networks in coronary artery disease.
New gene regulatory network tools can integrate multiomics data to elucidate complex disease mechanisms at unprecedented cellular and spatial resolutions. At the same time, updates to coronary artery disease expression data in existing databases have enabled researchers to build gene regulatory networks to study novel disease mechanisms. Gene regulatory networks have proven extremely useful in understanding CAD heritability beyond what is explained by GWAS loci and in identifying mechanisms and key driver genes underlying disease onset and progression. Gene regulatory networks can holistically and comprehensively address the complex nature of coronary artery disease. In this review, we discuss key algorithmic approaches to construct gene regulatory networks and highlight state-of-the-art methods that model specific modes of gene regulation. We also explore recent applications of these tools in coronary artery disease patient data repositories to understand disease heritability and shared and distinct disease mechanisms and key driver genes across tissues, between sexes, and between species.
冠状动脉疾病是一种复杂的疾病，也是全球范围内导致死亡的主要原因。随着高通量多组学数据生成技术的进步，基因调控网络建模已成为理解冠状动脉疾病的一种越来越强大的工具。本文总结了用于批量组织和单细胞数据的最新和新颖的基因调控网络工具、用于网络构建的现有数据库，以及基因调控网络在冠状动脉疾病中的应用。
新的基因调控网络工具可以整合多组学数据，以前所未有的细胞和空间分辨率阐明复杂的疾病机制。同时，现有数据库中冠状动脉疾病表达数据的更新使研究人员能够构建基因调控网络来研究新的疾病机制。基因调控网络已被证明在理解 GWAS 基因座之外的 CAD 遗传性方面非常有用，并确定了疾病发病和进展的机制和关键驱动基因。基因调控网络可以全面综合地解决冠状动脉疾病的复杂性。在这篇综述中，我们讨论了构建基因调控网络的关键算法方法，并强调了用于模拟特定基因调控模式的最新方法。我们还探讨了这些工具在冠状动脉疾病患者数据库中的最新应用，以了解疾病遗传性以及组织之间、性别之间和物种之间的共享和独特的疾病机制和关键驱动基因。
