Meng Qingying, Mäkinen Ville-Petteri, Luk Helen, Yang Xia
Department of Integrative Biology and Physiology, University of California (UCLA), 610 Charles E. Young Dr E., Terasaki Life Sciences Building, Los Angeles, CA 90095 USA.
Curr Cardiovasc Risk Rep. 2013 Feb;7(1):73-83. doi: 10.1007/s12170-012-0280-y. Epub 2012 Oct 18.
The metabolically connected triad of obesity, diabetes, and cardiovascular diseases is a major public health threat, and is expected to worsen due to the global shift toward energy-rich and sedentary living. Despite decades of intense research, a large part of the molecular pathogenesis behind complex metabolic diseases remains unknown. Recent advances in genetics, epigenomics, transcriptomics, proteomics and metabolomics enable us to obtain large-scale snapshots of the etiological processes in multiple disease-related cells, tissues and organs. These datasets provide us with an opportunity to go beyond conventional reductionist approaches and to pinpoint the specific perturbations in critical biological processes. In this review, we summarize systems biology methodologies such as functional genomics, causality inference, data-driven biological network construction, and higher-level integrative analyses that can produce novel mechanistic insights, identify disease biomarkers, and uncover potential therapeutic targets from a combination of omics datasets. Importantly, we also demonstrate the power of these approaches by application examples in obesity, diabetes, and cardiovascular diseases.
肥胖、糖尿病和心血管疾病这一代谢相关的三联征是对公众健康的重大威胁,并且由于全球生活方式向高能量摄入和久坐不动转变,预计情况会恶化。尽管经过数十年的深入研究,但复杂代谢疾病背后的大部分分子发病机制仍不为人知。遗传学、表观基因组学、转录组学、蛋白质组学和代谢组学的最新进展使我们能够获取多个疾病相关细胞、组织和器官病因过程的大规模快照。这些数据集为我们提供了超越传统还原论方法的机会,并能精准定位关键生物学过程中的特定扰动。在本综述中,我们总结了系统生物学方法,如功能基因组学、因果推断、数据驱动的生物网络构建以及更高级别的综合分析,这些方法能够从组学数据集的组合中产生新的机制见解、识别疾病生物标志物并揭示潜在的治疗靶点。重要的是,我们还通过肥胖、糖尿病和心血管疾病的应用实例展示了这些方法的强大之处。