Department of Community Nursing, School of Nursing and Health, Zhengzhou University, High-Tech Development Zone of States, Zhengzhou, 450001, Henan, People's Republic of China.
Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, LA, 70112, USA.
Hum Genomics. 2022 May 14;16(1):15. doi: 10.1186/s40246-022-00388-x.
Obesity is a complex, multifactorial condition in which genetic play an important role. Most of the systematic studies currently focuses on individual omics aspect and provide insightful yet limited knowledge about the comprehensive and complex crosstalk between various omics levels.
Therefore, we performed a most comprehensive trans-omics study with various omics data from 104 subjects, to identify interactions/networks and particularly causal regulatory relationships within and especially those between omic molecules with the purpose to discover molecular genetic mechanisms underlying obesity etiology in vivo in humans.
By applying differentially analysis, we identified 8 differentially expressed hub genes (DEHGs), 14 differentially methylated regions (DMRs) and 12 differentially accumulated metabolites (DAMs) for obesity individually. By integrating those multi-omics biomarkers using Mendelian Randomization (MR) and network MR analyses, we identified 18 causal pathways with mediation effect. For the 20 biomarkers involved in those 18 pairs, 17 biomarkers were implicated in the pathophysiology of obesity or related diseases.
The integration of trans-omics and MR analyses may provide us a holistic understanding of the underlying functional mechanisms, molecular regulatory information flow and the interactive molecular systems among different omic molecules for obesity risk and other complex diseases/traits.
肥胖是一种复杂的多因素疾病,其中遗传起着重要作用。目前大多数系统研究都集中在个体组学方面,对各种组学层面之间的全面和复杂的相互作用提供了有见地但有限的知识。
因此,我们对 104 名受试者的各种组学数据进行了最全面的跨组学研究,以识别组内和组间分子之间的相互作用/网络,特别是因果调控关系,目的是发现肥胖病因的分子遗传机制在人类体内。
通过应用差异分析,我们分别鉴定了 8 个差异表达的枢纽基因(DEHGs)、14 个差异甲基化区域(DMRs)和 12 个差异积累代谢物(DAMs)与肥胖有关。通过使用孟德尔随机化(MR)和网络 MR 分析整合这些多组学生物标志物,我们鉴定出了 18 个具有中介效应的因果途径。对于涉及这 18 对的 20 个生物标志物,有 17 个生物标志物与肥胖或相关疾病的病理生理学有关。
跨组学和 MR 分析的整合可能为我们提供对不同组学分子之间潜在功能机制、分子调控信息流和相互作用分子系统的整体理解,从而了解肥胖风险和其他复杂疾病/特征的发生机制。