Yan Zhi-Tao, Huang Jin-Mei, Luo Wen-Li, Liu Ji-Wen, Zhou Kang
Department of Cardiology, The First Affiliated Hospital of the Medical College, Shihezi University, Shihezi, Xinjiang 832000, P.R. China.
Department of General Surgery, The First Affiliated Hospital of the Medical College, Shihezi University, Shihezi, Xinjiang 832000, P.R. China.
Exp Ther Med. 2019 May;17(5):3929-3934. doi: 10.3892/etm.2019.7443. Epub 2019 Mar 26.
Metabolites in atrial fibrillation (AF) were characterized to further explore the molecular mechanisms of AF by integrating metabolic, phenomic and genomic data. Gene expression data on AF (E-GEOD-79768) were downloaded from the EMBL-EBI database, followed by identification of differentially expressed genes (DEGs) which were used to construct gene-gene network. Then, multi-omics composite networks were constructed. Subsequently, random walk with restart was expanded to a multi-omics composite network to identify and prioritize the metabolites according to the AF-related seed genes deposited in the OMIM database, the whole metabolome as candidates and the phenotype of AF. Using the interaction score among metabolites, we extracted the top 50 metabolites, and identified the top 100 co-expressed genes interacted with the top 50 metabolites. Based on the FDR <0.05, 622 DEGs were extracted. In order to demonstrate the intrinsic mode of this method, we sorted the metabolites of the composite network in descending order based on the interaction scores. The top 5 metabolites were respectively weighed potassium, sodium ion, chitin, benzo[a]pyrene-7,8-dihydrodiol-9,10-oxide, and celebrex (TN). Potassium and sodium ion possessed higher degrees in the subnetwork of the entire composite network and the co-expressed network. Metabolites such as potassium and sodium ion may provide valuable clues for early diagnostic and therapeutic targets for AF.
通过整合代谢组学、表型组学和基因组学数据，对心房颤动（AF）中的代谢物进行表征，以进一步探索AF的分子机制。从EMBL-EBI数据库下载AF的基因表达数据（E-GEOD-79768），随后鉴定差异表达基因（DEG），并用于构建基因-基因网络。然后，构建多组学复合网络。随后，将带重启的随机游走扩展到多组学复合网络，根据OMIM数据库中存储的AF相关种子基因、作为候选的全代谢组和AF表型对代谢物进行识别和优先级排序。利用代谢物之间的相互作用得分，提取前50种代谢物，并鉴定与前50种代谢物相互作用的前100个共表达基因。基于FDR<0.05，提取622个DEG。为了证明该方法的内在模式，我们根据相互作用得分对复合网络的代谢物进行降序排序。前5种代谢物分别是重水合钾、钠离子、几丁质、苯并[a]芘-7,8-二氢二醇-9,10-环氧化物和塞来昔布（TN）。钾离子和钠离子在整个复合网络的子网和共表达网络中具有较高的度数。钾离子和钠离子等代谢物可能为AF的早期诊断和治疗靶点提供有价值的线索。
