Department of Pathology, School of Basic Medical Science, Guangzhou Medical University, Guangzhou, Guangdong 511436, P.R. China.
Department of Hematology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou 510080, P.R. China.
J Microbiol Biotechnol. 2022 Mar 28;32(3):387-395. doi: 10.4014/jmb.2106.06027.
Deciphering the metabolites of human diseases is an important objective of biomedical research. Here, we aimed to capture the core metabolites of Fanconi anemia (FA) using the bioinformatics method of a multi-omics composite network. Based on the assumption that metabolite levels can directly mirror the physiological state of the human body, we used a multi-omics composite network that integrates six types of interactions in humans (gene-gene, disease phenotype-phenotype, disease-related metabolite-metabolite, gene-phenotype, gene-metabolite, and metabolite-phenotype) to procure the core metabolites of FA. This method is applicable in predicting and prioritizing disease candidate metabolites and is effective in a network without known disease metabolites. In this report, we first singled out the differentially expressed genes upon different groups that were related with FA and then constructed the multi-omics composite network of FA by integrating the aforementioned six networks. Ultimately, we utilized random walk with restart (RWR) to screen the prioritized candidate metabolites of FA, and meanwhile the co-expression gene network of FA was also obtained. As a result, the top 5 metabolites of FA were tenormin (TN), guanosine 5'-triphosphate, guanosine 5'-diphosphate, triphosadenine (DCF) and adenosine 5'-diphosphate, all of which were reported to have a direct or indirect relationship with FA. Furthermore, the top 5 co-expressed genes were CASP3, BCL2, HSPD1, RAF1 and MMP9. By prioritizing the metabolites, the multi-omics composite network may provide us with additional indicators closely linked to FA.
解析人类疾病的代谢物是生物医学研究的一个重要目标。在这里，我们旨在使用多组学综合网络的生物信息学方法来捕捉范可尼贫血症 (FA) 的核心代谢物。基于代谢物水平可以直接反映人体生理状态的假设，我们使用了一种多组学综合网络，该网络整合了人类的六种相互作用类型（基因-基因、疾病表型-表型、疾病相关代谢物-代谢物、基因-表型、基因-代谢物和代谢物-表型），以获取 FA 的核心代谢物。这种方法适用于预测和优先考虑疾病候选代谢物，并且在没有已知疾病代谢物的网络中也是有效的。在本报告中，我们首先挑选出与 FA 相关的不同组之间差异表达的基因，然后通过整合上述六种网络构建 FA 的多组学综合网络。最终，我们利用随机游走重启 (RWR) 筛选 FA 的优先候选代谢物，同时还获得了 FA 的共表达基因网络。结果，FA 的前 5 种代谢物是替米沙坦 (TN)、鸟苷 5'-三磷酸、鸟苷 5'-二磷酸、三磷酸腺嘌呤 (DCF) 和腺苷 5'-二磷酸，这些都被报道与 FA 有直接或间接关系。此外，前 5 个共表达基因是 CASP3、BCL2、HSPD1、RAF1 和 MMP9。通过对代谢物进行优先级排序，多组学综合网络可能为我们提供与 FA 密切相关的其他指标。
