(1)研究背景:人体肠道中存在大量多样的微生物种群,支持肠道稳态和宿主的健康。分泌短链脂肪酸(SCFA)的微生物还产生几种代谢产物,对各种恶性肿瘤和免疫性炎症具有良好的调节作用。肠道SCFA参与肾脏疾病,如各种肾脏恶性肿瘤和炎症,近年来已成为一个引人入胜的研究领域。然而,SCFA产生菌产生的SCFA和其他代谢产物抗肾癌和炎症的机制尚未研究.(2)方法:我们考虑了来自gutM基因数据库的177种不同的产生SCFA的微生物物种和114种代谢物。Further,我们使用不同的基于在线的数据库平台来预测与代谢物相关的1890个基因靶标.此外,DisGeNet,OMIM,和Genecard数据库用于考虑13,104个疾病相关基因靶标。我们使用了维恩图和各种蛋白质-蛋白质相互作用(PPI),KEGG途径,和GO分析,用于基因靶标的功能分析。此外,蛋白质-蛋白质相互作用的子网络(通过字符串和细胞景观平台)用于通过程度中心性选择前20%的基因靶标,中间性中心性,和紧密的中心性。筛选可能的候选化合物,我们对ADMET进行了分析(吸收,分布,新陈代谢,排泄,和毒性)代谢物的特性,然后使用分子对接模拟找到最佳的结合亲和力。(3)结果:最后,我们发现了与合适的化合物相互作用的关键基因靶标,并具有对抗肾癌和炎症的功能,如MTOR(与甘氨胆酸),PIK3CA(含11-甲氧基卷曲素,甘胆酸,和异槲皮苷),IL6(含异槲皮苷),PTGS2(加异槲皮苷),和IGF1R(与2-氨基-1-甲基-6-苯基咪唑并[4,5-b]吡啶,异槲皮苷),显示较低的结合亲和力。(4)结论:本研究提供了证据来支持产生SCFA的微生物代谢产物对肾癌和炎症的积极作用,并提出了可用于指导未来研究的综合研究建议。
(1) Background: A large and diverse microbial population exists in the human intestinal tract, which supports gut homeostasis and the health of the host. Short-chain fatty acid (SCFA)-secreting microbes also generate several metabolites with favorable regulatory effects on various malignancies and immunological inflammations. The involvement of intestinal SCFAs in kidney diseases, such as various kidney malignancies and inflammations, has emerged as a fascinating area of study in recent years. However, the mechanisms of SCFAs and other metabolites produced by SCFA-producing bacteria against kidney cancer and inflammation have not yet been investigated. (2) Methods: We considered 177 different SCFA-producing microbial species and 114 metabolites from the gutMgene database. Further, we used different online-based database platforms to predict 1890 gene targets associated with metabolites. Moreover, DisGeNET, OMIM, and Genecard databases were used to consider 13,104 disease-related gene targets. We used a Venn diagram and various protein-protein interactions (PPIs), KEGG pathways, and GO analyses for the functional analysis of gene targets. Moreover, the subnetwork of protein-protein interactions (through string and cytoscape platforms) was used to select the top 20% of gene targets through degree centrality, betweenness centrality, and closeness centrality. To screen the possible candidate compounds, we performed an analysis of the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of metabolites and then found the best binding affinity using molecular docking simulation. (3) Results: Finally, we found the key gene targets that interact with suitable compounds and function against kidney cancer and inflammation, such as MTOR (with glycocholic acid), PIK3CA (with 11-methoxycurvularin, glycocholic acid, and isoquercitrin), IL6 (with isoquercitrin), PTGS2 (with isoquercitrin), and IGF1R (with 2-amino-1-methyl-6-phenylimidazo[4,5-b] pyridine, isoquercitrin), showed a lower binding affinity. (4) Conclusions: This study provides evidence to support the positive effects of SCFA-producing microbial metabolites that function against kidney cancer and inflammation and makes integrative research proposals that may be used to guide future studies.