Yao Weiguo, Huo Jinlin, Ji Jing, Liu Kun, Tao Pengyu
Department of Nephrology, Jinshan District Central Hospital, Shanghai University of Medicine & Health Sciences, Shanghai, China.
Institute of Precision Medicine, The First Affiliated Hospital of Shantou University Medical College, Shantou, China.
Mol Med. 2024 Dec 20;30(1):263. doi: 10.1186/s10020-024-01033-0.
Extensive research has underscored the criticality of preserving diversity and equilibrium within the gut microbiota for optimal human health. However, the precise mechanisms by which the metabolites and targets of the gut microbiota exert their effects remain largely unexplored. This study utilizes a network pharmacology methodology to elucidate the intricate interplay between the microbiota, metabolites, and targets in the context of DM, thereby facilitating a more comprehensive comprehension of this multifaceted disease.
In this study, we initially extracted metabolite information of gut microbiota metabolites from the gutMGene database. Subsequently, we employed the SEA and STP databases to discern targets that are intricately associated with these metabolites. Furthermore, we leveraged prominent databases such as Genecard, DisGeNET, and OMIM to identify targets related to diabetes. A protein-protein interaction (PPI) network was established to screen core targets. Additionally, we conducted comprehensive GO and KEGG enrichment analyses utilizing the DAVID database. Moreover, a network illustrating the relationship among microbiota-substrate-metabolite-target was established.
We identified a total of 48 overlapping targets between gut microbiota metabolites and diabetes. Subsequently, we selected IL6, AKT1 and PPARG as core targets for the treatment of diabetes. Through the construction of the MSMT comprehensive network, we discovered that the three core targets exert therapeutic effects on diabetes through interactions with 8 metabolites, 3 substrates, and 5 gut microbiota. Additionally, GO analysis revealed that gut microbiota metabolites primarily regulate oxidative stress, inflammation and cell proliferation. KEGG analysis results indicated that IL-17, PI3K/AKT, HIF-1, and VEGF are the main signaling pathways involved in DM.
Gut microbiota metabolites primarily exert their therapeutic effects on diabetes through the IL6, AKT1, and PPARG targets. The mechanisms of gut microbiota metabolites regulating DM might involve signaling pathways such as IL-17 pathways, HIF-1 pathways and VEGF pathways.
广泛的研究强调了维持肠道微生物群的多样性和平衡对人类最佳健康状态的至关重要性。然而，肠道微生物群的代谢产物及其作用靶点发挥作用的确切机制在很大程度上仍未得到探索。本研究采用网络药理学方法来阐明微生物群、代谢产物和靶点在糖尿病背景下的复杂相互作用，从而有助于更全面地理解这种多方面的疾病。
在本研究中，我们首先从gutMGene数据库中提取肠道微生物群代谢产物的代谢信息。随后，我们利用SEA和STP数据库来识别与这些代谢产物密切相关的靶点。此外，我们利用诸如Genecard、DisGeNET和OMIM等著名数据库来识别与糖尿病相关的靶点。建立了蛋白质-蛋白质相互作用（PPI）网络以筛选核心靶点。此外，我们利用DAVID数据库进行了全面的基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析。此外，还建立了一个说明微生物群-底物-代谢产物-靶点之间关系的网络。
我们共鉴定出肠道微生物群代谢产物与糖尿病之间的48个重叠靶点。随后，我们选择白细胞介素6（IL6）、蛋白激酶B（AKT1）和过氧化物酶体增殖物激活受体γ（PPARG）作为治疗糖尿病的核心靶点。通过构建微生物群-底物-代谢产物-靶点（MSMT）综合网络，我们发现这三个核心靶点通过与8种代谢产物、3种底物和5种肠道微生物群相互作用对糖尿病发挥治疗作用。此外，GO分析表明肠道微生物群代谢产物主要调节氧化应激、炎症和细胞增殖。KEGG分析结果表明，白细胞介素-17（IL-17）、磷脂酰肌醇-3激酶/蛋白激酶B（PI3K/AKT）、缺氧诱导因子-1（HIF-1）和血管内皮生长因子（VEGF）是参与糖尿病的主要信号通路。
肠道微生物群代谢产物主要通过IL6、AKT1和PPARG靶点对糖尿病发挥治疗作用。肠道微生物群代谢产物调节糖尿病的机制可能涉及IL-17通路、HIF-1通路和VEGF通路等信号通路。
