Laboratory for Diabetes, Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China.
Institute on Aging and Brain Disorders, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China.
Oxid Med Cell Longev. 2022 Jul 28;2022:8255550. doi: 10.1155/2022/8255550. eCollection 2022.
Type 2 diabetes mellitus (T2DM) is a metabolic disease with increasing prevalence and mortality year by year. The purpose of this study was to explore new therapeutic targets and candidate drugs for multitargets by single-cell RNA expression profile analysis, network pharmacology, and molecular docking. Single-cell RNA expression profiling of islet cell samples between T2DM patients and nondiabetic controls was conducted to identify important subpopulations and the marker genes. The potential therapeutic targets of T2DM were identified by the overlap analysis of insulin-related genes and diabetes-related genes, the construction of protein-protein interaction network, and the molecular complex detection (MCODE) algorithm. The network distance method was employed to determine the potential drugs of the target. Molecular docking and molecular dynamic simulations were carried out using AutoDock Vina and Gromacs2019, respectively. Eleven cell clusters were identified by single-cell RNA sequencing (scRNA-seq) data, and three of them (C2, C8, and C10) showed significant differences between T2DM samples and normal samples. Eight genes from differential cell clusters were found from differential cell clusters to be associated with insulin activity and T2DM. The MCODE algorithm built six key subnetworks, with five of them correlating with inflammatory pathways and immune cell infiltration. Importantly, CCR5 was a gene within the key subnetworks and was differentially expressed between normal samples and T2DM samples, with the highest area under the ROC curve (AUC) of 82.5% for the diagnosis model. A total of 49 CCR5-related genes were screened, and DB05494 was identified as the most potential drug with the shortest distance to CCR5-related genes. Molecular docking illustrated that DB05494 stably bound with CCR5 (-8.0 kcal/mol) through multiple hydrogen bonds (LYS26, TYR37, TYR89, CYS178, and GLN280) and hydrophobic bonds (TRP86, PHE112, ILE198, TRP248, and TYR251). This study identified CCR5 as a potential therapeutic target and screened DB05494 as a potential drug for T2DM treatment.
2 型糖尿病(T2DM)是一种代谢疾病,其患病率和死亡率逐年上升。本研究旨在通过单细胞 RNA 表达谱分析、网络药理学和分子对接,探索多靶点的新治疗靶点和候选药物。对 T2DM 患者和非糖尿病对照者胰岛细胞样本进行单细胞 RNA 表达谱分析,以鉴定重要的亚群和标记基因。通过胰岛素相关基因和糖尿病相关基因的重叠分析、蛋白质-蛋白质相互作用网络的构建和分子复合物检测(MCODE)算法,确定 T2DM 的潜在治疗靶点。采用网络距离法确定潜在药物靶点。采用 AutoDock Vina 和 Gromacs2019 分别进行分子对接和分子动力学模拟。通过单细胞 RNA 测序(scRNA-seq)数据鉴定了 11 个细胞簇,其中 3 个(C2、C8 和 C10)在 T2DM 样本和正常样本之间存在显著差异。从差异细胞簇中发现了与胰岛素活性和 T2DM 相关的 8 个差异表达基因。MCODE 算法构建了 6 个关键子网络,其中 5 个与炎症途径和免疫细胞浸润相关。重要的是,CCR5 是关键子网络中的一个基因,在正常样本和 T2DM 样本之间差异表达,诊断模型的 AUC 最高为 82.5%。共筛选出 49 个 CCR5 相关基因,其中 DB05494 与 CCR5 相关基因的距离最短,被鉴定为最具潜力的药物。分子对接表明,DB05494 通过多个氢键(LYS26、TYR37、TYR89、CYS178 和 GLN280)和疏水键(TRP86、PHE112、ILE198、TRP248 和 TYR251)与 CCR5 稳定结合(-8.0kcal/mol)。本研究鉴定了 CCR5 作为一个潜在的治疗靶点,并筛选出 DB05494 作为治疗 T2DM 的潜在药物。