MicroRNAs （miRNAs） are a group of regulatory RNAs that regulate gene expression post-transcriptionally by the degradation or translational inhibition of their target messenger RNAs （mRNAs）. Regulation is accomplis...MicroRNAs （miRNAs） are a group of regulatory RNAs that regulate gene expression post-transcriptionally by the degradation or translational inhibition of their target messenger RNAs （mRNAs）. Regulation is accomplished when the 22-25 nucleotide miRNAs bind to complementary sequences in the 3＇-untranslated regions （UTR）. One barrier to miRNA research is to find target genes. Although computational target predictions have shed light on important aspects of microRNA target recognition, questions remain concerning the rates of false positives. In addition, we do not completely understand how microRNAs can recognize and regulate their targets. As such, experimental positive predictions and allow for an unbiased stu ap dy proaches are required, which can reflect in vivo processes, eliminating false of microRNA target recognition. In this review, we summarized experimental approaches that have been described for the identification and validation of mRNA targets associated with specific miRNAs.展开更多
[目的]运用网络药理学和体外实验探讨玉泉胶囊治疗糖尿病肾病的潜在作用机制。[方法]借助中药系统药理学数据库与分析平台(Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform,TCMSP)和中医药综合数据...[目的]运用网络药理学和体外实验探讨玉泉胶囊治疗糖尿病肾病的潜在作用机制。[方法]借助中药系统药理学数据库与分析平台(Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform,TCMSP)和中医药综合数据库(Traditional Chinese Medicine Integrated Database,TCMID)筛选玉泉胶囊有效成分及靶点基因,基因组注释数据平台(Genome Annotation Database Platform,GeneCards)、疾病基因网络(Disease Gene Network,DisGeNET)数据库和治疗靶点数据库(Therapeutic Target Database,TTD)筛选糖尿病肾病的疾病相关基因,STRING 11.5数据库构建蛋白互作(protein-protein interaction,PPI)网络,并利用Cytoscape 3.7.2软件可视化,利用Cytoscape 3.7.2软件的CytoHubba和MCODE插件筛选关键化合物与核心靶点,利用R软件(ClusterProfiler包)进行基因本体(gene ontology,GO)富集分析和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)信号通路富集分析,利用AutoDock和PyMOL软件进行分子对接验证,最后对网络药理学预测结果进行体外实验验证。[结果]玉泉胶囊抗糖尿病肾病活性成分有177个,活性成分作用靶点共477个,作用于糖尿病肾病的靶点有135个,核心靶点包括丝氨酸/苏氨酸激酶1(serine/threonine kinase 1,AKT1)、白细胞介素-1β(interleukin-1 beta,IL1B)、过氧化氢酶(catalase,CAT)、一氧化氮合酶(nitric oxide synthase 3,NOS3)、瘦素(leptin,LEP)、胰岛素样生长因子1(insulin-like growth factor 1,IGF1)和趋化因子配体2(C-C motif chemokine ligand 2,CCL2);GO富集分析共得到相关条目2557个,其中生物过程1415个、细胞组成657个、分子功能48个;KEGG通路分析结果显示玉泉胶囊作用于糖尿病肾病的靶点富集在66条通路上,其中环磷酸腺苷(cyclic adenosine monophosphate,cAMP)信号通路、高级糖基化终末产物-受体(advanced glycation end products-receptor for advanced glycation end products,AGE-RAGE)信号通路在糖尿病并发症中的作用等可能是玉泉胶囊治疗糖尿病肾病的关键通路。分子对接结果显示核心靶点与关键化合物均能较好地结合。实验验证显示,CCL2蛋白水平呈药物剂量依赖性下降,CAT、内皮型一氧化氮合酶(endothelial nitric oxide synthase,eNOs)蛋白水平呈药物剂量依赖性上升。[结论]玉泉胶囊治疗糖尿病肾病具有多成分、多靶点的特点,其机制可能与下调CCL2、上调CAT、eNOs的蛋白表达,抑制糖尿病肾病的氧化应激,调节关键通路相关。展开更多
药物靶标作用关系预测是一种重要的辅助药物研发手段,而生物实验验证药物靶标作用关系耗钱耗时,因此,在数据库中查询验证预测的药物靶标作用关系是对预测方法的重要评价.基于KEGG,DrugBank,ChEMBL这3个数据库,利用爬虫获取信息的方式设...药物靶标作用关系预测是一种重要的辅助药物研发手段,而生物实验验证药物靶标作用关系耗钱耗时,因此,在数据库中查询验证预测的药物靶标作用关系是对预测方法的重要评价.基于KEGG,DrugBank,ChEMBL这3个数据库,利用爬虫获取信息的方式设计开发了药物靶标作用关系查询验证方法DTcheck(drug-target check),实现了对于提供KEGG DRUG ID及KEGG GENES ID的药物靶标对的高效查询验证功能,并利用DTcheck分别为Enzyme,IC(ion channel),GPCR(G-protein-coupled receptor),NR(nuclear receptor)四个标准数据集扩充新增药物靶标作用关系907,766,458,40对.此外,结合DTcheck查询验证,以BLM(bipartite local models)方法为例分析了预测结果的评价问题,结果表明,采用AUC(area under curve)值评价药物靶标作用关系预测方法没有Top N 评价合理,且AUC值低的BLMd方法在预测新的药物靶标作用关系时优于AUC值高的BLMmax方法.展开更多
基金Supported by the National Natural Science Foundation of China (30570990, 30471059, 31171578)the "863" project (2008AA10Z153)+2 种基金the Key Research Plan of Heilongjiang Province (GA06B103-3)the Innovation Research Group of NEAU (CXT004)the Research Fund for the Doctoral Program of Higher Education of China (20102325120002)
文摘MicroRNAs （miRNAs） are a group of regulatory RNAs that regulate gene expression post-transcriptionally by the degradation or translational inhibition of their target messenger RNAs （mRNAs）. Regulation is accomplished when the 22-25 nucleotide miRNAs bind to complementary sequences in the 3＇-untranslated regions （UTR）. One barrier to miRNA research is to find target genes. Although computational target predictions have shed light on important aspects of microRNA target recognition, questions remain concerning the rates of false positives. In addition, we do not completely understand how microRNAs can recognize and regulate their targets. As such, experimental positive predictions and allow for an unbiased stu ap dy proaches are required, which can reflect in vivo processes, eliminating false of microRNA target recognition. In this review, we summarized experimental approaches that have been described for the identification and validation of mRNA targets associated with specific miRNAs.
文摘药物靶标作用关系预测是一种重要的辅助药物研发手段,而生物实验验证药物靶标作用关系耗钱耗时,因此,在数据库中查询验证预测的药物靶标作用关系是对预测方法的重要评价.基于KEGG,DrugBank,ChEMBL这3个数据库,利用爬虫获取信息的方式设计开发了药物靶标作用关系查询验证方法DTcheck(drug-target check),实现了对于提供KEGG DRUG ID及KEGG GENES ID的药物靶标对的高效查询验证功能,并利用DTcheck分别为Enzyme,IC(ion channel),GPCR(G-protein-coupled receptor),NR(nuclear receptor)四个标准数据集扩充新增药物靶标作用关系907,766,458,40对.此外,结合DTcheck查询验证,以BLM(bipartite local models)方法为例分析了预测结果的评价问题,结果表明,采用AUC(area under curve)值评价药物靶标作用关系预测方法没有Top N 评价合理,且AUC值低的BLMd方法在预测新的药物靶标作用关系时优于AUC值高的BLMmax方法.
