循环Micro RNA在缺血性卒中炎性反应通路中调控作用的研究进展

缺血性卒中(ischcmic stroke,IS)是一种由脑血管狭窄或完全阻塞而导致的脑组织缺血性坏死及神经功能损伤的疾病,其重要的病理生理机制之一为炎性反应。炎性反应通过调控下游信号通路促进IS的发生、发展。Micro RNA是一类18～24个核苷酸大小的非编码RNA,参与调节基因的转录与翻译。越来越多的研究证实,Micro RNA在IS炎性反应通路的调控中发挥关键作用。现就Micro RNA在IS炎性反应通路调控作用的相关研究做一综述,为防治IS提供新策略。     

伴视神经炎的多发性硬化患者外周血CD19+B细胞中基因差异表达及相关通路的研究

目的利用生物信息学方法分析伴视神经炎的多发性硬化患者外周血CD19+B细胞中基因标记物的特征。方法从GEO数据库获取伴视神经炎的多发性硬化患者外周血CD19+B细胞中基因芯片表达谱,利用GEO2R软件进行差异表达分析,应用GO和KEGG对差异基因进行功能注释和通路分析,并进一步应用string-db数据库进行蛋白相互作用分析。结果共发现561个差异表达基因,这些在差异性表达的基因被富集到不同的生物学过程或分子功能的子集中,主要富集在免疫反应、炎症反应、信号传导调控等功能上,主要调控细胞因子受体互作等通路。结论伴有视神经炎的多发性硬化患者差异基因主要集中在免疫反应以及介导细胞因子受体互作通路上。     

红花黄色素治疗颅脑损伤作用机制的生物信息学分析

目的 应用生物信息学方法分析红花黄色素（YS）治疗颅脑损伤（TBI）的作用机制。方法 通过GEO数据库查询检索基因表达微阵列芯片数据集GSE21854,筛选差异表达基因（DEGs）, 并与YS靶点基因互相印射取交集。采用DAVID/String数据库对共同靶基因进行GO分析和KEGG通路分析,构建PPI网络。结果 筛选出140个DEGs,与YS靶点基因互相印射后得到26个共同靶基因;进一步GO分析前5个基因功能富集为信号转导、DNA 转录调节、血小板活化、信号与聚集、免疫系统及转录调控区 DNA 结合,KEGG通路途径分析筛选出前5条信号通路包括MAPK信号通路、趋化因子信号通路、PI3K/AKT信号通路、EGFR1信号通路和肿瘤信号通路。PPI分析筛选发现节点最高的中心蛋白编码基因是MAPK3。结论 应用生物信息学方法,可有效获取YS治疗TBI靶向基因的生物学功能、相关信号通路及核心关键蛋白,为后续研究和临床治疗提供了依据。     

Notch信号通路基因在人脑动静脉畸形和周围脑组织中的差异表达

目的:应用基因芯片研究人脑动静脉畸形(BAVM)的畸形团及周围脑组织的Notch信号通路相关基因的差异表达。方法:收集有出血史的5例BAVM标本,在畸形团、周围脑组织分别取材,进行Notch信号通路基因芯片杂交,得到基因芯片上各基因点数值型信号强度,计算其均数和标准差。计算畸形团芯片及周围脑组织芯片上各基因点的比值。取比值>1.5或<0.67的因子,归纳总结出表达水平有明显差异的基因。以realtime PCR方法对其中任意选取的2个基因进行验证。结果:在113个Notch信号通路基因中,共筛选出27个明显变化的基因,其中,上调4个,下调23个。较为重要的基因包括Notch信号通路配体DLL1、DLL3,受体及配体裂解关键基因ADAM10、ADAM17,Notch信号通路靶基因HES5,Notch相关的Sonic Hedgehog通路基因GLI1、Wnt信号通路基因FZD1、原癌基因LMO2。realtime PCR验证DLL1和HES5两个基因,其检测结果与芯片结果基本相符。部分Notch信号通路关键基因在两个部位表达无差异。结论:多个Notch信号通路基因在BAVM畸形团及周围脑组织存在差异表达,提示BAVM的发生发展与Notch信号通路有关。周围脑组织中,Notch信号通路亦可能存在异常。差异表达的基因可作为BAVM进一步研究的切入点。     

阿尔茨海默病的组学机制分析及药物预测

目的通过生物信息学的方法分析阿尔茨海默病(AD)患者和正常对照之间的差异表达基因以及相应的靶向药物。方法从基因表达数据库(GEO)中获取AD患者和正常对照的相关数据芯片(GSE5281),利用R语言LIMMA程序包进行差异表达基因分析,筛选差异表达基因,使用DAVID数据库对进行基因本体(GO)及京都基因与基因组百科全书(KEGG)信号通路富集分析,再利用STRING数据库进行蛋白相互作用网络(PPI)分析,筛选出核心基因和关键基因,以核心基因为靶点的筛选出现有药物。结果共筛选出863个差异表达基因,包括246个上调基因和617个下调基因,GO富集分析结果示差异基因主要功能为ATP结合等,KEGG通路富集分析为帕金森病、朊蛋白病等多种神经退行性疾病,以及长时程增强作用、轴突导向等,STRING数据库分析得到5个核心基因(PSMA7、PSMA3、PSMB7、PSMC5和PSMC3),和31个关键基因(包括23个上调基因和8个下调基因),以核心基因为靶点筛选出8种现有药物,3组基因芯片两两比较筛选出13个共同的差异表达基因。结论基于GEO数据库的生物信息学分析,AD患者与正常对照之间存在差异表达基因并筛选出现有药物。     

阿尔茨海默病相关基因的生物信息学分析

目的 通过生物信息学方法探讨阿尔茨海默病的发病相关基因,为阿尔茨海默病的基础研究和临床治疗提供新思路. 方法 从基因芯片公共数据库GEO中下载阿尔茨海默病相关基因芯片数据,利用String、KEGG、Panther等在线分析软件对差异表达基因进行生物信息学分析. 结果 在34个差异表达基因所编码的蛋白中,有25个蛋白与其他蛋白存在相互作用关系;相互作用过程中涉及多种生物学通路,与多个生物学过程和分子功能相关. 结论 阿尔茨海默病是多种基因相互作用的结果,其中与钙离子信号相关基因关系较为密切.     

颞叶内侧癫痫基因表达的生物信息学分析

目的通过生物信息学方法分析颞叶内侧癫痫(Mesio-temporal lobe epilepsy,MTLE)模型小鼠的海马组织与正常海马组织差异表达的基因,以及其生物学功能、细胞定位、参与的信号通路等,筛选出在MTLE发生发展过程中的关键基因,并挑选关键基因中处于整个调控网络的核心基因(与其它基因关系最密切者),为MTLE的发病机制及其防治研究提供新思路。方法①从基因芯片公共数据库(Gene expression omnibus,GEO)中搜索下载MTLE相关基因芯片数据集(GSE88992),该芯片含有17个癫痫模型小鼠的海马标本[包括8个病例组和9个对照组,其中将通过注射海藻氨酸(Kainic acid,KA;1 nmol/50 nL)获得的MTLE小鼠模型作为病例组,注射生理盐水的小鼠作为平行对照组,在注射后6、12及24 h提取海马标本];②通过GEO2R工具筛选和鉴定出MTLE小鼠的差异表达基因(Differentially expressed genes,DEGs),调整后P值0.05,|log2(fold-change)|1作为截断值的标准;③使用Venny2.1.0对上调和下调的差异表达基因分别取交集,筛选出共同差异表达的基因;④利用DAVID在线网站及FUNRICH软件分析基因的信号通路、基因蛋白表达等信息;⑤利用STRING在线网站及Cytoscape软件分析差异基因表达蛋白的相互作用以及挑选出5个与周围基因高度相关性(度值,Degree)的中枢基因,实现蛋白相互作用的可视化及分析核心模块基因。结果①在GEO数据集GSE88992,注射后6、12和24 h的结果中筛选出688、1 294和1 916个DEGs,分别包括549、797和870个上调基因以及139、497和1 046个下调基因;②所有DEGs中,有285个上调的DEGs和46个下调的DEGs,共有331个DEGs在3个结果中共存,提示这331个共表达DEGs具有重要意义;③筛选出DEGs中的核心模块以及挑选出10个核心基因(Il6、Fos、Stat3、Ptgs2、Ccl2、Timp1、Cd44、Icam1、Atf3、Cxcl1),生物信息学分析提示核心模块基因主要富集在细胞质膜与细胞间隙中;涉及的生物学过程集中在中性粒细胞趋化、炎症反应、正向调控ERK1和ERK2级联反应;生物分子功能集中在趋化因子活性、细胞因子活性和趋化因子受体结合等;KEGG信号通路分析DEGs主要参与趋化因子信号通路、细胞因子互作信号通路等信号通路。结论通过生物信息学分析表明MTLE的发病机制错综复杂,是多个基因相互作用的结果,多条信号通路参与了MTLE的发病,对MTLE基因表达的进一步分析有利于揭示其发病机制以及确定新的候选治疗靶点。     

基于GEO数据库筛选垂体泌乳素瘤发生的关键差异基因

目的 利用GEO数据库探究泌乳素瘤发生发展过程中的差异基因及潜在的相互作用机制,为泌乳素瘤的研究和治疗提供新的靶点。方法 挑选GEO数据库GSE119063为研究对象,使用R语言筛选出芯片中的差异基因,利用筛选出的基因进行功能富集分析,构建蛋白质互相作用网络图,探究泌乳素瘤发生发展的相关机制。结果 筛选出泌乳素瘤与正常垂体组织之间的差异基因279个,其中表达上调基因23个,表达下调基因256个。基因本体(gene ontology,GO)富集分析显示,差异基因主要分布于细胞外基质、内质网内腔等细胞结构,主要参与生殖、感官及视觉系统的发育、受体配体活动、生长因子活性、跨膜受体蛋白酪氨酸激酶活性等分子功能。通过对京都基因与基因百科全书(kyoto encyclopedia of genes and genonmes,KEGG)通路富集分析发现,差异基因主要在TGF-β信号通路、干细胞多能性调控信号通路、视黄醇新陈代谢通路等通路上富集。通过蛋白质互相作用网络,进一步挖掘得到可能参与垂体瘤发生发展的20个关键基因,包括SOX2、PAX6、SOX9、POMC等。结论本研究筛选得到SOX2、PAX6、SOX9、POMC等垂体泌乳素瘤发生的关键差异基因,并发现其主要通过影响TGF-β信号通路,从而影响泌乳素瘤的发生发展,可为后续泌乳素瘤的基础研究和治疗提供新靶点。     

基于基因表达谱筛查小鼠脑缺血再灌注后星形胶质细胞活化相关基因

目的 利用全基因组表达谱芯片筛查脑缺血再灌注后星形胶质细胞中起关键作用的信号通路,并对 星形胶质细胞活化过程中可能参与的关键基因进行分析。 方法 从美国基因表达汇编（gene e xpression o mnibus,GEO）数据库中选取小鼠星形胶质细胞基因表 达谱芯片（Affymetrix Gene Chip Mouse Genome 430 2.0 Arrays）数据8张。小鼠分为脑缺血再灌注组 （脑缺血1 h再灌注24 h）和假手术组,每组4只。筛选差异表达基因,并进行信号通路分析,筛选核心 信号通路,分析具有重要调控作用的基因。 结果 共筛查出1966个在脑缺血再灌注后星形胶质细胞中差异表达的基因,其中有1210个基因表达 上调,756个基因表达下调。信号通路分析得到154个差异表达的信号通路。进一步分析发现,在脑 缺血再灌注后参与星形胶质细胞活化的关键信号通路主要有丝裂原活化蛋白激酶（mitogen-activated protein kinases,MAPK）信号通路、凋亡（apoptosis）、细胞周期（cell cycle）和p53信号通路。对信号通路 中差异基因的分析发现,Flnc 和Ccnd1基因在脑缺血再灌注后星形胶质细胞活化过程中具有重要作用。 结论 脑缺血再灌注后星形胶质细胞中有大量基因表达异常。通过对差异基因的分析,筛选出脑缺 血再灌注损伤过程中潜在的发挥重要调控作用的新靶点。     

Toll样受体4相关信号通路在缺血性脑卒中炎症损伤中的研究进展

缺血性脑卒中的发生发展机制目前尚未完全阐明, 但炎症损伤已被公认在其中起着重要作用。Toll样受体4(TLR4)相关信号通路的激活可促进缺血性脑卒中后相关促炎细胞因子和抗炎细胞因子的表达, 从而影响着缺血性脑卒中的进展及预后。笔者现围绕TLR4相关信号通路在缺血性脑卒中炎症损伤中的研究进展进行综述, 以期为可用于缺血性脑卒中治疗的靶向TLR4抗炎药物的研发提供一些思路及借鉴。     

Potential therapeutic drugs for ischemic stroke based on bioinformatics analysis

Abstract

Ischemic stroke (IS) is a complex disease affected by various environmental factors, genetic factors and their interactions. Because genetic factors occupy an irreplaceable place in the pathogenesis of IS, the identification of genetic factors has become one of the hot spots in the current research. In the present study, we aimed to identify possible gene targets and relevant drug molecules in the pathogenesis of IS. Microarray dataset of GSE16561 was downloaded from Gene Expression Omnibus database. The differentially expressed genes (DEGs) between IS group and control group were obtained using limma package in R. Ground-Operation Simulation package in R language was used to cluster DEGs according to their biological process, cellular components and molecular functions with respect to the GO annotation. The DEGs were analyzed by Search Tool for the Retrieval of Interacting Genes online database and Cytoscape software to predict their interaction relationship. Finally, the DEGs were submitted to DGIdb dataset and related drug molecules were retrieved. 20 DEGs were identified from IS group including 1 downregulated and 19 upregulated genes. The function enrichment analysis revealed that the DEGs were enriched in three GO terms, mainly including inflammatory response, positive regulation of protein kinase activity and innate immune response. Finally, 10 drug molecules were identified from the DEGs. Our study identified some potential biological targets and drug molecules for the treatment of IS.     

Effects of gender on gene expression in the blood of ischemic stroke patients

This study examined the effects of gender on RNA expression after ischemic stroke (IS). RNA obtained from blood of IS patients (n=51; 153 samples at ⩽3, 5, and 24 hours) and from matched controls (n=52) were processed on Affymetrix microarrays. Analyses of covariance for stroke versus control samples were performed separately for both genders and the regulated genes for females compared with males. In all, 242, 227, and 338 male-specific genes were regulated at ⩽3, 5, and 24 hours after IS, respectively, of which 59 were regulated at all time points. Overall, 774, 3,437, and 571 female-specific stroke genes were regulated at ⩽3, 5, and 24 hours, respectively, of which 152 were regulated at all time points. Male-specific stroke genes were associated with integrin, integrin-liked kinase, actin, tight junction, Wnt/β-catenin, RhoA, fibroblast growth factors (FGF), granzyme, and tumor necrosis factor receptor (TNFR)2 signaling. Female-specific stroke genes were associated with p53, high-mobility group box-1, hypoxia inducible factor (HIF)1α, interleukin (IL)1, IL6, IL12, IL18, acute-phase response, T-helper, macrophage, and estrogen signaling. Cell death signaling was overrepresented in both genders, although the molecules and pathways differed. Gender affects gene expression in the blood of IS patients, which likely implies gender differences in immune, inflammatory, and cell death responses to stroke.     

MiR-107 Aggravates Oxygen-Glucose Deprivation/Reoxygenation (OGD/R)-Induced Injury Through Inactivating PI3K-AKT Signalling Pathway by Targeting FGF9/FGF12 in PC12 Cells

ObjectivesThe aberrant expression of miR-107 has been confirmed in some neurological diseases, including ischemic stroke (IS). However, the function of miR-107 and underlying mechanisms are ambiguous.Materials and methodsOxygen-Glucose Deprivation/Reoxygenation (OGD/R)-induced PC12 cells were used to mimic IS condition. MiR-107 expression and differentially expressed genes (DEGs) responding to IS were analyzed by GSE97532 and GSE61616 datasets, respectively. The target genes of miR-107 were predicted by TargetScan and confirmed by dual-luciferase reporter assay. Cell counting kit-8 and apoptosis assays were conducted to explore the role of miR-107 in biological behaviors of OGD/R-induced PC12 cells.ResultsBioinformatics analysis revealed that miR-107 expression was elevated in rats with middle cerebral artery occlusion (MCAO), which was confirmed in OGD/R-treated PC12 cells. Notably, miR-107 strongly inhibited the proliferation of OGD/R-treated PC12 cells. As most DEGs were enriched in PI3K-AKT signaling pathway, which was critical for IS, DEGs in this pathway was compared with the down-regulated genes and the predicted genes to obtain potential target genes of miR-107, and ultimately fibroblast growth factor (FGF)9 and FGF12 stood out. The experiments demonstrated that miR-107 inhibited viability and promoted apoptosis of OGD/R-treated PC12 cells by down-regulating FGF9/FGF12 level. Mechanically, for the first time, we clarified the mechanism via which miR-107 inactivated PI3K-AKT signaling pathway by targeting FGF9/FGF12.ConclusionsWe summarized that miR-107 aggravates OGD/R-induced injury through inactivating PI3K-AKT signaling pathway via targeting FGF9/FGF12. Therefore, our study elucidates the neurotoxicity of miR-107 in IS development and provides a new promising therapy strategy for IS.     

Bioinformatics Analysis of Microarray Profiling Identifies That the miR-203-3p Target Ppp2ca Aggravates Seizure Activity in Mice

This study aimed to identify key genes (microRNA and messenger RNA (mRNA)) and associated signaling-regulated pathways in a drug-induced epilepsy model in mice by microarray profiling. The related microarray dataset of seizures was obtained from the NCBI Gene Expression Omnibus database (GEO), and differentially expressed genes (DEGs) between two control samples or multi-treated samples and samples were analyzed using the statistical software R. To identify the expected function of DEGs, Gene Set Enrichment Analysis (GSEA) was utilized to conduct Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The interaction relationship between microRNAs (miRNAs) and mRNAs in normal and epilepsy mouse models was identified using Cytoscape software. TargetScan7.1 was applied to determine the binding sites of DEGs. The dual-luciferase assay was used to verify the target relationship between miRNA and mRNA. Four miRNAs were identified as differentially expressed genes in both 24-h and 28-day status epilepticus (SE)-treated samples. Ppp2ca expression in the mitogen-activated protein kinase (MAPK) signaling pathway was downregulated in the pilocarpine-induced SE mouse model. The expression of Ppp2ca was also downregulated in the kinase-induced SE model group compared with that in the untreated group and MAP kinase (MEK) inhibitor-treated group of mice. KEGG pathway analysis indicated that the MAPK signaling pathway was upregulated in the kinase-induced SE model group compared with that in both the untreated group and the MEK inhibitor-treated group of mice. miR-203 had a targeted relationship with Ppp2ca in both humans and mice. The miR-203-3p target Ppp2ca aggravates the seizures of the SE model in mice.     

颅内动脉瘤破裂关键基因的生信分析

目的 探讨颅内动脉瘤破裂的关键基因.方法 采用生信分析方法,GEO数据库下载数据集GSE13353、GSE15629、GSE54083,R语言筛选差异表达基因(DEG),WGCNA算法分析动脉瘤破裂关键基因,采用GO和KEGG分析关键基因的生物学功能,GSEA软件进行基因富集分析.应用GSE122897数据集进行验证....     

Identification of key genes involved in post-traumatic stress disorder: Evidence from bioinformatics analysis

BACKGROUNDPost-traumatic stress disorder (PTSD) is a serious stress-related disorder.AIMTo identify the key genes and pathways to uncover the potential mechanisms of PTSD using bioinformatics methods.METHODSGene expression profiles were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified by using GEO2R. Gene functional annotation and pathway enrichment were then conducted. The gene-pathway network was constructed with Cytoscape software. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was applied for validation, and text mining by Coremine Medical was used to confirm the connections among genes and pathways.RESULTSWe identified 973 DEGs including 358 upregulated genes and 615 downregulated genes in PTSD. A group of centrality hub genes and significantly enriched pathways (MAPK, Ras, and ErbB signaling pathways) were identified by using gene functional assignment and enrichment analyses. Six genes (KRAS, EGFR, NFKB1, FGF12, PRKCA, and RAF1) were selected to validate using qRT-PCR. The results of text mining further confirmed the correlation among hub genes and the enriched pathways. It indicated that these altered genes displayed functional roles in PTSD via these pathways, which might serve as key signatures in the pathogenesis of PTSD.CONCLUSIONThe current study identified a panel of candidate genes and important pathways, which might help us deepen our understanding of the underlying mechanism of PTSD at the molecular level. However, further studies are warranted to discover the critical regulatory mechanism of these genes via relevant pathways in PTSD.     

Identification of microRNAs and messenger RNAs involved in human umbilical cord mesenchymal stem cell treatment of ischemic cerebral infarction using integrated bioinformatics analysis

In recent years, a large number of differentially expressed genes have been identified in human umbilical cord mesenchymal stem cell(hUMSC) transplants for the treatment of ischemic cerebral infarction. These genes are involved in various biochemical processes, but the role of microRNAs(miRNAs) in this process is still unclear. From the Gene Expression Omnibus(GEO) database, we downloaded two microarray datasets for GSE78731(messenger RNA(mRNA) profile) and GSE97532(miRNA profile). The differentially expressed genes screened were compared between the hUMSC group and the middle cerebral artery occlusion group. Gene ontology enrichment and pathway enrichment analyses were subsequently conducted using the online Database for Annotation, Visualization, and Integrated Discovery. Identified genes were applied to perform weighted gene co-suppression analyses, to establish a weighted co-expression network model. Furthermore, the protein-protein interaction network for differentially expressed genes from turquoise modules was built using Cytoscape(version 3.40) and the most highly correlated subnetwork was extracted from the protein-protein interaction network using the MCODE plugin. The predicted target genes for differentially expressed miRNAs were also identified using the online database starBase v3.0. A total of 3698 differentially expressed genes were identified. Gene ontology analysis demonstrated that differentially expressed genes that are related to hUMSC treatment of ischemic cerebral infarction are involved in endocytosis and inflammatory responses. We identified 12 differentially expressed miRNAs in middle cerebral artery occlusion rats after hUMSC treatment, and these differentially expressed miRNAs were mainly involved in signaling in inflammatory pathways, such as in the regulation of neutrophil migration. In conclusion, we have identified a number of differentially expressed genes and differentially expressed mRNAs, miRNA-mRNAs, and signaling pathways involved in the hUMSC treatment of ischemic cerebral infarction. Bioinformatics and interaction analyses can provide novel clues for further research into hUMSC treatment of ischemic cerebral infarction.