基于IRES序列的多基因共表达载体构建

目的:构建一个IRES序列介导的多基因共表达载体,实现两个目的基因和筛选标记基因共用一个启动子高效表达,提高多基因稳定共表达细胞株的筛选效率。方法:以实验室前期构建的载体pLV-MCS-Puro为骨架,设计并全基因合成双基因克隆表达元件,连接到骨架载体,构建多基因共表达载体pLV-2MCS-Puro,以DsRed2和EGFP荧光蛋白基因验证该载体用于多基因稳定共表达细胞株筛选的效率。结果:成功构建了pLV-2MCS-Puro载体以及DsRed2和EGFP共表达重组质粒pLV-DsRed2-EGFP-Puro。瞬时转染实验证明该载体能介导多基因共表达。抗性筛选获得了MDCK和HeLa两种细胞的多基因稳定共表达细胞池。细胞池涂片荧光显微镜观察和计数表明抗性细胞池DsRed2和EGFP双阳率接近100%。基因组和转录水平PCR及蛋白质免疫印迹实验表明,DsRed2和EGFP稳定整合到抗性细胞基因组,并且两种蛋白质表达水平较为一致。结论:成功构建了多基因共表达载体pLV-2MCS-Puro,实现了两个目的基因和抗性基因串联共表达,并且具有高效的多基因稳定共表达细胞株筛选效率。该载体在研究蛋白质相互作用及工程细胞构建等方面具有一定的应用前景。     

伴HBV感染性肝癌调控枢纽基因筛选与初步鉴定

慢性乙型肝炎病毒(Hepatitis B virus,HBV)感染引起的原发性肝癌涉及多种基因、转录本和蛋白质的相互作用及调控。从单个基因的角度来看,某个基因的表达量的改变只能对肝癌发生发展的局部作出解释而无法从整体行为进行深入和全面的探索,无法满足高度复杂性的调控研究需要。筛选乙肝相关性肝癌的基因芯片数据获取差异表达基因后,应用加权基因共表达网络分析算法构建基因共表达网络,识别与肝癌发生相关的模块,利用可视化筛选枢纽基因,并针对枢纽基因进行基因本体富集分析和初步验证。富集分析和文献挖掘一致发现,某些枢纽基因确实与多种癌症的发生与发展存在显著的关联。权重基因共表达网络分析方法被证明是一个高效的系统生物学方法,应用该方法发现了新的HBV相关性肝癌枢纽基因。经实验验证,发现枢纽基因SHARPIN促进细胞迁移。该研究对肝癌发生的调控机制以及发现HBV慢性感染导致肝癌的新型诊断标志物和(或)药物作用靶点提供了新的视野。     

基于蛋白质互作网络挖掘自闭症谱系障碍的功能模块与核心基因

自闭症谱系障碍(autism spectrum disorder, ASD)是一种具有高遗传性、临床异质性和生物复杂性的神经行为障碍类疾病。为挖掘ASD发生发展过程中的功能模块与核心基因,本文从自闭症谱系障碍疾病数据库获取ASD相关基因;利用STRING数据库构建ASD相关基因的蛋白质互作网络;通过MCODE算法对蛋白质互作网络进行模块分析并筛选核心基因;最后对各功能模块进行KEGG通路分析,根据富集到的通路类别评估功能模块之间的相互作用。结果显示, 3个疾病基因数据库筛选出182个共有基因,构建的蛋白质互作网络包含171个节点和1 041条边,其中NRXN1、GRIN2B、GRIN2A、DLG4、NLGN3、MECP2、CNTNAP2、BDNF、NLGN4X、FMR1等23个基因具有较高的连通度(degree)。从蛋白质互作网络中分析得到5个功能模块,包括68个核心基因。KEGG富集分析发现功能模块参与多个生物学通路,包括细胞黏附分子、钙离子通路、神经活性的配体-受体相互作用、多巴胺能神经突触等。分析结果提示,挖掘的ASD功能模块和核心基因大多集中在神经元活动、信号分子和信号传导等,且各模块相互作用共同影响ASD的发生发展。     

合浦珠母贝海洋酸化和温度胁迫下基因共表达网络分析

利用共表达网络分析法(WGCNA)筛选出合浦珠母贝外套膜在高温胁迫下影响显著的1个响应基因模块和海水酸化胁迫下的5个响应基因模块,并对模块中包含的基因进行功能富集;分析贝壳蛋白与模块其他基因的关联性,构建矿化基因的应激网络。高温显著响应模块基因富集结果显示细胞骨架蛋白发生了显著的变化,如ARHGEF4、SAP和myosin。酸化应答模块共5个,响应的基因包括呼吸相关的血红素A合成酶、芳香族氨基酸代谢相关的酶、钙粘蛋白和血小板反应蛋白等。另外,高温应答模块聚集了最多矿化基因,包括MSI、Tyrosinase、Fibronectin-like protein等重要的矿化基因,说明这些矿化基因对温度较为敏感。构建基因共表达网络的结果显示钙离子结合蛋白、细胞骨架蛋白、蛋白质糖基化相关的基因关系密切。     

甜高粱与高粱的多组学比较分析

高粱(Sorghum bicolor)是禾本科(Poaceae)一年生草本植物,也是研究C₄植物的模式生物。甜高粱相对于普通高粱有其独特的糖代谢积累机理,发掘其糖合成、运输和积累相关的调控基因和代谢途径,有助于糖分和碳源分配的遗传改良设计,又可为其他重要的C₄作物提供参考。本研究结合基因组、转录组和代谢组等多组学数据,从蔗糖转运、蔗糖合成相关基因家族以及基因共表达网络等多个角度对甜高粱及高粱的组学数据进行比较分析。结果如下：(1)构建了甜高粱及普通高粱品种的泛基因组,大小为908 Mb,比单个参考基因组增加约24.6%,进行了核心基因、非核心基因以及基因组存在/缺失变异区域相关基因的分类统计及功能富集分析,对多样本多个时期的RNA-seq数据进行差异表达分析,构建基因共表达网络,对基因集进行筛选验证,进一步筛选得到了糖分相关基因;(2)利用12份不同时期的甜高粱(Rio)、高粱(BTx406)以及两者子代(R9188)的RNA-seq数据进行差异表达分析与基因共表达分析,通过与对应时期蔗糖浓度变化数据进行相关性分析,得到与蔗糖代谢相关的基因集...     

生长素信号转导途径与植物胁迫反应相互作用的证据(英)

生长素影响植物多种生理过程 ,有报道显示生长素可能影响植物对逆境胁迫的反应。我们利用cDNA阵列技术鉴定拟南芥 (Arabidopsisthaliana (L .)Heynh .)的生长素应答基因 ,发现多个胁迫应答基因受生长素抑制 ,包括ArabidopsishomologofMEKkinase1(ATMEKK1) ,RelA/SpoThomolog 3(At_RSH3) ,Catalase 1(Cat1)和Ferritin 1(Fer1) ,说明生长素可调节胁迫应答基因的表达。此外 ,我们还证明吲哚乙酸 (IAA)合成途径中的腈水解酶基因nitrilase 1(NIT1)和nitrilase 2 (NIT2 )受盐胁迫诱导 ,提示在逆境条件下IAA的合成可能随之增加。我们利用生长素不敏感突变体研究生长素与逆境反应相互作用的信号转导 ,发现胁迫应答基因在野生型和生长素不敏感突变体auxinresistant2 (axr2 )中可被盐胁迫诱导 ,而在auxinresistant1_3(axr1_3)中则不被诱导 ,说明生长素与逆境胁迫反应的相互作用可能发生在泛素途径。     

生长素信号转导途径与植物胁迫反应相互作用的证据

生长素影响植物多种生理过程,有报道显示生长素可能影响植物对逆境胁迫的反应.我们利用cDNA阵列技术鉴定拟南芥(Arabidopsis thaliana (L.) Heynh.)的生长素应答基因,发现多个胁迫应答基因受生长素抑制,包括Arabidopsis homolog of MEK kinase1 (ATMEKK1),RelA/SpoT homolog 3 (At-RSH3),Catalase 1 (Cat1) 和Ferritin 1 (Fer1),说明生长素可调节胁迫应答基因的表达.此外,我们还证明吲哚乙酸(IAA)合成途径中的腈水解酶基因nitrilase 1 (NIT1) 和nitrilase 2 (NIT2) 受盐胁迫诱导,提示在逆境条件下IAA的合成可能随之增加.我们利用生长素不敏感突变体研究生长素与逆境反应相互作用的信号转导,发现胁迫应答基因在野生型和生长素不敏感突变体auxin resistant 2 (axr2) 中可被盐胁迫诱导,而在auxin resistant 1-3 (axr1-3)中则不被诱导,说明生长素与逆境胁迫反应的相互作用可能发生在泛素途径.     

生长素信号转导途径与植物胁迫反应相互作用的证据

生长素影响植物多种生理过程,有报道显示生长素可能影响植物对逆境胁迫的反应。我们利用cDNA阵列技术鉴定拟南芥（Arabiopsis thaliana (L.)Heynh.）的生长素应答基因,发现多个胁迫应答基因受生长素抑制,包括Arabidopsis homolog of MEK kinasel（ATMEKK1）,RelA/SpoT homolog 3(At-RSH3),Catalase 1(Cat1)和Ferriitn 1(Fer1)。说明生长素可调节胁迫应答基因的表达,此外,我们还证明吲哚乙酸（LAA）合成途径中的腈水解酶基因nitrilase 1(NIT1)和nitrilae 2（NIT2）受盐胁迫诱导,提示在逆境条件下1AA的合成可能随之增加,我们利用生长素不敏感突变体研究生长素与逆境反应相互作用的信号转导,发现胁迫应答基因在野生型和生长素不敏感突变体auxin resistant 2（axr2）中可被盐胁迫诱导,而在auxin resitant1-3（axl-3)中则不被诱导,说明生长素与逆境胁迫反应的相互作用可能发生在泛素途径。     

龋病感染牙髓相关基因的筛选与生物信息学分析

目的:综合应用生物信息学技术,从分子水平对龋坏牙髓与正常牙髓的差异基因进行筛选分析,初步探讨其作用机制。方法:从GEO基因表达数据库中下载龋坏牙髓相关芯片数据集,采用MORPHEUS在线筛选差异表达基因,结合DAIVID、STRING等在线分析工具对差异表达基因进行GO功能富集分析及KEGG通路分析,后用Cytoscape软件构建蛋白质相互作用网络。结果:共筛选出375个差异表达基因,其中表达上调253个、下调122个,主要涉及免疫应答、炎症反应、细胞因子应答和生物矿化组织发育等生物过程,以及抗原加工提呈和NF-κB信号等生物通路。通过蛋白质互作网络构建分析发现,MMP9、IL-8、PTPRC、CXCR4等10个基因处于核心节点位置。结论:借助生物信息学方法能得到可靠的相关差异基因信息,能够有效指导进一步的研究。得到的差异基因可以作为龋病诊断的指示因子和机制研究的候选基因。     

MTA1调控基因网络在胃腺癌中的作用

本研究利用TCGA数据库提供的胃腺癌数据集,结合cBio Cancer Genomics Portal数据库和GeneCards数据库筛选出与肿瘤转移相关基因(MTA1)存在共表达和相互作用的83个基因。利用DAVID、STRING等分析软件发现这些基因主要富集在细胞周期、WNT通路、P53信号通路、胃癌和DNA修复等癌症相关通路上,并进一步利用String数据库和Cytoscape筛选出与MTA1紧密联系基因,同时结合大样本临床数据的生存曲线分析认为这些基因与胃腺癌生存率密切相关,MTA1可能与这些基因相互作用调控细胞增殖,影响胃腺癌细胞侵袭转移。通过研究胃腺癌组织中MTA1调控的基因网络,有助于揭示胃腺癌发病机制。找到有效生物学标记物组合作为胃癌的预测指标,可以为相关药物研发及临床诊断治疗提供新的思路和依据。     

Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis

Osteoarthritis (OA) significantly influences the quality life of people around the world. It is urgent to find an effective way to understand the genetic etiology of OA. We used weighted gene coexpression network analysis (WGCNA) to explore the key genes involved in the subchondral bone pathological process of OA. Fifty gene expression profiles of GSE51588 were downloaded from the Gene Expression Omnibus database. The OA‐associated genes and gene ontologies were acquired from JuniorDoc. Weighted gene coexpression network analysis was used to find disease‐related networks based on 21756 gene expression correlation coefficients, hub‐genes with the highest connectivity in each module were selected, and the correlation between module eigengene and clinical traits was calculated. The genes in the traits‐related gene coexpression modules were subject to functional annotation and pathway enrichment analysis using ClusterProfiler. A total of 73 gene modules were identified, of which, 12 modules were found with high connectivity with clinical traits. Five modules were found with enriched OA‐associated genes. Moreover, 310 OA‐associated genes were found, and 34 of them were among hub‐genes in each module. Consequently, enrichment results indicated some key metabolic pathways, such as extracellular matrix (ECM)‐receptor interaction (hsa04512), focal adhesion (hsa04510), the phosphatidylinositol 3'‐kinase (PI3K)‐Akt signaling pathway (PI3K‐AKT) (hsa04151), transforming growth factor beta pathway, and Wnt pathway. We intended to identify some core genes, collagen (COL)6A3, COL6A1, ITGA11, BAMBI, and HCK, which could influence downstream signaling pathways once they were activated. In this study, we identified important genes within key coexpression modules, which associate with a pathological process of subchondral bone in OA. Functional analysis results could provide important information to understand the mechanism of OA.     

Gene coexpression analysis offers important modules and pathway of human lung adenocarcinomas

Lung adenocarcinomas injured greatly on the people worldwide. Although clinic experiments and gene profiling analyses had been well performed, to our knowledge, systemic coexpression analysis of human genes for this cancer is still limited to date. Here, using the published data GSE75037, we built the coexpression modules of genes by Weighted Gene Co-Expression Network Analysis (WGCNA), and investigated function and protein–protein interaction network of coexpression genes by Database for Annotation, visualization, and Integrated Discovery (DAVID) and String database, respectively. First, 11 coexpression modules were conducted for 5,000 genes in the 83 samples recently. Number of genes for each module ranged from 90 to 1,260, with the mean of 454. Second, interaction relationships of hub-genes between pairwise modules showed great differences, suggesting relatively high scale independence of the modules. Third, functional enrichment of the coexpression modules showed great differences. We found that genes in modules 8 significantly enriched in the biological process and/or pathways of cell adhesion, extracellular matrix (ECM)–receptor interaction, focal adhesion, and PI3K-Akt signaling pathway, and so forth. It was inferred as the key module underlying lung adenocarcinomas. Furthermore, PPI analysis revealed that the genes COL1A1, COL1A2, COL3A1, CTGF, and BGN owned the largest number of adjacency genes, unveiling that they may functioned importantly during the occurrence of lung adenocarcinomas. To summary, genes involved in cell adhesion, ECM–receptor interaction, focal adhesion, and PI3K-Akt signaling pathway play crucial roles in human lung adenocarcinomas.     

Identification of gene coexpression modules,hub genes,and pathways related to spinal cord injury using integrated bioinformatics methods

Spinal cord injury (SCI) is characterized by dramatic neurons loss and axonal regeneration suppression. The underlying mechanism associated with SCI-induced immune suppression is still unclear. Weighted gene coexpression network analysis (WGCNA) is now widely applied for the identification of the coexpressed modules, hub genes, and pathways associated with clinic traits of diseases. We performed this study to identify hub genes associated with SCI development. Gene Expression Omnibus (GEO) data sets GSE45006 and GSE20907 were downloaded and the significant correlativity and connectivity between them were detected using WGCNA. Three significant consensus modules, including 567 eigengenes, were identified from the master GSE45006 data following the preconditions of approximate scale-free topology for WGCNA. Further bioinformatics analysis showed these eigengenes were involved in inflammatory and immune responses in SCI. Three hub genes Rac2, Itgb2, and Tyrobp and one pathway “natural killer cell-mediated cytotoxicity” were identified following short time-series expression miner, protein-protein interaction network, and functional enrichment analysis. Gradually upregulated expression patterns of Rac2, Itgb2, and Tyrobp genes at 0, 3, 7, and 14 days after SCI were confirmed based on GSE45006 and GSE20907 data set. Finally, we found that Rac2, Itgb2, and Tyrobp genes might take crucial roles in SCI development through the “natural killer cell–mediated cytotoxicity” pathway.     

Key genes and functional coexpression modules involved in the pathogenesis of systemic lupus erythematosus

We performed a systematic review of genome‐wide gene expression datasets to identify key genes and functional modules involved in the pathogenesis of systemic lupus erythematosus (SLE) at a systems level. Genome‐wide gene expression datasets involving SLE patients were searched in Gene Expression Omnibus and ArrayExpress databases. Robust rank aggregation (RRA) analysis was used to integrate those public datasets and identify key genes associated with SLE. The weighted gene coexpression network analysis (WGCNA) was adapted to identify functional modules involved in SLE pathogenesis, and the gene ontology enrichment analysis was utilized to explore their functions. The aberrant expressions of several randomly selected key genes were further validated in SLE patients through quantitative real‐time polymerase chain reaction. Fifteen genome‐wide gene expression datasets were finally included, which involved a total of 1,778 SLE patients and 408 healthy controls. A large number of significantly upregulated or downregulated genes were identified through RRA analysis, and some of those genes were novel SLE gene signatures and their molecular roles in etiology of SLE remained vague. WGCNA further successfully identified six main functional modules involved in the pathogenesis of SLE. The most important functional module involved in SLE included 182 genes and mainly enriched in biological processes, including defense response to virus, interferon signaling pathway, and cytokine‐mediated signaling pathway. This study identifies a number of key genes and functional coexpression modules involved in SLE, which provides deepening insights into the molecular mechanism of SLE at a systems level and also provides some promising therapeutic targets.     

基于转录组的肝豆状核变性调控网络的构建和分析

本研究旨在利用生物信息学方法构建经铜诱导的ATP7B基因敲除HepG2细胞系的转录调控网络。探讨关键转录因子在肝豆状核变性发生、发展中的潜在作用机制。收集公共基因表达数据库(gene expression omnibus, GEO)中包含野生型、ATP7B基因敲除型、铜诱导的野生型和铜诱导的ATP7B基因敲除型HepG2细胞系数据。筛选由铜诱导产生的差异表达基因(differentially expressed genes,DEGs)后进行基因本体论(gene ontology,GO)、京都基因和基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)富集分析。基于蛋白相互作用网络,识别疾病关键基因和功能模块,并对关键功能模块中的基因进行富集分析。最后,构建转录调控网络,筛选核心转录因子。共筛选出1 034个差异表达基因,其中上调525个,下调509个。上、下调关键功能模块分别包括了3785个和3931个基因。关键功能模块中的基因主要定位于细胞-基质连接、染色体、剪接复合体、核糖体等区域,共同参与了mRNA加工、组蛋白修饰、RNA剪切...     

Coexpression network analysis linked H2AFJ to chemoradiation resistance in colorectal cancer

Neoadjuvant chemoradiotherapy (CRT) resistance is a complex phenomenon and it remains a major problem for patients with a priori resistant tumor. Therefore, there is a strong need to investigate molecular biomarkers which may guide for treatment decision-making. In our study, weighted gene coexpression network analysis was applied to identify CRT-resistance hub modules in 12 colorectal cancer (CRC) cell lines with different CRT sensitivities from GSE20298 data set. The green module and purple module had the highest correlations with CRT resistance. Gene ontology enrichment analysis indicated that the function of these two modules focused on interferon-mediated signaling pathway, immune response, chromatin modulation, Rho GTPases activities, and regulation of apoptotic process. Then, 15 hub genes in both the coexpression and protein-protein interaction networks were selected. Among these hub genes, higher H2A histone family member J (H2AFJ) expression was independently validated in patient cohorts from two testing data sets of GSE46862 and GSE68204 to be related to CRT resistance. The receiver operating characteristic curve showed that H2AFJ could efficiently distinguish CRT-resistance cases from CRT-sensitive cases in another two testing data sets. Furthermore, meta-analysis of 12 Gene Expression Omnibus–sourced data sets showed that H2AFJ messenger RNA levels were significantly higher in CRC tissues than in normal colon tissues. High H2AFJ expression was correlated with a significant worse event- and relapse-free survival by analyzing the data from the R2: Genomics Analysis and Visualization Platform. Gene set enrichment analysis determined that the mechanism of H2AFJ-mediated CRT resistance might involve the ERK5 (MAPK7), human immunodeficiency virus Nef (HIV Nef), and inflammatory pathways. This study is the first, to the best of our knowledge, to implicate and verify H2AFJ as an effective new marker for CRT response prediction.     

Gene coexpression networks analysis of sickle stroke risk

Stroke is one of the most destructive complications of sickle cell disease (SCD), and SCD is also the most common cause of childhood stroke. Sickle cell stroke is complex and has a genetic endothelial basis. Here, we further investigated this genetic basis using weighted gene coexpression network analysis. This systems biology approach revealed the correlation between coexpressed gene modules and sickle stroke risk. The pink module was significantly correlated with stroke risk and genes in this module were mainly related to GO:0044877 (protein-containing complex binding). In addition hub genes were identified through protein-protein interaction enrichment analysis, including CXCR7, VCAM1, CD44, BMP2, SMAD3, BCL2L1, ITPR2, ITPR3, etc. These hub genes were significantly enriched for three Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways including “gastric acid secretion,” “pathways in cancer,” and “TGF- β signaling pathway.” Altogether, our results based on this innovative method provided some novel understanding of the pathology of sickle cell stroke. Hub genes identified in this study could be potential targets for screening and prevention of stroke risk in SCD children.