埃及伊蚊不同组织的基因共表达模式分析

【目的】利用权重基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)探索埃及伊蚊Aedes aegypti不同组织基因共表达模式。【方法】从NCBI SRA数据库中选择埃及伊蚊不同组织的转录组数据中具代表性的9种组织(雌雄成蚊的触角和脑,雌蚊的喙、下颚须和卵巢,雄成蚊的前足、中足、后足和腹部末端)的双端测序数据;经过缺失值移除以及方差计算后,筛选出方差最大的5 000个基因,利用R软件中WGCNA包建立埃及伊蚊成蚊不同组织的基因共表达网络并划分模块;然后利用clusterProfiler包对组织特异性模块内的基因进行GO(Gene Ontology)和KEGG(Kyoto Encyclopediaof Genes and Genomes)富集分析,并用Cytoscape软件中的CytoHubba插件筛选共表达模块内的hub基因。【结果】从埃及伊蚊成蚊不同组织中共鉴定出11个基因共表达模块,在雌蚊触角、喙、卵巢、下颚须以及雄蚊脑、腹部末端组织中各鉴定出1个特异性表达模块,雄蚊前足、中足和后足组织中无特异性表达模块。6个组织特异性表达模块内基因功能注释到组织生物学功能;其中,雌蚊触角特异性green模块内基因具有气味结合和嗅觉受体活性等功能;雌蚊喙特异性purple模块内基因具有丝氨酸型肽链内切酶活性和丝氨酸水解酶活性等功能;雄蚊脑特异性blue模块内基因在生物学过程调节、信号转导和神经系统过程等生物学过程中发挥主要作用。利用CytoHubba进一步鉴定出所选组织特异性共表达模块中具有高连通性的hub基因,包括AAEL010426, AAEL002896, AAEL002600, AAEL000961, AAEL007784和AAEL006429。【结论】本研究依据埃及伊蚊不同组织转录组数据,利用WGCNA方法发现了许多重要的基因共表达模块。本研究的结果为蚊虫基因共表达模式分析提供新思路和方法基础,对探究蚊虫不同组织特有的基因资源信息以及功能基因生物信息学研究有参考价值。     

Rat Hepatocytes Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Liver Regeneration after Partial Hepatectomy

The recovery of liver mass is mainly mediated by proliferation of hepatocytes after 2/3 partial hepatectomy (PH) in rats. Studying the gene expression profiles of hepatocytes after 2/3 PH will be helpful to investigate the molecular mechanisms of liver regeneration (LR). We report here the first application of weighted gene co-expression network analysis (WGCNA) to analyze the biological implications of gene expression changes associated with LR. WGCNA identifies 12 specific gene modules and some hub genes from hepatocytes genome-scale microarray data in rat LR. The results suggest that upregulated MCM5 may promote hepatocytes proliferation during LR; BCL3 may play an important role by activating or inhibiting NF-kB pathway; MAPK9 may play a permissible role in DNA replication by p38 MAPK inactivation in hepatocytes proliferation stage. Thus, WGCNA can provide novel insight into understanding the molecular mechanisms of LR.     

基于SNP共表达网络肝癌分子分型及预后分析

基于SNP突变数据与mRNA表达谱关联分析,构建一种肝癌分子分型方法并对比不同分型预后的差异,并对不同分型肝癌的发生发展机制进一步研究。首先通过TCGA数据库收集359例肝细胞癌患者的SNP突变数据和mRNA表达数据,采用Wilcoxon秩和检验,筛选突变后差异表达基因,并通过生物信息学工具String和Cytoscape 构建差异表达基因的蛋白互作网络,筛选连接度最高的10个Hub基因。利用Consensus Cluster Plus软件包,基于Hub基因mRNA表达水平构建NMF分子分型模型,再结合生存数据评估各分型患者的预后。最后利用加权基因共表达网络分析(WGCNA),识别与肝癌分子分型相关的模块,并针对关键模块的基因进行通路富集,从而对不同分型肝癌的基因表达谱进行比较。结果:NMF模型将肝癌分为高危、低危2个分型,其中CDKN2A和FOXO1基因对分型贡献度高。生存分析显示低危组患者的生存情况显著优于高危组,高危组富集多个与肿瘤细胞侵蚀、转移、复发过程相关的信号通路,低危组则与细胞周期和胰液分泌相关。本研究在无先验性信息的前提下,基于突变后显著差异表达的Hub基因表达水平构建的肝癌分子分型对肝癌患者预后评估具有一定的指导意义,其中CDKN2A和FOXO1突变是肝癌患者的不良预后因素,针对二者的靶向药研发,可能为肝癌患者提供新的治疗策略。     

Biological and Molecular Characterization of Taiwanese Isolates of Cucumber mosaic virus Associated with Banana Mosaic Disease

Banana mosaic disease (BMD) caused by Cucumber mosaic virus (CMV) has become an important threat to the banana industry. We collected and characterized 10 CMV isolates associated with BMD in Taiwan and compared their biological characteristics and coat protein sequences. The isolates fell into four pathotypes on the basis of the symptoms they induce on banana, Nicotiana glutinosa and Vigna unguiculata (cowpea). Double-stranded RNA analysis revealed that the different pathotypes are not related to the presence of CMV satellite RNA. Phylogenetic analysis of worldwide CMV coat protein sequences revealed that among the currently known CMV subgroups IA, IB and II, subgroup IB is phylogenetically unresolved. Our CMV isolates form a new subgroup, IT, within subgroup I. In addition, we resolved another new CMV subgroup, IS, within subgroup I. The analysis also revealed that isolates within different subgroups can infect the banana.     

Identification and functional analysis of spermatogenesis-associated gene modules in azoospermia by weighted gene coexpression network analysis

Nonobstructive azoospermia (NOA) or testicular failure is the most severe form of male infertility. A variety of conditions, both acquired and congenital, can cause azoospermia. However, in a large number of azoospermia patients who are classified as idiopathic cases, the etiology remains poorly understand mainly due to the lack of knowledge of all the genetic causes and molecular mechanisms responsible for spermatogenesis failure. Identification of the key gene modules and pathways-related spermatogenesis failure might help to reveal the mechanisms of idiopathic azoospermia. Therefore, the expression patterns of spermatogenesis-associated genes in NOA were analyzed by weighted gene coexpression network analysis (WGCNA) based on two public microarray data sets (GSE45885 and GSE45887), which included 51 samples and 32,321 genes. We identified a module (turquoise) that was significantly related to the Johnsen score of the testicular samples. In addition, the results of function and pathway enrichment analyses based on the online bioinformatics database Metascape revealed that genes in the turquoise module were mainly related to the process of spermatogenesis and spermatid development. To further identify spermatogenesis-associated genes, a microarray data set (GSE926) of murine testis at different developmental time points was analyzed by WGCNA. The blue module in GSE926 was significantly related to the time of murine testis development. The overlap study and k-core analysis based on protein–protein interaction network revealed that spermatogenesis- and spermatid development–associated genes, including glyceraldehyde-3-phosphate dehydrogenase, ADAM metallopeptidase domain 2, transition protein 1, testis-specific serine kinase 2, transition protein 2, and germ cell-associated 1 (GSG1), were further identified in the selected modules. The expression profile of GSG1 in human testis was chosen for further study using immunochemistry staining. Taken together, these screened gene modules and pathways provided a more detailed genetic and molecular mechanism underlying spermatogenesis failure occurrence and holds promise as potential diagnosis biomarkers and therapeutic targets.     

基于转录组和WGCNA的甘薯花青素合成相关基因共表达网络的构建及核心基因的挖掘*

加权基因共表达网络分析(weighted gene co-expression network analysis, WGCNA)可通过聚类鉴定共表达的基因模块来研究生物学数据与相应性状之间的关系。甘薯[Ipomoea batatas (L.)Lam.]是世界上营养丰富的块根作物之一,紫薯是甘薯的一种特殊品种,因含有大量的花青素而具有较高的营养价值。因此,培育花青素含量高的优质紫薯一直以来都是甘薯育种家所追求的目标。利用传统的育种方法已经培育了一些紫薯品种,但其周期长、工作量大、见效慢,所以急需通过分子设计育种手段来培育高产优质的紫薯新品种。花青素合成相关关键基因的挖掘对紫薯的分子育种具有重要意义。为了挖掘甘薯花青素合成相关基因,以紫薯品种‘徐紫薯3号'和白薯品种‘徐薯18号'的块根为材料进行了转录组测序(RNA-seq),并结合公共数据库中已公布的甘薯基因组信息以及43份紫薯和45份非紫薯块根的RNA-seq数据,通过分析在不同样本间表达量差异大的前50%的基因中选择了26 760个基因进行WGCNA分析。结果表明,利用WGCNA鉴定出28个共表达模块,其中4个为紫薯特异性模块(Grey60模块和Black模块与紫薯显著正相关,Brown模块和Blue模块与紫薯显著负相关)。利用GO功能富集分析发现紫薯特异性模块Grey60可以显著富集到类黄酮和花青素代谢过程。通过计算模块内基因的连通性,分析挖掘到Grey60模块中有47个核心基因,其中包括已报道的8个花青素合成相关基因MYB113、CHS、3个CHI、F3H、GST和LDOX。利用qRT-PCR验证了其中7个核心基因的表达模式。通过构建核心基因的互作网络发现:MYB不仅与已知的花青素合成相关基因bHLH、CHI、GST、F3'H、CHS等存在互作,同时也与DUF914、ABCC4等转运蛋白基因互作;WRKY3与多个核心基因存在互作,如LDOX、GST、CHS等。为高花青素含量紫薯新品种的培育和紫薯花青素生物合成机制的解析提供了理论基础和新思路。     

Subgroups of Tcr α chains and correlation with T-cell function

T-cell receptor (Tcr) chains are classified into four subgroups (I, II, III, and miscellaneous) based on the amino acid residues at positions 61 and 62. Subgroup I has Gly Phe at these positions, subgroup II has Arg Phe, subgroup III has Arg Leu, and subgroup miscellaneous has several other combinations. Variability plots for subgroups I, II, and III sequences show higher values around positions 93–103, 105, 108, 111, 113, and 115, suggesting that these positions may interact with the processed antigen molecules. Smaller peaks are present at various other regions which may bind the major histocompatibility complex class I or II molecules. The patterns of variability within one subgroup are similar for all species, for human alone, and for mouse alone. These subgroup patterns appear much less complicated than patterns for sequences in all subgroups taken together, implying that subgroups may be related to Tcr functions. Among 83 mouse chains, 15 are from cytotoxic cells and 40 from helper cells. Of the 15 from cytotoxic cells, 11, 2, 0, and 2 are in subgroups I, II, III, and miscellaneous; and of the 40 from helper cells, 9, 16, 12, ans 3 are in subgroups I, II, III, and miscellaneous, respectively. Thus, a correlation between sequence and function of Tcr chains seems possible. Address correspondence and offprint requests to: M. Schiffer.     

The ACE gene D/I polymorphism as a modulator of severity of cystic fibrosis

ABSTRACT: BACKGROUND: Cystic Fibrosis (CF) is a monogenic disease with complex expression because of the action of genetic and environmental factors. We investigated whether the ACE gene D/I polymorphism is associated with severity of CF. METHODS: A cross-sectional study was performed, from 2009 to 2011, at University of Campinas - UNICAMP. We analyzed 180 patients for the most frequent mutations in the CFTR gene, presence of the ACE gene D/I polymorphism and clinical characteristics of CF. RESULTS: There was an association of the D/D genotype with early initiation of clinical manifestations (OR: 1.519, CI: 1.074 to 2.146), bacterium Burkholderia cepacia colonization (OR: 3.309, CI: 1.476 to 6.256) and Bhalla score (BS) (p = 0.015). The association was observed in subgroups of patients which were defined by their CFTR mutation genotype (all patients; subgroup I: no mutation detected; subgroup II: one CFTR allele identified to mutation class I, II or III; subgroup III: both CFTR alleles identified to mutation class I, II and/or III). CONCLUSION: An association between the D allele in the ACE gene and the severity of CF was found in our study.     

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.     

IRF2-ferroptosis related gene is associated with prognosis and EMT in gliomas

Ferroptosis is a new type of programmed cell death that has excellent anti-tumor potential in different tumors. However, the research on ferroptosis in glioma is still incomplete. In this study, we aimed to revealed the relationship between ferroptosis-related genes (FRGs) and glioma. We collected gene expression profiles of glioma patients from the TCGA and CGGA databases. All glioma samples were classified into five subtypes using the R software ConsensusClusterPlus. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between different subtypes and immune status and ferroptosis. Then co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA). A Gene Ontology (GO) analysis was conducted to analyze the potential biological functions of the genes in the modules. Finally, we identified 10 hub genes using the PPI network. The in vitro experiments were used to validate our predictions. We found that the expression level of IRF2 is positively correlated with the grade of glioma. The overexpression of IRF2 could protect glioma cells from ferroptosis and enhance the invasive and migratory abilities. Silence of IRF2 had the opposite effect. In conclusion, we demonstrated a novel ferroptosis-related signature for predicting prognosis, and IRF2 could be a potential biomarker for diagnosis and treatment in glioma.