金银花花形基因的发掘及生物信息学分析

金银花作为我国重要的中药材,具有消炎、抗菌、抗病毒、抗氧化、防癌等多种功效。随着金银花市场供需矛盾日益加剧,通过分子标记辅助选择育种方法来培育高产优质品种势在必行。通过NCBI的Blast工具扫描金银花蛋白组数据发掘花形候选基因,并执行候选基因的亲缘关系分析、结构域分析、表达模式分析、理化性质分析、蛋白质结构预测等一系列生物信息学分析。依据拟南芥调控花形的ABE类基因,通过NCBI-Blast工具扫描金银花氨基酸序列,筛选出包含MADS结构域的8个调控花形的金银花候选基因。经LjaFGD表达模式分析发现,金银花的花中GWHGAAZE016592和GWHGAAZE014905表达量显著高于其他部位,可能正向调控金银花花形。GWHGAAZE014905是一个包含MADS结构域的调控花器官发育的B类基因;GWHGAAZE016592是AP3同源基因。生物信息学分析发现,GWHGAAZE016592和GWHGAAZE014905均是稳定的亲水蛋白,属于非分泌蛋白,包括Motif1、Motif3、Motif4、Motif2、Motif6和Motif5,蛋白质三级结构模板为6byy.2.A和4ox0.2.C。GWHGAAZE014905被定位到细胞核上,而GWHGAAZE016592被定位到叶绿体上,且包含1个位于151~173 bp的跨膜螺旋区域,属于膜蛋白。研究结果为分子标记辅助选择方式培育道地高产优质金银花品种提供了基因资源和分子标记。     

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。     

Apple gene function and gene family database: an integrated bioinformatics database for apple research

The apple (Malus domestica) is one of the most economically important fruit crops in the world, due its importance to human nutrition and health. To analyze the function and evolution of different apple genes, we developed apple gene function and gene family database (AppleGFDB) for collecting, storing, arranging, and integrating functional genomics information of the apple. The AppleGFDB provides several layers of information about the apple genes, including nucleotide and protein sequences, chromosomal locations, gene structures, and any publications related to these annotations. To further analyze the functional genomics data of apple genes, the AppleGFDB was designed to enable users to easily retrieve information through a suite of interfaces, including gene ontology, protein domain and InterPro. In addition, the database provides tools for analyzing the expression profiles and microRNAs of the apple. Moreover, all of the analyzed and collected data can be downloaded from the database. The database can also be accessed using a convenient web server that supports a full-text search, a BLAST sequence search, and database browsing. Furthermore, to facilitate cooperation among apple researchers, AppleGFDB is presented in a user-interactive platform, which provides users with the opportunity to modify apple gene annotations and submit publication information for related genes. AppleGFDB is available at http://www.applegene.org or http://gfdb.sdau.edu.cn/.     

Co-expression analysis provides important module and pathways of human dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is a heart disease that injured greatly to the people wordwide. Systemic co-expression analysis for this cancer is still limited, although massive clinic experiments and gene profiling analyses had been well performed previously. Here, using the public RNA-Seq data “GSE116250” and gene annotation of Ensembl database, we built the co-expression modules for DCM by Weighted Gene Co-Expression Network Analysis, and investigated the function enrichment and protein-protein interaction (PPI) network of co-expression genes of each module by Database for Annotation, Visualization, and Integrated Discovery and Search Tool for the Retrieval of Interacting Genes/Proteins database, respectively. First, 5,000 genes in the 37 samples were screened and 11 co-expression modules were conducted. The number of genes for each module ranged from 77 to 936, with a mean of 455. Second, interaction relationships of hub-genes between pairwise modules showed great differences, suggesting relatively high-scale independence of the modules. Third, functional enrichments of the co-expression modules exhibited great differences. We found that genes in module 3 were significantly enriched in the pathways of focal adhesion and ubiquitin-mediated proteolysis. This module was inferred as the key module involved in DCM. In addition, PPI analysis revealed that the genes HSP90AA1, CTNNB1, MAPK1, GART, and PPP2CA owned the largest number of adjacency genes, unveiling that they may function importantly during the occurrence of DCM. Focal adhesion and ubiquitin-mediated proteolysis play important roles in human DCM.     

iBIG: An Integrative Network Tool for Supporting Human Disease Mechanism Studies

Understanding the mechanism of complex human diseases is a major scientific challenge. Towards this end, we developed a web-based network tool named iBIG (stands for integrative BIoloGy), which incorporates a variety of information on gene interaction and regulation. The generated network can be annotated with various types of information and visualized directly online. In addition to the gene networks based on physical and pathway interactions, networks at a functional level can also be constructed. Furthermore, a supplementary R package is provided to process microarray data and generate a list of important genes to be used as input for iBIG. To demonstrate its usefulness, we collected 54 microarrays on common human diseases including cancer, neurological disorders, infectious diseases and other common diseases. We processed the microarray data with our R package and constructed a network of functional modules perturbed in common human diseases. Networks at the functional level in combination with gene networks may provide new insight into the mechanism of human diseases. iBIG is freely available at http://lei.big.ac.cn/ibig.     

AtGGM2014, an Arabidopsis gene co-expression network for functional studies

Gene co-expression networks provide an important tool for systems biology studies. Using microarray data from the Array Express database, we constructed an Arabidopsis gene co-expression network, termed At GGM2014, based on the graphical Gaussian model, which contains 102,644 co-expression gene pairs among 18,068 genes. The network was grouped into 622 gene co-expression modules. These modules function in diverse house-keeping, cell cycle, development, hormone response, metabolism, and stress response pathways. We developed a tool to facilitate easy visualization of the expression patterns of these modules either in a tissue context or their regulation under different treatment conditions. The results indicate that at least six modules with tissue-specific expression pattern failed to record modular regulation under various stress conditions. This discrepancy could be best explained by the fact that experiments to study plant stress responses focused mainly on leaves and less on roots, and thus failed to recover specific regulation pattern in other tissues. Overall, the modular structures revealed by our network provide extensive information to generate testable hypotheses about diverse plant signaling pathways. At GGM2014 offers a constructive tool for plant systems biology studies.     

长白落叶松过氧化氢酶LoCAT1基因克隆及表达分析

为了解长白落叶松过氧化氢酶(CAT)基因的相关信息,探究该基因在长白落叶松不同组织中及不同逆境胁迫下的表达特性,本研究根据长白落叶松转录组数据库中获得的CAT1基因全长序列设计引物,克隆得到长白落叶松CAT1基因,命名为LoCAT1。该基因完整的开放阅读框(ORF)长度为954bp,共编码317个氨基酸。系统进化树分析结果显示,LoCAT1基因与北美云杉、银杏等CAT基因亲缘关系较近。利用实时定量RT-PCR技术分析了LoCAT1基因在长白落叶松中的组织表达特异性和应对非生物胁迫的表达模式。结果表明:LoCAT1基因在长白落叶松的根、茎、叶中均有表达,其中在茎部表达量最低,在叶中相对表达量最高。在非生物胁迫下,LoCAT1基因在长白落叶松根、茎、叶中的表达均发生了变化,但表达模式不同。在NaCl处理后,根和茎中LoCAT1基因均表现为下调表达,在12h时表达量最低,而叶中LoCAT1基因表达在24h明显受抑制,随后被上调表达,胁迫96h时表达量最高。PEG6000处理后,根和茎中LoCAT1基因的表达在胁迫早期被明显抑制,随后被上调表达。而叶中LoCAT1基因的表达在所有时间点均表现为上调表达。本研究推测长白落叶松LoCAT1基因可能参与了植物响应逆境胁迫的应答。     

GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation

Gene co-expression network analysis has been widely used in gene function annotation, especially for long noncoding RNAs (lncRNAs). However, there is a lack of effective cross-platform analysis tools. For biologists to easily build a gene co-expression network and to predict gene function, we developed GCEN, a cross-platform command-line toolkit developed with C++. It is an efficient and easy-to-use solution that will allow everyone to perform gene co-expression network analysis without the requirement of sophisticated programming skills, especially in cases of RNA-Seq research and lncRNAs function annotation. Because of its modular design, GCEN can be easily integrated into other pipelines.     

MEGO: gene functional module expression based on gene ontology

Existing analysis tools to study the collective properties of gene functional modules cannot return highly homogeneous modules and do not provide quantitative measures of module activity level. By partitioning genes according to multiple gene functional categorization principles and summarizing gene expression values into module expression values, MEGO (module expression based on gene ontology), a standalone microarray data analysis program, is able to extract highly activated gene functional modules that are of much interest to microarray experimenters. With multiple functional categorization principles simultaneously introduced in MEGO, the partition of genes is more delicate, and the collective property of a group of genes is sharpened and easier to capture. The quantitative measures of module activity levels returned by MEGO give users a quick impression of the direction and degree of module regulation. MEGO efficiently determines the answers to frequently asked questions, such as which functional classes have been induced or repressed under a specific experiment and to which levels these functional classes have been affected. MEGO is available free of charge for academic use and may be downloaded from http://www.dxy.cn/mego/MEGOInstall.EXE. Supplementary information can be found on the authors' web page at http://www.dxy.cn/mego/ and at the BioTechniques' web site at http://www. BioTechniques.com/February2005/TuSupplementary.html.     

The ClosTron: a universal gene knock-out system for the genus Clostridium

Progress in exploiting clostridial genome information has been severely impeded by a general lack of effective methods for the directed inactivation of specific genes. Those few mutants that have been generated have been almost exclusively derived by single crossover integration of a replication-deficient or defective plasmid by homologous recombination. The mutants created are therefore unstable. Here we have adapted a mutagenesis system based on the mobile group II intron from the ltrB gene of Lactococcus lactis (Ll.ltrB) to function in clostridial hosts. Integrants are readily selected on the basis of acquisition of resistance to erythromycin, and are generated from start to finish in as little as 10 to 14 days. Unlike single crossover plasmid integrants, the mutants are extremely stable. The system has been used to make 6 mutants of Clostridium acetobutylicum and 5 of Clostridium difficile, exceeding the number of published mutants ever generated in these species. Genes have also been inactivated for the first time in Clostridium botulinum and Clostridium sporogenes, suggesting the system will be universally applicable to the genus. The procedure is highly efficient and reproducible, and should revolutionize functional genomic studies in clostridia.     

The intron-containing ribosomal protein-encoding genes L5, L7a and L37a are unlinked in chicken

Chicken ribosomal protein (rp)-encoding genes are currently being studied at the nucleotide level and three independent recombinant phages have been isolated from chicken genomic libraries using cloned cDNA probes. Each of these was shown to include an intron-containing rp gene of chicken (L5, L7a, L37a). In this study the chromosomal location of these three intron-containing rp from the large subunit of the chicken ribosome was determined by fluorescence in situ hybridization. L7a mapped to a microchromosome, whereas L5 and L37a mapped to macrochromosomes 8 and 7, respectively. The results demonstrate that these functionally related genes are widely dispersed in the genome. Furthermore, as in the case of many other evolutionarily advanced eukaryotes, there is no apparent linkage of rp and rRNA genes.     

SARS-CoV-2病毒感染潜在关键分子生物标志物及免疫浸润特征分析

应用生物信息学方法筛选新型冠状病毒肺炎（corona virus disease 2019,COVID-19）感染的潜在关键分子生物标志物并分析其免疫浸润特征。从GEO数据库下载GSE152418数据集,其中COVID-19患者17例,健康对照17例。用加权基因共表达网络分析（weighted gene co-expression network analysis,WGCNA）方法筛选出COVID-19最相关的模块基因。与差异基因取交集得到共同基因,进行功能及信号通路富集分析,构建蛋白互作网络筛选关键基因,构建关键基因的miRNATF-mRNA调控网络,用CIBERSORT算法预测样本免疫细胞浸润特征。差异分析得到2 049个差异基因。WGCNA分析7个模块中“土耳其蓝色”模块与COVID-19相关性最高（r=0.91,P<0.001）。模块中基因显著性和模块隶属度呈显著正相关（r=0.96,P<0.001）。得到共同基因766个,主要参与有丝分裂、微管结合、阳离子通道活性及卵母细胞减数分裂、细胞衰老等。蛋白互作网络筛选到前10位关键基因分别为CDK1、BUB1、CCNA2...