宽果苁菔适应昼夜变化ESTs的分离与分析

以流石滩地区植物广布种宽果苁菔为实验材料,应用磁珠介导的抑制消减杂交方法,分离昼夜差异基因。随机挑选了136个差异ESTs克隆并进行测序,测序结果使用Blast2go程序进行功能注释和分析。结果表明绝大部分ESTs功能与稳定细胞状态和抗性响应相关,其次与物质能量代谢和信号传导相关,宽果苁菔适应昼夜变化过程的机制非常复杂,本研究为进一步研究高山植物适应流石滩恶劣环境的机制奠定了基础。     

番茄茎腺毛差异表达序列分离与分析

腺毛是参与植物防御性反应与次生代谢挥发物合成的特异化器官,了解腺毛专一性表达序列标签将有助于我们进一步认识植物腺毛的特异性功能.本文应用磁珠介导的抑制消减杂交方法(SSH),以番茄茎腺毛为检测子(tester),去除腺毛的番茄茎为驱动子(driver),构建番茄茎腺毛差异表达cDNA文库,分离腺毛差异表达基因.随机挑选了108个差异ESTs进行测序,测序结果使用Blast2go程序进行blastx比对、功能注释和KEGG代谢路径分析.结果表明,绝大部分ESTs功能与胁迫响应、物质代谢、生物及非生物刺激反应相关,具有结合、催化功能.这些分离的差异ESTs为进一步研究番茄腺毛的植物防御性机制奠定了基础.     

小麦抗白粉病侵染初期的表达序列标签分析

以抗白粉病品系“百农 32 17×Mardler”　BC5F4为材料 ,构建了一个白粉病菌接种初期的抑制消减杂交cDNA文库 ,测序获得 76 0条ESTs。与GenBank序列进行BLASTx分析 ,获功能已知ESTs 2 71条。通过分析抗病相关基因 ,推测G蛋白介导的信号传导途径、SA信号传递系统、MAP相关信号传递系统等参与了小麦抗白粉病过程。SAR基因在抗病相关ESTs中的种类与数量最多。数据显示苯丙烷代谢途径、细胞壁结构修饰作用、细胞保卫机制参与了抗病过程。未知功能ESTs与GenBank序列进行BLASTn分析 ,其中许多与病原菌、非病原菌诱导cDNA文库来源的ESTs同源 ;新ESTs占全部ESTs的 16 6 %。     

黄瓜抗黑星病相关基因的差异表达分析

以抗黑星病黄瓜材料HX1为试材,接种黑星病菌（Cladosporium cucumerinum）2h、8h、20h、32h和72h的叶片作为试验方（Tester）,相应的未接种叶片作为对照方（Driver）,利用SSH技术,构建了黑星病菌侵染初期的正向和反向cDNA-SSH文库。用巢式引物PCR检测插入片段,获得了200个阳性克隆,通过测序,除去重复序列,共得到105个Unique ESTs。与非冗余蛋白数据库进行BLASTx比对,结果显示,17条ESTs未找到同源序列,88条非重复序列和已知基因的同源性较高,占全部ESTs序列的83.8%,其中86条ESTs与非冗余蛋白数据库已知功能的蛋白具有高度的相似性。结合高密度点阵膜杂交差异筛选,阳性率为75.0%。经初步分析这些序列的功能,差异表达的ESTs功能涉及能量和基础代谢、信号转导、蛋白和核酸代谢、光合作用及逆境中特异表达的基因等方面。为研究黄瓜抗黑星病基因提供了依据。     

西藏八角莲与桃儿七根及根茎SSH文库的建立

以西藏八角莲(Dysosma tsayuensis Ying)和桃儿七[Sinopodophyllum hexandrum(Royle)Ying]为材料,构建其根及根茎的SSH文库,从中筛选鬼臼类植物属种间与鬼臼毒素生物合成相关的差异表达基因。从文库中随机挑取201个阳性克隆测序后得到183条ESTs。去除载体序列和冗余序列,聚类拼接得到17个西藏八角莲的unique ESTs。经BLAST同源比较和功能查寻,有功能注释的unique ESTs共12个,占70.6%,所编码的蛋白涉及光合作用、合成代谢、转录调控等功能;无功能注释和匹配结果的共5个,占29.4%。该研究成功构建了西藏八角莲和桃儿七SSH文库,为进一步揭示鬼臼毒素生物合成途径及其调控机制奠定了基础。     

镰刀菌诱导的泸定百合SSH文库构建及抗病相关基因筛选

该研究以泸定百合(Lilium sargentiae Wilson)为材料,构建其组培苗经百合尖孢镰刀菌侵染后的叶片SSH文库,从中筛选镰刀菌枯萎病抗病相关基因。从正向SSH文库中随机挑取300个单克隆测序后得到280条ESTs,进行功能比对分析后,除去未知功能、沉冗蛋白以及无同源序列,得到有功能的ESTs共168条,其功能涉及信号传导、蛋白质合成与代谢、抗病与防御、物质与能量代谢、转录相关等多种途径,其中有31条ESTs与抗病防御相关。从抗病防御相关基因中选取8条ESTs:过氧化氢酶、ATP结合盒转运蛋白(ABC transporter)、Kunitz型胰蛋白酶抑制剂4(Kunitz trypsin inhibitor 4)、丝氨酸乙醛酸氨基转移酶、多聚泛素、脂氧合酶I(Lipoxygenase I)、丝氨酸/苏安酸蛋白激酶(Serine/Threonine-protein kinase)、抗坏血酸过氧化物酶(Arabidopsis thaliana),通过RTPCR对其表达情况进行分析,发现经镰刀菌诱导后均为上调表达,推测它们可能参与了泸定百合镰刀菌枯萎病的抗病反应途径。     

一种新的cDNA末端快速扩增获取全长cDNA的方法

为克隆精子发生相关基因的全长cDNA,根据mRNA差异显示获得的ESTs设计引物。利用一种新的cDNA末端快速扩增方法（SMART RACE）扩增该EST的5′末端,并进行克隆测序,与cDNA差异显示获得ESTs拼接后,获得了三个新的全长cDNA。结果表明：SMAR RACE是一种简便、有效的克隆cDNA5′末端未知序列的技术。     

小麦抗病基因表达谱中的文库构建与筛选方法研究

以抗白粉病品系“百农 32 17×Mardler”BC5F4为材料 ,构建了白粉病菌诱导的普通cDNA文库和抑制消减杂交(SSH)cDNA文库。分别对两文库进行了一定规模的测序 ,获得普通cDNA文库不重复ESTs 387条和SSHcDNA文库ESTs 76 0条。将获得的ESTs与GenBank序列进行了BLASTn、BLASTx同源性分析。结果表明 :在普通文库中 ,一些参与光合作用与核糖体构成等的基因出现频率较高 ,而获得的抗病相关基因则较少。消减文库在构建方法、抗病相关基因的富集等方面具有明显的优越性 ,是目前抗病基因表达谱研究中的较好方法。利用高密度点阵膜杂交技术对两文库的筛选结果表明 ,该方法具有相对简便易操作、杂交膜可反复使用等优点 ;但也存在mRNA及同位素用量大等问题。经筛选 ,消减文库中有 5 4 1%的功能已知ESTs为抗病相关基因 ,被证明参与了小麦抗白粉病反应     

梅花鹿鹿茸尖端组织ESTs分析

文章通过对东北梅花鹿(Cervus nippon hortulorum)鹿茸尖端组织cDNA文库随机测序获得了906条高质量ESTs,906条ESTs拼接后代表了701个Unigenes,其中包括重叠群86个,单拷贝615个。Blast分析显示具已知和推测功能的基因580个(82.7%),通过Gene Ontology(GO)分类对获得的580个功能基因进行了包括分子功能、生物过程和细胞组分在内的3个层次的功能注释,并根据BLAST的注释结果及进一步的筛选与分析,共得到39条与鹿茸尖端组织生长发育相关的基因。cDNA文库的构建和ESTs分析填补了鹿科动物在NCBI公共数据库上基因组信息的空白,并为科学的开发和利用梅花鹿资源提供了重要的理论依据。     

一种新的cDNA 末端快速扩增获取全长cDNA的方法

为克隆精子发生相关基因的全长cDNA,根据mRNA差异显示获得的ESTs设计引物,利用一种新的cDNA末端快速扩增方法(SMARTRACE)扩增该EST的5′末端,并进行克隆测序,与mRNA差异显示获得ESTs拼接后,获得了三个新的全长cDNA.结果表明,SMARTRACE是一种简便、有效的克隆cDNA5′末端未知序列的技术. Abstract:To clone the full-length cDNAs of genes related to spermatogenesis,ESTs obtained by mRNA differential display were used to design gene-specific primer.Then SMART RACE was performed to obtain the 5′ region of these ESTs.After cloning,sequencing and splicing with ESTs obtained by mRNA differential display,three full-length cDNAs were obtained.The results indicate that SMART RACE is a simple and an effective technique for cloning 5′-end unknown sequence of gene.     

Affymetrix GeneChip microarray preprocessing for multivariate analyses

Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses-detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common; however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.     

Evaluation of two-dimensional differential gel electrophoresis for proteomic expression analysis of a model breast cancer cell system

The technique of fluorescent two-dimensional (2D) difference gel electrophoresis for differential protein expression analysis has been evaluated using a model breast cancer cell system of ErbB-2 overexpression. Labeling of paired cell lysate samples with N-hydroxy succinimidyl ester-derivatives of fluorescent Cy3 and Cy5 dyes for separation on the same 2D gel enabled quantitative, sensitive, and reproducible differential expression analysis of the cell lines. SyproRuby staining was shown to be a highly sensitive and 2D difference gel electrophoresis-compatible method for post-electrophoretic visualization of proteins, which could then be picked and identified by matrix-assisted laser-desorption ionization mass spectroscopy. Indeed, from these experiments, we have identified multiple proteins that are likely to be involved in ErbB-2-mediated transformation. A triple dye labeling methodology was used to identify proteins differentially expressed in the cell system over a time course of growth factor stimulation. A Cy2-labeled pool of samples was used as a standard with all Cy3- and Cy5-labeled sample pairs to facilitate cross-gel quantitative analysis. DeCyder (Amersham Biosciences, Inc.) software was used to distinguish clear statistical differences in protein expression over time and between the cell lines.     

Ground based studies of gene expression in Arabidopsis exposed to gravity stresses

As a link in the preparation of the MULTIGEN experiment, which will take place on the International Space Station, ground based studies of the gene expression in Arabidopsis thaliana were performed. Microarray technology was used to screen Arabidopsis seedlings exposed to simulated hypogravity on a Random Positioning Machine and a 1 x g control sample. This screening showed differential expression in 177 out of approximately 8000 genes. Some of these genes can be grouped into functional categories, e.g. general metabolism, biogenesis of cellular components, cellular transport and transport facilitation, and cell rescue and defense response. However, about 50% of the genes encode proteins with unknown function. Based on the above results a new "in-house" cDNA microarray was constructed. Some of the selected genes on this microarray (e.g. Xyloglucan endotransglycosylase, At2g18800) showed differential expression both in Arabidopsis exposed to hypergravity and simulated hypogravity by use of a centrifuge and a Random Positioning Machine.     

Bayesian hierarchical model for identifying changes in gene expression from microarray experiments.

Recent developments in microarrays technology enable researchers to study simultaneously the expression of thousands of genes from one cell line or tissue sample. This new technology is often used to assess changes in mRNA expression upon a specified transfection for a cell line in order to identify target genes. For such experiments, the range of differential expression is moderate, and teasing out the modified genes is challenging and calls for detailed modeling. The aim of this paper is to propose a methodological framework for studies that investigate differential gene expression through microarrays technology that is based on a fully Bayesian mixture approach (Richardson and Green, 1997). A case study that investigated those genes that were differentially expressed in two cell lines (normal and modified by a gene transfection) is provided to illustrate the performance and usefulness of this approach.     

Monitoring of representational difference analysis subtraction procedures by global microarrays

Various approaches to the study of differential gene expression are applied to compare cell lines and tissue samples in a wide range of biological contexts. The compromise between focusing on only the important genes in certain cellular processes and achieving a complete picture is critical for the selection of strategy. We demonstrate how global microarray technology can be used for the exploration of the differentially expressed genes extracted through representational difference analysis (RDA). The subtraction of ubiquitous gene fragments from the two samples was demonstrated using cDNA microarrays including more than 32 000 spotted, PCR-amplified human clones. Hybridizations indicated the expression of 9100 of the microarray elements in a macrophage/foam cell atherosclerosis model system, of which many were removed during the RDA process. The stepwise subtraction procedure was demonstrated to yield an efficient enrichment of gene fragments overrepresented in either sample (18% in the representations, 86% after the first subtraction, and 88% after the second subtraction), many of which were impossible to detect in the starting material. Interestingly, the method allowed for the observation of the differential expression of several members of the low-abundant nuclear receptor gene family. We also observed a certain background level in the difference products of nondifferentially expressed gene fragments, warranting a verification strategy for selected candidate genes. The differential expression of several genes was verified by real-time PCR.     

Quantifying reproducibility for differential proteomics: noise analysis for protein liquid chromatography-mass spectrometry of human serum

SUMMARY: Using replicated human serum samples, we applied an error model for proteomic differential expression profiling for a high-resolution liquid chromatography-mass spectrometry (LC-MS) platform. The detailed noise analysis presented here uses an experimental design that separates variance caused by sample preparation from variance due to analytical equipment. An analytic approach based on a two-component error model was applied, and in combination with an existing data driven technique that utilizes local sample averaging, we characterized and quantified the noise variance as a function of mean peak intensity. The results indicate that for processed LC-MS data a constant coefficient of variation is dominant for high intensities, whereas a model for low intensities explains Poisson-like variations. This result leads to a quadratic variance model which is used for the estimation of sample preparation noise present in LC-MS data.     

Identification of lncRNA‐associated differential subnetworks in oesophageal squamous cell carcinoma by differential co‐expression analysis

Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co‐expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co‐expression of lncRNAs and protein‐coding genes (PCGs) between normal oesophageal tissue and ESCC tissues, and constructed a lncRNA‐PCG differential co‐expression network (DCN). DCN was characterized as a scale‐free, small‐world network with modular organization. Focusing on lncRNAs, a total of 107 differential lncRNA‐PCG subnetworks were identified from the DCN by integrating both differential expression and differential co‐expression. These differential subnetworks provide a valuable source for revealing lncRNA functions and the associated dysfunctional regulatory networks in ESCC. Their consistent discrimination suggests that they may have important roles in ESCC and could serve as robust subnetwork biomarkers. In addition, two tumour suppressor genes (AL121899.1 and ELMO2), identified in the core modules, were validated by functional experiments. The proposed method can be easily used to investigate differential subnetworks of other molecules in other cancers.