果蝇蛋白质组学研究进展

蛋白质组学旨在阐明基因组所表达的真正执行生命活动的全部蛋白质的表达规律和生物功能。随着人类基因组学计划的逐渐成熟,分子水平的实验技术不断发展,蛋白质组学的研究被提高到了前所未有的高度。果蝇是生命科学领域最为常用的一种模式生物,长期的系统研究也使果蝇的基因组成为至今注释最好的基因组之一,为功能基因组研究奠定了基础。但由于技术的限制,迄今有关果蝇蛋白质组学研究的报道尚不多见。近年来果蝇蛋白质组学的研究主要包括表达谱、修饰谱、比较蛋白质组学和疾病模型蛋白质组等四个方向,为进一步开展人类疾病临床蛋白质组学研究奠定了基础。     

基于LC-MS/MS策略建立水牛卵母细胞蛋白质表达谱的方法研究

本研究采用了LC-MS/MS策略对如何建立水牛卵母细胞蛋白质表达谱进行了探讨,为后续研究不同时期的水牛卵母细胞和早期胚胎卵裂分化过程中蛋白质组变化建立研究平台。本实验收集水牛GV期和MⅡ期卵母细胞各500枚,去除透明带后将两个时期的细胞混合后提取蛋白质,蛋白质酶解后通过强阳离子交换色谱预分离多肽混合物,预分离后的馏分采用EASY-n LC结合LTQ-Orbitrap质谱鉴定蛋白质表达谱。通过SEQUEST检索,共鉴定出574种蛋白质,生物信息学分析表明,有明确分子功能相关的蛋白质有487种,与组成细胞成分相关的蛋白质有476种,与生物学进程相关的蛋白质有490种,初步建立了一套获得水牛卵母细胞蛋白质表达谱的方法。结果表明建立的LC-MS/MS策略方法适合于水牛卵母细胞蛋白质组学的研究,卵母细胞蛋白质表达谱数据为今后研究水牛不同时期的卵母细胞和早期胚胎卵裂分化过程中蛋白质组学奠定了方法与理论基础。     

活性蛋白质表达谱分析与病毒学研究

活性蛋白质表达谱（activity-based protein profiling, ABPP）分析技术是功能蛋白质组学的一种策略,属于化学蛋白质组学的一部分.它借助化学小分子从功能角度直接切入蛋白质组的研究,能够直接对蛋白质组中感兴趣的靶酶蛋白的活性进行检测,为药物的发现提供强有力的支持.因此,ABPP技术被认为是基于功能的新一代蛋白质组学技术.随着ABPP分析技术和方法的不断成熟,其应用领域也不断扩展.最近一系列研究表明, 今后ABPP分析技术可能成为病毒学研究的又一重要武器.本文综述了ABPP分析技术的基本原理及其在病毒学研究中的应用.     

ER/PR阳性和阴性乳腺癌的定量蛋白质组学和生物信息学比较研究

目的:建立雌/孕激素受体(ER/PR)阴性和阳性乳腺癌的蛋白质表达谱,寻找ER/PR阴性和阳性乳腺癌中差异表达蛋白,为乳腺癌患者提供新的预后预测指标和治疗新靶点。方法:应用蛋白质组学i TRAQ技术建立ER/PR阳性和阴性乳腺癌的蛋白质差异表达谱,鉴定两组乳腺癌的差异表达蛋白,对部分差异表达蛋白进行生物信息学分析,包括蛋白功能注释和分类GO分析和KEGG通路分析。结果:应用i TRAQ蛋白质组学技术对乳腺癌组织进行了蛋白组学分析,鉴定出ER/PR阳性和阴性组间有差异表达的蛋白4999种,以ER/PR阳性:ER/PR阴性≥3为上调标准,确定ER/PR阳性组上调蛋白101种。以ER/PR阳性:ER/PR阴性≤0.5为下调标准,ER/PR阳性组下调蛋白122种。GO分析结果显示ER/PR受体阴性和阳性乳腺癌的差异表达蛋白的分子功能、生物过程、细胞定位较为复杂,并且在上调蛋白和下调蛋白上存在分布差异。KEGG通路分析发现部分差异表达蛋白涉及201条信号通路。结论:ER/PR阳性和阴性乳腺癌间存在差异表达蛋白,这些蛋白涉及复杂的分子功能、生物过程和信号通路。     

应用超高分辨双向凝胶电泳技术进行正常人类肝脏蛋白质组研究

肝脏是人体最大的实质器官, 承担着人体许多关键的生理功能, 在生命活动中占有重要地位. 根据人类肝脏蛋白质组计划, 本实验旨在构建人肝脏蛋白质双向凝胶电泳表达谱, 尽可能分离和鉴定更多的蛋白. 在双向凝胶电泳第一向等电聚焦水平上, 从上样方式、水化液配方、聚焦时间等方面优化了碱性蛋白的分离条件, 利用超放大胶分离技术搭建了高分辨的双向凝胶电泳分离平台, 构建了人类肝脏蛋白质2-DE参考谱, 检测到5481个蛋白质点. 这是目前国际上最为全面的人体器官蛋白质组2-DE参考谱, 为其他肝病的研究提供了较好的参照系. 成功鉴定了429个非冗余蛋白, 对pH 4.0~7.0, 5.0~6.0, 5.5~6.7, 6.0~9.0部分蛋白质点在胶上进行了注释, 由此构建了人肝2-DE蛋白质表达谱数据库. 对蛋白质的理化性质、功能及亚细胞定位进行了全面分析. 研究中构建的人类肝脏蛋白质2-DE图谱将为肝脏比较蛋白质组学研究提供参考.     

应用蛋白质组学技术筛选胃癌SGC-7901细胞中的let-7a功能相关蛋白

为探讨let-7a表达下调在胃癌发病中的机制,高通量地检测了与let-7a功能相关的蛋白质.首先采用基因克隆技术稳定过表达SGC-7901细胞系的let-7a基因,然后用蛋白质组学技术研究稳定过表达该基因对SGC-7901细胞蛋白质表达谱的影响.通过对SGC-7901/let-7a细胞的蛋白质表达谱改变的研究,并用基质辅助激光解吸电离飞行时间质谱(MALDI-TOF-MS)分析鉴定了10个差异表达蛋白质.这些差异表达蛋白质可能是let-7a功能相关蛋白质,其中抗氧化蛋白2、胰岛素样生长因子结合蛋白2、二硫化蛋白异构酶A2、四氢叶酸合成酶、细胞周期素依赖性激酶抑制蛋白1、Rho-GTPsae激活蛋白4表达上调,Skp2蛋白、血小板黏附蛋白CD41、纤维连接蛋白、Cks1蛋白表达下调.部分差异表达蛋白质如细胞周期素依赖性激酶抑制蛋白1、Skp2蛋白和纤维连接蛋白经蛋白质印迹分析进行了验证.在SGC-7901/let-7a中鉴定的10个差异表达蛋白质涉及到细胞周期的调控、分子基因表达调控、细胞黏附、细胞代谢等众多事件,它们可能作为let-7a功能相关蛋白质,为阐明let-7a表达下调在胃癌发病中的机制提供了重要线索.     

微生物蛋白质组学的定量分析

越来越多的微生物基因组序列数据为系统地研究基因的调节和功能创造了有利条件.由于蛋白质是具有生物功能的分子,蛋白质组学在微生物基因组的功能研究中异军突起、蓬勃发展.微生物蛋白质组学的基本原则是,用比较研究来阐明和理解不同微生物之间或不同生长条件下基因的表达水平.显而易见,定量分析技术是比较蛋白质组学中急需发展的核心技术.对蛋白质组学定量分析技术在微生物蛋白质组研究中的进展进行了综述.     

鼻咽癌细胞系与永生化的鼻咽上皮细胞系蛋白质表达差异分析

鼻咽癌对我国南部居民的健康造成严重的威胁.为了研究鼻咽癌的发病机理,本研究采用了蛋白质组学技术分析和比较了鼻咽癌细胞系（HNE1和CNE1）与永生化的鼻咽上皮细胞系的蛋白质表达谱.采用双向凝胶电泳分离提取的全细胞蛋白质,通过PDQuest软件分析找出在肿瘤中表达变化的蛋白质点,用基质辅助激光解析电离飞行时间串联质谱(MALDI- TOF/TOF-MS)进行鉴定.共得到了15个在肿瘤细胞系中表达上调和18个在肿瘤细胞系中表达下调的蛋白质,并对其中一些蛋白质的表达进行免疫印迹的验证.这些表达差异的蛋白质与细胞的增殖和调亡、癌症的转移,细胞骨架,信号传导等有关.本研究鉴定了一批可能作为鼻咽癌治疗的药物靶标的蛋白质,并对研究鼻咽癌发病机理提供了相关的线索.     

水稻叶绿体蛋白质在生长发育过程中的表达研究

在植物中,叶绿体是负责光合作用的细胞器,对叶绿体内的各种生物过程人们已经积累了很多知识,但对叶绿体蛋白质的表达还所知甚少．为了解水稻叶绿体蛋白质在正常生长发育过程中的表达情况,尝试基于抗体的水稻蛋白质组学策略．选取了10个水稻叶绿体基因,利用表达的蛋白质或合成的抗原决定簇片段制备了抗体,用Western blotting检测了相应蛋白质在5个发育时期的根、茎、叶及穗组织中的表达．发现10个蛋白质均在叶片中表达,在根中不表达．与原初反应相关的叶绿素A/B结合蛋白1和2(CAB1和CAB2)、与电子传递相关的放氧增强蛋白1(OEE1)及与活性氧清除相关的过氧还蛋白过氧化物酶(2-CysP)和硫氧还蛋白(Trx)在茎中表达．而在卡尔文循环中发挥作用的Rubisco活化酶(RCA)、甘油醛-3-磷酸脱氢酶(GAPDH)、果糖二磷酸醛缩酶(FBPA)和景天庚酮糖-1, 7-二磷酸酶(SBPase)蛋白质在茎中不表达．在穗中,这些蛋白质的表达时序不同,CAB2和2-CysP在穗发育的全程表达,CAB1和OEE1在中后期表达,而卡尔文循环中的蛋白质只在中期表达．有意思的是,卡尔文循环中的蛋白质表达模式相似,这一结果从蛋白质表达水平支持它们之间的相互衔接关系．此外,实验还揭示了可能的蛋白质修饰、二聚体及不同的转录本现象．将目标基因的表达谱与转录谱进行比较,发现二者间有一定的平行性,但也有明显的区别．以水稻叶绿体蛋白质为对象,直观并相对定量地揭示了它们的表达模式,为阐释其功能提供了信息,也为基于抗体的水稻蛋白质组学策略提供了一个初步数据．     

水稻类钙调磷酸酶亚基B蛋白质在叶片生长和白叶枯病抗性反应中的表达

钙信号介导植物对多种外部刺激的反应并参与调控广泛的生理学过程。钙离子结合蛋白质, 如类钙调磷酸酶亚基B蛋白质(calcineurin B-like protein, CBL), 对感受和传递钙信号具有重要作用。根据基因组测序及注释分析, 水稻(Oryza sativa)基因组中有10个CBL家族成员。采用基于抗体的蛋白质组学策略, 利用免疫印迹方法研究了水稻CBL蛋白质在叶片生长不同时期的表达, 揭示了其在正常发育过程中的表达模式。然后对Xa21介导的水稻白叶枯病抗性反应不同时间点的蛋白质表达进行检测, 发现OsCBL-1、OsCBL-3、OsCBL-5、OsCBL-9和OsCBL-10这5个蛋白质的表达发生了变化; 进一步比较它们在抗病、感病和对照处理中的表达情况, 发现其在不同反应间的表达也有区别, 提示了CBL蛋白质在水稻-白叶枯病菌互作过程中的功能。该研究为深入了解水稻CBL蛋白质的功能提供了有价值的线索。     

An overview of human protein databases and their application to functional proteomics in health and disease

Functional proteomics can be defined as a strategy to couple proteomic information with biochemical and physiological analyses with the aim of understanding better the functions of proteins in normal and diseased organs. In recent years, a variety of publicly available bioinformatics databases have been developed to support protein-related information management and biological knowledge discovery. In addition to being used to annotate the proteome, these resources also offer the opportunity to develop global approaches to the study of the functional role of proteins both in health and disease. Here, we present a comprehensive review of the major human protein bioinformatics databases. We conclude this review by discussing a few examples that illustrate the importance of these databases in functional proteomics research.     

GS2PATH: a web-based integrated analysis tool for finding functional relationships using gene ontology and biochemical pathway data

GS2PATH is a Web-based pipeline tool to permit functional enrichment of a given gene set from prior knowledge databases, including gene ontology (GO) database and biological pathway databases. The tool also provides an estimation of gene set enrichment, in GO terms, from the databases of the KEGG and BioCarta pathways, which may allow users to compute and compare functional over-representations. This is especially useful in the perspective of biological pathways such as metabolic, signal transduction, genetic information processing, environmental information processing, cellular process, disease, and drug development. It provides relevant images of biochemical pathways with highlighting of the gene set by customized colors, which can directly assist in the visualization of functional alteration.

Availability     

Gene Ontology annotation quality analysis in model eukaryotes

Functional analysis using the Gene Ontology (GO) is crucial for array analysis, but it is often difficult for researchers to assess the amount and quality of GO annotations associated with different sets of gene products. In many cases the source of the GO annotations and the date the GO annotations were last updated is not apparent, further complicating a researchers’ ability to assess the quality of the GO data provided. Moreover, GO biocurators need to ensure that the GO quality is maintained and optimal for the functional processes that are most relevant for their research community. We report the GO Annotation Quality (GAQ) score, a quantitative measure of GO quality that includes breadth of GO annotation, the level of detail of annotation and the type of evidence used to make the annotation. As a case study, we apply the GAQ scoring method to a set of diverse eukaryotes and demonstrate how the GAQ score can be used to track changes in GO annotations over time and to assess the quality of GO annotations available for specific biological processes. The GAQ score also allows researchers to quantitatively assess the functional data available for their experimental systems (arrays or databases).     

A graph-based semantic similarity measure for the gene ontology

Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.     

Get ready to GO! A biologist's guide to the Gene Ontology

The Gene Ontology (GO) project provides a controlled vocabulary to facilitate high-quality functional gene annotation for all species. Genes in biological databases are linked to GO terms, allowing biologists to ask questions about gene function in a manner independent of species. This tutorial provides an introduction for biologists to the GO resources and covers three of the most common methods of querying GO: by individual gene, by gene function and by using a list of genes. [For the sake of brevity, the term 'gene' is used throughout this paper to refer to genes and their products (proteins and RNAs). GO annotations are always based on the characteristics of gene products, even though it may be the gene that is cited in the annotation.].     

A Draft Map of Rhesus Monkey Tissue Proteome for Biomedical Research

Though the rhesus monkey is one of the most valuable non-human primate animal models for various human diseases because of its manageable size and genetic and proteomic similarities with humans, proteomic research using rhesus monkeys still remains challenging due to the lack of a complete protein sequence database and effective strategy. To investigate the most effective and high-throughput proteomic strategy, comparative data analysis was performed employing various protein databases and search engines. The UniProt databases of monkey, human, bovine, rat and mouse were used for the comparative analysis and also a universal database with all protein sequences from all available species was tested. At the same time, de novo sequencing was compared to the SEQUEST search algorithm to identify an optimal work flow for monkey proteomics. Employing the most effective strategy, proteomic profiling of monkey organs identified 3,481 proteins at 0.5% FDR from 9 male and 10 female tissues in an automated, high-throughput manner. Data are available via ProteomeXchange with identifier PXD001972. Based on the success of this alternative interpretation of MS data, the list of proteins identified from 12 organs of male and female subjects will benefit future rhesus monkey proteome research.     

A probabilistic generative model for GO enrichment analysis

The Gene Ontology (GO) is extensively used to analyze all types of high-throughput experiments. However, researchers still face several challenges when using GO and other functional annotation databases. One problem is the large number of multiple hypotheses that are being tested for each study. In addition, categories often overlap with both direct parents/descendents and other distant categories in the hierarchical structure. This makes it hard to determine if the identified significant categories represent different functional outcomes or rather a redundant view of the same biological processes. To overcome these problems we developed a generative probabilistic model which identifies a (small) subset of categories that, together, explain the selected gene set. Our model accommodates noise and errors in the selected gene set and GO. Using controlled GO data our method correctly recovered most of the selected categories, leading to dramatic improvements over current methods for GO analysis. When used with microarray expression data and ChIP-chip data from yeast and human our method was able to correctly identify both general and specific enriched categories which were overlooked by other methods.     

Semantic search among heterogeneous biological databases based on gene ontology

Various biological database systems including datacapture, data storage, data retrieval and other data pro-cessing methods have been developed. These systems havebecome effective tools for today’s genomics and relatedstudies. However, the highly distribu…