A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates.     

差异表达基因克隆技术的进展

分离并克隆差异表达基因,不仅有助于揭示生命的奥秘,而且还能为基因诊断与治疗提供重要的理论依据。基因差异表达的变化有两种,即新出现的基因表达与表达量差异的基因表达。以前,过多强调克隆新出现的表达基因,忽略表达量差异的基因。目前研究认为,表达量差异的基因...     

Identifying Differentially Expressed Genes in Time Course Microarray Data

Identifying differentially expressed (DE) genes across conditions or treatments is a typical problem in microarray experiments. In time course microarray experiments (under two or more conditions/treatments), it is sometimes of interest to identify two classes of DE genes: those with no time-condition interactions (called parallel DE genes, or PDE), and those with time-condition interactions (nonparallel DE genes, NPDE). Although many methods have been proposed for identifying DE genes in time course experiments, methods for discerning NPDE genes from the general DE genes are still lacking. We propose a functional ANOVA mixed-effect model to model time course gene expression observations. The fixed effect of (the mean curve) of the model decomposes bivariate functions of time and treatments (or experimental conditions) as in the classic ANOVA method and provides the associated notions of main effects and interactions. Random effects capture time-dependent correlation structures. In this model, identifying NPDE genes is equivalent to testing the significance of the time-condition interaction, for which an approximate F-test is suggested. We examined the performance of the proposed method on simulated datasets in comparison with some existing methods, and applied the method to a study of human reaction to the endotoxin stimulation, as well as to a cell cycle expression data set.     

克隆差异表达基因的新策略

基因表达的变化有两种,即新出现的基因表达与表达量差异的基因表达.表达量差异的基因克隆技术主要有mRNA差异展示,此技术是目前筛选差异表达基因最有效的方法之一,但主要存在假阳性率高的不足,针对此缺点,近几年提出了新的策略与方法,如差异消减展示、基于PCR和减法杂交基础上的差异表达基因克隆技术,这些技术具有显著优势.     

4个棉花ADF基因的分子鉴定及其差异表达

肌动蛋白解聚合因子(actin-depolymerizing factor, ADF)是一种在真核生物中广泛存在的低分子量的肌动蛋白结合蛋白,它在调控细胞内肌动蛋白纤丝的解聚合和再聚合中起着关键作用。我们在棉纤维cDNA文库中分离克隆了4个ADF基因(cDNAs),分别命名为GhADF2,GhADF3,GhADF4,GhADF5。GhADF2 cDNA 长度为705 bp,编码139个氨基酸;GhADF3 cDNA长度为819 bp,编码139个氨基酸;GhADF4 cDNA长度为804 bp,编码143个氨基酸;GhADF5 cDNA长度为644 bp,编码141个氨基酸。分析表明,GhADF2与GhADF3的氨基酸序列同源性为99%。而且,GhADF2/3与矮牵牛PeADF2之间的氨基酸序列同源性也高达89%。GhADF4与拟南芥AtADF6的亲缘关系较近,二者的氨基酸序列同源性为78%。GhADF5与拟南芥AtADF5的亲缘关系较近,氨基酸序列的同源性为83%。上述结果表明植物ADF基因在进化中具有高度保守性。RT-PCR分析表明,GhADF2在纤维中优势表达,而GhADF5基因则在子叶中表达量最高。另一方面,GhADF3和GhADF4似乎不具有组织特异性或偏爱性表达。同一组织中不同GhADF基因表达量有较大的差异,表明它们可能涉及棉花不同组织生长发育过程的调节。而且,在进化过程中,各ADF同分异构体之间可能发展形成某种功能上的差异性。     

鼠精子发生时期差异表达基因的克隆(英文)

为了分离鼠精子发生时期表达的基因,本文采用mRNA差异显示法,以鼠的粗线期卵母细胞为对照,检测了出生后60天和16天鼠的睾丸。得到12个有差异的片段(Fig.1&Table 1)。克隆测序结果表明,其中5个与已知基因非常吻合,另外6个与一些未知功能的cDNA、ESTs有较高的同源性,只有1个与已知序列没有同源性。Northern杂交分析显示sp1和sp8主要在成年鼠睾丸表达(Fig.4B)。采用5RACE对sp1的cDNA进行了全长分析,该基因编码一个推测是高度磷酸化蛋白的541个氨基酸(Fig.2),其中包括一个核定位信号,无论在核苷酸水平上,还是在氨基酸水平上均没有明显的同源性,仅在2个蛋白区有少量同源氨基酸(Fig.3)。该基因在20-60天龄鼠的睾丸均有表达,并且具有很高的组织特异性只在睾丸里表达(Fig.4A)。因而,这个基因有可能参与减数分裂及其以后的整个过程。可以认为这是一个新基因。我们把它命名为peat (predominantly expressed in adult testis)。     

肝细胞癌相关差异基因的生物信息学及预后分析

肝细胞癌是全球癌症相关死亡的主要原因,目前对肝细胞癌的发病机制研究尚不完善,探索肝细胞癌发生、发展相关的分子标志物及其预后具有重要意义。从GEO数据库获得肝细胞癌组织和非癌组织的基因表达阵列数据GSE84402,利用GEO2R筛选差异表达基因;采用DAVID数据库对差异基因进行GO富集分析和KEGG通路分析;通过STRING数据库和Cytoscape软件构建差异表达基因对应的蛋白质相互作用网络,并从网络中筛选出核心基因(hub genes);结合KM plotter数据库的临床信息对hub genes进行预后分析。结果显示:共得到1 307个差异表达基因,其中上调基因741个,下调基因566个,这些差异表达基因主要涉及细胞分裂、细胞周期、DNA复制及物质代谢等生物学过程及生物通路。通过GO、KEGG及蛋白质相互作用网络筛选出BUB1、BUB1B、CCNA2、CCNB1、CCNB2、CDC20、CDK1、MAD2L1、PLK1等9个hub genes,进一步分析发现hub genes均与细胞周期的调控相关,表明细胞周期的调控失常在肝细胞癌的发生、发展过程中具有重要作用。生存分析显示9个hub genes在肝细胞癌患者中均为表达上调的基因,且与患者预后不良相关,这为寻找肝细胞癌患者预后相关生物标志物的研究提供了线索。     

Function and Chromosome Location of Differentially Expressed Genes in Gastric Cancer

Using Affymetrix U133A oligonucleotide microarrays, screening was done for genes that were differentially expressed in gastric cancer (T) and normal gastric mucosa (C), and their chromosome location was characterized by bioinformatics. A total of 270 genes were found to have a difference in expression levels of more than eight times. Of them 157 were up-regulated (Signal Log Ratio [SLR]≥3), and 113 were down-regulated (SLR≤-3). Except for, four genes with unknown localization, a vast majority of the genes were sporadically distributed over every chromosome. However, chromosome 1 contained the most differentially expressed genes (26 genes, or 9.8%), followed by chromosomes 11 and 19 (both 24 genes, or 9.1%). These genes were also more likely to be on the short-arm of the chromosome (q), which had 173 (65%). When these genes were classified according to their functions, it was found that most (67 genes, 24.8%) belonged to the enzymes and their regulators groups. The next group was the signal transduction genes group (43 genes, 15.9%). The rest of the top three groups were nucleic acid binding genes (17, 6.3%), transporter genes (15, 5.5%), and protein binding genes (12, 4.4%). These made up 56.9% of all the differentially expressed genes. There were also 50 genes of unknown function (18.5%). Therefore it was concluded that differentially expressed genes in gastric cancer seemed to be sporadically distributed across the genome, but most were found on chromosomes 1, 11 and 19. The five groups associated genes abnormality were important genes for further study on gastric cancer.     

Three Dynamin-Encoding Genes Are Differentially Expressed in Developing Rat Brain

Abstract: Dynamin proteins are members of a recently described family of GTPases involved in receptor-mediated processes. To date, three different dynamin-encoding genes have been identified in mammalian tissues. Dynamin I is expressed only in neurons, whereas dynamin II is ubiquitously expressed. A third isoform, dynamin III, was originally isolated from a rat testis cDNA library and shown to be testis-specific. However, here we report the cloning and characterization of dynamin III from brain and lung, demonstrating a more extended pattern of expression for this isoform. In addition, we have investigated the temporal pattern of expression of these three genes during brain development. We find that both dynamin I and dynamin III mRNA levels are up-regulated during embryogenesis, whereas dynamin II mRNA levels remain unchanged. From these results, we conclude that dynamin III is not a testis-specific isoform and, furthermore, that rat brain expresses three different dynamin-encoding genes that are differentially regulated during development. Therefore, this large isoform diversity of dynamin proteins in brain predicts a significant complexity in the understanding of dynamin-based processes in this tissue.     

胃癌差异表达基因在染色体上的定位及其功能分析

利用标准化的Affymetrix公司生产的U133A基因芯片检测胃癌（T）与切缘正常胃黏膜（C）基因表达谱差异,并利用生物信息学方法对检测结果进行差异基因在染色体定位和功能分析。结果表明：胃癌与正常胃黏膜比较差异8倍以上共有270个基因,其中表达上调[信号比的对数值（SLR）≥3]有157个,表达下调（SLR≤-3）有113个。从表达差异的基因在染色体定位分析,发现除4个基因未知其定位外,其余所有差异表达基因散在分布和各条染色体上,但以1号染色体为最多,有26个（占9．8％）,其次是11和19号染色体上分别有24个（各占9．1％）。而差异表达的基因发生在染色体短臂（q）上有173个（占65％）。从表达差异的基因功能分类看,属于酶和酶调控子基因最多（67个,24．8占％）,其次是信号传导基因（43个,占15．9％）,第3类是核酸结合基因（17个,占6．3％）,第4类是转运子基因（15个,占5．5％）,第5类是蛋白结合基因（12个,占4．4％）,还有功能未知的基因有50个,占18．5％。以上5大类共占基因总数56．9％。胃癌差异表达基因散在分布在各条染色体上,但以1、11、19号染色体差异表达基因居多。这5大类（酶和酶调控子、信号传导、核酸结合、转运子、蛋白结合）相关基因异常是今后研究胃癌的重要基因。     

A Robust Statistical Method for Detecting Differentially Expressed Genes

DNA microarray technology allows researchers to monitor the expressions of thousands of genes under different conditions, and to measure the levels of thousands of different DNA molecules at a given point in the life of an organism, tissue or cell. A wide variety of different diseases that are characterised by unregulated gene expression, DNA replication, cell division and cell death, can be detected early using microarrays. One of the major objectives of microarray experiments is to identify differentially expressed genes under various conditions. The detection of differential gene expression under two different conditions is very important in biological studies, and allows us to identify experimental variables that affect different biological processes. Most of the tests available in the literature are based on the assumption of normal distribution. However, the assumption of normality may not be true in real-life data, particularly with respect to microarray data.A test is proposed for the identification of differentially expressed genes in replicated microarray experiments conducted under two different conditions. The proposed test does not assume the distribution of the parent population; thus, the proposed test is strictly nonparametric in nature. We calculate the p-value and the asymptotic power function of the proposed test statistic. The proposed test statistic is compared with some of its competitors under normal, gamma and exponential population setup using the Monte Carlo simulation technique. The application of the proposed test statistic is presented using microarray data. The proposed test is robust and highly efficient when populations are non-normal.     

Analyzing Kernel Matrices for the Identification of Differentially Expressed Genes

One of the most important applications of microarray data is the class prediction of biological samples. For this purpose, statistical tests have often been applied to identify the differentially expressed genes (DEGs), followed by the employment of the state-of-the-art learning machines including the Support Vector Machines (SVM) in particular. The SVM is a typical sample-based classifier whose performance comes down to how discriminant samples are. However, DEGs identified by statistical tests are not guaranteed to result in a training dataset composed of discriminant samples. To tackle this problem, a novel gene ranking method namely the Kernel Matrix Gene Selection (KMGS) is proposed. The rationale of the method, which roots in the fundamental ideas of the SVM algorithm, is described. The notion of ''''the separability of a sample'''' which is estimated by performing -like statistics on each column of the kernel matrix, is first introduced. The separability of a classification problem is then measured, from which the significance of a specific gene is deduced. Also described is a method of Kernel Matrix Sequential Forward Selection (KMSFS) which shares the KMGS method''s essential ideas but proceeds in a greedy manner. On three public microarray datasets, our proposed algorithms achieved noticeably competitive performance in terms of the B.632+ error rate.     

用基因表达谱芯片研究人正常肝和肝细胞癌中差异表达的基因

以基因表达谱芯片对人正常肝及肝癌组织基因表达的差异性进行了研究比较。奖４０９６条人ｃＤＮＡ用点样仪点在特制玻片上制备成表达谱芯片;利用肝和肝癌组织的ｍＲＮＡ通过逆转录方法,将Ｃｙ３和Ｃｙ５２种荧光分别标记到两种组织的ｃＤＮＡ上,制备成ｃＤＮＡ探针,并与表达谱芯片进行杂交及扫描,重复４次实验,通过计算机数据处理判定基因是否在上述２种组织中有表达差异,筛选出差异表达的基因共９０３条。基因芯片技术可同时     

差异表达基因分离技术的研究进展

分离并克隆差异表达基因是生命科学的研究热点.近年来,以差示筛选、扣除杂交等基本方法为基础,先后出现了抑制差减杂交,微阵列技术等多种分析差异表达基因的技术, 使差异表达基因分离方法不断完善.对这些方法的优缺点、发展趋势及应用前景进行了简要综述.     

帕金森病多巴胺神经细胞的差异表达基因分析

帕金森病(Parkinson’s disease,PD)是一种常见的神经退行性疾病,影响群体广泛。该文分析了帕金森病多巴胺细胞的表达和DNA甲基化信息,识别出了新的表达或者DNA甲基化异常的基因,并分析了这些基因与帕金森病的关联。结果表明:相比于正常细胞,帕金森病细胞中与微管形成相关的基因表达上调,这些基因包括SLAIN1、TAGLN3和TUBB2B;天然免疫关联的基因(如LY96)下调。另一个上调基因SCG5推测与免疫应激响应相关。DNA甲基化变化在启动子区显著,除了调节基因转录,这些变化可能通过PRC1和Pc G复合物改变染色质的活性水平。此外,表达水平和DNA甲基化同时调整的基因与轴突定向、胞内运输、神经元分化及迁移等功能有关。以上结果提供了对帕金森病机理特征的新的认识。     

Suppression Subtractive Hybridization Identified Differentially Expressed Genes in Pistil Mutations in Wheat

To understand the molecular mechanism of the three pistils mutation in wheat, two forward subtractive cDNA libraries from two pairs of near-isogenic wheat lines, Chuanmai 28 three pistils (CMTP) and Chinese Spring three pistils (CSTP), were constructed using SSH. A total of 68 clones in CMTP lines and 197 clones in CSTP lines were identified as potentially over-expressed clones. Thirty-two out of 68 clones in CMTP lines belonged to unknown proteins; while, the remaining 30 clones shared homology to diverse classes of genes involved in protein modulation and protein synthesis, signal transduction, and ion transporters. Approximately 67% of genes in CSTP lines were either unclassified or had no matches (“no hits”) in the database and about 33% of identified genes encoded polypeptides with known functions. Sequence comparisons of cDNA clones between the two forward cDNA libraries revealed that four genes, encoding thioredoxin H, ubiquitin protein ligases, MCM2, and ubiquinol-cytochrome C reductase complex 14 kDa proteins, were over-expressed in both libraries. These genes would likely play an important role in determining the three pistils trait in the mutant wheat line.