基于PCR的基因差异表达分析技术

基因差异表达分析是研究许多生物学过程的分子基础的一条直接、有效的途径。自DDRT-PCR技术建立以来,一系列基于PCR的基因差异表达分析技术,如SAGE、SSH、RDA和DNA微阵列等相继发展起来,为分析和克隆差异表达的基因提供了更为快速、灵敏的工具。本对这几种方法进行了简要综述,比较了不同方法的优缺点,并展望了今后基因差异表达研究技术的发展方向。     

A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R

This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.     

基因差异表达与杂种优势形成机制探讨

对杂种优势这一普遍而重要的生物学现象研究虽有百余年的历史, 但其根本机理尚未阐述清楚。继基因组组成差异及基因效应研究之后, 基因表达差异成为探寻杂种优势分子机理新的切入点。旨在通过揭示杂种中等位基因差异表达、杂种与亲本间基因差异表达的调控机制, 来认识杂种优势形成的分子机理, 从而达到指导育种实践的目的。文章概述了杂种等位基因差异表达现象及其产生机理, 总结了杂种与亲本相比所呈现出的加性、显性和超显性等多种差异基因表达模式, 归纳了表达谱研究筛选出的与杂种优势形成有关的基因, 以及某些关键生化代谢途径对杂种优势形成的贡献。但由于杂种优势机理的复杂性, 基因表达研究并没有得出统一的表达模式, 大多数杂种优势基因也不能被归属为同一类别。尽管如此, 基因表达谱研究毕竟迈出了解析杂种优势形成复杂基因表达网络的第一步, 随着表达谱技术和生物信息学的不断更新和发展, 杂种优势形成的分子机理有望在基因表达层面上取得突破。     

PlantRGS: a web server for the identification of most suitable candidate reference genes for quantitative gene expression studies in plants

Normalization of quantitative gene expression data with a suitable reference gene is essential for accurate and reliable results. However, the availability and choice of most suitable reference gene(s) showing uniform expression across all the experimental conditions remain a drawback. We have developed a web server, PlantRGS (http://www.nipgr.res.in/PlantRGS), for the identification of most suitable candidate reference gene(s) at the whole-genome level using microarray data for quantitative gene expression studies in plants. Microarray data from more than 11 000 tissue samples for nine plant species have been included in the PlantRGS for meta-analysis. The web server provides a user-friendly graphical user interface-based analysis tool for the identification of most suitable reference genes in the selected plant species under user-defined experimental conditions. Various parameter options and output formats will help users to investigate desired number of most suitable reference genes with wide range of expression levels. Validation of results revealed that novel reference genes identified by the PlantRGS outperforms the traditionally used reference genes in terms of expression stability. We anticipate that the PlantRGS will provide a platform for the identification of most suitable reference gene(s) under given experimental conditions and facilitate quantitative gene expression studies in plants.     

Deconstructing language by comparative gene expression: from neurobiology to microarray

Language is a defining characteristic of our species that has emerged quite recently on an evolutionary timescale. Understanding the neurobiological substrates and genetic underpinnings of language constitutes a basic challenge for both neuroscience and genetics. The functional localization of language in the brain has been progressively refined over the last century through studies of aphasics and more recently through neuroimaging. Concurrently, structural specializations in these brain regions have been identified by virtue of their lateralization in humans and also through comparisons with homologous brain regions in non-human primate species. Comparative genomics has revealed the genome of our closest living relative, the chimpanzee, to be astonishingly similar to our own. To explore the role that changes in the regulation of gene expression have had in recent human evolution, several groups have used microarrays to compare expression levels for thousands of genes in the brain between humans and chimpanzees. By applying this approach to the increasingly refined peri-sylvian network of brain regions involved in language, it may be possible to discern functionally significant changes in gene expression that are universal among humans but unique to our species, thus casting light on the molecular basis of language in the brain.     

Deconstructing language by comparative gene expression: from neurobiology to microarray

Language is a defining characteristic of our species that has emerged quite recently on an evolutionary timescale. Understanding the neurobiological substrates and genetic underpinnings of language constitutes a basic challenge for both neuroscience and genetics. The functional localization of language in the brain has been progressively refined over the last century through studies of aphasics and more recently through neuroimaging. Concurrently, structural specializations in these brain regions have been identified by virtue of their lateralization in humans and also through comparisons with homologous brain regions in non-human primate species. Comparative genomics has revealed the genome of our closest living relative, the chimpanzee, to be astonishingly similar to our own. To explore the role that changes in the regulation of gene expression have had in recent human evolution, several groups have used microarrays to compare expression levels for thousands of genes in the brain between humans and chimpanzees. By applying this approach to the increasingly refined peri-sylvian network of brain regions involved in language, it may be possible to discern functionally significant changes in gene expression that are universal among humans but unique to our species, thus casting light on the molecular basis of language in the brain.     

Microarray analysis of differential gene expression in sensitive and resistant pig to Escherichia coli F18

In this study, Agilent two‐colour microarray‐based gene expression profiling was used to detect differential gene expression in duodenal tissues collected from eight full‐sib pairs of Sutai pigs differing in adhesion phenotype (sensitivity and resistance to Escherichia coli F18). Using a two‐fold change minimum threshold, we found 18 genes that were differentially expressed (10 up‐regulated and eight down‐regulated) between the sensitive and resistant animal groups. Our gene ontology analysis revealed that these differentially expressed genes are involved in a variety of biological processes, including immune responses, extracellular modification (e.g. glycosylation), cell adhesion and signal transduction, all of which are related to the anabolic metabolism of glycolipids, as well as to inflammation‐ and immune‐related pathways. Based on the genes identified in the screen and the pathway analysis results, real‐time PCR was used to test the involvement of ST3GAL1 and A genes (of glycolipid‐related pathways), SLA‐1 and SLA‐3 genes (of inflammation‐ and immune‐related pathways), as well as the differential genes FUT1, TAP1 and SLA‐DQA. Subsequently, real‐time PCR was performed to validate seven differentially expressed genes screened out by the microarray approach, and sufficient consistency was observed between the two methods. The results support the conclusion that these genes are related to the E. coli F18 receptor and susceptibility to E. coli F18.     

家蚕羧酸酯酶基因克隆及差异表达

家蚕浓核病毒 (Bombyx mori densonucleosis virus,BmDNV)是蚕业生产上危害比较严重的一类病毒。用完全抗浓核病中国镇江株(BmDNV-Z)的家蚕品系秋丰、感性品系华八及以华八为轮回亲本回交8代和自交8代构建的近等基因系BC₈为材料,采用mRNA荧光差显技术首次分离克隆了家蚕羧酸酯酶（B. mori carboxylesterase,BmCarE）基因全长cDNA,并用实时荧光定量PCR检测了添毒后12 h、36 h、72 h BmCarE在感、抗BmDNV-Z家蚕品系中肠内的表达差异。结果表明： （1）添毒后12 h不同品系家蚕中肠BmCarE表达差异最大,抗性品系BC₈和秋丰分别是感性品系华八的17.714倍和3.602倍,三者彼此间的差异达到极显著水平;（2）同一品系添毒后12 h与添清水后12 h BmCarE表达也有较大差异, BC₈添毒是BC₈添清水的15.08倍, 秋丰添毒是秋丰添清水的3.39倍, 差异达到极显著水平,而华八添毒和添清水的BmCarE表达量均低,二者差异不显著;（3）同一品系添毒后不同时间BmCarE表达也有较大差异, BC₈和秋丰添毒后12 h BmCarE表达量最高,显著高于各自添毒后36 h和72 h表达水平,而添毒后36 h与72 h表达无显著差异;华八添毒后12 h、36 h和72 h,BmCarE表达无显著差异。上述结果提示羧酸酯酶基因可能与家蚕抗浓核病毒有一定关系。     

Tuber on a chip: differential gene expression during potato tuber development

Potato tuber development has proven to be a valuable model system for studying underground sink organ formation. Research on this topic has led to the identification of many genes involved in this complex process and has aided in the unravelling of the mechanisms underlying starch synthesis. However, less attention has been paid to the biochemical pathways of other important metabolites or to the changing metabolic fluxes occurring during potato tuber development. In this paper, we describe the construction of a potato complementary DNA (cDNA) microarray specifically designed for genes involved in processes related to tuber development and tuber quality traits. We present expression profiles of 1315 cDNAs during tuber development where the predominant profiles were strong up- and down-regulation. Gene expression profiles showing transient increases or decreases were less abundantly represented and followed more moderate changes, mainly during tuber initiation. In addition to the confirmation of gene expression patterns during tuber development, many novel differentially expressed genes were identified and are considered as candidate genes for direct involvement in potato tuber development. A detailed analysis of starch metabolism genes provided a unique overview of expression changes during tuber development. Characteristic expression profiles were often clearly different between gene family members. A link between differential gene expression during tuber development and potato tissue specificity is described. This dataset provides a firm basis for the identification of key regulatory genes in a number of metabolic pathways that may provide researchers with new tools to achieve breeding goals for use in industrial applications.     

Combining microarrays and genetic analysis

Gene expression can be studied at a genome-wide scale with the aid of modern microarray technologies. Expression profiling of tens to hundreds of individuals in a genetic population can reveal the consequences of genetic variation. In this paper it is argued that the design and analysis of such a study is not a matter of simply applying the existing and more-or-less standard computational tools for microarrays to a new type of experimental data. It is shown how to fully exploit the power of genetics through optimal experimental design and analysis for two major microarray technologies, cDNA two-colour arrays and Affymetrix short oligonucleotide arrays.     

Bayesian robust inference for differential gene expression in microarrays with multiple samples

We consider the problem of identifying differentially expressed genes under different conditions using gene expression microarrays. Because of the many steps involved in the experimental process, from hybridization to image analysis, cDNA microarray data often contain outliers. For example, an outlying data value could occur because of scratches or dust on the surface, imperfections in the glass, or imperfections in the array production. We develop a robust Bayesian hierarchical model for testing for differential expression. Errors are modeled explicitly using a t-distribution, which accounts for outliers. The model includes an exchangeable prior for the variances, which allows different variances for the genes but still shrinks extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and it can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. The method is illustrated using two publicly available gene expression data sets. We compare our method to six other baseline and commonly used techniques, namely the t-test, the Bonferroni-adjusted t-test, significance analysis of microarrays (SAM), Efron's empirical Bayes, and EBarrays in both its lognormal-normal and gamma-gamma forms. In an experiment with HIV data, our method performed better than these alternatives, on the basis of between-replicate agreement and disagreement.     

Microarray technology GEM microarrays and drug discovery

Incyte Genomics' GEM™ Gene Expression Microarray is a proven genomics tool used by a large number of pharmaceutical companies to speed up the drug discovery and development process. The development and integration of this technology, together with Incyte's sequence databases and clone resources, have resulted in GEM microarrays that span approximately 60,000 human genes as well as approximately 60,000 plant, rat, mouse, yeast, and bacterial genes. The technology underlying the use of these arrays and their application to the drug discovery process is highlighted. Journal of Industrial Microbiology & Biotechnology (2002) 28, 180–185 DOI: 10.1038/sj/jim/7000136 Received 16 November 2000/ Accepted in revised form 01 March 2001     

芯片数据标准化方法比较研究

在基因芯片实验中,基因表达水平之间的相关性在推断基因间相互关系时起到非常重要的作用.未经标准化处理的芯片数据基因之间往往都呈现出很强的相关性,这些高相关性一部分是由基因表达水平变化引起的,而另外一部分是由系统偏差引起的.对芯片数据进行标准化处理的目的之一是消除系统偏差引起的高相关性,同时保留由真正生物学原因引起的基因表达水平高相关性.虽然目前对标准化方法已经有了不少比较研究,但还较少有人研究标准化方法对基因之间相关系数的影响,以及哪种方法最有利于恢复基因之间的相关性结构.通过对基因表达水平数据的模拟,具体比较了几种常用标准化方法的效果,从而给出最有利于恢复基因之间相关性结构的那种标准化方法.     

Cancer outlier differential gene expression detection

We study statistical methods to detect cancer genes that are over- or down-expressed in some but not all samples in a disease group. This has proven useful in cancer studies where oncogenes are activated only in a small subset of samples. We propose the outlier robust t-statistic (ORT), which is intuitively motivated from the t-statistic, the most commonly used differential gene expression detection method. Using real and simulation studies, we compare the ORT to the recently proposed cancer outlier profile analysis (Tomlins and others, 2005) and the outlier sum statistic of Tibshirani and Hastie (2006). The proposed method often has more detection power and smaller false discovery rates. Supplementary information can be found at http://www.biostat.umn.edu/~baolin/research/ort.html.     

Involvement of differential gene expression and mRNA stability in the developmental regulation of the hsp 30 gene family in heat-shocked Xenopus laevis embryos

Four complete hsp 30 genes have been isolated from Xenopus laevis: hsp 30A, hsp 30B (a pseudogene), hsp 30C, and hsp 30D. The hsp 30A and hsp 30C genes are first heat inducible at the early tailbud stage, as determined by RNase protection and RT-PCR assays. In this study, we determined by RT-PCR that the hsp 30D gene was first heat inducible (33^oC for 1 h) at the mid-tailbud stage, approximately 1 day later in development than hsp 30A and hsp 30C. Furthermore, using Northern blot analysis, we detected the presence of very low levels of hsp 30 mRNA at the heat-shocked late blastula stage. The relative levels of these pre-tailbud (PTB) hsp 30 mRNAs increased at the gastrula and neurula stage followed by a dramatic enhancement in heat shocked tail-bud and tadpole stage embryos (50- to 100- fold relative to late blastula). Interestingly, treatment of blastula or gastrula embryos at high temperatures (37^oC for 1 h) or with the protein synthesis inhibitor, cycloheximide, followed by heat shock, led to enhanced accumulation of the pre-tailbud (PTB) hsp 30 mRNAs. hsp 70, hsp 87, and actin messages were not stabilized at high temperatures or by cycloheximide treatment. Finally, hsp 30D mRNA was not detected by RT-PCR analysis of cycloheximidetreated, heat-shocked blastula stage embryos, confirming that it is not a member of the PTB hsp 30 mRNAs. This study indicates that differential gene expression and mRNA stability are involved in the regulation of hsp 30 gene expression during early Xenopus laevis development. © 1995 Wiley-Liss, Inc.