表达谱基因芯片

表达谱基因芯片具有高通量、缩微、多参数、平行化等优点.在功能基因组学研究中正发挥越来越重要的作用.就其原理、应用、存在问题及解决策略等进行了综述.     

基因芯片在骨骼肌老龄化相关基因表达谱中的应用

对老龄组大鼠 (30月龄 )和年轻对照组大鼠 (3月龄 )的腓肠肌超微结构进行观察 ,可以看到前者肌肉肌纤维萎缩伴有线粒体空泡变性。并进行总RNA抽提、mRNA纯化、探针制备 ,应用基因芯片筛选老龄化相关基因 ,两组大鼠骨骼肌重复出现的差异表达基因 12 7个 ,下调基因涉及能量代谢、信号转导 ,上调基因涉及蛋白质分解、细胞凋亡     

基因芯片在研究病毒感染宿主基因表达谱中的应用

人类基因组计划提供的海量基因信息以及功能基因组学理论和技术的出现,为研究病毒与宿主相互作用提供了难得的历史机遇。病毒作为一种严格的细胞内寄生物,感染宿主后会引起宿主细胞形态和功能的改变。这些变化主要由于病毒感染导致宿主细胞基因表达变化所引起。通过基因芯片技术研究病毒感染宿主的基因表达谱变化,可以发现病毒感染宿主细胞在分子水平上的应答,从而为病毒性疾病的预防、诊断和临床治疗提供新的策略。     

应用cDNA微阵列研究肿瘤基因表达谱

随着研究水平的深入 ,对基因在发育过程中、生理反应中和疾病发展中表达上的时空差别 ,基因蛋白质产物的亚细胞定位和细胞分子间的相互作用等研究领域的开拓 ,所涉及到的基因的广泛性用传统方法研究已很难达到要求 ,因此一些新的研究方法应运而生 ,基因表达谱的研究即为其中之一。基因表达谱 ( ｇｅｎｅｅｘｐｒｅｓｓｉｏｎｐｒｏｆｉｌｅ)是指细胞中所有基因表达的格局 ,具体说来就是指细胞中产生所有ｍＲＮＡ的总和。对于所有的ｍＲＮＡ来说 ,通常采用的研究方法是将其逆转录成ｃＤＮＡ ,通过构建ｃＤＮＡ文库 ,再进行分析研究。原先采用…     

应用基因芯片技术检测肝组织的基因表达

为检测正常肝组织中的基因表达状况,采用cDNA microarray技术对正常肝组织中表达的1500个基因进行定量,结果为97个基因较高表达,1010个基因中度表达,380个基因低表达,结果是cDNA microarray技术是大规模检测基因表达的有效方法。     

基因芯片技术在微生物学研究中的应用

近年来,基因芯片技术的诞生使得在一个实验中就可以同时对成千上万个基因进行转录水平的表达和DNA同源性分析成为可能。该项技术已被应用于揭示许多微生物体的转录表达和基因组的差异,随着越来越多的微生物基因组全序列测定的完成,基因芯片正逐渐成为许多微生物学研究领域中的一项常规技术。归纳了该技术在微生物生理,致病性,流行病学,生态,进化,代谢工程及发酵优化等研究中的应用。     

基因表达谱芯片杂交影响因素的初步研究

通过对K562细胞基因表达谱芯片杂交影响因素的研究表明,用限制性显示技术制备的cDNA探针长度较均一,适合基因芯片杂交;在42℃条件下含甲酰胺的杂交液中杂交16．20h,可保证样品的有效杂交。并表现出很好的杂交特异性;用RD-PCR或线性PCR对少量样品进行扩增,并用荧光(Cy3或Cy5)标记的通用引物对样品进行标记,可提高芯片检测的灵敏度;一次杂交反应总RNA的用量仅需0．5～10μg,在每cm^2约含1000～1500个点的阵列中杂交时,标记样品用量1～2μg为宜;用PCR产物纯化柱对荧光标记产物进行纯化,可大大减小背景荧光,提高信噪比;同一批芯片经同一样品杂交结果的重复性很好,相关系数高达97．8％     

用基因芯片技术分析铝胁迫下小麦的基因表达谱

目的:采用基因表达谱分析方法,探讨小麦耐铝的分子机理。方法:利用抑制消减杂交(SSH)技术,以小麦的铝敏感品种Chisholm及其耐铝近等基因系Chisholm-T(其耐铝性来自小麦品种Atlas66)的根尖为材料,构建了2个铝胁迫后的SSHcDNA文库,共含有1628个表达序列标签(EST),利用这些EST制作了小麦根系的cDNA基因芯片。以cDNA基因芯片为平台,在铝胁迫后6h、1d、3d和7d,分别比较Chisholm和Chisholm-T之间的基因表达谱差异。结果:在各个时间点,耐铝和不耐铝小麦材料之间约有5%的EST表现出差异表达。对所有差异表达的EST进行测序分析,序列数据经Pipe-Online2.0进行毗连序列群(contig)拼接,发现只有8.3%的重复序列。结论:SSH是一种非常有效的差减和均一化的建库方法。对有功能注释的差异表达基因进行功能分类分析,表明这些基因参与了植物体内的电子传递、信号传导、植物保护和次生物质的代谢活动。     

免疫性损伤小鼠肝脏的基因表达谱变化

采用卡介苗和脂多糖联合诱导的方法建立小鼠免疫性肝损伤模型, 分别提取正常对照组与免疫性肝损伤组小鼠的肝脏总RNA, 经反转录用Cy3-dUTP和Cy5-dUTP分别标记制备对照组与模型组来源的cDNA探针, 并将cDNA探针与小鼠基因表达谱芯片杂交, 杂交结果经芯片扫描仪扫描并用相关软件进行分析. 结果表明, 与对照组相比, 免疫性肝损伤组有293条基因发生了差异表达, 其中188条基因表达量明显上调, 另外105条基因表达量明显下调. 通过对一些关键差异表达基因的生物学功能分析表明, 卡介苗与脂多糖联合诱导的小鼠免疫性肝损伤的发生、发展过程与肝细胞的免疫反应、细胞合成与代谢、细胞凋亡及转运等过程密切相关, 这对进一步阐明免疫性肝损伤高度相关的基因表达调控网络, 进而阐明免疫性肝损伤的病理机制具有十分重要的作用.     

基因芯片分析干扰素对乙型肝炎病毒转染细胞基因表达的影响

乙型肝炎病毒 (HepatitisBvirus ,HBV)的持续性感染是影响人类健康的重大问题 ,而α 干扰素 (IFN α)和γ 干扰素(IFN γ)分别具有抗HBV的作用。为了研究HBV持续存在对IFN效应基因表达的影响 ,将HepG2细胞和来源于HepG2细胞并整合有HBV基因组的HepG2 .2 .15细胞经IFN α或IFN γ处理 6h后 ,用含 14 112个靶基因的人cDNA基因芯片检测了各组基因表达谱的差异。结果证实 ,许多与细胞周期、增殖、凋亡相关的基因及部分EST和功能未知基因受IFN调节 ;IFN诱导后 ,一些与激酶和信号转导、转录调节、抗原递呈和处理相关的基因在两株细胞间表达存在差异 ,提示HBV影响IFN诱导的细胞基因的表达。进一步挑选部分显著差异表达的基因 ,研究其对HBV复制的影响 ,结果发现在两个细胞株中IFN诱导后基因表达差异的双遍在蛋白 (Diubiquitin)和髓样细胞分化蛋白 (MyD88)均可显著降低HBV抗原表达 ,并证实MyD88能抑制HBV复制。这有助于揭示IFN抗病毒效应及HBV持续性感染机制 ,为分子水平寻找新型抗HBV药物的靶点打下基础。     

基因芯片技术及其在肿瘤基因组学中的应用

基因芯片又称DNA微阵列,分为cDNA微阵列和寡聚核苷酸微阵列。DNA微阵列技术是探索基因组功能的一种强有力工具。扼要介绍基因芯片、表达谱芯片技术和原理,以及基因芯片技术在肿瘤基因组学中的应用。     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Microarray analysis of gene expression profiles in the bovine mammary gland during lactation

Mammary glands undergo functional and metabolic changes during virgin, lactation and dry periods. A total of 122 genes were identified as differentially expressed, including 79 up-regulated and 43 down-regulated genes during lactation compared with virgin and dry periods. Gene ontology analysis showed the functional classification of the up-regulated genes in lactation, including transport, biosynthetic process, signal transduction, catalytic activity, immune system process, cell death, and positive regulation of the developmental process. Microarray data clarified molecular events in bovine mammary gland lactation.     

根据基因表达谱筛选间接互作蛋白质进行蛋白质功能预测

利用基因表达谱数据,通过计算互作蛋白质的表达相关系数,来筛选、优化蛋白质互作网络。结果显示,利用经过筛选的互作数据,根据邻居计数法和卡方法进行功能预测的预测效果明显提高,距离待测蛋白质较远的邻居也包含着与待测蛋白质功能一致的信息。     

Mining gene expression profiles: an integrated implementation of kernel principal component analysis and singular value decomposition

The detection of genes that show similar profiles under different experimental conditions is often an initial step in inferring the biological significance of such genes. Visualization tools are used to identify genes with similar profiles in microarray studies. Given the large number of genes recorded in microarray experiments, gene expression data are generally displayed on a low dimensional plot, based on linear methods. However, microarray data show nonlinearity, due to high-order terms of interaction between genes, so alternative approaches, such as kernel methods, may be more appropriate. We introduce a technique that combines kernel principal component analysis (KPCA) and Biplot to visualize gene expression profiles. Our approach relies on the singular value decomposition of the input matrix and incorporates an additional step that involves KPCA. The main properties of our method are the extraction of nonlinear features and the preservation of the input variables (genes) in the output display. We apply this algorithm to colon tumor, leukemia and lymphoma datasets. Our approach reveals the underlying structure of the gene expression profiles and provides a more intuitive understanding of the gene and sample association.     

Early embryonic death-associated changes in genome-wide gene expression profiles in the fetal placenta of the cow carrying somatic nuclear-derived cloned embryo

Successful somatic nuclear transfer-derived cloning has been reported in cattle; however, the cloned embryo is highly susceptible to death around day 60 of gestation leading to early embryonic loss. The early embryonic death is postulated to possibly arise in part from an atypical placentation. We have performed cDNA macroarray analysis using 3,353 of the previously cataloged 4,165 genes, in order to characterize the early embryonic death-associated changes in genome-wide gene expression profiles in the fetal placenta of the cow carrying somatic nuclear transfer-derived cloned embryo. A more marked difference in the expression profiles was observed between the fetal placentas of the cows with the cloned immotile embryo (CD) and with the cloned motile embryo (CL) or artificial insemination-derived motile embryo (AI), as compared to between the CL and AI placentas, suggesting an aberration of the expression profile in the CD placenta among the three placentas. Further, 291 and 77 genes showed more than twofold elevation and less than 50% reduction, respectively, in either or both of two CD (CD1 and CD2) placentas in comparison with the CL placenta, but no differential expression between the CL and AI placentas. The expression patterns of six genes in the AI, CL, and CD placentas were confirmed in an experiment with an additional sample for each of the three placentas. Among the placental genes showing the early embryonic death-associated changes of expression in the cow with the cloned embryo, IGF2 (elevated gene), and HBA1, HBA2, SPTB, and SPTBN1 genes (reduced gene) are intriguing in that the changes of expression in these genes were observed in an additional sample of CD placenta as well as the CD1 and CD2 placentas, and in that overexpression (for IGF2) and dysfunction or deficiency (for HBA1, HBA2, SPTB, and SPTBN1) result in embryonic lethality.     

Large-scale analysis of gene expression profiles

The accumulation of DNA microarray data has now made it possible to use gene expression profiles to analyse expression data. A gene expression profile contains the expression data for a given gene over various samples, and can be contrasted with an expression signature, which contains the expression data for a single sample. Gene expression profiles are most revealing when samples are grouped appropriately, either by standard clinical or pathological categories or by categories discovered through cluster analysis techniques. Expression profiles can exist at various levels of abstraction, yielding information across various tissues or across diseases within a particular tissue. Hypothesis tests may be applied to expression profiles on a large scale to identify candidate genes of interest.