肝癌差异表达基因HCUTAL2在酵母Pichia pastoris中的表达

利用正常肝组织和肝癌组织的mRNA,通过逆转录方法,将Cy3和Cy5二种荧光分别标记到2种组织的cDNA上,制备成cDNA探针,并与包含4096条各种人类基因的DNA表达谱芯片进行杂交及扫描,重复11次实验,通过计算机数据处理判定基因是否在上述2种组织中存在表达差异,从而鉴定参与肿瘤发生的基因,其中1类差异表达基因为CUTA的高度同源基因,该基因可能参与重金属离子在体的代谢,对该基因拟编码的蛋白质进行酵母表达。     

地高辛标记cDNA探针检测植物谷胱甘肽过氧化物酶基因表达及方法研究

本文利用RT-PCR和PCR技术制备地高辛标记的谷胱甘肽过氧化物酶(ATGPX3)cDNA探针,分别进行DNA和RNA斑点和印迹杂交分析.通过调整杂交液组分的浓度和增加10%硫酸葡聚糖的方法,改进杂交反应.实验结果表明,改良的方法不仅提高了杂交效率,而且明显检测到RNA杂交印迹反应.另外,利用地高辛标记cDNA探针技术也检测到了植物激素ABA诱导的ATGPX3基因的表达,同时证明了该方法可以用来检测植物基因的表达.     

应用表达谱基因芯片筛选植物激活蛋白处理水稻相关差异基因

应用基因表达谱检测植物激活蛋白处理水稻相关差异基因的表达,建立相关基因表达谱。用Cy5和Cy3分别标记激活蛋白处理和对照cDNA,将两种荧光探针混合,与载有10368位点的水稻表达谱cDNA基因芯片进行杂交,并用芯片扫描系统进行扫描,通过Cy5与Cy3信号强度比值的计算研究基因的表达差异。共获得97个差异表达基因,其中上调基因4个,下调基因93个。应用基因表达谱芯片成功筛选了植物激活蛋白处理水稻差异表达的基因,为深入揭示该激活蛋白的作用机制研究提供依据。     

非修饰性寡核苷酸芯片用于HLA-DQA分型的初步研究

利用非修饰寡核苷酸基因芯片技术,根据HLAⅡ类抗原DQA位点不同基因亚型的特异性序列设计探针,制成分型芯片;待测样本经PCR反应标记上荧光之后,与探针在芯片上进行杂交,根据杂交产生的荧光信号值,确定样品DQA位点基因亚型。将这一方法应用于125例临床样本和10例标准品的HLA-DQA分型,该方法分型的结果与准确结果相比,准确率达到93.3%。实验结果表明:非修饰性寡核苷酸芯片用于HLA-DQA分型技术可行。     

用基因芯片研究苦丁茶甙对K562细胞基因表达的影响

以人红白血病K562细胞株为材料，应用基因芯片技术研究苦丁茶甙对人红白血病K562细胞作用前后基因表达的差异，以诱导分化药物羟基脲处理作为正对照，分别提取药物处理前后细胞RNA进行逆转录cDNA，使获得的cDNA分别标记上Cy3和Cy5两种荧光物质；然后与由1632条cDNA片段制作的基因表达谱芯片杂交，经扫描及对获得的数据用相关软件分析，确定K562细胞经苦丁茶甙处理前后差异表达基因48条，有41条与羧基脲处理相同，其中上调表达的基因有32条，下调表达的基因有16条，这将为进一步建立基因芯片技术药物筛选模型奠定基础。     

基于微分的cDNA基因芯片图像自动划格算法

针对cDNA基因芯片数据分析中划格对提取杂交荧光样点杂交强度信息的重要作用，提出了一种基于微分的cDNA基因芯片图像自动划格算法，采用NCBI上GEO数据库中cDNA基因芯片图像进行划格实验，验证了该算法的有效性。     

水稻基因表达谱分析寡核苷酸芯片制备的研究

 首先有目的地搜集了50个水稻花序相关基因,进行了探针的设计、筛选及合成纯化,用点样仪以微阵列的形式将其点于醛基化的玻璃片上;将3个不同生长阶段的水稻花序材料的总RNA经荧光标记反转录后与寡核苷酸芯片进行杂交.用ScanArray3000对获得的表达谱进行扫描分析显示,芯片图像背景均匀,信号清晰.用ImaGene4.0软件对表达谱分析表明,候选基因在水稻花序3个不同发育阶段的材料中,表达水平有显著差异.为进一步进行水稻寡核苷酸芯片的制备及应用奠定了基础.     

0-1整数规划问题的巨磁电阻型DNA计算模型

给出了基于GMR(巨磁电阻)型DNA芯片技术的0-1整数规划问题的DNA计算模型。将问题的变量编码成DNA链,在GMR型芯片表面固定DNA探针,然后将被生物素标记的待分析目标DNA链与探针进行充分杂交,通过芯片上的GMR传感器对芯片上纳米磁珠的检测,以电信号方式输出,得到问题的解,避免了荧光分析中的信号转换而引起的失真。该模型具有较高灵敏度,信号检测和分析较为简单,对信号检测设备要求较低。     

光生物素标记DNA荧光法微孔板DNA杂交测定类单胞菌DNA-DNA相关性的研究

应用光生物素标记DNA荧光法微孔板DNA杂交测定类单胞菌的分离菌株和参考菌株DNA-DNA相关性.热变性DNA固化在96孔微孔板,光生物素标记DNA与固化DNA杂交,测定酶链霉抗生物素偶联β-D-半乳糖苷酶(Strept-avidin-conjugated β-D-galctosidase)作用于敏感荧光底物4-甲基荧光素-β-D-半乳糖苷(4-methylumbelliferyl-β-D-galactoside).在波长365nm激发450nm发射,荧光分析仪测定杂交的荧光强度.根据荧光强度,计算分离菌株和参考菌株DNA-DNA相关性.试验结果表明:光生物素标记ONA荧光法微孔板DNA杂交是更敏感的方法,与光生物标记DNA硝酸纤维膜比色法点杂交、DNA动力学测定DNA同源性,数值分类法的结果基本相符.     

甲基乙二醛诱导牙周膜成纤维细胞基因表达与分析

运用基因芯片研究甲基乙二醛诱导人牙周膜成纤维细胞基因表达谱的变化。原代培养人牙周膜成纤维细胞,诱导组以终质量浓度为0.1 g/L的甲基乙二醛刺激培养细胞,对照组不含甲基乙二醛。24 h后收获细胞,提取mRNA,逆转录cDNA时用Cy3和Cy5荧光染料标记,制备成cDNA探针,与表达谱芯片进行杂交、扫描和分析。芯片检测结果用实时定量聚合酶链反应验证和生物信息学分析。结果共有18条基因显著差异表达,其中上调基因有11条,下调基因有7条,差异性表达的基因按功能可分为程序性细胞死亡、信号转导、细胞因子、代谢酶类、载体蛋白和未知基因等。与程序性细胞死亡、信号转导和细胞因子相关基因的差异表达可能是甲基乙二醛通过线粒体信号通路,诱导人牙周膜成纤维程序性细胞死亡,破坏牙周组织增生,从而导致牙周病发生的机制。     

Detection and analysis system for hybridization images of lab-in-a-tube microarray

A lab-in-a-tube microarray system is developed for sample inspection and signal detection by fabricating a fiat transparent window cap of the Eppendorf tube. The oligonucleotide microarray is immobilized on the inner surface of the cap. A small vessel is placed in an Eppendorf tube for storing hybridization solutions. With the microarray system, the full biochemical processes, including gene fragment amplification, fluorescence labeling, hybridization, and fluorescence detection, have been performed in the sealed tube without opening the cap. The images are obtained from a fluorescence microscope and captured by a CCD, and the data are transported to a computer through the universal serial bus （USB）. After noise reduction, signal intensity is determined from hybridization image and the presence of gene fragments is identified. The final data output includes sample information, process steps, and hybridization results. A lab-ina-tube microarray system for detecting ten respiratory viruses at a single detection is designed. High detection throughput and accuracy have been demonstrated with the system.     

Gene association study with SVM, MLP and cross-validation for the diagnosis of diseases

Gene association study is one of the major challenges of biochip technology both for gene diagnosis where only a gene subset is responsible for some diseases, and for the treatment of the curse of dimensionality which occurs especially in DNA microarray datasets where there are more than thousands of genes and only a few number of experiments (samples). This paper presents a gene selection method by training linear support vector machine (SVM)/nonlinear MLP (multilayer perceptron) classifiers and testing them with cross-validation for finding a gene subset which is optimal/suboptimal for the diagnosis of binary/multiple disease types. Genes are selected with linear SVM classifier for the diagnosis of each binary disease types pair and tested by leave-one-out cross-validation; then, genes in the gene subset initialized by the union of them are deleted one by one by removing the gene which brings the greatest decrease of the generalization power, for samples, on the gene subset after removal, where generalization is measured by training MLPs with leave-one-out and leave-four-out cross-validations. The proposed method was tested with experiments on real DNA microarray MIT data and NCI data. The result shows that it outperforms conventional SNR method in the separability of the data with expression levels on selected genes. For real DNA microarray MIT/NCI data, which is composed of 7129/2308 effective genes with only 72/64 labeled samples belonging to 2/4 disease classes, only 11/6 genes are selected to be diagnostic genes. The selected genes are tested by the classification of samples on these genes with SVM/MLP with leave-one-out/both leave-one-out and leave-four-out cross-validations. The result of no misclassification indicates that the selected genes can be really considered as diagnostic genes for the diagnosis of the corresponding diseases.     

Gene association study with SVM, MLP and cross validation for diagnosis of diseases

Gene association study is one of the major challenges of biochip technology both for gene diagnosis where only a gene subset is responsible to some diseases, and for treatment of curse of dimensionality which occurs especially in DNA microarray datasets where there are more than thousands of genes and only a few number of experiments (samples). This paper presents a gene selection method by training linear support vector machine (SVM)/nonlinear MLP (multi-layer perceptron) classifiers and testing them with cross validation for finding a gene subset which is optimal/suboptimal for diagnosis of binary/multiple disease types. Genes are selected with linear SVM classifier for the diagnosis of each binary disease types pair and tested by leave-one-out cross validation; then, genes in the gene subset initialized by the union of them are deleted one by one by removing the gene which brings the greatest decrease of the generalization power, for samples, on the gene subset after removal, where generalization is measured by training MLPs with leave-one-out and leave-4-out cross validations. The proposed method was tested with experiments on real DNA microarray MIT data and NCI data. The result shows that it outperforms conventional SNR method in separability of the data with expression levels on selected genes. For real DNA microarray MIT/NCI data, which is composed of 7129/2308 effective genes with only 72/64 labeled samples belonging to 2/4 disease classes, only 11/6 genes are selected to be diagnostic genes. The selected genes are tested by classification of samples on these genes with SVM/MLP with leave-one-out/both leave-one-out and leave-4-out cross validations. The result of no misclassification indicates that the selected genes can be really considered as diagnostic genes for the diagnosis of the corresponding diseases.     

Gene association study with SVM, MLP and cross validation for diagnosis of diseases

Gene association study is one of the major challenges of biochip technology both for gene diagnosis where only a gene subset is responsible for some diseases, and for the treatment of the curse of dimensionality which occurs especially in DNA microarray datasets where there are more than thousands of genes and only a few number of experiments （samples）. This paper presents a gene selection method by training linear support vector machine （SVM）/nonlinear MLP （multilayer perceptron） classifiers and testing them with cross-validation for finding a gene subset which is optimal/suboptimal for the diagnosis of binary/multiple disease types. Genes are selected with linear SVM classifier for the diagnosis of each binary disease types pair and tested by leave-one-out cross-validation; then, genes in the gene subset initialized by the union of them are deleted one by one by removing the gene which brings the greatest decrease of the generalization power, for samples, on the gene subset after removal, where generalization is measured by training MLPs with leaveone-out and leave-four-out cross-validations. The proposed method was tested with experiments on real DNA microarray MIT data and NCI data. The result shows that it outperforms conventional SNR method in the separability of the data with expression levels on selected genes. For real DNA microarray MIT/NCI data, which is composed of 7129/2308 effective genes with only 72/64 labeled samples belonging to 2/4 disease classes, only 11/6 genes are selected to be diagnostic genes. The selected genes are tested by the classification of samples on these genes with SVM/MLP with leave-one-out/both leave-one-out and leave-four-out cross-validations. The result of no misclassification indicates that the selected genes can be really considered as diagnostic genes for the diagnosis of the corresponding diseases.     

Design and application of 60mer oligonucleotide microarray in SARS coronavirus detection

In April 2003, a novel coronavirus[1,2] which was associated with cases of Severe Acute Respiratory Syn-drome (SARS) was first isolated and sequenced in Canada. The genome of SARS coronavirus (SARS-CoV) is 29727[3] nucleotides in length and has 11 known open reading frames (ORFs). Although the genome organiza-tion of this virus is similar to that of other coronaviruses, phylogenetic analyses and sequence alignment show that SARS-CoV is not closely related to any of the previously ch…     

Gene expression profiles of the developing human retina

Retinaplaysimportantrolesintheperception,proc-essandtransmissionofvisualsignalsandthefunctionsoftheretinadepend,toalargeextent,onitshighlyorganizedstructure.During3—6weeksinhumanembryogenesis,theneuralectodermgrowsoutfromthediencephalonstoformtheopticvesicleandtheninvaginatestoformtheopticcup.Theouterlayeroftheopticcupbecomesthenon-neuralretinalpigmentepithelium(RPE)andtheinnerlayerbecomestheneuralretina.RPEcellsproliferateslowlyandappeardifferentiatedandpigmentedasearlyas6—8weeksandremain…     

基于DNA探针和FISH技术对两种有害浮游植物的检测研究

针对两种典型的有害浮游植物微小原甲藻和Takayama pulchellum(T.pulchellum)各设计了4条特异性探针,并采用基于PCR管载体的荧光原位杂交检测方法检测了这些探针的标记效果。实验结果表明,针对微小原甲藻和T.pulchellum核糖体大、小亚基DNA(LSU和SSU rDNA)的序列信息所设计的8条荧光标记探针均能够特异性地识别这些目标藻。各探针的标记效果有一定差异,经过标记的目标藻细胞在单种和自然水样混合样品中均可以通过荧光显微镜进行识别和区分。针对微小原甲藻和T.pulchellum的荧光原位杂交检测方法的建立将有助于对样品中这些目标藻进行快速准确地检测和监测。     

低能离子束辐照诱导的水稻差异表达基因的调控网络研究

用基因芯片检测鉴定离子束辐照下水稻中的差异表达基因（DEGs）.结果共检测到26个上调和6个下调的差异表达基因.共表达网络分析（RiceNET）表明,这些基因都存在着直接或间接的互作网络,暗示水稻存在着以前没有充分认识的基因网络.为进一步研究植物应答离子束辐照候选基因提供了基础.