棉花细胞壁蛋白基因分离鉴定与表达分析

棉纤维起源于棉花胚珠外层表皮细胞．研究棉纤维发育,具有重要理论和实践意义．从棉纤维等组织cDNA文库中分离了186个棉花细胞壁结构蛋白基因,按照所编码的蛋白质结构特点,可将这些基因分为三类;富含脯氨酸细胞壁蛋白（Proline—rich cell wall proteins,PRPs）基因、伸展蛋白（Extensins）或者富含羟脯氨酸糖蛋白（Hydroxyproline—rich glycoprotein,HRGP）基因,富含甘氨酸蛋白（Glycine—rich proteins,GRPs）基因．采用cDNA microarray技术,比较上述基因在开花后10天的野生型棉花纤维和无絮无绒（fuzzless—lintless,fl）突变体胚珠表皮中表达谱发现,其中有7个基因在野生型纤维中特异性或高效性表达,表明它们可能与纤维发育有关．     

授粉后红球姜雌性生殖器官qRT-PCR的内参基因筛选

在研究分析红球姜（Zingiber zerumet (L.) Smith）败育关键调控基因的表达中，筛选授粉后雌性生殖器官发育过程中的内参基因至关重要。本研究根据授粉后不同时间点的红球姜转录组数据库以及相关文献报道的传统内参基因，筛选出10个表达相对稳定的基因Actin-2（ACT2）、Actin-7（ACT7）、Beta tubulin-1（TUB1）、Beta tubulin-5（TUB5）、Alpha tubulin-3（TUA3）、Ubiquitin（UBQ）、Glyceraldehyde-3-phosphate dehydrogenase（GAPDH）、Elongation factor 1-alpha（EF-1）、Cyclophilin（CYP）、Histone（H2A）作为候选内参基因，采用qRT-PCR技术，结合GeNorm、NormFinder和BestKeeper软件对候选内参基因的表达稳定性进行分析。结果表明，在红球姜雌性生殖器官授粉后的发育过程中，GAPDH和UBQ的表达稳定性最好，均适合作为内参基因，且同时使用两种作为内参基因能使实时荧光定量PCR标准化分析结果更精确。因此，最终选择GAPDH和UBQ作为实时荧光定量PCR标准化分析红球姜雌性生殖器官相关基因表达的内参基因。本研究将为探究红球姜败育的分子机理奠定基础，也为近源姜属植物内参基因的筛选提供线索。     

天然棕色棉纤维发育过程中生化物质变化规律的研究

目的:比较棕色棉和白色棉纤维发育过程中生化物质含量的差异,分析棕色棉纤维色素合成与纤维发育的关系,为棕色棉育种提供理论依据。方法:以白色棉泗棉3号为对照,测定4种棕色棉各发育阶段纤维中主要生化物质含量及其动态变化规律,分析了棕色棉纤维各发育时期生化物质含量与棕色棉纤维色素含量相关关系。结果:棕色棉纤维40DPA含水率、10DPA还原糖含量低于白色棉,30-40DPA还原糖含量高于白色棉。白色棉20DPA可溶性蛋白质出现高峰期,棕色棉相应的蛋白质高峰期出现在25DPA,棕色棉纤维生长的各阶段纤维素的含量均低于白色棉;棕色棉和白色棉,含水率、还原性糖、可溶性蛋白质的含量都随着棉纤维发育不断降低,而纤维素含量不断增加。成熟棉纤维色素含量与10DPA还原性糖含量、30-40DPA纤维素含量负相关达到显著水平(p<0.05),与35DPA还原性糖含量正相关达到极显著水平(p<0.01),与15DPA纤维素含量负相关达到极显著水平(p<0.01)。结论:棕色棉纤维的各发育阶段的生化物质含量与白色棉表现明显的差异,但是二者在动态变化规律上表现一致;棕色棉色素合成与棉纤维发育过程中生化物质组成关系密切。     

Gene cloning and molecular breeding to improve fiber qualities in cotton

Cotton fiber is one of known natural resources comprising the highest purity cellulose. It plays an important role world wide in the textile industry. With the acceleration of spinning speeds and the improvement of the people‘s living level, the demand of improving cotton fiber qualities is getting stronger and stronger. So, making clear the developmental model of fiber cell and elucidating systematically the molecular mechanisms of cotton fiber development and regulation will produce a great significance to make full use of cotton gene resources, raise cotton yield and improve fiber quality, and even develop man-made fiber. In the paper, the status of the gene cloning and the molecular breeding to improve cotton fiber quality were reviewed, the importance and potential of gene cloning related with cotton fiber quality were put forward and the proposal and prospect on fiber quality improvement were made. Using national resources available and through the creative exploration in corresponding research, some international leading patents in genes or markers linked with cotton fiber development having Chinese own intellectual property should be licensed quickly. And they can be used to improve cotton fiber quality in cotton breeding practice.     

SCFP,a novel fiber-specific promoter in cotton

To investigate the expression pattern of GhSCFP which was isolated from cotton fiber cDNA library, a 1006 bp upstream fragment of the gene was cloned by chromosome walking and fused to GUSand GFP respectively. Histochemical GUS and GFP fluorescence analysis revealed that the expression of the report genes driven by the promoter sequence was detectable only in outer layer cells during the seed development in the transgentic tobaccos. In transgenic cotton, strong GUS activity was observed in spherical protrusions on 0 dpa （days post anthesis） ovule surface, and in the 2-36 dpa fiber cells, while no GUS signals were detected in the root, leaves, stem, corolla, anther and stigma. Our data demonstrated that GhSCFP upstream sequence is a cotton fiber-specific promoter and this promoter will be useful in the molecular research on fiber cell development and in cotton fiber improvements by genetic modification.     

Prediction of multiple drug resistance phenotype in cancer cell lines using gene expression profiles and phylogenetic trees

When microarray gene expression data are used to predict multiple drug resistance (MDR) phenotypes for anticancer drugs, the normalization strategy and the quality of the selected signature genes are usually the main causes of inconsistency among different experiments. A stable statistical drug response prediction model is urgently required in oncology. In this study, the microarray gene expression data of multiple cancer cell lines with MDR was analyzed. For each probe-set, the expression value was defined as present/absent (1/0) and was classified into a gene set defined with protein domain organization (PDO). After employing the gene content method of phylogenetic analysis, a phylogenetic model (cell tree) for MDR phenotype prediction was built at the PDO gene set level. The results indicate that classification of cancer cell lines is predominantly affected by both the histopathological features and the MDR phenotype (paclitaxel and vinblastine). When applying this model to predict the MDR phenotype of independent samples, the phylogenetic model performs better than signature gene models. Although the utility of our procedure is limited due to sample heterogeneity, it still has potential application in MDR research, especially for hematological tumors or established cell lines.     

优选Atlas微阵列检测胶质瘤基因表达谱的分析方法

从Atlas微阵列多种分析法中优选获得差异基因信息量大且较准确的方法。使用Atlas微阵列检测2例新鲜胶质母细胞瘤组织、获得相应的基因表达谱,选取不同的标准化基值及内参照共组合成8种不同方法,比较2例肿瘤组织的差异表达基因,并随机抽取部分基因行RT-PCR验证,由不同分析法得到的结果不同,以一种看家基因为标准化基值,且以所有基因表达值的中位数为内参照比较时得到的信息最多,且经RT-PCR证实可靠。证明以看家基因为标准化基值、并以所有差异基因表达值的中位数为内参照,可能是分析Atlas微阵列检测结果的较优方法     

Specific expression of a foreign beta-globin gene in erythroid cells of transgenic mice

The globin gene family represents an attractive system for the study of gene regulation during mammalian development, as its expression is subject to both tissue-specific and temporal regulation. While many aspects of globin gene structure and expression have been described extensively, relatively little is known about the cis-acting DNA sequences involved in the developmental regulation of globin gene expression. To begin to experimentally define these regulatory sequences, we have taken the approach of introducing cloned globin genes into the mouse germ line and examining their expression in the resulting transgenic animals. Here we describe a series of transgenic mice carrying a hybrid mouse/human adult beta-globin gene, several of which express the gene exclusively or predominantly in erythroid tissues. These studies demonstrate that regulatory sequences closely linked to the beta-globin gene are sufficient to specify a correct pattern of tissue-specific expression in a developing mouse, when the gene is integrated at a subset of foreign chromosomal positions.     

低功率毫米波辐射对HL60细胞基因表达谱的影响

摘要：研究低功率毫米波辐射对HL60白血病细胞基因表达谱的影响。应用基因芯片检测频率41.32GHz的毫米波辐射HL60白血病细胞和未辐射毫米波HL60白血病细胞组基因表达差异,并进行RT-PCR方法验证IL-7、EGF和LGALS3基因变化。 结果与对照组比较,毫米波辐射60min后,HL60细胞增殖,基因芯片检出基因表达上调18个和下调306个,在下调的基因中,RT-PCR 检出IL-7、EGF和LGALS3基因下调与基因芯片结果一致。表明低功率毫米波可导致HL60细胞基因表达谱发生变化,这些变化的基因与HL60细胞增殖功能相关。提示基因表达变化是低功率毫米波辐射HL60细胞所致生物学反应的重要因素。     

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。     

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.     

转录物组学解析植物生长发育的生物钟调控模

基于拟南芥的时间序列的基因组芯片数据,分析了植物生长的昼夜调节模式相关的基因表达规律,发现有2.4%的基因的日振幅达到了显著差异水平.从整体基因转录组水平分析,白天诱导表达的基因主要参与调控植物与环境之间的相互作用,而夜晚表达上调的基因主要参与调节植物的生长发育.此外,植物叶绿素和血红素的生物合成也受到了生物钟的调控.对整个基因组水平上生物钟核心震荡调节子CCA1/LHY和TOC1的共表达基因做了基因组水平上的扫描鉴定,得到了一些新的潜在的生物节律调节因子.这些结果为今后更为系统地完善植物的生物节律的调控网络提供了参考.     

基于最优正交质心特征选取的DNA微阵列数据分析

微阵列数据具有样本小、维度高的特点,给数据分析带来了困难。因此,在生物信息学的研究和应用中,从微阵列数据里挑选主基因(特征选取)是十分重要和有意义的。本文采用基于最优正交质心特征选取算法(OCFS)来挑选主基因,并与基于信噪比的主基因挑选法和基于遗传算法的主基因挑选法进行了对比。利用挑选出的主基因,采用支持向量机(SVM)对数据样本进行了分类研究。通过实验,在经典的白血病数据集上,对于34个样本的测试集,达到了33/34的分类准确率,表明了本方法的适用性。     

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.