SV40 PolyA序列及其AATAAA信号对上游GFP基因表达及转录终止的影响

SV40 PolyA(猴空泡病毒PolyA,简称PolyA)序列是有转录终止作用和使转录的mRNA添加PolyA尾的DNA序列(240 bp),含有AATAAA六核苷酸多腺苷化信号(Polyadenylation signal)。在pEGFP-C1质粒的GFP基因下游插入14个同向串联的Alu序列(Alu14),构建pAlu14质粒,瞬时转染HeLa细胞,用Northern blot检测和荧光显微镜观察GFP RNA和GFP蛋白表达,发现Alu串联序列强烈抑制GFP基因表达,该序列没有转录终止作用产生高分子量GFP融合RNA。又在pAlu14质粒GFP基因和Alu串联序列之间按正、反方向插入PolyA序列及去除AATAAA信号的PolyA序列,插入的这些PolyA序列均能部分解除Alu14对GFP基因的抑制作用;去除AATAAA信号的PolyA正、反序列仍然引起转录终止。将PolyA反序(PolyAas)分为4段每段60 bp,中间的2段分别称为2F2R和3F3R,将2F2R或3F3R插在pAlu14质粒的Alu串联序列的上游,随着插入2F2R片段拷贝数的增加转录的GFP融合RNA的分子量增加;2F2R的下游如果依然是2F2R那么2F2R可以支持转录延伸,如果2F2R下游是Alu串联序列则2F2R导致转录终止。无论插入一个3F3R或插入64个3F3R,均产生低分子量GFP RNA。     

SV40PolyA片段串联拷贝数目影响GFP报告基因表达

Alu元件（约280bp）是人类基因组中的重要重复序列,串联Alu呈长度依赖性下调GFP报告基因表达,在Alu串联序列上游正向或反向插入SV40PolyA（简称PolyA,240bp）,解除Alu串联序列对GFP基因的抑制作用.PCR法扩增PolyA反序（PolyAas）不同位置的60bp片段,插入pAlu14质粒（14个Alu正向串联插入pEGFP-C1）的GFP基因和Alu14之间,瞬时转染HeLa细胞,通过荧光显微镜观察和Northern检测,1F1R（PolyAas5′端第1个60bp片段）和4F4R（自PolyAas5′端计算第4个60bp片段）不能活化基因;2F2R和3F3R（PolyAas中间的2段）可以解除Alu14对GFP基因的抑制作用.将2F2R和3F3R各自反复首尾串联,分别插入pAlu14质粒GFP基因和Alu串联序列之间,瞬时转染HeLa细胞,用插入4个2F2R的pAlu28,插入4个3F3R的pAlu18,插入4个3F3R的pAlu28作长度对照,以排除由于插入片段长度增加可能对结果造成的干扰,发现2～4个拷贝的2F2R和3F3R活化基因作用高于一个拷贝的同样片段,但是多于8拷贝以后,活化GFP基因作用减弱.本实验证明,SV40 PolyAas至少含有两段活化基因序列,活化基因序列（2F2R,3F3R）的最适活化基因条件需要合适的拷贝数.     

T7启动子在哺乳类动物细胞中启动外源基因表达的研究

人低密度脂蛋白（LDL）受体基因cDNA和氯霉素已酞转移酶基因（CAT）及PolyA信号序列被克隆进pGEM4载体的T7噬茵体启动子下游,构建成质粒pT7LDLR和pT7CAT．两个重组质粒转化CHO细胞．PCR和CAT酶实验显示：两个基因被T7噬菌体启动子所启动．结果证实真核生物RNA聚合酶能够识别T7启动子,转录外源基因．常用的含有T7启动子的质粒可同时作为原核生物和真核生物的表达载体．     

SV40PolyA顺式活化基因元件中不完整茎环结构的发现和序列研究

研究室的前期工作发现,Alu串连序列插入pEGFP-C1质粒的GFP基因下游,瞬时转染HeLa细胞抑制GFP基因表达,2F2R(来自SV40PolyA反序5′端的第2个60 bp)插入GFP和Alu串连序列之间可以解除Alu序列对GFP基因的抑制作用。文章通过删减2F2R发现,45R(2F2R 5′端的45 bp)、30R和22R可以活化基因,且二串连体活化基因作用高于单体。Secloop(2F2R近中部的22 bp)和Poly4(2F2R 3′端的30 bp)不能活化基因。30R与Poly4用9碱基连接形成30R-Poly4,其活化基因作用低于2F2R,两个22R之间连接碱基数对活化GFP基因作用没有明显的影响。22R(5′-GTGAAAAAAATGCTTTATTTGT-3′)含有不完整的回文序列,可以形成不完整的茎环结构,包括一个3碱基loop、3 bp第一茎、2碱基泡和3 bp第二茎。改变22R茎环结构的碱基突变明显影响其活化GFP基因的作用,过多互补和过少互补的茎环结构均不利于活化基因,提示适当的不完整茎环结构与活化基因有关。     

用人工神经网络方法确定真核基因启动子

本文运用神经网络方法,并结合分子生物学的有关理论与实验统计事实,对真核基因启动子区域进行了识别．文中选择了人类、牛、猪、猫、山羊、兔、绵羊、大鼠、小鼠、小马、仓鼠、鸡、鸭、大豆等共13种真核生物360个基因组,作为研究对象．学习组选择了300个基因组,预测组选择了60基因组．结果表明,将神经网络模型与基因理论相结合,能够运用计算方法,从大量的可能启动子组合中排列出唯一的启动子区域．     

南方菜豆花叶病毒外壳蛋白基因序列的分析

用重组DNA技术及序列分析法测定了南方菜豆花叶病毒RNA基因组3′端1,000个碱基的序列,以及由此序列推导出的整个外壳蛋白的氨基酸顺序,它与巳报导的基本上一致。介绍了用DNA的寡核苷酸水解混合物作为起始引物,以3′端不含PolyA尾巴且不能加上PolyA的病毒RNA作为模板合成互补DNA,及进一步无性繁殖此cDNA的方法。     

芜菁花叶病毒的3’末端序列克隆与CP基因序列分析

目的:克隆芜菁花叶病毒(Turnip mosaic virus,TuMV)的3'末端序列,并进行CP基因序列分析.方法:以TuMV杭州榨菜分离物(TuMv-HZZC)接种病叶为材料,利用病毒粒子吸附法制备病毒RNA模板,经RT-PCR扩增获得了TuMV-HZZC 3'末端序列,将其克隆到PMD 18-T质粒上进行序列分析.结果:TuMV-HZZC分离物3'末端序列包括部分的Nib基因、完整的TuMVCP基因和3'-UTR,CP基因为864bp,分别编码288个氨基酸,3'-UTR序列(不包括PolyA尾巴)为213bp.经过与其他TuMV分离物的CP基因核苷酸和氨基酸比较,同源性分别达到88.0%～97.6%和91.0%-96.5%.结论:TuMV的系统进化具有典型的地域和寄主关联性.     

基于生物信息学的HERV研究现状与发展趋势

近年来研究证实,人内源性逆转录病毒（HERV）中长末端重复序列（LTR）和囊膜蛋白基因（env）与基因异常表达关系密切。本文阐述了HERV国内外研究现状与发展动态。从生物信息学角度对LTR中启动子、多聚腺苷化（PolyA）信号和增强子等顺式作用元件以及env中的免疫抑制区（ISD）进行了重点分析。指出上述元件和潜在功能区在基因的异常表达和囊膜蛋白的免疫抑制中起关键作用,必将成为HERV与基因异常表达关系研究的热点与趋势。通过此类研究,以期为癌症疾病的早期诊断和治疗靶位点的选择奠定理论基础。     

基于生物信息学方法识别非小细胞肺癌(NSCLC)潜在治疗靶基因

本研究运用生物信息学方法识别非吸烟女性非小细胞肺癌(NSCLC)潜在的靶基因,并从分子水平探索其潜在的发病机制。从GEO数据库下载非吸烟女性非小细胞肺癌相关基因芯片数据集,经癌症组和癌旁对照组差异表达基因识别,并利用R软件对差异基因进行层次聚类分析,DAVID进行基因本体(gene ontology)和KEGG通路富集分析,STRING和Cytoscape软件构建蛋白-蛋白交互(PPI)网络,以及运用PASTAA分析,识别NSCLC相关转录因子,构建转录因子-基因共表达网络。结果表明,185个基因在NSCLC中差异表达,其中40个上调,145个下调;通过PASTAA分析识别出5个NSCLC基因相关转录因子。差异基因与胶原分解代谢过程、炎症反应的正调控等生物过程密切相关,基因的产物主要参与蛋白质细胞外基质、胶原三聚体等细胞组分,且主要发挥调节金属内肽酶活性、肝素结合和调节受体活性等分子功能;KEGG通路富集分析表明差异基因显著富集到胞外基质-受体信号通路、粘着斑信号通路、PPAR信号通和PI3K-Akt信号通路等,与非小细胞肺癌的发生发展密切相关。通过生物信息学方法,最终筛选到4个NSCLC关键基因:IL6、MMP1、COL1A1、CD36,其可能是非吸烟女性NSCLC潜在的治疗靶点。     

高等植物性别分化研究进展

高等植物性别分化研究主要包括三个方面：性别分化特异大分子标记物的鉴定(分化程序)；诱导信号(如植物激素)的分析和性决定基因的分离与分析。近年来，植物性别分化研究取得了较大进展，本文主要介绍这一研究在分化程序、诱导信号和性决定基因等方面的研究进展。     

A methodology based on molecular interactions and pathways to find candidate genes associated to diseases: its application to schizophrenia and Alzheimer's disease

Experimental techniques for the identification of genes associated with diseases are expensive and have certain limitations. In this scenario, computational methods are useful tools to identify lists of promising genes for further experimental verification. This paper describes a flexible methodology for the in silico prediction of genes associated with diseases combining the use of available tools for gene enrichment analysis, gene network generation and gene prioritization. A set of reference genes, with a known association to a disease, is used as bait to extract candidate genes from molecular interaction networks and enriched pathways. In a second step, prioritization methods are applied to evaluate the similarities between previously selected candidates and the set of reference genes. The top genes obtained by these programs are grouped into a single list sorted by the number of methods that have selected each gene. As a proof of concept, top genes reported a few years ago in SzGene and AlzGene databases were used as references to predict genes associated to schizophrenia and Alzheimer's disease, respectively. In both cases, we were able to predict a statistically significant amount of genes belonging to the updated lists.     

A Novel Strategy for Gene Selection of Microarray Data Based on Gene-to-Class Sensitivity Information

To obtain predictive genes with lower redundancy and better interpretability, a hybrid gene selection method encoding prior information is proposed in this paper. To begin with, the prior information referred to as gene-to-class sensitivity (GCS) of all genes from microarray data is exploited by a single hidden layered feedforward neural network (SLFN). Then, to select more representative and lower redundant genes, all genes are grouped into some clusters by K-means method, and some low sensitive genes are filtered out according to their GCS values. Finally, a modified binary particle swarm optimization (BPSO) encoding the GCS information is proposed to perform further gene selection from the remainder genes. For considering the GCS information, the proposed method selects those genes highly correlated to sample classes. Thus, the low redundant gene subsets obtained by the proposed method also contribute to improve classification accuracy on microarray data. The experiments results on some open microarray data verify the effectiveness and efficiency of the proposed approach.     

人工神经原网络处理PCR数据在细菌鉴定中的应用

目的16SrRNA和16S-23SrRNA间区片段是常用细菌分类鉴定靶点,本研究探讨人工神经原网络（ANN）对上述位点PCR扩增产物数据分析在细菌快速鉴定方面的价值。方法2对15SrRNA基因荧光引物和1对16S-23SrRNA区间基因引物用于扩增血液标本中分离出的317株细菌。相关毛细管电泳（CE）限制性片段长度多态性（RFLP）和单链构象多态性（SSCP）数据进行人工神经原网络分析。结果16S-23SrRNA基因的RFLP数据对未知菌鉴定的准确率高于16SrRNA基因的SSCP数据,分别为98．0％和79．6％。结论实验证明了人工神经原网络作为一种模式识别方法对于简化细菌鉴定十分有价值。     

A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data

MOTIVATION: Microarray experiments are affected by numerous sources of non-biological variation that contribute systematic bias to the resulting data. In a dual-label (two-color) cDNA or long-oligonucleotide microarray, these systematic biases are often manifested as an imbalance of measured fluorescent intensities corresponding to Sample A versus those corresponding to Sample B. Systematic biases also affect between-slide comparisons. Making effective corrections for these systematic biases is a requisite for detecting the underlying biological variation between samples. Effective data normalization is therefore an essential step in the confident identification of biologically relevant differences in gene expression profiles. Several normalization methods for the correction of systemic bias have been described. While many of these methods have addressed intensity-dependent bias, few have addressed both intensity-dependent and spatiality-dependent bias. RESULTS: We present a neural network-based normalization method for correcting the intensity- and spatiality-dependent bias in cDNA microarray datasets. In this normalization method, the dependence of the log-intensity ratio (M) on the average log-intensity (A) as well as on the spatial coordinates (X,Y) of spots is approximated with a feed-forward neural network function. Resistance to outliers is provided by assigning weights to each spot based on how distant their M values is from the median over the spots whose A values are similar, as well as by using pseudospatial coordinates instead of spot row and column indices. A comparison of the robust neural network method with other published methods demonstrates its potential in reducing both intensity-dependent bias and spatial-dependent bias, which translates to more reliable identification of truly regulated genes.     

用人工神神经网络法辨识发酵动力学模型参数

本文提出发酵动力学模型参数辩识的人工神经网络方法,并对几种典型的模型进行了具体尝试,结果表明,用这种方法估计参数效果极好。     

基于决策森林特征基因的两种识别方法

应用DNA芯片可获得成千上万个基因的表达谱数据。寻找对疾病有鉴别力的特征基因 ,滤掉与疾病无关的基因是基因表达谱数据分析的关键问题。利用决策森林方法的集成优势 ,提出基于决策森林的两种特征基因识别方法。该方法先由决策森林按照一定的显著性水平滤掉大部分与疾病类别无关的基因 ,然后采用统计频数法和扰动法 ,根据所选特征对分类的贡献程度对初选的特征基因作更加精细地选择。最后 ,选用神经网络作为外部分类器对所选的特征基因子集进行评价 ,将提出的方法应用于 4 0例结肠癌组织与 2 2例正常组织中 2 0 0 0个基因的表达谱实验数据。结果表明 :上述两种方法选出的特征基因均具有较高的疾病鉴别能力 ,均可获得最优特征基因子集 ,基于决策森林的统计频数法优于扰动法。     

A Computational Systems Biology Study for Understanding Salt Tolerance Mechanism in Rice

Salinity is one of the most common abiotic stresses in agriculture production. Salt tolerance of rice (Oryza sativa) is an important trait controlled by various genes. The mechanism of rice salt tolerance, currently with limited understanding, is of great interest to molecular breeding in improving grain yield. In this study, a gene regulatory network of rice salt tolerance is constructed using a systems biology approach with a number of novel computational methods. We developed an improved volcano plot method in conjunction with a new machine-learning method for gene selection based on gene expression data and applied the method to choose genes related to salt tolerance in rice. The results were then assessed by quantitative trait loci (QTL), co-expression and regulatory binding motif analysis. The selected genes were constructed into a number of network modules based on predicted protein interactions including modules of phosphorylation activity, ubiquity activity, and several proteinase activities such as peroxidase, aspartic proteinase, glucosyltransferase, and flavonol synthase. All of these discovered modules are related to the salt tolerance mechanism of signal transduction, ion pump, abscisic acid mediation, reactive oxygen species scavenging and ion sequestration. We also predicted the three-dimensional structures of some crucial proteins related to the salt tolerance QTL for understanding the roles of these proteins in the network. Our computational study sheds some new light on the mechanism of salt tolerance and provides a systems biology pipeline for studying plant traits in general.     

Use of combined in silico expression data and phylogenetic analysis to identify new oocyte genes encoding RNA binding proteins in the mouse

During folliculogenesis, oocytes accumulate maternal mRNAs in preparation for the first steps of early embryogenesis. The processing of oocyte mRNAs is ensured by heterogeneous nuclear ribonucleoproteins (hnRNPs) genes that encode RNA binding proteins implied in mRNA biogenesis, translation, alternative splicing, nuclear exportation, and degradation. In the present work, by combining phylogenetic analyses and, when available, in silico expression data, we have identified three new oocyte-expressed genes encoding RNA binding proteins by using two strategies. Firstly, we have identified mouse orthologs of the Car1 gene, known to be involved in regulation of germ cell apoptosis in C. elegans, and of the Squid gene, required for the establishment of anteroposterior polarity in the Drosophila oocyte. Secondly, we have identified, among genes whose ESTs are highly represented in oocyte libraries, a paralog of Poly(A) binding protein--Interacting Protein 2 (Paip2) gene, known to inhibit the interaction of the Poly(A)-Binding Protein with Poly(A) tails of mRNAs. For all of these genes, the expression in oocyte was verified by in situ hybridization. Overall, this work underlines the efficiency of in silico methodologies to identify new genes involved in biological processes such as oogenesis.