高效位点特异性链霉菌ψC31噬菌体整合酶的研究进展

链霉菌ψC31噬菌体整合酶(ψC31-int)属于位点特异性重组酶的解离酶/转化酶系,该家族催化机制由丝氨酸介导,能识别噬菌体附着位点(attP)和宿主基因组上的细菌附着位点(attB),介导同源序列之间的位点特异性重组.实验证实,ψC31-int是一种高效位点特异性整合工具酶,与其他整合策略相比,具有集高效和安全于一体的优点,通过介导位点特异性整合,将外源基因特异地整合到宿主基因组,使目的基因得以持续、高效的表达,并且具有DNA容量方面的优势.随着研究的深入,人们对ψC31-int介导整合作用机制和影响因素有了进一步的认识.通过一系列成功应用ψC31-int进行的基因治疗的动物学实验,为今后如何利用非病毒载体系统进行基因治疗及转基因动物模型的建立提供了一种新的选择.     

高效位点特异性链霉菌φC31噬菌体整合酶的研究进展

链霉菌φC31噬菌体整合酶（φC31-int）属于位点特异性重组酶的解离酶／转化酶系,该家族催化机制由丝氨酸介导,能识别噬菌体附着位点（attP）和宿主基因组上的细菌附着位点（attB）,介导同源序列之间的位点特异性重组。实验证实,φC31-int是一种高效位点特异性整合工具酶,与其他整合策略相比,具有集高效和安全于一体的优点,通过介导位点特异性整合,将外源基因特异地整合到宿主基因组,使目的基因得以持续、高效的表达,并且具有DNA容量方面的优势。随着研究的深入,人们对φC31-int介导整合作用机制和影响因素有了进一步的认识。通过一系列成功应用φC31-int进行的基因治疗的动物学实验,为今后如何利用非病毒载体系统进行基因治疗及转基因动物模型的建立提供了一种新的选择。     

不同特征描述下H1N1病毒血凝素蛋白序列的比较分析

在传统表征蛋白质序列的40维特征向量的基础上,依据氨基酸的种类与理化性质,将蛋白质序列40维向量分解为20维、4维和16维3种子特征向量描述。结合33条H1N1流感病毒血凝素(hemagglutinin,HA)蛋白质序列和统计学相关性分析理论,进行了蛋白质序列两两之间及每条病毒蛋白质序列对应的不同子特征向量之间的相关性分析,发现病毒蛋白质序列之间存在高度相关性,且每条病毒蛋白质序列对应的20维子特征向量与其他两种子特征向量之间均不显著相关,而4维与16维子特征向量之间显著相关。进一步依据不同的特征向量对33条HA蛋白质序列进行分类,研究发现依据40维特征向量与16维特征向量进行的分类结果高度一致。因此,在不影响表征病毒序列特性的前提下,对于已有的表征蛋白质序列的40维特征向量,可以用16维的特征向量进行代替,以减少计算复杂度。     

一种新的非翻译区剪接位点识别方法

为提高非翻译区剪接位点识别的精度,提出一种统计概率与支持向量机相结合的识别方法 .该方法主要分为两个阶段,第一阶段应用统计学方法对非翻译区(UTR)序列进行描述,将序列中各碱基之间的相关性、位置特异性、保守性等特征用概率形式描述,以概率参数作为第二阶段支持向量机的输入向量,第二阶段应用带有多项式核函数的支持向量机(SVM)对剪接位点进行识别.通过对人类5′UTR剪接位点数据集进行测试,结果表明:该方法对非翻译区剪接位点的识别取得了很好的效果.     

应用反向PCR克隆慢病毒介导的转基因小鼠整合位点序列

目的：为分析慢病毒介导的转基因小鼠中外源基因整合位点的信息,应用反向PCR克隆整合位点序列。方法：小鼠基因组总DNA酶解和自连接后,针对慢病毒载体的特点在LTR附近设计一组特异的PCR引物,优化半巢式PCR的各种参数,提高整合位点序列克隆的效率。结果：克隆了分别携带绿色荧光蛋白（GFP）和转铁蛋白（TF）基因的慢病毒介导的转基因小鼠家系7只小鼠中10个外源基因整合位点序列。结论：本方法可用于慢病毒介导的转基因小鼠整合位点序列的克隆,为分析整合位点与外源基因表达之间的关系等提供了科学依据。     

基于位置关联权重矩阵及序列组分的多样性增量识别剪接位点

前体mRNA的剪接是真核基因表达的关键阶段,识别剪接位点对基因表达也起着至关重要的作用。作者用紧邻与非紧邻的位置关联权重矩阵及组成分的多样性增量得到的五维特征向量来表示序列,应用支持向量机对供体位点和受体位点进行识别。采用5-fold交叉检验,得到供体和受体位点的马修斯相关系数分别为0.924和0.947,ROC曲线下面积分别为99.08%和99.54%。与一些传统方法相比,这一方法考虑了位点之间的相关性和序列的生物信息,表现出特征少、精度高等优点。     

基于位置特异性谱和输入加权神经网络的蛋白质亚细胞定位预测

蛋白质必须处于正确的亚细胞位置才能行使其功能。文章利用PSI-BLAST工具搜索蛋白质序列,提取位点特异性谱中的位点特异性得分矩阵作为蛋白质的一类特征,并计算4等分序列的氨基酸含量以及1～7阶二肽含量作为另外两类特征,由这三类特征一共得到蛋白质序列的12个特征向量。通过设计一个简单加权函数对各类特征向量加权处理,作为神经网络预测器的输入,并使用Levenberg-Marquardt算法代替传统的EBP算法来调整网络权值和阈值,大大提高了训练速度。对具有4类亚细胞位置和12类亚细胞位置的两种蛋白质数据集分别进行＂留一法＂测试和5倍交叉验证测试,总体预测精度分别达到88.4%和83.3%。其中,对4类亚细胞位置数据集的预测效果优于普通BP神经网络、隐马尔可夫模型、模糊K邻近等预测方法,对12类亚细胞位置数据集的预测效果优于支持向量机分类方法。最后还对三类特征采取不同加权比例对预测精度的影响进行了讨论,对选择的八种加权比例的预测结果表明,分别给予三类特征合适的权值系数可以进一步提高预测精度。     

转基因植物中外源基因的整合特性及其研究策略

综述了外源基因在宿主中的整合特性及结构变化的多样性。将外源基因在宿主中的整合方式分为3种：单拷贝单位点整合、串联多联体单位点整合及单拷贝或多拷贝在多位点整合。分析了不同整合方式对表达的影响。详细讨论了植物转基因的整合机制,阐述了以基因组的双链断裂修复为基础的整合模型。最后介绍了国内外的研究者对不同的整合方式所采取的研究策略。     

转基因植物中外源基因的整合特性及其研究策略

综述了外源基因在宿主中的整合特性及结构变化的多样性.将外源基因在宿主中的整合方式分为3种:单拷贝单位点整合、串联多联体单位点整合及单拷贝或多拷贝在多位点整合.分析了不同整合方式对表达的影响.详细讨论了植物转基因的整合机制,阐述了以基因组的双链断裂修复为基础的整合模型.最后介绍了国内外的研究者对不同的整合方式所采取的研究策略.     

高危型人乳头瘤病毒基因组整合的研究进展

高危型人乳头瘤病毒(HPV)基因组整合在宫颈癌等多种肿瘤中被发现。流行病学和实验证据支持高危型HPV基因组整合在宫颈癌等相关肿瘤中起重要作用,其发生可能与宿主染色体不稳定和DNA甲基化相关。近年来发现克隆选择与干扰素通路相关。另外,整合对宿主细胞基因影响和整合位点检测方法方面的研究也取得了重要进展。本文就高危型HPV基因组整合研究的最新进展作一综述。     

Impact of Nucleoporin-Mediated Chromatin Localization and Nuclear Architecture on HIV Integration Site Selection

It has been known for a number of years that integration sites of human immunodeficiency virus type 1 (HIV-1) DNA show a preference for actively expressed chromosomal locations. A number of viral and cellular proteins are implicated in this process, but the underlying mechanism is not clear. Two recent breakthrough publications advance our understanding of HIV integration site selection by focusing on the localization of the preferred target genes of integration. These studies reveal that knockdown of certain nucleoporins and components of nucleocytoplasmic trafficking alter integration site preference, not by altering the trafficking of the viral genome but by altering the chromatin subtype localization relative to the structure of the nucleus. Here, we describe the link between the nuclear basket nucleoporins (Tpr and Nup153) and chromatin organization and how altering the host environment by manipulating nuclear structure may have important implications for the preferential integration of HIV into actively transcribed genes, facilitating efficient viral replication.     

HIV integration targeting: a pathway involving Transportin-3 and the nuclear pore protein RanBP2

Genome-wide siRNA screens have identified host cell factors important for efficient HIV infection, among which are nuclear pore proteins such as RanBP2/Nup358 and the karyopherin Transportin-3/TNPO3. Analysis of the roles of these proteins in the HIV replication cycle suggested that correct trafficking through the pore may facilitate the subsequent integration step. Here we present data for coupling between these steps by demonstrating that depletion of Transportin-3 or RanBP2 altered the terminal step in early HIV replication, the selection of chromosomal sites for integration. We found that depletion of Transportin-3 and RanBP2 altered integration targeting for HIV. These knockdowns reduced HIV integration frequency in gene-dense regions and near gene-associated features, a pattern that differed from that reported for depletion of the HIV integrase binding cofactor Psip1/Ledgf/p75. MLV integration was not affected by the Transportin-3 knockdown. Using siRNA knockdowns and integration targeting analysis, we also implicated several additional nuclear proteins in proper target site selection. To map viral determinants of integration targeting, we analyzed a chimeric HIV derivative containing MLV gag, and found that the gag replacement phenocopied the Transportin-3 and RanBP2 knockdowns. Thus, our data support a model in which Gag-dependent engagement of the proper transport and nuclear pore machinery mediate trafficking of HIV complexes to sites of integration.     

Repair of Oxidative DNA Base Damage in the Host Genome Influences the HIV Integration Site Sequence Preference

Host base excision repair (BER) proteins that repair oxidative damage enhance HIV infection. These proteins include the oxidative DNA damage glycosylases 8-oxo-guanine DNA glycosylase (OGG1) and mutY homolog (MYH) as well as DNA polymerase beta (Polβ). While deletion of oxidative BER genes leads to decreased HIV infection and integration efficiency, the mechanism remains unknown. One hypothesis is that BER proteins repair the DNA gapped integration intermediate. An alternative hypothesis considers that the most common oxidative DNA base damages occur on guanines. The subtle consensus sequence preference at HIV integration sites includes multiple G:C base pairs surrounding the points of joining. These observations suggest a role for oxidative BER during integration targeting at the nucleotide level. We examined the hypothesis that BER repairs a gapped integration intermediate by measuring HIV infection efficiency in Polβ null cell lines complemented with active site point mutants of Polβ. A DNA synthesis defective mutant, but not a 5′dRP lyase mutant, rescued HIV infection efficiency to wild type levels; this suggeted Polβ DNA synthesis activity is not necessary while 5′dRP lyase activity is required for efficient HIV infection. An alternate hypothesis that BER events in the host genome influence HIV integration site selection was examined by sequencing integration sites in OGG1 and MYH null cells. In the absence of these 8-oxo-guanine specific glycosylases the chromatin elements of HIV integration site selection remain the same as in wild type cells. However, the HIV integration site sequence preference at G:C base pairs is altered at several positions in OGG1 and MYH null cells. Inefficient HIV infection in the absence of oxidative BER proteins does not appear related to repair of the gapped integration intermediate; instead oxidative damage repair may participate in HIV integration site preference at the sequence level.     

Data mining on DNA sequences of hepatitis B virus

Extraction of meaningful information from large experimental data sets is a key element in bioinformatics research. One of the challenges is to identify genomic markers in Hepatitis B Virus (HBV) that are associated with HCC (liver cancer) development by comparing the complete genomic sequences of HBV among patients with HCC and those without HCC. In this study, a data mining framework, which includes molecular evolution analysis, clustering, feature selection, classifier learning, and classification, is introduced. Our research group has collected HBV DNA sequences, either genotype B or C, from over 200 patients specifically for this project. In the molecular evolution analysis and clustering, three subgroups have been identified in genotype C and a clustering method has been developed to separate the subgroups. In the feature selection process, potential markers are selected based on Information Gain for further classifier learning. Then, meaningful rules are learned by our algorithm called the Rule Learning, which is based on Evolutionary Algorithm. Also, a new classification method by Nonlinear Integral has been developed. Good performance of this method comes from the use of the fuzzy measure and the relevant nonlinear integral. The nonadditivity of the fuzzy measure reflects the importance of the feature attributes as well as their interactions. These two classifiers give explicit information on the importance of the individual mutated sites and their interactions toward the classification (potential causes of liver cancer in our case). A thorough comparison study of these two methods with existing methods is detailed. For genotype B, genotype C subgroups C1, C2, and C3, important mutation markers (sites) have been found, respectively. These two classification methods have been applied to classify never-seen-before examples for validation. The results show that the classification methods have more than 70 percent accuracy and 80 percent sensitivity for most data sets, which are considered high as an initial scanning method for liver cancer diagnosis.     

New algorithms for multi-class cancer diagnosis using tumor gene expression signatures

MOTIVATION: The increasing use of DNA microarray-based tumor gene expression profiles for cancer diagnosis requires mathematical methods with high accuracy for solving clustering, feature selection and classification problems of gene expression data. RESULTS: New algorithms are developed for solving clustering, feature selection and classification problems of gene expression data. The clustering algorithm is based on optimization techniques and allows the calculation of clusters step-by-step. This approach allows us to find as many clusters as a data set contains with respect to some tolerance. Feature selection is crucial for a gene expression database. Our feature selection algorithm is based on calculating overlaps of different genes. The database used, contains over 16 000 genes and this number is considerably reduced by feature selection. We propose a classification algorithm where each tissue sample is considered as the center of a cluster which is a ball. The results of numerical experiments confirm that the classification algorithm in combination with the feature selection algorithm perform slightly better than the published results for multi-class classifiers based on support vector machines for this data set. AVAILABILITY: Available on request from the authors.