4株SARS冠状病毒基因组5′端序列的分析与比较

利用5′RACE试剂盒对从中国不同地区、不同SARS患者体中分离的SARS—CoV基因组5′端序列进行RT-PCR扩增,并将扩增产物克隆至Teasy vector。扩增片段的序列测定结果表明：所分离的4株SARS—CoV基因组5′端非编码区的核苷酸序列和其他国家和地区报道的序列基本一致,而且所形成二级结构也完全相同,但与已知普通冠状病毒的差别较大。同时发现在依赖于RNA的RNA聚合酶起始密码子上游—197nt处有冠状病毒典型的转录调控核心保守序列5′-CUAAAC-3′。     

SARS病毒基因组的多态性

对SARS病人粪便样本直接测序,得到SRAS—CoV BJ202全基因组序列（AY864806）。应用比较基因组研究方法对GenBank中公布的115株SARS—CoV基因组序列以及BJ202进行分析。以GZ02序列为参照,发现2个以上基因组中同时存在单核苷酸多态（SNP）位点共278个。多态位点在SARS—CoV基因组中呈偏态分布,大约一半突变位点（50．4％,140／278）发生在基因组3’末端1／3区域。编码Orf10-11、Orf3／4、E蛋白、M蛋白和S蛋白区域突变率较高。克隆并测序含有BJ202基因组12个多态位点的11个cDNA以及4个不含已知多态位点的cDNA片段（15个片段总长度为6．0kb）,结果显示：BJ202特有的3个多态位点（13804、1503l和20792）以及另外3个多态位点（26428、26477和27243）均检出两种不同核苷酸;位点18379虽在已公布的115株SARS—CoV基因组中未发现突变,实际上也是多态位点。14个克隆中有8个克隆该位点为A,6个克隆为G。全部116个SARS—CoV基因组中共有18种缺失类型和2种插入类型。大部分缺失发生在编码ORF9和ORF10-11区域（基因组序列27700—28000bp处）。以邻位连接法（Neighbor-Joining）构建了116株SARS—CoV系统发育树,BJ202与BJ01和LLJ-2004等SARS—CoV的亲缘关系较接近。     

SARS病毒与其他冠状病毒的基因组比较

本文利用生物信息学方法比较SARS病毒和其他冠状病毒基因组。通过数据库搜索,找出与SARS病毒基因组相似的核酸或蛋白质序列,并对相似序列进行比对,分析它们的共性和差异。结果表明,SARS病毒在基因组的组织上及结构蛋白质方面与现有冠状病毒有比较大的相似性,SARS病毒基因组与冠状病毒基因组相关。但是,SARS病毒基因组还存在一些特异性序列,ORF1a和S蛋白(特别是S1)的变化以及SARS—CoV特异性的非结构蛋白可能是SARS发病机理与传染特性区别于其他冠状病毒的分子基础。在全基因组水平上进行核酸单词出现频率分析,结果表明,SARS病毒远离已知的其他冠状病毒,单独成为一类。     

SARS冠状病毒的起源研究进展*

SARS冠状病毒(SARS—CoV)是一种新现病毒,可感染多种动物,果子狸是人类SARS—CoV重要的动物宿主之一,是已知较理想的实验动物模型。遗传因素在SARS-CoV的出现过程中起重要作用,它很可能是哺乳动物和鸟类冠状病毒之间重组产生的新物种,但发生基因重组不是SARS在人群中暴发的原因。     

严重急性呼吸综合征(SARS)冠状病毒S蛋白的鉴定与分析

利用293细胞对SARS病人样品进行病毒扩增,培养上清中病毒颗粒经过纯化后,利用蛋白质组学技术,对纯化得到的SARS冠状病毒颗粒蛋白进行初步分离与鉴定。其中质谱分析结果最终表明,分子量约150kD的蛋白质的氨基酸序列与SARS—CoV基因组所预测S蛋白质序列高度吻合,从而首次从蛋白质水平对SARS冠状病毒S蛋白的氨基酸序列进行了证实。     

肿瘤患者血清中SARS-CoV抗体阳性原因分析

探讨SARS冠状病毒(SARS—CoV)抗体在SARS病原学诊断中的特异性及其在肿瘤患血清中的假阳性问题。应用ELISA和荧光定量RT-PCR技术检测了111例正常对照和40例肿瘤患血清中SARS—CoV抗体的阳性率。在111例正常对照中,IgM抗体均阴性,IgG抗体的阳性率为3．6％(4／111);IgG抗体诊断SARS的特异性为96．4％,两种抗体同时阳性诊断SARS的特异性为100％。40例肿瘤患中,IgM抗体均阴性,IgG抗体阳性率17．5％(7／40)。经RT—PCR检测,上述肿瘤患阳性病例均为阴性。结果表明,同时测定SARS—CoV的两种抗体可降低诊断的假阳性率,提高诊断的特异性。用非纯化SARS—CoV抗原制备的ELISA试剂盒测定肿瘤患的SARS—CoV抗体,可能出现假阳性。在肿瘤患中出现假阳性的原因可能与包被的抗原有关。     

002核酸扩增试验筛查有助于防止严重急性呼吸综合征-冠状病毒的输血传播

已知引起严重急性呼吸综合征(SAILS)的病原体是SARS冠状病毒(SARS—CoV)，主要经飞沫或接触传播，对于它是否经输血传播知之甚少。为此，作者采用了一种实时定量聚合酶链反应(PCR)检测试剂盒，利用核酸扩增试验(NAT)方法对献血员进行了大批量的检测。     

SARS病毒与其他冠状病毒的基因组比较

本文利用生物信息学方法比较SARS病毒和其他冠状病毒基因组.通过数据库搜索,找出与SARS病毒基因组相似的核酸或蛋白质序列,并对相似序列进行比对,分析它们的共性和差异.结果表明,SARS病毒在基因组的组织上及结构蛋白质方面与现有冠状病毒有比较大的相似性,SARS病毒基因组与冠状病毒基因组相关.但是,SARS病毒基因组还存在一些特异性序列,ORF1a和S蛋白(特别是S1)的变化以及SARS-CoV特异性的非结构蛋白可能是SARS发病机理与传染特性区别于其他冠状病毒的分子基础.在全基因组水平上进行核酸单词出现频率分析,结果表明,SARS病毒远离已知的其他冠状病毒,单独成为一类.     

SARS-CoV S蛋白基因的克隆及其与VSV-G融合表达载体的构建

为了解重症急性呼吸综合征冠状病毒(SARS—CoV)表面S蛋白的受体结合功能域及其在宿主细胞上的作用受体,应用PCR技术从SARS—CoV cDNA中克隆到S蛋白的全长基因,并构建了S蛋白与疱疹性口腔炎病毒胞膜蛋白(VSV—G)融合表达载体pVSV—G‘-SG,进而为制备含有SARS—CoVS蛋白膜外区的逆转录病毒假毒粒奠定了实验基础。     

4株SARS冠状病毒中国分离株3′非编码区的序列测定及分析

分别对 4株SARS冠状病毒中国株基因组 3′非编码区序列进行测定和分析 ,然后应用Blast、DNAstarGeneQuest等软件对中国株的这段序列与其它SARS和非SARS冠状病毒的序列进行比较 .结果显示 ,所测 4株SARS冠状病毒中国分离株的 3′非编码区长度均为 339nt,与其它已测序SARS冠状病毒的相应序列完全一致 .SARS冠状病毒的非编码区含有冠状病毒保守的“伪结”结构 .在距末端 138nt处还存在 1个长约 32nt的Ⅱ型茎 环结构的序列 ,该序列在其它已知人冠状病毒中并不存在 ,而与禽传染性支气管炎病毒和星状病毒中的对应序列高度同源 .     

Assigning function to yeast proteins by integration of technologies

Interpreting genome sequences requires the functional analysis of thousands of predicted proteins, many of which are uncharacterized and without obvious homologs. To assess whether the roles of large sets of uncharacterized genes can be assigned by targeted application of a suite of technologies, we used four complementary protein-based methods to analyze a set of 100 uncharacterized but essential open reading frames (ORFs) of the yeast Saccharomyces cerevisiae. These proteins were subjected to affinity purification and mass spectrometry analysis to identify copurifying proteins, two-hybrid analysis to identify interacting proteins, fluorescence microscopy to localize the proteins, and structure prediction methodology to predict structural domains or identify remote homologies. Integration of the data assigned function to 48 ORFs using at least two of the Gene Ontology (GO) categories of biological process, molecular function, and cellular component; 77 ORFs were annotated by at least one method. This combination of technologies, coupled with annotation using GO, is a powerful approach to classifying genes.     

用生物信息学软件分析风信子HPI1基因及其产物蛋白

利用生物信息学方法对风信子HPI1基因及其产物蛋白进行序列、结构分析和功能预测。所研究的HPI1基因与许多植物的花器官决定基因具有较高的相似性 ,推测它们可能为同源基因 ,具有相似的生物学功能 ,即可预测HPI1基因与花器官的形成有关。     

Mitochondrial location of severe acute respiratory syndrome coronavirus 3b protein

Severe acute respiratory syndrome-associated coronavirus (SARS-CoV), a distant member of the Group 2 coronaviruses, has recently been identified as the etiological agent of severe acute respiratory syndrome (SARS). The genome of SARS-CoV contains four structural genes that are homologous to genes found in other coronaviruses, as well as six subgroup-specific open reading frames (ORFs). ORF3 encodes a predicted 154-amino-acid protein that lacks similarity to any known protein, and is designated 3b in this article. We reported previously that SARS-CoV 3b is predominantly localized in the nucleolus, and induces G0/G1 arrest and apoptosis in transfected cells. In this study, we show that SARS-CoV 3b fused with EGFP at its N- or C- terminus co-localized with a mitochondria-specific marker in some transfected cells. Mutation analysis of SARS-CoV 3b revealed that the domain spanning amino acids 80 to 138 was essential for its mitochondria localization. These results provide new directions for studies of the role of SARS-CoV 3b protein in SARS pathogenesis.     

Proteogenomic mapping as a complementary method to perform genome annotation

The accelerated rate of genomic sequencing has led to an abundance of completely sequenced genomes. Annotation of the open reading frames (ORFs) (i.e., gene prediction) in these genomes is an important task and is most often performed computationally based on features in the nucleic acid sequence. Using recent advances in proteomics, we set out to predict the set of ORFs for an organism based principally on expressed protein-based evidence. Using a novel search strategy, we mapped peptides detected in a whole-cell lysate of Mycoplasma pneumoniae onto a genomic scaffold and extended these "hits" into ORFs bound by traditional genetic signals to generate a "proteogenomic map". We were able to generate an ORF model for M. pneumoniae strain FH using proteomic data with a high correlation to models based on sequence features. Ultimately, we detected over 81% of the genomically predicted ORFs in M. pneumoniae strain M129 (the originally sequenced strain). We were also able to detect several new ORFs not originally predicted by genomic methods, various N-terminal extensions, and some evidence that would suggest that certain predicted ORFs are bogus. Some of these differences may be a result of the strain analyzed but demonstrate the robustness of protein analysis across closely related genomes. This technique is a cost-effective means to add value to genome annotation, and a prerequisite for proteome quantitation and in vivo interaction measures.     

Identification of the J and K genes in the bacteriophage Mu genome sequence

Bacteriophage Mu was the first transposable phage to be discovered and still serves as the model for a large family of related transposable phages and prophages. The Mu genome sequence is known (NC-000929.1 GI:9633494), but not all of the genes have been assigned to the ORFs in the genome sequence. For this paper, we have sequenced an approximately 3-kb DNA region containing four predicted ORFs, Mup35-Mup38, from lysogens containing amber mutant prophages defective in either the J or the K gene. Amber mutations in prophages with J gene mutations mapped to the Mup36 ORF, and those in the K gene were found in Mup37, identifying the ORFs corresponding to these genes.     

Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes

A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator''s organism of interest. Sequence-based search methods (e.g. BLAST) have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction.We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT) dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics techniques and will help biologists systematically integrate prior knowledge from diverse systems to direct targeted experiments in their organism of study.     

Intrinsic and extrinsic approaches for detecting genes in a bacterial genome.

The unannotated regions of the Escherichia coli genome DNA sequence from the EcoSeq6 database, totaling 1,278 'intergenic' sequences of the combined length of 359,279 basepairs, were analyzed using computer-assisted methods with the aim of identifying putative unknown genes. The proposed strategy for finding new genes includes two key elements: i) prediction of expressed open reading frames (ORFs) using the GeneMark method based on Markov chain models for coding and non-coding regions of Escherichia coli DNA, and ii) search for protein sequence similarities using programs based on the BLAST algorithm and programs for motif identification. A total of 354 putative expressed ORFs were predicted by GeneMark. Using the BLASTX and TBLASTN programs, it was shown that 208 ORFs located in the unannotated regions of the E. coli chromosome are significantly similar to other protein sequences. Identification of 182 ORFs as probable genes was supported by GeneMark and BLAST, comprising 51.4% of the GeneMark 'hits' and 87.5% of the BLAST 'hits'. 73 putative new genes, comprising 20.6% of the GeneMark predictions, belong to ancient conserved protein families that include both eubacterial and eukaryotic members. This value is close to the overall proportion of highly conserved sequences among eubacterial proteins, indicating that the majority of the putative expressed ORFs that are predicted by GeneMark, but have no significant BLAST hits, nevertheless are likely to be real genes. The majority of the putative genes identified by BLAST search have been described since the release of the EcoSeq6 database, but about 70 genes have not been detected so far. Among these new identifications are genes encoding proteins with a variety of predicted functions including dehydrogenases, kinases, several other metabolic enzymes, ATPases, rRNA methyltransferases, membrane proteins, and different types of regulatory proteins.