Yoon Sung Ho, Hur Cheol-Goo, Kang Ho-Young, Kim Yeoun Hee, Oh Tae Kwang, Kim Jihyun F
Genome Research Center, Korea Research Institute of Bioscience and Biotechnology, 52 Oun-dong, Yuseong, Daejeon 305-333, Korea.
BMC Bioinformatics. 2005 Jul 21;6:184. doi: 10.1186/1471-2105-6-184.
Pathogenicity islands (PAIs), distinct genomic segments of pathogens encoding virulence factors, represent a subgroup of genomic islands (GIs) that have been acquired by horizontal gene transfer event. Up to now, computational approaches for identifying PAIs have been focused on the detection of genomic regions which only differ from the rest of the genome in their base composition and codon usage. These approaches often lead to the identification of genomic islands, rather than PAIs.
We present a computational method for detecting potential PAIs in complete prokaryotic genomes by combining sequence similarities and abnormalities in genomic composition. We first collected 207 GenBank accessions containing either part or all of the reported PAI loci. In sequenced genomes, strips of PAI-homologs were defined based on the proximity of the homologs of genes in the same PAI accession. An algorithm reminiscent of sequence-assembly procedure was then devised to merge overlapping or adjacent genomic strips into a large genomic region. Among the defined genomic regions, PAI-like regions were identified by the presence of homolog(s) of virulence genes. Also, GIs were postulated by calculating G+C content anomalies and codon usage bias. Of 148 prokaryotic genomes examined, 23 pathogenic and 6 non-pathogenic bacteria contained 77 candidate PAIs that partly or entirely overlap GIs.
Supporting the validity of our method, included in the list of candidate PAIs were thirty four PAIs previously identified from genome sequencing papers. Furthermore, in some instances, our method was able to detect entire PAIs for those only partial sequences are available. Our method was proven to be an efficient method for demarcating the potential PAIs in our study. Also, the function(s) and origin(s) of a candidate PAI can be inferred by investigating the PAI queries comprising it. Identification and analysis of potential PAIs in prokaryotic genomes will broaden our knowledge on the structure and properties of PAIs and the evolution of bacterial pathogenesis.
致病岛（PAIs）是病原体中编码毒力因子的独特基因组片段，代表了通过水平基因转移事件获得的基因组岛（GIs）亚组。到目前为止，用于识别致病岛的计算方法主要集中在检测基因组区域，这些区域仅在碱基组成和密码子使用上与基因组的其余部分不同。这些方法常常导致识别出的是基因组岛，而非致病岛。
我们提出了一种通过结合序列相似性和基因组组成异常来检测完整原核生物基因组中潜在致病岛的计算方法。我们首先收集了207个包含部分或全部已报道致病岛位点的GenBank登录号。在已测序的基因组中，基于同一致病岛登录号中基因同源物的邻近性来定义致病岛同源物条带。然后设计了一种类似于序列组装程序的算法，将重叠或相邻的基因组条带合并成一个大的基因组区域。在定义的基因组区域中，通过毒力基因同源物的存在来识别致病岛样区域。此外，通过计算G+C含量异常和密码子使用偏倚来推测基因组岛。在所检测的148个原核生物基因组中，23种致病细菌和6种非致病细菌包含77个候选致病岛，这些致病岛部分或完全与基因组岛重叠。
我们的方法的有效性得到了支持，候选致病岛列表中包括先前从基因组测序论文中鉴定出的34个致病岛。此外，在某些情况下，对于那些仅有部分序列可用的致病岛，我们的方法能够检测到完整的致病岛。在我们的研究中，我们的方法被证明是一种划分潜在致病岛的有效方法。此外，通过研究组成候选致病岛的查询序列，可以推断出候选致病岛的功能和起源。原核生物基因组中潜在致病岛的识别和分析将拓宽我们对致病岛的结构和特性以及细菌致病机制进化的认识。
