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对一个大型荷兰嗜肺军团菌菌株的比较基因组分析确定了五个与临床菌株高度相关的标记物。

Comparative genome analysis of a large Dutch Legionella pneumophila strain collection identifies five markers highly correlated with clinical strains.

机构信息

Regional Public Health Laboratory of Haarlem, Haarlem, the Netherlands.

出版信息

BMC Genomics. 2010 Jul 15;11:433. doi: 10.1186/1471-2164-11-433.

Abstract

BACKGROUND

Discrimination between clinical and environmental strains within many bacterial species is currently underexplored. Genomic analyses have clearly shown the enormous variability in genome composition between different strains of a bacterial species. In this study we have used Legionella pneumophila, the causative agent of Legionnaire's disease, to search for genomic markers related to pathogenicity. During a large surveillance study in The Netherlands well-characterized patient-derived strains and environmental strains were collected. We have used a mixed-genome microarray to perform comparative-genome analysis of 257 strains from this collection.

RESULTS

Microarray analysis indicated that 480 DNA markers (out of in total 3360 markers) showed clear variation in presence between individual strains and these were therefore selected for further analysis. Unsupervised statistical analysis of these markers showed the enormous genomic variation within the species but did not show any correlation with a pathogenic phenotype. We therefore used supervised statistical analysis to identify discriminating markers. Genetic programming was used both to identify predictive markers and to define their interrelationships. A model consisting of five markers was developed that together correctly predicted 100% of the clinical strains and 69% of the environmental strains.

CONCLUSIONS

A novel approach for identifying predictive markers enabling discrimination between clinical and environmental isolates of L. pneumophila is presented. Out of over 3000 possible markers, five were selected that together enabled correct prediction of all the clinical strains included in this study. This novel approach for identifying predictive markers can be applied to all bacterial species, allowing for better discrimination between strains well equipped to cause human disease and relatively harmless strains.

摘要

背景

目前,许多细菌种内临床株和环境株之间的区分还未得到充分探索。基因组分析清楚地表明,同一细菌种内不同菌株的基因组组成存在巨大的可变性。在这项研究中,我们使用嗜肺军团菌(军团病的病原体)来寻找与致病性相关的基因组标记。在荷兰的一项大型监测研究中,收集了特征明确的患者分离株和环境分离株。我们使用混合基因组微阵列对该采集菌株中的 257 株进行了比较基因组分析。

结果

微阵列分析表明,480 个 DNA 标记(共 3360 个标记中的)在单个菌株之间存在明显的存在差异,因此选择这些标记进行进一步分析。对这些标记进行无监督统计分析表明,该物种内存在巨大的基因组变异,但与致病表型没有任何相关性。因此,我们使用有监督的统计分析来识别有区别的标记。遗传编程既用于识别预测标记,也用于定义它们之间的相互关系。开发了一个由五个标记组成的模型,该模型能够正确预测 100%的临床株和 69%的环境株。

结论

提出了一种用于识别预测标记的新方法,可用于区分嗜肺军团菌的临床株和环境分离株。在 3000 多个可能的标记中,选择了五个标记,它们共同能够正确预测本研究中包含的所有临床菌株。这种用于识别预测标记的新方法可应用于所有细菌种,能够更好地区分具有引起人类疾病能力的菌株和相对无害的菌株。

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