利用泛基因组DNA微阵列测定克罗诺杆菌的系统发育亲缘关系及其作为了解克罗诺杆菌生物学的数据挖掘工具。

Ben D Tall, Jayanthi Gangiredla, Christopher J Grim, Isha R Patel, Scott A Jackson, Mark K Mammel, Mahendra H Kothary, Venugopal Sathyamoorthy, Laurenda Carter, Séamus Fanning, Carol Iversen, Franco Pagotto, Roger Stephan, Angelika Lehner, Jeffery Farber, Qiong Q Yan, Gopal R Gopinath
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引用次数: 7

摘要

克罗诺杆菌(以前称为阪崎肠杆菌)是肠杆菌科革兰氏阴性、兼性厌氧、氧化酶阴性、过氧化氢酶阳性的棒状细菌属。这些微生物引起各种疾病,如新生儿和婴儿的脑膜炎、坏死性小肠结肠炎和败血症,以及成人的尿路、伤口、脓肿或手术部位感染、败血症和肺炎。利用最近报道的DNA微阵列技术测定了379株克罗诺杆菌及其分类相关分离株的总基因含量。克罗诺杆菌微阵列作为一种基因分型工具,为全球食品安全界提供了一种快速方法,以识别和捕获爆发分离株的总基因组内容,用于食品安全、环境和临床监测目的。它能够将7种克罗诺杆菌相互区分,并与非克罗诺杆菌物种区分开来。微阵列还能够将每个物种内的菌株聚集到定义明确的亚群中。这些结果也支持了以往关于该属物种成员系统发育分离的研究,并清楚地强调了该属各物种与系统发育相关物种之间的进化序列差异。这篇综述扩展了这些研究,并说明了微阵列也可以作为一种研究工具来挖掘菌株的基因组数据集。三个案例研究描述了微阵列的使用,包括:(1)确定具有毒力质粒pESA3的阪崎克罗诺杆菌菌株之间的等位基因差异;(2)挖掘都柏林克罗诺杆菌亚种间丙二酸盐和肌醇等位基因,确定亚种的同一性;(3)最后利用微阵列分析了240株克罗诺杆菌及其系统发育相关菌株中13个外膜蛋白等位基因的序列差异和系统发育相关性趋势。这篇综述的目的是描述微阵列作为一个强大的工具,基因组学研究这一分类和重要的属,对未来的预防措施的发展标准,以消除这一食源性病原体从全球食品供应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Use of a Pan-Genomic DNA Microarray in Determination of the Phylogenetic Relatedness among Cronobacter spp. and Its Use as a Data Mining Tool to Understand Cronobacter Biology.

Use of a Pan-Genomic DNA Microarray in Determination of the Phylogenetic Relatedness among Cronobacter spp. and Its Use as a Data Mining Tool to Understand Cronobacter Biology.

Cronobacter (previously known as Enterobacter sakazakii) is a genus of Gram-negative, facultatively anaerobic, oxidase-negative, catalase-positive, rod-shaped bacteria of the family Enterobacteriaceae. These organisms cause a variety of illnesses such as meningitis, necrotizing enterocolitis, and septicemia in neonates and infants, and urinary tract, wound, abscesses or surgical site infections, septicemia, and pneumonia in adults. The total gene content of 379 strains of Cronobacter spp. and taxonomically-related isolates was determined using a recently reported DNA microarray. The Cronobacter microarray as a genotyping tool gives the global food safety community a rapid method to identify and capture the total genomic content of outbreak isolates for food safety, environmental, and clinical surveillance purposes. It was able to differentiate the seven Cronobacter species from one another and from non-Cronobacter species. The microarray was also able to cluster strains within each species into well-defined subgroups. These results also support previous studies on the phylogenic separation of species members of the genus and clearly highlight the evolutionary sequence divergence among each species of the genus compared to phylogenetically-related species. This review extends these studies and illustrates how the microarray can also be used as an investigational tool to mine genomic data sets from strains. Three case studies describing the use of the microarray are shown and include: (1) the determination of allelic differences among Cronobacter sakazakii strains possessing the virulence plasmid pESA3; (2) mining of malonate and myo-inositol alleles among subspecies of Cronobacter dublinensis strains to determine subspecies identity; and (3) lastly using the microarray to demonstrate sequence divergence and phylogenetic relatedness trends for 13 outer-membrane protein alleles among 240 Cronobacter and phylogenetically-related strains. The goal of this review is to describe microarrays as a robust tool for genomics research of this assorted and important genus, a criterion toward the development of future preventative measures to eliminate this foodborne pathogen from the global food supply.

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来源期刊
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审稿时长
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期刊介绍: High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: Microarrays, DNA Sequencing, RNA Sequencing, Protein Identification and Quantification, Cell-based Approaches, Omics Technologies, Imaging, Bioinformatics, Computational Biology/Chemistry, Statistics, Integrative Omics, Drug Discovery and Development, Microfluidics, Lab-on-a-chip, Data Mining, Databases, Multiplex Assays.
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