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Interplay of heritage and habitat in the distribution of bacterial signal transduction systems

机译:细菌信号传导系统分布中遗产与栖息地的相互作用

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Comparative analysis of the complete genome sequences from a variety of poorly studied organisms aims at predicting ecological and behavioral properties of these organisms and helping in characterizing their habitats. This task requires finding appropriate descriptors that could be correlated with the core traits of each system and would allow meaningful comparisons. Using the relatively simple bacterial models, first attempts have been made to introduce suitable metrics to describe the complexity of organism's signaling machinery, which included introducing the "bacterial IQ" score. Here, we use an updated census of prokaryotic signal transduction systems to improve this parameter and evaluate its consistency within selected bacterial phyla. We also introduce a more elaborate descriptor, a set of profiles of relative abundance of members of each family of signal transduction proteins encoded in each genome. We show that these family profiles are well conserved within each genus and are often consistent within families of bacteria. Thus, they reflect evolutionary relationships between organisms as well as individual adaptations of each organism to its specific ecological niche.
机译:对来自各种未经充分研究的生物的完整基因组序列进行比较分析的目的是预测这些生物的生态和行为特性,并有助于表征其栖息地。该任务需要找到适当的描述符,这些描述符可以与每个系统的核心特征相关联,并可以进行有意义的比较。使用相对简单的细菌模型,首先尝试引入合适的指标来描述生物体信号传导机制的复杂性,其中包括引入“细菌智商”评分。在这里,我们使用原核信号转导系统的最新人口普查来改善此参数并评估其在选定细菌门内的一致性。我们还介绍了一个更详尽的描述符,即在每个基因组中编码的每个信号转导蛋白家族的成员相对丰度的一组配置文件。我们表明,这些家族谱在每个属中都是保守的,并且在细菌家族中通常是一致的。因此,它们反映了生物之间的进化关系以及每种生物对其特定生态位的个体适应。

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  • 来源
    《Molecular BioSystems》 |2010年第4期|p.721-728|共8页
  • 作者单位

    National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, Maryland 20894, USA;

    Bioinformatics & High-throughput Analysis Laboratory, Seattle Children's Research Institute, 1900 9th Ave., Seattle, Washington 98101, USA Predictive Analytics, Seattle Children's Hospital, 4800 Sand Point Way NE, Seattle, Washington 98105, USA;

    Bioinformatics & High-throughput Analysis Laboratory, Seattle Children's Research Institute, 1900 9th Ave., Seattle, Washington 98101, USA Predictive Analytics, Seattle Children's Hospital, 4800 Sand Point Way NE, Seattle, Washington 98105, USA Biomedical and Health Informatics Division, Medical Education and Biomedical Health Department, School of Medicine, University of Washington, Seattle, Washington 98105, USA;

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