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Long-term phenotypic evolution of bacteria

机译：细菌的长期表型进化

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摘要

For many decades comparative analyses of protein sequences and structures have been used to investigate fundamental principles of molecular evolution. In contrast, relatively little is known about the long-term evolution of species' phenotypic and genetic properties. This represents an important gap in our understanding of evolution, as exactly these proprieties play key roles in natural selection and adaptation to diverse environments. Here we perform a comparative analysis of bacterial growth and gene deletion phenotypes using hundreds of genome-scale metabolic models. Overall, bacterial phenotypic evolution can be described by a two-stage process with a rapid initial phenotypic diversification followed by a slow long-term exponential divergence. The observed average divergence trend, with approximately similar fractions of phenotypic properties changing per unit time, continues for billions of years. We experimentally confirm the predicted divergence trend using the phenotypic profiles of 40 diverse bacterial species across more than 60 growth conditions. Our analysis suggests that, at long evolutionary distances, gene essentiality is significantly more conserved than the ability to utilize different nutrients, while synthetic lethality is significantly less conserved. We also find that although a rapid phenotypic evolution is sometimes observed within the same species, a transition from high to low phenotypic similarity occurs primarily at the genus level.
机译：数十年来，蛋白质序列和结构的比较分析已用于研究分子进化的基本原理。相反，关于物种的表型和遗传特性的长期演变知之甚少。在我们对进化的理解中，这代表了一个重要的空白，因为这些专有性在自然选择和适应各种环境中起着关键作用。在这里，我们使用数百个基因组规模的代谢模型对细菌生长和基因缺失表型进行了比较分析。总的来说，细菌的表型进化可以通过一个两阶段的过程来描述，首先是快速的初始表型多样化，然后是缓慢的长期指数差异。观察到的平均发散趋势（大约每单位时间的表型特性分数变化）持续了数十亿年。我们使用40多种细菌在60多个生长条件下的表型特征，通过实验确定了预测的趋同趋势。我们的分析表明，在长距离进化中，基因本质比利用不同营养素的能力要保守得多，而合成杀伤力则要保守得多。我们还发现，尽管有时在同一物种中观察到快速的表型进化，但从高表型相似性到低表型相似性的转变主要发生在属水平上。

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  • 来源
    《Nature》 |2015年第7534期|369-372|共4页
  • 作者

    German Plata; Christopher S. Henry; Dennis Vitkup;

  • 作者单位

    Department of Systems Biology, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York 10032, USA,Integrated Program in Cellular, Molecular, Structural and Genetic Studies, Columbia University, New York, New York 10032, USA;

    Mathemathics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois 60439, USA;

    Department of Systems Biology, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York 10032, USA,Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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  • 正文语种 eng
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