Long-term phenotypic evolution of bacteria.

Germán Plata, Christopher S Henry, Dennis Vitkup

Journal: Nature 2015;517(7534):369-72

PMID: 25363780

Abstract

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.

Address: 1] Department of Systems Biology, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York 10032, USA [2] 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.; 1] Department of Systems Biology, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York 10032, USA [2] Department of Biomedical Informatics, Columbia University, New York, New York 10032, USA.

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