Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates

David Heckmann; Daniel C. Zielinski; Bernhard O. Palsson

doi:10.1038/s41467-018-07649-1

Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates

David Heckmann
, Daniel C. Zielinski
, Bernhard O. Palsson

University of Iceland

University of Iceland

Research output: Contribution to journal › Article › peer-review

Abstract

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (k_cats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved k_cats. Diminishing returns epistasis prevents enzymes from developing higher k_cats in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows k_cat evolution to be convergent. Predicted k_cat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern k_cats and the whole of metabolism.

Original language	English
Article number	5270
Journal	Nature Communications
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41467-018-07649-1
Publication status	Published - 1 Dec 2018

Bibliographical note

Funding Information: The authors would like to thank Abdelmoneim Amer Desouki for his support in using the sybilccFBA package, and Ron Milo and Laurence Yang for helpful discussion. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy grant number DE-SC0008701. This work was supported by the Novo Nordisk Foundation grant number NNF10CC1016517. Publisher Copyright: © 2018, The Author(s).

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title = "Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates",

abstract = "Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (kcats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. Diminishing returns epistasis prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern kcats and the whole of metabolism.",

author = "David Heckmann and Zielinski, \{Daniel C.\} and Palsson, \{Bernhard O.\}",

note = "Funding Information: The authors would like to thank Abdelmoneim Amer Desouki for his support in using the sybilccFBA package, and Ron Milo and Laurence Yang for helpful discussion. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy grant number DE-SC0008701. This work was supported by the Novo Nordisk Foundation grant number NNF10CC1016517. Publisher Copyright: {\textcopyright} 2018, The Author(s).",

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N2 - Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (kcats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. Diminishing returns epistasis prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern kcats and the whole of metabolism.

AB - Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (kcats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. Diminishing returns epistasis prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern kcats and the whole of metabolism.

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Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates

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