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.
- Authors:
-
[2]
- Univ. of California, San Diego, CA (United States)
- Univ. of California, San Diego, CA (United States); Technical Univ. of Denmark, Lyngby (Denmark)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, San Diego, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1543751
- Grant/Contract Number:
- SC0008701
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
Heckmann, David, Zielinski, Daniel C., and Palsson, Bernhard O. Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates. United States: N. p., 2018.
Web. doi:10.1038/s41467-018-07649-1.
Heckmann, David, Zielinski, Daniel C., & Palsson, Bernhard O. Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates. United States. https://doi.org/10.1038/s41467-018-07649-1
Heckmann, David, Zielinski, Daniel C., and Palsson, Bernhard O. Mon .
"Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates". United States. https://doi.org/10.1038/s41467-018-07649-1. https://www.osti.gov/servlets/purl/1543751.
@article{osti_1543751,
title = {Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates},
author = {Heckmann, David and Zielinski, Daniel C. and Palsson, Bernhard O.},
abstractNote = {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.},
doi = {10.1038/s41467-018-07649-1},
journal = {Nature Communications},
number = 1,
volume = 9,
place = {United States},
year = {Mon Dec 10 00:00:00 EST 2018},
month = {Mon Dec 10 00:00:00 EST 2018}
}
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