Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

David Heckmann, Anaamika Campeau, Colton J. Lloyd, Patrick V. Phaneuf, Ying Hefner, Marvic Carrillo-Terrazas, Adam M. Feist, David J. Gonzalez, Bernhard O. Palsson*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Enzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo kcats using metabolic specialist Escherichia coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo kcats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivokcats predict unseen proteomics data with much higher precision than in vitro kcats. The results demonstrate that in vivo kcats can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models.
Original languageEnglish
JournalProceedings of the National Academy of Sciences of the United States of America
Volume117
Issue number37
Pages (from-to)23182-23190
Number of pages9
ISSN0027-8424
DOIs
Publication statusPublished - 2020

Keywords

  • In vivo
  • Turnover number
  • Proteomics
  • kcats
  • Gene knockout

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