Protein cost allocation explains metabolic strategies in Escherichia coli

• Verfasst von: Grigaitis, Pranas [VerfasserIn]
• Verfasst von: Olivier, Brett G. [VerfasserIn]
• Verfasst von: Fiedler, Tomas [VerfasserIn]
• Verfasst von: Teusink, Bas [VerfasserIn]
• Verfasst von: Kummer, Ursula [VerfasserIn]
• Verfasst von: Veith, Nadine [VerfasserIn]
• Titel: Protein cost allocation explains metabolic strategies in Escherichia coli
• Verf.angabe: Pranas Grigaitis, Brett G. Olivier, Tomas Fiedler, Bas Teusink, Ursula Kummer, Nadine Veith
• Jahr: 2021
• Jahr des Originals: 2020
• Umfang: 10 S.
• Teil: volume:327
• Teil: year:2021
• Teil: pages:54-63
• Teil: extent:10
• Fussnoten: Available online 10 December 2020 ; Gesehen am 31.03.2021
• Titel Quelle: Enthalten in: Journal of biotechnology
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1984
• Jahr Quelle: 2021
• Band/Heft Quelle: 327(2021), Seite 54-63
• ISSN Quelle: 1873-4863
• DOI: doi:10.1016/j.jbiotec.2020.11.003
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Genome-scale models
• Sach-SW: Microbial metabolism
• Sach-SW: Quantitative proteomics
• Sach-SW: Resource allocation
• K10plus-PPN: 1752969723

Abstract

In-depth understanding of microbial growth is crucial for the development of new advances in biotechnology and for combating microbial pathogens. Condition-specific proteome expression is central to microbial physiology and growth. A multitude of processes are dependent on the protein expression, thus, whole-cell analysis of microbial metabolism using genome-scale metabolic models is an attractive toolset to investigate the behaviour of microorganisms and their communities. However, genome-scale models that incorporate macromolecular expression are still inhibitory complex: the conceptual and computational complexity of these models severely limits their potential applications. In the need for alternatives, here we revisit some of the previous attempts to create genome-scale models of metabolism and macromolecular expression to develop a novel framework for integrating protein abundance and turnover costs to conventional genome-scale models. We show that such a model of Escherichia coli successfully reproduces experimentally determined adaptations of metabolism in a growth condition-dependent manner. Moreover, the model can be used as means of investigating underutilization of the protein machinery among different growth settings. Notably, we obtained strongly improved predictions of flux distributions, considering the costs of protein translation explicitly. This finding in turn suggests protein translation being the main regulation hub for cellular growth.