Whole-cell modeling of E. coli confirms that in vitro tRNA aminoacylation measurements are insufficient to support cell growth and predicts a positive feedback mechanism regulating arginine biosynthesis

Abstract

In Escherichia coli, inconsistencies between in vitro tRNA aminoacylation measurements and in vivo protein synthesis demands were postulated almost 40 years ago, but have proven difficult to confirm. Whole-cell modeling can test whether a cell behaves in a physiologically correct manner when parameterized with in vitro measurements by providing a holistic representation of cellular processes in vivo. Here, a mechanistic model of tRNA aminoacylation, codon-based polypeptide elongation, and N-terminal methionine cleavage was incorporated into a developing whole-cell model of E. coli. Subsequent analysis confirmed the insufficiency of aminoacyl-tRNA synthetase kinetic measurements for cellular proteome maintenance, and estimated aminoacyl-tRNA synthetase kcats that were on average 7.6-fold higher. Simulating cell growth with perturbed kcats demonstrated the global impact of these in vitro measurements on cellular phenotypes. For example, an insufficient kcat for HisRS caused protein synthesis to be less robust to the natural variability in aminoacyl-tRNA synthetase expression in single cells. More surprisingly, insufficient ArgRS activity led to catastrophic impacts on arginine biosynthesis due to underexpressed N-acetylglutamate synthase, where translation depends on repeated CGG codons. Overall, the expanded E. coli model deepens understanding of how translation operates in an in vivo context.

Multi-scale models of whole cells: progress and challenges

“…The additions significantly improve the WC-EC's ability to simulate dynamics of cellular responses as a response to environmental perturbations. Another update (Choi and Covert, 2023) added accurate tRNA aminoacylation, codon-based polypeptide elongation, and N-terminal methionine cleavage mechanisms to WC-EC which permits better examination of inconsistencies between different types of measurements. The updated model was used to verify that in vitro tRNA aminoacylation measurements are insufficient for cellular proteome maintenance.…”

Accelerated design ofEscherichia coligenomes with reduced size using a whole-cell model and machine learning

“…By cross-validating numerous E. coli datasets during the construction of the WCM, Macklin et al identified several inconsistencies in the literature, which upon resolution, enabled the model to accurately predict in vitro protein half-lives [4]. Additionally, the E. coli WCM has recently been applied to better understand the differences between in vivo and in vitro measurements of tRNA aminoacylation [9]. The population-level E. coli WCM, which considers many independent cells within a shared environment, has been instrumental in assessing antibiotic responses and identifying sub-generationally expressed genes that significantly impact cell survival [7].…”

“…Design-build-test-learn cycles can also be optimized using WCMs, with the M. genitalium WCM being used to predict minimal genomes in silico [10, 11, 12]. Furthermore, the relationship between gene knock outs, cellular subsystems, and phenotypes has been better understood through the application of WCMs [9, 13, 14].…”

A modular model integrating metabolism, growth, and cell cycle predicts that fermentation is required to modulate cell size in yeast populations

“…1B). 18 A bottom-up approach has been used to construct a genome-wide whole-cell model for the small parasitic bacterium Mycoplasma genitalium 37 , and a similar approach has been used to model Escherichia coli 38,39 . In this study, we applied a top-down systems biology approach to understand how metabolism controls the growth and division of eukaryotic cells under a given set of nutritional conditions 40,41 .…”