Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses.

Autor: Skalnik, Christopher J., Cheah, Sean Y., Yang, Mica Y., Wolff, Mattheus B., Spangler, Ryan K., Talman, Lee, Morrison, Jerry H., Peirce, Shayn M., Agmon, Eran, Covert, Markus W.
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Zdroj: PLoS Computational Biology; 6/16/2023, Vol. 19 Issue 6, p1-29, 29p, 2 Color Photographs, 2 Diagrams, 1 Chart
Abstrakt: Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in "whole-cell" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the "whole-colony" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival. Author summary: Antibiotic-resistant bacteria pose a threat to human health, making current treatments for infection less effective or even obsolete. Computational modeling has been used to investigate phenomena related to antibiotic resistance at various scales, from diffusion of antibiotic molecules across cell barriers to the spread of resistance in hospitals. However, these models fail to capture phenomena that occur across multiple scales simultaneously. By combining multiple instances of a detailed mathematical model of individual Escherichia coli cells in a shared spatial environment, we were able to simulate bacterial colonies with single-cell detail of molecular mechanisms. We used this model to investigate the response of E. coli to two antibiotics with very different modes of action, evaluating how these responses were impacted by cell-to-cell variation in gene and protein expression. This work has implications for understanding emergent colony-level responses to antibiotics, and may offer a valuable approach to modeling colony-scale emergent phenomena more generally. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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