Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses.
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...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-06-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1011232 |
| _version_ | 1797787216406118400 |
|---|---|
| author | Christopher J Skalnik Sean Y Cheah Mica Y Yang Mattheus B Wolff Ryan K Spangler Lee Talman Jerry H Morrison Shayn M Peirce Eran Agmon Markus W Covert |
| author_facet | Christopher J Skalnik Sean Y Cheah Mica Y Yang Mattheus B Wolff Ryan K Spangler Lee Talman Jerry H Morrison Shayn M Peirce Eran Agmon Markus W Covert |
| author_sort | Christopher J Skalnik |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T01:19:48Z |
| format | Article |
| id | doaj.art-c586e70966d047e2adda5d4777ce5255 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-13T01:19:48Z |
| publishDate | 2023-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-c586e70966d047e2adda5d4777ce52552023-07-05T05:30:59ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-06-01196e101123210.1371/journal.pcbi.1011232Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses.Christopher J SkalnikSean Y CheahMica Y YangMattheus B WolffRyan K SpanglerLee TalmanJerry H MorrisonShayn M PeirceEran AgmonMarkus W CovertAntibiotic 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.https://doi.org/10.1371/journal.pcbi.1011232 |
| spellingShingle | Christopher J Skalnik Sean Y Cheah Mica Y Yang Mattheus B Wolff Ryan K Spangler Lee Talman Jerry H Morrison Shayn M Peirce Eran Agmon Markus W Covert Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses. PLoS Computational Biology |
| title | Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses. |
| title_full | Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses. |
| title_fullStr | Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses. |
| title_full_unstemmed | Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses. |
| title_short | Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses. |
| title_sort | whole cell modeling of e coli colonies enables quantification of single cell heterogeneity in antibiotic responses |
| url | https://doi.org/10.1371/journal.pcbi.1011232 |
| work_keys_str_mv | AT christopherjskalnik wholecellmodelingofecolicoloniesenablesquantificationofsinglecellheterogeneityinantibioticresponses AT seanycheah wholecellmodelingofecolicoloniesenablesquantificationofsinglecellheterogeneityinantibioticresponses AT micayyang wholecellmodelingofecolicoloniesenablesquantificationofsinglecellheterogeneityinantibioticresponses AT mattheusbwolff wholecellmodelingofecolicoloniesenablesquantificationofsinglecellheterogeneityinantibioticresponses AT ryankspangler wholecellmodelingofecolicoloniesenablesquantificationofsinglecellheterogeneityinantibioticresponses AT leetalman wholecellmodelingofecolicoloniesenablesquantificationofsinglecellheterogeneityinantibioticresponses AT jerryhmorrison wholecellmodelingofecolicoloniesenablesquantificationofsinglecellheterogeneityinantibioticresponses AT shaynmpeirce wholecellmodelingofecolicoloniesenablesquantificationofsinglecellheterogeneityinantibioticresponses AT eranagmon wholecellmodelingofecolicoloniesenablesquantificationofsinglecellheterogeneityinantibioticresponses AT markuswcovert wholecellmodelingofecolicoloniesenablesquantificationofsinglecellheterogeneityinantibioticresponses |