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

ABSTRACT: 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.

SUBMITTER: Skalnik CJ

PROVIDER: S-EPMC10310025 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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

Skalnik Christopher J CJ Cheah Sean Y SY Yang Mica Y MY Wolff Mattheus B MB Spangler Ryan K RK Talman Lee L Morrison Jerry H JH Peirce Shayn M SM Agmon Eran E Covert Markus W MW

PLoS computational biology 20230616 6

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 ...[more]

PMID: 37327241

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Project description:BackgroundWhole genome sequencing has revolutionised the interrogation of mycobacterial genomes. Recent studies have reported conflicting findings on the genomic stability of Mycobacterium tuberculosis during the evolution of drug resistance. In an age where whole genome sequencing is increasingly relied upon for defining the structure of bacterial genomes, it is important to investigate the reliability of next generation sequencing to identify clonal variants present in a minor percentage of the population. This study aimed to define a reliable cut-off for identification of low frequency sequence variants and to subsequently investigate genetic heterogeneity and the evolution of drug resistance in M. tuberculosis.MethodsGenomic DNA was isolated from single colonies from 14 rifampicin mono-resistant M. tuberculosis isolates, as well as the primary cultures and follow up MDR cultures from two of these patients. The whole genomes of the M. tuberculosis isolates were sequenced using either the Illumina MiSeq or Illumina HiSeq platforms. Sequences were analysed with an in-house pipeline.ResultsUsing next-generation sequencing in combination with Sanger sequencing and statistical analysis we defined a read frequency cut-off of 30% to identify low frequency M. tuberculosis variants with high confidence. Using this cut-off we demonstrated a high rate of genetic diversity between single colonies isolated from one population, showing that by using the current sequencing technology, single colonies are not a true reflection of the genetic diversity within a whole population and vice versa. We further showed that numerous heterogeneous variants emerge and then disappear during the evolution of isoniazid resistance within individual patients. Our findings allowed us to formulate a model for the selective bottleneck which occurs during the course of infection, acting as a genomic purification event.ConclusionsOur study demonstrated true levels of genetic diversity within an M. tuberculosis population and showed that genetic diversity may be re-defined when a selective pressure, such as drug exposure, is imposed on M. tuberculosis populations during the course of infection. This suggests that the genome of M. tuberculosis is more dynamic than previously thought, suggesting preparedness to respond to a changing environment.

Dataset Information

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

Publications

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

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