Dataset Information

Dynamics of intracellular bacterial replication at the single cell level.

ABSTRACT: Several important pathogens cause disease by surviving and replicating within host cells. Bacterial proliferation is the product of both replication and killing undergone by the population. However, these processes are difficult to distinguish, and are usually assessed together by determination of net bacterial load. In addition, measurement of net load does not reveal heterogeneity within pathogen populations. This is particularly important in persistent infections in which slow or nongrowing bacteria are thought to have a major impact. Here we report the development of a reporter system based on fluorescence dilution that enables direct quantification of the replication dynamics of Salmonella enterica serovar Typhimurium (S. Typhimurium) in murine macrophages at both the population and single-cell level. We used this technique to demonstrate that a major S. Typhimurium virulence determinant, the Salmonella pathogenicity island 2 type III secretion system, is required for bacterial replication but does not have a major influence on resistance to killing. Furthermore, we found that, upon entry into macrophages, many bacteria do not replicate, but appear to enter a dormant-like state. These could represent an important reservoir of persistent bacteria. The approach could be extended to other pathogens to study the contribution of virulence and host resistance factors to replication and killing, and to identify and characterize nonreplicating bacteria associated with chronic or latent infections.

SUBMITTER: Helaine S

PROVIDER: S-EPMC2840444 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Dynamics of intracellular bacterial replication at the single cell level.

Helaine Sophie S Thompson Jessica A JA Watson Kathryn G KG Liu Mei M Boyle Cliona C Holden David W DW

Proceedings of the National Academy of Sciences of the United States of America 20100202 8

Several important pathogens cause disease by surviving and replicating within host cells. Bacterial proliferation is the product of both replication and killing undergone by the population. However, these processes are difficult to distinguish, and are usually assessed together by determination of net bacterial load. In addition, measurement of net load does not reveal heterogeneity within pathogen populations. This is particularly important in persistent infections in which slow or nongrowing b ...[more]

PMID: 20133586

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Project description:A few bacterial cells may be sufficient to produce a food-borne illness outbreak, provided that they are capable of adapting and proliferating on a food matrix. This is why any quantitative health risk assessment policy must incorporate methods to accurately predict the growth of bacterial populations from a small number of pathogens. In this aim, mathematical models have become a powerful tool. Unfortunately, at low cell concentrations, standard deterministic models fail to predict the fate of the population, essentially because the heterogeneity between individuals becomes relevant. In this work, a stochastic differential equation (SDE) model is proposed to describe variability within single-cell growth and division and to simulate population growth from a given initial number of individuals. We provide evidence of the model ability to explain the observed distributions of times to division, including the lag time produced by the adaptation to the environment, by comparing model predictions with experiments from the literature for Escherichia coli, Listeria innocua, and Salmonella enterica. The model is shown to accurately predict experimental growth population dynamics for both small and large microbial populations. The use of stochastic models for the estimation of parameters to successfully fit experimental data is a particularly challenging problem. For instance, if Monte Carlo methods are employed to model the required distributions of times to division, the parameter estimation problem can become numerically intractable. We overcame this limitation by converting the stochastic description to a partial differential equation (backward Kolmogorov) instead, which relates to the distribution of division times. Contrary to previous stochastic formulations based on random parameters, the present model is capable of explaining the variability observed in populations that result from the growth of a small number of initial cells as well as the lack of it compared to populations initiated by a larger number of individuals, where the random effects become negligible.

Dataset Information

Dynamics of intracellular bacterial replication at the single cell level.

Publications

Dynamics of intracellular bacterial replication at the single cell level.

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