Stochastic dynamics of Francisella tularensis infection and replication

Carruthers, Jonathan; Lythe, Grant; López‐García, Martín; Gillard, Joseph; Laws, Thomas R.; Lukaszewski, Roman A.; Molina-Parı́s, Carmen

doi:10.1371/journal.pcbi.1007752

Cited by 7 publications

(16 citation statements)

References 90 publications

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“…Hosts mitigate acute illness through deployment of the immune system. Infection progression and outcome is ultimately determined by a combination of genetics, environment, and stochastic events (Carruthers et al, 2020;Duneau et al, 2017). Determining the relative contribution of each of these factors to individual prognosis will enable the identification of genetic markers and early predictors of infection outcome.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal monitoring of individual infection progression in Drosophila melanogaster

Love

Chandrasekaran

et al. 2021

Preprint

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The innate immune system is critical for host survival of infection. Infection models in organisms like Drosophila melanogaster are key for understanding evolution and dynamics of innate immunity. However, current toolsets for fly infection studies are limited in their ability to resolve changes in pathogen load on the hours time-scale, along with stochastic responses to infection in individuals. Here we report a novel bioluminescent imaging strategy enabling non-invasive characterization of pathogen load over time. We demonstrate that photon flux from autobioluminescent reporter bacteria can be used to estimate pathogen count. Escherichia coli expressing the ilux operon were imaged in whole, living flies at relevant concentrations for immune study. Because animal sacrifice was not necessary to estimate pathogen load, stochastic responses to infection were characterized in individuals for the first time. The high temporal resolution of bioluminescence imaging also enabled visualization of the fine dynamics of microbial clearance on the hours time-scale. Overall, this non-invasive imaging strategy provides a simple and scalable platform to observe changes in pathogen load in vivo over time.

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Section: Introductionmentioning

confidence: 99%

Longitudinal monitoring of individual infection progression in Drosophila melanogaster

Love

Chandrasekaran

et al. 2021

Preprint

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“…Carruthers et al in ( 16 ) studied a very similar process to the one described here, for the non-sporulating bacteria F. tularensis . In this section we use some of their results.…”

Section: Methodsmentioning

confidence: 72%

“…Still, we make use of a stochastic approach, instead of a deterministic one, to describe the population dynamics of spores and bacteria. We follow the methods recently developed by Carruthers et al ( 16 ) for Francisella tularensis infection, extended here to include spores and spore germination, since B. anthracis is a spore-forming bacteria and F. tularensis is not.…”

Section: Introductionmentioning

confidence: 99%

“…These summary statistics can then play an important role when considering within-host infection dynamics, such as in the model by Day et al ( 17 ), or when linking to dose-response data ( 18 ), as considered in the Discussion section. In the same way as Carruthers et al ( 16 ), we assume that an infected macrophage’s rupture probability per unit time is proportional to its bacterial load. Thus, cells with a high bacterial load at a given time are more likely to rupture than those with a lower one.…”

Section: Introductionmentioning

confidence: 99%

“…For this model, we show how to compute the probability of either rupture or recovery of the infected cell and the conditional mean times taken to reach these fates. Furthermore, we adapt some of the results from ( 16 ) in order to compute the probability distribution of rupture times, which is shown to be proportional to the mean number of vegetative bacteria in the cell over time. We are also able to compute the probability distribution of the rupture size, which is the number of bacteria that are eventually released into the extracellular environment from one single infected cell.…”

Section: Introductionmentioning

confidence: 99%

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A Stochastic Intracellular Model of Anthrax Infection With Spore Germination Heterogeneity

et al. 2021

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We present a stochastic mathematical model of the intracellular infection dynamics of Bacillus anthracis in macrophages. Following inhalation of B. anthracis spores, these are ingested by alveolar phagocytes. Ingested spores then begin to germinate and divide intracellularly. This can lead to the eventual death of the host cell and the extracellular release of bacterial progeny. Some macrophages successfully eliminate the intracellular bacteria and will recover. Here, a stochastic birth-and-death process with catastrophe is proposed, which includes the mechanism of spore germination and maturation of B. anthracis. The resulting model is used to explore the potential for heterogeneity in the spore germination rate, with the consideration of two extreme cases for the rate distribution: continuous Gaussian and discrete Bernoulli. We make use of approximate Bayesian computation to calibrate our model using experimental measurements from in vitro infection of murine peritoneal macrophages with spores of the Sterne 34F2 strain of B. anthracis. The calibrated stochastic model allows us to compute the probability of rupture, mean time to rupture, and rupture size distribution, of a macrophage that has been infected with one spore. We also obtain the mean spore and bacterial loads over time for a population of cells, each assumed to be initially infected with a single spore. Our results support the existence of significant heterogeneity in the germination rate, with a subset of spores expected to germinate much later than the majority. Furthermore, in agreement with experimental evidence, our results suggest that most of the spores taken up by macrophages are likely to be eliminated by the host cell, but a few germinated spores may survive phagocytosis and lead to the death of the infected cell. Finally, we discuss how this stochastic modelling approach, together with dose-response data, allows us to quantify and predict individual infection risk following exposure.

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Analysis of Single Bacterium Dynamics in a Stochastic Model of Toxin-Producing Bacteria

Paterson¹,

López‐García²,

Gillard³

et al. 2021

Lecture Notes in Computer Science

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We stochastically model two bacterial populations which can produce toxins. We propose to analyse this biological system by following the dynamics of a single bacterium during its lifetime, as well as its progeny. We study the lifespan of a single bacterium, the number of divisions that this bacterium undergoes, and the number of toxin molecules that it produces during its lifetime. We also compute the mean number of bacteria in the genealogy of the original bacterium and the number of toxin molecules produced by its genealogy. We illustrate the applicability of our methods by considering the bacteria Bacillus anthracis and antibiotic treatment, making use of in vitro experimental data. We quantify, for the first time, bacterial toxin production by exploiting an in vitro assay for the A16R strain, and make use of the resulting parameterised model to illustrate our techniques.

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Stochastic dynamics of Francisella tularensis infection and replication

Cited by 7 publications

References 90 publications

Longitudinal monitoring of individual infection progression in Drosophila melanogaster

Longitudinal monitoring of individual infection progression in Drosophila melanogaster

A Stochastic Intracellular Model of Anthrax Infection With Spore Germination Heterogeneity

Analysis of Single Bacterium Dynamics in a Stochastic Model of Toxin-Producing Bacteria

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