A new cell assay to determine the virulence of Francisella tularensis

Mack, Karl D.; Fulop, Mark; Manchee, R. J.; Stirling, C. J.

doi:10.1111/j.1472-765x.1994.tb00931.x

Cited by 6 publications

(7 citation statements)

References 2 publications

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“…Here, the parameters are estimated in four stages ( §2.2), starting with the birth rate. The reciprocal of this is estimated through the median time until cell death-found to be 41.2 h. The magnitude of this duration is supported by the only other similar in vitro study that could be found [53], in which at 24 h 100% of cells are still healthy (cf. 92% in [28]).…”

Section: Parametrizationmentioning

confidence: 67%

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A dose and time response Markov model for the in-host dynamics of infection with intracellular bacteria following inhalation: with application toFrancisella tularensis

Wood

Egan

Hall

2014

J. R. Soc. Interface.

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In a novel approach, the standard birth–death process is extended to incorporate a fundamental mechanism undergone by intracellular bacteria, phagocytosis. The model accounts for stochastic interaction between bacteria and cells of the immune system and heterogeneity in susceptibility to infection of individual hosts within a population. Model output is the dose–response relation and the dose-dependent distribution of time until response, where response is the onset of symptoms. The model is thereafter parametrized with respect to the highly virulent Schu S4 strain of Francisella tularensis, in the first such study to consider a biologically plausible mathematical model for early human infection with this bacterium. Results indicate a median infectious dose of about 23 organisms, which is higher than previously thought, and an average incubation period of between 3 and 7 days depending on dose. The distribution of incubation periods is right-skewed up to about 100 organisms and symmetric for larger doses. Moreover, there are some interesting parallels to the hypotheses of some of the classical dose–response models, such as independent action (single-hit model) and individual effective dose (probit model). The findings of this study support experimental evidence and postulations from other investigations that response is, in fact, influenced by both in-host and between-host variability.

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Section: Parametrizationmentioning

confidence: 67%

“…92% in [28]). Pooling data from other, related studies is not undertaken, because the timing of cell degradation is dependent on a number of factors, such as the strain [28,34], host species [53] and route of infection [19].…”

Section: Parametrizationmentioning

confidence: 99%

A dose and time response Markov model for the in-host dynamics of infection with intracellular bacteria following inhalation: with application toFrancisella tularensis

Wood

Egan

Hall

2014

J. R. Soc. Interface.

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“…We expected a slightly different value for this parameter due to different extrapolation techniques from Lindemann et al (2011) . The reciprocal of the birth rate λ is estimated through the median time until cell death-found to be 43.4 h. This duration is supported by another similar in vitro study ( Mack et al, 1994 ), in which at 24 h 100% of cells are still healthy (92% in Lindemann et al (2011) ).…”

Section: The Parametrisation Of Our Francisella Tularensis mentioning

confidence: 66%

A human time dose response model for Q fever

2017

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The causative agent of Q fever, Coxiella burnetii, has the potential to be developed for use in biological warfare and it is classified as a bioterrorism threat agent by the Centers for Disease Control and Prevention (CDC) and as a category B select agent by the National Institute of Allergy and Infectious Diseases (NIAID). In this paper we focus on the in-host properties that arise when an individual inhales a dose of C. burnetii and establish a human time-dose response model. We also propagate uncertainty throughout the model allowing us to robustly estimate key properties including the infectious dose and incubation period. Using human study data conducted in the 1950's we conclude that the dose required for a 50% probability of infection is about 15 organisms, and that one inhaled organism of C. burnetti can cause infection in 5% of the exposed population. In addition, we derive a low dose incubation period of 17.6 days and an extracellular doubling time of half a day. In conclusion this paper provides a framework for detailing the parameters and approaches that would be required for risk assessments associated with exposures to C. burnetii that might cause human infection.

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“…After 8 hours the vast majority of bacteria are located within the cytosol [10,12,15,18-20]. Work by Mack and colleagues [21] as well as Edwards and colleagues [22] has demonstrated, through direct comparisons, that F. tularensis infections show an essentially identical disease progression in J774A.1 cells as P388D1 cells, the murine macrophage line used in this study.…”

Section: Resultsmentioning

confidence: 99%

Use of a Capture-Based Pathogen Transcript Enrichment Strategy for RNA-Seq Analysis of the Francisella Tularensis LVS Transcriptome during Infection of Murine Macrophages

et al. 2013

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Francisella tularensis is a zoonotic intracellular pathogen that is capable of causing potentially fatal human infections. Like all successful bacterial pathogens, F. tularensis rapidly responds to changes in its environment during infection of host cells, and upon encountering different microenvironments within those cells. This ability to appropriately respond to the challenges of infection requires rapid and global shifts in gene expression patterns. In this study, we use a novel pathogen transcript enrichment strategy and whole transcriptome sequencing (RNA-Seq) to perform a detailed characterization of the rapid and global shifts in F. tularensis LVS gene expression during infection of murine macrophages. We performed differential gene expression analysis on all bacterial genes at two key stages of infection: phagosomal escape, and cytosolic replication. By comparing the F. tularensis transcriptome at these two stages of infection to that of the bacteria grown in culture, we were able to identify sets of genes that are differentially expressed over the course of infection. This analysis revealed the temporally dynamic expression of a number of known and putative transcriptional regulators and virulence factors, providing insight into their role during infection. In addition, we identified several F. tularensis genes that are significantly up-regulated during infection but had not been previously identified as virulence factors. These unknown genes may make attractive therapeutic or vaccine targets.

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A new cell assay to determine the virulence of Francisella tularensis

Cited by 6 publications

References 2 publications

A dose and time response Markov model for the in-host dynamics of infection with intracellular bacteria following inhalation: with application toFrancisella tularensis

A dose and time response Markov model for the in-host dynamics of infection with intracellular bacteria following inhalation: with application toFrancisella tularensis

A human time dose response model for Q fever

Use of a Capture-Based Pathogen Transcript Enrichment Strategy for RNA-Seq Analysis of the Francisella Tularensis LVS Transcriptome during Infection of Murine Macrophages

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