Intracellular Demography and the Dynamics of Salmonella enterica Infections |
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Authors: | Sam P Brown Sam P Brown Sam P Brown Sam P Brown Sam P Brown |
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Institution: | 1 Department of Zoology, University of Cambridge, Cambridge, United Kingdom, 2 Section of Integrative Biology, University of Texas at Austin, Austin, Texas, United States of America, 3 Institute of Integrative and Comparative Biology, University of Leeds, Leeds, United Kingdom, 4 Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom, 5 Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, Pennsylvania, United States of America, 6 Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America |
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Abstract: | An understanding of within-host dynamics of pathogen interactions with eukaryotic cells can shape the development of effective preventive measures and drug regimes. Such investigations have been hampered by the difficulty of identifying and observing directly, within live tissues, the multiple key variables that underlay infection processes. Fluorescence microscopy data on intracellular distributions of Salmonella enterica serovar Typhimurium (S. Typhimurium) show that, while the number of infected cells increases with time, the distribution of bacteria between cells is stationary (though highly skewed). Here, we report a simple model framework for the intensity of intracellular infection that links the quasi-stationary distribution of bacteria to bacterial and cellular demography. This enables us to reject the hypothesis that the skewed distribution is generated by intrinsic cellular heterogeneities, and to derive specific predictions on the within-cell dynamics of Salmonella division and host-cell lysis. For within-cell pathogens in general, we show that within-cell dynamics have implications across pathogen dynamics, evolution, and control, and we develop novel generic guidelines for the design of antibacterial combination therapies and the management of antibiotic resistance. |
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