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Abstract

Cryptococcus neoformans is an opportunistic fungal infection that causes cryptococcal meningitis in immunocompromised individuals. Macrophages play a critical role in determining the outcome of infection, and can either phagocytose and kill the cryptococcal cells, or disseminate infection. While it is known that macrophages impact the progression of cryptococcal disease, it is not known how the macrophage intracellular niche contributes to complex infection outcomes. Clinical and experimental studies have identified potential genetic differences, in both host and pathogen, but statistical robustness has been difficult to achieve due the large variability in the outcome of infections. Therefore, in attempt to quantitatively explain this variability, we have used a zebrafish model of cryptococcal infection where we can directly relate the initial level of fungal infection with final infection outcome. We find that at low levels of initial fungal burden the outcome of infection is stochastic, while over high ranges of initial infection the outcome is linearly related to the initial burden, but with a further stochastic component that contributes to increased variability. Using these experimental data and data from clinical trials we have generated a computational simulation of in vivo cryptococcal infections which allows us to consider different infection variables and how they alter the progression of cryptococcosis. By combining such computational simulations with our experimental models we demonstrate that the macrophage intracellular niche determines the unknown stochastic component, independent of fungal burden. Using this knowledge, we can better identify the molecular, population genetic, and clinical parameters associated with the outcome of cryptococcosis.

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/content/journal/acmi/10.1099/acmi.ac2019.po0237
2019-04-08
2019-09-16
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http://instance.metastore.ingenta.com/content/journal/acmi/10.1099/acmi.ac2019.po0237
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