A dose response model for Staphylococcus aureus

Abstract Dose-response models (DRMs) are used to predict the probability of microbial infection when a person is exposed to a given number of pathogens. In this study, we propose a new DRM for Staphylococcus aureus (SA), which causes skin and soft-tissue infections. The current approach to SA dose-r...

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Autores principales:	Srikiran Chandrasekaran, Sunny C. Jiang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/053d0c8f7f3b4f2fa1acb6df22846eff
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spelling	oai:doaj.org-article:053d0c8f7f3b4f2fa1acb6df22846eff2021-12-02T16:04:26ZA dose response model for Staphylococcus aureus10.1038/s41598-021-91822-y2045-2322https://doaj.org/article/053d0c8f7f3b4f2fa1acb6df22846eff2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91822-yhttps://doaj.org/toc/2045-2322Abstract Dose-response models (DRMs) are used to predict the probability of microbial infection when a person is exposed to a given number of pathogens. In this study, we propose a new DRM for Staphylococcus aureus (SA), which causes skin and soft-tissue infections. The current approach to SA dose-response is only partially mechanistic and assumes that individual bacteria do not interact with each other. Our proposed two-compartment (2C) model assumes that bacteria that have not adjusted to the host environment decay. After adjusting to the host, they exhibit logistic/cooperative growth, eventually causing disease. The transition between the adjusted and un-adjusted states is a stochastic process, which the 2C DRM explicitly models to predict response probabilities. By fitting the 2C model to SA pathogenesis data, we show that cooperation between individual SA bacteria is sufficient (and, within the scope of the 2C model, necessary) to characterize the dose-response. This is a departure from the classical single-hit theory of dose-response, where complete independence is assumed between individual pathogens. From a quantitative microbial risk assessment standpoint, the mechanistic basis of the 2C DRM enables transparent modeling of dose-response of antibiotic-resistant SA that has not been possible before. It also enables the modeling of scenarios having multiple/non-instantaneous exposures, with minimal assumptions.Srikiran ChandrasekaranSunny C. JiangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Srikiran Chandrasekaran Sunny C. Jiang A dose response model for Staphylococcus aureus
description	Abstract Dose-response models (DRMs) are used to predict the probability of microbial infection when a person is exposed to a given number of pathogens. In this study, we propose a new DRM for Staphylococcus aureus (SA), which causes skin and soft-tissue infections. The current approach to SA dose-response is only partially mechanistic and assumes that individual bacteria do not interact with each other. Our proposed two-compartment (2C) model assumes that bacteria that have not adjusted to the host environment decay. After adjusting to the host, they exhibit logistic/cooperative growth, eventually causing disease. The transition between the adjusted and un-adjusted states is a stochastic process, which the 2C DRM explicitly models to predict response probabilities. By fitting the 2C model to SA pathogenesis data, we show that cooperation between individual SA bacteria is sufficient (and, within the scope of the 2C model, necessary) to characterize the dose-response. This is a departure from the classical single-hit theory of dose-response, where complete independence is assumed between individual pathogens. From a quantitative microbial risk assessment standpoint, the mechanistic basis of the 2C DRM enables transparent modeling of dose-response of antibiotic-resistant SA that has not been possible before. It also enables the modeling of scenarios having multiple/non-instantaneous exposures, with minimal assumptions.
format	article
author	Srikiran Chandrasekaran Sunny C. Jiang
author_facet	Srikiran Chandrasekaran Sunny C. Jiang
author_sort	Srikiran Chandrasekaran
title	A dose response model for Staphylococcus aureus
title_short	A dose response model for Staphylococcus aureus
title_full	A dose response model for Staphylococcus aureus
title_fullStr	A dose response model for Staphylococcus aureus
title_full_unstemmed	A dose response model for Staphylococcus aureus
title_sort	dose response model for staphylococcus aureus
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/053d0c8f7f3b4f2fa1acb6df22846eff
work_keys_str_mv	AT srikiranchandrasekaran adoseresponsemodelforstaphylococcusaureus AT sunnycjiang adoseresponsemodelforstaphylococcusaureus AT srikiranchandrasekaran doseresponsemodelforstaphylococcusaureus AT sunnycjiang doseresponsemodelforstaphylococcusaureus
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A dose response model for Staphylococcus aureus

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