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A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation.
Järvenpää, Marko; Sater, Mohamad R Abdul; Lagoudas, Georgia K; Blainey, Paul C; Miller, Loren G; McKinnell, James A; Huang, Susan S; Grad, Yonatan H; Marttinen, Pekka.
Afiliação
  • Järvenpää M; Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
  • Sater MRA; Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, MA, USA.
  • Lagoudas GK; Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, MA, USA.
  • Blainey PC; Department of Biological Engineering, MIT, Cambridge, MA, USA.
  • Miller LG; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • McKinnell JA; Department of Biological Engineering, MIT, Cambridge, MA, USA.
  • Huang SS; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Grad YH; Infectious Disease Clinical Outcomes Research Unit, Division of Infectious Diseases, LA Biomed Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA.
  • Marttinen P; Infectious Disease Clinical Outcomes Research Unit, Division of Infectious Diseases, LA Biomed Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA.
PLoS Comput Biol ; 15(4): e1006534, 2019 04.
Article em En | MEDLINE | ID: mdl-31009452
Bacterial populations that colonize a host can play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. With the use of whole genome sequencing to determine genetic distance between isolates, a common solution to estimate acquisition and clearance rates has been to assume a fixed genetic distance threshold below which isolates are considered to represent the same strain. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we present a fully Bayesian model that provides probabilities of whether two strains should be considered the same, allowing us to determine bacterial clearance and acquisition from genomes sampled over time. Our method explicitly models the within-host variation using population genetic simulation, and the inference is done using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with multiple carefully conducted simulations and demonstrate its use in practice by analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates from a large recently completed longitudinal clinical study. An R-code implementation of the method is freely available at: https://github.com/mjarvenpaa/bacterial-colonization-model.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Infecções Estafilocócicas / Biologia Computacional / Interações Hospedeiro-Patógeno / Staphylococcus aureus Resistente à Meticilina / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Infecções Estafilocócicas / Biologia Computacional / Interações Hospedeiro-Patógeno / Staphylococcus aureus Resistente à Meticilina / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2019 Tipo de documento: Article