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PLoS Comput Biol. 2019 Apr 22;15(4):e1006534. doi: 10.1371/journal.pcbi.1006534. eCollection 2019 Apr.

A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation.

Author information

1
Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
2
Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, MA, USA.
3
Department of Biological Engineering, MIT, Cambridge, MA, USA.
4
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
5
Infectious Disease Clinical Outcomes Research Unit, Division of Infectious Diseases, LA Biomed Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA.
6
Division of Infectious Diseases and Health Policy Research Institute, University of California, Irvine School of Medicine, Irvine, CA, USA.

Abstract

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.

PMID:
31009452
PMCID:
PMC6497309
DOI:
10.1371/journal.pcbi.1006534
[Indexed for MEDLINE]
Free PMC Article

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