期刊
MOLECULAR BIOLOGY AND EVOLUTION
卷 28, 期 5, 页码 1593-1603出版社
OXFORD UNIV PRESS
DOI: 10.1093/molbev/msq319
关键词
Bayesian inference; phylogeography; phylogenetics; measurably evolving population
资金
- UF Emerging Pathogens Institute Seed
- Department of Homeland Security
- Fogarty International Center
- US National Institutes of Health
- National Cancer Institute, National Institutes of Health [CA09126]
- Bill & Melinda Gates Foundation [49446]
- Transportation Research Board of the National Academies [ACRP 02-20]
- Royal Society
- National Institute of Allergy and Infectious Diseases, National Institutes of Health [R01 NS063897]
Staphylococcus aureus is a common cause of infections that has undergone rapid global spread over recent decades. Formal phylogeographic methods have not yet been applied to the molecular epidemiology of bacterial pathogens because the limited genetic diversity of data sets based on individual genes usually results in poor phylogenetic resolution. Here, we investigated a whole-genome single nucleotide polymorphism (SNP) data set of health care-associated Methicillin-resistant S. aureus sequence type 239 (HA-MRSA ST239) strains, which we analyzed using Markov spatial models that incorporate geographical sampling distributions. The reconstructed timescale indicated a temporal origin of this strain shortly after the introduction of Methicillin, followed by global pandemic spread. The estimate of the temporal origin was robust to the molecular clock, coalescent prior, full/intergenic/synonymous SNP inclusion, and correction for excluded invariant site patterns. Finally, phylogeographic analyses statistically supported the role of human movement in the global dissemination of HA-MRSA ST239, although it was unable to conclusively resolve the location of the root. This study demonstrates that bacterial genomes can indeed contain sufficient evolutionary information to elucidate the temporal and spatial dynamics of transmission. Future applications of this approach to other bacterial strains may provide valuable epidemiological insights that may justify the cost of genome-wide typing.
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