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Elife. 2019 Oct 8;8. pii: e46402. doi: 10.7554/eLife.46402. [Epub ahead of print]

Improved characterisation of MRSA transmission using within-host bacterial sequence diversity.

Author information

1
Big Data Institute, University of Oxford, Oxford, United Kingdom.
2
School of Medicine, University of St Andrews, St Andrews, United Kingdom.
3
Department of Pediatrics, Sunpasitthiprasong Hospital, Ubon Ratchathani, Thailand.
4
Department of Medicine, Sunpasitthiprasong Hospital, Ubon Ratchathani, Thailand.
5
Mahidol Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.
6
Department of Biology and Biochemistry, University of Bath, Bath, United Kingdom.
7
Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom.
8
Department of Infectious Diseases, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom.
9
Department of Medicine, University of Cambridge, Cambridge, United Kingdom.

Abstract

Methicillin-resistant Staphylococcus aureus (MRSA) transmission in the hospital setting has been a frequent subject of investigation using bacterial genomes, but previous approaches have not yet fully utilised the extra deductive power provided when multiple pathogen samples are acquired from each host. Here, we use a large dataset of MRSA sequences from multiply-sampled patients to reconstruct colonisation of individuals in a high-transmission setting in a hospital in Thailand. We reconstructed transmission trees for MRSA. We also investigated transmission between anatomical sites on the same individual, finding that this either occurs repeatedly or involves a wide transmission bottleneck. We examined the between-subject bottleneck, finding a wide range in the amount of diversity transmitted. Finally, we compared our approach to the simpler method of identifying transmission pairs using single nucleotide polymorphism (SNP) counts. This suggested that the optimum threshold for identifying a pair is 39 SNPs, if sensitivities and specificities are equally weighted.

KEYWORDS:

epidemiology; global health; human

PMID:
31591959
DOI:
10.7554/eLife.46402
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