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Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis

BACKGROUND: Whole-genome sequencing (WGS) can be used in genomic epidemiology investigations to confirm or refute outbreaks of bacterial pathogens, and to support targeted and efficient infection control interventions. We aimed to define a genetic relatedness cutoff, quantified as a number of single...

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Autores principales: Coll, Francesc, Raven, Kathy E, Knight, Gwenan M, Blane, Beth, Harrison, Ewan M, Leek, Danielle, Enoch, David A, Brown, Nicholas M, Parkhill, Julian, Peacock, Sharon J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721685/
https://www.ncbi.nlm.nih.gov/pubmed/33313577
http://dx.doi.org/10.1016/S2666-5247(20)30149-X
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author Coll, Francesc
Raven, Kathy E
Knight, Gwenan M
Blane, Beth
Harrison, Ewan M
Leek, Danielle
Enoch, David A
Brown, Nicholas M
Parkhill, Julian
Peacock, Sharon J
author_facet Coll, Francesc
Raven, Kathy E
Knight, Gwenan M
Blane, Beth
Harrison, Ewan M
Leek, Danielle
Enoch, David A
Brown, Nicholas M
Parkhill, Julian
Peacock, Sharon J
author_sort Coll, Francesc
collection PubMed
description BACKGROUND: Whole-genome sequencing (WGS) can be used in genomic epidemiology investigations to confirm or refute outbreaks of bacterial pathogens, and to support targeted and efficient infection control interventions. We aimed to define a genetic relatedness cutoff, quantified as a number of single-nucleotide polymorphisms (SNP), for meticillin-resistant Staphylococcus aureus (MRSA), above which recent (ie, within 6 months) patient-to-patient transmission could be ruled out. METHODS: We did a retrospective genomic and epidemiological analysis of MRSA data from two prospective observational cohort studies in the UK to establish SNP cutoffs for genetic relatedness, above which recent transmission was unlikely. We used three separate approaches to calculate these thresholds. First, we applied a linear mixed model to estimate the S aureus substitution rate and 95th percentile within-host diversity in a cohort in which multiple isolates were sequenced per individual. Second, we applied a simulated transmission model to this same genomic dataset. Finally, in a second cohort, we determined the genetic distance (ie, the number of SNPs) that would capture 95% of epidemiologically linked cases. We applied the three approaches to both whole-genome and core-genome sequences. FINDINGS: In the linear mixed model, the estimated substitution rate was roughly 5 whole-genome SNPs (wgSNPs) or 3 core-genome SNPs (cgSNPs) per genome per year, and the 95th percentile within-host diversity was 19 wgSNPs or 10 cgSNPs. The combined SNP cutoffs for detection of MRSA transmission within 6 months per this model were thus 24 wgSNPs or 13 cgSNPs. The simulated transmission model suggested that cutoffs of 17 wgSNPs or 12 cgSNPs would detect 95% of MRSA transmission events within the same timeframe. Finally, in the second cohort, cutoffs of 22 wgSNPs or 11 cgSNPs captured 95% of epidemiologically linked cases within 6 months. INTERPRETATION: On the basis of our results, we propose conservative cutoffs of 25 wgSNPs or 15 cgSNPS above which transmission of MRSA within the previous 6 months can be ruled out. These cutoffs could potentially be used as part of a genomic sequencing approach to the management of outbreaks of MRSA in conjunction with traditional epidemiological techniques. FUNDING: UK Department of Health, Wellcome Trust, UK National Institute for Health Research.
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spelling pubmed-77216852020-12-11 Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis Coll, Francesc Raven, Kathy E Knight, Gwenan M Blane, Beth Harrison, Ewan M Leek, Danielle Enoch, David A Brown, Nicholas M Parkhill, Julian Peacock, Sharon J Lancet Microbe Articles BACKGROUND: Whole-genome sequencing (WGS) can be used in genomic epidemiology investigations to confirm or refute outbreaks of bacterial pathogens, and to support targeted and efficient infection control interventions. We aimed to define a genetic relatedness cutoff, quantified as a number of single-nucleotide polymorphisms (SNP), for meticillin-resistant Staphylococcus aureus (MRSA), above which recent (ie, within 6 months) patient-to-patient transmission could be ruled out. METHODS: We did a retrospective genomic and epidemiological analysis of MRSA data from two prospective observational cohort studies in the UK to establish SNP cutoffs for genetic relatedness, above which recent transmission was unlikely. We used three separate approaches to calculate these thresholds. First, we applied a linear mixed model to estimate the S aureus substitution rate and 95th percentile within-host diversity in a cohort in which multiple isolates were sequenced per individual. Second, we applied a simulated transmission model to this same genomic dataset. Finally, in a second cohort, we determined the genetic distance (ie, the number of SNPs) that would capture 95% of epidemiologically linked cases. We applied the three approaches to both whole-genome and core-genome sequences. FINDINGS: In the linear mixed model, the estimated substitution rate was roughly 5 whole-genome SNPs (wgSNPs) or 3 core-genome SNPs (cgSNPs) per genome per year, and the 95th percentile within-host diversity was 19 wgSNPs or 10 cgSNPs. The combined SNP cutoffs for detection of MRSA transmission within 6 months per this model were thus 24 wgSNPs or 13 cgSNPs. The simulated transmission model suggested that cutoffs of 17 wgSNPs or 12 cgSNPs would detect 95% of MRSA transmission events within the same timeframe. Finally, in the second cohort, cutoffs of 22 wgSNPs or 11 cgSNPs captured 95% of epidemiologically linked cases within 6 months. INTERPRETATION: On the basis of our results, we propose conservative cutoffs of 25 wgSNPs or 15 cgSNPS above which transmission of MRSA within the previous 6 months can be ruled out. These cutoffs could potentially be used as part of a genomic sequencing approach to the management of outbreaks of MRSA in conjunction with traditional epidemiological techniques. FUNDING: UK Department of Health, Wellcome Trust, UK National Institute for Health Research. Elsevier Ltd 2020-12 /pmc/articles/PMC7721685/ /pubmed/33313577 http://dx.doi.org/10.1016/S2666-5247(20)30149-X Text en © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Articles
Coll, Francesc
Raven, Kathy E
Knight, Gwenan M
Blane, Beth
Harrison, Ewan M
Leek, Danielle
Enoch, David A
Brown, Nicholas M
Parkhill, Julian
Peacock, Sharon J
Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis
title Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis
title_full Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis
title_fullStr Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis
title_full_unstemmed Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis
title_short Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis
title_sort definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant staphylococcus aureus: a genomic epidemiology analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721685/
https://www.ncbi.nlm.nih.gov/pubmed/33313577
http://dx.doi.org/10.1016/S2666-5247(20)30149-X
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