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The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology
BACKGROUND: Tracking recent transmission is a vital part of controlling widespread pathogens such as Mycobacterium tuberculosis. Multiple methods with specific performance characteristics exist for detecting recent transmission chains, usually by clustering strains based on genotype similarities. Wi...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284411/ https://www.ncbi.nlm.nih.gov/pubmed/30341041 http://dx.doi.org/10.1016/j.ebiom.2018.10.013 |
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author | Meehan, Conor J. Moris, Pieter Kohl, Thomas A. Pečerska, Jūlija Akter, Suriya Merker, Matthias Utpatel, Christian Beckert, Patrick Gehre, Florian Lempens, Pauline Stadler, Tanja Kaswa, Michel K. Kühnert, Denise Niemann, Stefan de Jong, Bouke C. |
author_facet | Meehan, Conor J. Moris, Pieter Kohl, Thomas A. Pečerska, Jūlija Akter, Suriya Merker, Matthias Utpatel, Christian Beckert, Patrick Gehre, Florian Lempens, Pauline Stadler, Tanja Kaswa, Michel K. Kühnert, Denise Niemann, Stefan de Jong, Bouke C. |
author_sort | Meehan, Conor J. |
collection | PubMed |
description | BACKGROUND: Tracking recent transmission is a vital part of controlling widespread pathogens such as Mycobacterium tuberculosis. Multiple methods with specific performance characteristics exist for detecting recent transmission chains, usually by clustering strains based on genotype similarities. With such a large variety of methods available, informed selection of an appropriate approach for determining transmissions within a given setting/time period is difficult. METHODS: This study combines whole genome sequence (WGS) data derived from 324 isolates collected 2005–2010 in Kinshasa, Democratic Republic of Congo (DRC), a high endemic setting, with phylodynamics to unveil the timing of transmission events posited by a variety of standard genotyping methods. Clustering data based on Spoligotyping, 24-loci MIRU-VNTR typing, WGS based SNP (Single Nucleotide Polymorphism) and core genome multi locus sequence typing (cgMLST) typing were evaluated. FINDINGS: Our results suggest that clusters based on Spoligotyping could encompass transmission events that occurred almost 200 years prior to sampling while 24-loci-MIRU-VNTR often represented three decades of transmission. Instead, WGS based genotyping applying low SNP or cgMLST allele thresholds allows for determination of recent transmission events, e.g. in timespans of up to 10 years for a 5 SNP/allele cut-off. INTERPRETATION: With the rapid uptake of WGS methods in surveillance and outbreak tracking, the findings obtained in this study can guide the selection of appropriate clustering methods for uncovering relevant transmission chains within a given time-period. For high resolution cluster analyses, WGS-SNP and cgMLST based analyses have similar clustering/timing characteristics even for data obtained from a high incidence setting. |
format | Online Article Text |
id | pubmed-6284411 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-62844112018-12-13 The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology Meehan, Conor J. Moris, Pieter Kohl, Thomas A. Pečerska, Jūlija Akter, Suriya Merker, Matthias Utpatel, Christian Beckert, Patrick Gehre, Florian Lempens, Pauline Stadler, Tanja Kaswa, Michel K. Kühnert, Denise Niemann, Stefan de Jong, Bouke C. EBioMedicine Research paper BACKGROUND: Tracking recent transmission is a vital part of controlling widespread pathogens such as Mycobacterium tuberculosis. Multiple methods with specific performance characteristics exist for detecting recent transmission chains, usually by clustering strains based on genotype similarities. With such a large variety of methods available, informed selection of an appropriate approach for determining transmissions within a given setting/time period is difficult. METHODS: This study combines whole genome sequence (WGS) data derived from 324 isolates collected 2005–2010 in Kinshasa, Democratic Republic of Congo (DRC), a high endemic setting, with phylodynamics to unveil the timing of transmission events posited by a variety of standard genotyping methods. Clustering data based on Spoligotyping, 24-loci MIRU-VNTR typing, WGS based SNP (Single Nucleotide Polymorphism) and core genome multi locus sequence typing (cgMLST) typing were evaluated. FINDINGS: Our results suggest that clusters based on Spoligotyping could encompass transmission events that occurred almost 200 years prior to sampling while 24-loci-MIRU-VNTR often represented three decades of transmission. Instead, WGS based genotyping applying low SNP or cgMLST allele thresholds allows for determination of recent transmission events, e.g. in timespans of up to 10 years for a 5 SNP/allele cut-off. INTERPRETATION: With the rapid uptake of WGS methods in surveillance and outbreak tracking, the findings obtained in this study can guide the selection of appropriate clustering methods for uncovering relevant transmission chains within a given time-period. For high resolution cluster analyses, WGS-SNP and cgMLST based analyses have similar clustering/timing characteristics even for data obtained from a high incidence setting. Elsevier 2018-10-16 /pmc/articles/PMC6284411/ /pubmed/30341041 http://dx.doi.org/10.1016/j.ebiom.2018.10.013 Text en © 2018 The Authors 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 | Research paper Meehan, Conor J. Moris, Pieter Kohl, Thomas A. Pečerska, Jūlija Akter, Suriya Merker, Matthias Utpatel, Christian Beckert, Patrick Gehre, Florian Lempens, Pauline Stadler, Tanja Kaswa, Michel K. Kühnert, Denise Niemann, Stefan de Jong, Bouke C. The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology |
title | The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology |
title_full | The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology |
title_fullStr | The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology |
title_full_unstemmed | The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology |
title_short | The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology |
title_sort | relationship between transmission time and clustering methods in mycobacterium tuberculosis epidemiology |
topic | Research paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284411/ https://www.ncbi.nlm.nih.gov/pubmed/30341041 http://dx.doi.org/10.1016/j.ebiom.2018.10.013 |
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