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A population-level strain genotyping method to study pathogen strain dynamics in human infections

A hallmark of chronic bacterial infections is the long-term persistence of 1 or more pathogen species at the compromised site. Repeated detection of the same bacterial species can suggest that a single strain or lineage is continually present. However, infection with multiple strains of a given spec...

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Autores principales: Morgan, Sarah J., Durfey, Samantha L., Ravishankar, Sumedha, Jorth, Peter, Ni, Wendy, Skerrett, Duncan T., Aitken, Moira L., McKone, Edward F., Salipante, Stephen J., Radey, Matthew C., Singh, Pradeep K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Clinical Investigation 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8783678/
https://www.ncbi.nlm.nih.gov/pubmed/34935640
http://dx.doi.org/10.1172/jci.insight.152472
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author Morgan, Sarah J.
Durfey, Samantha L.
Ravishankar, Sumedha
Jorth, Peter
Ni, Wendy
Skerrett, Duncan T.
Aitken, Moira L.
McKone, Edward F.
Salipante, Stephen J.
Radey, Matthew C.
Singh, Pradeep K.
author_facet Morgan, Sarah J.
Durfey, Samantha L.
Ravishankar, Sumedha
Jorth, Peter
Ni, Wendy
Skerrett, Duncan T.
Aitken, Moira L.
McKone, Edward F.
Salipante, Stephen J.
Radey, Matthew C.
Singh, Pradeep K.
author_sort Morgan, Sarah J.
collection PubMed
description A hallmark of chronic bacterial infections is the long-term persistence of 1 or more pathogen species at the compromised site. Repeated detection of the same bacterial species can suggest that a single strain or lineage is continually present. However, infection with multiple strains of a given species, strain acquisition and loss, and changes in strain relative abundance can occur. Detecting strain-level changes and their effects on disease is challenging because most methods require labor-intensive isolate-by-isolate analyses, and thus, only a few cells from large infecting populations can be examined. Here, we present a population-level method for enumerating and measuring the relative abundance of strains called population multi-locus sequence typing (PopMLST). The method exploits PCR amplification of strain-identifying polymorphic loci, next-generation sequencing to measure allelic variants, and informatic methods to determine whether variants arise from sequencing errors or low-abundance strains. These features enable PopMLST to simultaneously interrogate hundreds of bacterial cells that are cultured en masse from patient samples or are present in DNA directly extracted from clinical specimens without ex vivo culture. This method could be used to detect epidemic or super-infecting strains, facilitate understanding of strain dynamics during chronic infections, and enable studies that link strain changes to clinical outcomes.
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spelling pubmed-87836782022-01-26 A population-level strain genotyping method to study pathogen strain dynamics in human infections Morgan, Sarah J. Durfey, Samantha L. Ravishankar, Sumedha Jorth, Peter Ni, Wendy Skerrett, Duncan T. Aitken, Moira L. McKone, Edward F. Salipante, Stephen J. Radey, Matthew C. Singh, Pradeep K. JCI Insight Resource and Technical Advance A hallmark of chronic bacterial infections is the long-term persistence of 1 or more pathogen species at the compromised site. Repeated detection of the same bacterial species can suggest that a single strain or lineage is continually present. However, infection with multiple strains of a given species, strain acquisition and loss, and changes in strain relative abundance can occur. Detecting strain-level changes and their effects on disease is challenging because most methods require labor-intensive isolate-by-isolate analyses, and thus, only a few cells from large infecting populations can be examined. Here, we present a population-level method for enumerating and measuring the relative abundance of strains called population multi-locus sequence typing (PopMLST). The method exploits PCR amplification of strain-identifying polymorphic loci, next-generation sequencing to measure allelic variants, and informatic methods to determine whether variants arise from sequencing errors or low-abundance strains. These features enable PopMLST to simultaneously interrogate hundreds of bacterial cells that are cultured en masse from patient samples or are present in DNA directly extracted from clinical specimens without ex vivo culture. This method could be used to detect epidemic or super-infecting strains, facilitate understanding of strain dynamics during chronic infections, and enable studies that link strain changes to clinical outcomes. American Society for Clinical Investigation 2021-12-22 /pmc/articles/PMC8783678/ /pubmed/34935640 http://dx.doi.org/10.1172/jci.insight.152472 Text en © 2021 Morgan et al. https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Resource and Technical Advance
Morgan, Sarah J.
Durfey, Samantha L.
Ravishankar, Sumedha
Jorth, Peter
Ni, Wendy
Skerrett, Duncan T.
Aitken, Moira L.
McKone, Edward F.
Salipante, Stephen J.
Radey, Matthew C.
Singh, Pradeep K.
A population-level strain genotyping method to study pathogen strain dynamics in human infections
title A population-level strain genotyping method to study pathogen strain dynamics in human infections
title_full A population-level strain genotyping method to study pathogen strain dynamics in human infections
title_fullStr A population-level strain genotyping method to study pathogen strain dynamics in human infections
title_full_unstemmed A population-level strain genotyping method to study pathogen strain dynamics in human infections
title_short A population-level strain genotyping method to study pathogen strain dynamics in human infections
title_sort population-level strain genotyping method to study pathogen strain dynamics in human infections
topic Resource and Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8783678/
https://www.ncbi.nlm.nih.gov/pubmed/34935640
http://dx.doi.org/10.1172/jci.insight.152472
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