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Deciphering the low abundance microbiota of presumed aseptic hip and knee implants
16S rRNA gene sequencing of DNA extracted from clinically uninfected hip and knee implant samples has revealed polymicrobial populations. However, previous studies assessed 16S rRNA gene sequencing as a technique for the diagnosis of periprosthetic joint infections, leaving the microbiota of presume...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8439452/ https://www.ncbi.nlm.nih.gov/pubmed/34520499 http://dx.doi.org/10.1371/journal.pone.0257471 |
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author | Carr, Charles Wilcox, Hannah Burton, Jeremy P. Menon, Sharanya Al, Kait F. O’Gorman, David Lanting, Brent A. Vasarhelyi, Edward M. Neufeld, Michael Teeter, Matthew G. |
author_facet | Carr, Charles Wilcox, Hannah Burton, Jeremy P. Menon, Sharanya Al, Kait F. O’Gorman, David Lanting, Brent A. Vasarhelyi, Edward M. Neufeld, Michael Teeter, Matthew G. |
author_sort | Carr, Charles |
collection | PubMed |
description | 16S rRNA gene sequencing of DNA extracted from clinically uninfected hip and knee implant samples has revealed polymicrobial populations. However, previous studies assessed 16S rRNA gene sequencing as a technique for the diagnosis of periprosthetic joint infections, leaving the microbiota of presumed aseptic hip and knee implants largely unstudied. These communities of microorganisms might play important roles in aspects of host health, such as aseptic loosening. Therefore, this study sought to characterize the bacterial composition of presumed aseptic joint implant microbiota using next generation 16S rRNA gene sequencing, and it evaluated this method for future investigations. 248 samples were collected from implants of 41 patients undergoing total hip or knee arthroplasty revision for presumed aseptic failure. DNA was extracted using two methodologies—one optimized for high throughput and the other for human samples—and amplicons of the V4 region of the 16S rRNA gene were sequenced. Sequencing data were analyzed and compared with ancillary specific PCR and microbiological culture. Computational tools (SourceTracker and decontam) were used to detect and compensate for environmental and processing contaminants. Microbial diversity of patient samples was higher than that of open-air controls and differentially abundant taxa were detected between these conditions, possibly reflecting a true microbiota that is present in clinically uninfected joint implants. However, positive control-associated artifacts and DNA extraction methodology significantly affected sequencing results. As well, sequencing failed to identify Cutibacterium acnes in most culture- and PCR-positive samples. These challenges limited characterization of bacteria in presumed aseptic implants, but genera were identified for further investigation. In all, we provide further support for the hypothesis that there is likely a microbiota present in clinically uninfected joint implants, and we show that methods other than 16S rRNA gene sequencing may be ideal for its characterization. This work has illuminated the importance of further study of microbiota of clinically uninfected joint implants with novel molecular and computational tools to further eliminate contaminants and artifacts that arise in low bacterial abundance samples. |
format | Online Article Text |
id | pubmed-8439452 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-84394522021-09-15 Deciphering the low abundance microbiota of presumed aseptic hip and knee implants Carr, Charles Wilcox, Hannah Burton, Jeremy P. Menon, Sharanya Al, Kait F. O’Gorman, David Lanting, Brent A. Vasarhelyi, Edward M. Neufeld, Michael Teeter, Matthew G. PLoS One Research Article 16S rRNA gene sequencing of DNA extracted from clinically uninfected hip and knee implant samples has revealed polymicrobial populations. However, previous studies assessed 16S rRNA gene sequencing as a technique for the diagnosis of periprosthetic joint infections, leaving the microbiota of presumed aseptic hip and knee implants largely unstudied. These communities of microorganisms might play important roles in aspects of host health, such as aseptic loosening. Therefore, this study sought to characterize the bacterial composition of presumed aseptic joint implant microbiota using next generation 16S rRNA gene sequencing, and it evaluated this method for future investigations. 248 samples were collected from implants of 41 patients undergoing total hip or knee arthroplasty revision for presumed aseptic failure. DNA was extracted using two methodologies—one optimized for high throughput and the other for human samples—and amplicons of the V4 region of the 16S rRNA gene were sequenced. Sequencing data were analyzed and compared with ancillary specific PCR and microbiological culture. Computational tools (SourceTracker and decontam) were used to detect and compensate for environmental and processing contaminants. Microbial diversity of patient samples was higher than that of open-air controls and differentially abundant taxa were detected between these conditions, possibly reflecting a true microbiota that is present in clinically uninfected joint implants. However, positive control-associated artifacts and DNA extraction methodology significantly affected sequencing results. As well, sequencing failed to identify Cutibacterium acnes in most culture- and PCR-positive samples. These challenges limited characterization of bacteria in presumed aseptic implants, but genera were identified for further investigation. In all, we provide further support for the hypothesis that there is likely a microbiota present in clinically uninfected joint implants, and we show that methods other than 16S rRNA gene sequencing may be ideal for its characterization. This work has illuminated the importance of further study of microbiota of clinically uninfected joint implants with novel molecular and computational tools to further eliminate contaminants and artifacts that arise in low bacterial abundance samples. Public Library of Science 2021-09-14 /pmc/articles/PMC8439452/ /pubmed/34520499 http://dx.doi.org/10.1371/journal.pone.0257471 Text en © 2021 Carr et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Carr, Charles Wilcox, Hannah Burton, Jeremy P. Menon, Sharanya Al, Kait F. O’Gorman, David Lanting, Brent A. Vasarhelyi, Edward M. Neufeld, Michael Teeter, Matthew G. Deciphering the low abundance microbiota of presumed aseptic hip and knee implants |
title | Deciphering the low abundance microbiota of presumed aseptic hip and knee implants |
title_full | Deciphering the low abundance microbiota of presumed aseptic hip and knee implants |
title_fullStr | Deciphering the low abundance microbiota of presumed aseptic hip and knee implants |
title_full_unstemmed | Deciphering the low abundance microbiota of presumed aseptic hip and knee implants |
title_short | Deciphering the low abundance microbiota of presumed aseptic hip and knee implants |
title_sort | deciphering the low abundance microbiota of presumed aseptic hip and knee implants |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8439452/ https://www.ncbi.nlm.nih.gov/pubmed/34520499 http://dx.doi.org/10.1371/journal.pone.0257471 |
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