Cargando…
Bacterial Identification using 16S rRNA Gene Sequencing in a University Teaching Hospital
BACKGROUND: 16S rRNA gene sequencing can identify bacteria that are not identified using manual and automated systems, and characterize previously undescribed species. We describe our experience with 16S sequencing at the 1500-bed Yale New Haven Hospital (YNHH) in New Haven, CT, USA. METHODS: The mi...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5630713/ http://dx.doi.org/10.1093/ofid/ofx163.1576 |
_version_ | 1783269274837581824 |
---|---|
author | Poonawala, Husain Peaper, David |
author_facet | Poonawala, Husain Peaper, David |
author_sort | Poonawala, Husain |
collection | PubMed |
description | BACKGROUND: 16S rRNA gene sequencing can identify bacteria that are not identified using manual and automated systems, and characterize previously undescribed species. We describe our experience with 16S sequencing at the 1500-bed Yale New Haven Hospital (YNHH) in New Haven, CT, USA. METHODS: The microbiology laboratory at YNHH performs 16S sequencing weekly on bacterial isolates that cannot be identified with biochemical tests (API, RapID, Vitek) or MALDI-TOF MS. Bacterial colonies are processed using MicroSeq sequencing kits and a validated protocol. Sequence data are analyzed using MicroSeq and BLAST databases, and identification is assigned using Clinical and Laboratory Standards Institute (CLSI) guidelines MM-18. We reviewed 16S rRNA sequencing results obtained between April 2011 and July 2016. RESULTS: 586 isolates from 581 clinical specimens belonging to 513 patients were sequenced. Most isolates were from the blood (34%), respiratory (33%) and wound (25%) benches (Figure 1). Anaerobes (32%), mycobacteria (23%), and Gram-negative rods (21%), accounted for 76% of isolates. For 566 (97%) of isolates, an identification was made to the species (62%), complex (14%) or genus (24%) (Figure 2). These 566 isolates were distributed among 108 unique genera (Figure 3). The five most common genera were Mycobacteria (24%), Actinomyces (13%), Nocardia (6%), Clostridium (5%), and Aerococcus (2%) species. 49 genera were represented by a single isolate, 17 genera by 2 isolates each, and 11 genera by 3 isolates each. Of the remaining 20 isolates (3%), 11 were identified at the taxonomical level of family or order. For 5 isolates, no identification was possible, while 4 isolates were potentially novel species. Following introduction of MALDI-TOF MS there was a decline in isolates needing 16S sequencing for identification. CONCLUSION: Our experience with 16S sequencing demonstrates its utility in clinical microbiology. We identified organisms from a wide variety of specimen types with an identification at the level of genus or below in 97% of isolates. 566 isolates belonged to 108 unique genera, with 4 potential novel species, suggesting conventional identification methods may underestimate the diversity of bacteria in clinical specimens. DISCLOSURES: All authors: No reported disclosures. |
format | Online Article Text |
id | pubmed-5630713 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-56307132017-11-07 Bacterial Identification using 16S rRNA Gene Sequencing in a University Teaching Hospital Poonawala, Husain Peaper, David Open Forum Infect Dis Abstracts BACKGROUND: 16S rRNA gene sequencing can identify bacteria that are not identified using manual and automated systems, and characterize previously undescribed species. We describe our experience with 16S sequencing at the 1500-bed Yale New Haven Hospital (YNHH) in New Haven, CT, USA. METHODS: The microbiology laboratory at YNHH performs 16S sequencing weekly on bacterial isolates that cannot be identified with biochemical tests (API, RapID, Vitek) or MALDI-TOF MS. Bacterial colonies are processed using MicroSeq sequencing kits and a validated protocol. Sequence data are analyzed using MicroSeq and BLAST databases, and identification is assigned using Clinical and Laboratory Standards Institute (CLSI) guidelines MM-18. We reviewed 16S rRNA sequencing results obtained between April 2011 and July 2016. RESULTS: 586 isolates from 581 clinical specimens belonging to 513 patients were sequenced. Most isolates were from the blood (34%), respiratory (33%) and wound (25%) benches (Figure 1). Anaerobes (32%), mycobacteria (23%), and Gram-negative rods (21%), accounted for 76% of isolates. For 566 (97%) of isolates, an identification was made to the species (62%), complex (14%) or genus (24%) (Figure 2). These 566 isolates were distributed among 108 unique genera (Figure 3). The five most common genera were Mycobacteria (24%), Actinomyces (13%), Nocardia (6%), Clostridium (5%), and Aerococcus (2%) species. 49 genera were represented by a single isolate, 17 genera by 2 isolates each, and 11 genera by 3 isolates each. Of the remaining 20 isolates (3%), 11 were identified at the taxonomical level of family or order. For 5 isolates, no identification was possible, while 4 isolates were potentially novel species. Following introduction of MALDI-TOF MS there was a decline in isolates needing 16S sequencing for identification. CONCLUSION: Our experience with 16S sequencing demonstrates its utility in clinical microbiology. We identified organisms from a wide variety of specimen types with an identification at the level of genus or below in 97% of isolates. 566 isolates belonged to 108 unique genera, with 4 potential novel species, suggesting conventional identification methods may underestimate the diversity of bacteria in clinical specimens. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2017-10-04 /pmc/articles/PMC5630713/ http://dx.doi.org/10.1093/ofid/ofx163.1576 Text en © The Author 2017. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Abstracts Poonawala, Husain Peaper, David Bacterial Identification using 16S rRNA Gene Sequencing in a University Teaching Hospital |
title | Bacterial Identification using 16S rRNA Gene Sequencing in a University Teaching Hospital |
title_full | Bacterial Identification using 16S rRNA Gene Sequencing in a University Teaching Hospital |
title_fullStr | Bacterial Identification using 16S rRNA Gene Sequencing in a University Teaching Hospital |
title_full_unstemmed | Bacterial Identification using 16S rRNA Gene Sequencing in a University Teaching Hospital |
title_short | Bacterial Identification using 16S rRNA Gene Sequencing in a University Teaching Hospital |
title_sort | bacterial identification using 16s rrna gene sequencing in a university teaching hospital |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5630713/ http://dx.doi.org/10.1093/ofid/ofx163.1576 |
work_keys_str_mv | AT poonawalahusain bacterialidentificationusing16srrnagenesequencinginauniversityteachinghospital AT peaperdavid bacterialidentificationusing16srrnagenesequencinginauniversityteachinghospital |