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...

Descripción completa

Detalles Bibliográficos
Autores principales: Poonawala, Husain, Peaper, David
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