Longitudinal Metagenomic Analysis of Hospital Air Identifies Clinically Relevant Microbes.
We describe the sampling of sixty-three uncultured hospital air samples collected over a six-month period and analysis using shotgun metagenomic sequencing. Our primary goals were to determine the longitudinal metagenomic variability of this environment, identify and characterize genomes of potentia...
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doaj-d38ee2d84ab246f6b67429e062285f092020-11-24T22:12:25ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01118e016012410.1371/journal.pone.0160124Longitudinal Metagenomic Analysis of Hospital Air Identifies Clinically Relevant Microbes.Paula KingLong K PhamShannon WaltzDan SpharRobert T YamamotoDouglas ConradRandy TaplitzFrancesca TorrianiR Allyn ForsythWe describe the sampling of sixty-three uncultured hospital air samples collected over a six-month period and analysis using shotgun metagenomic sequencing. Our primary goals were to determine the longitudinal metagenomic variability of this environment, identify and characterize genomes of potential pathogens and determine whether they are atypical to the hospital airborne metagenome. Air samples were collected from eight locations which included patient wards, the main lobby and outside. The resulting DNA libraries produced 972 million sequences representing 51 gigabases. Hierarchical clustering of samples by the most abundant 50 microbial orders generated three major nodes which primarily clustered by type of location. Because the indoor locations were longitudinally consistent, episodic relative increases in microbial genomic signatures related to the opportunistic pathogens Aspergillus, Penicillium and Stenotrophomonas were identified as outliers at specific locations. Further analysis of microbial reads specific for Stenotrophomonas maltophilia indicated homology to a sequenced multi-drug resistant clinical strain and we observed broad sequence coverage of resistance genes. We demonstrate that a shotgun metagenomic sequencing approach can be used to characterize the resistance determinants of pathogen genomes that are uncharacteristic for an otherwise consistent hospital air microbial metagenomic profile.http://europepmc.org/articles/PMC4970769?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Paula King Long K Pham Shannon Waltz Dan Sphar Robert T Yamamoto Douglas Conrad Randy Taplitz Francesca Torriani R Allyn Forsyth |
spellingShingle |
Paula King Long K Pham Shannon Waltz Dan Sphar Robert T Yamamoto Douglas Conrad Randy Taplitz Francesca Torriani R Allyn Forsyth Longitudinal Metagenomic Analysis of Hospital Air Identifies Clinically Relevant Microbes. PLoS ONE |
author_facet |
Paula King Long K Pham Shannon Waltz Dan Sphar Robert T Yamamoto Douglas Conrad Randy Taplitz Francesca Torriani R Allyn Forsyth |
author_sort |
Paula King |
title |
Longitudinal Metagenomic Analysis of Hospital Air Identifies Clinically Relevant Microbes. |
title_short |
Longitudinal Metagenomic Analysis of Hospital Air Identifies Clinically Relevant Microbes. |
title_full |
Longitudinal Metagenomic Analysis of Hospital Air Identifies Clinically Relevant Microbes. |
title_fullStr |
Longitudinal Metagenomic Analysis of Hospital Air Identifies Clinically Relevant Microbes. |
title_full_unstemmed |
Longitudinal Metagenomic Analysis of Hospital Air Identifies Clinically Relevant Microbes. |
title_sort |
longitudinal metagenomic analysis of hospital air identifies clinically relevant microbes. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2016-01-01 |
description |
We describe the sampling of sixty-three uncultured hospital air samples collected over a six-month period and analysis using shotgun metagenomic sequencing. Our primary goals were to determine the longitudinal metagenomic variability of this environment, identify and characterize genomes of potential pathogens and determine whether they are atypical to the hospital airborne metagenome. Air samples were collected from eight locations which included patient wards, the main lobby and outside. The resulting DNA libraries produced 972 million sequences representing 51 gigabases. Hierarchical clustering of samples by the most abundant 50 microbial orders generated three major nodes which primarily clustered by type of location. Because the indoor locations were longitudinally consistent, episodic relative increases in microbial genomic signatures related to the opportunistic pathogens Aspergillus, Penicillium and Stenotrophomonas were identified as outliers at specific locations. Further analysis of microbial reads specific for Stenotrophomonas maltophilia indicated homology to a sequenced multi-drug resistant clinical strain and we observed broad sequence coverage of resistance genes. We demonstrate that a shotgun metagenomic sequencing approach can be used to characterize the resistance determinants of pathogen genomes that are uncharacteristic for an otherwise consistent hospital air microbial metagenomic profile. |
url |
http://europepmc.org/articles/PMC4970769?pdf=render |
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