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Longitudinal Metagenomic Analysis of Hospital Air Identifies Clinically Relevant Microbes.
King, Paula; Pham, Long K; Waltz, Shannon; Sphar, Dan; Yamamoto, Robert T; Conrad, Douglas; Taplitz, Randy; Torriani, Francesca; Forsyth, R Allyn.
Afiliação
  • King P; FLIR Systems, Inc., La Jolla, California, United States of America.
  • Pham LK; Singlera Genomics, Inc., La Jolla, California, United States of America.
  • Waltz S; FLIR Systems, Inc., La Jolla, California, United States of America.
  • Sphar D; FLIR Systems, Inc., La Jolla, California, United States of America.
  • Yamamoto RT; FLIR Systems, Inc., La Jolla, California, United States of America.
  • Conrad D; Zova Systems, LLC, San Diego, California, United States of America.
  • Taplitz R; Department of Medicine, Division of Pulmonary Medicine, UC San Diego Health System, San Diego, California, United States of America.
  • Torriani F; Department of Medicine, Division of Infectious Diseases and Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health System, San Diego, California, United States of America.
  • Forsyth RA; Department of Medicine, Division of Infectious Diseases and Infection Prevention and Clinical Epidemiology Unit, UC San Diego Health System, San Diego, California, United States of America.
PLoS One ; 11(8): e0160124, 2016.
Article em En | MEDLINE | ID: mdl-27482891
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Penicillium / Aspergillus / Stenotrophomonas maltophilia / Microbiologia do Ar / Metagenoma / Consórcios Microbianos Tipo de estudo: Observational_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Penicillium / Aspergillus / Stenotrophomonas maltophilia / Microbiologia do Ar / Metagenoma / Consórcios Microbianos Tipo de estudo: Observational_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2016 Tipo de documento: Article