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SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment.
Marotz, Clarisse; Belda-Ferre, Pedro; Ali, Farhana; Das, Promi; Huang, Shi; Cantrell, Kalen; Jiang, Lingjing; Martino, Cameron; Diner, Rachel E; Rahman, Gibraan; McDonald, Daniel; Armstrong, George; Kodera, Sho; Donato, Sonya; Ecklu-Mensah, Gertrude; Gottel, Neil; Salas Garcia, Mariana C; Chiang, Leslie Y; Salido, Rodolfo A; Shaffer, Justin P; Bryant, Mac Kenzie; Sanders, Karenina; Humphrey, Greg; Ackermann, Gail; Haiminen, Niina; Beck, Kristen L; Kim, Ho-Cheol; Carrieri, Anna Paola; Parida, Laxmi; Vázquez-Baeza, Yoshiki; Torriani, Francesca J; Knight, Rob; Gilbert, Jack; Sweeney, Daniel A; Allard, Sarah M.
  • Marotz C; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Belda-Ferre P; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
  • Ali F; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Das P; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
  • Huang S; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Cantrell K; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Jiang L; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
  • Martino C; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Diner RE; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
  • Rahman G; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
  • McDonald D; Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
  • Armstrong G; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
  • Kodera S; Division of Biostatistics, University of California, San Diego, La Jolla, CA, USA.
  • Donato S; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Ecklu-Mensah G; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
  • Gottel N; Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
  • Salas Garcia MC; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Chiang LY; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
  • Salido RA; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Shaffer JP; Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
  • Bryant MK; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Sanders K; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Humphrey G; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
  • Ackermann G; Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
  • Haiminen N; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Beck KL; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
  • Kim HC; Microbiome Core, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Carrieri AP; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Parida L; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
  • Vázquez-Baeza Y; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Torriani FJ; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
  • Knight R; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Gilbert J; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
  • Sweeney DA; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
  • Allard SM; Infection Prevention and Clinical Epidemiology Unit at UC San Diego Health, Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego, San Diego, CA, USA.
Microbiome ; 9(1): 132, 2021 06 08.
Article in English | MEDLINE | ID: covidwho-1262519
ABSTRACT

BACKGROUND:

SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting.

METHODS:

We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model.

RESULTS:

Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic.

CONCLUSIONS:

These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment. Video Abstract.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Variants Limits: Humans Language: English Journal: Microbiome Year: 2021 Document Type: Article Affiliation country: S40168-021-01083-0

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Variants Limits: Humans Language: English Journal: Microbiome Year: 2021 Document Type: Article Affiliation country: S40168-021-01083-0