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Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built 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; Garcia, Mariana C Salas; Chiang, Leslie Y; Salido, Rodolfo A; Shaffer, Justin P; Bryant, MacKenzie; 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 A; Sweeney, Daniel A; Allard, Sarah M.
Affiliation
  • Marotz C; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Belda-Ferre P; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.
  • Ali F; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Das P; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.
  • Huang S; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Cantrell K; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Jiang L; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.
  • Martino C; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Diner RE; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.
  • Rahman G; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.
  • McDonald D; Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.
  • Armstrong G; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.
  • Kodera S; Division of Biostatistics, University of California, San Diego, La Jolla, California, USA.
  • Donato S; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Ecklu-Mensah G; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.
  • Gottel N; Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.
  • Garcia MCS; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Chiang LY; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.
  • Salido RA; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Shaffer JP; Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.
  • Bryant M; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Sanders K; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Humphrey G; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.
  • Ackermann G; Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.
  • Haiminen N; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Beck KL; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.
  • Kim HC; Microbiome Core, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Carrieri AP; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Parida L; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.
  • Vázquez-Baeza Y; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Torriani FJ; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.
  • Knight R; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Gilbert JA; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.
  • Sweeney DA; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.
  • Allard SM; Department of Bioengineering, University of California San Diego, La Jolla, California, USA.
medRxiv ; 2020 Nov 22.
Article in En | MEDLINE | ID: mdl-33236030
ABSTRACT
Synergistic effects of bacteria on viral stability and transmission are widely documented but remain unclear in the context of SARS-CoV-2. We collected 972 samples from hospitalized ICU patients with coronavirus disease 2019 (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 contextualized the massive microbial diversity in this dataset in a meta-analysis of over 20,000 samples. Sixteen percent of surfaces from COVID-19 patient rooms were positive, with the highest prevalence in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples increasingly resembled the patient microbiome throughout their stay, SARS-CoV-2 was less frequently detected there (11%). Despite surface contamination in almost all patient rooms, no health care workers providing COVID-19 patient care contracted the disease. SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity across human and surface samples, and higher biomass in floor samples. 16S microbial community profiles allowed for 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 was highly predictive of SARS-CoV-2 across sample types, and had higher prevalence in positive surface and human samples, even when comparing to samples from patients in another intensive care unit prior to the COVID-19 pandemic. These results suggest that bacterial communities contribute to viral prevalence both in the host and hospital environment.

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prevalence_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: MedRxiv Year: 2020 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prevalence_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: MedRxiv Year: 2020 Document type: Article Affiliation country: