Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment.

Autor: Marotz C; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA., Belda-Ferre P; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Center for Microbiome Innovation, Jacobs School of Engineering, 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; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Scripps Institution of Oceanography, 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.; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA., Cantrell K; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.; Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA., Jiang L; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.; Division of Biostatistics, 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.; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.; Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA., Diner RE; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA., Rahman G; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA., McDonald D; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA., Armstrong G; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.; Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA., Kodera S; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA., Donato S; Microbiome Core, School of Medicine, University of California San Diego, La Jolla, California, USA., Ecklu-Mensah G; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA., Gottel N; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Scripps Institution of Oceanography, 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.; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA., Chiang LY; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA., Salido RA; Department of Bioengineering, University of California San Diego, La Jolla, California, USA., Shaffer JP; Department of Pediatrics, School of Medicine, 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; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA., Ackermann G; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA., Haiminen N; IBM, T.J Watson Research Center, Yorktown Heights, New York, USA., Beck KL; AI and Cognitive Software, IBM Research-Almaden, San Jose, California, USA., Kim HC; AI and Cognitive Software, IBM Research-Almaden, San Jose, California, USA., Carrieri AP; AI and Cognitive Software, IBM Research-Almaden, San Jose, California, USA., Parida L; AI and Cognitive Software, IBM Research-Almaden, San Jose, California, USA., Vázquez-Baeza Y; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA., Torriani FJ; 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., Knight R; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.; Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA.; Department of Bioengineering, University of California San Diego, La Jolla, California, USA., Gilbert JA; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA., Sweeney DA; Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, University of California San Diego, La Jolla, California, USA., Allard SM; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, California, USA.; Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA.
Jazyk: angličtina
Zdroj: MedRxiv : the preprint server for health sciences [medRxiv] 2020 Nov 22. Date of Electronic Publication: 2020 Nov 22.
DOI: 10.1101/2020.11.19.20234229
Abstrakt: 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.
Databáze: MEDLINE