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 Supplementary Information The online version contains supplementary material available at 10.1186/s40168-021-01083-0.
【저자키워드】 COVID-19, SARS-CoV-2, built environment, 16S rRNA, Microbiome, 【초록키워드】 coronavirus disease, viruses, Coronavirus disease 2019, pandemic, Health care, COVID-19 pandemic, hospital, variant, intensive care unit, hospitalized patients, Prevalence, 16S rRNA, RT-qPCR, Stool, Health, Hospital environment, Viral, Accuracy, Patient, Random forest, SARS-CoV-2 RNA, Viral RNA, RNA virus, Admission, Care, microbial community, predict, rRNA, Bacterial, SARS-CoV-2 RNA detection, Amplicon sequencing, COVID-19 patient, Bacterial community, Phylogenetic, microbial, bed rail, patient rooms, Classifier, supplementary material, profile, complex, sequence, profiles, genus, nares, random, 16S rRNA gene, positive, positive sample, forehead, antagonistic effect, Host, synergistic, Occurrence, Result, greater, highest, responsible, predicted, identify, collected, the patient, detectable, screened, characterized, less, hospitalized patient, with COVID-19, 【제목키워드】 hospital, SARS-CoV-2 detection, Patient, Community, Bacterial,