tetano
Editor, Senior Moderator
Microbiome
. 2021 Jun 8;9(1):132.
doi: 10.1186/s40168-021-01083-0.
SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment
Clarisse Marotz[SUP] #[/SUP][SUP] 1 2 [/SUP], Pedro Belda-Ferre[SUP] #[/SUP][SUP] 1 3 [/SUP], Farhana Ali[SUP] 1 [/SUP], Promi Das[SUP] 1 2 [/SUP], Shi Huang[SUP] 1 3 [/SUP], Kalen Cantrell[SUP] 3 4 [/SUP], Lingjing Jiang[SUP] 3 5 [/SUP], Cameron Martino[SUP] 1 3 6 [/SUP], Rachel E Diner[SUP] 1 2 [/SUP], Gibraan Rahman[SUP] 1 6 [/SUP], Daniel McDonald[SUP] 1 [/SUP], George Armstrong[SUP] 1 3 6 [/SUP], Sho Kodera[SUP] 1 2 [/SUP], Sonya Donato[SUP] 7 [/SUP], Gertrude Ecklu-Mensah[SUP] 1 2 [/SUP], Neil Gottel[SUP] 1 2 [/SUP], Mariana C Salas Garcia[SUP] 1 2 [/SUP], Leslie Y Chiang[SUP] 1 [/SUP], Rodolfo A Salido[SUP] 8 [/SUP], Justin P Shaffer[SUP] 1 [/SUP], Mac Kenzie Bryant[SUP] 1 [/SUP], Karenina Sanders[SUP] 1 [/SUP], Greg Humphrey[SUP] 1 [/SUP], Gail Ackermann[SUP] 1 [/SUP], Niina Haiminen[SUP] 9 [/SUP], Kristen L Beck[SUP] 10 [/SUP], Ho-Cheol Kim[SUP] 10 [/SUP], Anna Paola Carrieri[SUP] 11 [/SUP], Laxmi Parida[SUP] 9 [/SUP], Yoshiki Vázquez-Baeza[SUP] 3 [/SUP], Francesca J Torriani[SUP] 8 [/SUP], Rob Knight[SUP] 1 3 4 12 [/SUP], Jack Gilbert[SUP] 1 2 3 [/SUP], Daniel A Sweeney[SUP] 13 [/SUP], Sarah M Allard[SUP] 14 15 [/SUP]
Affiliations
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.
Keywords: 16S rRNA; Built environment; COVID-19; Microbiome; SARS-CoV-2.
. 2021 Jun 8;9(1):132.
doi: 10.1186/s40168-021-01083-0.
SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment
Clarisse Marotz[SUP] #[/SUP][SUP] 1 2 [/SUP], Pedro Belda-Ferre[SUP] #[/SUP][SUP] 1 3 [/SUP], Farhana Ali[SUP] 1 [/SUP], Promi Das[SUP] 1 2 [/SUP], Shi Huang[SUP] 1 3 [/SUP], Kalen Cantrell[SUP] 3 4 [/SUP], Lingjing Jiang[SUP] 3 5 [/SUP], Cameron Martino[SUP] 1 3 6 [/SUP], Rachel E Diner[SUP] 1 2 [/SUP], Gibraan Rahman[SUP] 1 6 [/SUP], Daniel McDonald[SUP] 1 [/SUP], George Armstrong[SUP] 1 3 6 [/SUP], Sho Kodera[SUP] 1 2 [/SUP], Sonya Donato[SUP] 7 [/SUP], Gertrude Ecklu-Mensah[SUP] 1 2 [/SUP], Neil Gottel[SUP] 1 2 [/SUP], Mariana C Salas Garcia[SUP] 1 2 [/SUP], Leslie Y Chiang[SUP] 1 [/SUP], Rodolfo A Salido[SUP] 8 [/SUP], Justin P Shaffer[SUP] 1 [/SUP], Mac Kenzie Bryant[SUP] 1 [/SUP], Karenina Sanders[SUP] 1 [/SUP], Greg Humphrey[SUP] 1 [/SUP], Gail Ackermann[SUP] 1 [/SUP], Niina Haiminen[SUP] 9 [/SUP], Kristen L Beck[SUP] 10 [/SUP], Ho-Cheol Kim[SUP] 10 [/SUP], Anna Paola Carrieri[SUP] 11 [/SUP], Laxmi Parida[SUP] 9 [/SUP], Yoshiki Vázquez-Baeza[SUP] 3 [/SUP], Francesca J Torriani[SUP] 8 [/SUP], Rob Knight[SUP] 1 3 4 12 [/SUP], Jack Gilbert[SUP] 1 2 3 [/SUP], Daniel A Sweeney[SUP] 13 [/SUP], Sarah M Allard[SUP] 14 15 [/SUP]
Affiliations
- PMID: 34103074
- DOI: 10.1186/s40168-021-01083-0
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.
Keywords: 16S rRNA; Built environment; COVID-19; Microbiome; SARS-CoV-2.