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

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 patients with coronavirus disease 2019 (COVID-19), their health care providers, and hospital surfaces before, during,...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7685343/
https://www.ncbi.nlm.nih.gov/pubmed/33236030
http://dx.doi.org/10.1101/2020.11.19.20234229
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author 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.
author_facet 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.
author_sort Marotz, Clarisse
collection PubMed
description 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 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 through 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 over time, SARS-CoV-2 was detected less there (11%). Despite viral surface contamination in almost all patient rooms, no health care workers contracted the disease, suggesting that personal protective equipment was effective in preventing transmissions. 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 SARS-CoV-2 classifier accuracy in not only nares, but also forehead, stool, and floor samples. Across 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 compared to samples from patients in another intensive care unit prior to the COVID-19 pandemic. These results suggest that bacterial communities may contribute to viral prevalence both in the host and hospital environment.
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spelling pubmed-76853432020-11-25 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. medRxiv Article 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 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 through 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 over time, SARS-CoV-2 was detected less there (11%). Despite viral surface contamination in almost all patient rooms, no health care workers contracted the disease, suggesting that personal protective equipment was effective in preventing transmissions. 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 SARS-CoV-2 classifier accuracy in not only nares, but also forehead, stool, and floor samples. Across 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 compared to samples from patients in another intensive care unit prior to the COVID-19 pandemic. These results suggest that bacterial communities may contribute to viral prevalence both in the host and hospital environment. Cold Spring Harbor Laboratory 2020-11-22 /pmc/articles/PMC7685343/ /pubmed/33236030 http://dx.doi.org/10.1101/2020.11.19.20234229 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
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.
Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment
title Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment
title_full Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment
title_fullStr Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment
title_full_unstemmed Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment
title_short Microbial context predicts SARS-CoV-2 prevalence in patients and the hospital built environment
title_sort microbial context predicts sars-cov-2 prevalence in patients and the hospital built environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7685343/
https://www.ncbi.nlm.nih.gov/pubmed/33236030
http://dx.doi.org/10.1101/2020.11.19.20234229
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