Evidence map›Paper›PMID 34103074›Full record

ArticleMicrobiome2021

SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment.

Clarisse Marotz, Pedro Belda-Ferre, Farhana Ali, Promi Das, Shi Huang, Kalen Cantrell, Lingjing Jiang, Cameron Martino, Rachel E Diner, Gibraan Rahman and 25 more

Open access · goldAbstract readVideo-Audio Media
PubMed Full text at PMC Publisher
In one paragraph

Article in Microbiome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 2 pooled it
4.1field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 60 citations in OpenAlex.

  1. Teaching genomics to life science undergraduates using cloud computing platforms with open datasets.Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology · 2022
    Pooled it
  2. Pooled it
  3. Trial
  4. Observational
  5. Review
  6. Article
  7. Article
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  9. Loss of sialic acid side-chainProceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. Observational
  14. Article
  15. The indoors microbiome and human health.Nature reviews. Microbiology · 2024
    Review
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  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

35 authors at 6 institutions in 2 countries.

Clarisse Marotz *Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Pedro Belda-Ferre *Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Farhana AliDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Promi DasDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Shi HuangDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Kalen CantrellCenter for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
Lingjing JiangCenter for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
Cameron MartinoDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Rachel E DinerDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Gibraan RahmanDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Daniel McDonaldDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
George ArmstrongDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Sho KoderaDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Sonya DonatoMicrobiome Core, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Gertrude Ecklu-MensahDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Neil GottelDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Mariana C Salas GarciaDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Leslie Y ChiangDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Rodolfo A SalidoInfection 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.
Justin P ShafferDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Mac Kenzie BryantDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Karenina SandersDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Greg HumphreyDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Gail AckermannDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Niina HaiminenIBM, T.J Watson Research Center, Yorktown Heights, New York, USA.
Kristen L BeckAI and Cognitive Software, IBM Research-Almaden, San Jose, CA, USA.
Ho-Cheol KimAI and Cognitive Software, IBM Research-Almaden, San Jose, CA, USA.
Anna Paola CarrieriIBM Research UK, The Hartree Centre, Warrington, UK.
Laxmi ParidaIBM, T.J Watson Research Center, Yorktown Heights, New York, USA.
Yoshiki Vázquez-BaezaCenter for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA.
Francesca J TorrianiInfection 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.
Rob KnightDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Jack GilbertDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Daniel A SweeneyDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, University of California San Diego, La Jolla, CA, USA. dasweeney@health.ucsd.edu.
Sarah M AllardDepartment of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA. smallard@ucsd.edu.ORCID 0000-0001-7626-0472
University of California San Diego · USScripps Institution of Oceanography · USUC San Diego Health System · USIBM Research - Almaden · USIBM Research - Thomas J. Watson Research Center · USIBM (United Kingdom) · GB

Funding

UCSD/SDSU IRACDAK12GM068524 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI TREJO, JOANN · 2003 to 2025
$24.4M
San Diego Digestive Diseases Research CenterP30DK120515 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Bernd G. Schnabl · 2019 to 2026
$10.8M
Mapping host-microbe-metabolite interactions in 3D to find diet-derived enhancers of immunityDP1AT010885 · NCCIH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KNIGHT, ROB · 2019 to 2023
$5.5M
AI Horizons Network NANational Science Foundation P2011025NIDDK NIH HHS 1P30DK120515NIGMS NIH HHS GM068524NIH HHS 1DP1AT010885
6 · The paper itself

Abstract

backgroundSARS-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.

methodsWe 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.

resultsSixteen 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.

conclusionsThese 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.

Indexed as

COVID-19SARS-CoV-2HospitalsHumansPandemicsPhylogenyRNA, Ribosomal, 16SRNA, ViralRNA, Ribosomal, 16SRNA, Viral16S rRNABuilt environmentCOVID-19MicrobiomeSARS-CoV-2

Identifiers

PMID34103074
PMCPMC8186369
OpenAlexW3168330745

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.