SARS-CoV-2 Subgenomic RNA Kinetics in Longitudinal Clinical Samples.

COVID-19 SARS-CoV-2 cohort infectiousness subgenomic RNA

Journal

Open forum infectious diseases
ISSN: 2328-8957
Titre abrégé: Open Forum Infect Dis
Pays: United States
ID NLM: 101637045

Informations de publication

Date de publication:
Jul 2021
Historique:
received: 20 05 2021
accepted: 09 06 2021
entrez: 23 7 2021
pubmed: 24 7 2021
medline: 24 7 2021
Statut: epublish

Résumé

Given the persistence of viral RNA in clinically recovered coronavirus disease 2019 (COVID-19) patients, subgenomic RNAs (sgRNAs) have been reported as potential molecular viability markers for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, few data are available on their longitudinal kinetics, compared with genomic RNA (gRNA), in clinical samples. We analyzed 536 samples from 205 patients with COVID-19 from placebo-controlled, outpatient trials of peginterferon Lambda-1a (Lambda; n = 177) and favipiravir (n = 359). Nasal swabs were collected at 3 time points in the Lambda (days 1, 4, and 6) and favipiravir (days 1, 5, and 10) trials. N-gene gRNA and sgRNA were quantified by quantitative reverse transcription polymerase chain reaction. To investigate the decay kinetics in vitro, we measured gRNA and sgRNA in A549 At 6 days in the Lambda trial and 10 days in the favipiravir trial, sgRNA remained detectable in 51.6% (32/62) and 49.5% (51/106) of the samples, respectively. Cycle threshold (Ct) values for gRNA and sgRNA were highly linearly correlated (marginal In clinical samples and in vitro, sgRNA was highly correlated with gRNA and did not demonstrate different decay patterns to support its application as a viability marker.

Sections du résumé

BACKGROUND BACKGROUND
Given the persistence of viral RNA in clinically recovered coronavirus disease 2019 (COVID-19) patients, subgenomic RNAs (sgRNAs) have been reported as potential molecular viability markers for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, few data are available on their longitudinal kinetics, compared with genomic RNA (gRNA), in clinical samples.
METHODS METHODS
We analyzed 536 samples from 205 patients with COVID-19 from placebo-controlled, outpatient trials of peginterferon Lambda-1a (Lambda; n = 177) and favipiravir (n = 359). Nasal swabs were collected at 3 time points in the Lambda (days 1, 4, and 6) and favipiravir (days 1, 5, and 10) trials. N-gene gRNA and sgRNA were quantified by quantitative reverse transcription polymerase chain reaction. To investigate the decay kinetics in vitro, we measured gRNA and sgRNA in A549
RESULTS RESULTS
At 6 days in the Lambda trial and 10 days in the favipiravir trial, sgRNA remained detectable in 51.6% (32/62) and 49.5% (51/106) of the samples, respectively. Cycle threshold (Ct) values for gRNA and sgRNA were highly linearly correlated (marginal
CONCLUSIONS CONCLUSIONS
In clinical samples and in vitro, sgRNA was highly correlated with gRNA and did not demonstrate different decay patterns to support its application as a viability marker.

Identifiants

pubmed: 34295944
doi: 10.1093/ofid/ofab310
pii: ofab310
pmc: PMC8291522
doi:

Types de publication

Journal Article

Langues

eng

Pagination

ofab310

Subventions

Organisme : NIGMS NIH HHS
ID : R25 GM086262
Pays : United States
Organisme : NIAID NIH HHS
ID : U01 AI150741
Pays : United States

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press on behalf of Infectious Diseases Society of America.

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Auteurs

Renu Verma (R)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.

Eugene Kim (E)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.

Giovanny Joel Martínez-Colón (GJ)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.

Prasanna Jagannathan (P)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.

Arjun Rustagi (A)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.

Julie Parsonnet (J)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.
Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA.

Hector Bonilla (H)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.

Chaitan Khosla (C)

Departments of Chemistry and Chemical Engineering, Stanford University, Stanford, California, USA.

Marisa Holubar (M)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.

Aruna Subramanian (A)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.

Upinder Singh (U)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.
Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California, USA.

Yvonne Maldonado (Y)

Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.

Catherine A Blish (CA)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.

Jason R Andrews (JR)

Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USA.

Classifications MeSH