Metagenomic Next-Generation Sequencing of Nasopharyngeal Specimens Collected from Confirmed and Suspect COVID-19 Patients

Study information

Needs review

study design

cross-sectional observational, not case-control

Citation

PMID PubMed identifier for scientific articles.

33219095

DOI Digital object identifier for electronic documents.

10.1128/mBio.01969-20

URI Uniform resource identifier for web resources.

Authors

Mostafa HH, Fissel JA, Fanelli B, Bergman Y, Gniazdowski V, Dadlani M, Carroll KC, Colwell RR, Simner PJ

Journal

mBio

Year

2020

Metagenomic next-generation sequencing (mNGS) offers an agnostic approach for emerging pathogen detection directly from clinical specimens. In contrast to targeted methods, mNGS also provides valuable information on the composition of the microbiome and might uncover coinfections that may associate with disease progression and impact prognosis. To evaluate the use of mNGS for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and/or other infecting pathogens, we applied direct Oxford Nanopore long-read third-generation metatranscriptomic and metagenomic sequencing. Nasopharyngeal (NP) swab specimens from 50 patients under investigation for CoV disease 2019 (COVID-19) were sequenced, and the data were analyzed by the CosmosID bioinformatics platform. Further, we characterized coinfections and the microbiome associated with a four-point severity index. SARS-CoV-2 was identified in 77.5% (31/40) of samples positive by RT-PCR, correlating with lower cycle threshold (Ct) values and fewer days from symptom onset. At the time of sampling, possible bacterial or viral coinfections were detected in 12.5% of SARS-CoV-2-positive specimens. A decrease in microbial diversity was observed among COVID-19-confirmed patients (Shannon diversity index, P = 0.0082; Chao richness estimate, P = 0.0097; Simpson diversity index, P = 0.018), and differences in microbial communities were linked to disease severity (P = 0.022). Furthermore, statistically significant shifts in the microbiome were identified among SARS-CoV-2-positive and -negative patients, in the latter of whom a higher abundance of Propionibacteriaceae (P = 0.028) and a reduction in the abundance of Corynebacterium accolens (P = 0.025) were observed. Our study corroborates the growing evidence that increased SARS-CoV-2 RNA detection from NP swabs is associated with the early stages rather than the severity of COVID-19. Further, we demonstrate that SARS-CoV-2 causes a significant change in the respiratory microbiome. This work illustrates the utility of mNGS for the detection of SARS-CoV-2, for diagnosing coinfections without viral target enrichment or amplification, and for the analysis of the respiratory microbiome.IMPORTANCE SARS-CoV-2 has presented a rapidly accelerating global public health crisis. The ability to detect and analyze viral RNA from minimally invasive patient specimens is critical to the public health response. Metagenomic next-generation sequencing (mNGS) offers an opportunity to detect SARS-CoV-2 from nasopharyngeal (NP) swabs. This approach also provides information on the composition of the respiratory microbiome and its relationship to coinfections or the presence of other organisms that may impact SARS-CoV-2 disease progression and prognosis. Here, using direct Oxford Nanopore long-read third-generation metatranscriptomic and metagenomic sequencing of NP swab specimens from 50 patients under investigation for COVID-19, we detected SARS-CoV-2 sequences by applying the CosmosID bioinformatics platform. Further, we characterized coinfections and detected a decrease in the diversity of the microbiomes in these patients. Statistically significant shifts in the microbiome were identified among COVID-19-positive and -negative patients, in the latter of whom a higher abundance of Propionibacteriaceae and a reduction in the abundance of Corynebacterium accolens were observed. Our study also corroborates the growing evidence that increased SARS-CoV-2 RNA detection from NP swabs is associated with the early stages of disease rather than with severity of disease. This work illustrates the utility of mNGS for the detection and analysis of SARS-CoV-2 from NP swabs without viral target enrichment or amplification and for the analysis of the respiratory microbiome.

Experiment 1

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Needs review

Curated date: 2021/06/05

Curator: Claregrieve1

Revision editor(s): Claregrieve1, WikiWorks

Subjects

Location of subjects: United States of America

Host species Species from which microbiome was sampled (if applicable): Homo sapiens

Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology: Nasopharynx Nasenrachenraum,Epipharynx,Nasal part of pharynx,Pars nasalis pharyngis,Rhinopharynx,Nasopharynx

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology: COVID-19 2019 novel coronavirus,2019 novel coronavirus infection,2019-nCoV,2019-nCoV infection,beta-CoV,beta-CoVs,betacoronavirus,coronavirus disease 2019,SARS-coronavirus 2,SARS-CoV-2,severe acute respiratory syndrome coronavirus 2,severe acute respiratory syndrome coronavirus 2 infectious disease,β-coronavirus,β-CoV,β-CoVs,COVID-19

Group 0 name Corresponds to the control (unexposed) group for case-control studies: COVID-19 negative controls
Group 1 name Corresponds to the case (exposed) group for case-control studies: COVID-19 cases
Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: Positive for SaRS-CoV-2 by diagnostic RT-PCR
Group 0 sample size Number of subjects in the control (unexposed) group: 10
Group 1 sample size Number of subjects in the case (exposed) group: 40

Lab analysis

Sequencing type: WMS

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Nanopore

Statistical Analysis

Statistical test: Mann-Whitney (Wilcoxon)

Significance threshold p-value or FDR threshold used for differential abundance testing (if any): 0.05
MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?: No

Alpha Diversity

Shannon Estimator of species richness and species evenness: more weight on species richness: decreased
Chao1 Abundance-based estimator of species richness: decreased
Simpson Estimator of species richness and species evenness: more weight on species evenness: decreased