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

Abstract

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.

Metagenomic analysis of RNA sequencing data reveals SARS-CoV-2-mediated progressive dysbiosis of upper respiratory tract microbiota

“…1 B). This is in correlation with the host transcriptomics response [ 16 ], where the elicitation of cytokines and other immune responses are higher in IAV treatment and muted during SARS-CoV-2 treatment. As previously reported [ 11 , 12 ], we observed proliferation of betaproteobacteria particularly Pseudomonadales during IAV infection.…”

“…Blanco-Melo et al [ 16 ] reported the unique transcriptional signature in cell lines and nasal washes of ferrets infected with SARS-CoV-2 (strain USA-WA1/2020) and influenza A virus (IAV; pH1N1; A/California/04/2009 strain). The nasal washes are collected from the infected and control animals on specific days and bulk RNA sequencing was performed by Blanco-Melo et al group and the raw data files are deposited in NCBI-SRA (Accession No.…”

“…A recent study revealed the changes in gut microbiota induced by SARS-CoV-2 is different from influenza patients [ 13 , 14 ]. Though numerous efforts are being executed in understanding the viral genome, vaccine development and host response to SARS-CoV-2, the effect of this viral infection on microbiota of respiratory tract is less unexplored [ 15 , 16 ]. This study is aimed at analyzing the bacterial diversity of upper respiratory tract by mining into the publically available metaranscriptomics data generated from the SARS-CoV-2 infected ferrets.…”

Role of Multiomics Data to Understand Host–Pathogen Interactions in COVID-19 Pathogenesis

“… 55 In a similar study, a higher abundance of Propionibacteriaceae and reductions in Corynebacterium accolens was observed in COVID-19 negative patients compared to COVID-19 positive patients. 56 Next-gen immunosequencing performed from blood collected from COVID-19 positive patients as well as recovered patients has been used to generate a library of more than 14 billion B and T-cell receptor sequences to decode host humoral responses generated postinfection. 57 The same study showed that converging IGHV3-driven BCR clusters is mostly responsible for producing SARS-CoV-2 antibodies, whereas interferon (especially IFN-I & III) responses and early CD4+ and CD8+ T cell activation were key factors driving the clonality of T cell receptors.…”

Mucosal immune responses in COVID19 - a living review

“…In addition, an increase in certain Clostridial and Pseudomonas opportunistic pathogens have been associated with COVID-19 severity, possibly due to increased risk of secondary bacterial infection [ 21 , 22 , 24 ]. Similarly, infection with SARS-CoV2 may also reduce the diversity of the nasopharyngeal microbiome [ 27 ], and increase abundance of specific genii that can play a role in mucosal immunity, such as Prevotella , the presence of which has been associated with more severe symptoms [ 28 ]. However, data are currently limited to small-scale studies, and more prospective, longitudinal analysis of larger cohorts will be required to unpick the many variables that may contribute to microbial changes.…”