Although the first detection of SARS-CoV-2 in the United States lagged two months behind the virus’ initial discovery in China, implementation of quantitative reverse transcriptase PCR (qRT-PCR) was delayed. Unbiased detection methodology that bypasses the requirement for emerging viral sequence data is an appealing strategy for diagnosing novel infections that have yet to be characterized. Metagenomic sequencing could provide this alternative.
Led by Vikas Peddu of the Greninger lab, along with collaborators from UW Virology, Boston University, and the Vaccine and Infectious Disease Division, a recent study in Clinical Chemistry demonstrated that unbiased, metagenomic next-generation sequencing (mNGS) of nasopharyngeal swab samples can not only detect whole viral genomes but also identify co-infections that may increase mortality in emerging infectious diseases. While PCR is cheaper, it relies on primers that are designed to detect a known viral sequence, mNGS uses an unbiased approach to detect all sequences, that are then aligned to known viral and bacterial reference databases to identify known and unknown pathogens.
To validate that mNGS could detect SARS-CoV-2 in samples derived from qRT-PCR-confirmed cases, the sequences recovered were aligned to the 2019 Genbank nucleotide database using CLOMP, a metagenomic analysis pipeline developed by the Greninger lab. All six confirmed SARS-CoV-2-positive samples matched the reference database for “severe acute respiratory syndrome-related coronavirus,” based on previously sequenced SARS-like coronaviruses. Additionally, in all samples, the nearly complete SARS-CoV-2 genome could be reconstructed from the unassigned reads, suggesting that mNGS sequencing coverage is sufficiently thorough.
When the recovered SARS-CoV-2 sequences were fit within a phylogenic tree, the analysis revealed that the sequences clustered within two distinct clades. Five samples clustered with the local Washington State SARS-CoV-2 clade, while one clustered with the European clade, demonstrating that mNGS can distinguish between distinct transmission chains.
In addition to identifying SARS-CoV-2, mNGS also revealed coinfection with bacterial species in several patients, and quantified increased bacterial burden in some patients over others. This suggests that mNGS can identify co-infections that could lead to increased morbidity or mortality on a patient-by-patient basis.
In addition to diagnosing potential comorbidity, mNGS could also prevent misdiagnosis of SARS-CoV-2. Peddu explained that “early in the pandemic, due to the shortage of testing kits, viral respiratory panels for common respiratory pathogens were used to ‘rule out’ SARS-CoV-2 infections rather than testing directly for SARS-CoV-2.” However, mNGS detected one patient’s co-infection with human parainfluenza virus 3, demonstrating that the presence of one respiratory virus does not rule out SARS-CoV-2. “With the evidence of a viral coinfection, we show that the "rule out" approach could potentially report false negatives, and as a result SARS-CoV-2 must be directly tested for,” he said. Going forward, Peddu explained that he’s “interested in the rate of coinfections, both bacterial and viral, whether certain coinfections happen more frequently, and how the number of coinfections declines as social distancing guidelines remain in effect.”
Emerging zoonotic infections often spread throughout human populations before the novel pathogen is identified, necessitating early and rapid pathogen identification. Diagnosis is necessary to prevent pandemic-level spread. This study demonstrates “the usefulness of Metagenomic sequencing in the context of rapidly emerging infectious disease, in that we are able to detect and assemble the full viral genome despite its absence in our reference database,” Peddu said. Just 36 hours after the sample was received, mNGS produced taxonomical assessment just could be a faster alternative to qRT-PCR and provide a lifesaving tool for diagnosing and curbing future emerging infections and co-infections.
This work was supported by Amazon Web Services.
UW/Fred Hutch Cancer Consortium member Keith Jerome contributed to this work.
Peddu V, Shean RC, Xie H, Shrestha L, Perchetti GA, Minot SS, Roychoudhury P, Huang ML, Nalla A, Reddy SB, Phung Q, Reinhardt A, Jerome KR, and Greninger AL. 2020. Metagenomic analysis reveals clinical SARS-CoV-2 infection and bacterial or viral superinfection and colonization. Clinical Chemistry. doi: 10.1093/clinchem/hvaa106