A metagenomics workflow for SARS-CoV-2 identification, co-pathogen detection, and overall diversity.

ABSTRACT

An unbiased metagenomics approach to virus identification can be essential in the initial phase of a pandemic. Better molecular surveillance strategies are needed for the detection of SARS-CoV-2 variants of concern and potential co-pathogens triggering respiratory symptoms. Here, a metagenomics workflow was developed to identify the metagenome diversity by SARS-CoV-2 diagnosis (npositive = 65; nnegative = 60), symptomatology status (nsymptomatic = 71; nasymptomatic = 54) and anatomical swabbing site (nnasopharyngeal = 96; nthroat = 29) in 125 individuals. Furthermore, the workflow was able to identify putative respiratory co-pathogens, and the SARS-CoV-2 lineage across 29 samples. The diversity analysis showed a significant shift in the DNA-metagenome by symptomatology status and anatomical swabbing site. Additionally, metagenomic diversity differed between SARS-CoV-2 infected and uninfected asymptomatic individuals. While 31 co-pathogens were identified in SARS-CoV-2 infected patients, no significant increase in pathogen or associated reads were noted when compared to SARS-CoV-2 negative patients. The Alpha SARS-CoV-2 VOC and 2 variants of interest (Zeta) were successfully identified for the first time using a clinical metagenomics approach. The metagenomics pipeline showed a sensitivity of 86% and a specificity of 72% for the detection of SARS-CoV-2. Clinical metagenomics can be employed to identify SARS-CoV-2 variants and respiratory co-pathogens potentially contributing to COVID-19 symptoms. The overall diversity analysis suggests a complex set of microorganisms with different genomic abundance profiles in SARS-CoV-2 infected patients compared to healthy controls. More studies are needed to correlate severity of COVID-19 disease in relation to potential disbyosis in the upper respiratory tract. A metagenomics approach is particularly useful when novel pandemic pathogens emerge.