Project name: Metagenomics COVID-19

Description: 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. A metagenomics workflow was developed to identify the genome structure of SARS-CoV-2 and variants in symptomatic and asymptomatic individuals (n=125), the overall metagenome diversity, and the presence or absence of co-pathogens. SARS-CoV-2 VOC (B.1.1.7) and 2 variants of interest (P.2) were successfully identified for the first time using a clinical metagenomics approach. Shifts in overall metagenomic diversity were observed between symptomatic and asymptomatic individuals regardless of SARS-CoV-2 infection status and anatomic site. The diversity analysis had a significant shift in the DNA-metagenome by symptomatic individuals and anatomical swabbing site. Additionally, metagenomic diversity differed between SARS-CoV-2 infected and uninfected asymptomatic individuals. While 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 pipeline developed as an unbiased diagnostic tool for SARS-CoV-2 identification (AUC=0.86, sensitivity=0.85; specificity = 0.72) performed well. Clinical metagenomics can be employed to identify SARS-CoV-2 variants and respiratory co-pathogenspotentially 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.

Data type: Other

Sample scope: Monoisolate

Last updated: 2021-10-12