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

Area of Science:

• Microbiology
• Virology
• Genomics

Background:

• Accurate virus identification is crucial during pandemics for surveillance.
• Detecting SARS-CoV-2 variants and co-pathogens requires improved molecular strategies.
• Metagenomics offers an unbiased approach for identifying diverse microbial agents.

Purpose of the Study:

• To develop and validate a metagenomics workflow for identifying SARS-CoV-2 and respiratory co-pathogens.
• To analyze metagenome diversity in relation to SARS-CoV-2 infection, symptomatology, and sample site.
• To identify SARS-CoV-2 variants and assess the pipeline's diagnostic performance.

Main Methods:

• A metagenomics workflow was applied to 125 individuals with varying SARS-CoV-2 status and symptoms.
• DNA-metagenome diversity was analyzed based on symptomatology and anatomical swabbing site.
• The workflow identified SARS-CoV-2 lineages, co-pathogens, and assessed diagnostic sensitivity and specificity.

Main Results:

• Metagenome diversity significantly shifted with symptomatology and swabbing site.
• SARS-CoV-2 infected individuals showed different metagenomic diversity compared to uninfected controls.
• The workflow successfully identified Alpha and Zeta SARS-CoV-2 variants, with 86% sensitivity and 72% specificity for SARS-CoV-2 detection.

Conclusions:

• Clinical metagenomics is effective for identifying SARS-CoV-2 variants and potential COVID-19 co-pathogens.
• SARS-CoV-2 infection is associated with distinct microbial abundance profiles in the upper respiratory tract.
• Further research is needed to link upper respiratory tract dysbiosis to COVID-19 severity.