Metagenomic Approach Reveals the Circulation of Pathogenic Viruses Associated with Respiratory Infections in Peru

Abstract

Acute respiratory infections (ARIs) are diseases that significantly impact public health, traditional PCR panels often fail to detect acute respiratory infections (ARIs) due to unmonitored viruses, primer mismatches, or geographically isolated variants. To overcome these diagnostic limitations, the primary purpose of this study is to uncover hidden viral etiologies through a stringent mNGS bioinformatic algorithm. By applying untargeted metagenomic sequencing to 300 previously test-negative nasopharyngeal samples across diverse cohorts in Peru, this research aims to characterize undetected pathogens and support national genomic surveillance. Genomic material was amplified via sequence-independent single-primer amplification (SISPA) and sequenced using the Illumina platform. Following quality filtering and human read depletion, taxonomic identification was performed using Kraken2. A stringent custom filtration algorithm based on taxon-specific reads per million ratios (RPMr), SMNR and MAPQ thresholds. Unbiased mNGS of 300 test-negative respiratory samples revealed a viral landscape dominated by Coronaviridae (67.2%; predominantly SARS-CoV-2), followed by Picornaviridae (13.6%), Orthoherpesviridae (12.0%), Paramyxoviridae (5.6%), Anelloviridae (0.8%), and Adenoviridae (0.8%). Agnostic mNGS bypasses targeted assay limitations, revealing that 35.66% of previously negative samples contained hidden pathogens. This approach proves pivotal for respiratory virus surveillance, enhancing pathogen tracking and outbreak detection.