Genomic surveillance of SARS-CoV-2: a resource-efficient wastewater-based workflow

SARS-CoV-2 has been deeply characterized. However, large-scale clinical sequencing remains unaffordable or is declining in many regions, threatening the detection of new viral variants. While clinical mutations can be identified by analyzing wastewater samples, the potential of this tool for diversity analyses remains underexploited. Additionally, current procedures for virus concentration and purification continue to undergo refinement.

This study presents a workflow integrating a high-purity laboratory procedure and an information theory-based R package to generate high-coverage, high-depth genomic data and to profile amino acid-level diversity. The workflow was validated using samples from a single urban node, successfully recovering known features of SARS-CoV-2 diversity at two contrasting stages of the COVID-19 pandemic: early regional endemism and late-stage Omicron cosmopolitanism. Clinically elusive mutations and single-nucleotide variants attributable to countrywide lineages were effectively identified in both early and late samples. The resulting analysis also revealed spatial distribution signatures consistent with the regional endemism typical of ancestral lineages and the global dissemination of Omicron subvariants. The workflow also allowed detecting location-specific mutation frequency differences, revealing biogeographical trends that may escape clinical surveillance. Finally, hotspots of high amino acid diversity were identified at receptor binding domain positions known to drive infectivity, transmissibility and immune escape.

Together, these results indicate that wastewater can support effective genomic monitoring. The workflow proposed provides an accessible complement to clinical surveillance, especially in settings where large-scale, patient-level sequencing is limited or unavailable.