A fit-for-purpose sequencing strategy for West Nile virus genomic surveillance using NAT-reactive blood donations
Milani, P.; Chafets, D.; Montalvo, L.; Stone, M.; Green, V.; Lanteri, M.; Busch, M. P.
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Background. West Nile virus (WNV) genomic surveillance in the United States relies largely on mosquito and avian sequencing, while human-derived genomes remain scarce. Nucleic acid testing (NAT)-reactive blood donations provide a standardized source of acute human-phase virus, but low donor viremia complicates genome recovery. This study evaluated a sequencing strategy for WNV surveillance using these samples. Study Design and Methods. Amplicon sequencing, hybridization capture, and shotgun RNA-seq were evaluated for WNV lineage 1a recovery from donor plasma. Amplicon performance was characterized using a WHO International Standard dilution panel quantified by RT-dPCR, contemporary 2025 donations, archival 2010-2011 donations, and technical replicates. Two donations were processed by all three methods from matched plasma to compare performance metrics and consensus concordance. Results. Amplicon sequencing recovered near-complete genomes across the full dilution panel, including the lowest measured input, and across the viral-load range represented by the selected donor samples. Recovery from the two archival plasma samples was similar to that observed among contemporary donations. In the two matched donations, all three methods generated identical consensus sequences across shared callable positions. At lower input, amplicon and capture maintained near-complete recovery, whereas shotgun RNA-seq decreased to 87.2% coverage at 10X. For libraries achieving near-complete recovery, WNV-mapped-read requirements were similar, but amplicon sequencing required substantially fewer total reads. Discussion. NAT-reactive blood donations can support WNV genomic surveillance. Amplicon sequencing is an efficient first-pass approach for expected lineage 1a WNV, with capture and shotgun RNA-seq serving as escalation strategies for divergent lineages or unbiased pathogen detection.
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