Speaker
Description
Influenza A virus (IAV) has placed a significant strain on public health systems by causing seasonal outbreaks and, in severe cases, pandemics. This burden is compounded by its rapid evolution, which drives immune escape and requires the frequent redesign of vaccines and antiviral drugs. Therefore, genomic surveillance of IAV is crucial for monitoring its evolution and informing vaccine and drug development. Due to its unbiased approach and cost-effectiveness, wastewater-based surveillance (WBS) has been used for monitoring infectious diseases. However, while genomic WBS for SARS-CoV-2 using next-generation sequencing (NGS) is well-established, its application to IAV has not yet been fully explored.
We developed a novel primer scheme for genomic WBS of IAV using 400 bp tiling amplicon sequencing. The scheme was designed to target the three surface protein encoding segments (HA, NA, and M) of IAV in both H1N1 and H3N2 subtypes seasonally circulating in humans. Utilizing this primer scheme, we successfully sequenced the target segments in two clinical RNA extracts and in 42 wastewater samples from the 2022/2023 and 2023/2024 IAV season in Switzerland, including 12 time-series samples collected weekly during 2023/2024. To process the IAV sequencing data and to generate IAV clade abundance estimates, we extended the viral bioinformatics pipline V-pipe. We found a statistically significant correlation between clinical- and wastewater-based abundance estimates of 6B.1A.5a.2a and 6B.1A.5a.2a.1, the two dominating H1N1 clades during the 2023/2024 winter season. Further, we found mutational changes in vaccine and drug target sites over time, in concordance with clinical findings during that season.
Overall our findings show that IAV evolution can be monitored through WBS and inform IAV vaccine and drug development. It provides an effective low-cost method for population-level genomic surveillance of future outbreaks.
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