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Lilley, A.

Publications and source records attributed to Lilley, A..

2 recordsLinked to original sources

Post-infection pig and ferret antisera show similar antigenic profiles for human influenza A(H1N1pdm09) viruses

BackgroundMonitoring antigenic drift in human influenza A viruses is essential for vaccine strain selection and ensuring protection against circulating strains. Antigenic drift is traditionally assessed using ferret antisera, which provide monospecific responses, and human vaccinee sera, which reflect exposure to multiple antigens. In this study we evaluated the pig as an alternative source of antisera to study antigenic drift compared to immune responses in ferrets and humans. We included seasonal influenza A(H1N1pdm09) human viruses that had shown different antigenic characteristics when using ferret or human antisera. MethodsPairs of pigs were inoculated with six human A(H1N1)pdm09 viruses circulating between 2019 and 2023, a period of marked antigenic drift. Pig and ferret antisera were analysed by hemagglutination inhibition (HI) and virus neutralization (VN) assays. ResultsPigs were successfully infected with all strains, shedding virus and producing antibody responses, confirming their susceptibility to human influenza A viruses. Antigenic reactivity of pig antisera was qualitatively comparable to ferret antisera in both HI and VN assays, although maximum homologous antibody titres were significantly higher in ferrets. The antisera raised against viruses in circulation in 2019 and before, exempified by A/Guangdong-Maonan/SWL1536/2019, clade 5a.1, were clearly differentiated by both ferret and pig antisera from those in clade 5a.2 and its derivatives that became predominant. ConclusionsFerrets and pigs showed comparable responses and both distinguished clade 5a.1 from clade 5a.2. However, neither model recognised antigenically drifted variants from 2019-2022, including subclades 5a.2-C, 5a.2a-C.1/C.1.9, and .5a.2a.1-C.1.1/D, which were distinguishable using human post-vaccination antisera.

immunology↗

Integrative modeling of seasonal influenza evolution via AI-powered antigenic cartography

Seasonal influenza viruses evade host immunity through rapid antigenic evolution. Antigenicity is assessed by serological assays and typically visualized as antigenic maps, which represent antigenic differences among virus strains. However, conventional maps cannot directly infer the antigenicity of unexamined variants from their genotypes. Here, we present PLANT, a protein language model that projects influenza A/H3N2 viruses onto an antigenic map using HA protein sequences. Using PLANT-based cartography, we show that (i) H3N2 antigenic evolution accelerates during periods of disrupted global circulation, (ii) antigenic novelty accounts for a substantial portion of viral fitness advantage, and (iii) vaccine strains are often antigenically distant from circulating viruses. We further propose a PLANT-based framework for selecting vaccine strains with improved antigenic match than the WHO-recommended strains. This study provides a statistical foundation for integrated modeling of viral genotype, antigenicity, and fitness, offering quantitative insights into seasonal influenza virus evolution and supporting rational vaccine design.

microbiology↗