Evolution and Vaccination of Influenza Virus

Molecular characterization and evolution dynamics of influenza B viruses circulating in Germany from season 1996/1997 to 2019/2020

Influenza B viruses are responsible for significant disease burden caused by viruses of both the Yamagata- and Victoria-lineage. Since the circulating patterns of influenza B viruses in different countries vary we investigated molecular properties and evolution dynamics of influenza B viruses circulating in Germany between 1996 and 2020. A change of the dominant lineage occurred in Germany in seven seasons in over past 25 years. A total of 676 sequences of hemagglutinin coding domain 1 (HA1) and 516 sequences of neuraminidase (NA) genes of Yamagata- and Victoria-lineage viruses were analyzed using time-scaled phylogenetic tree. Phylogenetic analysis demonstrated that Yamagata-lineage viruses are more diverse than the Victoria-lineage viruses and could be divided into nine genetic groups whereas Victoria-lineage viruses presented six genetic groups. Comparative phylogenetic analyses of both the HA and NA segments together revealed a number of inter-lineage as well as inter- and intra-clade reassortants. We identified key amino acid substitutions in major HA epitopes such as in four antigenic sites and receptor-binding sites (RBS) and in the regions close to them, with most substitutions in the 120-loop of both lineage viruses. Altogether, seventeen substitutions were fixed over time within the Yamagata-lineage with twelve of them in the antigenic sites. Thirteen substitutions were identified within the Victoria-lineage, with eleven of them in the antigenic sites. Moreover, all Victoria-lineage viruses of the 2017/2018 season were characterized by a deletion of two amino acids at the position 162-163 in the antigenic site of HA1. The viruses with triple deletion Δ162-164 were found in Germany since season 2018/2019. We highlighted the interplay between substitutions in the glycosylation sites and RBS and antigenic epitope during HA evolution. The results obtained underscore the need for continuous monitoring of circulating influenza B viruses. Early detection of strains with genetic and antigenic variation is essential to predict the circulation patterns for the following season. Such information is important for the development of optimal vaccines and strategies for prevention and control of influenza.

Microarray Gene Expression Dataset Re-analysis Reveals Variability in Influenza Infection and Vaccination

Influenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination, including variability due to age and sex. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted p < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted p < 0.05) disease-age and disease-sex interactions, respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.