Influenza A(H7N9) Pandemic Preparedness: Assessment of the Breadth of Heterologous Antibody Responses to Emerging Viruses from Multiple Pre-Pandemic Vaccines and Population Immunity.

Influenza A(H7N9) viruses remain as a high pandemic threat. The continued evolution of the A(H7N9) viruses poses major challenges in pandemic preparedness strategies through vaccination. We assessed the breadth of the heterologous neutralizing antibody responses against the 3rd and 5th wave A(H7N9) viruses using the 1st wave vaccine sera from 4 vaccine groups: 1. inactivated vaccine with 2.8 µg hemagglutinin (HA)/dose + AS03A; 2. inactivated vaccine with 5.75 µg HA/dose + AS03A; 3. inactivated vaccine with 11.5 µg HA/dose + MF59; and 4. recombinant virus like particle (VLP) vaccine with 15 µg HA/dose + ISCOMATRIX™. Vaccine group 1 had the highest antibody responses to the vaccine virus and the 3rd/5th wave drifted viruses. Notably, the relative levels of cross-reactivity to the drifted viruses as measured by the antibody GMT ratios to the 5th wave viruses were similar across all 4 vaccine groups. The 1st wave vaccines induced robust responses to the 3rd and Pearl River Delta lineage 5th wave viruses but lower cross-reactivity to the highly pathogenic 5th wave A(H7N9) virus. The population in the United States was largely immunologically naive to the A(H7N9) HA. Seasonal vaccination induced cross-reactive neuraminidase inhibition and binding antibodies to N9, but minimal cross-reactive antibody-dependent cell-mediated cytotoxicity (ADCC) antibodies to A(H7N9).

LABEL: fast and accurate lineage assignment with assessment of H5N1 and H9N2 influenza A hemagglutinins.

The evolutionary classification of influenza genes into lineages is a first step in understanding their molecular epidemiology and can inform the subsequent implementation of control measures. We introduce a novel approach called Lineage Assignment By Extended Learning (LABEL) to rapidly determine cladistic information for any number of genes without the need for time-consuming sequence alignment, phylogenetic tree construction, or manual annotation. Instead, LABEL relies on hidden Markov model profiles and support vector machine training to hierarchically classify gene sequences by their similarity to pre-defined lineages. We assessed LABEL by analyzing the annotated hemagglutinin genes of highly pathogenic (H5N1) and low pathogenicity (H9N2) avian influenza A viruses. Using the WHO/FAO/OIE H5N1 evolution working group nomenclature, the LABEL pipeline quickly and accurately identified the H5 lineages of uncharacterized sequences. Moreover, we developed an updated clade nomenclature for the H9 hemagglutinin gene and show a similarly fast and reliable phylogenetic assessment with LABEL. While this study was focused on hemagglutinin sequences, LABEL could be applied to the analysis of any gene and shows great potential to guide molecular epidemiology activities, accelerate database annotation, and provide a data sorting tool for other large-scale bioinformatic studies.