Abstract:
The nasal microbiome and its dynamics appear to play a role in the progression of respiratory illnesses. Understanding these changes would contribute to the mechanistic deconvolution of vaccine-related pathways, open up the possibility of identifying bacterial biomarkers as correlates of protection and may have broader implications on vaccine design. To explore these dynamics further, we characterised changes in the nasal microbiome in response to the trivalent-inactivated influenza vaccine, Cadiflu-S and compared microbial profiles between individuals with high and low immunity to influenza (as inferred from serum antibody titres). Nasal swabs were used to sample the participant’s anterior nares on Day 0 before vaccine administration and on Day 8 and Day 30 post-vaccination. A single swab was collected from the unvaccinated participants for cross-sectional analysis of the nasal microbiome in the context of immunity to influenza. Microbiome profiling was done using 16S rRNA gene amplification and next-generation sequencing, and mucosal IgA levels were measured through ELISA. Significant differences were noted in alpha-diversity (p-value = 0.0025, Shannon) and beta diversity (p-value = 0.004, Weighted UniFrac) between influenza-associated immune and non-immune individuals. Individuals with the highest antibody titres demonstrated a significant decrease in alpha diversity when compared to the non-immune. No changes were observed between samples collected at different time points prior to and post-vaccination. Significant correlation was established between alpha diversity measures and total mucosal IgA concentrations (Correlation coefficient = -0.1835 and p-value = 0.02, Shannon). These results hint that both the local and systemic immune response of an individual may play a major role in shaping the microbiota. However, we are still a long way from establishing the nature of the relationship and the immune-microbiome interplay. While the results look appealing, one must keep in mind that the presence of multiple confounders often leads to misleading results in such clinical studies.
