PBMC transcriptomes after
influenza vaccination contain valuable information about factors affecting
vaccine responses. However, distilling meaningful knowledge out of these complex datasets is often difficult and requires advanced data mining algorithms. We investigated the use of the data-driven Weighted Gene Correlation Network Analysis (WGCNA) gene clustering method to identify
vaccine response-related genes in PBMC transcriptomic datasets collected from 138 healthy older adults (ages 50-74) before and after 2010-2011 seasonal trivalent
influenza vaccination. WGCNA separated the 14,197 gene dataset into 15 gene clusters based on observed gene expression patterns across subjects. Eight clusters were strongly enriched for genes involved in specific immune cell types and processes, including B cells, T cells, monocytes, platelets, NK cells, cytotoxic T cells, and
antiviral signaling. Examination of gene cluster membership identified signatures of cellular and humoral responses to seasonal
influenza vaccination, as well as pre-existing cellular immunity. The results of this study illustrate the utility of this publically available analysis methodology and highlight genes previously associated with
influenza vaccine responses (e.g., CAMK4, CD19), genes with functions not previously identified in
vaccine responses (e.g., SPON2, MATK, CST7), and previously uncharacterized genes (e.g. CORO1C, C8orf83) likely related to
influenza vaccine-induced immunity due to their expression patterns.