Abstract
CD4+ T-cell mechanisms are implied in protection against pneumococcal colonization; however, their target antigens and function are not well defined. In contrast to high-throughput protein arrays for serology, basic antigen tools for CD4+ T-cell studies are lacking. Here, we evaluate the potential of a bioinformatics tool for in silico prediction of immunogenicity as a method to reveal domains of pneumococcal proteins targeted by human CD4+ T cells. For 100 pneumococcal proteins, CD4+ T-cell immunogenicity was predicted based on HLA-DRB1 binding motifs. For 20 potentially CD4+ T-cell immunogenic proteins, epitope regions were verified by testing synthetic peptides in T-cell assays using peripheral blood mononuclear cells from healthy adults. Peptide pools of 19 out of 20 proteins evoked T-cell responses. The most frequent responses (detectable in ≥20% of donors tested) were found to SP_0117 (PspA), SP_0468 (putative sortase), SP_0546 (BlpZ), SP_1650 (PsaA), SP_1923 (Ply), SP_2048 (conserved hypothetical protein), SP_2216 (PscB), and SPR_0907 (PhtD). Responding donors had diverging recognition patterns and profiles of signature cytokines (gamma interferon [IFN-γ], tumor necrosis factor alpha [TNF-α], interleukin-13 [IL-13], and/or IL-17A) against single-epitope regions. Natural HLA-DR-restricted presentation and recognition of a predicted SP_1923-derived epitope were validated through the isolation of a CD4+ T-cell clone producing IFN-γ, TNF-α, and IL-17A in response to the synthetic peptide, whole protein, and heat-inactivated pneumococcus. This proof of principle for a bioinformatics tool to identify pneumococcal protein epitopes targeted by human CD4+ T cells provides a peptide-based strategy to study cell-mediated immune mechanisms for the pneumococcal proteome, advancing the development of immunomonitoring assays and targeted vaccine approaches.