Heterologous prime-boost immunization combining parenteral and mucosal routes with different adjuvants mounts long-lived CD4+ T cell responses in lungs
Bibliographic record
Abstract
Introduction Airway mucosa represents the main entry point for several human pathogens, and as such vaccines against respiratory diseases should ideally elicit protective immune responses in the airways. We have previously reported two immunomodulatory adjuvants based on non-toxic derivatives of Cholera toxin (CT), namely mmCT and CTB-CpG with strong ability to mount mucosal immune responses. Methods Herein, we aimed to pinpoint the potential of prime-boost immunization approaches using the fusion-protein based Mycobacterium tuberculosis subunit vaccine candidate H56 as a model antigen, combined with adjuvants CAF01, mmCT, and CTB-CpG in mice. This included a parenteral H56+CAF01 priming followed by an intranasal boost with H56+CAF01, H56+mmCT, or H56+CTB-CpG, compared with repeated homologous intranasal administrations of H56 with each adjuvant. Results We observed that a parenteral prime with H56+CAF01 followed by an intranasal H56+CTB-CpG booster immunization triggered a Th1-skewed immune response. Conversely, combining the parenteral H56+CAF01 prime with an intranasal H56+mmCT boost resulted in a mixed Th1/Th17-skewed immune response. Notably, the latter combination also engendered anamnestic, long-lived T-cell responses in the lungs which homologous intranasal H56+mmCT immunizations failed to induce. Discussion These results suggest that an immunization regimen consists of parenteral priming with H56+CAF01 followed by an airway boosting with H56 protein and mucosal adjuvants holds promise in mounting combined systemic and mucosal immune responses to Mycobacterium tuberculosis, and as such warrants further exploration. Given the rising interest in mucosal vaccines for respiratory pathogens, these findings offer an important immunological framework for future translational studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".