Nasal Immunization with<i>Burkholderia multivorans</i>Outer Membrane Proteins and the Mucosal Adjuvant Adamantylamide Dipeptide Confers Efficient Protection against Experimental Lung Infections with<i>B. multivorans</i>and<i>B. cenocepacia</i>
Bibliographic record
Abstract
Chronic lung infection by opportunistic pathogens, such as Pseudomonas aeruginosa and members of the Burkholderia cepacia complex, is a major cause of morbidity and mortality in patients with cystic fibrosis. Outer membrane proteins (OMPs) of gram-negative bacteria are promising vaccine antigen candidates. In this study, we evaluated the immunogenicity, protection, and cross-protection conferred by intranasal vaccination of mice with OMPs from B. multivorans plus the mucosal adjuvant adamantylamide dipeptide (AdDP). Robust mucosal and systemic immune responses were stimulated by vaccination of naive animals with OMPs from B. multivorans and B. cenocepacia plus AdDP. Using a mouse model of chronic pulmonary infection, we observed enhanced clearance of B. multivorans from the lungs of vaccinated animals, which correlated with OMP-specific secretory immunoglobulin A responses. Furthermore, OMP-immunized mice showed rapid resolution of the pulmonary infection with virtually no lung pathology after bacterial challenge with B. multivorans. In addition, we demonstrated that administration of B. multivorans OMP vaccine conferred protection against B. cenocepacia challenge in this mouse infection model, suggesting that OMPs provide cross-protection against the B. cepacia complex. Therefore, we concluded that mucosal immunity to B. multivorans elicited by intranasal vaccination with OMPs plus AdDP could prevent early steps of colonization and infection with B. multivorans and also ameliorate lung tissue damage, while eliciting cross-protection against B. cenocepacia. These results support the notion that therapies leading to increased mucosal immunity in the airways may help patients with cystic fibrosis.
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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) | 0.000 | 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 | 0.000 | 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".