Mucosal vaccine immunity induced by a new auxotrophic <i>Pseudomonas aeruginosa</i> strain is linked to Th17 and IgA responses
Bibliographic record
Abstract
ABSTRACT Pseudomonas aeruginosa ( P.a ) is a Gram-negative opportunistic pathogen that poses a major global health threat, particularly in immunocompromised individuals, patients with cystic fibrosis, and those with burn injuries or ventilator-associated pneumonia. Despite intense efforts, no licensed vaccine is currently available for human use. In this context, live attenuated vaccines (LAVs) represent a promising but underexplored approach, offering the potential to elicit robust, long-lasting, and multifaceted immune responses including that of inducing trained immunity. Here, we sub-cultured Δ LasB PAO1 (a P.a strain that we have shown previously shown to have reduced virulence) in artificial sputum medium (ASM), a culture medium mimicking CF sputum in which bacteria often show auxotrophy. We showed that such a strain (designed here ‘V’ for vaccine) was auxotrophic, less virulent, and had characteristics of ‘CF-like strains’. Crucially, V was able to induce both local (IgA) and systemic humoral responses as well as memory Th17 immune responses, and could, when administered intra-tracheally (but not intra-muscularly), fully protected mice against a lethal PAO1 infection. Overall, the present study demonstrates that our vaccine formulation, in addition to providing an advantageous auxotrophic phenotype adapted to the CF setting, was efficient, when given mucosally, in preferentially inducing secretory IgA and Th17 pathway at mucosal surfaces, a critical barrier that neutralizes pathogens before tissue invasion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".