Intranasal multivalent adenoviral-vectored vaccine protects against replicating and dormant M.tb in conventional and humanized mice
Bibliographic record
Abstract
Abstract Viral-vectored vaccines are highly amenable for respiratory mucosal delivery as a means of inducing much-needed mucosal immunity at the point of pathogen entry. Unfortunately, current monovalent viral-vectored tuberculosis (TB) vaccine candidates have failed to demonstrate satisfactory clinical protective efficacy. As such, there is a need to develop next-generation viral-vectored TB vaccine strategies which incorporate both vaccine antigen design and delivery route. In this study, we have developed a trivalent chimpanzee adenoviral-vectored vaccine to provide protective immunity against pulmonary TB through targeting antigens linked to the three different growth phases (acute/chronic/dormancy) of Mycobacterium tuberculosis (M.tb) by expressing an acute replication-associated antigen, Ag85A, a chronically expressed virulence-associated antigen, TB10.4, and a dormancy/resuscitation-associated antigen, RpfB. Single-dose respiratory mucosal immunization with our trivalent vaccine induced robust, sustained tissue-resident multifunctional CD4+ and CD8+ T-cell responses within the lung tissues and airways, which were further quantitatively and qualitatively improved following boosting of subcutaneously BCG-primed hosts. Prophylactic and therapeutic immunization with this multivalent trivalent vaccine in conventional BALB/c mice provided significant protection against not only actively replicating M.tb bacilli but also dormant, non-replicating persisters. Importantly, when used as a booster, it also provided marked protection in the highly susceptible C3HeB/FeJ mice, and a single respiratory mucosal inoculation was capable of significant protection in a humanized mouse model. Our findings indicate the great potential of this next-generation TB vaccine strategy and support its further clinical development for both prophylactic and therapeutic applications.
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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".