Expression of Exogenous Antigens in the <em>Mycobacterium bovis</em> BCG Vaccine via Non-genetic Surface Decoration with the Avidin-biotin System
Bibliographic record
Abstract
Tuberculosis (TB) is a serious infectious disease and the only available vaccine M. bovis bacillus Calmette-Guérin (BCG) is safe and effective for protection against children's severe TB meningitis and some forms of disseminated TB, but fails to protect against pulmonary TB, which is the most prevalent form of the disease. Promising strategies to improve BCG currently rely either on its transformation with genes encoding immunodominant M. tuberculosis (Mtb)-specific antigens and/or complementation with genes encoding co-factors that would stimulate antigen presenting cells. Major limitations to these approaches include low efficiency, low stability, and the uncertain level of safety of expression vectors. In this study, we present an alternative approach to vaccine improvement, which consists of BCG complementation with exogenous proteins of interest on the surface of bacteria, rather than transformation with plasmids encoding corresponding genes. First, proteins of interest are expressed in fusion with monomeric avidin in standard E. coli expression systems and then used to decorate the surface of biotinylated BCG. Animal experiments using BCG surface decorated with surrogate ovalbumin antigen demonstrate that the modified bacterium is fully immunogenic and capable of inducing specific T cell responses. Altogether, the data presented here strongly support a novel and efficient method for reshaping the current BCG vaccine that replaces the laborious conventional approach of complementation with exogenous nucleic acids.
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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 | 0.001 |
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".