Improving the Immunogenicity of the Mycobacterium bovis BCG Vaccine by Non-Genetic Bacterial Surface Decoration Using the Avidin-Biotin System
Bibliographic record
Abstract
Current strategies to improve the current BCG vaccine attempt to over-express genes encoding specific M. tuberculosis (Mtb) antigens and/or regulators of antigen presentation function, which indeed have the potential to reshape BCG in many ways. However, these approaches often face serious difficulties, in particular the efficiency and stability of gene expression via nucleic acid complementation and safety concerns associated with the introduction of exogenous DNA. As an alternative, we developed a novel non-genetic approach for rapid and efficient display of exogenous proteins on bacterial cell surface. The technology involves expression of proteins of interest in fusion with a mutant version of monomeric avidin that has the feature of reversible binding to biotin. Fusion proteins are then used to decorate the surface of biotinylated BCG. Surface coating of BCG with recombinant proteins was highly reproducible and stable. It also resisted to the freeze-drying shock routinely used in manufacturing conventional BCG. Modifications of BCG surface did not affect its growth in culture media neither its survival within the host cell. Macrophages phagocytized coated BCG bacteria, which efficiently delivered their surface cargo of avidin fusion proteins to MHC class I and class II antigen presentation compartments. Thereafter, chimeric proteins corresponding to a surrogate antigen derived from ovalbumin and the Mtb specific ESAT6 antigen were generated and tested for immunogenicity in vaccinated mice. We found that BCG displaying ovalbumin antigen induces an immune response with a magnitude similar to that induced by BCG genetically expressing the same surrogate antigen. We also found that BCG decorated with Mtb specific antigen ESAT6 successfully induces the expansion of specific T cell responses. This novel technology, therefore, represents a practical and effective alternative to DNA-based gene expression for upgrading the current BCG vaccine.
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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 | 0.000 |
| 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".