Immunoproteomic discovery of Mycobacterium bovis antigens, including the surface lipoprotein Mpt83 as a T cell antigen useful for vaccine development
Bibliographic record
Abstract
Tuberculosis (TB) is one of the leading causes of death from infectious diseases, killing approximately 1.3 million people worldwide in 2022 alone. The current vaccine for TB contains a live attenuated bacterium, Mycobacterium bovis BCG (Bacille Calmette-Guérin). The BCG vaccine is highly effective in preventing severe forms of childhood TB but does not protect against latent infection or disease in older age groups. A new or improved BCG vaccine for prevention of pulmonary TB is urgently needed. In this study, we infected murine bone marrow derived dendritic cells from C57BL/6 mice with M. bovis BCG followed by elution and identification of BCG-derived MHC class I and class II-bound peptides using tandem mass spectrometry. We identified 1436 MHC-bound peptides of which 94 were derived from BCG. Fifty-five peptides were derived from MHC class I molecules and 39 from class II molecules. We tested the 94 peptides for their immunogenicity using IFN- γ ELISPOT assay with splenocytes purified from BCG immunized mice and 10 showed positive responses. Seven peptides were derived from MHC II and three from MHC class I. In particular, MHC class II binding peptides derived from the mycobacterial surface lipoprotein Mpt83 were highly antigenic. Further evaluations of these immunogenic BCG peptides may identify proteins useful as new TB vaccine candidates.
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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) | 0.000 | 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".