Loss of Lipid Virulence Factors Reduces the Efficacy of the BCG Vaccine
Bibliographic record
Abstract
Bacille Calmette-Guérin (BCG), an attenuated strain of Mycobacterium bovis, is the only vaccine available for tuberculosis (TB) control. BCG comprises a number of substrains that exhibit genetic and biochemical differences. Whether and how these differences affect BCG efficacy remain unknown. Compared to other BCG strains, BCG-Japan, -Moreau, and -Glaxo are defective in the production of phthiocerol dimycocerosates (PDIMs) and phenolic glycolipids (PGLs), two lipid virulence factors. To determine if the loss of PDIMs/PGLs affects BCG efficacy, we constructed a PDIM/PGL-deficient strain of BCG-Pasteur by deleting fadD28, and compared virulence, immunogenicity, and protective efficacy in animal models. SCID mouse infection experiments showed that ∆fadD28 was more attenuated than wild type (WT). The ∆fadD28 and WT strains induced equivalent levels of antigen specific IFN-γ by CD4(+) and CD8(+) T cells; however, ∆fadD28 was less effective against Mycobacterium tuberculosis challenge in both BALB/c mice and guinea pigs. These results indicate that the loss of PIDMs/PGLs reduces the virulence and protective efficacy of BCG. Since the loss of PDIMs/PGLs occurs naturally in a subset of BCG strains, it also suggests that these strains may have been over-attenuated, which compromises their effectiveness. Our finding has important implications for current BCG programs and future vaccine development.
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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 | 0.000 |
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".