Novel genome polymorphisms in BCG vaccine strains and impact on efficacy
Bibliographic record
Abstract
Bacille Calmette-Guérin (BCG) is an attenuated strain of Mycobacterium bovis currently used as a vaccine against tuberculosis. Global distribution and propagation of BCG has contributed to the in vitro evolution of the vaccine strain and is thought to partially account for the different outcomes of BCG vaccine trials. Previous efforts by several molecular techniques effectively identified large sequence polymorphisms among BCG daughter strains, but lacked the resolution to identify smaller changes. In this study, we have used a NimbleGen tiling array for whole genome comparison of 13 BCG strains. Using this approach, in tandem with DNA resequencing, we have identified six novel large sequence polymorphisms including four deletions and two duplications in specific BCG strains. Moreover, we have uncovered various polymorphisms in the phoP-phoR locus. Importantly, these polymorphisms affect genes encoding established virulence factors including cell wall complex lipids, ESX secretion systems, and the PhoP-PhoR two-component system. Our study demonstrates that major virulence factors are different among BCG strains, which provide molecular mechanisms for important vaccine phenotypes including adverse effect profile, tuberculin reactivity and protective efficacy. These findings have important implications for the development of a new generation of vaccines.
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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 | 0.001 |
| 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".