Differential expression of a virulence factor in pathogenic and non‐pathogenic mycobacteria
Bibliographic record
Abstract
The pathogenicity of mycobacterial infections depends on virulence factors that mediate survival inside host macrophages. These virulence factors are generally believed to be specific for pathogenic species and absent or mutated in non-pathogenic strains. The serine/threonine protein kinase G (PknG) mediates survival of mycobacteria within macrophages by blocking lysosomal delivery. Here we describe a gene of the non-pathogenic species Mycobacterium smegmatis that is 78% identical with pknG of Mycobacterium tuberculosis and M. bovis bacillus Calmette-Guérin (BCG). When cloned into expression vectors, the M. smegmatis pknG orthologue produced an active kinase and performed the same function as its M. bovis BCG counterpart in intracellular survival. In addition, similar levels of pknG transcripts were found in M. bovis BCG and M. smegmatis. However, virtually no translation product of chromosomal pknG could be detected in M. smegmatis both after in vitro growth and after macrophage infection. This lack of efficient translation was shown to be caused by regulatory elements in the upstream region of the M. smegmatis gene. The data reveal dramatically increased translational efficiency of a virulence gene in a pathogenic mycobacterium compared with a non-pathogenic mycobacterium suggesting that changes in expression levels may underlie evolution of pknG and other pathogenicity genes in mycobacterium.
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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".