Osmolarity affects Bvg-mediated virulence regulation by Bordetella pertussis
Bibliographic record
Abstract
Bordetella pertussis dramatically alters its phenotype by sensing its environment via the BvgAS regulatory system. Increased concentrations of specific chemicals are used in vitro to induce modulation of the bacterium from the Bvg(+) virulent phenotype to a fully Bvg(-) phenotype. Varied expression of sets of Bvg(-)regulated molecules depends on the modulating capacity of the environment. We examined the effect of a number of chemicals on the modulating capacity of B. pertussis growth media, both alone and in combination with known modulators. It was demonstrated that under certain conditions the Bvg(-)intermediate protein, BipA, is coexpressed with the Bvg(-) antigen, VraA. This demonstrates that the patterns of molecules expressed in the different phenotypes of B. pertussis are more fluid than has previously been demonstrated. The in vitro modulator, sulfate, was found to be a relatively inefficient modulator of our Tohama I-derived B. pertussis strain. However, addition of nicotinic acid, MgCl2, or sucrose in combination with relatively low sulfate concentrations resulted in effective modulation. This suggests that multiple signals may affect modulation through the BvgAS system or possibly through other regulatory networks. In addition, the cooperative modulating effect of sucrose implicates osmolarity as an environmental stimulus that affects phenotypic modulation.
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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".