Regulation of phenylacetic acid degradation genes of Burkholderia cenocepacia K56-2
Bibliographic record
Abstract
BACKGROUND: Metabolically versatile soil bacteria Burkholderia cepacia complex (Bcc) have emerged as opportunistic pathogens, especially of cystic fibrosis (CF). Previously, we initiated the characterization of the phenylacetic acid (PA) degradation pathway in B. cenocepacia, a member of the Bcc, and demonstrated the necessity of a functional PA catabolic pathway for full virulence in Caenorhabditis elegans. In this study, we aimed to characterize regulatory elements and nutritional requirements that control the PA catabolic genes in B. cenocepacia K56-2. RESULTS: Translational fusions of the PA degradation gene promoters with eGFP were constructed and introduced in B. cenocepacia K56-2. eGFP expression was observed when the reporter strains were grown in minimal media containing glycerol and PA or other compounds expected to proceed through the PA pathway, and in synthetic CF medium (SCFM). Addition of succinate or glucose to the PA containing medium repressed eGFP expression. To show that BCAL0210, a putative TetR-type regulator gene encodes a regulator for the PA genes in B. cenocepacia, we developed a BCAL0210 insertional mutant reporter strain. Results show that these strains exhibit fluorescence regardless of the presence of PA in the culture. CONCLUSION: The PA catabolic genes of B. cenocepacia K56-2 are induced by PA and other related compounds, are negatively regulated by PaaR (named herein), a TetR-type regulator, and are subjected to catabolic repression by glucose and succinate. As the PA catabolic pathway of B. cenocepacia appears to be induced during growth in synthetic cystic fibrosis medium (SCFM), further research is necessary to determine the relevance of this pathway in CF-like conditions and in other host-pathogen interactions.
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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) | 0.001 | 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 | 0.001 |
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".