Differential expression of the seven rRNA operon promoters from the plant growth-promoting bacterium<i>Pseudomonas</i>sp. UW4
Bibliographic record
Abstract
Bacteria often have multiple copies of ribosomal RNA (rrn) genes in their genomes. The presence of multiple rrn operons suggests an advantage to the organism, perhaps through adjustable control of protein expression in response to altered environmental conditions. In the work described here, the strengths of the seven rRNA promoters of Pseudomonas sp. UW4 were individually assessed by separately cloning each promoter region into an expression vector and monitoring the activity of the reporter protein, the Escherichia coli lacZ gene product. The lacZ expression was the highest for the rrnE promoter under all growth conditions, with the various promoters demonstrating a range of strengths. These findings indicate that these promoters are not functionally identical. This observation suggests that the differential expression of rrn operons under various physiological conditions and growth stages allows better regulation of rRNA, conferring an advantage to P.sp. UW4 through a more fine-tuned control of protein expression in a wide range of environmental situations. The plant growth-promoting bacterium Pseudomonas sp. UW4 has seven rRNA operons in its genome, and the promoters of these operons displayed different strengths when the bacterium was grown under various conditions. The plant growth-promoting bacterium Pseudomonas sp. UW4 has seven rRNA operons in its genome, and the promoters of these operons displayed different strengths when the bacterium was grown under various conditions.
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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 | 0.001 |
| 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".