Regulation of <i>perR</i> Expression by Iron and PerR in Campylobacter jejuni
Bibliographic record
Abstract
Campylobacter jejuni is a leading food-borne pathogen causing gastroenteritis in humans. Although OxyR is a widespread oxidative stress regulator in many Gram-negative bacteria, C. jejuni lacks OxyR and instead possesses the metalloregulator PerR. Despite the important role played by PerR in oxidative stress defense, little is known about the factors influencing perR expression in C. jejuni. In this study, a perR promoter-lacZ fusion assay demonstrated that iron significantly reduced the level of perR transcription, whereas other metal ions, such as copper, cobalt, manganese, and zinc, did not affect perR transcription. Notably, a perR mutation substantially increased the level of perR transcription and in trans complementation restored the transcriptional changes, suggesting perR is transcriptionally autoregulated in C. jejuni. In the perR mutant, iron did not repress perR transcription, indicating the iron dependence of perR expression results from perR autoregulation. Electrophoretic mobility shift assays showed that PerR binds to the perR promoter, and DNase I footprinting assays identified a PerR binding site overlapping the -35 region of the two perR promoters, further supporting perR autoregulation at the transcriptional level. Alignment of the PerR binding sequence in the perR promoter with the regulatory region of other PerR regulon genes of C. jejuni revealed a 16-bp consensus PerR binding sequence, which shares high similarities to the Bacillus subtilis PerR box. The results of this study demonstrated that PerR directly interacts with the perR promoter and regulates perR transcription and that perR autoregulation is responsible for the repression of perR transcription by iron in C. jejuni.
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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".