Anaerobic adaptation in <i>Pseudomonas aeruginosa</i> : definition of the Anr and Dnr regulons
Bibliographic record
Abstract
The anaerobic metabolism of the opportunistic pathogen Pseudomonas aeruginosa is important for growth and biofilm formation during persistent infections. The two Fnr-type transcription factors Anr and Dnr regulate different parts of the underlying network in response to oxygen tension and NO. Little is known about all members of the Anr and Dnr regulons and the mediated immediate response to oxygen depletion. Comprehensive transcriptome and bioinformatics analyses in combination with a limited proteome analyses were used for the investigation of the P. aeruginosa response to an immediate oxygen depletion and for definition of the corresponding Anr and Dnr regulons. We observed at first the activation of fermentative pathways for immediate energy generation followed by induction of alternative respiratory chains. A solid position weight matrix model was deduced from the experimentally identified Anr boxes and used for identification of 170 putative Anr boxes in potential P. aeruginosa promoter regions. The combination with the experimental data unambiguously identified 130 new members for the Anr and Dnr regulons. The basis for the understanding of two regulons of P. aeruginosa central to biofilm formation and infection is now defined.
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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".