Sigma factors in<i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
In Pseudomonas aeruginosa, as in most bacterial species, the expression of genes is tightly controlled by a repertoire of transcriptional regulators, particularly the so-called sigma (sigma) factors. The basic understanding of these proteins in bacteria has initially been described in Escherichia coli where seven sigma factors are involved in core RNA polymerase interactions and promoter recognition. Now, 7 years have passed since the completion of the first genome sequence of the opportunistic pathogen P. aeruginosa. Information from the genome of P. aeruginosa PAO1 identified 550 transcriptional regulators and 24 putative sigma factors. Of the 24 sigma, 19 were of extracytoplasmic function (ECF). Here, basic knowledge of sigma and ECF proteins was reviewed with particular emphasis on their role in P. aeruginosa global gene regulation. Summarized data are obtained from in silico analysis of P. aeruginosasigma and ECF including rpoD (sigma(70)), RpoH (sigma(32)), RpoF (FliA or sigma(28)), RpoS (sigma(S) or sigma(38)), RpoN (NtrA, sigma(54) or sigma(N)), ECF including AlgU (RpoE or sigma(22)), PvdS, SigX and a collection of uncharacterized sigma ECF, some of which are implicated in iron transport. Coupled to systems biology, identification and functional genomics analysis of P. aeruginosasigma and ECF are expected to provide new means to prevent infection, new targets for antimicrobial therapy, as well as new insights into the infection process.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".