Integration of two essential virulence modulating signals at the<i>Erwinia chrysanthemi pel</i>gene promoters: a role for Fis in the growth‐phase regulation
Bibliographic record
Abstract
Production of the essential virulence factors, called pectate lyases (Pels), in the phytopathogenic bacterium Erwinia chrysanthemi is controlled by a complex regulation system and responds to various stimuli, such as the presence of pectin or plant extracts, growth phase, temperature and iron concentration. The presence of pectin and growth phase are the most important signals identified. Eight regulators modulating the expression of the pel genes (encoding Pels) have been characterized. These regulators are organized in a network allowing a sequential functioning of the regulators during infection. Although many studies have been carried out, the mechanisms of control of Pel production by growth phase have not yet been elucidated. Here we report that a fis mutant of E. chrysanthemi showed a strong increase in transcription of the pel genes during exponential growth whereas induction of expression in the parental strain occurred at the end of exponential growth. This reveals that Fis acts to prevent an efficient transcription of pel genes at the beginning of exponential growth and also provides evidence of the involvement of Fis in the growth-phase regulation of the pel genes. By using in vitro DNA-protein interactions and transcription experiments, we find that Fis directly represses the pel gene expression at the transcription initiation step. In addition, we show that Fis acts in concert with KdgR, the main repressor responding to the presence of pectin compounds, to shut down the pel gene transcription. Finally, we find that active Fis is required for the efficient translocation of the Pels in growth medium. Together, these data indicate that Fis tightly controls the availability of Pels during pathogenesis by acting on both their production and their translocation in the external medium.
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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 | 0.001 |
| 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".