Repression of the Antifungal Activity of Pseudomonas sp. Strain DF41 by the Stringent Response
Bibliographic record
Abstract
The stringent response (SR) enables bacteria to adapt to nutrient limitation through production of the nucleotides guanosine tetraphosphate and guanosine pentaphosphate, collectively known as (p)ppGpp. Two enzymes are responsible for the intracellular pools of (p)ppGpp: RelA acts as a synthetase, while SpoT can function as either a synthetase or a hydrolase. We investigated how the SR affects the ability of the biological control agent Pseudomonas sp. strain DF41 to inhibit the fungal pathogen Sclerotinia sclerotiorum (Lib.) de Bary. Strain DF41 relA and relA spoT mutants were generated and found to exhibit increased antifungal activity. Strain DF41 produces a lipopeptide (LP) molecule that is essential for Sclerotinia biocontrol. LP production and protease activity were both elevated in the relA and relA spoT mutants. Addition of relA but not spoT in trans restored the mutant phenotype to that of the parent. Next, we investigated whether an association exists between the SR and known regulators of biocontrol, including the Gac system and RpoS. A gacS mutant of strain DF41 produced less (p)ppGpp and exhibited a 1.7-fold decrease in relA expression compared to the wild type, suggesting that relA forms part of the Gac regulon. We discovered that rpoS transcription was reduced significantly in the SR mutants. Furthermore, rpoS provided in trans restored protease activity to wild-type levels but did not attenuate antifungal activity. Finally, relA expression was decreased in the mutants, indicating that the SR is required for maximum expression of relA.
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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 | 0.000 | 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".