The non-motile phenotype of Salmonella hha ydgT mutants is mediated through PefI-SrgD
Bibliographic record
Abstract
BACKGROUND: Two ancestral nucleoid-associated proteins called Hha and YdgT contribute to the negative regulation of several virulence-associated genes in Salmonella enterica serovar Typhimurium. Our previous work showed that Hha and YdgT proteins are required for negative regulation of Salmonella Pathogenicity Island-2 and that hha ydgT double mutants are attenuated for murine infection. Interestingly, hha ydgT mutant bacteria exhibited a non-motile phenotype suggesting that Hha and YdgT have a role in flagellar regulation. RESULTS: In this study we show that the non-motile phenotype of hha ydgT mutants is due to decreased levels of the master transcriptional regulator FlhD4C2 resulting in down-regulation of class II/III and class III flagellar promoters and lack of surface flagella on these cells. The horizontally acquired pefI-srgD region was found to be partially responsible for this phenotype since deletion of pefI-srgD in a hha ydgT deletion background resulted in transient restoration of class II/III and III transcription, expression of surface flagella, and motility in the quadruple mutant. CONCLUSION: These data extend our current understanding of the mechanisms through which Hha and YdgT regulate flagellar biosynthesis and further describe how S. Typhimurium has integrated horizontal gene acquisitions into ancestral regulatory networks.
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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".