MvfR, a key <i>Pseudomonas aeruginosa</i> pathogenicity LTTR‐class regulatory protein, has dual ligands
Bibliographic record
Abstract
MvfR (PqsR), a Pseudomonas aeruginosa LysR-type transcriptional regulator, plays a critical role in the virulence of this pathogen. MvfR modulates the expression of multiple quorum sensing (QS)-regulated virulence factors; and the expression of the phnAB and pqsA-E genes that encode functions mediating 4-hydroxy-2-alkylquinolines (HAQs) signalling compounds biosynthesis, including 3,4-dihydroxy-2heptylquinoline (PQS) and its precursor 4-hydroxy-2-heptylquinoline (HHQ). PQS enhances the in vitro DNA-binding affinity of MvfR to the pqsA-E promoter, to suggest it might function as the in vivo MvfR ligand. Here we identify a novel MvfR ligand, as we show that HHQ binds to the MvfR ligand-binding-domain and potentiates MvfR binding to the pqsA-E promoter leading to transcriptional activation of pqsA-E genes. We show that HHQ is highly produced in vivo, where it is not fully converted into PQS, and demonstrate that it is required for MvfR-dependent gene expression and pathogenicity; PQS is fully dispensable, as pqsH-mutant cells, which produce HHI but completely lack PQS, display normal MvfR-dependent gene expression and virulence. Conversely, PQS is required for full production of pyocyanin. These results uncover a novel biological role for HHQ; and provide novel insights on MvfR activation that may aid in the development of therapies that prevent or treat P. aeruginosa infections in humans.
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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 | 0.000 |
| 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".