Analysis of <i>Pseudomonas aeruginosa</i> 4-hydroxy-2-alkylquinolines (HAQs) reveals a role for 4-hydroxy-2-heptylquinoline in cell-to-cell communication
Bibliographic record
Abstract
Bacterial communities use "quorum sensing" (QS) to coordinate their population behavior through the action of extracellular signal molecules, such as the N-acyl-l-homoserine lactones (AHLs). The versatile and ubiquitous opportunistic pathogen Pseudomonas aeruginosa is a well-studied model for AHL-mediated QS. This species also produces an intercellular signal distinct from AHLs, 3,4-dihydroxy-2-heptylquinoline (PQS), which belongs to a family of poorly characterized 4-hydroxy-2-alkylquinolines (HAQs) previously identified for their antimicrobial activity. Here we use liquid chromatography (LC)/MS, genetics, and whole-genome expression to investigate the structure, biosynthesis, regulation, and activity of HAQs. We show that the pqsA-E operon encodes enzymes that catalyze the biosynthesis of five distinct classes of HAQs, and establish the sequence of synthesis of these compounds, which include potent cytochrome inhibitors and antibiotics active against human commensal and pathogenic bacteria. We find that anthranilic acid, the product of the PhnAB synthase, is the primary precursor of HAQs and that the HAQ congener 4-hydroxy-2-heptylquinoline (HHQ) is the direct precursor of the PQS signaling molecule. Significantly, whereas phnAB and pqsA-E are positively regulated by the virulence-associated transcription factor MvfR, which is also required for the expression of several QS-regulated genes, the conversion of HHQ to PQS is instead controlled by LasR. Finally, our results reveal that HHQ is itself both released from, and taken up by, bacterial cells where it is converted into PQS, suggesting that it functions as a messenger molecule in a cell-to-cell communication pathway. HAQ signaling represents a potential target for the pharmacological intervention of P. aeruginosa-mediated infections.
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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) | 0.000 | 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".