Synthesis of multiple <i>Pseudomonas aeruginosa</i> biofilm matrix exopolysaccharides is post‐transcriptionally regulated
Bibliographic record
Abstract
Exopolysaccharide is a critical biofilm matrix component, yet little is known about how the synthesis of multiple exopolysaccharides is regulated. Pseudomonas aeruginosa can produce several biofilm matrix exopolysaccharides that include alginate, Psl and Pel. Here we demonstrated that AlgC, a key enzyme that provides sugar precursors for the synthesis of alginate and lipopolysaccharides (LPS) is also required for both Psl and Pel production. We showed that forced-synthesis of Psl in alginate-producing mucoid bacteria reduced alginate production but this was not due to transcription of the alginate biosynthesis-operon. Likewise, when either alginate or Psl were overproduced, levels of B-band LPS decreased. Induction of Pel resulted in a reduction of Psl levels. Because the effects of reduced exopolysaccharide synthesis when another is overproduced didn't appear to be regulated at the transcriptional level, this suggests that the biosynthesis pathways of Psl, Pel, alginate, and LPS compete for common sugar precursors. As AlgC is the only enzyme that provides precursors for each of these exopolysaccharides, we propose that AlgC is a key checkpoint enzyme that coordinates the total amount of exopolysaccharide biosynthesis by controlling sugar precursor pool. Our data also provide a plausible strategy that P.aeruginosa utilizes to modulate its biofilm matrix exopolysaccharides.
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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 | 0.001 | 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".