<scp>C</scp> yclic‐di‐ <scp>GMP</scp> levels affect <scp> <i>P</i> </scp> <i>seudomonas aeruginosa</i> fitness in the presence of imipenem
Bibliographic record
Abstract
A large number of genes coding for enzymes predicted to synthesize and degrade 3'-5'-cyclic diguanylic acid (c-di-GMP) is found in most bacterial genomes and this dinucleotide emerged as an intracellular signal-controlling bacterial behaviour. An association between high levels of c-di-GMP and antibiotic resistance may be expected because c-di-GMP regulates biofilm formation and this mode of growth leads to enhanced antibiotic resistance. However, a clear understanding of this correlation has not been established. We found that increased levels of c-di-GMP in Pseudomonas aeruginosa improve fitness in the presence of imipenem, even when grown as planktonic cells. P. aeruginosa post-transcriptionally regulates the amounts of five porins in response to c-di-GMP, including OprD, responsible for imipenem uptake. Cells with low c-di-GMP levels are consequently more sensitive to this antibiotic. Main efflux pumps or β-lactamase genes did not show altered mRNA levels in P. aeruginosa strains with modified different c-di-GMP concentrations. Together, our findings show that c-di-GMP levels modulate fitness of planktonic cultures in the presence of imipenem.
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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) | 0.002 | 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".