Resistance-nodulation-division efflux pump acrAB is modulated by florfenicol and contributes to drug resistance in the fish pathogen<i>Piscirickettsia salmonis</i>
Bibliographic record
Abstract
Piscirickettsia salmonis is a fastidious intracellular pathogen responsible for high mortality rates in farmed salmonids, with serious economic consequences for the Chilean aquaculture industry. Oxytetracycline and florfenicol are the most frequently used antibiotics against P. salmonis, but routine use could contribute to drug resistance. This study identified differentiated florfenicol susceptibilities in two P. salmonis strains, LF-89 and AUSTRAL-005. The less susceptible isolate, AUSTRAL-005, also showed a high ethidium bromide efflux rate, indicating a higher activity of general efflux pump genes than LF-89. The P. salmonis genome presented resistance nodulation division (RND) family members, a family containing typical multidrug resistance-related efflux pumps in Gram-negative bacteria. Additionally, efflux pump acrAB genes were overexpressed in AUSTRAL-005 following exposure to the tolerated maximal concentration of florfenicol, in contrast to LF-89. These results indicate that tolerated maximum concentrations of florfenicol can modulate RND gene expression and increase efflux pump activity. We propose that the acrAB efflux pump is essential for P. salmonis survival at critical florfenicol concentrations and for the generation of antibiotic-resistant bacterial strains.
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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".