Ciprofloxacin Induction of a Susceptibility Determinant in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
With few novel antimicrobials in development, resistance to the current selection of antibiotics increasingly encroaches on our ability to control microbial infections. One limitation in our understanding of the basis of the constraints on current therapies is our poor understanding of antibiotic interactions with bacteria on a global scale. Custom DNA microarrays were used to characterize the response of Pseudomonas aeruginosa to ciprofloxacin, a fluoroquinolone commonly used in therapy against chronic infections by this intrinsically resistant bacterium. Of the approximately 5,300 open reading frames (ORFs) on the array, 941 genes showed statistically significant (P </= 0.05) differential expression in response to 0.3x MIC of ciprofloxacin; 554 were promoted and 387 were repressed. Most striking among the responsive genes was the region between PA0613 and PA0648, which codes for the bacteriophage-like R2/F2 pyocins. In this region, virtually every ORF was increased by 0.3x MIC of ciprofloxacin and even more dramatically up-regulated (7- to 19-fold) following treatment with 1x MIC of ciprofloxacin. Pyocin gene expression was confirmed with lux reporter mutants and real-time PCR studies; pyocin-like particles were also present in transmission electron micrographs of supernatants from cells treated with 1x MIC of ciprofloxacin. Interestingly, mutants in this region exhibited >/=8-fold-increased resistance to ciprofloxacin and other fluoroquinolones, demonstrating that this region is a susceptibility determinant. Since this region is known to be variably present in the genomes of clinical isolates of P. aeruginosa (R. K. Ernst et al., Environ. Microbiol. 5:1341-1349, 2003, and M. C. Wolfgang et al., Proc. Natl. Acad. Sci. USA 100:8484-8489, 2003), these findings demonstrate that the R2/F2 pyocin region is a "loaded gun" that can mediate fluoroquinolone susceptibility in P. aeruginosa.
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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 | 0.001 |
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".