Target optimization for peptide nucleic acid (PNA)-mediated antisense inhibition of the CmeABC multidrug efflux pump in Campylobacter jejuni
Bibliographic record
Abstract
OBJECTIVES: CmeABC is a resistance-nodulation-cell division (RND)-type multidrug efflux pump conferring resistance to clinically important antibiotics in Campylobacter. This study aimed to identify the optimal target sites for the inhibition of CmeABC with antisense peptide nucleic acid (PNA). METHODS: Eighteen PNAs were designed to bind to the translational initiation regions of cmeABC, spanning the ribosome-binding site (RBS) and the start codon of the cmeABC genes. Campylobacter jejuni was treated with CmeABC-specific PNAs (CmeABC-PNAs) at various concentrations and subjected to western blotting to measure changes in the level of CmeABC expression. The MICs of ciprofloxacin and erythromycin were measured to evaluate the impact of CmeABC knockdown on antibiotic susceptibility. RESULTS: While antisense PNA significantly affected CmeA and CmeB expression, interestingly, CmeC expression was not altered by any of the CmeC-PNAs used in this study. A CmeA-PNA targeting the RBS of cmeA and its upstream region reduced CmeA expression most efficiently, and CmeB expression was most significantly decreased by PNA binding to the RBS of cmeB and its downstream region. CmeA- and CmeB-PNAs increased the susceptibility of C. jejuni to ciprofloxacin and erythromycin in proportion to the inhibition levels observed in western blotting. CONCLUSIONS: The cmeA gene is the best target to knockdown CmeABC with antisense PNA. The RBS is the major target for the PNA-mediated antisense inhibition of CmeABC. However, regions in its vicinity also significantly influence the effectiveness of the PNA-based knockdown of CmeABC.
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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".