Colistin-phage combinations decrease antibiotic resistance in <i>Acinetobacter baumannii</i> via changes in envelope architecture
Bibliographic record
Abstract
ABSTRACT Few emergency-use antibiotics remain for the treatment of multidrug-resistant bacterial infections. Infections with resistant bacteria are becoming increasingly common. Phage therapy has reemerged as a promising strategy to treat such infections, as microbial viruses are not affected by bacterial resistance to antimicrobial compounds. However, phage therapy is impeded by rapid emergence of phage-resistant bacteria during therapy. In this work, we studied phage-resistance of colistin sensitive and resistant A. baumannii strains. Using whole genome sequencing, we determined that phage resistant strains displayed mutations in genes that alter the architecture of the bacterial envelope. In contrast to previous studies where phage-escape mutants showed decreased binding of phages to the bacterial envelope, we obtained several not uninfectable isolates that allowed similar phage adsorption compared to the susceptible strain. When phage-resistant bacteria emerged in the absence of antibiotics, we observed that the colistin resistance levels often decreased, while the antibiotic resistance mechanism per se remained unaltered. In particular the two mutated genes that conveyed phage resistance, a putative amylovoran-biosynthesis and a lipo-oligosaccharide (LOS) biosynthesis gene, impact colistin resistance as the mutations increased sensitivity to the antibiotic.
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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".