Colistin-phage combinations decrease antibiotic resistance in <i>Acinetobacter baumannii</i> via changes in envelope architecture
Bibliographic record
Abstract
Multidrug-resistant bacterial infections are becoming increasingly common, with only few last-resort antibiotics such as colistin available for clinical therapy. An alternative therapeutic strategy gaining momentum is phage therapy, which has the advantage of not being affected by bacterial resistance to antibiotics. However, a major challenge in phage therapy is the rapid emergence of phage-resistant bacteria. In this work, our main aim was to understand the mechanisms of phage-resistance used by the top priority pathogen Acinetobacter baumannii. We isolated the novel phage Phab24, capable of infecting colistin-sensitive and -resistant strains of A. baumannii. After co-incubating Phab24 with its hosts, we obtained phage-resistant mutants which were characterized on both genotypic and phenotypic levels. Using whole genome sequencing, we identified phage-resistant strains that displayed mutations in genes that alter the architecture of the bacterial envelope at two levels: the capsule and the outer membrane. Using an adsorption assay, we confirmed that phage Phab24 uses the bacterial capsule as its primary receptor, with the outer membrane possibly serving as the secondary receptor. Interestingly, the phage-resistant isolates were less virulent compared to the parental strains in a Galleria mellonella infection model. Most importantly, we observed that phage-resistant bacteria that evolved in the absence of antibiotics exhibited an increased sensitivity to colistin, even though the antibiotic resistance mechanism per se remained unaltered. This increase in antibiotic sensitivity is a direct consequence of the phage-resistance mechanism, and could potentially be exploited in the clinical setting.
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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".