Burkholderia cepacia Complex Phage-Antibiotic Synergy (PAS): Antibiotics Stimulate Lytic Phage Activity
Bibliographic record
Abstract
The Burkholderia cepacia complex (Bcc) is a group of at least 18 species of Gram-negative opportunistic pathogens that can cause chronic lung infection in cystic fibrosis (CF) patients. Bcc organisms possess high levels of innate antimicrobial resistance, and alternative therapeutic strategies are urgently needed. One proposed alternative treatment is phage therapy, the therapeutic application of bacterial viruses (or bacteriophages). Recently, some phages have been observed to form larger plaques in the presence of sublethal concentrations of certain antibiotics; this effect has been termed phage-antibiotic synergy (PAS). Those reports suggest that some antibiotics stimulate increased production of phages under certain conditions. The aim of this study is to examine PAS in phages that infect Burkholderia cenocepacia strains C6433 and K56-2. Bcc phages KS12 and KS14 were tested for PAS, using 6 antibiotics representing 4 different drug classes. Of the antibiotics tested, the most pronounced effects were observed for meropenem, ciprofloxacin, and tetracycline. When grown with subinhibitory concentrations of these three antibiotics, cells developed a chain-like arrangement, an elongated morphology, and a clustered arrangement, respectively. When treated with progressively higher antibiotic concentrations, both the sizes of plaques and phage titers increased, up to a maximum. B. cenocepacia K56-2-infected Galleria mellonella larvae treated with phage KS12 and low-dose meropenem demonstrated increased survival over controls treated with KS12 or antibiotic alone. These results suggest that antibiotics can be combined with phages to stimulate increased phage production and/or activity and thus improve the efficacy of bacterial killing.
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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) | 0.002 | 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".