Bacteriophage steering of <i>Burkholderia cenocepacia</i> toward reduced virulence and increased antibiotic sensitivity
Bibliographic record
Abstract
ABSTRACT Antibiotic resistance in bacteria is a growing global concern and has spurred increasing efforts to find alternative therapeutics, such as the use of bacterial viruses, or bacteriophages. One promising approach is to use phages that not only kill pathogenic bacteria but also select phage-resistant survivors that are newly sensitized to traditional antibiotics, in a process called “phage steering.” Members of the bacterial genus Burkholderia , which includes various human pathogens, are highly resistant to most antimicrobial agents, including serum immune components, antimicrobial peptides, and polymixin-class antibiotics. However, the application of phages in combination with certain antibiotics can produce synergistic effects that more effectively kill pathogenic bacteria. Herein, we demonstrate that Burkholderia cenocepacia serum resistance is due to intact lipopolysaccharide (LPS) and membranes, and phage-induced resistance altering LPS structure can enhance bacterial sensitivity not only to immune components in serum but also to membrane-associated antibiotics such as colistin. IMPORTANCE Bacteria frequently encounter selection pressure from both antibiotics and lytic phages, but little is known about the interactions between antibiotics and phages. This study provides new insights into the evolutionary trade-offs between phage resistance and antibiotic sensitivity. The creation of phage resistance through changes in membrane structure or lipopolysaccharide composition can simultaneously be a major cause of antibiotic sensitivity. Our results provide evidence of synergistic therapeutic efficacy in phage-antibiotic interactions and have implications for the future clinical use of phage steering in phage therapy applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".