Temperate Phage-Antibiotic Synergy
Bibliographic record
Abstract
The escalating threat of antimicrobial resistance has intensified the exploration of alternative treatments, with bacteriophage (phage) therapy emerging as a potential substitute for antibiotics. While strictly lytic phages rapidly kill bacteria, temperate phages can also go dormant in their hosts. Accordingly, despite their prevalence, they are considered unsuitable for therapy. My systematic review of phage therapy in urinary tract infections (UTIs) highlighted this. This review motivated me to explore how the potential of these phages could be leveraged. Chapter 3 introduces a novel strategy to do so, exploring whether the fluoroquinolone antibiotic ciprofloxacin could synergize with temperate phages. This innovative strategy exploits the ability of the antibiotic to awaken dormant temperate phages, driving a potent synergy (≥8 log reduction) able to result in bacterial eradication. This is a potential breakthrough in the use of phages. Chapter 4 expands on this finding, establishing that a synergy exists across various drug classes with diverse mechanisms of action. Surprisingly, the synergy extends beyond antibiotics triggering the bacterial SOS-response known to wake temperate phages and also includes protein synthesis inhibitors, offering a new approach to influence the phage lysis-lysogeny decision. Chapter 5 explores the identified synergy in antibiotic-resistant models, focusing on the impact of antibiotic resistance on the effect of combining temperate phages with antibiotics. While the majority of cases demonstrated synergy comparable to the absence of antibiotic resistance, an exception was noted in the acetylation-resistant models for both gentamicin and ciprofloxacin. These resistance genes abolished synergy with the temperate phage, emphasizing the importance of the resistance mechanism within temperate phage antibiotic synergy (tPAS). In conclusion, this thesis underscores the lack of interest in temperate phages for therapy and demonstrates a scalable strategy to overcome the major barriers to their use. I uncover the mechanisms underlying the synergy and show that these concepts are applicable even in the context of resistance to the synergizing antibiotic. These findings propose a remarkable shift in how antimicrobial therapy approaches are viewed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".