Temperate phage-antibiotic synergy across antibiotic classes reveals new mechanism for preventing lysogeny
Bibliographic record
Abstract
Abstract A recent demonstration of potent synergy between a temperate phage and the antibiotic ciprofloxacin suggested a scalable approach to exploiting temperate phages in therapy, termed temperate phage-antibiotic synergy (tPAS), which specifically interacted with the lysis-lysogeny decision. To determine whether this would hold true more broadly across antibiotics, we challenged Escherichia coli K12 with the well-studied model λ-like phage HK97 and a set of 13 antibiotics spanning seven classes. As expected, given the conserved induction pathway targeted, we observed synergy with classes of drugs known to induce an SOS response; a sulfa drug, other quinolones, Mitomycin C, and some β-lactams. Curiously, we observed an equally potent synergy with antibiotics not known to induce the SOS response: protein synthesis inhibitors gentamicin, kanamycin, tetracycline, and azithromycin. The synergy results in an 8-fold reduction in the effective MIC of gentamicin, complete eradication of the bacteria, and, when deliberately administered at sub-optimal doses, drastically decreases the frequency of lysogens emerging from the combined challenge – more so even than was previously described for ciprofloxacin. However, unlike ciprofloxacin, lysogens exhibit no increased sensitivity to the antibiotic, synergy was maintained in the absence of RecA, and, when quantified over time, the antibiotic reduced the initial frequency of lysogeny rather than result in selection against formed lysogens. Not only do our results confirm that SOS-inducing antibiotics broadly result in temperate-phage specific synergy, but we other antibiotics can interact with temperate phages specifically and result in potent synergy. This is the first report of a means of chemically blocking entry into lysogeny, providing a new means for manipulating the key lysis-lysogeny decision point in temperate phages.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".