High-throughput screening reveals diverse lambdoid prophage inducers
Bibliographic record
Abstract
Prophages, dormant bacteriophage genomes integrated within the bacterial chromosome, play pivotal roles in shaping microbial communities when awakened. Our current understanding of prophage activation is largely shaped by a narrow set of traditional DNA-damaging inducers, such as mitomycin C and ciprofloxacin, which trigger the bacterial SOS response. This study employed high-throughput screening of 3,921 compounds to identify novel prophage inducers using model lambdoid prophage HK97. We identified multiple new inducers across diverse pharmacological classes, including dietary supplements and therapeutics. Despite the variety in compounds, all acted through SOS-dependent pathways. However, bleomycin, an antineoplastic antibiotic, demonstrated broad-spectrum and potent prophage induction exceeding standard inducers, with activity validated across multiple phage-host pairings. These findings expand the repertoire of prophage inducers into commonly ingested xenobiotics and introduce bleomycin as a powerful, cost-effective tool for prophage research. IMPORTANCE: Around 75% of bacteria carry within them dormant viruses (prophages), which can awaken when the bacterium is stressed, killing the bacterium. Historically, this has been done using DNA-damaging antibiotics, but increasingly, more such signals have been discovered. Here, through a high-throughput screen, we identify phage-waking activity in several commonly consumed compounds, such as the SSRI Prozac, as well as a new DNA-damaging agent that is much more effective in waking phages than the previous gold standard.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".