Engineering conjugative plasmids for inducible horizontal DNA transfer
Bibliographic record
Abstract
Rapidly developing microbial resistance to existing antimicrobials poses a growing threat to public health and global food security. Current chemical-based treatments target cells by inhibiting growth or metabolic function, but their effectiveness is diminishing. To address the growing antimicrobial resistance crisis, there is an urgent need for innovative therapies. Conjugative plasmids, a natural mechanism of horizontal gene transfer in bacteria, have been repurposed to deliver toxic genetic cargo to recipient cells, showing promise as next-generation antimicrobial agents. However, the ecological risks posed by unintended gene transfer require robust biocontainment strategies. In this study, we developed inducible conjugative plasmids to solve these challenges. Utilizing an arabinose-inducible promoter, we evaluated 13 plasmids with single essential gene deletions, identifying trbC and trbF as strong candidates for stringent regulation. These plasmids demonstrated inducibility in both cis and trans configurations, with induction resulting in up to a 5-log increase in conjugation efficiency compared to uninduced conditions. Although challenges such as reduced conjugation efficiency and promoter leakiness persist, this work establishes a foundation for the controlled transfer of plasmids, paving the way for safer and more effective antimicrobial technologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".