Superior Conjugative Plasmids Delivered by Bacteria to Diverse Fungi
Bibliographic record
Abstract
Fungi are nature's recyclers, allowing for ecological nutrient cycling and, in turn, the continuation of life on Earth.Some fungi inhabit the human microbiome where they can provide health benefits, while others are opportunistic pathogens that can cause disease.Yeasts, members of the fungal kingdom, have been domesticated by humans for the production of beer, bread, and, recently, medicine and chemicals.Still, the great untapped potential exists within the diverse fungal kingdom.However, many yeasts are intractable, preventing their use in biotechnology or in the development of novel treatments for pathogenic fungi.Therefore, as a first step for the domestication of new fungi, an efficient DNA delivery method needs to be developed.Here, we report the creation of superior conjugative plasmids and demonstrate their transfer via conjugation from bacteria to 7 diverse yeast species including the emerging pathogen Candida auris.To create our superior plasmids, derivatives of the 57 kb conjugative plasmid pTA-Mob 2.0 were built using designed gene deletions and insertions, as well as some unintentional mutations.Specifically, a cluster mutation in the promoter of the conjugative gene traJ had the most significant effect on improving conjugation to yeasts.In addition, we created Golden Gate assembly-compatible plasmid derivatives that allow for the generation of custom plasmids to enable the rapid insertion of designer genetic cassettes.Finally, we demonstrated that designer conjugative plasmids harboring engineered restriction endonucleases can be used as a novel antifungal agent, with important applications for the development of next-generation antifungal therapeutics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".