Bibliographic record
Abstract
ABSTRACT Fungal infections are increasingly difficult to treat due to rising antifungal resistance and the limited number of effective drug classes. To address this challenge, we developed a conjugation-based CRISPR antifungal (COBRA) platform that enables delivery of programmable gene-targeting machinery from Escherichia coli to Saccharomyces cerevisiae . We engineered a mobilizable pVenom plasmids containing an oriT for conjugation, Cas9 and guide RNAs for elimination of yeast. To prevent toxicity in E. coli , yeast ACT1 intron is inserted in Cas9 . Seven guide RNAs targeting essential genes involved in cell cycle progression, ribosome function, and DNA replication were first assessed by using electroporation as delivery and demonstrated strong lethality for guides targeting CDC28 and MCM2 . When delivered by conjugation, these guides again reproduced these results, whereas an intron-targeting control remained non-lethal. Dual-guide plasmids eliminated yeast colony formation entirely. Together, these findings demonstrate that conjugation-delivered CRISPR machinery can successfully target endogenous fungal genes and, especially when multiplexed, offers an effective and programmable antifungal strategy. Graphical Abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.019 | 0.007 |
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".