HyperdCas12a-based multiplexed genetic regulation in <i>Candida albicans</i>
Bibliographic record
Abstract
Complex microbial phenotypes involve the combined activity of diverse gene regulatory networks. However, the majority of reverse genetics approaches in microbial pathogenesis research have focused on single-gene perturbation studies, in part due to the lack of available genetic tools in many pathogens. Developing enhanced versions of CRISPR-Cas platforms holds significant promise for improving the scalability of microbial functional genomics research. Here, we demonstrate highly efficient, inducible, and multiplexed activation and repression in the major human fungal pathogen Candida albicans by translating the hyperdCas12a variant to the fungal kingdom. This represents the first application of a CRISPR-Cas12 system in a human fungal pathogen. We profile the effectiveness of our new CRISPR activation and CRISPR interference tools and achieve tunable levels of target modulation. Further, we demonstrate that perturbing combinations of genes in the drug efflux and ergosterol biosynthesis pathways reveals important redundancies and synergistic properties in drug resistance circuitry. Our hyperdCas12a platform is thus an efficient system for the rapid generation of combinatorial mutants that will enable the mechanistic understanding of genetic interactions involved in diverse phenotypes in C. albicans. The enhanced activity with hyperdCas12a in fungi suggests it could be translated to other microbes as a powerful tool for studying genetic interactions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".