Multiplex Genome Editing in Yeast by CRISPR/Cas9 – A Potent and Agile Tool to Reconstruct Complex Metabolic Pathways
Bibliographic record
Abstract
Numerous important pharmaceuticals and nutraceuticals originate from plant specialized metabolites, most of which are synthesized via complex biosynthetic pathways. The elucidation of these pathways is critical for the applicable uses of these compounds. Although the rapid progress of the omics technology has revolutionized the identification of candidate genes involved in these pathways, the functional characterization of these genes remains a major bottleneck. Baker’s yeast ( Saccharomyces cerevisiae ) has been used as a microbial platform for characterizing newly discovered metabolic genes in plant specialized metabolism. Using yeast for the investigation of numerous plant enzymes is a streamlined process because of yeast’s efficient transformation, limited endogenous specialized metabolism, partially sharing its primary metabolism with plants, and its capability of post-translational modification. Despite these advantages, reconstructing complex plant biosynthetic pathways in yeast can be time intensive. Since its discovery, CRISPR/Cas9 has greatly stimulated metabolic engineering in yeast. Yeast is a popular system for genome editing due to its efficient homology-directed repair mechanism, which allows precise integration of heterologous genes into its genome. One practical use of CRISPR/Cas9 in yeast is multiplex genome editing aimed at reconstructing complex metabolic pathways. This system has the capability of integrating multiple genes of interest in a single transformation, simplifying the reconstruction of complex pathways. As plant specialized metabolites usually have complex multigene biosynthetic pathways, the multiplex CRISPR/Cas9 system in yeast is suited well for functional genomics research in plant specialized metabolism. Here, we review the most advanced methods to achieve efficient multiplex CRISPR/Cas9 editing in yeast. We will also discuss how this powerful tool has been applied to benefit the study of plant specialized metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".