The Green Monster Process for the Generation of Yeast Strains Carrying Multiple Gene Deletions
Bibliographic record
Abstract
Phenotypes for a gene deletion are often revealed only when the mutation is tested in a particular genetic background or environmental condition(1,2). There are examples where many genes need to be deleted to unmask hidden gene functions(3,4). Despite the potential for important discoveries, genetic interactions involving three or more genes are largely unexplored. Exhaustive searches of multi-mutant interactions would be impractical due to the sheer number of possible combinations of deletions. However, studies of selected sets of genes, such as sets of paralogs with a greater a priori chance of sharing a common function, would be informative. In the yeast Saccharomyces cerevisiae, gene knockout is accomplished by replacing a gene with a selectable marker via homologous recombination. Because the number of markers is limited, methods have been developed for removing and reusing the same marker(5,6,7,8,9,10). However, sequentially engineering multiple mutations using these methods is time-consuming because the time required scales linearly with the number of deletions to be generated. Here we describe the Green Monster method for routinely engineering multiple deletions in yeast(11). In this method, a green fluorescent protein (GFP) reporter integrated into deletions is used to quantitatively label strains according to the number of deletions contained in each strain (Figure 1). Repeated rounds of assortment of GFP-marked deletions via yeast mating and meiosis coupled with flow-cytometric enrichment of strains carrying more of these deletions lead to the accumulation of deletions in strains (Figure 2). Performing multiple processes in parallel, with each process incorporating one or more deletions per round, reduces the time required for strain construction. The first step is to prepare haploid single-mutants termed 'ProMonsters,' each of which carries a GFP reporter in a deleted locus and one of the 'toolkit' loci-either Green Monster GMToolkit-a or GMToolkit-α at the can1Δ locus (Figure 3). Using strains from the yeast deletion collection(12), GFP-marked deletions can be conveniently generated by replacing the common KanMX4 cassette existing in these strains with a universal GFP-URA3 fragment. Each GMToolkit contains: either the a- or α-mating-type-specific haploid selection marker(1) and exactly one of the two markers that, when both GMToolkits are present, collectively allow for selection of diploids. The second step is to carry out the sexual cycling through which deletion loci can be combined within a single cell by the random assortment and/or meiotic recombination that accompanies each cycle of mating and sporulation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".