Complementation of Yeast Genes with Human Genes as an Experimental Platform for Functional Testing of Human Genetic Variants
Bibliographic record
Abstract
While the pace of discovery of human genetic variants in tumors, patients, and diverse populations has rapidly accelerated, deciphering their functional consequence has become rate-limiting. Using cross-species complementation, model organisms like the budding yeast, Saccharomyces cerevisiae, can be utilized to fill this gap and serve as a platform for testing human genetic variants. To this end, we performed two parallel screens, a one-to-one complementation screen for essential yeast genes implicated in chromosome instability and a pool-to-pool screen that queried all possible essential yeast genes for rescue of lethality by all possible human homologs. Our work identified 65 human cDNAs that can replace the null allele of essential yeast genes, including the nonorthologous pair yRFT1/hSEC61A1. We chose four human cDNAs (hLIG1, hSSRP1, hPPP1CA, and hPPP1CC) for which their yeast gene counterparts function in chromosome stability and assayed in yeast 35 tumor-specific missense mutations for growth defects and sensitivity to DNA-damaging agents. This resulted in a set of human-yeast gene complementation pairs that allow human genetic variants to be readily characterized in yeast, and a prioritized list of somatic mutations that could contribute to chromosome instability in human tumors. These data establish the utility of this cross-species experimental approach.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".