Cross-Species Complementation of Nonessential Yeast Genes Establishes Platforms for Testing Inhibitors of Human Proteins
Bibliographic record
Abstract
Abstract Given the broad utility of humanized yeast to model and study human biology, a reference set of human genes that can replace cognate yeast genes and operate in yeast is needed. Hamza et al. present... Cross-species complementation can be used to generate humanized yeast, which is a valuable resource with which to model and study human biology. Humanized yeast can be used as an in vivo platform to screen for chemical inhibition of human protein drug targets. To this end, we report the systematic complementation of nonessential yeast genes implicated in chromosome instability (CIN) with their human homologs. We identified 20 human–yeast complementation pairs that are replaceable in 44 assays that test rescue of chemical sensitivity and/or CIN defects. We selected a human–yeast pair (hFEN1/yRAD27), which is frequently overexpressed in cancer and is an anticancer therapeutic target, to perform in vivo inhibitor assays using a humanized yeast cell-based platform. In agreement with published in vitro assays, we demonstrate that HU-based PTPD is a species-specific hFEN1 inhibitor. In contrast, another reported hFEN1 inhibitor, the arylstibonic acid derivative NSC-13755, was determined to have off-target effects resulting in a synthetic lethal phenotype with yRAD27-deficient strains. Our study expands the list of human–yeast complementation pairs to nonessential genes by defining novel cell-based assays that can be utilized as a broad resource to study human drug targets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".