Using genetic buffering relationships identified in fission yeast to reveal susceptibilities in cells lacking hamartin or tuberin function
Bibliographic record
Abstract
Tuberous sclerosis complex is an autosomal dominant disorder characterized by benign tumors arising from the abnormal activation of mTOR signaling in cells lacking TSC1 (hamartin) or TSC2 (tuberin) activity. To expand the genetic framework surrounding this group of growth regulators, we utilized the model eukaryote Schizosaccharomyces pombe to uncover and characterize genes that buffer the phenotypic effects of mutations in the orthologous tsc1 or tsc2 loci. Our study identified two genes: fft3 (encoding a DNA helicase) and ypa1 (encoding a peptidyle-prolyl cis/trans isomerase). While the deletion of fft3 or ypa1 has little effect in wild-type fission yeast cells, their loss in tsc1Δ or tsc2Δ backgrounds results in severe growth inhibition. These data suggest that the inhibition of Ypa1p or Fft3p might represent an “Achilles’ heel” of cells defective in hamartin/tuberin function. Furthermore, we demonstrate that the interaction between tsc1/tsc2 and ypa1 can be rescued through treatment with the mTOR inhibitor, torin-1, and that ypa1Δ cells are resistant to the glycolytic inhibitor, 2-deoxyglucose. This identifies ypa1 as a novel upstream regulator of mTOR and suggests that the effects of ypa1 loss, together with mTOR activation, combine to result in a cellular maladaptation in energy metabolism that is profoundly inhibitory to growth.
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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) | 0.001 | 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) | 0.001 | 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".