A Global Kinase and Phosphatase Interaction Network in the Budding Yeast Reveals Novel Effectors of the Target of Rapamycin (TOR) Pathway
Bibliographic record
Abstract
In the budding yeast Saccharomyces cerevisiae, the evolutionarily conserved Target of Rapamycin (TOR) signaling network regulates cell growth in accordance with nutrient and stress conditions. In this work, I present evidence that the TOR complex 1 (TORC1)-interacting proteins Nnk1, Fmp48, Mks1, and Sch9 link TOR to various facets of nitrogen metabolism and mitochondrial function. The Nnk1 kinase controlled nitrogen catabolite repression-sensitive gene expression via Ure2 and Gln3, and physically interacted with the NAD+-linked glutamate dehydrogenase Gdh2 that catalyzes deamination of glutamate to alpha-ketoglutarate and ammonia. In turn, Gdh2 modulated rapamycin sensitivity, was phosphorylated in Nnk1 immune complexes in vitro, and was relocalized to a discrete cytoplasmic focus in response to NNK1 overexpression or respiratory growth. The Fmp48 kinase regulated respiratory function and mitochondrial morphology, while Mks1 linked TORC1 to the mitochondria-to-nucleus retrograde signaling pathway. The Sch9 kinase appeared to act as both an upstream regulator and downstream sensor of mitochondrial function. Loss of Sch9 conferred a respiratory growth defect, a defect in mitochondrial DNA transmission, lower mitochondrial membrane potential, and decreased levels of reactive oxygen species. Conversely, loss of mitochondrial DNA caused loss of Sch9 enrichment at the vacuolar membrane, loss of Sch9 phospho-isoforms, and small cell size suggestive of reduced Sch9 activity. Sch9 also exhibited dynamic relocalization in response to stress, including enrichment at mitochondria under conditions that have previously been shown to induce apoptosis in yeast. Taken together, this work reveals intimate connections between TORC1, nitrogen metabolism, and mitochondrial function, and has implications for the role of TOR in regulating aging, cancer, and other human diseases.
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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) | 0.000 | 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) | 0.001 | 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".