Target of rapamycin signaling in pea embryos is dependent on glutamine but detached from seed storage protein biosynthesis
Bibliographic record
Abstract
Target of rapamycin (TOR) kinase is the hub of a eukaryotic master signaling network that integrates nutritional and hormonal signals into cellular activities. Most studies on TOR in plants have focused on seedlings, where TOR is most responsive to light and sucrose. Here, we observed differences in nutrient regulation of TOR across plant tissues. Biochemical analyses highlighted the predominance of Gln-TOR signaling in mature Arabidopsis leaves and developing pea seeds, and its integration with hormone signaling and amino acid metabolism. Phosphoproteomic and transcriptomic analysis of developing pea seeds identified established and novel components of TOR signaling, which were enriched for proteins/genes regulating gene expression and autophagy. Unexpectedly, Gln-TOR signaling in pea embryos inhibited or delayed growth and protein accumulation during seed filling. A developmental profile was evident wherein high TOR activity and Gln levels during pea cotyledon cellularization reduced sharply as embryos progressed to seed filling. We observed strong interactions between TOR and abscisic acid (ABA) signaling such that TOR-inhibited embryos were hypersensitive to ABA-induced protein accumulation. We propose that legume seed storage protein biosynthesis displays atypical regulatory properties because it occurs in the face of increasing desiccation stress and is promoted by ABA signaling rather than TOR signaling.
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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) | 0.002 | 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".