Target of Rapamycin Signaling Regulates Metabolism, Growth, and Life Span in <i>Arabidopsis</i>
Bibliographic record
Abstract
Target of Rapamycin (TOR) is a major nutrition and energy sensor that regulates growth and life span in yeast and animals. In plants, growth and life span are intertwined not only with nutrient acquisition from the soil and nutrition generation via photosynthesis but also with their unique modes of development and differentiation. How TOR functions in these processes has not yet been determined. To gain further insights, rapamycin-sensitive transgenic Arabidopsis thaliana lines (BP12) expressing yeast FK506 Binding Protein12 were developed. Inhibition of TOR in BP12 plants by rapamycin resulted in slower overall root, leaf, and shoot growth and development leading to poor nutrient uptake and light energy utilization. Experimental limitation of nutrient availability and light energy supply in wild-type Arabidopsis produced phenotypes observed with TOR knockdown plants, indicating a link between TOR signaling and nutrition/light energy status. Genetic and physiological studies together with RNA sequencing and metabolite analysis of TOR-suppressed lines revealed that TOR regulates development and life span in Arabidopsis by restructuring cell growth, carbon and nitrogen metabolism, gene expression, and rRNA and protein synthesis. Gain- and loss-of-function Ribosomal Protein S6 (RPS6) mutants additionally show that TOR function involves RPS6-mediated nutrition and light-dependent growth and life span in Arabidopsis.
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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 | 0.001 |
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".