Faculty Opinions recommendation of Metabolite regulatory interactions control plant respiratory metabolism via target of rapamycin (TOR) kinase activation.
Bibliographic record
Abstract
Respiration rate measurements provide an important readout of energy expenditure and mitochondrial activity in plant cells during the night.As plants inhabit a changing environment, regulatory mechanisms must ensure that respiratory metabolism rapidly and effectively adjusts to the metabolic and environmental conditions of the cell.Using a high-throughput approach, we have directly identified specific metabolites that exert transcriptional, translational, and posttranslational control over the nighttime O 2 consumption rate (R N ) in mature leaves of Arabidopsis (Arabidopsis thaliana).Multi-hour R N measurements following leaf disc exposure to a wide array of primary carbon metabolites (carbohydrates, amino acids, and organic acids) identified phosphoenolpyruvate (PEP), Pro, and Ala as the most potent stimulators of plant leaf R N .Using metabolite combinations, we discovered metabolite-metabolite regulatory interactions controlling R N .Many amino acids, as well as Glc analogs, were found to potently inhibit the R N stimulation by Pro and Ala but not PEP.The inhibitory effects of amino acids on Pro-and Ala-stimulated R N were mitigated by inhibition of the Target of Rapamycin (TOR) kinase signaling pathway.Supporting the involvement of TOR, these inhibitory amino acids were also shown to be activators of TOR kinase.This work provides direct evidence that the TOR signaling pathway in plants responds to amino acid levels by eliciting regulatory effects on respiratory energy metabolism at night, uniting a hallmark mechanism of TOR regulation across eukaryotes.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.293 | 0.178 |
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".