Identification and characterization of TOR in Macrobrachium rosenbergii and its role in muscle protein and lipid production
Bibliographic record
Abstract
The recent scarcity of fishmeal and other resources means that studies on the intrinsic mechanisms of nutrients in the growth and development of aquatic animals at the molecular level have received widespread attention. The target of rapamycin (TOR) pathway has been reported to receive signals from nutrients and environmental stresses, and regulates cellular anabolism and catabolism to achieve precise regulation of cell growth and physiological activities. In this study, we cloned and characterized the full-length cDNA sequence of the TOR gene of Macrobrachium rosenbergii (MrTOR). MrTOR was expressed in all tissues, with higher expression in heart and muscle tissues. In situ hybridization also indicated that MrTOR was expressed in muscle, mainly around the nucleus. RNA interference decreased the expression levels of MrTOR and downstream protein synthesis-related genes (S6K, eIF4E, and eIF4B) (P < 0.05) and the expression and enzyme activity of the lipid synthesis-related enzyme, fatty acid synthase (FAS), and increased enzyme activity of the lipolysis-related enzyme, lipase (LPS). In addition, amino acid injection significantly increased the transcript levels of MrTOR and downstream related genes (S6K, eIF4E, eIF4B, and FAS), as well as triglyceride and total cholesterol tissue levels and FAS activity. Starvation significantly increased transcript levels and enzyme activities of adenylate-activated protein kinase and LPS and decreased transcript levels and enzyme activities of FAS, as well as transcript levels of MrTOR and its downstream genes (P < 0.05), whereas amino acid injection alleviated the starvation-induced decreases in transcript levels of these genes. These results suggested that arginine and leucine activated the TOR signaling pathway, promoted protein and lipid syntheses, and alleviated the pathway changes induced by starvation.
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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) | 0.000 | 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".