Regulation of Amino Acid Transporters and Sensors in Response to a High protein Diet: A Randomized Controlled Trial in Elderly Men
Bibliographic record
Abstract
BACKGROUND: The mammalian target of rapamycin complex 1 (mTORC1) is fundamental for many cellular processes, yet it is often dysregulated with aging. Increased amino acid (AA) availability is correlated with the expression of AA transporters (AAT) and mTORC1 activity. Although many AA sensors and mediators have been proposed to relay the AA signal to mTORC1, it has not yet been determined if chronic dietary intervention affects the expression of AAT, sensors and mediators and their relationships with mTORC1 activity. OBJECTIVE AND DESIGN: This study investigated whether the consumption of a diet containing either the current recommended daily allowance (RDA) of protein intake (0.8 g/kg/d) or twice the RDA (2RDA) for ten weeks affected the expression of targets associated with AA transport, sensing and mTORC1 regulation in 26 older men (70-81 years). METHOD: Muscle biopsies were collected before and after the intervention under fasting conditions. Diets were controlled by providing fully prepared meals and snacks. Western blot and quantitative polymerase chain reaction were used to measure protein and gene expression respectively. RESULTS: Consumption of 2RDA reduced the protein expression of L-type amino acid transporter 1 (LAT1). However, plasma leucine concentration and basal mTORC1 activity were unaltered. The downregulation of LAT1 did not affect the expression of AA sensors and mediators, including leucyl tRNA synthetase (LRS), cytosolic arginine sensor for mTORC1 (CASTOR1), Sestrin2 and Rag proteins. Instead, total ribosomal protein S6 (RPS6) was upregulated with 2RDA. CONCLUSION: Ten weeks of 2RDA diet did not affect the fasting mTORC1 signaling, but increased total RPS6 might suggest improved muscular translational capacity to maintain muscular mass.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".