Nutrient availability and lactogenic hormones regulate mammary protein synthesis through the mammalian target of rapamycin signaling pathway
Bibliographic record
Abstract
The nutritional and endocrine factors affecting protein translation in the bovine mammary gland, and the molecular mechanisms mediating their effects, are not well understood. The objective of this study was to assess the role of the mammalian target of rapamycin (mTOR) signaling pathway in the regulation of mammary protein synthesis by nutrients and hormones. Mammary epithelial acini isolated from lactating dairy cows were treated in medium containing, alone or in combination, a mixture of AA or glucose and acetate (GA) as energy substrates, or a combination of the lactogenic hormones hydrocortisone, insulin, and prolactin (HIP). Changes in the rate of mammary protein synthesis and the phosphorylation state of components of the mTOR signaling pathway were measured. Mammary protein synthesis was 50% higher with increased availability of AA in medium. The presence of GA or treatment of mammary acini with HIP alone did not affect mammary protein synthesis. The stimulation of mammary protein synthesis by AA was enhanced by HIP treatment, but not by the presence of GA in medium. Treatment of mammary acini with HIP induced the phosphorylation of protein kinase B. This effect was augmented in the presence of either AA or GA in medium. The stimulation of mammary protein synthesis by AA and its enhancement by HIP were associated with increased phosphorylation of the mTOR substrates, p70 ribosomal protein S6 kinase-1, and eukaryotic initiation factor 4E (eIF4E)-binding protein-1 (4E-BP1), and dissociation of 4E-BP1 from eIF4E. The results suggest that nutrients and hormones may modulate mammary protein synthesis through the mTOR signaling pathway.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".