Branched-chain amino acid and lysine deficiencies exert different effects on mammary translational regulation
Bibliographic record
Abstract
Deficiencies and imbalances of specific group II essential amino acids (EAA) were created in lactating cows by an infusion subtraction protocol to explore effects on milk production and abundance and phosphorylation state of regulators of mRNA translation in the mammary glands. Five lactating cows on a diet of 11.2% crude protein were infused abomasally for 5d with saline, 563 g/d of a complete EAA mix, or EAA mixes without the branched-chain amino acids (BCAA), Leu, or Lys in a 5 × 5 Latin square design. Milk protein yield was stimulated by EAA infusion and returned to saline levels upon subtraction of BCAA, Leu, or Lys. Mammary abundance of phosphorylated S6K1 was measured as an indicator of mammalian target of rapamycin complex 1 (mTORC1) activity and was found not to be affected by the complete EAA mix but was increased by the mixture lacking Lys. Total S6K1 abundances in mammary tissue were elevated by complete and BCAA-lacking infusions. All of the EAA treatments except the one lacking BCAA upregulated mammary eIF2Bε and eIF2α abundances, which is stimulatory to global mRNA translation. Phosphorylation state of eIF2Bε tended to decrease when complete or Lys-lacking EAA mixtures were infused. Phosphorylation state of eIF2α was not affected by treatment. We detected a correlation of 0.62 between phosphorylation state of S6K1 and total eIF2Bε abundance, and a correlation of 0.58 between phosphorylation state of S6K1 and total eIF2α abundance, suggesting that mTORC1 activation may have upregulated eIF2Bε and eIF2α expression. Despite maintenance of mammary eIF2Bε and eIF2α abundances during Leu and Lys deficiencies, milk protein yield declined, suggesting that other factors are responsible for mediating effects of Lys and Leu. A deficiency of all 3 BCAA may impair milk protein yield through deactivation of mTORC1-mediated upregulation of eIF2Bε and eIF2α abundances.
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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 | 0.000 |
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".