Abundance and Phosphorylation State of Translation Initiation Factors in Mammary Glands of Lactating and Nonlactating Dairy Cows
Bibliographic record
Abstract
To test if control of mRNA translation is involved in the increase in protein synthesis by mammary glands during lactation, cellular contents and phosphorylation states of translation factors and their upstream regulators were measured in mammary parenchyma from 12 nonpregnant dairy cows. For a 42-d period, 6 cows in late lactation continued to be milked (L) and 6 at the same stage of lactation were dried off (NL). All cows were then slaughtered and mammary glands and tissue samples obtained. Alveoli and lobules tended to be larger in L cows. Mammary parenchymal mass, cell number, cell size, and RNA, DNA, and protein contents were greater in L cows. Increases (3.1- and 1.8-fold) in the abundance of active, phosphorylated ribosomal protein S6 and its kinase, S6K1, respectively, in L vs. NL parenchyma indicated an ability to sustain greater rates of synthesis of translational machinery, which was also evident in the 102% increase in parenchymal RNA:DNA between the 2 groups. Cellular abundances of the main eukaryotic translation initiation factors (eIF), eIF2 and eIF4E, were 2.6- and 3-fold greater, respectively, in L cows. That these differences were greater than the 102% greater RNA:DNA in L mammary parenchyma suggests an elevated translational efficiency in L glands. Abundance of phosphorylated rpS6 was not different between mammary parenchyma and liver, whereas eIF2alpha was 50% greater in mammary tissue. In semimembranosus muscle, abundances of phosphorylated rpS6 and eIF2alpha were 3 to 4 times lower than in mammary parenchyma. In both L and NL mammary glands, 11% of eIF2alpha was in the inhibitory, phosphorylated form and 48 to 60% of eIF4E was complexed with its binding protein, 4EBP1. It is concluded that up-regulation of initiation of mRNA translation occurs in the fully differentiated milk secretory cell and that, where crucial initiation factors are not present in a maximally active form, the initiation rate might be flexible in response to external stimuli.
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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".