Translation attenuation via 3′ terminal codon usage in bovine<i>csn1s2</i>is responsible for the difference in α<sub>s2</sub>- and β-casein profile in milk
Bibliographic record
Abstract
The rate of secretion of αs2-casein into bovine milk is approximately 25% of that of β-casein, yet mammary expression of their respective mRNA transcripts (csn1s2 and csn2) is not different. Our objective was to identify molecular mechanisms that explain the difference in translation efficiency between csn1s2 and csn2. Cell-free translational efficiency of csn2 was 5 times that of csn1s2. Transcripts of csn1s2 distributed into heavier polysomes than csn2 transcripts, indicating an attenuation of elongation and/or termination. Stimulatory and inhibitory effects of the 5' and 3' UTRs on translational efficiency were different with luciferase and casein sequences in the coding regions. Substituting the 5' and 3' UTRs from csn2 into csn1s2 did not improve csn1s2 translation, implicating the coding region itself in the translation difference. Deletion of a 28-codon fragment from the 3' terminus of the csn1s2 coding region, which displays codons with low correlations to cell fitness, increased translation to a par with csn2. We conclude that the usage of the last 28 codons of csn1s2 is the main regulatory element that attenuates its expression and is responsible for the differential translational expression of csn1s2 and csn2.
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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".