Translation attenuation via 3′ terminal codon usage in bovine <i>csn1s2</i> is responsible for the difference in αs2‐ and β‐casein profile in milk
Bibliographic record
Abstract
α s2 ‐casein mRNA ( csn1s2 ) is translated at 25% of the efficiency of β‐casein transcripts ( csn2 ); however, the molecular mechanisms governing the difference are unknown. The main objective of this study was to identify molecular mechanisms that explain differential translational regulation between bovine β‐ and α s2 ‐ casein by assessing the role of putative translational regulatory factors in both cellular and cell‐free translation systems. Sequence analysis indicated that the two transcripts share similar primary and secondary structures around the coding region. Deleting and exchanging untranslated regions (UTRs) on the transcripts suggested that the 3′ UTR of csn2 and the 5′ UTR of csn1s2 exert stimulatory effects on translation yet their effectiveness depends on the upstream and downstream sequences with which they are associated. A stronger effect on translational efficiency was found in the coding region of csn1s2 which displays unfavourable codons at the 3′ terminus. Deletion of a 28‐codon fragment from the 3′ terminus of the csn1s2 coding region increased translation to a par with csn2 . We conclude that the last 28 codons of csn1s2 is the main regulatory element that attenuates its expression and is responsible for the different translational expression of β‐ and α s2 ‐ casein mRNA. This research was supported by NSERC Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".