Post‐transcriptional regulation of the U3 small nucleolar RNA
Bibliographic record
Abstract
A high copy shuttle vector was used to express a “tagged” U3 snoRNA gene in Schizosaccharomyces pombe to examine regulatory responses to a high gene dosage. RNA analyses utilizing RT‐PCR amplification and restriction fragment length polymorphism indicated that the “tagged” gene was both proportionally and highly expressed and that downstream processing and/or termination were critical to U3 snoRNA stability. In contrast, direct measurements of the total cellular U3 snoRNA showed essentially normal levels of mature RNA although measurements of precursor levels confirmed a highly expressed gene construct. Taken together the results indicated that the steady state amounts of mature U3 snoRNA were primarily regulated at the post‐transcriptional level. This regulatory mechanism prevents an over accumulation of the cellular U3 snoRNA and can efficiently degrade mutant RNA molecules. Together with past studies on other 3’ extended RNA precursors, the results support post‐transcriptional regulation as a quality control mechanism in which appropriate amounts of functional RNA are stabilized by protein interaction while excess or defective RNA is rapidly degraded. Precursor processing in vitro and mutational analyses were consistent with this model. Supported by the Natural Sciences and Engineering Research Council of Canada.
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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 | 0.001 |
| 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".