AU-rich elements and the control of gene expression through regulated mRNA stability
Bibliographic record
Abstract
The regulation of gene expression is a fundamental cellular process that is controlled at multiple levels. Abnormal regulation of gene expression has been directly implicated in the pathogenesis of some diseases of animals and humans and may contribute to the disease process in unrecognized ways in many others. Furthermore, novel treatment strategies for a number of different diseases may hinge upon our ability to exploit mechanisms that normally alter the expression of endogenous genes. While the study of gene regulation has traditionally focused on transcription as a major regulator of gene expression, it has recently become apparent that the post-transcriptional control of gene expression may play an equally important role. In particular, rapid, context-specific regulation of the stability of mRNA transcripts encoding highly active proteins, such as cytokines, growth factors, oncogenes and cell-cycle regulators, appears to play a key role in the control of these molecules and the processes they mediate. Many of the known regulatory pathways for mRNA stability involve proteins that interact with specific AU-rich elements in the 3'-untranslated region of the transcript. This review will address some important aspects of the normal regulation of mRNA stability and known or potential contributions of RNA stability regulation to health and disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".