To cap it all off, again: dynamic capping and recapping of coding and non-coding RNAs to control transcript fate and biological activity
Bibliographic record
Abstract
The addition of the methyl-7-guanosine (m7G) “cap” on the 5' ends of coding and some non-coding RNAs is essential for their protein coding capacity and biochemical activity, respectively. It was previously considered that capping was a constitutive process that generates a complete cap on all transcripts at steady-state. However, development of new methodologies demonstrated that steady-state capping is a dynamic and regulatable feature of many coding and non-coding RNAs. Indeed, capping status of specific RNAs can flux during differentiation and development, thereby impacting on their protein-coding capacity and activity. Moreover, in some primary cancer specimens, capping can be elevated for transcripts encoding proteins involved in proliferation and survival corresponding to their increased protein levels. Overexpression of one of the capping enzymes (RNMT), the transcription factor MYC or the eukaryotic translation initiation factor eIF4E all led to increased levels of steady-state capping of selected transcripts. Additionally, transcripts can be decapped and recapped, allowing these to be sequestered until needed. This review provides a summary of the major advances in enzymatic and affinity-based approaches to quantify m7G capping. Further, we summarize the evidence for regulation of capping. Capping has emerged as a significant regulatory step in RNA metabolism which is poised to impact a myriad of biological processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".