Regulating the regulators: Serine/arginine‐rich proteins under scrutiny
Bibliographic record
Abstract
Serine/arginine-rich (SR) proteins are among the most studied splicing regulators. They constitute a family of evolutionarily conserved proteins that, apart from their initially identified and deeply studied role in splicing regulation, have been implicated in genome stability, chromatin binding, transcription elongation, mRNA stability, mRNA export and mRNA translation. Remarkably, this list of SR protein activities seems far from complete, as unexpected functions keep being unraveled. An intriguing aspect that awaits further investigation is how the multiple tasks of SR proteins are concertedly regulated within mammalian cells. In this article, we first discuss recent findings regarding the regulation of SR protein expression, activity and accessibility. We dive into recent studies describing SR protein auto-regulatory feedback loops involving different molecular mechanisms such asunproductive splicing, microRNA-mediated regulation and translational repression. In addition, we take into account another step of regulation of SR proteins, presenting new findings about a variety of post-translational modifications by proteomics approaches and how some of these modifications can regulate SR protein sub-cellular localization or stability. Towards the end, we focus in two recently revealed functions of SR proteins beyond mRNA biogenesis and metabolism, the regulation of micro-RNA processing and the regulation of small ubiquitin-like modifier (SUMO) conjugation.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".