SUMO-Modification of the La Protein Facilitates Binding to mRNA In Vitro and in Cells
Bibliographic record
Abstract
The RNA-binding protein La is involved in several aspects of RNA metabolism including the translational regulation of mRNAs and processing of pre-tRNAs. Besides its well-described phosphorylation by Casein kinase 2, the La protein is also posttranslationally modified by the Small Ubiquitin-like MOdifier (SUMO), but the functional outcome of this modification has not been defined. The objective of this study was to test whether sumoylation changes the RNA-binding activity of La. Therefore, we established an in vitro sumoylation assay for recombinant human La and analyzed its RNA-binding activity by electrophoretic mobility shift assays. We identified two novel SUMO-acceptor sites within the La protein located between the RNA recognition motif 1 and 2 and we demonstrate for the first time that sumoylation facilitates the RNA-binding of La to small RNA oligonucleotides representing the oligopyrimidine tract (TOP) elements from the 5' untranslated regions (UTR) of mRNAs encoding ribosomal protein L22 and L37 and to a longer RNA element from the 5' UTR of cyclin D1 (CCND1) mRNA in vitro. Furthermore, we show by RNA immunoprecipitation experiments that a La mutant deficient in sumoylation has impaired RNA-binding activity in cells. These data suggest that modulating the RNA-binding activity of La by sumoylation has important consequences on its functionality.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".