Bibliographic record
Abstract
In vertebrates, alternative splicing regulates gene expression in a cell- and developmental stage-specific manner, producing functional diversity of the corresponding proteins. Although its importance has been underscored, control mechanisms of alternative splicing are poorly understood. The N-methyl-D-aspartate receptor NR1 subunit (NMDAR1, GRIN1) contains three cassette exons (NI, CI and CII), and which are specifically expressed in mammalian brain. In this study, a minigene splicing reporter system of the CI cassette exon was utilized to study its splicing silencing mechanism in mammalian cell lines. This work focuses primarily on the identification of exonic UAGG motifs and a 5' splice site region G cluster. Individual motifs play a silencing role but the combination of all three is required for strong silencing. Whereas insertion of an extra UAGG motif in the exon shows almost complete silencing, removal of all three silencers results in loss of silencing. Therefore, the UAGG and G cluster motifs confer flexibility of control of CI cassette exon splicing and the number of the motifs determines the silencing strength in various tissues. The UAGG motif interacts with hnRNP A1 and the strong silencing of hnRNP A1 requires the G cluster. The splicing enhancing role of hnRNP F or H/H' is identified, and this effect largely requires the G cluster. Using bioinformatics searches, new groups of skipped exons containing the UAGG and GGGG (as a G cluster) motifs are identified from the human and mouse genomes. This study suggests that hnRNP H1 and H3 (HNRPH1 and HNRPH3) may be auto-regulated at the level of splicing. Overall, this work provides evidence for a splicing silencing mechanism that is important for the tissue-specificity of the CI cassette exon. This work also shows that the motif pattern can be used computationally to identify additional skipped exons that contain combinations of UAGG and GGGG motifs.
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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 | 0.000 |
| 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".