Neuronal-specific microexon splicing of <i>TAF1</i> mRNA is directly regulated by SRRM4/nSR100
Bibliographic record
Abstract
Neuronal microexons represent the most highly conserved class of alternative splicing events and their timed expression shapes neuronal biology, including neuronal commitment and differentiation. The six-nt microexon 34ʹ is included in the neuronal form of TAF1 mRNA, which encodes the largest subunit of the basal transcription factor TFIID. In this study, we investigate the tissue distribution of TAF1-34ʹ mRNA and protein and the mechanism responsible for its neuronal-specific splicing. Using isoform-specific RNA probes and antibodies, we observe that canonical TAF1 and TAF1-34ʹ have different distributions in the brain, which distinguish proliferating from post-mitotic neurons. Knockdown and ectopic expression experiments demonstrate that the neuronal-specific splicing factor SRRM4/nSR100 promotes the inclusion of microexon 34ʹ into TAF1 mRNA, through the recognition of UGC sequences in the poly-pyrimidine tract upstream of the regulated microexon. These results show that SRRM4 regulates temporal and spatial expression of alternative TAF1 mRNAs to generate a neuronal-specific TFIID complex.
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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".