Essential roles for the splicing regulator nSR100/SRRM4 during nervous system development
Bibliographic record
Abstract
Alternative splicing (AS) generates vast transcriptomic complexity in the vertebrate nervous system. However, the extent to which trans-acting splicing regulators and their target AS regulatory networks contribute to nervous system development is not well understood. To address these questions, we generated mice lacking the vertebrate- and neural-specific Ser/Arg repeat-related protein of 100 kDa (nSR100/SRRM4). Loss of nSR100 impairs development of the central and peripheral nervous systems in part by disrupting neurite outgrowth, cortical layering in the forebrain, and axon guidance in the corpus callosum. Accompanying these developmental defects are widespread changes in AS that primarily result in shifts to nonneural patterns for different classes of splicing events. The main component of the altered AS program comprises 3- to 27-nucleotide (nt) neural microexons, an emerging class of highly conserved AS events associated with the regulation of protein interaction networks in developing neurons and neurological disorders. Remarkably, inclusion of a 6-nt, nSR100-activated microexon in Unc13b transcripts is sufficient to rescue a neuritogenesis defect in nSR100 mutant primary neurons. These results thus reveal critical in vivo neurodevelopmental functions of nSR100 and further link these functions to a conserved program of neuronal microexon splicing.
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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".