Mechanisms of Neuronal Alternative Splicing and Strategies for Therapeutic Interventions
Bibliographic record
Abstract
Many cellular and physiological processes are coordinated by regulatory networks that produce a remarkable complexity of transcript isoforms. In the mammalian nervous system, alternative pre-mRNA splicing generates functionally distinct isoforms that play key roles in normal physiology, supporting development, plasticity, complex behaviors, and cognition. Neuronal splicing programs controlled by RNA-binding proteins, are influenced by chromatin modifications and can exhibit neuronal subtype specificity. As highlighted in recent publications, aberrant alternative splicing is a major contributor to disease phenotypes. Therefore, understanding the underlying mechanisms of alternative splicing regulation and identifying functional splicing isoforms with critical phenotypic roles are expected to provide a comprehensive resource for therapeutic development, as illuminated by recent successful interventions of spinal muscular atrophy. Here, we discuss the latest progress in the study of the emerging complexity of alternative splicing mechanisms in neurons, and how these findings inform new therapies to correct and control splicing defects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".