Neural Differentiation Modulates the Vertebrate Brain Specific Splicing Program
Bibliographic record
Abstract
Alternative splicing patterns are known to vary between tissues but these patterns have been found to be predominantly peculiar to one species or another, implying only a limited function in fundamental neural biology. Here we used high-throughput RT-PCR to monitor the expression pattern of all the annotated simple alternative splicing events (ASEs) in the Reference Sequence Database, in different mouse tissues and identified 93 brain-specific events that shift from one isoform to another (switch-like) between brain and other tissues. Consistent with an important function, regulation of a core set of 9 conserved switch-like ASEs is highly conserved, as they have the same pattern of tissue-specific splicing in all vertebrates tested: human, mouse and zebrafish. Several of these ASEs are embedded within genes that encode proteins associated with the neuronal microtubule network, and show a dramatic and concerted shift within a short time window of human neural stem cell differentiation. Similarly these exons are dynamically regulated in zebrafish development. These data demonstrate that although alternative splicing patterns often vary between species, there is nonetheless a core set of vertebrate brain-specific ASEs that are conserved between species and associated with neural differentiation.
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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 | 0.000 |
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".