The N-terminal Set-β Protein Isoform Induces Neuronal Death
Bibliographic record
Abstract
Set-β protein plays different roles in neurons, but the diversity of Set-β neuronal isoforms and their functions have not been characterized. The expression and subcellular localization of Set-β are altered in Alzheimer disease, cleavage of Set-β leads to neuronal death after stroke, and the full-length Set-β regulates retinal ganglion cell (RGC) and hippocampal neuron axon growth and regeneration in a subcellular localization-dependent manner. Here we used various biochemical approaches to investigate Set-β isoforms and their role in the CNS, using the same type of neurons, RGCs, across studies. We found multiple alternatively spliced isoforms expressed from the Set locus in purified RGCs. Set transcripts containing the Set-β-specific exon were the most highly expressed isoforms. We also identified a novel, alternatively spliced Set-β transcript lacking the nuclear localization signal and demonstrated that the full-length (∼39-kDa) Set-β is localized predominantly in the nucleus, whereas a shorter (∼25-kDa) Set-β isoform is localized predominantly in the cytoplasm. Finally, we show that an N -terminal Set-β cleavage product can induce neuronal death. Background: Set-β protein can suppress or promote neuronal recovery. We investigated Set-β isoforms to understand its different roles in neurons. Results: Neurons express several Set-β transcripts, Set-β proteins localize to different compartments, and the N -terminal Set-β induces neuronal death. Conclusion: N -terminal Set-β induces neuronal death. Other isoforms could play different roles. Significance: Different Set-β isoforms could underlie its diverse roles in neurons.
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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 | 0.001 |
| 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".