Regulating Set-β's Subcellular Localization Toggles Its Function between Inhibiting and Promoting Axon Growth and Regeneration
Bibliographic record
Abstract
The failure of the CNS neurons to regenerate axons after injury or stroke is a major clinical problem. Transcriptional regulators like Set-β are well positioned to regulate intrinsic axon regeneration capacity, which declines developmentally in maturing CNS neurons. Set-β also functions at cellular membranes and its subcellular localization is disrupted in Alzheimer's disease, but many of its biological mechanisms have not been explored in neurons. We found that Set-β was upregulated postnatally in CNS neurons, and was primarily localized to the nucleus but was also detected in the cytoplasm and adjacent to the plasma membrane. Remarkably, nuclear Set-β suppressed, whereas Set-β localized to cytoplasmic membranes promoted neurite growth in rodent retinal ganglion cells and hippocampal neurons. Mimicking serine 9 phosphorylation, as found in Alzheimer's disease brains, delayed nuclear import and furthermore blocked the ability of nuclear Set-β to suppress neurite growth. We also present data on gene regulation and protein binding partner recruitment by Set-β in primary neurons, raising the hypothesis that nuclear Set-β may preferentially regulate gene expression whereas Set-β at cytoplasmic membranes may regulate unique cofactors, including PP2A, which we show also regulates axon growth in vitro. Finally, increasing recruitment of Set-β to cellular membranes promoted adult rat optic nerve axon regeneration after injury in vivo. Thus, Set-β differentially regulates axon growth and regeneration depending on subcellular localization and phosphorylation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".