Progranulin promotes neurite outgrowth and neuronal differentiation by regulating GSK-3β
Bibliographic record
Abstract
Progranulin (PGRN) has recently emerged as a key player in a subset of frontotemporal dementias (FTD). Numerous mutations in the progranulin gene have been identified in patients with familial or sporadic frontotemporal lobar degeneration (FTLD). In order to understand the molecular mechanisms by which PGRN deficiency leads to FTLD, we examined activity of PGRN in mouse cortical and hippocampal neurons and in human neuroblastoma SH-SY5Y cells. Treatment of mouse neurons with PGRN protein resulted in an increase in neurite outgrowth, supporting the role of PGRN as a neurotrophic factor. PGRN treatment stimulated phosphorylation of glycogen synthase kinase-3 beta (GSK-3β) in cultured neurons. Knockdown of PGRN in SH-SY5Y cells impaired retinoic acid induced differentiation and reduced the level of phosphorylated GSK-3β. PGRN knockdown cells were also more sensitized to staurosporine-induced apoptosis. These results reveal an important role of PGRN in neurite outgrowth and involvement of GSK-3β in mediating PGRN activity. Identification of GSK-3β activation as a downstream event for PGRN signaling provides a mechanistic explanation for PGRN activity in the nervous system. Our work also suggest that loss of axonal growth stimulation during neural injury repair or deficits in axonal repair may contribute to neuronal damage or axonal loss in FTLD associated with PGRN mutations. Finally, our study suggests that modulating GSK-3β or similar signaling events may provide therapeutic benefits for FTLD cases associated with PGRN mutations.
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 | 0.000 | 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".