Activity-dependent secretion of progranulin from synapses
Bibliographic record
Abstract
The secreted growth factor progranulin (PGRN) has been shown to be important for regulating neuronal survival and outgrowth, as well as synapse formation and function. Mutations in the PGRN gene that result in PGRN haploinsufficiency have been identified as a major cause of frontotemporal dementia (FTD). Here we demonstrate that PGRN is colocalized with dense-core vesicle markers and is co-transported with brain-derived neurotrophic factor (BDNF) within axons and dendrites of cultured hippocampal neurons in both anterograde and retrograde directions. We also show that PGRN is secreted in an activity-dependent manner from synaptic and extrasynaptic sites, and that the temporal profiles of secretion are distinct in axons and dendrites. Neuronal activity is also shown to increase the recruitment of PGRN to synapses and to enhance the density of PGRN clusters along axons. Finally, treatment of neurons with recombinant PGRN is shown to increase synapse density, while decreasing the size of the presynaptic compartment and specifically the number of synaptic vesicles per synapse. Together, this indicates that activity-dependent secretion of PGRN can regulate synapse number and structure.
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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".