Multiple Molecular Pathways Are Influenced by Progranulin in a Neuronal Cell Model–A Parallel Omics Approach
Bibliographic record
Abstract
Progranulin (PGRN) is critical in supporting a healthy CNS. Its haploinsufficiency results in frontotemporal dementia, while in experimental models of age-related neurodegenerative diseases, the targeted expression of PGRN greatly slows the onset of disease phenotypes. Nevertheless, much remains unclear about how PGRN affects its target cells. In previous studies we found that PGRN showed a remarkable ability to support the survival of NSC-34 motor neuron cells under conditions that would otherwise lead to their apoptosis. Here we used the same model to investigate other phenotypes of PGRN expression in NSC-34 cells. PGRN significantly influenced morphological differentiation, resulting in cells with enlarged cell bodies and extended projections. At a molecular level this correlated with pathways associated with the cytoskeleton and synaptic differentiation. Depletion of PGRN led to increased expression of several neurotrophic receptors, which may represent a homeostatic mechanism to compensate for loss of neurotrophic support from PGRN. The exception was RET, a neurotrophic tyrosine receptor kinase, which, when PGRN levels are high, shows increased expression and enhanced tyrosine phosphorylation. Other receptor tyrosine kinases also showed higher tyrosine phosphorylation when PGRN was elevated, suggesting a generalized enhancement of receptor activity. PGRN was found to bind to multiple plasma membrane proteins, including RET, as well as proteins in the ER/Golgi apparatus/lysosome pathway. Understanding how these various pathways contribute to PGRN action may provide routes toward improving neuroprotective therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".