Insights into the molecular role of Progranulin: lessons learnt from small model organisms
Bibliographic record
Abstract
Progranulin (PGRN) is a broadly expressed trophic factor in which loss-of-function mutations lead to Frontotemporal dementia (FTD), a devastating neurodegenerative disease with no known cure or therapy. PGRN has been shown to regulate a number of cellular functions, one of which is lysosomal function. However, the mechanistic link between the two remains unclear. Here, we have turned to the nematode, Caenorhabditis elegans, to better understand the cellular and molecular disturbances that lead to PGRN pathologies and as a drug screening tool to identify new, potential therapies for this disease.In our search for a link between PGRN and lysosomal function, we have found that many enzymes in the sphingolipid (SL) biosynthetic pathway appear to be important. We demonstrate that the RNAi knockdown of multiple genes involved in SL metabolism restore lysosomal defects in nematodes in vivo. Interestingly, the knockdown of some of these enzymes also restore other phenotypes, such as autophagy and motility, to WT levels suggesting an important role for SLs in PGRN pathology. This was especially true for cgt-3 and asah-1, the worm homologs of the mammalian enzymes UGCG and ASAH1 respectively, whose knockdowns restored all tested phenotypes. We further used these nematodes as an in vivo tool for an unbiased, high-throughput drug screen and validated our hits in PGRN-deficient cells. We identified two promising drug candidates, rottlerin and rivastigmine, that were able to restore WT levels of many phenotypes in the nematodes, both individually and in combination.Our work not only provides important insight into PGRN’s function, but presents two promising drug candidates deserving of further testing, opens to door to other therapeutic avenues, and helps identify broader indications for them, beyond FTD
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".