Novel regulators of heparan sulfate proteoglycans modulate cellular uptake of α-synuclein fibrils
Bibliographic record
Abstract
Abstract Synucleinopathies are characterized by the accumulation and propagation of α-synuclein (α-syn) aggregates throughout the brain, leading to neuronal dysfunction and death. In this study, we used an unbiased FACS-based genome-wide CRISPR/Cas9 knockout screening to identify genes that regulate the entry and accumulation of α-syn preformed fibrils (PFFs) in cells. We identified key genes and pathways specifically implicated in α-syn PFFs intracellular accumulation, including heparan sulfate proteoglycans (HSPG) biosynthesis and Golgi trafficking. All confirmed hits affected heparan sulfate (HS), a post-translational modification known to act as a receptor for proteinaceous aggregates including α-syn and tau. Intriguingly, deletion of SLC39A9 and C3orf58 genes, encoding respectively a Golgi-localized exporter of Zn2+, and the Golgi-localized putative kinase DIPK2A, specifically impaired the uptake of α-syn PFFs, by preventing the binding of PFFs to the cell surface. Mass spectrometry-based analysis of HS chains in SLC39A9 -/- and C3orf58 -/- cells indicated major defects in HS homeostasis. Additionally, Golgi accumulation of NDST1, a prime HSPG biosynthetic enzyme, was detected in C3orf58 -/- cells. Interestingly, C3orf58 -/- human iPSC-derived microglia and dopaminergic neurons exhibited a strong reduction in their ability to internalize α-syn PFFs. Altogether, our data identifies new modulators of HSPGs that regulate α-syn PFFs cell surface binding and uptake.
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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".