Glycine-to-aspartic acid mutation at codon 51 in <i>Snca</i> disrupts the synaptic localisation of α-synuclein and enhances its propensity for synucleinopathy
Bibliographic record
Abstract
Abstract Point mutations in the SNCA gene, which encodes α-synuclein (αSyn), are a known cause of familial Parkinson’s disease. The glycine-51-aspartic acid (G51D) mutation causes early-onset neurodegeneration with complex, wide-spread αSyn pathology. We used CRISPR/Cas9 gene editing to introduce the G51D point mutation into the endogenous rat Snca gene. Our goal was to investigate whether the G51D αSyn mutation gives rise to synucleinopathy and neurodegenerative phenotypes in rats. Co-localisation immunostaining studies with synaptic proteins revealed that αSynG51D protein fails to efficiently localise to synapses. Furthermore, biochemical isolation of synaptosomes from rat cortex demonstrated a significant depletion of αSyn in SncaG51D/+ and SncaG51D/G51D rats. Unbiased proteomic investigation of the cortex identified significant synaptic dysregulation in SncaG51D/G51D animals. Finally, we compared the propensity for synucleinopathy of Snca+/+ and SncaG51D/G51D rats by stereotaxically delivering αSyn pre-formed fibrils (PFFs) into the pre-frontal cortex. At an early time-point, 6 weeks post-injection, we observed discrete Lewy pathology-like structures positive for phosphoserine-129-αSyn (pS129-αSyn) only in SncaG51D/G51D brains. At 26 weeks post-injection of PFFs, SncaG51D/G51D brains exhibited intense, discrete pS129-αSyn-positive structures, while Snca+/+ brains exhibited diffuse pS129-αSyn immunostaining. In summary, G51D mutagenesis of the endogenous Snca rat gene caused reduced synaptic localisation of αSyn, proteomic evidence of early synaptic dysfunction, and enhanced propensity for αSyn pathology.
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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) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".