Alpha-synuclein induces epigenomic dysregulation of glutamate signaling and locomotor pathways
Bibliographic record
Abstract
Abstract Background Mutations and multiplications in the gene encoding for alpha-synuclein are associated with Parkinson’s disease (PD). However, not all individuals with alpha-synuclein variants develop PD, suggesting that additional factors are involved. We hypothesized that increased alpha-synuclein might alter epigenetic regulation of PD pathways. Objectives To identify genome-wide DNA methylation and hydroxymethylation changes induced by overexpression of two alpha-synuclein variants in human dopaminergic neurons, and to relate these to the corresponding transcriptome. Methods We assessed DNA methylation and hydroxymethylation at >850,000 CpGs using the EPIC BeadChip in LUHMES cells differentiated to dopaminergic neurons. Control LUHMES neurons, LUHMES neurons overexpressing wild type alpha-synuclein, and LUHMES neurons overexpressing A30P alpha-synuclein were compared. We used SMITE network analysis to identify functionally related genes with altered DNA methylation, DNA hydroxymethylation, and/or gene expression, incorporating LUHMES H3K4me1 ChIP-seq to delineate enhancers in addition to the default promoter and gene body regions. Results Using stringent statistical thresholds, we found that increased expression of wild type or A30P mutant alpha-synuclein induced DNA methylation changes at thousands of CpGs and DNA hydroxymethylation changes at hundreds of CpGs. Differentially methylated sites in both genotypes were enriched for several processes including movement-associated pathways and glutamate signaling. For glutamate and other signaling pathways (i.e. PDGF, insulin), this differential DNA methylation was also associated with transcriptional changes. Conclusions Our results indicated that alpha-synuclein altered the DNA methylome of dopaminergic neurons, influencing regulation of pathways involved in development, signaling, and metabolism. This supports a role for alpha-synuclein in the epigenetic etiology of PD.
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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".