A novel post-translational proteomics platform identifies neurite outgrowth impairments in Parkinson’s disease <i>GBA-N370S</i> dopamine neurons
Bibliographic record
Abstract
Abstract The causes of Parkinson’s disease (PD) likely involve complex interactions between environmental factors and susceptibility genes with variants at the GBA locus encoding the glucocerebrosidase (GCase) enzyme being the strongest common genetic risk factor for PD. To understand GBA -related disease mechanisms, we used a novel multipart-enrichment proteomics and post-translational modification workflow to simultaneously identify peptides with phosphorylation, reversible cysteine-modifications or sialylated N-linked glycosylation, alongside unmodified proteins. We identified large numbers of dysregulated proteins and post-translational modifications (PTMs) in heterozygous GBA - N370S PD patient induced pluripotent stem cells (iPSC)-derived dopamine neurons. Alterations in glycosylation status of lysosomal proteins identified disturbances in the autophagy-lysosomal pathway, concurrent with upstream perturbations in mTOR phosphorylation and activity in GBA-N370S iPSC-dopamine neurons. In addition, the strategy revealed several native and modified proteins encoded by PD-associated genes to be dysregulated in GBA-N370S neurons, enhancing our understanding of the wider role of GBA mutations on the neuronal proteome. Integrated pathway analysis of all datasets revealed impaired neuritogenesis in GBA-N370S PD iPSC-dopamine neurons and identified tau ( MAPT ) as a key mediator of this process. Using a functional assay, we confirmed neurite outgrowth deficits in GBA-N370S PD neurons and a central role for tau in this process. Furthermore, pharmacological restoration of GCase activity in GBA-N370S PD patient neurons rescued the neurite outgrowth deficit. Overall, this study demonstrates the potential of PTMomics to elucidate novel neurodegeneration-associated pathways and identify phenotypes and potential drug targets in complex disease models.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".