Mass Spectrometry-Based Comparative Analysis of <i>N</i> -Glycosylation Alterations in Three Human Body Fluids in Parkinson’s Disease
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Parkinson’s disease (PD) is a progressive neurodegenerative disorder lacking definitive diagnostic tests. To identify new diagnostic biomarkers, we employed glycoproteomics-mass spectrometry (MS) to investigate dynamic changes in protein N -glycosylation across the serum, urine, and saliva of PD patients. Our comparative analysis of differentially expressed glycoproteins (DEGs) between PD patients and healthy controls (HCs) revealed distinct patterns. Specifically, ATPase phospholipid transporter 11B (ATP11B) was significantly upregulated in the serum of PD patients, while urine and saliva showed an opposite trend. Other key findings included elevated myeloperoxidase (MPO) in urine and clusterin (CLU) in serum. Zinc-α-2-glycoprotein (AZGP1), detected in all three biofluids, displayed increased sialylation and core fucosylation in serum but decreased levels in the saliva and urine of PD patients, along with a distinct bifucosylation pattern in saliva. These glycoprotein expression changes were further validated using enzyme-linked immunoassay (ELISA). Pathway analysis indicated that these DEGs are primarily involved in inflammatory response, complement activation, and synaptic plasticity, suggesting that glycosylation dysregulation may contribute to PD progression by modulating neuroinflammation and protein homeostasis. This study represents the first comprehensive analysis of multibiofluid N -glycosylation in PD. The findings offer potential biomarkers and provide insights into the molecular mechanisms of the disease, which could ultimately inform early diagnosis and the development of targeted therapies.
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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 | 0.001 | 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".