Characterization of Heparin-induced Glyceraldehyde-3-phosphate Dehydrogenase Early Amyloid-like Oligomers and Their Implication in α-Synuclein Aggregation
Bibliographic record
Abstract
Lewy bodies and Lewy neurites, neuropathological hallmarks of several neurological diseases, are mainly made of filamentous assemblies of α-synuclein. However, other macromolecules including Tau, ubiquitin, glyceraldehyde-3-phosphate dehydrogenase, and glycosaminoglycans are routinely found associated with these amyloid deposits. Glyceraldehyde-3-phosphate dehydrogenase is a glycolytic enzyme that can form fibrillar aggregates in the presence of acidic membranes, but its role in Parkinson disease is still unknown. In this work, the ability of heparin to trigger the amyloid aggregation of this protein at physiological conditions of pH and temperature is demonstrated by infrared and fluorescence spectroscopy, dynamic light scattering, small angle x-ray scattering, circular dichroism, and fluorescence microscopy. Aggregation proceeds through the formation of short rod-like oligomers, which elongates in one dimension. Heparan sulfate was also capable of inducing glyceraldehyde-3-phosphate dehydrogenase aggregation, but chondroitin sulfates A, B, and C together with dextran sulfate had a negligible effect. Aided with molecular docking simulations, a putative binding site on the protein is proposed providing a rational explanation for the structural specificity of heparin and heparan sulfate. Finally, it is demonstrated that in vitro the early oligomers present in the glyceraldehyde-3-phosphate dehydrogenase fibrillation pathway promote α-synuclein aggregation. Taking into account the toxicity of α-synuclein prefibrillar species, the heparin-induced glyceraldehyde-3-phosphate dehydrogenase early oligomers might come in useful as a novel therapeutic strategy in Parkinson disease and other synucleinopathies.
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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) | 0.000 | 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".