Cyclized NDGA modifies dynamic α-synuclein monomers preventing aggregation and toxicity
Bibliographic record
Abstract
Growing evidence implicates α-synuclein aggregation as a key driver of neurodegeneration in Parkinson's disease (PD) and other neurodegenerative disorders. Herein, the molecular and structural mechanisms of inhibiting α-synuclein aggregation by novel analogs of nordihydroguaiaretic acid (NDGA), a phenolic dibenzenediol lignan, were explored using an array of biochemical and biophysical methodologies. NDGA analogs induced modest, progressive compaction of monomeric α-synuclein, preventing aggregation into amyloid-like fibrils. This conformational remodeling preserved the dynamic adoption of α-helical conformations, which are essential for physiological membrane interactions. Oxidation-dependent NDGA cyclization was required for the interaction with monomeric α-synuclein. NDGA analog-pretreated α-synuclein did not aggregate even without NDGA-analogs in the aggregation mixture. Strikingly, NDGA-pretreated α-synuclein suppressed aggregation of naïve untreated aggregation-competent monomeric α-synuclein. Further, cyclized NDGA reduced α-synuclein-driven neurodegeneration in Caenorhabditis elegans. The cyclized NDGA analogs may serve as a platform for the development of small molecules that stabilize aggregation-resistant α-synuclein monomers without interfering with functional conformations yielding potential therapies for PD and related disorders.
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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".