Understanding the Inhibitory and Antioxidant Effects of Pyrroloquinoline Quinone (PQQ) on Copper(II)-Induced α-Synuclein-119 Aggregation
Bibliographic record
Abstract
Parkinson's disease (PD) is associated with the aggregation and misfolding of a-synuclein (a-syn) protein in dopaminergic neurons. The misfolding process is heavily linked to copper dysregulation in PD. Experimental evidence supports the hypothesis that the co-presence of Cu(II) and α-syn facilitates the aggregation of α-syn, affecting the pathological development of PD. Recent literature has shown that pyrroloquinoline quinone (PQQ) contains strong neuroprotective activity by reducing the reactive oxygen species (ROS) production by α-syn. Despite these known facts, minimal studies have been done on the antioxidant effect of PQQ against ROS formation in the presence of Cu(II) and α-syn-119. Thus, it is of great significance to study the interaction between all three components, PQQ, Cu(II), and α-syn-119. In this proof-of-concept study, a variety of chemical techniques were employed to examine the antioxidant effect of PQQ on ROS that α-syn-119 produced in the presence of Cu(II). Our results showed that PQQ effectively prevented ROS formation in SH-SY5Y human differentiated neuronal cells. Thioflavin T (ThT) fluorescence assay, circular dichroism (CD) spectroscopy, and transmission electron microscopy (TEM) were applied, where PQQ was able to actively prevent fibrillation of α-syn-119 in the presence of Cu(II). This finding was further confirmed using electrochemical impedance spectroscopy (EIS), where the binding of PQQ to the α-syn-119 suppressed the aggregation process on the electrode surface. With these encouraging results, we envisage that PQQ and its derivatives can be a promising candidate for further studies as a multitarget therapeutic agent toward PD therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".