A functionalized hydroxydopamine quinone links thiol modification to neuronal cell death
Bibliographic record
Abstract
Recent findings suggest that dopamine oxidation contributes to the development of Parkinson's disease (PD); however, the mechanistic details remain elusive. Here, we compare 6-hydroxydopamine (6-OHDA), a product of dopamine oxidation that commonly induces dopaminergic neurodegeneration in laboratory animals, with a synthetic alkyne-functionalized 6-OHDA variant. This synthetic molecule provides insights into the reactivity of quinone and neuromelanin formation. Employing Huisgen cycloaddition chemistry (or "click chemistry") and fluorescence imaging, we found that reactive 6-OHDA p-quinones cause widespread protein modification in isolated proteins, lysates and cells. We identified cysteine thiols as the target site and investigated the impact of proteome modification by quinones on cell viability. Mass spectrometry following cycloaddition chemistry produced a large number of 6-OHDA modified targets including proteins involved in redox regulation. Functional in vitro assays demonstrated that 6-OHDA inactivates protein disulfide isomerase (PDI), which is a central player in protein folding and redox homeostasis. Our study links dopamine oxidation to protein modification and protein folding in dopaminergic neurons and the PD model.
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 | 0.000 |
| 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".