G6PD deficiency triggers dopamine loss and the initiation of Parkinson’s disease pathogenesis
Bibliographic record
Abstract
Loss of dopaminergic neurons in Parkinson's disease (PD) is preceded by loss of synaptic dopamine (DA) and accumulation of proteinaceous aggregates. Linking these deficits is critical to restoring DA signaling in PD. Using murine and human pluripotent stem cell (hPSC) models of PD coupled with human postmortem tissue, we show that accumulation of α-syn micro-aggregates impairs metabolic flux through the pentose phosphate pathway (PPP). This leads to decreased nicotinamide adenine dinucleotide phosphate (NADP/H) and glutathione (GSH) levels, resulting in DA oxidation and decreased total DA levels. We find that α-syn anchors the PPP enzyme G6PD to synaptic vesicles via the α-syn C terminus and that this interaction is lost in PD. Furthermore, G6PD clinical mutations are associated with PD diagnosis, and G6PD deletion phenocopies PD pathology. Finally, we show that restoring NADPH or GSH levels through genetic and pharmacological intervention blocks DA oxidation and rescues steady-state DA levels, identifying G6PD as a pharmacological target against PD.
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".