Crucial neuroprotective roles of the metabolite BH4 in dopaminergic neurons
Bibliographic record
Abstract
Dopa-responsive dystonia (DRD) and Parkinson's disease (PD) are movement disorders caused by the dysfunction of nigrostriatal dopaminergic neurons. Identifying druggable pathways and biomarkers for guiding therapies is crucial due to the debilitating nature of these disorders. Recent genetic studies have identified variants of GTP cyclohydrolase-1 (GCH1), the rate-limiting enzyme in tetrahydrobiopterin (BH4) synthesis, as causative for these movement disorders. Here, we show that genetic and pharmacological inhibition of BH4 synthesis in mice and human midbrain-like organoids accurately recapitulates motor, behavioral and biochemical characteristics of these human diseases, with severity of the phenotype correlating with extent of BH4 deficiency. We also show that BH4 deficiency increases sensitivities to several PD-related stressors in mice and PD human cells, resulting in worse behavioral and physiological outcomes. Conversely, genetic and pharmacological augmentation of BH4 protects mice from genetically- and chemically induced PD-related stressors. Importantly, increasing BH4 levels also protects primary cells from PD-affected individuals and human midbrain-like organoids (hMLOs) from these stressors. Mechanistically, BH4 not only serves as an essential cofactor for dopamine synthesis, but also independently regulates tyrosine hydroxylase levels, protects against ferroptosis, scavenges mitochondrial ROS, maintains neuronal excitability and promotes mitochondrial ATP production, thereby enhancing mitochondrial fitness and cellular respiration in multiple preclinical PD animal models, human dopaminergic midbrain-like organoids and primary cells from PD-affected individuals. Our findings pinpoint the BH4 pathway as a key metabolic program at the intersection of multiple protective mechanisms for the health and function of midbrain dopaminergic neurons, identifying it as a potential therapeutic target for PD.
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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".