One-Carbon Metabolism and Midbrain Dopaminergic Cells in Lesch-Nyhan Disease
Bibliographic record
Abstract
Background: Lesch-Nyhan disease (LND) is characterized by severe motor problems, self-injury, and gout. LND is caused by loss of function of HPRT which functions to salvage purine nucleotides, but the link between purine recycling and the neurological phenotypes remains unknown. One-carbon metabolism (OCM) is the utilization of single-carbon units for important cellular pathways such as de novo purine synthesis, the methionine cycle, and the transsulfuration pathway. Since purine salvage is lost in LND and one-carbon groups are required for de novo purine synthesis, there is an obligate need to re-route one-carbon flux in LND. Midbrain dopaminergic neurons have been associated with LND and may represent an important intersection point for OCM and LND. Summary: In this review, we analyze the relationships between HPRT loss, OCM, and the unique metabolic features of midbrain dopaminergic cells. Our hope is to better understand how changes to metabolic flux in OCM might affect midbrain dopaminergic cells and ultimately lead to the neurological phenotypes of LND. Key Messages: OCM provides important components for different processes and pathways including de novo purine synthesis, methionine cycle, transsulfuration pathway, and polyamine synthesis. Changes in flux toward de novo purine synthesis in LND may affect these interconnected processes and have potential effects on dopaminergic cells as discussed in this review.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".