Characterization of a novel mouse model of Dopamine Transporter Deficiency Syndrome and pharmacological therapeutic strategies
Bibliographic record
Abstract
Abstract The dopamine transporter (DAT) is an essential protein in the maintenance of dopamine homeostasis in the brain. Thus, single amino acid changes in the gene that encodes for DAT can be sufficient to induce disease, such as Dopamine Transporter Deficiency Syndrome (DTDS). DTDS-associated variants are posited to cause DAT protein misfolding, retention in the endoplasmic reticulum, and a consequent depletion or loss of DAT at the cell surface. In turn, proper dopaminergic regulation is lost. Current treatments for DTDS are largely ineffective, and improved therapeutic options are greatly needed. To this end, we have created a novel mouse model of DTDS harboring the A313V knock-in DAT variant, a proxy for the DTDS-causing A314V variant in humans. We show that the A313V knock-in DAT mice are hyperactive, have increased striatal tissue content of dopamine and its metabolites homovanillic acid (HVA) and DOPAC, and impaired dopamine uptake. We demonstrate that FDA approved compounds alpha-methyl-para-tyrosine (ɑMPT) and amphetamine (AMPH) ameliorate hyperactivity in this mouse model. Moreover, ɑMPT may be a disease-modifying treatment by addressing the hyperdopaminergic tone underlying this hyperactivity. In contrast, noribogaine, a pharmacological chaperone for DAT, is unable to rescue DAT expression. Taken together, these findings show that the A313V knock-in DAT variant mice recapitulate several defining phenotypes seen in patients with DTDS, and provide evidence for two novel treatments for the disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".