Increased expression of the dopamine transporter leads to loss of dopamine neurons, oxidative stress and l-DOPA reversible motor deficits
Bibliographic record
Abstract
The dopamine transporter is a key protein responsible for regulating dopamine homeostasis. Its function is to transport dopamine from the extracellular space into the presynaptic neuron. Studies have suggested that accumulation of dopamine in the cytosol can trigger oxidative stress and neurotoxicity. Previously, ectopic expression of the dopamine transporter was shown to cause damage in non-dopaminergic neurons due to their inability to handle cytosolic dopamine. However, it is unknown whether increasing dopamine transporter activity will be detrimental to dopamine neurons that are inherently capable of storing and degrading dopamine. To address this issue, we characterized transgenic mice that over-express the dopamine transporter selectively in dopamine neurons. We report that dopamine transporter over-expressing (DAT-tg) mice display spontaneous loss of midbrain dopamine neurons that is accompanied by increases in oxidative stress markers, 5-S-cysteinyl-dopamine and 5-S-cysteinyl-DOPAC. In addition, metabolite-to-dopamine ratios are increased and VMAT2 protein expression is decreased in the striatum of these animals. Furthermore, DAT-tg mice also show fine motor deficits on challenging beam traversal that are reversed with L-DOPA treatment. Collectively, our findings demonstrate that even in neurons that routinely handle dopamine, increased uptake of this neurotransmitter through the dopamine transporter results in oxidative damage, neuronal loss and LDOPA reversible motor deficits. In addition, DAT over-expressing animals are highly sensitive to MPTP-induced neurotoxicity. The effects of increased dopamine uptake in these transgenic mice could shed light on the unique vulnerability of dopamine neurons in Parkinson’s 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".