Membrane transporters as mediators of synaptic dopamine dynamics: implications for disease
Bibliographic record
Abstract
Dopamine was first identified as a neurotransmitter localized to the midbrain over 50 years ago. The dopamine transporter (DAT; SLC6A3) and the vesicular monoamine transporter 2 (VMAT2; SLC18A2) are regulators of dopamine homeostasis in the presynaptic neuron. DAT transports dopamine from the extracellular space into the cytosol of the presynaptic terminal. VMAT2 then packages this cytosolic dopamine into vesicular compartments for subsequent release upon neurotransmission. Thus, DAT and VMAT2 act in concert to move the transmitter efficiently throughout the neuron. Accumulation of dopamine in the neuronal cytosol can trigger oxidative stress and neurotoxicity, suggesting that the proper compartmentalization of dopamine is critical for neuron function and risk of disease. For decades, studies have examined the effects of reduced transporter function in mice (e.g. DAT-KO, VMAT2-KO, VMAT2-deficient). However, we have only recently been able to assess the effects of elevated transporter expression using BAC transgenic methods (DAT-tg, VMAT2-HI mice). Complemented with in vitro work and neurochemical techniques to assess dopamine compartmentalization, a new focus on the importance of transporter proteins as both models of human disease and potential drug targets has emerged. Here, we review the importance of DAT and VMAT2 function in the delicate balance of neuronal dopamine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".