Loss of neurexins disrupts inhibitory connectivity and increases vulnerability of dopamine neurons in culture
Bibliographic record
Abstract
Abstract Midbrain dopamine (DA) neurons are essential regulators of basal ganglia function. Their axonal structure is intricate, with numerous non-synaptic release sites and fewer synaptic terminals that notably release glutamate or GABA. Despite their significance, the molecular mechanisms governing DA neuron connectivity and neurochemical identity remain poorly understood. We hypothesize that trans-synaptic cell adhesion molecules such as neurexins (Nrxns) regulate the interactions of DA neuron axons with target cells and thereby influence axonal branching and synapse formation by DA neurons. We therefore examined neuronal survival, axonal growth and synapse formation in cultured DA neurons lacking all neurexins (DAT::NrxnsKO). Conditional deletion of all Nrxns in DA neurons revealed that loss of Nrxns does not disrupt the basic development of these neurons or the structure of their axonal terminals, including normal expression of the vesicular monoamine transporter (VMAT2) and the calcium sensor synaptotagmin 1 (Syt1). However, loss of Nrxns affects the survival of DA neurons and their formation of inhibitory synapses, suggesting that Nrxns regulate the axonal connectivity of these neurons.
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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 | 0.001 |
| 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".