Direct assessment of presynaptic modulation of cortico-striatal glutamate release in a Huntington’s disease mouse model
Bibliographic record
Abstract
Glutamate is the main excitatory neurotransmitter in the brain, and impairments in its signaling are associated with many neurological disorders, including Huntington’s disease (HD). Previous studies in HD mouse models demonstrate altered glutamate receptor distribution and signaling at cortico-striatal synapses, and some studies suggest that glutamate release is altered; however, traditional methods to study synaptic glutamate release are indirect or have poor temporal resolution. Here we utilize iGluSnFR, a modified green fluorescent protein reporter for real-time imaging of glutamate transmission, to study presynaptic modulation of cortical glutamate release in the striatum of the YAC128 HD mouse model. We determined that iGluSnFR can be used to accurately measure short- and long-term changes in glutamate release caused by modulation of extracellular Ca2+ levels, activation of presynaptic receptors, and high-frequency stimulation (HFS) protocols. We also confirmed a difference in the expression of HFS-induced long-term depression in YAC128. Together, this research demonstrates the utility of iGluSnFR in studying presynaptic modulation of glutamate release in healthy mice and disease models that display impairments in glutamate signaling. NEW & NOTEWORTHY We use iGluSnFR to directly assess presynaptic modulation of cortico-striatal glutamate release in brain slice and compare changes in glutamate release between wild type and a Huntington’s disease mouse model, YAC128. We observed reductions in glutamate release after low extracellular Ca2+ and activation of various presynaptic receptors. We also demonstrate a presynaptic mechanism of reduced glutamate release in high-frequency stimulation-induced long-term depression and show this to be altered in YAC128.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".