Genetic inhibition of glutamate allosteric potentiation of GABA<sub>A</sub>Rs in mice results in hyperexcitability, leading to neurobehavioral abnormalities
Bibliographic record
Abstract
Abstract The imbalance between neuronal excitation and inhibition (E/I) in neural circuit has been considered to be at the root of numerous brain disorders. We recently reported a novel feedback crosstalk between the excitatory neurotransmitter glutamate and inhibitory γ‐aminobutyric acid type A receptor (GABAAR)‐glutamate allosteric potentiation of GABAAR functions through a direct binding of glutamate to the GABAAR itself. Here, we investigated the physiological significance and pathological implications of this cross‐talk by generating the β3E182G knock‐in (KI) mice. We found that β3E182G KI, while had little effect on basal GABAAR‐mediated synaptic transmission, significantly reduced glutamate potentiation of GABAAR‐mediated responses. These KI mice displayed lower thresholds for noxious stimuli, higher susceptibility to seizures and enhanced hippocampus‐related learning and memory. Additionally, the KI mice exhibited impaired social interactions and decreased anxiety‐like behaviors. Importantly, hippocampal overexpression of wild‐type β3‐containing GABAARs was sufficient to rescue the deficits of glutamate potentiation of GABAAR‐mediated responses, hippocampus‐related behavioral abnormalities of increased epileptic susceptibility, and impaired social interactions. Our data indicate that the novel crosstalk among excitatory glutamate and inhibitory GABAAR functions as a homeostatic mechanism in fine‐tuning neuronal E/I balance, thereby playing an essential role in ensuring normal brain functioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".