Glutamate and GABAA receptor crosstalk mediates homeostatic regulation of neuronal excitation in the mammalian brain
Bibliographic record
Abstract
Abstract Maintaining a proper balance between the glutamate receptor-mediated neuronal excitation and the A type of GABA receptor (GABAAR) mediated inhibition is essential for brain functioning; and its imbalance contributes to the pathogenesis of many brain disorders including neurodegenerative diseases and mental illnesses. Here we identify a novel glutamate-GABAAR interaction mediated by a direct glutamate binding of the GABAAR. In HEK293 cells overexpressing recombinant GABAARs, glutamate and its analog ligands, while producing no current on their own, potentiate GABA-evoked currents. This potentiation is mediated by a direct binding at a novel glutamate binding pocket located at the α+/β− subunit interface of the GABAAR. Moreover, the potentiation does not require the presence of a γ subunit, and in fact, the presence of γ subunit significantly reduces the potency of the glutamate potentiation. In addition, the glutamate-mediated allosteric potentiation occurs on native GABAARs in rat neurons maintained in culture, as evidenced by the potentiation of GABAAR-mediated inhibitory postsynaptic currents and tonic currents. Most importantly, we found that genetic impairment of this glutamate potentiation in knock-in mice resulted in phenotypes of increased neuronal excitability, including decreased thresholds to noxious stimuli and increased seizure susceptibility. These results demonstrate a novel cross-talk between excitatory transmitter glutamate and inhibitory GABAAR. Such a rapid and short feedback loop between the two principal excitatory and inhibitory neurotransmission systems may play a critical homeostatic role in fine-tuning the excitation-inhibition balance (E/I balance), thereby maintaining neuronal excitability in the mammalian brain under both physiological and pathological conditions.
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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 | 0.000 |
| 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".