Glutamate as a co-agonist for acid-sensing ion channels to aggravate ischemic brain damage
Bibliographic record
Abstract
Glutamate is traditionally viewed as the first messenger to activate N-methyl-D-aspartate receptors (NMDARs) and downstream cell death pathways in stroke 1,2 , but unsuccessful clinical trials with NMDAR antagonists implicate the engagement of other NMDAR-independent mechanisms 3–7 . Here we show that glutamate and its structural analogs, including NMDAR antagonist L-AP5 (or APV), robustly potentiated currents mediated by acid-sensing ion channels (ASICs) which are known for driving acidosis-induced neurotoxicity in stroke 4 . Glutamate increased the proton affinity and open probability of ASICs, aggravating ischemic neurotoxicity in both in vitro and in vivo models. Site-directed mutagenesis and structure-based in silico molecular docking and simulations uncovered a novel glutamate binding cavity in the extracellular domain of ASIC1a. Computational drug screening of NMDAR competitive antagonist analogs identified a small molecule, LK-2, that binds to this cavity and abolishes glutamate-dependent potentiation of ASIC currents but spares NMDARs, providing strong neuroprotection efficacy comparable to that in ASIC1a or other cation ion channel knockout mouse models 4–7 . We conclude that glutamate serves as the first messenger for ASICs to exacerbate neurotoxicity, and that selective blockage of glutamate binding sites on ASICs without affecting NMDARs may be of strategic importance for developing effective stroke therapeutics devoid of the psychotic side effects of NMDAR antagonists.
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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) | 0.001 | 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) | 0.002 | 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".