Mutation of a conserved Gln residue does not abolish desensitization of acid-sensing ion channel 1
Bibliographic record
Abstract
Abstract Desensitization is a common feature of ligand-gated ion channels although the molecular cause varies widely between channel types. Mutations that substantially reduce or abolish desensitization have been described for many ligand-gated ion channels including glutamate, GABA, glycine and nicotinic receptors but not for acid-sensing ion channels (ASICs) until recently. Mutating Gln276 to a glycine in human ASIC1a was reported to mostly abolish desensitization at both the macroscopic and single channel levels, potentially providing a valuable tool for subsequent studies. However, we find that in both human and chicken ASIC1 the effect of Q276G is modest. In chicken ASIC1, the equivalent Q277G slightly reduces desensitization when using pH 6.5 as a stimulus but desensitizes essentially like wild type when using more acidic pH values. In addition, steady-state desensitization is intact, albeit right-shifted, and recovery from desensitization is accelerated. Molecular dynamics simulations indicate that the Gln277 side chain participates in a hydrogen bond network that might stabilize the desensitized conformation. Consistent with this, destabilizing this network with the Q277N or Q277L mutations largely mimics the Q277G phenotype. In human ASIC1a, Q276G does not substantially reduce desensitization but surprisingly slows entry to and exit from the desensitized state, thus requiring longer agonist applications to reach equilibrium. Our data reveal that while the Q/G mutation does not substantially impair desensitization as previously reported, it does point to unexpected differences between chicken and human ASICs and the need for careful scrutiny before using this mutation in future studies.
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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) | 0.003 | 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".