Distinct functional roles for the M4 α-helix from each homologous subunit in the heteropentameric ligand-gated ion channel nAChR
Bibliographic record
Abstract
The outermost lipid-exposed α-helix (M4) in each of the homologous α, β, δ, and γ/ε subunits of the muscle nicotinic acetylcholine receptor (nAChR) has previously been proposed to act as a lipid sensor. However, the mechanism by which this sensor would function is not clear. To explore how the M4 α-helix from each subunit in human adult muscle nAChR influences function, and thus explore its putative role in lipid sensing, we functionally characterized alanine mutations at every residue in αM4, βM4, δM4, and εM4, along with both alanine and deletion mutations in the post-M4 region of each subunit. Although no critical interactions involving residues on M4 or in post-M4 were identified, we found that numerous mutations at the M4-M1/M3 interface altered the agonist-induced response. In addition, homologous mutations in M4 in different subunits were found to have different effects on channel function. The functional effects of multiple mutations either along M4 in one subunit or at homologous positions of M4 in different subunits were also found to be additive. Finally, when characterized in both Xenopus oocytes and human embryonic kidney 293T cells, select αM4 mutations displayed cell-specific phenotypes, possibly because of the different membrane lipid environments. Collectively, our data suggest different functional roles for the M4 α-helix in each heteromeric nAChR subunit and predict that lipid sensing involving M4 occurs primarily through the cumulative interactions at the M4-M1/M3 interface, as opposed to the alteration of specific interactions that are critical to channel function.
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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 | 0.001 | 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".