Mechanistic basis for LQT1 caused by S3 mutations in the KCNQ1 subunit of <i>IKs</i>
Bibliographic record
Abstract
Long QT interval syndrome (LQTS) type 1 (LQT1) has been reported to arise from mutations in the S3 domain of KCNQ1, but none of the seven S3 mutations in the literature have been characterized with respect to trafficking or biophysical deficiencies. Surface channel expression was studied using a proteinase K assay for KCNQ1 D202H/N, I204F/M, V205M, S209F, and V215M coexpressed with KCNE1 in mammalian cells. In each case, the majority of synthesized channel was found at the surface, but mutant I(Ks) current density at +100 mV was reduced significantly for S209F, which showed approximately 75% reduction over wild type (WT). All mutants except S209F showed positively shifted V(1/2)'s of activation and slowed channel activation compared with WT (V(1/2) = +17.7 +/- 2.4 mV and tau(activation) of 729 ms at +20 mV; n = 18). Deactivation was also accelerated in all mutants versus WT (126 +/- 8 ms at -50 mV; n = 27), and these changes led to marked loss of repolarizing currents during action potential clamps at 2 and 4 Hz, except again S209F. KCNQ1 models localize these naturally occurring S3 mutants to the surface of the helices facing the other voltage sensor transmembrane domains and highlight inter-residue interactions involved in activation gating. V207M, currently classified as a polymorphism and facing lipid in the model, was indistinguishable from WT I(Ks). We conclude that S3 mutants of KCNQ1 cause LQTS predominantly through biophysical effects on the gating of I(Ks), but some mutants also show protein stability/trafficking defects, which explains why the kinetic gain-of-function mutation S209F causes LQT1.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".