One drug-sensitive subunit is sufficient for a near-maximal retigabine effect in KCNQ channels
Bibliographic record
Abstract
Retigabine is an antiepileptic drug and the first voltage-gated potassium (Kv) channel opener to be approved for human therapeutic use. Retigabine is thought to interact with a conserved Trp side chain in the pore of KCNQ2–5 (Kv7.2–7.5) channels, causing a pronounced hyperpolarizing shift in the voltage dependence of activation. In this study, we investigate the functional stoichiometry of retigabine actions by manipulating the number of retigabine-sensitive subunits in concatenated KCNQ3 channel tetramers. We demonstrate that intermediate retigabine concentrations cause channels to exhibit biphasic conductance–voltage relationships rather than progressive concentration-dependent shifts. This suggests that retigabine can exert its effects in a nearly “all-or-none” manner, such that channels exhibit either fully shifted or unshifted behavior. Supporting this notion, concatenated channels containing only a single retigabine-sensitive subunit exhibit a nearly maximal retigabine effect. Also, rapid solution exchange experiments reveal delayed kinetics during channel closure, as retigabine dissociates from channels with multiple drug-sensitive subunits. Collectively, these data suggest that a single retigabine-sensitive subunit can generate a large shift of the KCNQ3 conductance–voltage relationship. In a companion study (Wang et al. 2018. J. Gen. Physiol. https://doi.org/10.1085/jgp.201812014), we contrast these findings with the stoichiometry of a voltage sensor-targeted KCNQ channel opener (ICA-069673), which requires four drug-sensitive subunits for maximal effect.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".