Unexpected synergism between Tetrodotoxin and μ-conotoxin in blocking voltage-gated sodium channels
Bibliographic record
Abstract
Tetrodotoxin (TTX) is the quintessential ligand of voltage-gated sodium channels (NaVs). Like TTX, mu-conotoxin peptides are pore blockers, and both toxins have helped to define the properties of neurotoxin receptor Site 1 of NaVs. Here, we report unexpected results showing that the recently discovered mu-conotoxin KIIIA and TTX can simultaneously bind to Site 1 and act in concert. Results with saturating concentrations of peptide applied to voltage-clamped Xenopus oocytes expressing brain NaV1.2, and single-channel recordings from brain channels in lipid bilayers, show that KIIIA or its analog, KIIIA[K7A], block partially, with a residual current that can be completely blocked by TTX. In addition, the kinetics of block by TTX and peptide are each affected by the prior presence of the other toxin. For example, bound peptide slows subsequent binding of TTX (an antagonistic interaction) and slows TTX dissociation when both toxins are bound (a synergistic effect on block). The overall functional consequence resulting from the combined action of the toxins depends on the quantitative balance between these opposing actions. The results lead us to postulate that in the bi-liganded NaV complex, TTX is bound between the peptide and the selectivity filter. These observations refine our view of Site 1 and open new possibilities in NaV pharmacology.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".