KCNE4-dependent modulation of Kv1.3 pharmacology
Bibliographic record
Abstract
The voltage-dependent potassium channel Kv1.3 is a promising therapeutic target for the treatment of autoimmune and chronic inflammatory disorders. Kv1.3 blockers are effective in treating multiple sclerosis (fampridine) and psoriasis (dalazatide). However, most Kv1.3 pharmacological antagonists are not specific enough, triggering potential side effects and limiting their therapeutic use. Functional Kv are oligomeric complexes in which the presence of ancillary subunits shapes their function and pharmacology. In leukocytes, Kv1.3 associates with KCNE4, which reduces the surface abundance and enhances the inactivation of the channel. This mechanism exerts profound consequences on Kv1.3-related physiological responses. Because KCNE peptides alter the pharmacology of Kv channels, we studied the effects of KCNE4 on Kv1.3 pharmacology to gain insights into pharmacological approaches. To that end, we used margatoxin, which binds the channel pore from the extracellular space, and Psora-4, which blocks the channel from the intracellular side. While KCNE4 apparently did not alter the affinity of either margatoxin or Psora-4, it slowed the inhibition kinetics of the latter in a stoichiometry-dependent manner. The results suggested changes in the Kv1.3 architecture in the presence of KCNE4. The data indicated that while the outer part of the channel mouth remains unaffected, KCNE4 disturbs the intracellular architecture of the complex. Various leukocyte types expressing different Kv1.3/KCNE4 configurations participate in the immune response. Our data provide evidence that the presence of these variable architectures, which affect both the structure of the complex and their pharmacology, should be considered when developing putative therapeutic approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".