Regulation of Kv1.3 Channels in Activated Human T Lymphocytes by Ca<sup>2+</sup>-Dependent Pathways
Bibliographic record
Abstract
Activated T lymphoblasts respond more effectively to mitogenic stimuli than resting T cells, partly through differences in Ca(2+) signaling, which in turn depend on K(+) channel activity. Both Kv1.3 and Ca(2+)-activated K(+) (SK4) channels are up-regulated in T lymphoblasts. Since Ca(2+)- and calmodulin (CaM)-dependent signal-ing are key pathways in T-cell activation, we investigated their involvement in regulating the Kv1.3 current. Kv1.3 in lymphoblasts was significantly inhibited by elevating internal Ca(2+) to the micromolar level. It was also reduced in a Ca(2+)-dependent manner by inhibiting CaM with W-7 or calmidazolium. Part of the CaM-dependence is likely through CaM kinase since the current was also inhibited by the antagonist, KN-62, but not by the inactive analogue, KN-04. Kinase inhibition, unlike CaM inhibition, was only effective at physiological temperatures, a difference that implies involvement of more than one mechanism. We demonstrated a biochemical association of Kv1.3 protein in lymphoblasts with the multifunctional type II CaM kinase, but not with calmodulin. Thus, Kv1.3 forms a multi-protein complex with CaM kinase II (which binds to Ca(2+)/CaM) and previously identified proteins (e.g., PSD-95, src tyrosine kinase) that position the channel to respond to signaling pathways that are crucial for T-cell activation and proliferation.
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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 | 0.000 | 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 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".