Microglia Kv1.3 Channels Contribute to Their Ability to Kill Neurons
Bibliographic record
Abstract
Many CNS disorders involve an inflammatory response that is orchestrated by cells of the innate immune system: macrophages, neutrophils, and microglia (the endogenous CNS immune cell). Hence, there is considerable interest in anti-inflammatory strategies that target these cells. Microglia express Kv1.3 (KCNA3) channels, which we showed previously are important for their proliferation and the NADPH-mediated respiratory burst. Here, we demonstrate the potential for targeting Kv1.3 channels to control CNS inflammation. Rat microglia express Kv1.2, Kv1.3, and Kv1.5 transcripts and protein, but only a Kv1.3 current was detected. When microglia were activated with lipopolysaccharide or a phorbol ester, only the Kv1.3 transcript (but not protein) expression changed. Using a Transwell cell-culture system that allows separate drug treatment of microglia or neurons, we found that activated microglia killed postnatal hippocampal neurons through a process that requires Kv1.3 channel activity in microglia but not in neurons. A major neurotoxic molecule in this model was peroxynitrite, which is formed from superoxide and nitric oxide; thus, it is significant that Kv1.3 channel blockers reduced the respiratory burst, but not nitric oxide production, by the activated microglia. In addressing the biochemical pathway affected by Kv1.3 channel activity, we found that Kv1.3 acts via a different cellular mechanism from the broad-spectrum drug minocycline, which is often used in animal models of neuroinflammation. That is, the dose-dependent reduction in neuron killing by minocycline corresponded with a reduction in p38 mitogen-activated protein kinase activation in microglia; however, none of the Kv1.3 blockers affected p38 activation.
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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 | 0.001 |
| 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".