Expression and contributions of the Kir2.1 inward-rectifier K+ channel to proliferation, migration and chemotaxis of microglia in unstimulated and anti-inflammatory states
Bibliographic record
Abstract
When microglia respond to CNS damage, they can range from pro-inflammatory (classical, M1) to anti-inflammatory, alternative (M2) and acquired deactivation states. It is important to determine how microglial functions are affected by these activation states, and to identify molecules that regulate their behavior. Microglial proliferation and migration are crucial during development and following damage in the adult, and both functions are Ca(2+)-dependent. In many cell types, the membrane potential and driving force for Ca(2+) influx are regulated by inward-rectifier K(+) channels, including Kir2.1, which is prevalent in microglia. However, it is not known whether Kir2.1 expression and contributions are altered in anti-inflammatory states. We tested the hypothesis that Kir2.1 contributes to Ca(2+) entry, proliferation and migration of rat microglia. Kir2.1 (KCNJ2) transcript expression, current amplitude, and proliferation were comparable in unstimulated microglia and following alternative activation (IL-4 stimulated) and acquired deactivation (IL-10 stimulated). To examine functional roles of Kir2.1 in microglia, we first determined that ML133 was more effective than the commonly used blocker, Ba(2+); i.e., ML133 was potent (IC50 = 3.5 μM) and voltage independent. Both blockers slightly increased proliferation in unstimulated or IL-4 (but not IL-10)-stimulated microglia. Stimulation with IL-4 or IL-10 increased migration and ATP-induced chemotaxis, and blocking Kir2.1 greatly reduced both but ML133 was more effective. In all three activation states, blocking Kir2.1 with ML133 dramatically reduced Ca(2+) influx through Ca(2+)-release-activated Ca(2+) (CRAC) channels. Thus, Kir2.1 channel activity is necessary for microglial Ca(2+) signaling and migration under resting and anti-inflammatory states but the channel weakly inhibits 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 | 0.001 |
| 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".