The Ca2+ activated SK3 channel is expressed in microglia in the rat striatum and contributes to microglia-mediated neurotoxicity in vitro
Bibliographic record
Abstract
BACKGROUND: Small-conductance Ca2+ activated K+ channels are expressed in the CNS, where KCNN2/SK2/KCa2.2 and KCNN3/SK3/KCa2.3 help shape the electrical activity of some neurons. The SK3 channel is considered a potential therapeutic target for diseases and disorders involving neuron hyper-excitability but little is known about its expression and roles in non-neuronal cells in either the healthy or damaged CNS. The purpose of this study was to examine expression of KCNN3/SK3 in CNS microglia in vivo and in vitro, and to use an established in vitro model to determine if this channel contributes to the neurotoxic capacity of activated microglia. METHODS: KCNN3 mRNA (real-time RT-PCR) and SK3 immunoreactivity were examined in rat microglia. Lipopolysaccharide was then used to activate microglia (monitored by iNOS, nitric oxide, activation of NF-kappaB and p38 MAPK) and transform them to a neurotoxic state. Microglia-mediated neuron damage (TUNEL, activated caspase 3) and nitrotyrosine levels were quantified using a two-chamber system that allowed microglia to be treated with channel blockers, washed and then added to neuron/astrocyte cultures. Contributions of SK3 to these processes were discriminated using a subtractive pharmacological approach with apamin and tamapin. ANOVA and post-hoc tests were used to assess the statistical significance of differences between treatment groups. SK3 immunoreactivity was then compared in the normal and damaged adult rat striatum, by injecting collagenase (a hemorrhagic stroke) or endothelin-1 (a transient ischemic stroke). RESULTS: KCNN3 mRNA was prevalent in cultured microglia and increased after lipopolysaccharide-induced activation; SK3 channel blockade inhibited microglial activation and reduced their ability to kill neurons. SK3 immunoreactivity was prevalent in cultured microglia and throughout the adult rat striatum (except white matter tracts). After strokes, SK3 was highly expressed in activated microglia/macrophages within the lesions, but reduced in other cells. CONCLUSIONS: SK3 is expressed in microglia in both the healthy and damaged adult striatum, and mechanistic in vitro studies show it contributes to transformation of microglia to an activated neurotoxic phenotype. Thus, SK3 might be a therapeutic target for reducing inflammation-mediated acute CNS damage. Moreover, its roles in microglia must be considered when targeting this channel for CNS diseases, disorders and reducing neuron hyper-excitability.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".