Atorvastatin prevents age-related and amyloid-β-induced microglial activation by blocking interferon-γ release from natural killer cells in the brain
Bibliographic record
Abstract
BACKGROUND: Microglial function is modulated by several factors reflecting the numerous receptors expressed on the cell surface, however endogenous factors which contribute to the age-related increase in microglial activation remain largely unknown. One possible factor which may contribute is interferon-γ (IFNγ). IFNγ has been shown to increase in the aged brain and potently activates microglia, although its endogenous cell source in the brain remains unidentified. METHODS: Male Wistar rats were used to assess the effect of age and amyloid-β (Aβ) on NK cell infiltration into the brain. The effect of the anti-inflammatory compound, atorvastatin was also assessed under these conditions. We measured cytokine and chemokine (IFNγ, IL-2, monocyte chemoattractant protein-1 (MCP-1) and IFNγ-induced protein 10 kDa (IP-10)), expression in the brain by appropriate methods. We also looked at NK cell markers, CD161, NKp30 and NKp46 using flow cytometry and western blot. RESULTS: Natural killer (NK) cells are a major source of IFNγ in the periphery and here we report the presence of CD161+ NKp30+ cells and expression of CD161 and NKp46 in the brain of aged and Aβ-treated rats. Furthermore, we demonstrate that isolated CD161+ cells respond to interleukin-2 (IL-2) by releasing IFNγ. Atorvastatin, the HMG-CoA reductase inhibitor, attenuates the increase in CD161 and NKp46 observed in hippocampus of aged and Aβ-treated rats. This was paralleled by a decrease in IFNγ, markers of microglial activation and the chemokines, MCP-1 and IP-10 which are chemotactic for NK cells. CONCLUSIONS: We propose that NK cells contribute to the age-related and Aβ-induced neuroinflammatory changes and demonstrate that these changes can be modulated by atorvastatin treatment.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".