Loss of IRF2BP2 in Microglia Increases Inflammation and Functional Deficits after Focal Ischemic Brain Injury
Bibliographic record
Abstract
Ischemic stroke causes neuronal cell death and triggers a cascade of inflammatory signals that contribute to secondary brain damage. Microglia, the brain-resident macrophages that remove dead neurons, play a critical role in the brain's response to ischemic injury. Our previous studies showed that IRF2 binding protein 2 (IRF2BP2) regulates peripheral macrophage polarization, limits their inflammatory response and reduces susceptibility to atherosclerosis. Here, we show that loss of IRF2BP2 in microglia leads to increased inflammatory cytokine expression in response to lipopolysaccharide challenge and impaired activation of anti-inflammatory markers in response to interleukin-4 (IL4) stimulation. Focal ischemic brain injury of the sensorimotor cortex induced by photothrombosis caused more severe functional deficits in mice with IRF2BP2 ablated in macrophages/microglia, associated with elevated expression of inflammatory cytokines in the brain. These mutant mice had larger infarctions 4 days after stroke associated with fewer anti-inflammatory M2 microglia/macrophages recruited to the peri-infarct area, suggesting an impaired clearance of injured tissues. Since IRF2BP2 modulates interferon signaling, and interferon beta (IFNβ) has been reported to be anti-inflammatory and reduce ischemic brain injury, we asked whether loss of IRF2BP2 in macrophages/microglia would affect the response to IFNβ in our stroke model. IFNβ suppressed inflammatory cytokine production of macrophages and reduced infarct volumes at 4 days after photothrombosis in wild type mice. The anti-inflammatory effect of IFNβ was lost in IRF2BP2-deficient macrophages and IFNβ failed to protect mice lacking IRF2BP2 in macrophages/microglia from ischemic injury. In summary, IRF2BP2 expression in macrophages/microglia is important to limit inflammation and stroke injury, in part by mediating the beneficial effect of IFNβ.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".