Galectin-3 Is Required for Resident Microglia Activation and Proliferation in Response to Ischemic Injury
Bibliographic record
Abstract
Growing evidence suggests that galectin-3 is involved in fine tuning of the inflammatory responses at the periphery, however, its role in injured brain is far less clear. Our previous work demonstrated upregulation and coexpression of galectin-3 and IGF-1 in a subset of activated/proliferating microglial cells after stroke. Here, we tested the hypothesis that galectin-3 plays a pivotal role in mediating injury-induced microglial activation and proliferation. By using a galectin-3 knock-out mouse (Gal-3KO), we demonstrated that targeted disruption of the galectin-3 gene significantly alters microglia activation and induces ∼4-fold decrease in microglia proliferation. Defective microglia activation/proliferation was further associated with significant increase in the size of ischemic lesion, ∼2-fold increase in the number of apoptotic neurons, and a marked deregulation of the IGF-1 levels. Next, our results revealed that contrary to WT cells, the Gal3-KO microglia failed to proliferate in response to IGF-1. Moreover, the IGF-1-mediated mitogenic microglia response was reduced by N-glycosylation inhibitor tunicamycine while coimmunoprecipitation experiments revealed galectin-3 binding to IGF-receptor 1 (R1), thus suggesting that interaction of galectin-3 with the N-linked glycans of receptors for growth factors is involved in IGF-R1 signaling. While the canonical IGF-1 signaling pathways were not affected, we observed an overexpression of IL-6 and SOCS3, suggesting an overactivation of JAK/STAT3, a shared signaling pathway for IGF-1/IL-6. Together, our findings suggest that galectin-3 is required for resident microglia activation and proliferation in response to ischemic injury.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".