Glucose transporter 1 critically controls microglial activation through facilitating glycolysis
Bibliographic record
Abstract
BACKGROUND: Uncontrolled microglial activation contributes to the pathogenesis of various neurodegenerative diseases. Previous studies have shown that proinflammatory microglia are powered by glycolysis, which relays on high levels of glucose uptake. This study aimed to understand how glucose uptake is facilitated in active microglia and whether microglial activation can be controlled by restricting glucose uptake. METHODS: Primary murine brain microglia, BV2 cells and the newly established microglial cell line B6M7 were treated with LPS (100 ng/ml) + IFNγ (100 ng/ml) or IL-4 (20 ng/ml) for 24 h. The expression of glucose transporters (GLUTs) was examined by PCR and Western blot. Glucose uptake by microglia was inhibited using the GLUT1-specific inhibitor STF31. The metabolic profiles were tested using the Glycolysis Stress Test and Mito Stress Test Kits using the Seahorse XFe96 Analyser. Inflammatory gene expression was examined by real-time RT-PCR and protein secretion by cytokine beads array. The effect of STF31 on microglial activation and neurodegeneraion was further tested in a mouse model of light-induced retinal degeneration. RESULTS: The mRNA and protein of GLUT1, 3, 4, 5, 6, 8, 9, 10, 12, and 13 were detected in microglia. The expression level of GLUT1 was the highest among all GLUTs detected. LPS + IFNγ treatment further increased GLUT1 expression. STF31 dose-dependently reduced glucose uptake and suppressed Extracellular Acidification Rate (ECAR) in naïve, M(LPS + IFNγ) and M(IL-4) microglia. The treatment also prevented the upregulation of inflammatory cytokines including TNFα, IL-1β, IL-6, and CCL2 in M(LPS + IFNγ) microglia. Interestingly, the Oxygen Consumption Rates (OCR) was increased in M(LPS + IFNγ) microglia but reduced in M(IL-4) microglia by STF31 treatment. Intraperitoneal injection of STF31 reduced light-induced microglial activation and retinal degeneration. CONCLUSION: Glucose uptake in microglia is facilitated predominately by GLUT1, particularly under inflammatory conditions. Targeting GLUT1 could be an effective approach to control neuroinflammation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".