Glucose Stimulates Glial Cell Line-Derived Neurotrophic Factor Gene Expression in Microglia through a GLUT5-Independent Mechanism
Bibliographic record
Abstract
Feeding-regulating neurotrophic factors are expressed in both neurons and glial cells. However, nutritional regulation of anorexigenic glial cell line-derived neurotrophic factor (GDNF) and orexigenic mesencephalic astrocyte-derived neurotrophic factor (MANF) expression in specific cell types remains poorly understood. Hypothalamic glucose sensing plays a critical role in the regulation of food intake. It has been theorized that local glucose concentration modulates microglial activity partially via glucose transporter 5 (GLUT5). We hypothesized that an increased local glucose concentration stimulates GDNF expression while inhibiting MANF expression in the hypothalamus and microglia via GLUT5. The present study investigated the effect of glucose on Gdnf and Manf mRNA expression in the mouse hypothalamus and murine microglial cell line SIM-A9. Intracerebroventricular glucose treatment significantly increased Gdnf mRNA levels in the hypothalamus without altering Manf mRNA levels. Exposure to high glucose caused a significant increase in Gdnf mRNA expression and a time-dependent change in Manf mRNA expression in SIM-A9 cells. GLUT5 inhibitor treatment did not block glucose-induced Gdnf mRNA expression in these cells. These findings suggest that microglia are responsive to changes in the local glucose concentration and increased local glucose availability stimulates the expression of microglial GNDF through a GLUT5-independent mechanism, contributing to glucose-induced feeding suppression.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".