Neuroinflammatory inhibition of synaptic long‐term potentiation requires immunometabolic reprogramming of microglia
Bibliographic record
Abstract
Immunometabolism refers to the rearrangement of metabolic pathways in response to immune stimulation, and the ability of these metabolic pathways themselves to control immune functions. Many aspects of immunometabolism have been revealed through studies of peripheral immune cells. However, immunometabolic reprogramming of microglia, the resident immune cell of the central nervous system, and the consequential outcome on neuronal activity have remained difficult to unravel. Microglia are highly sensitive to subtle changes in their environment, limiting the techniques available to study their metabolic and inflammatory profiles. Here, using fluorescence lifetime imaging of endogenous NAD(P)H, we measure the metabolic activity of individual microglia within acute hippocampal slices. We observed an LPS-induced increase in aerobic glycolysis, which was blocked by the addition of 5 mM 2-deoxyglucose (2DG). This LPS-induced glycolysis in microglia was necessary for the stabilization of hypoxia inducible factor-1α (HIF-1α) and production of the proinflammatory cytokine, interleukin-1β (IL-1β). Upon release, IL-1β acted via the neuronal interleukin-1 receptor to inhibit the formation of synaptic long-term potentiation (LTP) following high frequency stimulation. Remarkably, the addition of 2DG to blunt the microglial glycolytic increase also inhibited HIF-1α accumulation and IL-1β production, and therefore rescued LTP in LPS-stimulated slices. Overall, these data reveal the importance of metabolic reprogramming in regulating microglial immune functions, with appreciable outcomes on cytokine release and neuronal activity.
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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 | 0.001 |
| 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".