The metabolic response to inflammation in astrocytes is regulated by nuclear factor‐kappa B signaling
Bibliographic record
Abstract
Inflammation and metabolism are intrinsically linked with inflammatory stimuli inducing metabolic changes in cells and, in turn, metabolic capacity determining cellular inflammatory responses. Although well characterized in peripheral immune cells there is comparatively less known about these "immunometabolic" responses in astrocytes. In this study, we tested the hypothesis that the astrocytic inflammatory response driven by nuclear factor-kappa B (NF-κB) signaling is dependent on glycolytic metabolism. Using mouse primary cortical astrocyte cultures, we assessed changes in cellular metabolism after exposure to lipopolysaccharide (LPS), with cytokine ELISAs and immunoblotting being used to measure inflammatory responses. Results indicate temporally distinct metabolic adaptations to pro-inflammatory stimulation in astrocytes: 3 hr LPS treatment increased glycolysis but did not alter mitochondrial metabolism, while following 24 hr of LPS treatment we observed increased oxidative phosphorylation, and decreased glycolytic capacity and glucose uptake, partly due to reduced glucose transporter 1 expression. Inhibition of NF-κB signaling with the IKK-beta inhibitor TPCA-1 prevented the LPS induced changes to glycolysis and oxidative phosphorylation. Furthermore, TPCA-1 treatment altered both glycolysis and oxidative phosphorylation independently from inflammatory stimulation, indicating a role for NF-κB signaling in regulation of basal metabolism in astrocytes. Inhibition of glycolysis with 2-deoxyglucose significantly attenuated LPS-induced cytokine release and NF-κB phosphorylation, indicating that intact glycolysis is required for the full inflammatory response to LPS. Together our data indicate that astrocytes display immunometabolic responses to acute LPS stimulation which may represent a potential therapeutic target for neuroinflammatory disorders.
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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".