Anti-TLR2 antibody triggers oxidative phosphorylation in microglia and increases phagocytosis of β-amyloid
Bibliographic record
Abstract
BACKGROUND: Microglia are multifunctional cells that are primarily neuroprotective and a deficit in their functional integrity is likely to be a contributory factor in the deteriorating neuronal function that occurs with age and neurodegeneration. One aspect of microglial dysfunction is reduced phagocytosis, and this is believed to contribute to the accumulation of amyloid-β (Aβ) in Alzheimer's disease (AD). Therefore, improving phagocytosis should be beneficial in limiting the amyloidosis that characterises AD. METHODS: Here, we investigated whether an antibody that targets toll-like receptor (TLR)2 might attenuate the inflammatory and metabolic changes induced by lipopolysaccharide (LPS) and amyloid-β. The impact on phagocytosis was assessed by immunohistochemistry. We evaluated the metabolic changes with the SeaHorse Extracellular Flux Analyser and studied the expression of key enzymes driving glycolysis by western blotting. For all experiments, statistical significance was determined by unpaired Student's t test and two-way analysis of variance (ANOVA). RESULTS: We have reported that, when exposed to an inflammatory stimulus, microglia switch their metabolism towards the metabolically- inefficient glycolysis; this potentially impacts on metabolically demanding functions like phagocytosis. Anti-TLR2 antibody increased phagocytosis of Aβ in LPS + Aβ-stimulated microglia and this was linked with the ability of the antibody to attenuate the LPS + Aβ-triggered inflammasome activation. LPS + Aβ increased glycolysis in microglia and increased the expression of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase (PFKFB)3, an enzyme that plays a key role in driving glycolysis; these effects were inhibited when cells were incubated with the anti-TLR2 antibody. The data also show that antibody treatment increased oxidative metabolism. CONCLUSIONS: Thus, microglia with an inflammatory phenotype, specifically cells in which the inflammasome is activated, are glycolytic; this may compromise the metabolic efficiency of microglia and thereby provide an explanation for the reduced phagocytic function of the cells. We propose that, by restoring oxidative metabolism and reducing inflammasome activation in microglia, phagocytic function is also restored.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".