Iron accumulation in microglia triggers a cascade of events that leads to altered metabolism and compromised function in APP/PS1 mice
Bibliographic record
Abstract
Among the changes that typify Alzheimer's disease (AD) are neuroinflammation and microglial activation, amyloid deposition perhaps resulting from compromised microglial function and iron accumulation. Data from Genome Wide Association Studies (GWAS) identified a number of gene variants that endow a significant risk of developing AD and several of these encode proteins expressed in microglia and proteins that are implicated in the immune response. This suggests that neuroinflammation and the accompanying microglial activation are likely to contribute to the pathogenesis of the disease. The trigger(s) leading to these changes remain to be identified. In this study, we set out to examine the link between the inflammatory, metabolic and iron-retentive signature of microglia in vitro and in transgenic mice that overexpress the amyloid precursor protein (APP) and presenilin 1 (PS1; APP/PS1 mice), a commonly used animal model of AD. Stimulation of cultured microglia with interferon (IFN)γ and amyloid-β (Aβ) induced an inflammatory phenotype and switched the metabolic profile and iron handling of microglia so that the cells became glycolytic and iron retentive, and the phagocytic and chemotactic function of the cells was reduced. Analysis of APP/PS1 mice by magnetic resonance imaging (MRI) revealed genotype-related hypointense areas in the hippocampus consistent with iron deposition, and immunohistochemical analysis indicated that the iron accumulated in microglia, particularly in microglia that decorated Aβ deposits. Isolated microglia prepared from APP/PS1 mice were characterized by a switch to a glycolytic and iron-retentive phenotype and phagocytosis of Aβ was reduced in these cells. This evidence suggests that the switch to glycolysis in microglia may kick-start a cascade of events that ultimately leads to microglial dysfunction and Aβ accumulation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".