Bioenergetic Mechanisms in Astrocytes May Contribute to Amyloid Plaque Deposition and Toxicity
Bibliographic record
Abstract
Alzheimer disease (AD) is characterized neuropathologically by synaptic disruption, neuronal loss, and deposition of amyloid β (Aβ) protein in brain structures that are critical for memory and cognition. There is increasing appreciation, however, that astrocytes, which are the major non-neuronal glial cells, may play an important role in AD pathogenesis. Unlike neurons, astrocytes are resistant to Aβ cytotoxicity, which may, in part, be related to their greater reliance on glycolytic metabolism. Here we show that, in cultures of human fetal astrocytes, pharmacological inhibition or molecular down-regulation of a main enzymatic regulator of glycolysis, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase (PFKFB3), results in increased accumulation of Aβ within and around astrocytes and greater vulnerability of these cells to Aβ toxicity. We further investigated age-dependent changes in PFKFB3 and astrocytes in AD transgenic mice (TgCRND8) that overexpress human Aβ. Using a combination of Western blotting and immunohistochemistry, we identified an increase in glial fibrillary acidic protein expression in astrocytes that paralleled the escalation of the Aβ plaque burden in TgCRND8 mice in an age-dependent manner. Furthermore, PFKFB3 expression also demonstrated an increase in these mice, although at a later age (9 months) than GFAP and Aβ. Immunohistochemical staining showed significant reactive astrogliosis surrounding Aβ plaques with increased PFKFB3 activity in 12-month-old TgCRND8 mice, an age when AD pathology and behavioral deficits are fully manifested. These studies shed light on the unique bioenergetic mechanisms within astrocytes that may contribute to the development of AD pathology.
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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 | 0.001 | 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".