Amyloid- Peptide Oligomers Disrupt Axonal Transport through an NMDA Receptor-Dependent Mechanism That Is Mediated by Glycogen Synthase Kinase 3 in Primary Cultured Hippocampal Neurons
Bibliographic record
Abstract
Disruption of axonal transport is a hallmark of several neurodegenerative diseases, including Alzheimer's disease (AD). Even though defective transport is considered an early pathologic event, the mechanisms by which neurodegenerative insults impact transport are poorly understood. We show that soluble oligomers of the amyloid-beta peptide (AbetaOs), increasingly recognized as the proximal neurotoxins in AD pathology, induce disruption of organelle transport in primary hippocampal neurons in culture. Live imaging of fluorescent protein-tagged organelles revealed a marked decrease in axonal trafficking of dense-core vesicles and mitochondria in the presence of 0.5 microm AbetaOs. NMDA receptor (NMDAR) antagonists, including d-AP5, MK-801, and memantine, prevented the disruption of trafficking, thereby identifying signals for AbetaO action at the cell membrane. Significantly, both pharmacological inhibition of glycogen synthase kinase-3beta (GSK-3beta) and transfection of neurons with a kinase-dead form of GSK-3beta prevented the transport defect. Finally, we demonstrate by biochemical and immunocytochemical means that AbetaOs do not affect microtubule stability, indicating that disruption of transport involves a more subtle mechanism than microtubule destabilization, likely the dysregulation of intracellular signaling cascades. Results demonstrate that AbetaOs negatively impact axonal transport by a mechanism that is initiated by NMDARs and mediated by GSK-3beta and establish a new connection between toxic Abeta oligomers and 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".