GSK3β Impairs KIF1A Transport in a Cellular Model of Alzheimer’s Disease but Does Not Regulate Motor Motility at S402
Bibliographic record
Abstract
Abstract Impairment of axonal transport is an early pathologic event that precedes neurotoxicity in Alzheimer’s disease (AD). Soluble amyloid-β oligomers (AβOs), a causative agent of AD, activate intracellular signaling cascades that trigger phosphorylation of many target proteins, including tau, resulting in microtubule destabilization and transport impairment. Here, we investigated how KIF1A, a kinesin-3 family motor protein required for the transport of neurotrophic factors, is impaired in mouse hippocampal neurons treated with AβOs. By live cell imaging, we observed that AβOs inhibit transport of KIF1A-GFP similarly in wild-type and tau knock-out neurons, indicating that tau is not required for this effect. Pharmacological inhibition of glycogen synthase kinase 3β (GSK3β), a kinase overactivated in AD, prevented the transport defects. By mass spectrometry on KIF1A immunoprecipitated from transgenic AD mouse brain, we detected phosphorylation at S402, which conforms to a highly conserved GSK3β consensus site. We confirmed that this site is phosphorylated by GSK3βin vitro. Finally, we tested whether a phosphomimic of S402 could modulate KIF1A motility in control and AβO-treated mouse neurons and in a Golgi dispersion assay devoid of endogenous KIF1A. In both systems, transport driven by mutant motors was similar to that of WT motors. In conclusion, GSK3β impairs KIF1A transport but does not regulate motor motility at S402. Further studies are required to determine the specific phosphorylation sites on KIF1A that regulate its cargo binding and/or motility in physiological and disease states.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".