Tyrosine phosphatase PTP1B impairs presynaptic NMDA receptor-mediated plasticity in a mouse model of Alzheimer's disease
Bibliographic record
Abstract
Mutations in the beta-amyloid protein (APP) cause familial Alzheimer's disease. In hAPP-J20 mice expressing mutant APP, pharmacological inhibition or genetic ablation of the tyrosine phosphatase PTP1B prevents CA3 hippocampus neuron loss and cognitive decline. However, how targeting PTP1B affects the cellular mechanisms underlying these cognitive deficits remains unknown. Changes in synaptic strength at the hippocampus can affect information processing for learning and memory. While prior studies have focused on post-synaptic mechanisms to account for synaptic deficits in Alzheimer's disease models, presynaptic mechanisms may also be affected. Here, using whole cell patch-clamp recording, coefficient of variation (CV) analysis suggested a profound presynaptic deficit in long-term potentiation (LTP) of CA3:CA1 synapses in hAPP-J20 mice. While the membrane-impermeable ionotropic NMDA receptor (NMDAR) blocker norketamine in the post-synaptic recording electrode had no effect on LTP, additional bath application of the ionotropic NMDAR blockers MK801 could replicate the deficit in LTP in wild type mice. In contrast to LTP, the paired-pulse ratio and short-term facilitation (STF) were aberrantly increased in hAPP-J20 mice. These synaptic deficits in hAPP-J20 mice were associated with reduced phosphorylation of NMDAR GluN2B and the synaptic vesicle recycling protein NSF (N-ethylmaleimide sensitive factor). Phosphorylation of both proteins, together with synaptic plasticity and cognitive function, were restored by PTP1B ablation or inhibition by the PTP1B-selective inhibitor Trodusquemine. Taken together, our results indicate that PTP1B impairs presynaptic NMDAR-mediated synaptic plasticity required for spatial learning in a mouse model of Alzheimer's disease. Since Trodusquemine has undergone phase 1/2 clinical trials to treat obesity, it could be repurposed to treat Alzheimer's disease.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".