Correction of mTORC1‐mediated brain protein synthesis rescues memory in mouse models of Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by synapse failure and cognitive decline. Brain mRNA translation is central to synaptic plasticity and cognition, and converging evidence indicates it is impaired in AD. The mammalian target of rapamycin complex 1 (mTORC1) pathway plays a key role in regulating protein synthesis, and mTORC1 signaling has received considerable attention in AD research. In this current work, we investigated whether stimulating mTORC1‐mediated protein synthesis can alleviate the impairments in synaptic plasticity and memory in AD mice. Method To address this question, we used two different approaches: 1. genetic reduction of the translational repressors, Fragile X messenger ribonucleoprotein (FMRP) or eukaryotic initiation factor 4E (eIF4E)‐binding protein 2 (4E‐BP2); and 2. pharmacological treatment with (2R,6R)‐hydroxynorketamine (HNK), an active metabolite of the antidepressant ketamine that stimulates mTORC1 signaling. Result Our results showed that genetic reduction of FMRP and 4E‐BP2 prevented the inhibition of hippocampal protein synthesis and memory impairment induced by amyloid‐β oligomers (AβOs) in mice. Reduction of 4E‐BP2 further rescued memory deficits in the APPswe/PS1dE9 (APP/PS1) transgenic mouse model of AD. Moreover, HNK treatment prevented deficits in long‐term potentiation (LTP) and fear memory in AbO‐infused and APP/PS1 mice. Conclusion Taken together, our findings indicate that strategies targeting mRNA translation correct hippocampal protein synthesis, synaptic plasticity and memory deficits in AD models, and raise the prospect that HNK could emerge as a therapeutic approach in AD.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".