The ketamine metabolite ( <i>2R,6R</i> )-hydroxynorketamine rescues hippocampal mRNA translation, synaptic plasticity and memory in mouse models of Alzheimer’s disease
Bibliographic record
Abstract
Abstract Impaired synaptic plasticity and progressive memory deficits are major hallmarks of Alzheimer’s disease (AD). Hippocampal mRNA translation, required for memory consolidation, is defective in AD. Here, we show that systemic treatment with ( 2R,6R )- hydroxynorketamine (HNK), an active metabolite of the antidepressant ketamine, prevented deficits in hippocampal mRNA translation, long-term potentiation (LTP) and memory induced by AD-linked amyloid-β oligomers (AβOs) in mice. HNK activated hippocampal extracellular signal-regulated kinase 1/2 (ERK1/2), mechanistic target of rapamycin (mTOR), and p70S6 kinase 1 (S6K1)/ribosomal protein S6 (S6), which promote protein synthesis and synaptic plasticity. Stimulation of S6 phosphorylation by HNK was mTORC1-dependent, while rescue of hippocampal LTP and memory in HNK-treated AβO-infused mice was ERK1/2-dependent and, partially, mTORC1- dependent. Remarkably, treatment with HNK corrected LTP and memory deficits in aged APP/PS1 mice. Transcriptomic analysis further showed that HNK rescued signaling pathways that are aberrant in APP/PS1 mice, including inflammatory and hormonal responses, and programmed cell death. Taken together, our findings demonstrate that HNK induces signaling and transcriptional responses that correct deficits in hippocampal synaptic plasticity and memory in AD mouse models. These results raise the prospect that HNK could serve as a therapeutic to prevent or reverse memory decline 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".