Attenuation of translational repression rescues memory impairment in Alzheimer`s disease mice
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is characterized by impaired synaptic plasticity and progressive memory deficits. Converging evidence indicates that hippocampal mRNA translation, required for memory consolidation, is defective in AD. Eukaryotic initiation factor 4E (eIF4E)‐binding protein 2 (4E‐BP2) and Fragile X mental retardation protein (FMRP) act as translational brakes at different stages of protein synthesis. While 4E‐BP2 represses protein synthesis at the initiation step through inhibition of ribosome‐mRNA complex formation, FMRP blocks translation at the mRNA elongation step through ribosome stalling Method Aβ oligomers were injected in 3‐month old mice lacking one allele of 4E‐BP2 and one or two alleles of Fmr1. Protein synthesis was assessed by non‐radioactive incorporation of puromycin (SuNSET) in hippocampal slices. Memory performance was assessed by contextual fear conditioning (CFC). Moreover, mice lacking one allele of 4E‐BP2 were crossed with APP/PS1 mice and memory was assessed by CFC at 12 month old. Result Genetic reduction of the translational repressors FMRP or 4E‐BP2, prevented the inhibition of hippocampal protein synthesis and memory impairment induced by Alzheimer’s‐linked amyloid‐β oligomers (AβOs) in mice. Moreover, 4E‐BP2 deletion rescued memory deficits in the APPswe/PS1dE9 (APP/PS1) transgenic mouse model of AD Conclusion Our findings demonstrate that strategies targeting translational repression correct hippocampal protein synthesis and memory deficits in AD models.
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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 | 0.001 | 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".