ELK1 inhibition alleviates amyloid pathology and memory decline by promoting the SYVN1-mediated ubiquitination and degradation of PS1 in Alzheimer’s disease
Bibliographic record
Abstract
ELK1 is a member of the E-twenty-six transcription factor family and is usually activated by phosphorylation at Ser383 and Ser389 by extracellular signal-regulated kinase 1/2 (ERK1/2). Dysregulation of ERK1/2 is involved in Alzheimer's disease (AD)-related neuropathogenesis and cognitive impairments. However, the role of ELK1 in AD pathogenesis remains unclear. Here we report that the expression of ELK1 was significantly increased in the brain tissues of patients with AD and AD model mice. The genetic knockdown of ELK1 or inhibition of its phosphorylation by an interfering peptide (TAT-DEF-ELK1 (TDE)) reduced amyloidogenic processing of APP by targeting PS1, consequently inhibiting Aβ generation and alleviating synaptic and memory impairments in APP23/PS45 double-transgenic AD model mice. In addition, we further found that ELK1 regulated the expression of PS1 by competitively inhibiting the interaction between PS1 and its E3 ubiquitin ligase synoviolin (SYVN1), thereby inhibiting the SYVN1-mediated ubiquitination and degradation of PS1. Our results demonstrate that ELK1 aberrantly increases in AD and genetic or pharmacological inhibition of ELK1 can alleviate AD-related pathology and memory impairments by enhancing the SYVN1-mediated PS1 ubiquitination and degradation, indicating that ELK1 may be a novel target for AD treatment.
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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".