Disruption of BAG3-mediated BACE1 stabilization alleviates neuropathology and memory deficits in a mouse model of Alzheimer’s disease
Bibliographic record
Abstract
β-Site amyloid precursor protein (APP)–cleaving enzyme 1 (BACE1) is the rate-limiting enzyme for amyloid-β (Aβ) generation and is considered promising drug target for Alzheimer’s disease (AD). The co-chaperone BAG3 (Bcl-2–associated athanogene 3) plays an important role in maintaining intracellular protein homeostasis by regulating heat shock protein 70 (HSP70). Here, we reported that BAG3 expression was significantly elevated in AD. It interacted with and stabilized BACE1 by delaying its degradation through ubiquitin-proteasome and autophagy-lysosomal pathways. BAG3 E455K and BAG3 R480A mutations reduced their interaction with BACE1. SPOT peptide arrays revealed that BACE1 carboxyl-terminal peptide fragments bound to the RQ domain of BAG3. This interaction can be disrupted by BACE1-derived peptide (Tat-BACE1 480-494 ), leading to decreased BACE1 stability. In APP23/PS45 double transgenic mice, Tat-BACE1 480-494 reduced BACE1 levels, decreased Aβ production, and improved synaptic and cognitive deficits. These findings indicate that BAG3 forms complex with HSP70 and BACE1 to stabilize BACE1, suggesting that Tat-BACE1 480-494 , may represent an ideal class of neuroprotective therapeutics against 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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".