Autophagy protein NRBF2 has reduced expression in Alzheimer’s brains and modulates memory and amyloid-beta homeostasis in mice
Bibliographic record
Abstract
BACKGROUND: Dysfunctional autophagy is implicated in Alzheimer's Disease (AD) pathogenesis. The alterations in the expression of many autophagy related genes (ATGs) have been reported in AD brains; however, the disparity of the changes confounds the role of autophagy in AD. METHODS: To further understand the autophagy alteration in AD brains, we analyzed transcriptomic (RNAseq) datasets of several brain regions (BA10, BA22, BA36 and BA44 in 223 patients compared to 59 healthy controls) and measured the expression of 130 ATGs. We used autophagy-deficient mouse models to assess the impact of the identified ATGs depletion on memory, autophagic activity and amyloid-β (Aβ) production. RESULTS: We observed significant downregulation of multiple components of two autophagy kinase complexes BECN1-PIK3C3 and ULK1/2-FIP200 specifically in the parahippocampal gyrus (BA36). Most importantly, we demonstrated that deletion of NRBF2, a component of the BECN1-PIK3C3 complex, which also associates with ULK1/2-FIP200 complex, impairs memory in mice, alters long-term potentiation (LTP), reduces autophagy in mouse hippocampus, and promotes Aβ accumulation. Furthermore, AAV-mediated NRBF2 overexpression in the hippocampus not only rescues the impaired autophagy and memory deficits in NRBF2-depleted mice, but also reduces β-amyloid levels and improves memory in an AD mouse model. CONCLUSIONS: Our data not only implicates NRBF2 deficiency as a risk factor for cognitive impairment associated with AD, but also support the idea of NRBF2 as a potential therapeutic target for 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 | 0.001 | 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".