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Record W2909969157 · doi:10.1172/jci.insight.123431

BACE2, a conditional β-secretase, contributes to Alzheimer’s disease pathogenesis

2019· article· en· W2909969157 on OpenAlexafffund
Zhe Wang, Qin Xu, Fang Cai, Yili Wu, Weihong Song

Bibliographic record

VenueJCI Insight · 2019
Typearticle
Languageen
FieldMedicine
TopicClusterin in disease pathology
Canadian institutionsUniversity of British Columbia
FundersRIKENUniversity of British ColumbiaCapital Medical UniversityNational Natural Science Foundation of ChinaRIKEN Brain Science InstituteCanadian Institutes of Health ResearchAlzheimer SocietyNational Science Foundation
KeywordsPathogenesisAmyloid precursor protein secretaseClusterinAmyloid precursor proteinSenile plaquesNeuropathologyPresenilinAlzheimer's diseaseP3 peptideCell biologyBiochemistry of Alzheimer's diseaseChemistryBiologyNeuroscienceMedicineDiseaseInternal medicineBiochemistryApoptosis

Abstract

fetched live from OpenAlex

Deposition of amyloid-β protein (Aβ) to form neuritic plaques is the characteristic neuropathology of Alzheimer's disease (AD). Aβ is generated from amyloid precursor protein (APP) by β- and γ-secretase cleavages. BACE1 is the β-secretase and its inhibition induces severe side effects, whereas its homolog BACE2 normally suppresses Aβ by cleaving APP/Aβ at the θ-site (Phe20) within the Aβ domain. Here, we report that BACE2 also processes APP at the β site, and the juxtamembrane helix (JH) of APP inhibits its β-secretase activity, enabling BACE2 to cleave nascent APP and aggravate AD symptoms. JH-disrupting mutations and clusterin binding to JH triggered BACE2-mediated β-cleavage. Both BACE2 and clusterin were elevated in aged mouse brains, and enhanced β-cleavage during aging. Therefore, BACE2 contributes to AD pathogenesis as a conditional β-secretase and could be a preventive and therapeutic target for AD without the side effects of BACE1 inhibition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.291
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations78
Published2019
Admission routes2
Has abstractyes

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