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Record W4390125626 · doi:10.1101/2023.12.21.572837

A Novel Pathway Implicated in Regulating Cognitive disfunction in a <i>Drosophila</i> Alzheimer’s Disease Model through Acer Inhibition and CG2233 Modulation

2023· preprint· en· W4390125626 on OpenAlexafffund
Judy Ghalayini, S H Lee, Oxana B. Gluscencova, Konstantin G. Iliadi, Gabrielle L. Boulianne

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsDiseaseNeuroscienceDementiaTransgeneMedicineMechanism (biology)NeuroprotectionBioinformaticsBiologyInternal medicineGeneticsGene

Abstract

Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for most dementia cases worldwide. Current therapies for AD have limited effectiveness in slowing disease progression or delivering a cure. As such, there is an immediate need for ongoing research and innovative strategies to tackle this multifaceted disease. Recently, several studies have implicated the renin-angiotensin system (RAS), known to regulate blood pressure, as a possible therapeutic target for AD. RAS-inhibiting drugs, including angiotensin-converting enzyme inhibitors (ACE-Is), have been shown to reduce the incidence and progression of AD. However, the literature describing their beneficial effects is inconsistent, with contradictory findings reporting no effects. How these drugs may function in AD remains poorly understood. Our previous work in Drosophila models expressing AD-related transgenes investigated the benefits of captopril, an ACE-I, and found it effectively rescued AD-related phenotypes including cognitive performance independent of Aβ42 changes. Importantly, our study implicated Acer, a homolog of mammalian ACE, as a key player. In our current study, we demonstrate that the beneficial outcomes of Acer inhibition depend on preventing its catalytic activity and downstream target processing. We identify CG2233 as a prospective target and reveal its functional interaction with Acer. Furthermore, we show CG2233 is implicated in AD-related pathways in Aβ42 expressing flies. Together, these findings provide a new avenue to study the role of ACE in AD. Significance Statement AD is a devastating neurodegenerative disorder with limited therapeutic success. Emerging research highlights the potential of inhibiting the renin-angiotensin system (RAS) in AD. Epidemiological findings and experimental studies have shown promising outcomes with RAS-targeting drugs including angiotensin-converting enzyme inhibitors (ACE-Is). Our previous work in Drosophila AD models revealed the efficacy of captopril, an ACE-I, in improving AD-related phenotypes. Moreover, we identified Acer as a key player in these mechanisms. Our current study further elucidates the role of Acer, identifies CG2233 as a potential target, and uncovers their functional interaction, shedding light on pathways relevant to AD phenotypes. This research underscores the significance of investigating ACE and ACE-I mechanisms in AD, offering potential innovative means for AD therapy.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: high

Drosophila model of Alzheimer's disease and ACE inhibition; a biomedical mechanism question.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

The study investigates biological mechanisms in an Alzheimer's disease model rather than research practice.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Drosophila Alzheimer disease model biology; object is disease pathways, not research practice.

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 score0.002
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0020.000

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 head0.057
GPT teacher head0.291
Teacher spread0.234 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

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