MétaCan
Menu
Back to cohort
Record W7117235796 · doi:10.1002/alz70855_105957

Regulatory Networks of circRNAs, miRNAs, and mRNAs in a APP/PS1 Mouse Model

2025· article· en· W7117235796 on OpenAlexaff
Lavínia Perquim, Marco Antônio De Bastiani, Oak Hatzimanolis, Alexandre Santos Cristino, Eduardo R. Zimmer

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCircular RNAs in diseases
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsGeneGene expressionDiseaseGene regulatory networkRegulation of gene expressionTranscriptomeGABAergic

Abstract

fetched live from OpenAlex

BACKGROUND: Recent advances have highlighted non-coding RNAs as key regulators of gene expression, including circRNAs, single-stranded, covalently closed RNAs, and miRNAs, small RNAs that inhibit mRNA translation. Growing evidence suggests that dysregulated expression of both circRNAs and miRNAs is linked to AD. In this study, we assessed whether circRNA transcription is altered in the hippocampus, a region highly vulnerable to AD, and explored the integrated regulation of circRNAs, miRNAs, and mRNAs in a transgenic amyloid mouse model. METHOD: We analyzed circRNAs from the hippocampus of the APPswe/PSEN1dE9 mouse model at 4, 6, and 8 months of age, using datasets available in the NCBI database. CircRNA identification was performed with the CIRI2 algorithm, and differential expression was evaluated using DESeq2 (p-value < 0.01) to compare transgenic mice with age-matched wild-type controls. Commonly altered circRNAs across studies were identified using Venn diagrams. A regulatory network of circRNAs and miRNAs was constructed with circFunBase, while miRDB was employed to investigate miRNA-mRNA interactions. Functional enrichment analysis of mRNAs was conducted using Gene Ontology (GO) terms with the ClusterProfiler R package. RESULT: We identified 41 differentially expressed circRNAs in 4-month-old mice, 82 at 6 months, and 425 at 8 months (Figure 1). The Venn Diagram results showed that a total of 18 circRNAs were shared among the groups (Figure 2A). Of those, we selected circRNA HOMER1, previously described in humans, which interacted with 36 miRNAs (Figure 2B), and these miRNAs, in turn, targeted 1682 genes (Target Score>100). The GO enrichment analysis revealed 51 gene ontologies associated with these genes (Figure 3). CONCLUSION: Our results indicate a growing dysregulation of circRNAs in the APPswe/PSEN1dE9, suggesting they follow disease severity. The dysregulation of circRNAs previously described in humans highlights their evolutionary conservation and the advantage of studying them in animal models. Investigating the interactions among circRNAs, miRNAs, and mRNAs revealed genes associated with AD pathophysiology, such as neural differentiation, GABAergic signaling, stress response, and neuroinflammation, suggesting an impact on gene expression in regions vulnerable to the disease.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.239
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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 teacher head, 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & DementiaSame topicCircular RNAs in diseasesFrench-language works237,207