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Abstract 12229: MicroRNA Regulation of JAK-STAT System in the Atrial Fibrillation-Related Fibrotic Response

2015· article· en· W4386665796 on OpenAlexaff
Yu Chen, Sirirat Surinkaew, Jiening Xiao, Chia Tung Wu, Hai Huang, Yiguo Sun, Dobromir Dobrev, Stanley Nattel

Bibliographic record

VenueCirculation · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicinemicroRNAVentriclePlatelet-derived growth factor receptorAtrial fibrillationSTAT3Western blotFibrosisInternal medicineHeart failureEndocrinologyGrowth factorPhosphorylationBiologyCell biologyReceptorGeneBiochemistry

Abstract

fetched live from OpenAlex

Introduction: MicroRNAs (miRNAs) are involved in cardiac remodeling, but their role in atrial fibrillation (AF) is poorly understood. JAK-STAT signalling is activated by the pro-fibrotic mediator PDGF, but its contribution to AF substrate formation is unknown. Here, we investigated miRNA regulation of the JAK-STAT system in in the heart failure (HF) induced AF fibrotic substrate. Methods: HF was induced in dogs by ventricular tachypacing (VTP, 240 bpm) for 2 weeks. Fibroblasts (FBs) isolated from the left atrium (LA) and left ventricle (LV) of control (CTL) dogs were treated with PDGF-AB. PDGF, JAK2, STAT3 and miRNA expression was assessed by qPCR. JAK2, STAT3 and collagen-I (COL-1) proteins were quantified by Western Blot. miRNA targets were validated by luciferase reporter assay. miRNA mimic or antisense oligonucleotides (AMOs) were used for miRNA manipulation in FBs. Results: HF dogs developed AF susceptibility and LA-selective fibrosis beginning at 1 wk VTP. PDGF and JAK2 mRNA, along with phosphorylated (activated) JAK2 protein, were increased in HF atria (by 1.4-2.9* fold, *p<0.05). miR-30a and miR-133a were predicted bioinformatically to target JAK2, and were downregulated by 51%-52%** and 48%-71%** (**p<0.01) in HF LA FBs at 1-2 wk VTP. PDGF-AB increased the expression of JAK2 and STAT3 mRNA (by 1.4-1.5* fold) and COL-1 protein (by 2*** fold, ***p<0.001) in canine LA FBs, while reducing the expression of miR-30a and miR-133a (by ~50%-80% ***). Smaller changes were seen in LV FBs with PDGF-stimulation. Luciferase assay confirmed miR-30a and miR-133a targeting of JAK2. Overexpression of miR-133a downregulated JAK2 mRNA (by 30%***) and protein (by 50%, p=0.05) in LA FBs. This effect was abolished by co-transfection of miR-133a and AMO-133a. The upregulation of JAK2 and COL-1 expression induced by PDGF-AB stimulation was reversed by miR-133a overexpression in LA FBs (reduced by 49%* and 53%*** versus PDGF stimulation alone). Conclusions: PDGF-induced disinhibition of JAK2 expression by downregulation of miR-30a and miR-133a may contribute to AF-associated fibrotic responses. MicroRNA targeting of the JAK-STAT system regulates LA FB function, appears to be involved in HF-induced LA fibrosis and is a potential therapeutic target for AF prevention.

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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.022
GPT teacher head0.279
Teacher spread0.258 · 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".

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Published2015
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