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Record W4411466134 · doi:10.1113/jp289040

Computational modelling of cardiac fibroblast signalling reveals a key role for Ca<sup>2+</sup> in driving atrial fibrillation‐associated fibrosis

2025· article· en· W4411466134 on OpenAlexaff
Najme Khorasani, Haibo Ni, Jeffrey J. Saucerman, Dobromir Dobrev, Stefano Morotti, Eleonora Grandi

Bibliographic record

VenueThe Journal of Physiology · 2025
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Institutes of Health
KeywordsFibrosisAtrial fibrillationAngiotensin IIExtracellular matrixPeriostinMyofibroblastInternal medicineCardiac fibrosisFibronectinChemistrySinus rhythmCell biologyEndocrinologyMedicineBiologyReceptor

Abstract

fetched live from OpenAlex

Abstract Atrial fibrillation (AF) is the most common arrhythmia, characterized by irregular atrial electrical activity resulting in asynchronous atrial contraction. AF is accompanied by extensive structural remodelling of atria, including extracellular matrix expansion (fibrosis), which affects both AF maintenance and treatment outcomes. However, no fibrosis‐specific therapies are currently available for AF. To identify the prominent pathways in atrial fibroblasts (Fb) that modulate atrial fibrosis and arrhythmogenesis, we developed the first atrial Fb signalling network model. This expands on the well‐established ventricular model by integrating atrial‐relevant elements involved in fibrogenesis and/or differentially expressed in chronic AF ( vs . normal sinus rhythm) patients and connections based on experimental evidence in an Fb‐related context. Our findings indicate that under high profibrotic signals, e.g. angiotensin‐II (AngII) and transforming growth factor β, inhibition of Ca 2+ fluxes reduced the abundance of key fibrotic markers such as collagen I, collagen III, periostin, plasminogen activator inhibitor‐1, connective tissue growth factor and α‐smooth muscle actin, via modulation of the Ca 2+ /calmodulin‐dependent protein kinase II/Smad3 pathway and extra domain A of fibronectin via the calcineurin pathway. Mechanistically, we found that the Ca 2+ ‐dependent regulation of collagen I and III is primarily at the level of gene transcription, with collagen I and collagen III exhibiting similar dynamics in the Fb model. Overall, our study highlights the pivotal role of Ca 2+ signalling in the evolution of AF‐associated fibrogenesis and provides novel insights into potential anti‐AF therapeutic strategies targeting fibrotic responses. Future work will investigate in greater detail the upstream mechanisms driving Ca 2+ increases in atrial Fbs during AF. image Key points A fibroblast signalling network was developed incorporating new atrial‐informed elements and reactions to identify the prominent pathways that modulate atrial fibrosis and associated arrhythmogenesis, including atrial fibrillation (AF). The model was validated against experimental data in cardiac fibroblasts. For atrial‐specific validation, we focused on the model responses to AF‐relevant profibrotic inputs, i.e. angiotensin‐II (AngII) and transforming growth factor β (TGFβ). The analysis underscores the critical role of Ca 2+ signalling in mediating profibrotic responses under AF‐relevant stimuli, AngII and TGFβ and shows that Ca 2+ /calmodulin‐dependent protein kinase II/Smad3 and calcineurin mediate the Ca 2+ ‐dependent upregulation of key fibrotic markers.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.033
GPT teacher head0.298
Teacher spread0.266 · 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 designSimulation or modeling
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

Citations2
Published2025
Admission routes1
Has abstractyes

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