Role of the Wnt‐Frizzled system in cardiac pathophysiology: a rapidly developing, poorly understood area with enormous potential
Bibliographic record
Abstract
Abstract The Wnt-Frizzled (Fzd) G-protein-coupled receptor system, involving 19 distinct Wnt ligands and 10 Fzd receptors, plays key roles in the development and functioning of many organ systems. There is increasing evidence that Wnt-Fzd signalling is important in regulating cardiac function. Wnt-Fzd signalling primarily involves a canonical pathway, with dishevelled-1-dependent nuclear translocation of β-catenin that derepresses Wnt-sensitive gene transcription, but can also include non-canonical pathways via phospholipase-C/Ca(2+) mobilization and dishevelled-protein activation of small GTPases. Wnt-Fzd effects vary with specific ligand/receptor interactions and associated downstream pathways. This paper reviews the biochemistry and physiology of the Wnt-Fzd complex, and presents current knowledge of Wnt signalling in cardiac remodelling processes such as hypertrophy and fibrosis, as well as disease states such as myocardial infarction (MI), heart failure and arrhythmias. Wnt signalling is activated during hypertrophy; inhibiting Wnt signalling by activating glycogen synthase kinase attenuates the hypertrophic response. Wnt signalling has complex and time-dependent actions post-MI, so that either beneficial or harmful effects might result from Wnt-directed interventions. Stem cell biology, a promising area for therapeutic intervention, is highly regulated by Wnt signalling. The Wnt system regulates fibroblast function, and is prominently altered in arrhythmogenic ventricular cardiomyopathy, a familial disease involving excess deposition of fibroadipose tissue. Wnt signalling controls connexin43 expression, thereby contributing to the regulation of cardiac electrical stability and arrhythmia generation. Although much has been learned about Wnt-Fzd signalling in hypertrophy and infarction, its role is poorly understood for a broad range of other heart disorders. Much more needs to be learned for its contributions to be fully appreciated, and to permit more effective exploitation of its enormous potential in therapeutic development.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".