The GSK-3 Family as Therapeutic Target for Myocardial Diseases
Bibliographic record
Abstract
Glycogen synthase kinase-3 (GSK-3) is one of the few signaling molecules that regulate a truly astonishing number of critical intracellular signaling pathways. It has been implicated in several diseases including heart failure, bipolar disorder, diabetes mellitus, Alzheimer disease, aging, inflammation, and cancer. Furthermore, a recent clinical trial has validated the feasibility of targeting GSK-3 with small molecule inhibitors for human diseases. In the current review, we will focus on its expanding role in the heart, concentrating primarily on recent studies that have used cardiomyocyte- and fibroblast-specific conditional gene deletion in mouse models. We will highlight the role of the GSK-3 isoforms in various pathological conditions including myocardial aging, ischemic injury, myocardial fibrosis, and cardiomyocyte proliferation. We will discuss our recent findings that deletion of GSK-3α specifically in cardiomyocytes attenuates ventricular remodeling and cardiac dysfunction after myocardial infarction by limiting scar expansion and promoting cardiomyocyte proliferation. The recent emergence of GSK-3β as a regulator of myocardial fibrosis will also be discussed. We will review our recent findings that specific deletion of GSK-3β in cardiac fibroblasts leads to fibrogenesis, left ventricular dysfunction, and excessive scarring in the ischemic heart. Finally, we will examine the underlying mechanisms that drive the aberrant myocardial fibrosis in the models in which GSK-3β is specifically deleted in cardiac fibroblasts. We will summarize these recent results and offer explanations, whenever possible, and hypotheses when not. For these studies we will rely heavily on our models and those of others to reconcile some of the apparent inconsistencies in the literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".