Glycogen Synthase Kinase-3β Is a Negative Regulator of Cardiomyocyte Hypertrophy
Bibliographic record
Abstract
Hypertrophy is a basic cellular response to a variety of stressors and growth factors, and has been best characterized in myocytes. Pathologic hypertrophy of cardiac myocytes leads to heart failure, a major cause of death and disability in the developed world. Several cytosolic signaling pathways have been identified that transduce prohypertrophic signals, but to date, little work has focused on signaling pathways that might negatively regulate hypertrophy. Herein, we report that glycogen synthase kinase-3beta (GSK-3beta), a protein kinase previously implicated in processes as diverse as development and tumorigenesis, is inactivated by hypertrophic stimuli via a phosphoinositide 3-kinase-dependent protein kinase that phosphorylates GSK-3beta on ser 9. Using adenovirus-mediated gene transfer of GSK-3beta containing a ser 9 to alanine mutation, which prevents inactivation by hypertrophic stimuli, we demonstrate that inactivation of GSK-3beta is required for cardiomyocytes to undergo hypertrophy. Furthermore, our data suggest that GSK-3beta regulates the hypertrophic response, at least in part, by modulating the nuclear/cytoplasmic partitioning of a member of the nuclear factor of activated T cells family of transcription factors. The identification of GSK-3beta as a transducer of antihypertrophic signals suggests that novel therapeutic strategies to treat hypertrophic diseases of the heart could be designed that target components of the GSK-3 pathway.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".