Cardiomyocyte specific overexpression of a 37 amino acid domain of regulator of G protein signalling 2 inhibits cardiac hypertrophy and improves function in response to pressure overload in mice
Bibliographic record
Abstract
Regulator of G protein signalling 2 (RGS2) is known to play a protective role in maladaptive cardiac hypertrophy and heart failure via its ability to inhibit G q - and G s - mediated GPCR signalling. We previously demonstrated that RGS2 can also inhibit protein translation and can thereby attenuate cell growth. This G protein-independent inhibitory effect has been mapped to a 37 amino acid domain (RGS2 eb ) within RGS2 that binds to eukaryotic initiation factor 2B (eIF2B). When expressed in neonatal rat cardiomyocytes, RGS2 eb attenuates both protein synthesis and hypertrophy induced by G q - and G s - activating agents. In the current study, we investigated the potential cardioprotective role of RGS2 eb by determining whether RGS2 eb transgenic (RGS2 eb TG) mice with cardiomyocyte specific overexpression of RGS2 eb show resistance to the development of hypertrophy in comparison to wild-type (WT) controls. Using transverse aortic constriction (TAC) in a pressure-overload hypertrophy model, we demonstrated that cardiac hypertrophy was inhibited in RGS2 eb TG mice compared to WT controls following four weeks of TAC. Expression of the hypertrophic markers atrial natriuretic peptide (ANP) and β-myosin heavy chain (MHC-β) was also reduced in RGS2 eb TG compared to WT TAC animals. Furthermore, cardiac function in RGS2 eb TG TAC mice was significantly improved compared to WT TAC mice. Notably, cardiomyocyte cell size was significantly decreased in TG compared to WT TAC mice. These results suggest that RGS2 may limit pathological cardiac hypertrophy at least in part via the function of its eIF2B-binding domain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".