Role of SIRT1 in Modulating Acetylation of the Sarco-Endoplasmic Reticulum Ca <sup>2+</sup> -ATPase in Heart Failure
Bibliographic record
Abstract
RATIONALE: -ATPase, is a critical determinant of cardiac function. Reduced level and activity of SERCA2a are major features of heart failure. Accordingly, intensive efforts have been made to develop efficient modalities for SERCA2a activation. We showed that the activity of SERCA2a is enhanced by post-translational modification with SUMO1 (small ubiquitin-like modifier 1). However, the roles of other post-translational modifications on SERCA2a are still unknown. OBJECTIVE: In this study, we aim to assess the role of lysine acetylation on SERCA2a function and determine whether inhibition of lysine acetylation can improve cardiac function in the setting of heart failure. METHODS AND RESULTS: The acetylation of SERCA2a was significantly increased in failing hearts of humans, mice, and pigs, which is associated with the reduced level of SIRT1 (sirtuin 1), a class III histone deacetylase. Downregulation of SIRT1 increased the SERCA2a acetylation, which in turn led to SERCA2a dysfunction and cardiac defects at baseline. In contrast, pharmacological activation of SIRT1 reduced the SERCA2a acetylation, which was accompanied by recovery of SERCA2a function and cardiac defects in failing hearts. Lysine 492 (K492) was of critical importance for the regulation of SERCA2a activity via acetylation. Acetylation at K492 significantly reduced the SERCA2a activity, presumably through interfering with the binding of ATP to SERCA2a. In failing hearts, acetylation at K492 appeared to be mediated by p300 (histone acetyltransferase p300), a histone acetyltransferase. CONCLUSIONS: These results indicate that acetylation/deacetylation at K492, which is regulated by SIRT1 and p300, is critical for the regulation of SERCA2a activity in hearts. Pharmacological activation of SIRT1 can restore SERCA2a activity through deacetylation at K492. These findings might provide a novel strategy for the treatment of heart failure.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".