Sirtuin 1 activation attenuates cardiac fibrosis in a rodent pressure overload model by modifying Smad2/3 transactivation
Bibliographic record
Abstract
Aims: Transforming growth factor β1 (TGF-β1) is a prosclerotic cytokine involved in cardiac remodelling leading to heart failure (HF). Acetylation/de-acetylation of specific lysine residues in Smad2/3 has been shown to regulate TGF-β signalling by altering its transcriptional activity. Recently, the lysine de-acetylase sirtuin 1 (SIRT1) has been shown to have a cardioprotective effect; however, SIRT1 expression and activity are paradoxically reduced in HF. Herein, we investigate whether pharmacological activation of SIRT1 would induce cardioprotection in a pressure overload model and assess the impact of SIRT1 activation on TGF-β signalling and the fibrotic response. Methods and results: Eight weeks old male C57BL/6 mice were randomized to undergo sham surgery or transverse aortic constriction (TAC) to induce pressure overload. Post-surgery, animals were further randomized to receive SRT1720 or vehicle treatment. Echocardiography, pressure-volume loops, and histological analysis revealed an impairment in cardiac function and deleterious left ventricular remodelling in TAC-operated animals that was improved with SRT1720 treatment. Genetic ablation and cell culture studies using a Smad-binding response element revealed SIRT1 to be a specific target of SRT1720 and identified Smad2/3 as a SIRT1 specific substrate. Conclusion: Overall, our data demonstrate that Smad2/3 is a specific SIRT1 target and suggests that pharmacological activation of SIRT1 may be a novel therapeutic strategy to prevent/reverse HF via modifying Smad activity.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".