Reduced Endoglin Activity Limits Cardiac Fibrosis and Improves Survival in Heart Failure
Bibliographic record
Abstract
BACKGROUND: Heart failure is a major cause of morbidity and mortality worldwide. The ubiquitously expressed cytokine transforming growth factor-β1 (TGFβ1) promotes cardiac fibrosis, an important component of progressive heart failure. Membrane-associated endoglin is a coreceptor for TGFβ1 signaling and has been studied in vascular remodeling and preeclampsia. We hypothesized that reduced endoglin expression may limit cardiac fibrosis in heart failure. METHODS AND RESULTS: We first report that endoglin expression is increased in the left ventricle of human subjects with heart failure and determined that endoglin is required for TGFβ1 signaling in human cardiac fibroblasts using neutralizing antibodies and an siRNA approach. We further identified that reduced endoglin expression attenuates cardiac fibrosis, preserves left ventricular function, and improves survival in a mouse model of pressure-overload-induced heart failure. Prior studies have shown that the extracellular domain of endoglin can be cleaved and released into the circulation as soluble endoglin, which disrupts TGFβ1 signaling in endothelium. We now demonstrate that soluble endoglin limits TGFβ1 signaling and type I collagen synthesis in cardiac fibroblasts and further show that soluble endoglin treatment attenuates cardiac fibrosis in an in vivo model of heart failure. CONCLUSION: Our results identify endoglin as a critical component of TGFβ1 signaling in the cardiac fibroblast and show that targeting endoglin attenuates cardiac fibrosis, thereby providing a potentially novel therapeutic approach for individuals with 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".