C-Reactive Protein Attenuates Endothelial Progenitor Cell Survival, Differentiation, and Function
Bibliographic record
Abstract
BACKGROUND: Myocardial ischemia provides a potent stimulus to angiogenesis, and the mobilization and differentiation of endothelial progenitor cells (EPCs) has been shown to be important in this process. An elevated level of C-reactive protein (CRP) has emerged as one of the most powerful predictors of cardiovascular disease. However, the impact of CRP on EPC biology is unknown. METHODS AND RESULTS: EPCs were isolated from the peripheral venous blood of healthy male volunteers. Cells were cultured in endothelial cell basal medium-2 in the absence and presence of CRP (5 to 20 microg/mL), rosiglitazone (1 micromol/L), and/or vascular endothelial growth factor. EPC differentiation, survival, and function were assayed. CRP at concentrations > or =15 microg/mL significantly reduced EPC cell number, inhibited the expression of the endothelial cell-specific markers Tie-2, EC-lectin, and VE-cadherin, significantly increased EPC apoptosis, and impaired EPC-induced angiogenesis. EPC-induced angiogenesis was dependent on the presence of nitric oxide, and CRP treatment caused a decrease in endothelial nitric oxide synthase mRNA expression by EPCs. However, all of these detrimental CRP-mediated effects on EPCs were attenuated by pretreatment with rosiglitazone, a peroxisome proliferator-activated receptor-gamma (PPARgamma) agonist. CONCLUSIONS: Human recombinant CRP, at concentrations known to predict adverse vascular outcomes, directly inhibits EPC differentiation, survival, and function, key components of angiogenesis and the response to chronic ischemia. This occurs in part via an effect of CRP to reduce EPC eNOS expression. The PPARgamma agonist rosiglitazone inhibits the negative effects of CRP on EPC biology. The ability of CRP to inhibit EPC differentiation and survival may represent an important mechanism that further links inflammation to cardiovascular disease.
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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) | 0.001 | 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".