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Quantification of Monocyte Transmigration and Foam Cell Formation from Individuals with Chronic Inflammatory Conditions
Published on: October 17, 2017
Suppression of inflammatory signaling in monocytes from patients with coronary artery disease
Stephan H Schirmer1, Joost O Fledderus, Anja M van der Laan
1Department of Cardiology, Academic Medical Center, University of Amsterdam, 1105AZ Amsterdam, The Netherlands. stephan.schirmer@uks.eu
Insights
Monocyte gene expression signatures, not T-cells, showed differences in coronary artery disease (CAD) patients. A monocyte signature accurately predicted CAD status, suggesting its potential diagnostic value.
Area of Science:
- Cardiovascular Biology
- Immunology
- Genomics
Background:
- Monocytes and T-cells are implicated in atherosclerotic coronary artery disease (CAD) pathogenesis.
- Understanding circulating cell transcriptomes may reveal CAD pathophysiology and diagnostic biomarkers.
Purpose of the Study:
- To investigate transcriptome differences in circulating mononuclear cells between CAD patients and controls.
- To identify potential diagnostic biomarkers for atherosclerotic CAD.
Main Methods:
- Genome-wide expression analysis of CD4+ T-cells, CD14+ monocytes, stimulated monocytes, and macrophages.
- Careful matching of 18 severe CAD patients with 13 controls, all on statin and aspirin therapy.
- Analysis of soluble-ICAM levels to assess vascular inflammation.
Main Results:
- CD14+ monocytes from CAD patients showed overexpression of negative gene regulators, unlike controls.
- These monocyte differences vanished upon stimulation or differentiation into macrophages.
- A monocyte gene expression signature predicted patient status with 84% accuracy, despite large inter-individual variability.
Conclusions:
- Transcriptome differences in circulating cells play a potentially smaller role in CAD susceptibility than previously assumed.
- Monocyte gene expression signatures show promise as diagnostic biomarkers for atherosclerotic CAD.
- Vascular inflammation, indicated by soluble-ICAM, was elevated in CAD patients.
Abstract:
Monocytes and T-cells play an important role in the development of atherosclerotic coronary artery disease (CAD). Transcriptome analysis of circulating mononuclear cells from carefully matched atherosclerotic and control patients will potentially provide insights into the pathophysiology of atherosclerosis and supply biomarkers for diagnostic purposes. From patients undergoing coronary angiography because of anginal symptoms, we carefully matched 18 patients with severe triple-vessel CAD to 13 control patients without angiographic signs of CAD. All patients were on statin and aspirin treatment. Elevated soluble-ICAM levels demonstrated increased vascular inflammation in atherosclerotic patients. RNA from circulating CD4+ T-cells, CD14+ monocytes, lipopolysaccharide-stimulated monocytes, and macrophages was subjected to genome-wide expression analysis. In CD14+ monocytes, few inflammatory genes were overexpressed in control patients, while atherosclerotic patients showed overexpression of a group of Krüppel-associated box - containing transcription factors involved in negative regulation of gene expression. These differences disappeared upon LPS-stimulation or differentiation towards macrophages. No consistent changes in T cell transcriptomes were detected. Large inter-individual variability prevented the use of single differentially expressed genes as biomarkers, while monocyte gene expression signature predicted patient status with an accuracy of 84%. In this comprehensive analysis of circulating cell transcriptomes in atherosclerotic CAD, cautious patient matching revealed only small differences in transcriptional activity in different mononuclear cell types. Only an indication of a negative feedback to inflammatory gene expression was detected in atherosclerotic patients. Transcriptome differences of circulating cells possibly play less of a role than hitherto thought in the individual patient's susceptibility to atherosclerotic CAD, when appropriately matched for clinical symptoms and medication taken.
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