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AtheroSpectrum Reveals Novel Macrophage Foam Cell Gene Signatures Associated With Atherosclerotic Cardiovascular
Chuan Li1, Lili Qu1, Alyssa J Matz1
1Department of Immunology (C.L., L.Q., A.J.M., A.T.V., B.Z.), University of Connecticut, Farmington.
Insights
A new tool, AtheroSpectrum, identifies inflammatory macrophage foam cells and a 30-gene signature. This signature improves prediction of atherosclerotic cardiovascular disease (ASCVD) risk when combined with traditional factors.
Area of Science:
- Cardiovascular research
- Genomics
- Computational biology
Background:
- Despite lipid-lowering interventions, atherosclerotic cardiovascular disease (ASCVD) event risks persist, indicating a need for better risk prediction.
- Monocytes and macrophages are crucial in atherosclerosis, yet detailed understanding of their role in ASCVD risk is limited.
Purpose of the Study:
- To develop and validate a novel computational tool and gene signature for improved ASCVD risk prediction.
- To identify specific macrophage populations and gene expression profiles associated with increased ASCVD risk.
Main Methods:
- Developed AtheroSpectrum, a tool analyzing macrophage lipid metabolism and inflammation using quantitative indices.
- Utilized machine learning to analyze monocyte transcriptomes from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort.
- Created a 30-gene cardiovascular disease risk score (CR-30) integrating gene expression with traditional risk factors.
Main Results:
- AtheroSpectrum identified distinct homeostatic and inflammatory pathogenic foaming programs in plaque macrophages.
- A 30-gene panel, derived from pathogenic foaming genes, significantly improved ASCVD risk prediction in validation cohorts.
- The CR-30 model demonstrated good performance across multiple datasets, outperforming traditional risk factors alone.
Conclusions:
- A novel computational program, AtheroSpectrum, identified a gene expression profile linked to inflammatory macrophage foam cells.
- A 30-gene signature in circulating monocytes, combined with traditional risk factors, enhances symptomatic atherosclerotic vascular disease prediction.
- These findings offer potential for improved mechanistic insights and therapeutic strategies for ASCVD.
Background:
Whereas several interventions can effectively lower lipid levels in people at risk for atherosclerotic cardiovascular disease (ASCVD), cardiovascular event risks remain, suggesting an unmet medical need to identify factors contributing to cardiovascular event risk. Monocytes and macrophages play central roles in atherosclerosis, but studies have yet to provide a detailed view of macrophage populations involved in increased ASCVD risk.
Methods:
A novel macrophage foaming analytics tool, AtheroSpectrum, was developed using 2 quantitative indices depicting lipid metabolism and the inflammatory status of macrophages. A machine learning algorithm was developed to analyze gene expression patterns in the peripheral monocyte transcriptome of MESA participants (Multi-Ethnic Study of Atherosclerosis; set 1; n=911). A list of 30 genes was generated and integrated with traditional risk factors to create an ASCVD risk prediction model (30-gene cardiovascular disease risk score [CR-30]), which was subsequently validated in the remaining MESA participants (set 2; n=228); performance of CR-30 was also tested in 2 independent human atherosclerotic tissue transcriptome data sets (GTEx [Genotype-Tissue Expression] and GSE43292).
Results:
Using single-cell transcriptomic profiles (GSE97310, GSE116240, GSE97941, and FR-FCM-Z23S), AtheroSpectrum detected 2 distinct programs in plaque macrophages-homeostatic foaming and inflammatory pathogenic foaming-the latter of which was positively associated with severity of atherosclerosis in multiple studies. A pool of 2209 pathogenic foaming genes was extracted and screened to select a subset of 30 genes correlated with cardiovascular event in MESA set 1. A cardiovascular disease risk score model (CR-30) was then developed by incorporating this gene set with traditional variables sensitive to cardiovascular event in MESA set 1 after cross-validation generalizability analysis. The performance of CR-30 was then tested in MESA set 2 (P=2.60×10-4; area under the receiver operating characteristic curve, 0.742) and 2 independent data sets (GTEx: P=7.32×10-17; area under the receiver operating characteristic curve, 0.664; GSE43292: P=7.04×10-2; area under the receiver operating characteristic curve, 0.633). Model sensitivity tests confirmed the contribution of the 30-gene panel to the prediction model (likelihood ratio test; df=31, P=0.03).
Conclusions:
Our novel computational program (AtheroSpectrum) identified a specific gene expression profile associated with inflammatory macrophage foam cells. A subset of 30 genes expressed in circulating monocytes jointly contributed to prediction of symptomatic atherosclerotic vascular disease. Incorporating a pathogenic foaming gene set with known risk factors can significantly strengthen the power to predict ASCVD risk. Our programs may facilitate both mechanistic investigations and development of therapeutic and prognostic strategies for ASCVD risk.
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