Prioritizing Candidates of Post-Myocardial Infarction Heart Failure Using Plasma Proteomics and Single-Cell Transcriptomics

Abstract

Background: Heart failure (HF) is the most common long-term complication of acute myocardial infarction (MI). Understanding plasma proteins associated with post-MI HF and their gene expression may identify new candidates for biomarker and drug target discovery.

Methods: We used aptamer-based affinity-capture plasma proteomics to measure 1305 plasma proteins at 1 month post-MI in a New Zealand cohort (CDCS [Coronary Disease Cohort Study]) including 181 patients post-MI who were subsequently hospitalized for HF in comparison with 250 patients post-MI who remained event free over a median follow-up of 4.9 years. We then correlated plasma proteins with left ventricular ejection fraction measured at 4 months post-MI and identified proteins potentially coregulated in post-MI HF using weighted gene co-expression network analysis. A Singapore cohort (IMMACULATE [Improving Outcomes in Myocardial Infarction through Reversal of Cardiac Remodelling]) of 223 patients post-MI, of which 33 patients were hospitalized for HF (median follow-up, 2.0 years), was used for further candidate enrichment of plasma proteins by using Fisher meta-analysis, resampling-based statistical testing, and machine learning. We then cross-referenced differentially expressed proteins with their differentially expressed genes from single-cell transcriptomes of nonmyocyte cardiac cells isolated from a murine MI model, and single-cell and single-nucleus transcriptomes of cardiac myocytes from murine HF models and human patients with HF.

Results: In the CDCS cohort, 212 differentially expressed plasma proteins were significantly associated with subsequent HF events. Of these, 96 correlated with left ventricular ejection fraction measured at 4 months post-MI. Weighted gene co-expression network analysis prioritized 63 of the 212 proteins that demonstrated significantly higher correlations among patients who developed post-MI HF in comparison with event-free controls (data set 1). Cross-cohort meta-analysis of the IMMACULATE cohort identified 36 plasma proteins associated with post-MI HF (data set 2), whereas single-cell transcriptomes identified 15 gene-protein candidates (data set 3). The majority of prioritized proteins were of matricellular origin. The 6 most highly enriched proteins that were common to all 3 data sets included well-established biomarkers of post-MI HF: N-terminal B-type natriuretic peptide and troponin T, and newly emergent biomarkers, angiopoietin-2, thrombospondin-2, latent transforming growth factor-β binding protein-4, and follistatin-related protein-3, as well.

Conclusions: Large-scale human plasma proteomics, cross-referenced to unbiased cardiac transcriptomics at single-cell resolution, prioritized protein candidates associated with post-MI HF for further mechanistic and clinical validation.

Keywords: heart failure; myocardial infarction; proteomics; transcriptome.

Figure 1.

Study design, proteomics, and transcriptomics workflow. Patients with recent MI were sampled at 30-day posthospitalization in both cohorts, CDCS and IMMACULATE. We performed an aptamer-based proteomics that measures 1305 proteins in a total of 654 patients post-MI and identified candidate proteins through differential expression and network analyses. We also analyzed 4 single-cell data sets to identify candidate genes that show consistent associations in murine and human MI and HF models. Candidates that were common to CDCS, IMMACULATE, and a single-cell data set were further investigated in murine and human cardiac cells subjected to specific HF conditions. CDCS indicates Coronary Artery Disease Cohort Study; DCM, dilated cardiomyopathy; DiNA, differential network analysis; HF, heart failure; IMMACULATE, Improving Outcomes in Myocardial Infarction through Reversal of Cardiac Remodelling; LVEF, left ventricular ejection fraction; MI, myocardial infarction; RT qPCR, real-time quantitative polymerase chain reaction; TAC, transverse aortic constriction; and WGCNA, weighted gene coexpression network analysis.

Figure 2.

Plasma proteins associated with post–myocardial infarction heart failure and 4-month post–myocardial infarction left ventricular ejection fraction. A, Volcano plot of the 1128 proteins measured in CDCS and their protein differential expression estimates by Limma. Colored dots represent significantly associated proteins at FDR ≤ 5%. B, Heat map of the protein expression levels vs left ventricular ejection fraction at 4 months (x axis). The strength of the correlation between protein expression and LVEF is indicated by the red and blue gradients of the heat map; a deeper shade of red indicates that higher protein levels (overexpression) correlate more strongly with a particular LVEF value, whereas a deeper shade of blue indicates that lower protein levels (underexpression) correlate more strongly with a particular LVEF value. The 17 proteins all show a negative correlation with LVEF such that high protein levels (deeper red) is observed with lower LVEF values. Patient group is indicated as HF in dark red and control in dark blue (top bar). A subset of the 96 significant proteins with the most highly correlated coefficients are shown (FDR ≤ 5%). The left bar shows the unsupervised hierarchical protein clusters. Protein expression levels have been smoothed by a nonparametric regression model. CDCS indicates Coronary Artery Disease Cohort Study; FC, fold change; FDR, false discovery rate; HF, heart failure; LVEF, left ventricular ejection fraction; and NT-proBNP, N-terminal pro B-type natriuretic peptide.

Figure 3.

Network analysis of plasma proteins. A, Hierarchical clustering highlighting the estimated, color-coded WGCNA modules in heart failure. B, The WGCNA Heart Failure network of the HF1 module proteins highlighting the significant coexpression hubs. In large font are the significant proteins of the Differential Network Analysis model. Only the connections with weighted correlations a_ii > 0.2 are shown. HF indicates heart failure; and WGCNA, weighted gene co-expression network analysis.

Figure 4.

Expression of top-priority candidate genes in human cardiac cell cultures. Human cardiac cells were cultured and stimulated in serum-free conditions. After stimulation, RNA was collected, and candidate gene expression was detected by quantitative polymerase chain reaction, relative to 18S/10 000 expression. Data show means, and error bars show standard deviation; n=3 biological replicates. Prohypertrophic (phenylephrine, isoproterenol, ET-1), fibrotic (TGFB), or inflammatory (IL1B). ANGPT2 indicates angiopoietin-2; CF, cardiac fibroblast; CM, cardiac myocyte; Ctrl, control; EC, endothelial cell; FSTL3, follistatin-related protein 3; IL1B, interleukin 1β; ISO, isoproterenol; LTBP4, latent transforming growth factor beta binding protein 4; NPPB, NT-proBNP/BNP; PE, phenylephrine; Rel. Exp., relative expression; SMC, smooth muscle cell; TGFB, transforming growth factor-B; THBS2, thrombospondin-2; and TNNT2, troponin T.

Figure 5.

Summary of post–myocardial infarction heart failure candidates. CDCS indicates Coronary Artery Disease Cohort Study; HF, heart failure; IMMACULATE, Improving Outcomes in Myocardial Infarction through Reversal of Cardiac Remodelling; LVEF, left ventricular ejection fraction; MI, myocardial infarction; QC, quality control; and RT qPCR, real-time quantitative polymerase chain reaction.