A plasma proteogenomic signature for fibromuscular dysplasia

Abstract

Aims: Fibromuscular dysplasia (FMD) is a poorly understood disease that predominantly affects women during middle-life, with features that include stenosis, aneurysm, and dissection of medium-large arteries. Recently, plasma proteomics has emerged as an important means to understand cardiovascular diseases. Our objectives were: (i) to characterize plasma proteins and determine if any exhibit differential abundance in FMD subjects vs. matched healthy controls and (ii) to leverage these protein data to conduct systems analyses to provide biologic insights on FMD, and explore if this could be developed into a blood-based FMD test.

Methods and results: Females with 'multifocal' FMD and matched healthy controls underwent clinical phenotyping, dermal biopsy, and blood draw. Using dual-capture proximity extension assay and nuclear magnetic resonance-spectroscopy, we evaluated plasma levels of 981 proteins and 31 lipid sub-classes, respectively. In a discovery cohort (Ncases = 90, Ncontrols = 100), we identified 105 proteins and 16 lipid sub-classes (predominantly triglycerides and fatty acids) with differential plasma abundance in FMD cases vs. controls. In an independent cohort (Ncases = 23, Ncontrols = 28), we successfully validated 37 plasma proteins and 10 lipid sub-classes with differential abundance. Among these, 5/37 proteins exhibited genetic control and Bayesian analyses identified 3 of these as potential upstream drivers of FMD. In a 3rd cohort (Ncases = 506, Ncontrols = 876) the genetic locus of one of these upstream disease drivers, CD2-associated protein (CD2AP), was independently validated as being associated with risk of having FMD (odds ratios = 1.36; P = 0.0003). Immune-fluorescence staining identified that CD2AP is expressed by the endothelium of medium-large arteries. Finally, machine learning trained on the discovery cohort was used to develop a test for FMD. When independently applied to the validation cohort, the test showed a c-statistic of 0.73 and sensitivity of 78.3%.

Conclusion: FMD exhibits a plasma proteogenomic and lipid signature that includes potential causative disease drivers, and which holds promise for developing a blood-based test for this disease.

Keywords: CD2AP; Fibromuscular dysplasia; Plasma protein; Proteomics.

Figure 1

Typical multifocal FMD and differential protein levels between FMD cases and matched healthy controls in the discovery cohort dataset. (A) Catheter-based angiographic image representative of the typical appearance of multifocal FMD affecting the carotid artery. (B) Typical catheter-based angiographic appearance of multifocal FMD affecting the renal artery, with so called ‘string-of-beads’ appearance. (C) Volcano plot of the differential protein level analysis comparing FMD patients to healthy controls; log2-fold change (log2-FC) on the horizontal axis, -log10 (P-value) on the vertical axis; the horizontal blue line marks the 10% FDR significance threshold. Selected proteins with large effect size or of further interest are labelled. Proteins that were subsequently validated (in the separate validation cohort) are represented with a blue halo. (D) Volcano plot of the differential lipid and lipoprotein level analysis in a fully adjusted model comparing FMD patients to healthy controls (co-variates were age, BMI, statin use, and non-statin lipid lowering medication use); log2-FC on the horizontal axis, -log10 (P-value) on the vertical axis; the horizontal blue line marks the 10% FDR significance threshold. Selected lipid and lipoprotein species with largest effect size are labelled. Lipids that were subsequently validated (in the separate validation cohort) are represented with a blue halo. ApoB, Apolipoprotein B; FreeC, free cholesterol; HDL-TG, triglycerides in HDL; LA, 18:2 linoleic acid; LDL-TG, triglycerides in LDL; MUFA, monounsaturated fatty acids 16:1, 18:1; PC, phosphatidylcholine and other cholines;. Remnant-C, remnant cholesterol (non-HDL, non-LDL-cholesterol); Serum-TG, serum total triglycerides; SFA, saturated fatty acids; TotFA, total fatty acids; UnSat, estimated degree of unsaturation of all fatty acids (the numeric value is an estimate of the average number of double bonds in the fatty acid chains); VLDL-C, total cholesterol in VLDL; VLDL-TG, triglycerides in VLDL.

Figure 2

The FMD protein signature is not due to medication use. FMD signature protein heatmap with the 105 signature FMD proteins from the discovery cohort presented in rows, and with z-scores of association with FMD shown in the first column, and different key medication classes in the other columns. FMD status (first column) was determined using all subjects, while medication use (other columns) was determined in FMD cases only. As expected, FMD disease status was strongly associated with each of these FMD signature proteins, while there was no association of these signature proteins with the use of differing classes of medications. z-Scores of protein associations with FMD disease status (all subjects) and differing classes of medication use (estimated within FMD patients only) corresponding to this figure are presented in Supplementary material online, Table S3.

Figure 3

FMD protein and lipid signature. Forest plot of association between validated FMD proteins and lipids, and FMD status, in three datasets: discovery cohort (grey lines), validation cohort (gold lines), and pooled cohort (blue lines). Estimated log2-fold changes on the horizontal axis, protein, and lipid labels on the vertical axis. Point estimates and 95% confidence intervals are further reported on the right.

Figure 4

Cross-correlations between validated FMD signature proteins and lipids. (A) Heatmap of Pearson cross-correlations: proteins in rows, lipids in columns. (B) Corresponding table of cross-correlations below the 10% FDR cut-off. NGID, Nightingale identification code.

Figure 5

A diagnostic test algorithm for FMD. Machine learning was used to generate diagnostic algorithms for predicting that a subject has FMD. Algorithms were exclusively generated using the discovery cohort datasets. Data shown in this figure represent the independent application of these algorithms to the validation cohort datasets. (A) Receiver operating characteristic (ROC) curve for a diagnostic algorithm for predicting FMD based only on plasma proteins (red), and also based on both the protein and lipid data (green). (B) Performance characteristics of the diagnostic test for FMD based only on protein data (as related to red line in A), when independently applied to the validation dataset. A total of 34 proteins were included in this model. (C) Performance characteristics of the diagnostic test for FMD based on the protein and lipid data (as related to green line in A), when independently applied to the validation dataset. A total of 34 proteins and all 31 lipid parameters were included in this model. Age and other demographic data did not enter into these models.

Figure 6

CD2AP is associated with FMD and is expressed by endothelial cells. (A) Locus zoom plot showing the peak of association with FMD risk, upstream of the gene encoding CD2AP. (B–D) Immune-fluorescence staining for CD2AP was performed on adult human non-FMD samples from (B) renal artery, (C) internal mammary artery, and (D) aorta. Endothelial cells were specifically identified by staining for CD31 (green), while CD2AP is shown in red. Nuclei were stained with DAPI (blue). Scale bar represents 25 μm. Inset panels on the right, representing an enlarged view of the area in the respective dashed squares, show endothelial cells at higher magnification. M, tunica media; L, lumen. All images are representative, and consistent results were obtained from staining of the aorta, internal mammary artery, and renal artery from at least three different subjects for each sample type.