Combined genome and transcriptome analysis identifies molecular signatures of aortic disease in patients with Marfan syndrome

Abstract

Introduction

Transcriptional dysregulation in patients with Marfan syndrome (MFS) is complex and not well-defined. There are likely patient-specific and general mechanisms in the aortic pathology. In this study, we combine genome and transcriptome data from patients with MFS to determine the transcriptional impacts of disease-causing variants in FBN1.

Methods

Prospectively enrolled participants provided blood and aortic tissue samples. Smooth muscle cells (SMCs) were cultured directly from the proximal aortic tissues of MFS cases undergoing aortic root replacement and controls during heart transplant. Genome sequencing (GS) analysis was combined with mRNA-sequencing (mRNA-seq) and single-cell gene expression profiling of SMCs. Findings in SMC culture analysis were further investigated in primary frozen aortic tissues.

Results

Automatic annotation of single-cell expression profiles classified 99% of cultured cells as SMCs. All disease-causing FBN1 variants were detected in both GS and SMC mRNA-seq reads. These included missense single nucleotide variants (SNVs), a whole-exon deletion, and a predicted stopgain SNV. Gene and allelic expression abnormalities in FBN1 were identified. Broadly, genes that were dysregulated in MFS were enriched for glycerophospholipid metabolism, immune, potassium channel, and extracellular matrix processes. Single-cell clustering analysis identified subtypes of SMCs. Some genes were differentially expressed in MFS across multiple SMC subtypes (e.g. TRPV2), whereas others were significant within specific SMC states (e.g. TGFB2 in SMCs expressing inflammatory markers).

Conclusions

mRNA-seq analysis of SMCs accurately identified FBN1 variants. General and patient-specific effects on allelic and gene expression were identified. Metabolism of glycerophospholipids may be dysregulated in aortic SMCs in MFS. Identifying pathogenic features with transcriptome analysis may guide novel diagnostic and therapeutic strategies.