Membrane Trafficking of TGF-β and Transcriptome Analysis in Marfan Syndrome

Abstract

Marfan Syndrome (MFS) is a rare, autosomal dominant disorder of the connective tissue that affects between 1.5 and 17.2 in 100.000 live births. MFS is caused by mutations in the extracellular matrix (ECM) glycoprotein Fibrillin-1 (FBN1). FBN1 is the main constituent of microfibrils, which, together with elastin, form the elastic fibres of the connective tissue throughout the body. MFS is multisystemic in its nature and can affect various organ systems in a variety of combinations and with consequences ranging from mild to severe. Approximately 3000 FBN1 mutations have been annotated to date and different mutation types have been attempted to be correlated with the organ system involved as well as the severity of disease outcome. Due to the large heterogeneity, mutations have been clustered into dominant negative and haploinsufficient phenotype based on the protein outcome. Yet, roughly a third of mutations cannot be assigned to either group and is therefore excluded from studies, which, to this date, show modest correlation. The most severe clinical outcome of MFS is the formation of ascending aortic aneurysms. These originate in the tunica media which consists of vascular smooth muscle cells (VSMC) and elastic fibres. FBN1 is crucial for the structural ECM integrity but has also been shown to be important in the regulation of Transforming Growth Factor-β (TGF-β). TGF-β is a ubiquitous cytokine whose release from the ECM is controlled by FBN1 binding. In MFS, the fragmentation of elastic fibres due to FBN1 mutation leads to increased release of TGF-β into the extracellular environment and increased TGF-β signalling. TGF-β endocytosis takes place through early endosomes and caveolin-1-positive vesicles. Endocytosis through early endosomes is associated with signal transduction and SARA-dependent recruitment of SMAD2 to the receptor complex as well as SMAD nuclear translocation which elicits changes in approximately 500 target genes. Increased TGF-β signalling induces the deposition of ECM proteins and is associated with fibrosis. CAV-1-dependent endocytosis on the other hand leads to inhibitory SMAD7 binding and signal abrogation. We assessed whether alterations in the compartmentalization of TGF-β contribute to increased TGF-β signalling in MFS. Furthermore, we examined transcriptomic alterations in MFS patients with ascending aortic aneurysms. We found increased membrane enrichment and TGF-β receptor interaction of SARA and SMAD2 as well as higher colocalization of SARA with the early endosome marker EEA1 in MFS VSMC. Using fluorescent TGF-β we found that internalized TGF-β showed equal colocalization with EEA1 and CAV-1 in VSMC from Marfan patients and controls. However, colocalization of TGF-β at SARA-positive early endosomes was increased in MFS, indicating increased signalling through the early endosomal pathway in MFS. In addition, RAB5 has been described to control SARA enrichment at early endosomes. We showed increased RAB5 enrichment at cell membranes as well as gene expression in MFS, indicating that the increased signalling at early endosomes might be controlled by RAB5. Furthermore, the CAV-1- and early endosome-associated pathways have been shown to partially merge to form double-positive vesicles associated with signal abrogation. We found decreased colocalization of TGF-β with EEA1/CAV-1 double positive structures, indicating decreased TGF-β signal abrogation through this pathway in MFS. We furthermore performed sequencing of VSMC derived mRNA from MFS and controls. We identified a FBN1 3´UTR mutation with a clear gene ontological profile of transient endoplasmic reticulum (ER) stress, potentially due to the loss of a micro RNA (miRNA) binding site. In clinical settings, non-coding regions are usually omitted, yet ER stress induced by mutations in these regions has been linked to other connective tissue and cardiovascular diseases. We suggest that the inclusion of non-coding regions could improve the low genotype-phenotype correlation and the prediction of disease severity.