Decoding the Genetic Basis of Mast Cell Hypersensitivity and Infection Risk in Hypermobile Ehlers-Danlos Syndrome

Abstract

Hypermobile Ehlers-Danlos syndrome (hEDS) is a connective tissue disorder marked by joint hypermobility, skin hyperextensibility, and tissue fragility. Recent studies have linked hEDS with mast cell activation syndrome (MCAS), suggesting a genetic interplay affecting immune regulation and infection susceptibility. This study aims to decode the genetic basis of mast cell hypersensitivity and increased infection risk in hEDS by identifying specific genetic variants associated with these conditions. We conducted whole-genome sequencing (WGS) on 18 hEDS participants and 7 first-degree relatives as controls, focusing on identifying genetic variants associated with mast cell dysregulation. Participants underwent clinical assessments to document hEDS symptoms and mast cell hypersensitivity, with particular attention to past infections and antihistamine response. Our analysis identified specific genetic variants in MT-CYB, HTT, MUC3A, HLA-B and HLA-DRB1, which are implicated in hEDS and MCAS. Protein-protein interaction (PPI) network analysis revealed significant interactions among identified variants, highlighting their involvement in pathways related to antigen processing, mucosal protection, and collagen synthesis. Notably, 61.1% of the hEDS cohort reported recurrent infections compared to 28.5% in controls, and 72.2% had documented mast cell hypersensitivity versus 14.2% in controls. These findings provide a plausible explanation for the complex interplay between connective tissue abnormalities and immune dysregulation in hEDS. The identified genetic variants offer insights into potential therapeutic targets for modulating mast cell activity and improving patient outcomes. Future research should validate these findings in larger cohorts and explore the functional implications of these variants to develop effective treatment strategies for hEDS and related mast cell disorders.

Keywords: Ehlers-Danlos syndrome; infection; mast cell activation syndrome; mast cell hypersensitivity; whole-genome sequencing.

Figure 1

Protein–protein interaction network highlighting mast cell hypersensitivity pathways. This figure illustrates the protein–protein interaction network, which demonstrated significantly more interactions than expected for a random set of proteins, with a PPI enrichment p-value of 1.49 × 10⁻¹⁰. The Markov cluster algorithm (MCL) identified at least six distinct clusters within the network. MCL is a method used to cluster proteins based on their interaction patterns within a protein-protein interaction network. This approach helps identify groups of proteins that interact more frequently with each other than with those outside the group, suggesting functional relatedness. The first cluster is involved in antigen processing and the presentation of endogenous peptide antigens and MHC protein complexes (red border). The second cluster relates to the defective GALNT3 causing hyperphosphatemic familial tumoral calcinosis (HFTC), including genes such as MUC3A, MUC16, MUC19, and ZNF717 (green circle). These MUC genes are major glycoprotein components of mucus gels, providing a protective barrier against particles and infectious agents at mucosal surfaces and potentially involved in ligand binding and intracellular signaling. The third cluster is associated with collagen chain trimerization and extracellular matrix structural constituents conferring tensile strength, including genes such as COL4A2, COL6A2 and MMP16 (yellow circle). The fourth cluster relates to retinoid and cholesterol metabolism, including genes such as LPL and LRP2 (blue circle). The fifth cluster is associated with mitochondrial complex I assembly model OXPHOS system, including genes such as MT-ND1 and ACAD9 (green rectangle). The last cluster relates to triplet repeat expansion, including genes such as SPTA1 (black circle). This refers to proteins encoded by a gene which has a triplet repeat expansion, i.e., the increase of triplet (trinucleotide) repeats within the gene sequence. The length of such repeats is frequently polymorphic, and there is often a correlation between repeat length and disease severity.

Figure 2

Protein–protein interaction network capturing hEDS-specific genes with potential relationships to established EDS genes. This figure illustrates the protein–protein interaction network, which showed significantly more interactions than expected for a random set of proteins, with a PPI enrichment p-value of 1.49 × 10⁻¹⁰. The red nodes represent the known genes associated with different types of EDS, while the green nodes represent genes with variations specific to hEDS subjects that have relationships with these known genes. All of these genes, except PHACTR1, are involved in collagen chain trimerization and extracellular matrix structural constituents conferring tensile strength. PHACTR1 (phosphatase and actin regulator 1) binds actin monomers (G actin) and plays a role in various processes, including the regulation of actin cytoskeleton dynamics, actin stress fibers formation, cell motility and survival, tubule formation by endothelial cells, and regulation of PPP1CA activity. It is also involved in the regulation of cortical neuron migration and dendrite arborization. To simplify the figure, pathways related to HLA and information repeated from Figure 1 have been removed from this pathway.

Figure 3

Protein–protein interaction network capturing MCAS-specific genetic variants in hEDS with potential relationships to established MCAS genes. This figure illustrates the protein–protein interaction network, which demonstrated significantly more interactions than would be expected for a random set of proteins, with a PPI enrichment p-value of 1.0 × 10⁻¹⁶. The red and green nodes represent known genes associated with mast cell activation syndrome (MCAS), while the yellow nodes represent genes with variations specific to hEDS subjects that have relationships with these known MCAS genes. The red nodes are involved in pathways related to hematopoietic or lymphoid organ development, whereas the yellow nodes participate in inflammatory responses and the positive regulation of interleukin-10 production. The results demonstrate that all known genes related to mast cell activation syndrome or mast cell hypersensitivity are interconnected, as anticipated. We identified additional genes within this network, including TLR1, RET, HP, ZNF521, and CCR5. Notably, ZNF521, a transcription factor, was also identified in the pathway depicted in Figure 2. It plays a role alongside RUNX2 in regulating osteoblast differentiation. To simplify the figure, pathways related to HLA and information repeated from Figure 1 and Figure 2 have been removed from this pathway.