Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes

Abstract

Exploring the molecular basis of disease severity in rare disease scenarios is a challenging task provided the limitations on data availability. Causative genes have been described for Congenital Myasthenic Syndromes (CMS), a group of diverse minority neuromuscular junction (NMJ) disorders; yet a molecular explanation for the phenotypic severity differences remains unclear. Here, we present a workflow to explore the functional relationships between CMS causal genes and altered genes from each patient, based on multilayer network community detection analysis of complementary biomedical information provided by relevant data sources, namely protein-protein interactions, pathways and metabolomics. Our results show that CMS severity can be ascribed to the personalized impairment of extracellular matrix components and postsynaptic modulators of acetylcholine receptor (AChR) clustering. This work showcases how coupling multilayer network analysis with personalized -omics information provides molecular explanations to the varying severity of rare diseases; paving the way for sorting out similar cases in other rare diseases.

Fig. 1. A schematic depiction of the main molecular activities of known Congenital Myasthenic Syndromes (CMS) causal genes (Methods) taking place at the neuromuscular junction (NMJ) in the presynaptic terminal (in blue), synaptic cleft (in white), and skeletal muscle fiber (in red) (for a detailed description of this system see Supplementary Information, Functions of CMS-associated genes in the neuromuscular junction).

ChAT Choline O-Acetyltransferase, LRP4 LDL Receptor Related Protein 4, AChR Acetylcholine Receptor, MuSK Muscle Associated Receptor Tyrosine Kinase, Na(V) 1.4 Na_v1.4 voltage-gated sodium channel.

Fig. 2. Analytical workflow designed to address the severity of a cohort of patients affected by Congenital Myasthenic Syndromes (CMS).

A multi-scale functional analysis approach, based on multilayer networks, was used to identify the functional relationships between genetic alterations obtained from omics data (Whole Genome Sequencing, WGS; RNA-sequencing, RNAseq) with known CMS causal genes. In green, compound heterozygous variants; in yellow, copy number variants (CNVs); in purple, known CMS causal genes. Modules of CMS linked genes are detected using graph community detection at a resolution range (γ) (Methods) where the most prominent changes in community structure occur. Modules that emerged from this analysis were characterized at single individual level.

Fig. 3. Identification of the largest module containing genes that are found in the same community in a range of modularity resolution (Methods).

In each module, genes are connected if they are found in the same multilayer communities at n values of the resolution parameter γ within the range under consideration (γ ∈ (0,4]). The arrows indicate the systematic increase of n. At n = 8, the module contains genes that are always found in the same community in the entire range of resolution (see Supplementary Information, Multilayer community detection analysis). The largest module containing the CMS linked gene set (highlighted in red), which includes known CMS causal genes, severe-specific heterozygous compound variants and CNVs, is shown. Source data are provided in the Github repository of the project (see Data Availability section).

Fig. 4. Largest multilayer network modules containing known CMS causal genes.

The largest modules, containing known CMS causal genes, within the multilayer communities of CMS linked genes specific to the not-severe (A) and severe (B) groups are reported. In green, compound heterozygous variants; in yellow, CNVs; in purple, known CMS causal genes. Being a CMS causal gene bearing compound heterozygous variants, AGRN is depicted using both green and purple. Source Cytoscape session is provided in the Github repository of the project (see Data Availability section).

Fig. 5. Incident interactions between the genes identified in the severe-specific module in the multilayer network.

LOXL3 is not depicted as it has incident interactions with genes in the module that are not CMS linked. USH2A is not present in the pathways layer, thus it is only depicted in the protein-protein interaction layer.