ArticleNature communications2024
Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Network-based framework for studying etiology and phenotypic diversity in primary ciliopathies.Genome biology · 2026Article
- PathoAnalyzer-I: An Integrative Bioinformatics Platform for Chronic Disease Analysis.Bioinformatics and biology insights · 2026Article
- Integrative Transcriptomic and Network-Based Analysis of Neuromuscular Diseases.International journal of molecular sciences · 2025Article
- Blood biomarker fingerprints in a cohort of patients with CHRNE-related congenital myasthenic syndrome.Acta neuropathologica communications · 2025Article
- Challenges and opportunities for digital twins in precision medicine from a complex systems perspective.NPJ digital medicine · 2025Review
- Lung Tissue Multilayer Network Analysis Uncovers the Molecular Heterogeneity of Chronic Obstructive Pulmonary Disease.American journal of respiratory and critical care medicine · 2024Article
- Inter-alpha-trypsin inhibitor heavy chain H3 is a potential biomarker for disease activity in myasthenia gravis.Acta neuropathologica · 2024Article
- Characterization of Clinical Phenotypes in Congenital Myasthenic Syndrome Associated with the c.1327delG Frameshift Mutation in CHRNE Encoding the Acetylcholine Receptor Epsilon Subunit.Journal of neuromuscular diseases · 2024Article
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18 authors.
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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.
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