Decoding Neuromuscular Disorders Using Phenotypic Clusters Obtained From Co-Occurrence Networks

Abstract

Neuromuscular disorders (NMDs) represent an important subset of rare diseases associated with elevated morbidity and mortality whose diagnosis can take years. Here we present a novel approach using systems biology to produce functionally-coherent phenotype clusters that provide insight into the cellular functions and phenotypic patterns underlying NMDs, using the Human Phenotype Ontology as a common framework. Gene and phenotype information was obtained for 424 NMDs in OMIM and 126 NMDs in Orphanet, and 335 and 216 phenotypes were identified as typical for NMDs, respectively. ‘Elevated serum creatine kinase’ was the most specific to NMDs, in agreement with the clinical test of elevated serum creatinine kinase that is conducted on NMD patients. The approach to obtain co-occurring NMD phenotypes was validated based on co-mention in PubMed abstracts. A total of 231 (OMIM) and 150 (Orphanet) clusters of highly connected co-occurrent NMD phenotypes were obtained. In parallel, a tripartite network based on phenotypes, diseases and genes was used to associate NMD phenotypes with functions, an approach also validated by literature co-mention, with KEGG pathways showing proportionally higher overlap than Gene Ontology and Reactome. Phenotype-function pairs were crossed with the co-occurrent NMD phenotype clusters to obtain 40 (OMIM) and 72 (Orphanet) functionally coherent phenotype clusters. As expected, many of these overlapped with known diseases and confirmed existing knowledge. Other clusters revealed interesting new findings, indicating informative phenotypes for differential diagnosis, providing deeper knowledge of NMDs, and pointing towards specific cell dysfunction caused by pleiotropic genes. This work is an example of reproducible research that i) can help better understand NMDs and support their diagnosis by providing a new tool that exploits existing information to obtain novel clusters of functionally-related phenotypes, and ii) takes us another step towards personalised medicine for NMDs.

Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View

“…There are also approaches that use HPO terms alongside data obtained directly from disease databases such as OMIM [ 43 ] and Orphanet [ 84 ]. For example, a recent study using the PhenoClusters methodology [ 85 ] gave insights into neuromuscular disease (NMD). The method obtains NMD related diseases from OMIM using keyword searching and expert manual curation.…”

“…Moreover, known disease–gene association data can be used to find clusters of related phenotypes that are associated with genes involved in similar pathways. Through collaboration with experts in the field of NMDs, it was possible to identify important clusters that can aid in differential diagnosis [ 85 ].…”

Hypermobile Ehlers–Danlos syndrome: A review and a critical appraisal of published genetic research to date

“…In light of the scant literature published to date, a search was also This information can then be integrated with genomic and transcriptomic data and with the use of bioinformatics tools, detect genotype-phenotype patterns and patient sub-clusters with similar phenotypes using developed statistical algorithms. This approach has already been applied for neuromuscular and neurodevelopmental disorders 65,66 and may thus show some promise in the study of EDS and musculoskeletal diseases. Assessing the effect, if any, of epigenetic modifications on the development of the disorder may also be an intriguing approach for future work.…”