Disentangling Multiple Sclerosis Phenotypes through Mendelian Disorders

Multiple sclerosis (MS) is a complex and heterogeneous neuroinflammatory disease of the central nervous system (CNS). It is characterized by multifocal immune-mediated attacks across the brain, spinal cord, and optic nerves, leading to a wide variety of acute and chronic clinical expressions. The disease's complexity is reflected in its diverse phenotypes, making it challenging to classify and treat effectively. Recent advancements in understanding MS pathophysiology underscore the need for an improved phenotypical framework that aligns with disease biology. This blog post delves into the insights presented by Bellucci et al. (2024) in their study "Disentangling Multiple Sclerosis Phenotypes through Mendelian Disorders: A Network Approach" published in the Multiple Sclerosis Journal.

Background
MS has traditionally been classified into different types based on clinical presentations and disease progression. However, these classifications often fall short in capturing the full spectrum of the disease's heterogeneity. The study by Bellucci et al. explores the genetic underpinnings of MS by leveraging the shared pathophysiology between MS and Mendelian disorders, which are monogenic diseases with well-characterized genetic causes. By doing so, they aim to create a novel phenotypical framework for MS that can better inform disease classification and therapeutic strategies.

Methods
The researchers performed an enrichment analysis of MS-associated genetic variants with genes implicated in Mendelian disorders. This analysis identified a network of MS-Mendelian genes (MSMGs), which were then analyzed to define phenotypic subnetworks and biological processes. A network-based drug screening was also implemented to identify potential phenotype-specific candidate drugs.

Key Findings
MS-Mendelian Molecular Network: The study identified a significant enrichment of monogenic disease genes within the MS genetic architecture. This network included 331 genes and 486 related disorders, which were categorized into four phenotypic classes: neurological, immunological, metabolic, and visual.

Phenotypic Subnetworks:
Neurological Subnetwork: Included 151 genes associated with diseases like ataxias, motor neuron degenerative diseases, and cognitive disorders. Key biological pathways involved synaptic function and neural morphogenesis.
Immunological Subnetwork: Consisted of 45 genes linked to immunodeficiencies and autoimmunity, with significant involvement of antiviral response pathways.
Metabolic Subnetwork: Comprised 77 genes related to mitochondrial disorders and glycolipid metabolism, highlighting pathways like sterol metabolism and PPAR-gamma signaling.
Visual Subnetwork: Included 70 genes associated with retinal degenerative diseases and optic atrophies, with significant roles in phototransduction and galactose metabolism.
Network-Based Drug Repurposing: The study identified 503 candidate drugs, with 27 molecules potentially effective across three phenotypic subnetworks and 140 in subnetwork pairs. Notable drugs included antiviral zidovudine and riluzole, a glutamatergic transmission modulator.

Conclusion
The findings from Bellucci et al. suggest that the genetic architecture of MS shares significant overlaps with various Mendelian disorders. This shared genetic basis allows for the identification of distinct pathophysiological axes—neurological, immunological, metabolic, and visual—that can refine MS phenotyping. The study's network-based approach also opens avenues for drug repurposing, offering potential new therapies tailored to specific MS phenotypes.

Implications for Future Research
The integration of genetic and phenotypic data provides a more precise framework for understanding MS. Future research should focus on validating these findings through large-scale studies and exploring the therapeutic potential of the identified drugs. This approach not only enhances our understanding of MS but also offers insights into related rare diseases, supporting the development of more effective treatments across a spectrum of neuroinflammatory and neurodegenerative disorders.

References:
Bellucci, G., Buscarinu, M. C., Reniè, R., Rinaldi, V., Bigi, R., Mechelli, R., Romano, S., Salvetti, M., & Ristori, G. (2024). Disentangling multiple sclerosis phenotypes through Mendelian disorders: A network approach. Multiple Sclerosis Journal, 30(3), 325–335. DOI: 10.1177/13524585241227119