Dataset Information

Multiscale networks in multiple sclerosis.

ABSTRACT: Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.

SUBMITTER: Kennedy KE

PROVIDER: S-EPMC10852301 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Multiscale networks in multiple sclerosis.

Kennedy Keith E KE Kerlero de Rosbo Nicole N Uccelli Antonio A Cellerino Maria M Ivaldi Federico F Contini Paola P De Palma Raffaele R Harbo Hanne F HF Berge Tone T Bos Steffan D SD Høgestøl Einar A EA Brune-Ingebretsen Synne S de Rodez Benavent Sigrid A SA Paul Friedemann F Brandt Alexander U AU Bäcker-Koduah Priscilla P Behrens Janina J Kuchling Joseph J Asseyer Susanna S Scheel Michael M Chien Claudia C Zimmermann Hanna H Motamedi Seyedamirhosein S Kauer-Bonin Josef J Saez-Rodriguez Julio J Rinas Melanie M Alexopoulos Leonidas G LG Andorra Magi M Llufriu Sara S Saiz Albert A Blanco Yolanda Y Martinez-Heras Eloy E Solana Elisabeth E Pulido-Valdeolivas Irene I Martinez-Lapiscina Elena H EH Garcia-Ojalvo Jordi J Villoslada Pablo P

PLoS computational biology 20240208 2

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healt ...[more]

PMID: 38329927

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Project description:BackgroundMultiple sclerosis (MS) is a complex disease in which the immune system attacks the central nervous system. The molecular mechanisms contributing to the etiology of MS remain poorly understood. Genome-wide association studies (GWAS) of MS have identified a small number of genetic loci significant at the genome level, but they are mainly non-coding variants. Network-assisted analysis may help better interpret the functional roles of the variants with association signals and potential translational medicine application. The Dense Module Searching of GWAS tool (dmGWAS version 2.4) developed in our team is applied to 2 MS GWAS datasets (GeneMSA and IMSGC GWAS) using the human protein interactome as the reference network. A dual evaluation strategy is used to generate results with reproducibility.ResultsApproximately 7500 significant network modules were identified for each independent GWAS dataset, and 20 significant modules were identified from the dual evaluation. The top modules included GRB2, HDAC1, JAK2, MAPK1, and STAT3 as central genes. Top module genes were enriched with functional terms such as "regulation of glial cell differentiation" (adjusted p-value = 2.58 × 10- 3), "T-cell costimulation" (adjusted p-value = 2.11 × 10- 6) and "virus receptor activity" (adjusted p-value = 1.67 × 10- 3). Interestingly, top gene networks included several MS FDA approved drug target genes HDAC1, IL2RA, KEAP1, and RELA, CONCLUSIONS: Our dmGWAS network analyses highlighted several genes (GRB2, HDAC1, IL2RA, JAK2, KEAP1, MAPK1, RELA and STAT3) in top modules that are promising to interpret GWAS signals and link to MS drug targets. The genes enriched with glial cell differentiation are important for understanding neurodegenerative processes in MS and for remyelination therapy investigation. Importantly, our identified genetic signals enriched in T cell costimulation and viral receptor activity supported the viral infection onset hypothesis for MS.

Dataset Information

Multiscale networks in multiple sclerosis.

Publications

Multiscale networks in multiple sclerosis.

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