Rinas, M. | Search bioRxiv

bioRxiv2023

Multiscale networks in multiple sclerosis

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. 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, and Boolean simulations identified paths within and among all layers. The path more commonly found from the boolean simulations connects MP2K, with Th17 cells, the retinal nerve fiber layer (RNFL) thickness and the age related MS severity score (ARMSS). Combinations of several proteins (HSPB1, MP2K1, SR6, KS6B1, SRC, MK03, LCK and STAT6)) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/530153v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@7dd26org.highwire.dtl.DTLVardef@482239org.highwire.dtl.DTLVardef@1bef03borg.highwire.dtl.DTLVardef@8dc6e6_HPS_FORMAT_FIGEXP M_FIG C_FIG Author SummaryComplex diseases such as Multiple Sclerosis (MS) involve the contribution of a wide range of biological processes. We conducted a systems biology study of MS based on network analysis and deep phenotyping in a prospective cohort of patients with clinical, imaging, genetics, and omics assessments. The gene, proteins and cell paths explained variation in central nervous system damage, and in metrics of disease severity. Such multilayer paths explain the different phenotypes of the disease and can be developed as biomarkers of MS.

systems biology↗

bioRxiv2019

Prediction of combination therapies based on topological modeling of the immune signaling network in Multiple Sclerosis

Signal transduction deregulation is a hallmark of many complex diseases, including Multiple Sclerosis (MS). Here, we performed ex vivo multiplexed phosphoproteomic assays in PBMCs from 180 MS patients either untreated or treated with fingolimod, natalizumab, interferon-beta, glatiramer acetate or the experimental therapy epigallocatechin gallate (EGCG), and from 60 matched healthy controls. Fitting a bespoke literature-derived network of MS-related pathways using logic modeling yielded a signaling network specific for each patient. Patient models were merged to characterize healthy-, disease- and drug-specific signaling networks. We defined a co-druggability score based on the topology for each drugs network. We used this score to identify kinase interactions whose activity could be reverted to a "healthy-like" status by combination therapy. We predicted several combinations with approved MS drugs. Specifically, TAK1 kinase, involved in TGF-B, toll-like receptor, B-cell receptor and response to inflammation pathways was found to be highly deregulated and co-druggable with four MS drugs. One of these predicted combinations, Fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. Our approach based on patient-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases. One sentence summaryA new approach to predict combination therapies based on modeling signaling architecture using phosphoproteomics from patients with Multiple Sclerosis characterizes deregulated signaling pathways and reveals new therapeutic targets and drug combinations.

systems biology↗