Kennedy, Keith E. and Kerlero de Rosbo, Nicole and Uccelli, Antonio and Cellerino, Maria and Ivaldi, Federico and Contini, Paola and De Palma, Raffaele and Harbo, Hanne F. and Berge, Tone and Bos, Steffan D. and Høgestøl, Einar A. and Brune-Ingebretsen, Synne and de Rodez Benavent, Sigrid A. and Paul, Friedemann and Brandt, Alexander U. and Bäcker-Koduah, Priscilla and Behrens, Janina and Kuchling, Joseph and Asseyer, Susanna and Scheel, Michael and Chien, Claudia and Zimmermann, Hanna and Motamedi, Seyedamirhosein and Kauer-Bonin, Josef and Saez-Rodriguez, Julio and Rinas, Melanie and Alexopoulos, Leonidas G. and Andorra, Magi and Llufriu, Sara and Saiz, Albert and Blanco, Yolanda and Martinez-Heras, Eloy and Solana, Elisabeth and Pulido-Valdeolivas, Irene and Martinez-Lapiscina, Elena H. and Garcia-Ojalvo, Jordi and Villoslada, Pablo and Zhang, Zhaolei (2024) Multiscale networks in multiple sclerosis. PLOS Computational Biology, 20 (2). e1010980. ISSN 1553-7358
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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.
Item Type: | Article |
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Subjects: | Article Archives > Biological Science |
Depositing User: | Unnamed user with email support@articlearchives.org |
Date Deposited: | 23 Mar 2024 12:11 |
Last Modified: | 23 Mar 2024 12:11 |
URI: | http://archive.paparesearch.co.in/id/eprint/2011 |