MSJ771838_Supplemental_figure – Supplemental material for Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis

<p>Supplemental material, MSJ771838_Supplemental_figure for Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis by Sara Llufriu, Maria A Rocca, Elisabetta Pagani, Gianna C Riccitelli, Elisabeth Solana, Bruno Colombo, Mariaemma Rodegher, Andrea Falini, Giancarlo Comi and Massimo Filippi in Multiple Sclerosis Journal</p

MSJ771838_Supplemental_tables – Supplemental material for Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis

<p>Supplemental material, MSJ771838_Supplemental_tables for Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis by Sara Llufriu, Maria A Rocca, Elisabetta Pagani, Gianna C Riccitelli, Elisabeth Solana, Bruno Colombo, Mariaemma Rodegher, Andrea Falini, Giancarlo Comi and Massimo Filippi in Multiple Sclerosis Journal</p

MSJ771838_Supplemental_appendix – Supplemental material for Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis

<p>Supplemental material, MSJ771838_Supplemental_appendix for Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis by Sara Llufriu, Maria A Rocca, Elisabetta Pagani, Gianna C Riccitelli, Elisabeth Solana, Bruno Colombo, Mariaemma Rodegher, Andrea Falini, Giancarlo Comi and Massimo Filippi in Multiple Sclerosis Journal</p

MSJ771838_Supplemental_methods – Supplemental material for Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis

<p>Supplemental material, MSJ771838_Supplemental_methods for Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis by Sara Llufriu, Maria A Rocca, Elisabetta Pagani, Gianna C Riccitelli, Elisabeth Solana, Bruno Colombo, Mariaemma Rodegher, Andrea Falini, Giancarlo Comi and Massimo Filippi in Multiple Sclerosis Journal</p

Pathways validation for canonical pathway.

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.</div

Sys4MS cohort: Clinical and imaging variables of MS patients and healthy controls.

Disability scales are shown as the mean ± SD, except for the EDSS which is displayed as the median (range).</p

Network permutation for negative controls of paths.

The five-layer network built using Pearson correlation is used as the base network. For each of the 100 repetitions, the network was permuted by swapping the edges between pairs of nodes. In permutation 1, the edge between B and C was swapped with the edge between D and E. In the permutation 2, the edge between A and E was swapped with the edge between B and C. In permutation 3, first the edge swap from the top network was applied, followed by the edge swap from the middle network. In each case, the edge swap can only be done if it does not result in two edges between the same pair of nodes. Making the permutation in this way keeps the original degree distribution of the network. The weights for each of the edges are permuted as well. This edge swapping technique is applied 10 times for each edge in the original network. After they are permuted, the top paths for each network are identified in the same manner as before. There are three possibilities for considering whether the paths from the original network appear in the paths from the permuted networks. In permutation 1, the path exists in the permuted network and furthermore was identified as a top path. In permutation 2, the original path does exist in the permuted network but was not identified as a top path. In permutation 3, the original path doesn’t exist in the permuted network at all.</p

Overlap between the paths identified in the single-cell analysis and the paths identified in the UNIPROT database.

Overlap between the paths identified in the single-cell analysis and the paths identified in the UNIPROT database.</p

Venn diagram describing the overlap between the paths identified in the single-cell analysis and the paths identified in the UNIPROT database.

(1) CD56 Neg > INL—mRNFL > EDSS—T25WT; (2) Total CD8 > NGMV—T2LV > EDSS - 9HPT–SDMT; (3) MK03 > Total T Cells > mRNFL > T25WT; (4) HSPB1 > B Memory > NBV > T25WT; (5) STAT6 > Th17 > NGMV Change > Years with Disease; (6) KS6B1—LCK > Total T Cells—Th1 Non Classic > NGMV—T2LV> LCVA Change—MSSS—Years since Relapse; (7) MP2K1—STAT6 > Th17 > mRNFL > T25WT—ARMSS (8) MP2K1—STAT6 > Th17 > INL > EDSS Change; (9) MP2K1 > CD8 Treg > GCIPL > EDSS Change; (10) Atypical B Memory–B Memory–Th1 Classic > mRNFL–T2LV > EDSS–T25WT.</p

Linear regression for HSPB1 node.

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.</div