IGF1 does not Induces IGF-IR and Clathrin Heavy Chain co-localization.

<p>HaCat cells were treated with IGF1(10 nM) for 5 min, fixed in 4% formaldehyde, permeabilized with Methanol at −20°C, labeled with a rabbit anti-Clathrin Heavy Chain (red) and a mouse anti-IGF-IR (green), antibody and imaged by confocal immunofluorescence microscopy.</p

Expression of CavY14F mutant decreases IGF-IR internalization in IGF1 stimulated cells.

<p>(A) Hacat cells were transiently transfected with pEGFPN1 Cav-1-wt and pEGFN1 Cav-1Y14F plasmids. 48 hours after transfection serum starved HaCat cells were stimulated with IGF1 10 nM for 5 min, fixed in 4% formaldehyde, permeabilized with Methanol at −20°C, labeled with a rabbit anti-PTRF/Cavin (red) and a mouse anti-IGF-IR (blue) imaged by confocal immunofluorescence microscopy. Merged fields show co-localization (white) of Cav-1wt, PTRF/Cavin, IGF-IR (upper pannels) and Cav-1Y14F, PTRF/Cavin and IGF-IR (bottom panels). (B) 48 hours from the transfection, serum-starved cells were treated with IGF110 nM for the indicated times, trypsined, washed, blocked and incubated with a mouse PE-conjugated IGF-IR.antibody. PE-conjugated IGF-IR labeled cells were analyzed by flow-cytometry to measure plasma membrane IGF-IR expression as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014157#s3" target="_blank">Materials and Methods</a>. Data are expressed as the mean ± SD. Statistical analysis was performed using Student's <i>t</i> test. *p<0.05.</p

Cav-1 and PTRF/Cavin are required for IGF-IR internalization and plasma membrane recovery.

<p>HaCat cells were transfected with siRNA for Cav-1 (Cav-1-siRNA), for PTRF/Cavin (PTRF/Cavin-siRNA), for Clathrin Heavy Chain (Clathrin HC-siRNA) and with scrambled control siRNA (Ctr-siRNA) as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014157#s3" target="_blank">materials and methods</a>. (A) 72 hours after transfection, serum-starved cells were treated with IGF110 nM for the indicated times, trypsined, washed, blocked and incubated with a mouse PE-conjugated IGF-IR antibody. PE-conjugated IGF-IR labeled cells were analyzed by flow-cytometry to measure plasma membrane IGF-IR expression as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014157#s3" target="_blank">Materials and Methods</a>. (B) Ctr-siRNA, Cav-1-siRNA, PTRF/Cavin-siRNA and Clathrin HC-siRNA HaCat cells were serum-starved and subjected to a biotinylation based endocytic assay with NH-SS-biotin at 4°C (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014157#s3" target="_blank">Materials and Methods</a>). The cells were then warmed at 37°C with medium containing IGF110 nM to allow IGF-IR internalization. Glutathione was used to reduce the proteins not internalized from the plasma membrane. IGF-IR was immunoprecipitated with IGF-IR antibody and the internalized IGF-IR was revealed by Western Blot with a Streptavidin-HRP antibody. Data were quantified using NIH-Image and plotted in the graph. The amount of biotinylated internalized IGF-IR was expressed as a percentage of the amount of IGF-IR on the surface at 4°C which we set as 100%. (C) 72 hours from the transfection serum-starved cells were lysed and equal amount of Ctr-siRNA and Cav-1-siRNA or Ctr-siRNA and PTRF/Cavin-siRNA and Clathrin HC-siRNA cell lysates were separated on SDS–PAGE, transferred on nitrocellulose and blotted with an antibody directed against Cav-1, PTRF/Cavin, Clathrin HC, IGF-IR, Flotillin-2 and actin proteins. Data are expressed as the mean ± SD. Statistical analysis was performed using Student's <i>t</i> test. *p<0.05.</p

Cav-1 and PTRF/Cavin co-immunoprecipitation with IGF-IR.

<p>Serum starved HaCat cells were stimulated with IGF110 nM for the indicated times and then lysed. Equal amount of cell lysates were immunoprecipitated (IP) and immunoblotted (IB) with the indicated antibodies. The graphs represent quantification of co-immunoprecipitation experiments following densitometric analysis of bands and are expressed as fold of increase. Data shown are representative of three independent experiments and are expressed as the mean ± SD.</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

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

Building multilayer networks using multi-omics, imaging, and clinical data.

(a) Illustration of network construction. The data from each layer is taken from the cohorts and used to create networks, where the nodes are the elements in the dataset (genomics (SNPs), phosphoproteomics, cytomics, CNS tissue imaging, and clinical data), and the edges correspond to the mutual information between element pairs across all subjects. Once individual networks are created, they are linked together, again using mutual information, following a hierarchy that connects each layer successively, starting with genomics and working up to the phenotypic (clinical) layer. (b-f) Topology of individual layer networks from the experimental data. In each of the networks, the degree of each node is color-coded, with higher degrees in darker colors. The edge weights are coded in gray scale in a similar manner, with a darker edge representing a higher weight, and thus a higher correlation between nodes. The genomics network was enriched with the previous knowledge on regulatory networks (f) and included the MS genetic burden scores (g). In the combined five-layer network, the layers are connected using the hierarchy described above, with genomics at the bottom and clinical phenotype at the top. These networks are meant to show the nodes that are more highly correlated with other nodes in the network. They provide the base to examine the topological structure of the overall multilayer network. High resolution network representations for single-layer networks are available at Github link https://keithtopher.github.io/single_networks/#/ and for multilayer networks at https://keithtopher.github.io/combo_networks/#/. Icons used in the figure are open source from onlinewebfonts.com, flaticon.com and icons8.com.</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

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