Fig 1.
A schematic representation of the steps to establish the context-dependent signaling-metabolic interconnection model.
Fig 2.
Establishment of signaling-metabolic pathway protein (S-M) interconnections for construction of SMIN.
(A) Fourteen signaling (left) and six groups of metabolic pathways (right; grouping of 81 individual metabolic pathways) were selected from the KEGG database to develop the S-M interconnected network (SMIN) through PPI. Experimentally validated data from the STRING protein-interaction database were used to construct an initial HPPIN. The levels of interaction were restricted at the interactors of interactors to create the network resulting in four possible linking paths through which selected signaling and metabolic pathway proteins could be connected directly or through one, two and three PPI. (B) Conversion of interconnecting paths into network representation.
Fig 3.
Development of a GBM-specific significant network from HPPIN.
(A) A human protein-protein interactome network (HPPIN) was established based on experimentally validated interactome data of STRING database (left panel: the total network where nodes and edges are marked in grey). The SMIN was extracted from the HPPIN as signaling-metabolic interconnected path-dependent network between fourteen signaling and six groups of metabolic pathways as indicated in Fig 2 (highlighted as orange nodes and blue edges within the HPPIN on the left panel). The interconnections in the SMIN between one signaling (CSNK2A1) and one metabolic (NDUFA13) pathway protein through PPIs were highlighted on the right panel.(B) A GBM-specific network (left panel with yellow nodes and green edges) was developed from the SMIN (orange nodes and blue edges within the total background network) considering interconnecting paths with at least one differentially expressed protein identified from comparative proteome analysis of U87MG (EGFRwt) and U87MGvIII (EGFRvIII) GBM. The interconnecting paths between the same signaling (CSNK2A1) and metabolic (NDUFA13) pathway protein after exclusion of non-GBM specific interconnections are shown on the right panel. (C) The GBM-specific network based on significant nodes (z ≥1) and significant interconnected paths (path score ≥80% of the highest path score of each SM pair) identified from the weighted network were indicated with blue nodes and red edges within the GBM-specific network with yellow node and green edge. The interconnecting paths in the GBM-specific significant network between CSNK2A1 and NDUFA13 are shown as examples on the right panel.
Table 1.
Detailed statistics of paths, pairs, and proteins with expression involved in stepwise filtration of the signaling-metabolic interaction network (SMIN) to the significant GBM-specific network with the differential expression states in the EGFR-mutated cell line U87MGvIII compared to U87MG with wild-type EGFR.
Fig 4.
Comparative proteome analysis between the GBM cell lines U87MG (EGFRwt) and U87MGvIII (EGFRvIII).
Protein identification and quantification were done by nano-HPLC Q-TOF MS/MS and peptide searches using the SwissProt databank for human proteins. Quantitation of the protein expressions was label-free by calculating the emPAI values for each protein. (A) Venn diagram for the statistics of proteins identified in U87MG and U87MGvIII. In three independent biological replicates of the proteome analysis for each cell line 907 proteins were identified, 528 common, 243 only for U87MG and 136 only for U87MGvIII cells. Of the shared proteins, 458 were equivalently and 70 differentially expressed. (B) Distribution of equivalently and differentially expressed proteins. Of the 449 differentially expressed proteins 243 were only present in U87MG and 136 in only U87MGvIII and 70 were present in both but in differential expression (down right). In total 268 were up-regulated and 181 down-regulated in U87MGvIII compared to U87MG. (C-D) Pathway over-representation analysis. Panels C and D plot the enriched pathways for proteins exclusively overexpressed in U87MGvIII (EGFRvIII) and U87MG (EGFRwt), respectively. One thirty four out of 136 and 235 out of 243 proteins overexpressed in U87MGvIII (EGFRvIII) and U87MG (EGFRwt) were mapped onto pathways. (E-F) Panels E and F plot enriched pathways of commonly up-regulated and down-regulated proteins between U87MGvIII and U87MG. Fourty five out of 45 and 24 out of 25 proteins commonly overexpressed in U87MGvIII (EGFRvIII) and U87MG (EGFRwt) were mapped onto pathways.
Fig 5.
Models for the identification of significant signaling-metabolic (SM) pathway protein pairs and interconnecting paths.
Models were implemented to identify significant SM pairs and paths from the weighted network. Weights of the nodes were defined based on whether the proteins were differentially expressed (dEXP), signal crosstalk (SC) proteins, rate-limiting enzymes (RLE) or none of these. The edge weights were defined as interaction probabilities derived from the gene expression values of two interacting proteins in GBM patients. (A) Model 1 was used to identify significant SM pairs from the GBM-specific weighted network. Four types of interconnection with none, one, two and three PPIs between S-M pairs were considered as separate models (i to iv). Two hypothetical proteins were considered on each side (SH and MH) to implement positional probabilities based on the node weights of all proteins including S and M. Using these models and the Hidden Markov Model (HMM) logic, the path scores were calculated from the node and edge weights. The path scores were converted to Z-scores, and scores above the cut-off value of Z≥1 or Z≥3 were considered as significant and highly significant, respectively, and the respective interconnected SM pairs as significant pairs. (B) Model 2 was used to identify important linking-paths among the total possible paths formed between significant SM pairs from Model 1. Path scores were calculated from the positional probabilities as above. Higher path scores indicate higher significance.
Fig 6.
GBM-specific significant network.
(A) The SM pairs and interconnecting paths between them that have a significant impact on EGFR-mediated GBM were filtered from the GBM-specific network using the mathematical model of Fig 5 and combined to generate the final signaling-metabolic interconnected network. The network parameters (color code of node and edge, node size, edge width mentioned in the figure) signified the cell-biological consequences of GBM driven by constitutive EGFR signaling. One representative signaling–metabolic connecting path for each of the fourteen signaling pathways is indicated at the side of the network.(B) Sub-network based on the 15 top-ranked signaling cross-talk proteins and their interconnection with all metabolic pathways.
Fig 7.
Importance of the individual nodes in signaling-metabolic pathway interconnection.
(A) Each of the 654 nodes of the final network was tested in perturbation studies by removing it and its associated connections from the network and recalculating all network parameters. The scores for significant SM pairs (Z≥1) and interconnected paths (path score ≥80% from the highest path score for each SM pair) were redefined for each signaling to all metabolic interconnections. The differences of the sums of all significant path scores before and after perturbation of the individual 654 nodes for each signaling to all metabolic pathway interconnections were converted to Z scores (blue dots) and plotted against the genes/nodes. (B) Summary of the nodes with a significant impact on the entire network. Nodes with significantly altered Z score after perturbation (-2≥Z ≥2) were considered as effective nodes with significant impact and are listed in S3 Table for all interconnections of the 14 signaling pathways to the 6 metabolic pathway groups and vice versa, the metabolic pathway groups to the signaling pathways. Drugable node/proteins according to the KEGG database are highlighted.
Fig 8.
Enrichment of significant S-M interconnections by successive filtering during the development of the signaling-metabolic pathway network.
The successive reduction of non-significant nodes and paths was followed for the inter-connection of the NOTCH pathway to all metabolic pathways. (A) Starting signaling-metabolic interaction network (SMIN) with the NOTCH pathway proteins in purple; (C) to GBM-specific network, and (E) the final GBM-specific significant (Z≥1) network. (B) Connections formed through twelve TOP Perturbation Impact (PI) nodes (red color) for NOTCH to all metabolic in SMIN; (D) connections formed through 11 nodes (in red color; out of 12 genes/proteins found with significant perturbation impact [PI]) in GBM-specific network and (F) connections formed through 9 nodes (in red color; out of 12) in the GBM-specific significant (Z≥1) network.
Fig 9.
Test of the GBM-specific significant network model in cell cultures.
The EGFRwt and EGFRvIII-mutated GBM cell lines U87MG and U87MGvIII were incubated with inhibitors for calmodulin (CALM2) with CGS 9343B (A), casein kinase II subunit alpha (CSNK2A1) with Emodin (B), 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma-1 (PLCG1) with U73122 (C), tyrosine-protein kinase ABL1 with Dasatinib(D), and B-cell lymphoma 2 (BCL2) with ABT-199 (E). The bar diagrams on the left show the effects of the inhibition of the expression of metabolic pathway proteins predicted to be interconnected to the targeted signaling pathway proteins. The line graphs on the right show the dose-dependent effects of the signaling protein inhibitors on the viability of the cells.