Figure 1.
Schematic of an integrated, proteomics-first approach for the discovery of functional, candidate sub-networks in a disease phenotype.
Disease targets significant for a phenotype (e.g. cancer) are used to seed an information-flow based search of the human interactome for candidate sub-networks subsequently classified as crosstalkers or interactors. Candidate sub-networks are then scored between test and control (e.g. normal vs. tumor) using the mutual information of aggregate mRNA expression data as a proxy for synergistic dysregulation. High-scoring sub-networks may be experimentally validated for their role in disease.
Table 1.
Definition of terminology used frequently in this paper.
Figure 2.
Crosstalkers are not significant at level of individual mRNA expression.
Cumulative distribution of differential expression for crosstalkers identified using two proteomic seeds (Nibbe et al., Friedman et al.), a seed of CRC driver genes (Sjöblom et al.), and all proteins in the HPRD PPI network, as quantified by mutual information with phenotype, using GSE8671 and GSE10950.
Figure 3.
Synergistic dysregulation versus network size for candidate sub-networks associated with proteomic seeds obtained from Nibbe et al.
Sub-network dysregulation (i.e. mutual information of sub-network mRNA expression profile with phenotype class) versus network size for candidate sub-networks. All interactors (green squares) and crosstalkers (red diamonds) were scored using (a) GSE10950 and (b) GSE8671. The blue lines represent the linear interpolation of the means of the estimated null distributions computed for random candidate sub-networks of size 2,4,8,16,32, and 64, using the respective arrays (see Materials and Methods for details). Vertical bars represent one standard deviation from the mean.
Figure 4.
Synergistic dysregulation versus network size for candidate sub-networks associated with proteomic seeds obtained from Friedman et al.
Please see Figure. 3 for annotation.
Figure 5.
Significant sub-networks induced by proteomic seeds.
Network graph visualization of sub-networks induced by Friedman seed, scored using GSE10950 (a) and Nibbe seed, scored using GSE8671 (b). Proteomic seeds that induced a significant crosstalker sub-network are shown in red, other proteomic seeds are shown in orange, crosstalkers are black and interactors are white. Visualization was performed with the Pajek software.
Figure 6.
Validation of select targets predicted to be dysregulated in TCP1 sub-network.
Immunoblot data were obtained from three (540, 534, 507) late-stage matched (N = normal/T = tumor) patient tissue biopsies not used in the original proteomic screen by Nibbe et. al. Values are in kilodalton (kDa). GSE8671 and GSE10950 represent the ratio of the mean mRNA value (tumor/normal) from the respective microarray array. Fold change was determined by densitometry.
Figure 7.
Synergistic dysregulation versus network size for candidate sub-networks associated with the CRC driver gene seeds obtained from Sjöblom et al.
Please see Figure 3 for annotation.
Figure 8.
Cross-validation performance comparison of sub-network based classifiers.
The sub-networks induced by proteomic and genomic seeds were first ranked by mutual information with phenotype (MI). Then the normalized mRNA expression values for the genes were aggregated to compute a feature for each sub-network with significant MI. These features were used to train an SVM-based classifier to distinguish normal from tumor using GSE10950, and then cross-validated on GSE8671 (a), and vice-versa (b).