A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma

Abstract

Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma.

Figure 1.

Overview of the disease module approach for asthma. (A) The stages of mapping out and validating the asthma disease module. (I) Construction of the interactome and compilation of the consensus disease gene list (seed genes). (II) Mapping the seed genes onto the interactome and identification of the disease module, the sub-network that contains disease-associated components, via DIAMOnD. (III) Bioinformatics validation of the asthma disease module using gene expression data, gene ontology, pathway information and comorbidity analysis. (IV) Detailed biological analysis and in vitro confirmation of novel asthma-related pathways with potential therapeutic relevance. (B) Subgraph of the full interactome showing the connections among the asthma seed genes. The largest cluster (proto-module) contains 37 seed genes, the rest is scattered in smaller fragments. The colors of the nodes indicate the source of each seed gene; the links are colored according to the source of the interaction. (C) Size distribution of the largest connected component expected for 129 randomly distributed genes. The observed proto-module for asthma is highly significant (z-score = 10.7, empirical P-value <10⁻⁶).

Figure 2.

DIAMOnD method for disease module identification and its validation: (A) Schematic network configuration with seed genes (red) and their neighbors (yellow). The P-values next to the yellow genes indicate how significant their respective number of links to seed genes are (see Supplementary Material, Section IX for details). (B) At each iteration step of the DIAMOnD algorithm the gene with the most significant number of connections to seeds and previously added genes is agglomerated into the module. (C) Schematic depiction of the relationship between the disease module predicted by DIAMOnD and the seed proto-module. We call the proteins/genes selected by the DIAMOnD algorithm DIAMOnD genes. The union of the DIAMOnD genes and the seed genes connected to them is the predicted disease module. (D) Validation of the DIAMOnD genes. Column 1 shows the number of DIAMONnD genes found in the different validation datasets, column 2 the corresponding statistical significance. Column 3 identifies the considered datasets (see Supplementary Material Sections III–VII for details): (i) differentially expressed genes compiled from nine sources; (ii) 35 asthma-specific pathways from GeneGO; (iii) MSIgDB pathways; (iv) gene ontology (biological processes); (v): genes associated with comorbid diseases. The values for the DIAMOnD genes are show in orange, the values for seed genes and random expectation in red and green, respectively. In Column 2 we used a sliding-window approach in order to compensate for the dependence of P-values on the underlying set size: At each iteration step i, we consider all DIAMOnD genes in the interval [i−129/2, i + 129/2], thereby obtaining sets of the same size as the seed genes that can be compared to each other. Column 1 shows that DIAMOnD always performs better than random expectation (orange line above green line), and in (i) and (ii) even better than the established seed genes themselves (orange line above red line). (E) Comparison of the number of seed genes contained in the final DIAMOnD module with the number obtained in 10⁵ random simulations. Connected sets that are constructed completely at random contain on average 51.6 ± 4.9 seed genes, which is significantly lower than in the real disease module. (F) Same as E, but choosing the connected genes not completely at random, but only from the immediate neighborhood of the seed genes. The random modules contain 76.0 ± 4.5 seed genes, again significantly lower than in the real disease module. (G) Schematic illustration of the disease module, together with a 3D depiction of the actual asthma module within the interactome, consisting of 441 genes (91 seed genes + 350 DIAMOnD genes). (H) Comparison of the enrichment of different gene sets with low P-value genes from the EVE GWAS meta-analysis dataset. The seed genes (red) have the highest enrichment. When the 33 GWAS genes are removed from the seed genes (yellow), their enrichment becomes comparable with the DIAMOnD genes (orange). The two random controls of genes with protein interactions to seed genes (green) and all interactome genes (blue) have significantly lower fractions of low P-value genes.

Figure 3.

Comparison of DIAMOnD with other methods. (A) Enrichment of the gene sets obtained by five gene-prioritization methods and DIAMOnD with non-asthma-specific validation data. For each method we use the 350 highest ranked genes. All tested methods show comparable performance. (B) Enrichment according to asthma-specific data. Here, DIAMOnD clearly shows stronger enrichment and outperforms the other methods. (C) Venn diagram of the gene sets of the three methods with the best performance in (A) and (B). While there is a significant overlap, each method also has a considerable number of unique genes. (D) Comparison of the degrees of the 232 unique DIAMOnD genes and the 267 combined unique genes from (C). The genes identified by the other two methods contain many network hubs that are absent in DIAMOnD.

Figure 4.

Overlap between Asthma disease module and the 11 other immune-related diseases. (A) Venn diagram of the three immune-related diseases with the highest overlap with the asthma module. Seventy-two genes are common among the four diseases. (B) Connected component in the interactome of the 65 genes unique to the asthma module. (C) The largest connected component of the asthma module with genes highlighted in purple that are differentially expressed between poorly and well-controlled asthma subjects. (D) The three pathways that are fully embedded in the asthma disease module (shown separately on the side). Seed genes are circled in red. (E) Effect of GC treatment in fibroblast cells. We determined the differentially expressed genes from ‘normal-untreated versus normal-treated’ and ‘asthma-untreated versus asthma-treated’ and mapped them to the interactome. The asthma module is significantly enriched with differentially expressed genes. These genes are highly interconnected inside the module (z-score = 7.8 and 6.5 for normal and asthmatic; orange bars), whereas the differentially expressed genes that fall outside the module are scattered and do not form significant clusters (blue bars). (F) Co-expression networks of GAB1 pathway genes in normal and asthmatic fibroblast cell lines.

Figure 5.

Putative GC mechanism effect via GAB1 in the asthma module. (A) The consensus functional GC response element (GRE) in target genes reads 5′-AGAACAnnnTGTTCT-3′. The probable GRE-binding site conserved in the GAB1 promoter region is 2742–2758 bp. (B) Expression of GAB1 and NFkB (p65) in Beas-2B cells transfected with GAB1-targeted or non-target (NT) control siRNA, as detected by western blot. β-Actin was used as loading control. Band density was quantified using Image J and then normalized to β-actin control. (C) Putative molecular mechanism for the downstream inhibition of NFkB after GC-treatment via the GAB1 signalsome pathway in the asthma module. The activation of NR3C1 via GCs can trigger GAB1 through several MAPK family members. Alternatively, GAB1 may also be a direct GC target as suggested by A. From the 11 genes in the GAB1 signalsome pathway, GAB1, PIK3R1 and PAG1 are differentially expressed both in normal and asthmatic cells. (D) Illustration that GAB1 regulates NFkB regardless of the presence of GCs.