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.

Big Data: Challenge and Opportunity for Translational and Industrial Research in Healthcare

“…Thus, they reasoned, the immediate vicinity of target proteins to disease modules should have been a proxy for effectiveness of the drug through the action of those targets. They introduced a proximity index that quantifies the topological relationship between drugs and disease proteins and used it to investigate relationship between drug targets and disease proteins (Sharma et al, 2015;Guney et al, 2016). Thousands of drug-disease associations either reported in the literature or unknown were grouped, for a total of more than 36 thousand associations.…”

Precision medicine in cardiology

“…The inter mediate goals of this approach are to identify the molecular determinants of a pheno type or patho phenotype; construct an interaction map that defines the physical and functional relationships among molecu lar partners in this phenotype network; demonstrate the consequences of biological (genetic or environmentally determined) variants of the gene products in the network; and, in the case of disease, understand the control points in the network that can be modulated to homeo statically restore the sys tem and phenotype function without substantial toxic effects [56][57][58] . These goals are not at all farfetched at this point, having been demonstrated to be useful in several interesting examples as proofsofconcept 59,60 .…”

From systems biology to P4 medicine: applications in respiratory medicine

“…They applied hierarchical clustering of differentially expressed genes as well as gene set variation analysis, gene-protein coexpression and pathway enrichment analysis. 3) SHARMA et al [100] used network-based tools to analyse the predictive value of the asthma interactome, and characterised high-impact pathways central to the disease heterogeneity and drug response. 4) QIU et al [101] used PANDA on participants of the Childhood Asthma Management Program cohort to assess the differential connectivity between the gene regulatory network of good responders to inhaled corticosteroids versus that of poor responders.…”