Integrative systems biology approaches in asthma pharmacogenomics

Abstract

In order to improve therapeutic outcomes, there is a tremendous need to identify patients who are likely to respond to a given asthma treatment. Pharmacogenomic studies have explained a portion of the variability in drug response and provided an increasing list of candidate genes and SNPs. However, as phenotypic variation arises from a network of complex interactions among genetic and environmental factors, rather than individual genes or SNPs, a multidisciplinary, systems-level approach is required in order to understand the inter-relationships among these factors. Systems biology, which seeks to capture interactions between genetic factors and other variables, offers a promising approach to improved therapeutic outcomes in asthma. This aritcle will review and update progress in the pharmacogenomics of asthma and then discuss the application of systems biology approaches to asthma pharmacogenomics.

Figure 1. GLCCI1 rs37972 SNP genotype predicts inhaled corticosteroid treatment response in asthma patient cohorts

The rs37972 mutant (T) allele was significantly associated with poorer therapeutic response (measured as the mean ± standard error ΔFEV₁, expressed as the percentage of the predicted value), after 4–8 weeks of therapy with inhaled glucocorticoids in four study groups: SOCS and SLIC trials (SS; n = 264), the Adult Study (n = 385), the LOCCS trial (n = 185) and the CARE network trials (n = 101). CC represents the homozygous reference genotype; CT represents the heterozygous genotype; TT represents the variant genotype. ΔFEV₁: Forced expiratory volume in 1 s. Reproduced with permission from [24] © 2011 Massachusetts Medical Society.

Figure 2. Bayesian network model of SNPs predictive of bronchodilator response

A Bayesian network, which was learned directly from the data, shows 15 SNPs from 15 candidate genes that are significant predictors of BDR. BDR: Bronchodilator response.

Figure 3. Module network analysis of a treatment-dependent protein interaction network

Ingenuity pathway analysis of a treatment response-induced, IL13-dependent cluster identified key regulatory genes TGF-β1 and JUNB. Eight of the network proteins were regulated by TGF-β1, which in turn is activated or induced by THBS1, MMP14 and IL13 in a positive-feedback loop. Proteins are represented by nodes, and edges indicate all interactions other than coexpression. Nodes of dark blue indicate IL13-dependent expression (i.e., genes that are induced by treatment only in the presence of IL13). Reproduced with permission from [108] © 2008, American Thoracic Society.

Figure 4. Pharmacogenetic categories of asthma phenotypes

Combined effects of genetic and environmental variation categorically affect drug and target response in four distinct ways: pharmacokinetics (therapeutic concentrations at the target site), pharmacodynamics (drug–target interactions), idiosyncratic drug reactions (immune hypersensitivity and drug–drug interactions) and genetic variation related to the natural history of the disease (pathogenesis and symptomatic response). IDR: Idiosyncratic drug reaction; NHD: Natural history of the disease; PD: Pharmacodynamic; PK: Pharmacokinetic.

Figure 5. Integrative gene network of 86 common edges from the CAMP and HapMap consortium (GeneVar) cohorts

From a subset of 608 genes, a subnetwork of 86 edges appeared in both CAMP and GeneVar networks using a threshold of posterior probability of 0.9. Hub nodes are indicated in pink for CAMP, cyan for GeneVar and yellow for both. The direction of the edges is shown by the edge color (green: both positive; black: both negative; red: opposite). Reproduced with permission from [126] © 2009 BioMed Central.