Differential Connectivity of Gene Regulatory Networks Distinguishes Corticosteroid Response in Asthma

Publication date: Available online 20 July 2017
Source:Journal of Allergy and Clinical Immunology
Author(s): Weiliang Qiu, Feng Guo, Kimberly Glass, Guo Cheng Yuan, John Quackenbush, Xiaobo Zhou, Kelan G. Tantisira
BackgroundVariations in drug response between individuals have prevented us from achieving high drug efficacy in treating many complex diseases, including asthma. Genetics plays an important role in accounting for such inter-individual variations in drug response. However, systematic approaches for addressing how genetic factors and their regulators determine variations in drug response in asthma treatment are lacking.MethodsWe used PANDA (Passing Attributes between Networks for Data Assimilations) to construct the gene regulatory networks associated with good responders and poor responders to inhaled corticosteroids based on a subset of 145 Caucasian asthmatic children who participated in the Childhood Asthma Management Cohort (CAMP). PANDA utilizes gene expression profiles and published relationships among genes, transcription factors (TFs), and proteins to construct the directed networks of TFs and genes. We assessed the differential connectivity between the gene regulatory network of good responders vs. that of poor responders.ResultsWhen compared to poor responders, the network of good responders has differential connectivity and distinct ontologies (e.g., pro-apoptosis enriched in network of good responders and anti-apoptosis enriched in network of poor responders). Many of the key hubs identified in conjunction with clinical response are also cellular response hubs. Functional validation demonstrated abrogation of differences in corticosteroid treated cell viability following siRNA knockdown of two TFs and differential downstream expression between good-responders and poor-responders.ConclusionsWe have identified and validated multiple transcription factors influencing asthma treatment response. Our results show that differential connectivity analysis can provide new insights into the heterogeneity of drug treatment effects.

Teaser

Almost half of asthmatic patients do not respond well to standard treatment. We proposed a network approach to identify key transcription factors and their target genes that may determine differential drug response in asthmatic patients.


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