Repositioning drugs by targeting network modules: a Parkinson's disease case study

• Published in: BMC bioinformatics 2017-12, Vol.18 (Suppl 14), p.532-532, Article 532
• Main Authors: Yue, Zongliang, Arora, Itika, Zhang, Eric Y, Laufer, Vincent, Bridges, S Louis, Chen, Jake Y
• Format: Article
• Language: English
• Subjects: Case studies; Disease susceptibility; Drug discovery; Drug Repositioning; Drugs; Gene Expression Regulation; Gene Ontology; Gene Regulatory Networks; Genes; Genome-Wide Association Study; Genomes; Genomics; Humans; Medical research; Parkinson Disease - drug therapy; Parkinson Disease - genetics; Phenotype; Protein Interaction Maps - genetics; Therapeutics; Transcription (Genetics)
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1889-0

Much effort has been devoted to the discovery of specific mechanisms between drugs and single targets to date. However, as biological systems maintain homeostasis at the level of functional networks robustly controlling the internal environment, such networks commonly contain multiple redundant mechanisms designed to counteract loss or perturbation of a single member of the network. As such, investigation of therapeutics that target dysregulated pathways or processes, rather than single targets, may identify agents that function at a level of the biological organization more relevant to the pathology of complex diseases such as Parkinson's Disease (PD). Genome-wide association studies (GWAS) in PD have identified common variants underlying disease susceptibility, while gene expression microarray data provide genome-wide transcriptional profiles. These genomic studies can illustrate upstream perturbations causing the dysfunction in signaling pathways and downstream biochemical mechanisms leading to the PD phenotype. We hypothesize that drugs acting at the level of a gene expression module specific to PD can overcome the lack of efficacy associated with targeting a single gene in polygenic diseases. Thus, this approach represents a promising new direction for module-based drug discovery in human diseases such as PD. We built a framework that integrates GWAS data with gene co-expression modules from tissues representing three brain regions-the frontal gyrus, the lateral substantia, and the medial substantia in PD patients. Using weighted gene correlation network analysis (WGCNA) software package in R, we conducted enrichment analysis of data from a GWAS of PD. This led to the identification of two over-represented PD-specific gene co-expression network modules: the Brown Module (Br) containing 449 genes and the Turquoise module (T) containing 905 genes. Further enrichment analysis identified four functional pathways within the Br module (cellular respiration, intracellular transport, energy coupled proton transport against the electrochemical gradient, and microtubule-based movement), and one functional pathway within the T module (M-phase). Next, we utilized drug-protein regulatory relationship databases (DMAP) and developed a Drug Effect Sum Score (DESS) to evaluate all candidate drugs that might restore gene expression to normal level across the Br and T modules. Among the drugs with the 12 highest DESS scores, 5 had been reported as potential treatments f