Human Disease-Drug Network Based on Genomic Expression Profiles

• Published in: PloS one Vol. 4; no. 8; p. e6536
• Main Authors: Hu, Guanghui, Agarwal, Pankaj
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06.08.2009; Public Library of Science (PLoS)
• Subjects: Apoptosis; Automation; Bipolar disorder; Cancer; Cancer therapies; Carcinogens; Chemotherapy; Cocaine; Computational Biology/Genomics; Crohn's Disease; Databases, Genetic; Datasets; Daunorubicin; Deoxyribonucleic acid; Dermatology; Disease; Diseases; DNA; DNA damage; DNA fragmentation; Drug discovery; Drugs; Gene expression; Gene Expression Profiling; Gene Expression Regulation - drug effects; Genes; Genetic research; Genetics and Genomics/Bioinformatics; Genetics and Genomics/Gene Expression; Genome, Human; Genomes; Genomics; Heart attacks; Hereditary spastic paraplegia; Humans; Huntington's disease; Keratosis; Liver cancer; Lobeline; Metabolites; Methamphetamine; Oligonucleotide Array Sequence Analysis; Pediatrics; Side effects; Squamous cell carcinoma; Studies; Tamoxifen; Target recognition; Type 2 diabetes; United States > US; Pennsylvania
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0006536

Drug repositioning offers the possibility of faster development times and reduced risks in drug discovery. With the rapid development of high-throughput technologies and ever-increasing accumulation of whole genome-level datasets, an increasing number of diseases and drugs can be comprehensively characterized by the changes they induce in gene expression, protein, metabolites and phenotypes. We performed a systematic, large-scale analysis of genomic expression profiles of human diseases and drugs to create a disease-drug network. A network of 170,027 significant interactions was extracted from the approximately 24.5 million comparisons between approximately 7,000 publicly available transcriptomic profiles. The network includes 645 disease-disease, 5,008 disease-drug, and 164,374 drug-drug relationships. At least 60% of the disease-disease pairs were in the same disease area as determined by the Medical Subject Headings (MeSH) disease classification tree. The remaining can drive a molecular level nosology by discovering relationships between seemingly unrelated diseases, such as a connection between bipolar disorder and hereditary spastic paraplegia, and a connection between actinic keratosis and cancer. Among the 5,008 disease-drug links, connections with negative scores suggest new indications for existing drugs, such as the use of some antimalaria drugs for Crohn's disease, and a variety of existing drugs for Huntington's disease; while the positive scoring connections can aid in drug side effect identification, such as tamoxifen's undesired carcinogenic property. From the approximately 37K drug-drug relationships, we discover relationships that aid in target and pathway deconvolution, such as 1) KCNMA1 as a potential molecular target of lobeline, and 2) both apoptotic DNA fragmentation and G2/M DNA damage checkpoint regulation as potential pathway targets of daunorubicin. We have automatically generated thousands of disease and drug expression profiles using GEO datasets, and constructed a large scale disease-drug network for effective and efficient drug repositioning as well as drug target/pathway identification.