TitleA Computational Systems Biology Approach for Identifying Candidate Drugs for Repositioning for Cardiovascular Disease.
Publication TypeJournal Article
Year of Publication2018
AuthorsYu, AZ, Ramsey, SA
JournalInterdiscip Sci
Date Published2018 Jun
KeywordsCardiovascular Diseases, Computational Biology, Drug Repositioning, Gene Expression Regulation, HL-60 Cells, Humans

We report an in silico method to screen for receptors or pathways that could be targeted to elicit beneficial transcriptional changes in a cellular model of a disease of interest. In our method, we integrate: (1) a dataset of transcriptome responses of a cell line to a panel of drugs; (2) two sets of genes for the disease; and (3) mappings between drugs and the receptors or pathways that they target. We carried out a gene set enrichment analysis (GSEA) test for each of the two gene sets against a list of genes ordered by fold-change in response to a drug in a relevant cell line (HL60), with the overall score for a drug being the difference of the two enrichment scores. Next, we applied GSEA for drug targets based on drugs that have been ranked by their differential enrichment scores. The method ranks drugs by the degree of anti-correlation of their gene-level transcriptional effects on the cell line with the genes in the disease gene sets. We applied the method to data from (1) CMap 2.0; (2) gene sets from two transcriptome profiling studies of atherosclerosis; and (3) a combined dataset of drug/target information. Our analysis recapitulated known targets related to CVD (e.g., PPARγ; HMG-CoA reductase, HDACs) and novel targets (e.g., amine oxidase A, δ-opioid receptor). We conclude that combining disease-associated gene sets, drug-transcriptome-responses datasets and drug-target annotations can potentially be useful as a screening tool for diseases that lack an accepted cellular model for in vitro screening.

Alternate JournalInterdiscip Sci
PubMed ID27778232
PubMed Central IDPMC5403631
Grant List1553728-DBI / / National Science Foundation /
Division of Health Sciences Interdisciplinary Research Grant / / Oregon State University /
In-Kind Computing Support / / Oregon State University Center for Genome Research and Biocomputing /
1557605-DMS / / National Science Foundation /
DeLoach Work Scholarship / / Oregon State University /
098807 / / National Heart, Lung, and Blood Institute /
New Investigator Grant / / Medical Research Foundation of Oregon /
K25 HL098807 / HL / NHLBI NIH HHS / United States