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A Computational Systems Biology Approach for Identifying Candidate Drugs for Repositioning for Cardiovascular Disease

Interdisciplinary Sciences: Computational Life Sciences volume 10, pages 449–454 (2018)Cite this article

Abstract

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.

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Acknowledgments

This work was supported by the US National Institutes of Health (award HL098807 to S.A.R.), the Medical Research Foundation of Oregon (New Investigator Grant award to S.A.R.), Oregon State University (Division of Health Sciences Interdisciplinary Research Grant award to S.A.R. and University Honors College DeLoach Work Scholarship to A.Y.), the National Science Foundation (award numbers 1557605-DMS and 1553728-DBI to S.A.R.), and the Oregon State University Center for Genome Research and Biocomputing.

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Affiliations

  1. Department of Biomedical Sciences, Oregon State University, 106 Dryden Hall, Corvallis, OR, 97331, USA

    Alvin Z. Yu & Stephen A. Ramsey

  2. School of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR, 97331, USA

    Stephen A. Ramsey

Authors
  1. Alvin Z. Yu
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  2. Stephen A. Ramsey
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Corresponding author

Correspondence to Stephen A. Ramsey.

Additional information

The software for this project is available under a free-software, open-source license at github.com/ramseylab/drugscan.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Online Resource 1

Quantitative results from an analysis of transcriptional responses of HL60 cells to 1229 drugs in which drugs were scored based on enrichments of genes from two atherosclerosis-related gene sets (Cagnin et al. and Puig et al. gene sets [7, 8]) among genes ordered by differential expression in response to drug treatment. P values are given in −log10 scale. (PDF 341 kb)

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Yu, A.Z., Ramsey, S.A. A Computational Systems Biology Approach for Identifying Candidate Drugs for Repositioning for Cardiovascular Disease. Interdiscip Sci Comput Life Sci 10, 449–454 (2018). https://doi.org/10.1007/s12539-016-0194-3

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  • DOI: https://doi.org/10.1007/s12539-016-0194-3

Keywords

  • Atherosclerosis
  • Gene expression analysis
  • Drug repositioning
  • Bioinformatics