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Identification of Alpha-Adrenergic Agonists as Potential Therapeutic Agents for Dermatomyositis through Drug-Repurposing Using Public Expression Datasets 

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Presentation on theme: "Identification of Alpha-Adrenergic Agonists as Potential Therapeutic Agents for Dermatomyositis through Drug-Repurposing Using Public Expression Datasets "— Presentation transcript:

1 Identification of Alpha-Adrenergic Agonists as Potential Therapeutic Agents for Dermatomyositis through Drug-Repurposing Using Public Expression Datasets  Hyunje Grace Cho, BA, David Fiorentino, MD, PhD, Matthew Lewis, MD, Marina Sirota, PhD, Kavita Y. Sarin, MD, PhD  Journal of Investigative Dermatology  Volume 136, Issue 7, Pages (July 2016) DOI: /j.jid Copyright © 2016 The Authors Terms and Conditions

2 Figure 1 Analytic workflow and disease signature expression. (a) To obtain a disease signature set, microarray expressions of skin samples from healthy control and active lesions from dermatomyositis patients were compared. After normalization, Rank Product (RankProd) and Significance Analysis of Microarrays (SAM) were used to obtain a signature of differentially expressed genes in dermatomyositis. The differential expression underwent a therapeutic analysis using target-based and expression-based approaches with DrugBank and Connectivity Map, respectively, in order to identify candidate therapeutic agents. (b) Heat map and pathway analysis of 885 signature genes revealed up-regulation in genes involved in cellular signaling (NF-κB, Toll-like receptor), pathologic immune processes (antigen presentation, IFN, epithelial remodeling), and cell cycle and apoptosis (cell cycle regulation, p53, protein ubiquitination). All P < 0.05. Journal of Investigative Dermatology  , DOI: ( /j.jid ) Copyright © 2016 The Authors Terms and Conditions

3 Figure 2 Expression-based analysis identifies potential therapeutic agents for dermatomyositis (DM). (a) A total of 23 agents were identified by both DrugBank and Connectivity Map (CMap), with 13 having therapeutic potential as inferred from negative drug-disease score (P < 0.05). Oxymetazoline (P < 0.01) was a top 15 drug. (b) The predicted targets of oxymetazoline included upstream regulators such as IFIH1 and STAT1, known regulators of IFN signaling. Journal of Investigative Dermatology  , DOI: ( /j.jid ) Copyright © 2016 The Authors Terms and Conditions


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