Systems approach to the pharmacological actions of HDAC inhibitors reveals EP300 activities and convergent mechanisms of regulation in diabetes

Given the skyrocketing costs to develop new drugs, repositioning of approved drugs, such as histone deacetylase (HDAC) inhibitors, may be a promising strategy to develop novel therapies. However, a gap exists in the understanding and advancement of these agents to meaningful translation for which new indications may emerge. To address this, we performed systems-level analyses of 33 independent HDAC inhibitor microarray studies. Based on network analysis, we identified enrichment for pathways implicated in metabolic syndrome and diabetes (insulin receptor signaling, lipid metabolism, immunity and trafficking). Integration with ENCODE ChIP-seq datasets identified suppression of EP300 target genes implicated in diabetes. Experimental validation indicates reversal of diabetes-associated EP300 target genes in primary vascular endothelial cells derived from a diabetic individual following inhibition of HDACs (by SAHA), EP300, or <i>EP300</i> knockdown. Our computational systems biology approach provides an adaptable framework for the prediction of novel therapeutics for existing disease.

鉴于开发新药的成本持续飙升，对已获批药物进行重定位（例如组蛋白去乙酰化酶（histone deacetylase, HDAC）抑制剂），或可成为开发新型治疗方案的极具前景的策略。然而，目前在理解此类药物并推动其实现具有临床意义的转化、发掘新适应症方面仍存在空白。为解决这一问题，本研究对33项独立的HDAC抑制剂微阵列研究开展了系统级分析。通过网络分析，我们发现与代谢综合征及糖尿病相关的通路（包括胰岛素受体信号通路、脂质代谢、免疫应答及物质转运通路）显著富集。结合DNA元件百科全书（Encyclopedia of DNA Elements, ENCODE）的染色质免疫共沉淀测序（chromatin immunoprecipitation sequencing, ChIP-seq）数据集，我们发现与糖尿病相关的EP300靶基因表达受到显著抑制。实验验证结果显示，在糖尿病个体来源的原代血管内皮细胞中，经HDAC抑制剂（SAHA）、EP300抑制剂或EP300基因敲低处理后，与糖尿病相关的EP300靶基因表达得到逆转。本研究提出的计算系统生物学方法，可为现有疾病的新型治疗药物预测提供可推广的研究框架。