Department of Pharmaceutical Sciences, Temple University School of Pharmacy, Philadelphia, PA 19140, USA.
Int J Mol Sci. 2024 Oct 16;25(20):11106. doi: 10.3390/ijms252011106.
Despite recent advances in chronic obstructive pulmonary disease (COPD) research, few studies have identified the potential therapeutic targets systematically by integrating multiple-omics datasets. This project aimed to develop a systems biology pipeline to identify biologically relevant genes and potential therapeutic targets that could be exploited to discover novel COPD treatments via drug repurposing or drug discovery. A computational method was implemented by integrating multi-omics COPD data from unpaired human samples of more than half a million subjects. The outcomes from genome, transcriptome, proteome, and metabolome COPD studies were included, followed by an interactome and drug-target information analysis. The potential candidate genes were ranked by a distance-based network computational model. Ninety-two genes were identified as COPD signature genes based on their overall proximity to signature genes on all omics levels. They are genes encoding proteins involved in extracellular matrix structural constituent, collagen binding, protease binding, actin-binding proteins, and other functions. Among them, 70 signature genes were determined to be druggable targets. The validation identified that the knockout or over-expression of , , , , , and genes may drive the cell transcriptomics to a status similar to or contrasting with COPD. While some genes identified in our pipeline have been previously associated with COPD pathology, others represent possible new targets for COPD therapy development. In conclusion, we have identified promising therapeutic targets for COPD. This hypothesis-generating pipeline was supported by unbiased information from available omics datasets and took into consideration disease relevance and development feasibility.
尽管在慢性阻塞性肺疾病 (COPD) 研究方面取得了一些进展，但很少有研究通过整合多组学数据集系统地确定潜在的治疗靶点。本项目旨在开发一个系统生物学管道，通过药物重用来识别有生物学意义的基因和潜在的治疗靶点，或通过药物发现来发现新的 COPD 治疗方法。通过整合来自超过 50 万例非配对人类样本的多组学 COPD 数据，实施了一种计算方法。包括基因组、转录组、蛋白质组和代谢组 COPD 研究的结果，然后进行互作组和药物靶点信息分析。通过基于距离的网络计算模型对潜在候选基因进行排序。根据它们与所有组学水平上的特征基因的整体接近程度，确定了 92 个基因作为 COPD 特征基因。这些基因编码涉及细胞外基质结构成分、胶原结合、蛋白酶结合、肌动蛋白结合蛋白和其他功能的蛋白质。其中，70 个特征基因被确定为可成药靶点。验证确定，基因的敲除或过表达可能会使细胞转录组学向类似于 COPD 的状态或与 COPD 相反的状态发展。虽然我们的管道中鉴定的一些基因以前与 COPD 病理学有关，但其他基因可能代表 COPD 治疗开发的新靶点。总之，我们已经确定了 COPD 的有前途的治疗靶点。该产生假说的管道由来自可用组学数据集的无偏信息支持，并考虑了疾病相关性和开发可行性。
