基于代谢分析的SARS-CoV-2药物靶点预测方法

Drug target prediction approach for SARS-CoV-2 based on metabolic analysis

提出一种基于代谢分析的抑制SARS-CoV-2复制的药物靶点预测方法。使用5组基因表达综合数据库(GEO)的人类肺部组织细胞的转录组学数据,提取出SARS-CoV-2入侵宿主细胞后显著高表达的基因,进而重构出病毒入侵肺部组织细胞后的代谢网络模型;之后采用基因敲除、毒性测试等系统生物学分析方法来预测药物靶点。对GEO中5个数据集的样本进行分析,结果显示各数据集预测的靶点基因具有一定的一致性。其中,PLPBP是5个数据集中预测的共有靶点基因,说明它对于SARS-CoV-2代谢活动具有重要作用,可作为治疗该疾病的潜在药物靶点;另外,BCAT1、BCAT2、ADI1也具有一定的研究价值。提出的方法为预测SARS-CoV-2的药物靶点提供一种新的思路,预测的药物靶点也具有进一步临床研究的潜力。

A metabolic profiling-based method for predicting the drug targets that inhibit SARS-CoV-2 replication is presented.Five sets of the transcriptomic data of human lung histiocytes from the Gene Expression Omnibus(GEO)database are used in the study.The highly expressed genes after SARS-CoV-2 invaded the host cells are extracted,and a metabolic network model after the virus invasion is reconstructed.Some systems biology approaches such as gene knockout and cytotoxicity test are adopted to discover the drug targets.The analysis on the samples from 5 datasets in GEO shows that the target genes predicted in each dataset has certain consistency.PLPBP is the common target gene predicted in the 5 datasets,indicating that it plays an important role in the metabolic activities of SARS-CoV-2 and can be served as a potential drug target.In addition,BCAT1,BCAT2,ADI1 are also worth further exploring.The proposed method provides a new idea for predicting the drug targets for SARS-CoV-2,and the predicted drug targets are potential for further clinical research.