[论文解读] Repurposing drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2

Li F, Michelson Ap+3 more|arXiv (Cornell University)|Jun 1, 2020

SARS-CoV-2 and COVID-19 Research参考文献 19被引用 8

一句话总结

本研究通过在人肺上皮细胞中进行转录组分析，识别出SARS-CoV-2干扰的宿主细胞信号通路，随后整合药物-靶点相互作用数据与逆向基因表达数据，对现有药物进行再利用。地塞米松被预测为最有效的药物，后续临床研究证实其可显著降低接受呼吸支持的重症COVID-19患者的死亡率。

ABSTRACT

The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling of host cells plays critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells, and drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment. In this study, we aimed to identify these potential molecular signaling pathways, and repurpose existing drugs as a potentially effective treatment of COVID-19 to facilitate the therapeutic discovery, based on the transcriptional response of host cells. We first identified dysfunctional signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through analysis of the altered gene expression profiles. In addition to the signaling pathway analysis, the activated gene ontologies (GOs) and super gene ontologies were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly identified. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. The dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support. The results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.

研究动机与目标

• 识别SARS-CoV-2感染人肺上皮细胞后失调的宿主分子信号通路。
• 利用转录组响应数据，优先筛选可靶向这些通路的现有药物，以探索潜在的COVID-19治疗策略。
• 整合药物-靶点数据与逆向基因表达数据，预测有效治疗药物，避免新药研发。
• 为病毒大流行期间的快速治疗发现提供系统生物学框架。

提出的方法

• 对SARS-CoV-2感染的人肺上皮细胞进行转录组分析，检测差异表达基因。
• 进行通路富集分析，识别功能失调的信号通路，包括MAPK、JNK、STAT、ERK、JAK-STAT、IRF7-NFκB和MYD88/CXCR6。
• 识别与宿主抗感染反应相关的激活基因本体（GOs）及超GOs。
• 整合药物-靶点相互作用数据库与逆向基因表达谱，预测可逆转SARS-CoV-2诱导的转录改变的药物候选。
• 基于药物逆转宿主转录组响应的能力，对药物进行优先排序。
• 将最高预测药物与已知临床结果进行验证，包括地塞米松对死亡率的影响。

实验结果

研究问题

• RQ1 SARS-CoV-2感染人肺上皮细胞后，哪些宿主细胞信号通路发生显著改变？
• RQ2 哪些现有药物可逆转宿主细胞中SARS-CoV-2诱导的转录组变化？
• RQ3 利用基因表达数据的系统生物学方法能否加速识别COVID-19的再利用药物？
• RQ4 预测的药物效果与重症COVID-19患者的真实临床结果之间有何关联？

主要发现

• SARS-CoV-2感染的宿主细胞中，MAPK、JNK、STAT、ERK、JAK-STAT、IRF7-NFκB和MYD88/CXCR6信号通路显著失调。
• 地塞米松因其逆转宿主转录组响应SARS-CoV-2的能力，被预测为最有效的药物候选。
• 地塞米松的预测疗效随后在临床中得到验证，其显著降低了需要呼吸支持的COVID-19患者的死亡率。
• 本研究识别出多个与免疫相关的信号通路，包括MYD88/CXCR6和NFκB，其在宿主对SARS-CoV-2的应答中起核心作用。
• 整合逆向基因表达与药物-靶点数据，实现了对再利用药物的快速计算机模拟优先排序，为治疗测试提供支持。