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基于宿主细胞对 SARS-CoV-2 的转录反应,进行药物再利用以治疗 COVID-19 的计算分析。

Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2.

机构信息

Institute for Informatics (I2), Washington University in St. Louis School of Medicine, St. Louis, MO, USA.

Department of Pediatrics, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.

出版信息

BMC Med Inform Decis Mak. 2021 Jan 7;21(1):15. doi: 10.1186/s12911-020-01373-x.

Abstract

BACKGROUND

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 pathways of host cells play 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. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment.

METHODS

In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells.

RESULTS

We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. 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. In addition to many drugs being evaluated in clinical trials, 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.

CONCLUSIONS

The integrative genomics data analysis and 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.

摘要

背景

2019 年冠状病毒病(COVID-19)大流行已在全球感染超过 1000 万人,死亡率相对较高。目前有许多疗法正在进行临床试验,但迄今为止尚无有效的疫苗或治疗方法。在感染严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)后,宿主细胞的分子信号通路在 SARS-CoV-2 的生命周期中发挥着关键作用。因此,确定宿主细胞内涉及的分子信号通路非常重要。针对这些分子信号通路的药物可能对 COVID-19 的治疗有效。

方法

本研究我们开发了一种新的综合分析方法,根据宿主细胞的转录反应,确定宿主细胞内与 SARS-CoV-2 感染相关的分子信号通路,并将药物重新用于 COVID-19 的潜在有效治疗。

结果

我们通过综合分析确定了与 SARS-CoV-2 感染相关的信号通路。然后,确定了激活的基因本体论(GO)和超级 GO。信号通路和 GO,如 MAPK、JNK、STAT、ERK、JAK-STAT、IRF7-NFkB 信号和 MYD88/CXCR6 免疫信号,特别活跃。基于鉴定的信号通路和 GO,通过整合药物-靶标和反向基因表达数据资源,重新利用了一组潜在有效的药物。除了许多正在临床试验中评估的药物外,地塞米松在预测中排名最高,这是第一个报道的能够显著降低接受呼吸支持的 COVID-19 患者死亡率的药物。

结论

综合基因组学数据分析和结果有助于了解宿主细胞内的相关分子信号通路,并促进 COVID-19 治疗有效药物的发现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c658/7791715/450ac6a53409/12911_2020_1373_Fig1_HTML.jpg

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