Ding Jun, Lugo-Martinez Jose, Yuan Ye, Huang Jessie, Hume Adam J, Suder Ellen L, Mühlberger Elke, Kotton Darrell N, Bar-Joseph Ziv
Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, Quebec, H4A 3J1, Canada.
Department of Computer Science, University of Puerto Rico, San Juan, Puerto Rico, 00925, USA.
bioRxiv. 2021 Dec 9:2020.06.01.127589. doi: 10.1101/2020.06.01.127589.
Several molecular datasets have been recently compiled to characterize the activity of SARS-CoV-2 within human cells. Here we extend computational methods to integrate several different types of sequence, functional and interaction data to reconstruct networks and pathways activated by the virus in host cells. We identify key proteins in these networks and further intersect them with genes differentially expressed at conditions that are known to impact viral activity. Several of the top ranked genes do not directly interact with virus proteins. We experimentally tested treatments for a number of the predicted targets. We show that blocking one of the predicted indirect targets significantly reduces viral loads in stem cell-derived alveolar epithelial type II cells (iAT2s).
最近已经汇编了几个分子数据集来描述严重急性呼吸综合征冠状病毒2（SARS-CoV-2）在人类细胞内的活性。在这里，我们扩展了计算方法，以整合几种不同类型的序列、功能和相互作用数据，从而重建病毒在宿主细胞中激活的网络和途径。我们确定了这些网络中的关键蛋白质，并进一步将它们与在已知影响病毒活性的条件下差异表达的基因进行交叉分析。几个排名靠前的基因并不直接与病毒蛋白相互作用。我们对许多预测靶点进行了实验性测试。我们表明，阻断其中一个预测的间接靶点可显著降低干细胞来源的II型肺泡上皮细胞（iAT2s）中的病毒载量。
