Digital Insights, QIAGEN, Redwood City, USA.
BMC Bioinformatics. 2021 May 3;22(1):229. doi: 10.1186/s12859-021-04148-x.
Leveraging previously identified viral interactions with human host proteins, we apply a machine learning-based approach to connect SARS-CoV-2 viral proteins to relevant host biological functions, diseases, and pathways in a large-scale knowledge graph derived from the biomedical literature. Our goal is to explore how SARS-CoV-2 could interfere with various host cell functions, and to identify drug targets amongst the host genes that could potentially be modulated against COVID-19 by repurposing existing drugs. The machine learning model employed here involves gene embeddings that leverage causal gene expression signatures curated from literature. In contrast to other network-based approaches for drug repurposing, our approach explicitly takes the direction of effects into account, distinguishing between activation and inhibition.
We have constructed 70 networks connecting SARS-CoV-2 viral proteins to various biological functions, diseases, and pathways reflecting viral biology, clinical observations, and co-morbidities in the context of COVID-19. Results are presented in the form of interactive network visualizations through a web interface, the Coronavirus Network Explorer (CNE), that allows exploration of underlying experimental evidence. We find that existing drugs targeting genes in those networks are strongly enriched in the set of drugs that are already in clinical trials against COVID-19.
The approach presented here can identify biologically plausible hypotheses for COVID-19 pathogenesis, explicitly connected to the immunological, virological and pathological observations seen in SARS-CoV-2 infected patients. The discovery of repurposable drugs is driven by prior knowledge of relevant functional endpoints that reflect known viral biology or clinical observations, therefore suggesting potential mechanisms of action. We believe that the CNE offers relevant insights that go beyond more conventional network approaches, and can be a valuable tool for drug repurposing. The CNE is available at https://digitalinsights.qiagen.com/coronavirus-network-explorer .
利用先前鉴定出的病毒与人宿主蛋白的相互作用，我们应用基于机器学习的方法，将 SARS-CoV-2 病毒蛋白与从生物医学文献中提取的大型知识图谱中的相关宿主生物学功能、疾病和途径联系起来。我们的目标是探索 SARS-CoV-2 如何干扰各种宿主细胞功能，并确定宿主基因中的药物靶点，这些靶点可能通过重新利用现有药物来针对 COVID-19 进行调节。这里使用的机器学习模型涉及利用从文献中整理的因果基因表达特征的基因嵌入。与其他用于药物再利用的网络方法不同，我们的方法明确考虑了效果的方向，区分激活和抑制。
我们构建了 70 个网络，将 SARS-CoV-2 病毒蛋白与反映病毒生物学、临床观察和 COVID-19 背景下合并症的各种生物学功能、疾病和途径联系起来。结果以通过网络界面——冠状病毒网络探索器（CNE）呈现的交互式网络可视化形式呈现，该界面允许探索潜在的实验证据。我们发现，针对这些网络中基因的现有药物在已经针对 COVID-19 进行临床试验的药物集中强烈富集。
这里提出的方法可以识别与 SARS-CoV-2 感染患者的免疫、病毒学和病理学观察明显相关的 COVID-19 发病机制的生物学上合理的假说。可再利用药物的发现是由反映已知病毒生物学或临床观察的相关功能终点的先验知识驱动的，因此提示了潜在的作用机制。我们相信，CNE 提供了超越更传统网络方法的相关见解，并且可以成为药物再利用的有价值的工具。CNE 可在 https://digitalinsights.qiagen.com/coronavirus-network-explorer 上获得。
