Verstraete Nina, Jurman Giuseppe, Bertagnolli Giulia, Ghavasieh Arsham, Pancaldi Vera, De Domenico Manlio
Centre de Recherches en Cancérologie de Toulouse (CRCT), UMR1037 Inserm, ERL5294 CNRS, Toulouse, France.
University Paul Sabatier III, Toulouse, France.
Netw Syst Med. 2020 Nov 17;3(1):130-141. doi: 10.1089/nsm.2020.0011. eCollection 2020.
We introduce in this study CovMulNet19, a comprehensive COVID-19 network containing all available known interactions involving SARS-CoV-2 proteins, interacting-human proteins, diseases and symptoms that are related to these human proteins, and compounds that can potentially target them. Extensive network analysis methods, based on a bootstrap approach, allow us to prioritize a list of diseases that display a high similarity to COVID-19 and a list of drugs that could potentially be beneficial to treat patients. As a key feature of CovMulNet19, the inclusion of symptoms allows a deeper characterization of the disease pathology, representing a useful proxy for COVID-19-related molecular processes. We recapitulate many of the known symptoms of the disease and we find the most similar diseases to COVID-19 reflect conditions that are risk factors in patients. In particular, the comparison between CovMulNet19 and randomized networks recovers many of the known associated comorbidities that are important risk factors for COVID-19 patients, through identified similarities with intestinal, hepatic, and neurological diseases as well as with respiratory conditions, in line with reported comorbidities. CovMulNet19 can be suitably used for network medicine analysis, as a valuable tool for exploring drug repurposing while accounting for the intervening multidimensional factors, from molecular interactions to symptoms.
在本研究中，我们引入了CovMulNet19，这是一个全面的COVID-19网络，包含所有已知的涉及严重急性呼吸综合征冠状病毒2（SARS-CoV-2）蛋白、与之相互作用的人类蛋白、与这些人类蛋白相关的疾病和症状以及可能靶向它们的化合物的相互作用。基于自举法的广泛网络分析方法，使我们能够对与COVID-19高度相似的疾病列表以及可能对治疗患者有益的药物列表进行优先级排序。作为CovMulNet19的一个关键特征，纳入症状可以更深入地描述疾病病理，代表了与COVID-19相关分子过程的有用替代指标。我们概括了该疾病的许多已知症状，并发现与COVID-19最相似的疾病反映了患者的危险因素。特别是，通过与肠道、肝脏和神经系统疾病以及呼吸道疾病的相似性识别，CovMulNet19与随机网络之间的比较重现了许多已知的相关合并症，这些合并症是COVID-19患者的重要危险因素，与报告的合并症一致。CovMulNet19可适用于网络医学分析，作为探索药物再利用的有价值工具，同时考虑从分子相互作用到症状的中间多维因素。
