A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19.
A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19.
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DOI：
10.1371/journal.pbio.3000970
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发表时间：
2020-11
期刊：
影响因子：
9.8
通讯作者：
Cheng F
中科院分区：
文献类型：
--
作者：
Zhou Y;Hou Y;Shen J;Mehra R;Kallianpur A;Culver DA;Gack MU;Farha S;Zein J;Comhair S;Fiocchi C;Stappenbeck T;Chan T;Eng C;Jung JU;Jehi L;Erzurum S;Cheng F
由严重急性呼吸道综合征冠状病毒2型（SARS-CoV-2）引起的2019年全球冠状病毒病（COVID-19）大流行导致了前所未有的社会和经济后果。COVID-19导致的发病率和死亡率的风险在共存的医疗条件下急剧增加，而潜在的机制仍不清楚。此外，COVID-19尚无获批疗法。本研究旨在通过沿着临床和多组学观察，使用网络医学方法学来确定SARS-CoV-2的发病机制、疾病表现和COVID-19治疗。我们将SARS-CoV-2病毒-宿主蛋白质-蛋白质相互作用，转录组学和蛋白质组学纳入人类相互作用组。网络邻近度测量揭示了广泛的COVID-19相关疾病表现的潜在发病机制。单细胞RNA测序数据分析显示，与未发炎组织相比，克罗恩病患者发炎回肠组织的吸收性肠上皮细胞中ACE 2和TMPRSS 2的共表达升高，揭示了COVID-19和炎症性肠病之间共有的病理生物学。对哮喘患者的代谢组学和转录组学（整体和单细胞）数据的综合分析表明，COVID-19与哮喘（包括IRAK 3和ADRB 2）具有中间炎症分子谱。为了优先考虑潜在的治疗方法，我们结合了基于网络的预测和来自COVID-19登记处的26，779名个体的倾向评分（PS）匹配观察性研究。我们发现，褪黑激素的使用（比值比[OR] = 0.72，95%CI 0.56-0.91）与28%的可能性减少阳性实验室检测结果的SARS冠状病毒-2证实逆转录聚合酶链反应测定。使用PS匹配用户活性对照设计，我们确定与使用血管紧张素II受体阻滞剂（OR = 0.70，95% CI 0.54-0.92）或血管紧张素转换酶抑制剂（OR = 0.69，95% CI 0.52-0.90）相比，使用褪黑激素与SARS-CoV-2阳性检测结果的可能性降低相关。重要的是，在使用PS匹配调整年龄、性别、种族、吸烟史和各种疾病合并症后，褪黑激素使用（OR = 0.48，95%CI 0.31-0.75）与非裔美国人SARS-CoV-2阳性实验室检测结果的可能性降低52%相关。综上所述，本研究提出了一个综合网络医学平台，用于预测与COVID-19相关的疾病表现，并确定褪黑激素用于COVID-19的潜在预防和治疗。这项研究使用网络医学方法沿着多组学观察（包括SARS-CoV-2和人类相互作用组、转录组和蛋白质组）来识别COVID-19的广泛疾病表现和药物再利用候选者（包括褪黑激素），并使用大规模患者登记数据库验证这些发现。
The global coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of coexisting medical conditions, while the underlying mechanisms remain unclear. Furthermore, there are no approved therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, disease manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus–host protein–protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measurement revealed underlying pathogenesis for broad COVID-19-associated disease manifestations. Analyses of single-cell RNA sequencing data show that co-expression of ACE2 and TMPRSS2 is elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn disease patients compared to uninflamed tissues, revealing shared pathobiology between COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicate that COVID-19 shares an intermediate inflammatory molecular profile with asthma (including IRAK3 and ADRB2). To prioritize potential treatments, we combined network-based prediction and a propensity score (PS) matching observational study of 26,779 individuals from a COVID-19 registry. We identified that melatonin usage (odds ratio [OR] = 0.72, 95% CI 0.56–0.91) is significantly associated with a 28% reduced likelihood of a positive laboratory test result for SARS-CoV-2 confirmed by reverse transcription–polymerase chain reaction assay. Using a PS matching user active comparator design, we determined that melatonin usage was associated with a reduced likelihood of SARS-CoV-2 positive test result compared to use of angiotensin II receptor blockers (OR = 0.70, 95% CI 0.54–0.92) or angiotensin-converting enzyme inhibitors (OR = 0.69, 95% CI 0.52–0.90). Importantly, melatonin usage (OR = 0.48, 95% CI 0.31–0.75) is associated with a 52% reduced likelihood of a positive laboratory test result for SARS-CoV-2 in African Americans after adjusting for age, sex, race, smoking history, and various disease comorbidities using PS matching. In summary, this study presents an integrative network medicine platform for predicting disease manifestations associated with COVID-19 and identifying melatonin for potential prevention and treatment of COVID-19. This study uses network medicine methodologies along with multiomic observations (including SARS-CoV-2 and human interactome, transcriptome, and proteome) to identify widespread disease manifestations and drug repurposing candidates (including melatonin) for COVID-19, validating these findings using a large-scale patient registry database.
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