Multi-platform omics analysis reveals molecular signature for COVID-19 pathogenesis, prognosis and drug target discovery.
Multi-platform omics analysis reveals molecular signature for COVID-19 pathogenesis, prognosis and drug target discovery.
复制标题
多平台组学分析揭示了COVID-19发病机制、预后和药物靶点发现的分子特征。
DOI：
10.1038/s41392-021-00508-4
复制
发表时间：
2021-04-15
影响因子：
39.3
通讯作者：
Xu Y
中科院分区：
文献类型：
--
作者：
Li Y;Hou G;Zhou H;Wang Y;Tun HM;Zhu A;Zhao J;Xiao F;Lin S;Liu D;Zhou D;Mai L;Zhang L;Zhang Z;Kuang L;Guan J;Chen Q;Wen L;Zhang Y;Zhuo J;Li F;Zhuang Z;Chen Z;Luo L;Liu D;Chen C;Gan M;Zhong N;Zhao J;Ren Y;Xu Y
2019冠状病毒病（COVID-19）的疾病进展预测和治疗药物靶点发现尤为重要，因为目前仍没有针对COVID-19重症患者的有效治疗策略。在此，我们对COVID-19期间收集的患者血浆和尿液样本进行了多平台组学分析。对这些组学数据的综合分析揭示了几个潜在的治疗靶点，如ANXA1和cle3b。血浆分子变化表明重症患者与非重症患者相比巨噬细胞失调和T细胞功能抑制。此外，我们选择了25个重要的分子特征作为预测疾病严重程度的潜在生物标志物。采用相应的新冠肺炎患者队列尿液和血浆样本验证预测能力，AUC分别达到0.904和0.988。总之，我们的组学数据不仅提出了潜在的治疗靶点，而且为了解重症COVID-19的发病机制提供了生物标志物。
Disease progression prediction and therapeutic drug target discovery for Coronavirus disease 2019 (COVID-19) are particularly important, as there is still no effective strategy for severe COVID-19 patient treatment. Herein, we performed multi-platform omics analysis of serial plasma and urine samples collected from patients during the course of COVID-19. Integrative analyses of these omics data revealed several potential therapeutic targets, such as ANXA1 and CLEC3B. Molecular changes in plasma indicated dysregulation of macrophage and suppression of T cell functions in severe patients compared to those in non-severe patients. Further, we chose 25 important molecular signatures as potential biomarkers for the prediction of disease severity. The prediction power was validated using corresponding urine samples and plasma samples from new COVID-19 patient cohort, with AUC reached to 0.904 and 0.988, respectively. In conclusion, our omics data proposed not only potential therapeutic targets, but also biomarkers for understanding the pathogenesis of severe COVID-19.
登录
查看更多内容
影响因子：
3.7
作者：
Currie SM;Findlay EG;McHugh BJ;Mackellar A;Man T;Macmillan D;Wang H;Fitch PM;Schwarze J;Davidson DJ
通讯作者：
Davidson DJ
影响因子：
30.3
作者：
Eisfeld AJ;Halfmann PJ;Wendler JP;Kyle JE;Burnum-Johnson KE;Peralta Z;Maemura T;Walters KB;Watanabe T;Fukuyama S;Yamashita M;Jacobs JM;Kim YM;Casey CP;Stratton KG;Webb-Robertson BM;Gritsenko MA;Monroe ME;Weitz KK;Shukla AK;Tian M;Neumann G;Reed JL;van Bakel H;Metz TO;Smith RD;Waters KM;N'jai A;Sahr F;Kawaoka Y
通讯作者：
Kawaoka Y
影响因子：
6.4
作者：
Khovidhunkit, W;Memon, RA;Grunfeld, C
通讯作者：
Grunfeld, C
影响因子：
--
作者：
Prasse, Antje;Pechkovsky, Dmitri V.;Mueller-Quernheirn, Joachim
通讯作者：
Mueller-Quernheirn, Joachim
影响因子：
--
作者：
Luzina, Irina G.;Papadimitriou, John C.;Atamas, Sergei P.
通讯作者：
Atamas, Sergei P.