Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19.
Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19.
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DOI：
10.1016/j.isci.2022.104612
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发表时间：
2022-07-15
期刊：
影响因子：
5.8
通讯作者：
Krumsiek, Jan
中科院分区：
文献类型：
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作者：
Buyukozkan, Mustafa;Alvarez-Mulett, Sergio;Racanelli, Alexandra C.;Schmidt, Frank;Batra, Richa;Hoffman, Katherine L.;Sarwath, Hina;Engelke, Rudolf;Gomez-Escobar, Luis;Simmons, Will;Benedetti, Elisa;Chetnik, Kelsey;Zhang, Guoan;Schenck, Edward;Suhre, Karsten;Choi, Justin J.;Zhao, Zhen;Racine-Brzostek, Sabrina;Yang, He S.;Choi, Mary E.;Choi, Augustine M. K.;Choo, Soo Jung;Krumsiek, Jan
新型冠状病毒病（COVID-19）疫情以前所未有的发病率和死亡率水平肆虐全球医疗保健。在这项研究中，我们对从COVID-19患者中获得的血清进行了大规模整合多组学分析，目的是揭示这种疾病的新致病复杂性，并确定预测临床结果的分子特征。我们通过有针对性的代谢组学和蛋白质组学分析，在330名COVID-19患者和97名非COVID住院对照中组装了一个蛋白质-代谢物相互作用网络。我们的网络确定了与免疫调节、能量和核苷酸代谢、血管稳态和胶原蛋白代谢相关的不同蛋白质代谢物串扰。此外，我们的数据将多种蛋白质和代谢物与长期死亡率和发病率相关的临床指标联系起来。最后，我们基于代谢组学数据开发了一种新的COVID-19疾病严重程度的复合结局指标。该模型预测严重疾病的一致性指数约为0.69，在两个独立的数据集中显示出0.83-0.93的高预测能力。COVID-19患者显示各种途径的血清变化代谢组学/蛋白质组学串扰定义疾病的途径COVID-19疾病严重程度与血液中的各种标志物相关COVID-19疾病严重程度可以通过一小组代谢物准确预测生物科学;临床发现;人体代谢;医学;生理学
The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83–0.93 in two independent datasets. COVID-19 patients show serum changes in various pathways Metabolomic/proteomic cross-talk defines pathways of disease COVID-19 disease severity correlates with various markers in blood COVID-19 disease severity can be accurately predicted by a small set of metabolites Biological sciences; Clinical finding; Human metabolism; Medicine; Physiology
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影响因子：
14.9
作者：
Kanehisa M;Furumichi M;Sato Y;Ishiguro-Watanabe M;Tanabe M
通讯作者：
Tanabe M
影响因子：
16.6
作者：
Benedetti E;Pučić-Baković M;Keser T;Wahl A;Hassinen A;Yang JY;Liu L;Trbojević-Akmačić I;Razdorov G;Štambuk J;Klarić L;Ugrina I;Selman MHJ;Wuhrer M;Rudan I;Polasek O;Hayward C;Grallert H;Strauch K;Peters A;Meitinger T;Gieger C;Vilaj M;Boons GJ;Moremen KW;Ovchinnikova T;Bovin N;Kellokumpu S;Theis FJ;Lauc G;Krumsiek J
通讯作者：
Krumsiek J
影响因子：
--
作者：
Krumsiek J;Suhre K;Illig T;Adamski J;Theis FJ
通讯作者：
Theis FJ
ruxolitinib在严重的covid-19中：多中心，前瞻性，单臂，开放标签临床研究的结果，以研究鲁唑替尼在Covid-19和严重急性呼吸综合症患者中的疗效和安全性。
DOI：
10.31053/1853.0605.v78.n3.32800
发表时间：
2021-09-07
期刊：
Revista de la Facultad de Ciencias Medicas (Cordoba, Argentina)
影响因子：
--
作者：
Iastrebner M;Castro J;García Espina E;Lettieri C;Payaslian S;Cuesta MC;Gutiérrez Fernández P;Mandrile A;Contreras AP;Gervasoni S;Verde G;Galetti C;Minoldo E;Caruso V
通讯作者：
Caruso V
影响因子：
30.5
作者：
Liu, Mingen;O'Connor, Roddy S.;Beatty, Gregory L.
通讯作者：
Beatty, Gregory L.