The Trans-omics Landscape of COVID-19
Summary System-wide molecular characteristics of COVID-19, especially in those patients without comorbidities, have not been fully investigated. We compared extensive molecular profiles of blood samples from 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Amongst the major findings, asymptomatic patients were characterized by highly activated anti-virus interferon, T/natural killer (NK) cell activation, and transcriptional upregulation of inflammatory cytokine mRNAs. However, given very abundant RNA binding proteins (RBPs), these cytokine mRNAs could be effectively destabilized hence preserving normal cytokine levels. In contrast, in critically ill patients, cytokine storm due to RBPs inhibition and tryptophan metabolites accumulation contributed to T/NK cell dysfunction. A machine-learning model was constructed which accurately stratified the COVID-19 severities based on their multi-omics features. Overall, our analysis provides insights into COVID-19 pathogenesis and identifies targets for intervening in treatment..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 04. Okt. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wu, Peng [VerfasserIn] |
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| doi: |
10.1101/2020.07.17.20155150 |
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| PPN (Katalog-ID): |
XBI018391400 |
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| 024 | 7 | |a 10.1101/2020.07.17.20155150 |2 doi | |
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| 100 | 1 | |a Wu, Peng |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a The Trans-omics Landscape of COVID-19 |
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| 337 | |a Computermedien |b c |2 rdamedia | ||
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| 520 | |a Summary System-wide molecular characteristics of COVID-19, especially in those patients without comorbidities, have not been fully investigated. We compared extensive molecular profiles of blood samples from 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Amongst the major findings, asymptomatic patients were characterized by highly activated anti-virus interferon, T/natural killer (NK) cell activation, and transcriptional upregulation of inflammatory cytokine mRNAs. However, given very abundant RNA binding proteins (RBPs), these cytokine mRNAs could be effectively destabilized hence preserving normal cytokine levels. In contrast, in critically ill patients, cytokine storm due to RBPs inhibition and tryptophan metabolites accumulation contributed to T/NK cell dysfunction. A machine-learning model was constructed which accurately stratified the COVID-19 severities based on their multi-omics features. Overall, our analysis provides insights into COVID-19 pathogenesis and identifies targets for intervening in treatment. | ||
| 650 | 4 | |a Biology |7 (dpeaa)DE-84 | |
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| 700 | 1 | |a Chen, Dongsheng |4 aut | |
| 700 | 1 | |a Ding, Wencheng |4 aut | |
| 700 | 1 | |a Wu, Ping |4 aut | |
| 700 | 1 | |a Hou, Hongyan |4 aut | |
| 700 | 1 | |a Bai, Yong |4 aut | |
| 700 | 1 | |a Zhou, Yuwen |4 aut | |
| 700 | 1 | |a Li, Kezhen |4 aut | |
| 700 | 1 | |a Xiang, Shunian |4 aut | |
| 700 | 1 | |a Liu, Panhong |4 aut | |
| 700 | 1 | |a Ju, Jia |4 aut | |
| 700 | 1 | |a Guo, Ensong |4 aut | |
| 700 | 1 | |a Liu, Jia |4 aut | |
| 700 | 1 | |a Yang, Bin |4 aut | |
| 700 | 1 | |a Fan, Junpeng |4 aut | |
| 700 | 1 | |a He, Liang |4 aut | |
| 700 | 1 | |a Sun, Ziyong |4 aut | |
| 700 | 1 | |a Feng, Ling |4 aut | |
| 700 | 1 | |a Wang, Jian |4 aut | |
| 700 | 1 | |a Wu, Tangchun |4 aut | |
| 700 | 1 | |a Wang, Hao |4 aut | |
| 700 | 1 | |a Cheng, Jin |4 aut | |
| 700 | 1 | |a Xing, Hui |4 aut | |
| 700 | 1 | |a Meng, Yifan |4 aut | |
| 700 | 1 | |a Li, Yongsheng |4 aut | |
| 700 | 1 | |a Zhang, Yuanliang |4 aut | |
| 700 | 1 | |a Luo, Hongbo |4 aut | |
| 700 | 1 | |a Xie, Gang |4 aut | |
| 700 | 1 | |a Lan, Xianmei |4 aut | |
| 700 | 1 | |a Tao, Ye |4 aut | |
| 700 | 1 | |a Yuan, Hao |4 aut | |
| 700 | 1 | |a Huang, Kang |4 aut | |
| 700 | 1 | |a Sun, Wan |4 aut | |
| 700 | 1 | |a Qian, Xiaobo |4 aut | |
| 700 | 1 | |a Li, Zhichao |4 aut | |
| 700 | 1 | |a Huang, Mingxi |4 aut | |
| 700 | 1 | |a Ding, Peiwen |4 aut | |
| 700 | 1 | |a Wang, Haoyu |4 aut | |
| 700 | 1 | |a Qiu, Jiaying |4 aut | |
| 700 | 1 | |a Wang, Feiyue |4 aut | |
| 700 | 1 | |a Wang, Shiyou |4 aut | |
| 700 | 1 | |a Zhu, Jiacheng |4 aut | |
| 700 | 1 | |a Ding, Xiangning |4 aut | |
| 700 | 1 | |a Chai, Chaochao |4 aut | |
| 700 | 1 | |a Liang, Langchao |4 aut | |
| 700 | 1 | |a Wang, Xiaoling |4 aut | |
| 700 | 1 | |a Luo, Lihua |4 aut | |
| 700 | 1 | |a Sun, Yuzhe |4 aut | |
| 700 | 1 | |a Yang, Ying |4 aut | |
| 700 | 1 | |a Zhuang, Zhenkun |4 aut | |
| 700 | 1 | |a Li, Tao |4 aut | |
| 700 | 1 | |a Tian, Lei |4 aut | |
| 700 | 1 | |a Zhang, Shaoqiao |4 aut | |
| 700 | 1 | |a Zhu, Linnan |4 aut | |
| 700 | 1 | |a Chen, Lei |4 aut | |
| 700 | 1 | |a Wu, Yiquan |4 aut | |
| 700 | 1 | |a Ma, Xiaoyan |4 aut | |
| 700 | 1 | |a Chen, Fang |4 aut | |
| 700 | 1 | |a Ren, Yan |4 aut | |
| 700 | 1 | |a Xu, Xun |4 aut | |
| 700 | 1 | |a Liu, Siqi |4 aut | |
| 700 | 1 | |a Wang, Jian |4 aut | |
| 700 | 1 | |a Yang, Huanming |4 aut | |
| 700 | 1 | |a Wang, Lin |4 aut | |
| 700 | 1 | |a Sun, Chaoyang |4 aut | |
| 700 | 1 | |a Ma, Ding |4 aut | |
| 700 | 1 | |a Jin, Xin |4 aut | |
| 700 | 1 | |a Chen, Gang |4 aut | |
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