$PBIO #covid-19 #coronavirus find: https://www.med
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Posted On: 04/13/2020 2:39:19 PM
$PBIO #covid-19 #coronavirus find: https://www.medrxiv.org/content/10.1101/2020....20054585v1
"The research group of T.G. is partly supported by Tencent, Thermo Fisher Scientific, SCIEX and Pressure Biosciences Inc."
Proteomic and Metabolomic Characterization of COVID-19 Patient Sera
View ORCID ProfileBo Shen, View ORCID ProfileXiao Yi, View ORCID ProfileYaoting Sun, Xiaojie Bi, Juping Du, Chao Zhang, Sheng Quan, View ORCID ProfileFangfei Zhang, View ORCID ProfileRui Sun, View ORCID ProfileLiujia Qian, View ORCID ProfileWeigang Ge, View ORCID ProfileWei Liu, View ORCID ProfileShuang Liang, View ORCID ProfileHao Chen, Ying Zhang, Jun Li, Jiaqin Xu, Zebao He, Baofu Chen, Jing Wang, Haixi Yan, Yufen Zheng, Donglian Wang, Jiansheng Zhu, Ziqing Kong, Zhouyang Kang, View ORCID ProfileXiao Liang, View ORCID ProfileXuan Ding, View ORCID ProfileGuan Ruan, View ORCID ProfileNan Xiang, View ORCID ProfileXue Cai, View ORCID ProfileHuanhuan Gao, View ORCID ProfileLu Li, View ORCID ProfileSainan Li, View ORCID ProfileQi Xiao, View ORCID ProfileTian Lu, View ORCID ProfileYi Judy Zhu, Huafen Liu, Haixiao Chen, View ORCID ProfileTiannan Guo
doi: https://doi.org/10.1101/2020.04.07.20054585
This article is a preprint and has not been peer-reviewed [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.
AbstractInfo/HistoryMetrics Preview PDF
Abstract
Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.
Competing Interest Statement
The research group of T.G. is partly supported by Tencent, Thermo Fisher Scientific, SCIEX and Pressure Biosciences Inc. C.Z., Z.K., Z.K. and S.Q. are employees of DIAN Diagnostics.
Clinical Trial
ChiCTR2000031365
Funding Statement
This work is supported by grants from Westlake Special Program for COVID-19 (2020), and Tencent foundation (2020), National Natural Science Foundation of China (81972492, 21904107, 81672086), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04).
Author Declarations
All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.
Yes
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
Yes
"The research group of T.G. is partly supported by Tencent, Thermo Fisher Scientific, SCIEX and Pressure Biosciences Inc."
Proteomic and Metabolomic Characterization of COVID-19 Patient Sera
View ORCID ProfileBo Shen, View ORCID ProfileXiao Yi, View ORCID ProfileYaoting Sun, Xiaojie Bi, Juping Du, Chao Zhang, Sheng Quan, View ORCID ProfileFangfei Zhang, View ORCID ProfileRui Sun, View ORCID ProfileLiujia Qian, View ORCID ProfileWeigang Ge, View ORCID ProfileWei Liu, View ORCID ProfileShuang Liang, View ORCID ProfileHao Chen, Ying Zhang, Jun Li, Jiaqin Xu, Zebao He, Baofu Chen, Jing Wang, Haixi Yan, Yufen Zheng, Donglian Wang, Jiansheng Zhu, Ziqing Kong, Zhouyang Kang, View ORCID ProfileXiao Liang, View ORCID ProfileXuan Ding, View ORCID ProfileGuan Ruan, View ORCID ProfileNan Xiang, View ORCID ProfileXue Cai, View ORCID ProfileHuanhuan Gao, View ORCID ProfileLu Li, View ORCID ProfileSainan Li, View ORCID ProfileQi Xiao, View ORCID ProfileTian Lu, View ORCID ProfileYi Judy Zhu, Huafen Liu, Haixiao Chen, View ORCID ProfileTiannan Guo
doi: https://doi.org/10.1101/2020.04.07.20054585
This article is a preprint and has not been peer-reviewed [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.
AbstractInfo/HistoryMetrics Preview PDF
Abstract
Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.
Competing Interest Statement
The research group of T.G. is partly supported by Tencent, Thermo Fisher Scientific, SCIEX and Pressure Biosciences Inc. C.Z., Z.K., Z.K. and S.Q. are employees of DIAN Diagnostics.
Clinical Trial
ChiCTR2000031365
Funding Statement
This work is supported by grants from Westlake Special Program for COVID-19 (2020), and Tencent foundation (2020), National Natural Science Foundation of China (81972492, 21904107, 81672086), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04).
Author Declarations
All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.
Yes
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
Yes
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