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  1. TI=Proteomic and Metabolomic Characterization of COVID 19 Patient Sera
  2. AU="Kaipainen, Aku L"

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  1. Article ; Online: Proteomic and Metabolomic Characterization of COVID-19 Patient Sera.

    Shen, Bo / Yi, Xiao / Sun, Yaoting / Bi, Xiaojie / Du, Juping / Zhang, Chao / Quan, Sheng / Zhang, Fangfei / Sun, Rui / Qian, Liujia / Ge, Weigang / Liu, Wei / Liang, Shuang / Chen, Hao / Zhang, Ying / Li, Jun / Xu, Jiaqin / He, Zebao / Chen, Baofu /
    Wang, Jing / Yan, Haixi / Zheng, Yufen / Wang, Donglian / Zhu, Jiansheng / Kong, Ziqing / Kang, Zhouyang / Liang, Xiao / Ding, Xuan / Ruan, Guan / Xiang, Nan / Cai, Xue / Gao, Huanhuan / Li, Lu / Li, Sainan / Xiao, Qi / Lu, Tian / Zhu, Yi / Liu, Huafen / Chen, Haixiao / Guo, Tiannan

    Cell

    2020  Volume 182, Issue 1, Page(s) 59–72.e15

    Abstract: ... we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals ... assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups ... Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here ...

    Abstract Early detection and effective treatment of severe COVID-19 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 was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.
    MeSH term(s) Adult ; Amino Acids/metabolism ; Biomarkers/blood ; COVID-19 ; Cluster Analysis ; Coronavirus Infections/blood ; Coronavirus Infections/physiopathology ; Female ; Humans ; Lipid Metabolism ; Machine Learning ; Macrophages/pathology ; Male ; Metabolomics ; Middle Aged ; Pandemics ; Pneumonia, Viral/blood ; Pneumonia, Viral/physiopathology ; Proteomics ; Severity of Illness Index
    Chemical Substances Amino Acids ; Biomarkers
    Keywords covid19
    Language English
    Publishing date 2020-05-28
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 187009-9
    ISSN 1097-4172 ; 0092-8674
    ISSN (online) 1097-4172
    ISSN 0092-8674
    DOI 10.1016/j.cell.2020.05.032
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  2. Article: Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

    Shen, Bo / Yi, Xiao / Sun, Yaoting / Bi, Xiaojie / Du, Juping / Zhang, Chao / Quan, Sheng / Zhang, Fangfei / Sun, Rui / Qian, Liujia / Ge, Weigang / Liu, Wei / Liang, Shuang / Chen, Hao / Zhang, Ying / Li, Jun / Xu, Jiaqin / He, Zebao / Chen, Baofu /
    Wang, Jing / Yan, Haixi / Zheng, Yufen / Wang, Donglian / Zhu, Jiansheng / Kong, Ziqing / Kang, Zhouyang / Liang, Xiao / Ding, Xuan / Ruan, Guan / Xiang, Nan / Cai, Xue / Gao, Huanhuan / Li, Lu / Li, Sainan / Xiao, Qi / Lu, Tian / Zhu, Yi / Liu, Huafen / Chen, Haixiao / Guo, Tiannan

    Cell. 2020 July 09, v. 182, no. 1

    2020  

    Abstract: ... we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals ... assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups ... Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here ...

    Abstract Early detection and effective treatment of severe COVID-19 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 was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.
    Keywords COVID-19 infection ; artificial intelligence ; biomarkers ; blood ; complement ; macrophages ; metabolites ; metabolomics ; model validation ; models ; patients ; proteomics
    Language English
    Dates of publication 2020-0709
    Size p. 59-72.e15.
    Publishing place Elsevier Inc.
    Document type Article
    ZDB-ID 187009-9
    ISSN 1097-4172 ; 0092-8674
    ISSN (online) 1097-4172
    ISSN 0092-8674
    DOI 10.1016/j.cell.2020.05.032
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  3. Article ; Online: Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

    Shen, Bo / Yi, Xiao / Sun, Yaoting / Bi, Xiaojie / Du, Juping / Zhang, Chao / Quan, Sheng / Zhang, Fangfei / Sun, Rui / Qian, Liujia / Ge, Weigang / Liu, Wei / Liang, Shuang / Chen, Hao / Zhang, Ying / Li, Jun / Xu, Jiaqin / He, Zebao / Chen, Baofu /
    Wang, Jing / Yan, Haixi / Zheng, Yufen / Wang, Donglian / Zhu, Jiansheng / Kong, Ziqing / Kang, Zhouyang / Liang, Xiao / Ding, Xuan / Ruan, Guan / Xiang, Nan / Cai, Xue / Gao, Huanhuan / Li, Lu / Li, Sainan / Xiao, Qi / Lu, Tian / Zhu, Yi Judy / Liu, Huafen / Chen, Haixiao / Guo, Tiannan

    Abstract: ... profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model ... changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation ... Severe COVID-19 patients account for most of the mortality of this disease. Early detection and ...

    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.
    Keywords covid19
    Publisher MedRxiv; WHO
    Document type Article ; Online
    DOI 10.1101/2020.04.07.20054585
    Database COVID19

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  4. Article ; Online: Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

    Shen, Bo / Yi, Xiao / Sun, Yaoting / Bi, Xiaojie / Du, Juping / Zhang, Chao / Quan, Sheng / Zhang, Fangfei / Sun, Rui / Qian, Liujia / Ge, Weigang / Liu, Wei / Liang, Shuang / Chen, Hao / Zhang, Ying / Li, Jun / Xu, Jiaqin / He, Zebao / Chen, Baofu /
    Wang, Jing / Yan, Haixi / Zheng, Yufen / Wang, Donglian / Zhu, Jiansheng / Kong, Ziqing / Kang, Zhouyang / Liang, Xiao / Ding, Xuan / Ruan, Guan / Xiang, Nan / Cai, Xue / Gao, Huanhuan / Li, Lu / Li, Sainan / Xiao, Qi / Lu, Tian / Zhu, Yi Judy / Liu, Huafen / Chen, Haixiao / Guo, Tiannan

    medRxiv

    Abstract: ... profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model ... changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation ... Severe COVID-19 patients account for most of the mortality of this disease. Early detection and ...

    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.
    Keywords covid19
    Language English
    Publishing date 2020-04-07
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.04.07.20054585
    Database COVID19

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  5. Article ; Online: Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

    Shen, Bo / Yi, Xiao / Sun, Yaoting / Bi, Xiaojie / Du, Juping / Zhang, Chao / Quan, Sheng / Zhang, Fangfei / Sun, Rui / Qian, Liujia / Ge, Weigang / Liu, Wei / Liang, Shuang / Chen, Hao / Zhang, Ying / Li, Jun / Xu, Jiaqin / He, Zebao / Chen, Baofu /
    Wang, Jing / Yan, Haixi / Zheng, Yufen / Wang, Donglian / Zhu, Jiansheng / Kong, Ziqing / Kang, Zhouyang / Liang, Xiao / Ding, Xuan / Ruan, Guan / Xiang, Nan / Cai, Xue / Gao, Huanhuan / Li, Lu / Li, Sainan / Xiao, Qi / Lu, Tian / Zhu, Yi / Liu, Huafen / Chen, Haixiao / Guo, Tiannan

    Cell

    2020  Volume 182, Issue 1, Page(s) 59–72.e15

    Keywords General Biochemistry, Genetics and Molecular Biology ; covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 187009-9
    ISSN 1097-4172 ; 0092-8674
    ISSN (online) 1097-4172
    ISSN 0092-8674
    DOI 10.1016/j.cell.2020.05.032
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1167: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 58: Show issues

    Order via subito

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  6. Article ; Online: Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

    Shen, Bo / Yi, Xiao / Sun, Yaoting / Bi, Xiaojie / Du, Juping / Zhang, Chao / Quan, Sheng / Zhang, Fangfei / Sun, Rui / Qian, Liujia / Ge, Weigang / Liu, Wei / Liang, Shuang / Chen, Hao / Zhang, Ying / Li, Jun / Xu, Jiaqin / He, Zebao / Chen, Baofu /
    Wang, Jing / Yan, Haixi / Zheng, Yufen / Wang, Donglian / Zhu, Jiansheng / Kong, Ziqing / Kang, Zhouyang / Liang, Xiao / Ding, Xuan / Ruan, Guan / Xiang, Nan / Cai, Xue / Gao, Huanhuan / Li, Lu / Li, Sainan / Xiao, Qi / Lu, Tian / Zhu, Yi / Liu, Huafen / Chen, Haixiao / Guo, Tiannan

    SSRN Electronic Journal ; ISSN 1556-5068

    2020  

    Keywords covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    DOI 10.2139/ssrn.3570565
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Proteomic and Metabolomic Characterization of SARS-CoV-2-Infected Cynomolgus Macaque at Early Stage.

    Wang, Tiecheng / Miao, Faming / Lv, Shengnan / Li, Liang / Wei, Feng / Hou, Lihua / Sun, Renren / Li, Wei / Zhang, Jian / Zhang, Cheng / Yang, Guang / Xiang, Haiyang / Meng, Keyin / Wan, Zhonghai / Wang, Busen / Feng, Guodong / Zhao, Zhongpeng / Luo, Deyan / Li, Nan /
    Tu, Changchun / Wang, Hui / Xue, Xiaochang / Liu, Yan / Gao, Yuwei

    Frontiers in immunology

    2022  Volume 13, Page(s) 954121

    Abstract: ... lymphopenia and massive "cytokines storm", main features of severe COVID-19 patients, were greatly weakened ... sera samples from COVID-19 CMs models. Our data demonstrated that innate immune response, neutrophile ... and platelet activation were mainly dysregulated in COVID-19 CMs. The symptom of neutrophilia ...

    Abstract Although tremendous effort has been exerted to elucidate the pathogenesis of severe COVID-19 cases, the detailed mechanism of moderate cases, which accounts for 90% of all patients, remains unclear yet, partly limited by lacking the biopsy tissues. Here, we established the COVID-19 infection model in cynomolgus macaques (CMs), monitored the clinical and pathological features, and analyzed underlying pathogenic mechanisms at early infection stage by performing proteomic and metabolomic profiling of lung tissues and sera samples from COVID-19 CMs models. Our data demonstrated that innate immune response, neutrophile and platelet activation were mainly dysregulated in COVID-19 CMs. The symptom of neutrophilia, lymphopenia and massive "cytokines storm", main features of severe COVID-19 patients, were greatly weakened in most of the challenged CMs, which are more semblable as moderate patients. Thus, COVID-19 model in CMs is rational to understand the pathogenesis of moderate COVID-19 and may be a candidate model to assess the safety and efficacy of therapeutics and vaccines against SARS-CoV-2 infection.
    MeSH term(s) Animals ; COVID-19 ; COVID-19 Vaccines ; Humans ; Macaca fascicularis ; Proteomics ; SARS-CoV-2
    Chemical Substances COVID-19 Vaccines
    Language English
    Publishing date 2022-07-12
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2606827-8
    ISSN 1664-3224 ; 1664-3224
    ISSN (online) 1664-3224
    ISSN 1664-3224
    DOI 10.3389/fimmu.2022.954121
    Database MEDical Literature Analysis and Retrieval System OnLINE

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