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    Proteomics and informatics for understanding phases and identifying biomarkers in COVID-19 disease

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    Authors
    Whetton, Anthony D
    Preston, G. W.
    Abubeker, S.
    Geifman, N.
    Affiliation
    Stoller Biomarker Discovery Centre, Faculty of Biology Medicine and Health (FBMH), University of Manchester, Manchester M20 4GJ
    Issue Date
    2020
    
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    Abstract
    The emergence of novel coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 coronavirus, has necessitated the urgent development of new diagnostic and therapeutic strategies. Rapid research and development, on an international scale, has already generated assays for detecting SARS-CoV-2 RNA and host immunoglobulins. However, the complexities of COVID-19 are such that fuller definitions of patient status, trajectory, sequelae, and responses to therapy are now required. There is accumulating evidence-from studies of both COVID-19 and the related disease SARS-that protein biomarkers could help to provide this definition. Proteins associated with blood coagulation (D-dimer), cell damage (lactate dehydrogenase), and the inflammatory response (e.g., C-reactive protein) have already been identified as possible predictors of COVID-19 severity or mortality. Proteomics technologies, with their ability to detect many proteins per analysis, have begun to extend these early findings. To be effective, proteomics strategies must include not only methods for comprehensive data acquisition (e.g., using mass spectrometry) but also informatics approaches via which to derive actionable information from large data sets. Here we review applications of proteomics to COVID-19 and SARS and outline how pipelines involving technologies such as artificial intelligence could be of value for research on these diseases.
    Citation
    Whetton AD, Preston GW, Abubeker S, Geifman N. Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease. J Proteome Res. 2020.
    Journal
    Journal of Proteome Research
    URI
    http://hdl.handle.net/10541/623179
    DOI
    10.1021/acs.jproteome.0c00326
    PubMed ID
    32657586
    Additional Links
    https://dx.doi.org/10.1021/acs.jproteome.0c00326
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1021/acs.jproteome.0c00326
    Scopus Count
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