Whetton, AD, Preston, GW orcid.org/0000-0003-4812-139X, Abubeker, S et al. (1 more author) (2020) Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease. Journal of Proteome Research, 19 (11). pp. 4219-4232. ISSN 1535-3893
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 American Chemical Society. This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. |
Keywords: | assay, biomarker, marker, SARS-CoV-2, COVID-19, proteomics, diagnosis, prognosis, artificial intelligence |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemistry (Leeds) > Organic Chemistry (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Oct 2022 10:16 |
Last Modified: | 14 Oct 2022 10:16 |
Status: | Published |
Publisher: | American Chemical Society |
Identification Number: | 10.1021/acs.jproteome.0c00326 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:191977 |