Article ; Online: Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease.
2020 Volume 19, Issue 11, Page(s) 4219–4232
Abstract: ... of both COVID-19 and the related disease SARS-that protein biomarkers could help to provide this definition ... applications of proteomics to COVID-19 and SARS and outline how pipelines involving technologies ... The emergence of novel coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 coronavirus ...
| 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. |
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| MeSH term(s) | Artificial Intelligence ; Betacoronavirus ; Biomarkers/analysis ; COVID-19 ; Coronavirus Infections/blood ; Coronavirus Infections/diagnosis ; Coronavirus Infections/metabolism ; Coronavirus Infections/physiopathology ; Diagnosis, Computer-Assisted ; Humans ; Pandemics ; Pneumonia, Viral/blood ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/metabolism ; Pneumonia, Viral/physiopathology ; Prognosis ; Proteomics ; SARS-CoV-2 |
| Chemical Substances | Biomarkers |
| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-07-24 |
| Publishing country | United States |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't ; Review |
| ZDB-ID | 2078618-9 |
| ISSN | 1535-3907 ; 1535-3893 |
| ISSN (online) | 1535-3907 |
| ISSN | 1535-3893 |
| DOI | 10.1021/acs.jproteome.0c00326 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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