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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