Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease

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.

Keywords: COVID-19; SARS-CoV-2; artificial intelligence; assay; biomarker; diagnosis; marker; prognosis; proteomics.

Figure 1

Protein biomarkers required in COVID-19. Proteins are the main stimulants of cellular mechanisms and are responsible for cellular homeostasis. The disruption of these cellular mechanisms is generally associated with many disease phenotypes. Therefore, in a complex multiorgan disease such as is associated with SARS-CoV-2, establishing the underlying proteins involved in the various stages of this disease will pave a way to the discovery of new biomarkers in the diagnosis and prognosis of COVID-19 and its complications. Markers for prognosis, diagnosis, and chronic effects are among those required. The second wave of disease depicted here infers that monitoring in the population for (re)infection would require biomarkers of sufficient specificity. In this respect, a generic inflammatory biomarker response would not be sufficiently specific. The measurement of IgG and IgM antibodies directed against the virus do offer opportunities for screening; however, these generally do not appear for several days following the onset of symptoms and are not always observed during the usual screening window.

Figure 2

Network analysis of the interactions between cytokines, cells, and diseases as captured from PubMed abstracts. Associations captured from MeSH terms in SARS- and coronavirus-related PubMed abstracts. (A) Joint network with SARS on the left and coronavirus on the right. (B) Subnetwork of first-neighbors of the disease node “common cold”. (C) Subnetwork of first-neighbors of the disease node “gastroenteritis”. (D) Subnetwork of first-neighbors of the disease node “pneumonia”. Yellow nodes represent cytokines, blue nodes represent diseases, and green nodes represent cell types. Purple edges represent associations derived from coronavirus-related PubMed abstracts, while pink edges represents those derived from SARS-related abstracts and gray edges represent those connections found in both corpuses. The size of the nodes corresponds to the count of that entity within both corpuses.

Figure 3

Integrated process for the rapid development of algorithms or markers of trajectory in infectious diseases; see also Figure 1. In 2010, 8% (524 million) of the world’s population was reported to be aged 65 or older. This figure is expected to triple by 2050 to about 1.5 billion. The regenerative or host defense capabilities for protection against SARS-CoV-2 infection diminish with age. This has led to the use of algorithms consisting of multiple markers, and factors like comorbidities, age, or sex can be used to develop “score”-based indicators of risk, prognosis, or trajectory. This is one reason for the rapid deployment of the pipeline described here inclusive of artificial intelligence.