Plasma Proteome Fingerprints Reveal Distinctiveness and Clinical Outcome of SARS-CoV-2 Infection

Abstract

Background: We evaluated how plasma proteomic signatures in patients with suspected COVID-19 can unravel the pathophysiology, and determine kinetics and clinical outcome of the infection. Methods: Plasma samples from patients presenting to the emergency department (ED) with symptoms of COVID-19 were stratified into: (1) patients with suspected COVID-19 that was not confirmed (n = 44); (2) non-hospitalized patients with confirmed COVID-19 (n = 44); (3) hospitalized patients with confirmed COVID-19 (n = 53) with variable outcome; and (4) patients presenting to the ED with minor diseases unrelated to SARS-CoV-2 infection (n = 20). Besides standard of care diagnostics, 177 circulating proteins related to inflammation and cardiovascular disease were analyzed using proximity extension assay (PEA, Olink) technology. Results: Comparative proteome analysis revealed 14 distinct proteins as highly associated with SARS-CoV-2 infection and 12 proteins with subsequent hospitalization (p < 0.001). ADM, IL-6, MCP-3, TRAIL-R2, and PD-L1 were each predictive for death (AUROC curve 0.80–0.87). The consistent increase of these markers, from hospital admission to intensive care and fatality, supported the concept that these proteins are of major clinical relevance. Conclusions: We identified distinct plasma proteins linked to the presence and course of COVID-19. These plasma proteomic findings may translate to a protein fingerprint, helping to assist clinical management decisions.

COVIDomics: The Proteomic and Metabolomic Signatures of COVID-19

“…Similar inflammatory markers were also detected by Bauer et al [ 38 ], analyzing with a PEA array the plasma proteome of 44 non-hospitalized and 53 hospitalized COVID-19 patients, and 44 non-COVID subjects. In fact, from the group comparison of COVID-19 vs. non-COVID-19, the authors identified 14 specific inflammatory proteins that can be related to SARS-CoV-2 infection, namely CXCL5, CXCL10, CXCL11, Gal-9, IL-18, IL-18R1, IFNγ, LIF-R, MCP-2, MCP-3, MERTK, MMP-1, PD-L1, and TNF.…”

“… The main findings obtained from the review of proteomics studies are summarized. In particular, the results from plasma [ 34 , 35 , 36 , 38 , 39 , 40 , 41 , 42 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ] and serum [ 43 , 44 , 45 , 46 , 47 , 83 , 86 ] studies were merged to identify the common proteins (top) that should represent the proteome signature of COVID-19. These protein entries were analyzed and clustered using STRING version 11.5, revealing the formation of three main clusters (bottom).…”

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery

“…Finally, we assessed which variables were associated with disease severity that were shared in all the above-mentioned approaches ( Figure 5 f and Table 4 ). The relative Neutrophil count, together with the inflammatory markers MCP3 [ 50 ], IL6 [ 16 , 51 ], TRANCE [ 63 ], and MCP1 [ 24 ]; the neurology-associated markers CD200R1 [ 54 ] and MATN3 [ 54 ]; and the cardiometabolic marker LTBP2 [ 39 ] emerged in every analysis conducted. Lymphocyte count, KIT, and α2-MRAP [ 54 ] were shared by the four different feature correlation algorithms.…”

“…Proximity extension assay (PEA) is a high-throughput technology developed for the high-multiplex analysis of proteins, allowing a successful detection and quantification of several biomarkers [ 23 ] by incubating a low amount of the biological sample. To date, some comprehensive studies identifying sera biomarkers correlated with COVID-19 disease severity have been published [ 24 , 25 , 26 , 27 , 28 , 29 ], but only few of them [ 25 ] have employed machine learning techniques to reach a holistic view that summarizes an extensive clinical characterization (including demographic, comorbidity, clinical, and hematochemical data) of the patients with a large number of potential biomarkers in order to identify those that more effectively classify patients with adverse prognosis.…”

Mapping the Serum Proteome of COVID-19 Patients; Guidance for Severity Assessment

“…Moreover, the observation frequency of the protein alterations across independent studies might be considered as a reference of its reliability and its association with COVID-19. Of the regulated protein panel, thirty-four proteins have been also reported in five or more studies, twenty-four in two to five, and four were reported only in one study from other authors [ 10 , 11 , 12 , 13 , 14 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ]; specifically, CD14, CRP, ITIH1, ITIH3, LBP, LRG1, SERPINA3, VWF, C9, ApoA1 and GSN have been associated with COVID-19 severity in at least eight independent studies ( Supplementary Table S3 ).…”