Plasma Proteomics Elucidated a Protein Signature in COVID-19 Patients with Comorbidities and Early-Diagnosis Biomarkers

Abstract

Despite great scientific efforts, deep understanding of coronavirus-19 disease (COVID-19) immunopathology and clinical biomarkers remains a challenge. Pre-existing comorbidities increase the mortality rate and aggravate the exacerbated immune response against the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, which can result in more severe symptoms as well as long-COVID and post-COVID complications. In this study, we applied proteomics analysis of plasma samples from 28 patients with SARS-CoV-2, with and without pre-existing comorbidities, as well as their corresponding controls to determine the systemic protein changes caused by the SARS-CoV-2 infection. As a result, the protein signature shared amongst COVID-19 patients with comorbidities was revealed to be characterized by alterations in the coagulation and complement pathways, acute-phase response proteins, tissue damage and remodeling, as well as cholesterol metabolism. These altered proteins may play a relevant role in COVID-19 pathophysiology. Moreover, several novel potential biomarkers for early diagnosis of the SARS-CoV-2 infection were detected, such as increased levels of keratin K22E, extracellular matrix protein-1 (ECM1), and acute-phase response protein α-2-antiplasmin (A2AP). Importantly, elevated A2AP may contribute to persistent clotting complications associated with the long-COVID syndrome in patients with comorbidities. This study provides new insights into COVID-19 pathogenesis and proposes novel potential biomarkers for early diagnosis that could be facilitated for clinical application by further validation studies.

Keywords: COVID-19; LC-MS/MS; SARS-CoV-2; acute-phase reaction; biomarker; comorbidity; complement; diagnosis; inflammation; plasma proteomics.

Figure 1

Quantification of plasma proteins from COVID-19 patients and their controls by tandem mass spectrometry coupled with liquid chromatography (LC-MS/MS). (a) Venn diagram with identified proteins in the three clinical groups. (b) Protein rank plot with the mean of the areas of the 235 identified proteins from each clinical group. (c) Correlation analysis of the protein quantified areas (after log₂ transformation) of three technical replicates from a representative patient and the corresponding controls with the R coefficients and p-values. DC, Disease Control; HC; Healthy Control; P, Patient.

Figure 2

Plasma protein changes in COVID-19 patients with comorbidities compared to their healthy controls. (a) Volcano plot of differential expression analysis between COVID-19 patients with comorbidities and age- and sex-matched healthy controls; (b) Network of selected pathways from the differentially expressed proteins (DEPs) with KEGG pathway enrichment analysis via active subnetworks from STRING database.

Figure 3

Plasma protein changes caused by SARS-CoV-2 infection in patients with comorbidities. (a) Volcano plot of differential expression analysis between COVID-19 patients with comorbidities and age- and sex-matched disease controls; (b) Network of KEGG pathway enrichment analysis of DEPs via active subnetworks using STRING database.

Figure 4

Protein changes in SARS-CoV-2 patients without comorbidities compared to age- and sex-matched healthy controls. (a) Heatmap of hierarchical clustering of selected DEPs after z-score normalization. Arrows indicate DEPs highlighted within the text; (b) Bubble plot of KEGG enriched terms from DEPs between patients without comorbidities and their healthy controls. NP, non-comorbidity patient; p, p-value.

Figure 5

Plasma protein changes in early SARS-CoV-2 infection. (a) Volcano plot of DEPs between patients with early SARS-CoV-2 virus infection and patients with late infection; (b) Network of KEGG pathway enrichment analysis with DEPs via active subnetworks from STRING protein–protein interaction database.