Plasma proteomics identify potential severity biomarkers from COVID-19 associated network

Abstract

Purpose: Coronavirus disease 2019 (COVID-19) continues to threaten public health globally. Severe acute respiratory coronavirus type 2 (SARS-CoV-2) infection-dependent alterations in the host cell signaling network may unveil potential target proteins and pathways for therapeutic strategies. In this study, we aim to define early severity biomarkers and monitor altered pathways in the course of SARS-CoV-2 infection.

Experimental design: We systematically analyzed plasma proteomes of COVID-19 patients from Turkey by using mass spectrometry. Different severity grades (moderate, severe, and critical) and periods of disease (early, inflammatory, and recovery) are monitored. Significant alterations in protein expressions are used to reconstruct the COVID-19 associated network that was further extended to connect viral and host proteins.

Results: Across all COVID-19 patients, 111 differentially expressed proteins were found, of which 28 proteins were unique to our study mainly enriching in immunoglobulin production. By monitoring different severity grades and periods of disease, CLEC3B, MST1, and ITIH2 were identified as potential early predictors of COVID-19 severity. Most importantly, we extended the COVID-19 associated network with viral proteins and showed the connectedness of viral proteins with human proteins. The most connected viral protein ORF8, which has a role in immune evasion, targets many host proteins tightly connected to the deregulated human plasma proteins.

Conclusions and clinical relevance: Plasma proteomes from critical patients are intrinsically clustered in a distinct group than severe and moderate patients. Importantly, we did not recover any grouping based on the infection period, suggesting their distinct proteome even in the recovery phase. The new potential early severity markers can be further studied for their value in the clinics to monitor COVID-19 prognosis. Beyond the list of plasma proteins, our disease-associated network unravels altered pathways, and the possible therapeutic targets in SARS-CoV-2 infection by connecting human and viral proteins. Follow-up studies on the disease associated network that we propose here will be useful to determine molecular details of viral perturbation and to address how the infection affects human physiology.

Keywords: SARS-CoV-2 infection; data integration; early severity biomarker; plasma proteome; turkey patient profile.

FIGURE 1

Global host proteome analysis of COVID‐19 patients in plasma. (A) Overview of the experimental outline and study cohort including five uninfected (black) individuals with single time point samples and 13 COVID‐19 patients (moderate: turquoise, severe: orange, and critical: brown) with three time point samples were collected after the onset of symptoms. (B) Pairwise Pearson correlation across all 52 proteome samples of uninfected individuals and COVID‐19 patients (red: high coefficients of variation, blue: low coefficients of variation). U, uninfected; M, moderate; S, severe; C, critical. (C) Pearson correlation coefficients of samples between severity grades (moderate, severe, and critical) and uninfected individuals. Kruskal–Wallis statistical test was used for statistical analysis: 0.01 < *p < 0.05, **p < 0.01, ***p < 0.001. (D) PCA of COVID‐19 patients (red, triangular) (n = 13) and uninfected individuals (blue, star) (n = 5). COVID‐19, coronavirus disease 2019; ns, non‐significant; PCA, principal component analysis.

FIGURE 2

Analysis of significantly deregulated proteins in coronavirus disease 2019 (COVID‐19) stages. (A) Upset plot showing the comparison of 111 deregulated plasma proteins with the significantly regulated proteins found in other proteomics data sets. (B) Venn diagram showing the validation of 111 deregulated proteins with the 153 deregulated proteins in the COMBAT plasma‐based proteomics data sets. p‐value: 2.2 e‐16. (C) Protein expression level trends of nine overlapping proteins with other proteomics data sets in different severity grades. Protein names and their corresponding trends were similarly color coded.

FIGURE 3

Stratification of COVID‐19 patients based on their plasma proteome profile. (A) PCA of COVID‐19 samples (M, moderate; S, severe; and C, critical) from different infection periods (early infection period: green, inflammatory response period: pink, and recovery period: blue). Biological variance showing each cluster profile. Cluster 1 (green) and cluster 2 (purple) are circled with dashed lines respectively. (B) Hierarchical clustering of COVID‐19 patients. *SAA1 – specific to critical patients’ cluster (cluster 1). (C) Volcano plot of deregulated proteins between cluster 1 and cluster 2. For statistical analysis of deregulated proteins, p‐value considered as 0.05. Student t‐test was applied for the statistical analysis. (D) Pathway analyses of proteins from cluster 1 and cluster 2, based on reactome annotation of g‐profiler website tool. Top 10 terms for each cluster were annotated and full annotation of a term marked with #: regulation of IGF transport and uptake by IGFBPs. COVID‐19, coronavirus disease 2019; IGF, insulin‐like growth factor; IGFBP, insulin‐like growth factor binding protein; PCA, principal component analysis; SAA1, serum amyloid A1.

FIGURE 4

Detection of individual severity biomarkers for the early prediction of the disease progression and the interaction network of plasma proteins with SARS‐CoV‐2 proteins. (A) Volcano plot showing five deregulated proteins from the comparison of critical and moderate–severe severity grades. (B) Protein expression level trends of the biomarkers for critical patients during the course of the disease (early infection period, inflammatory infection period, and recovery period). MB: purple, S100A9: orange, MST1: blue, ITIH2: green, CLEC3B: brown. (C) Expression levels of MST1, ITIH2, and CLEC3B in the early infection period of the disease. For the statistical analysis pairwise t‐test with 0.05 p‐value cutoff was used. 0.01<*p < 0.05, **p < 0.01, ***p < 0.001. (D) Interaction network of deregulated proteins with addition of intermediate proteins found using OI2. Subcellular location of proteins was retrieved from OI2 and illustrated with color backgrounds. GO annotation of each Louvain cluster obtained from PANTHER. Expression values of network proteins were represented by shape and font sizes. Critical‐specific deregulated proteins and our proposed severity biomarker proteins were indicated by black border and pink color, respectively. Steiner nodes were represented as yellow. GO, Gene Ontology; MB, myoglobin; OI2, Omics Integrator 2; SARS‐CoV‐2, severe acute respiratory coronavirus type 2.

FIGURE 5

Analysis of plasma proteins interaction with severe acute respiratory coronavirus type 2 (SARS‐CoV‐2) proteins and drug targets. (A) Overall view from viral proteins (blue) to its downstream deregulated communities of proteins (red) intermediated by modules of host proteins (green). (B) The downstream plasma proteins of ORF8 SARS‐CoV‐2 virus protein through a module of interacting host proteins. Same color codes were used. (C) The downstream plasma proteins of NSP5 and NSP5(C145A) SARS‐CoV‐2 virus proteins through HDAC2 and GPX1 host proteins. Same color codes were used.