Dataset Information

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.

SUBMITTER: Sahin AT

PROVIDER: S-EPMC9874836 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Sahin Ayse Tugce AT Yurtseven Ali A Dadmand Sina S Ozcan Gulin G Akarlar Busra A BA Kucuk Nazli Ezgi Ozkan NEO Senturk Aydanur A Ergonul Onder O Can Fusun F Tuncbag Nurcan N Ozlu Nurhan N

Proteomics. Clinical applications 20221025 2

<h4>Purpose</h4>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.<h4>Experimental design</h4>We systematically analyzed plasma proteomes of ...[more]

PMID: 36217943

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Project description:BackgroundPrognostic factors for the Coronavirus disease 2019 (COVID1-9) are not well established. This study aimed to summarize the available data on the association between the severity of COVID-19 and common hematological, inflammatory and biochemical parameters.MethodsEMBASE, MEDLINE, Web of sciences were searched to identify all published studies providing relevant data. Random-effects meta-analysis was used to pool effect sizes.ResultsThe bibliographic search yielded 287 citations, 31 of which were finally retained. Meta-analysis of standardized mean difference (SMD) between severe and non-severe COVID-19 cases showed that CK-MB (SMD = 0.68,95%CI: 0.48;0.87; P-value:< 0.001), troponin I (SMD = 0.71, 95%CI:0.42;1.00; P-value:< 0.001), D-dimer (SMD = 0.54,95%CI:0.31;0.77; P-value:< 0.001), prothrombin time (SMD = 0.48, 95%CI:0.23;0.73; P-value: < 0.001), procalcitonin (SMD = 0.72, 95%CI: 0.34;1,11; P-value:< 0.001), interleukin-6 (SMD = 0.93, 95%CI: 0.25;1.61;P-value: 0.007),C-reactive protein (CRP) (SMD = 1.34, 95%CI:0.83;1.86; P-value:< 0.001), ALAT (SMD = 0.53, 95%CI: 0.34;0,71; P-value:< 0.001), ASAT (SMD = 0.96, 95%CI: 0.58;1.34; P-value: < 0.001), LDH (SMD = 1.36, 95%CI: 0.75;1.98; P-value:< 0.001), CK (SMD = 0.48, 95%CI: 0.10;0.87; P-value:0.01), total bilirubin (SMD = 0.32, 95%CI: 0.18;0.47;P-value: < 0.001), γ-GT (SMD = 1.03, 95%CI: 0.83;1.22; P-value: < 0.001), myoglobin (SMD = 1.14, 95%CI: 0.81;1.47; P-value:< 0.001), blood urea nitrogen (SMD = 0.32, 95%CI: 0.18;0.47;P-value:< 0.001) and Creatininemia (SMD = 0.18, 95%CI: 0.01;0.35; P-value:0.04) were significantly more elevated in severe cases, in opposition to lymphocyte count (SMD = -0.57, 95%CI:-0.71; - 0.42; P-value: < 0.001) and proportion of lymphocytes (SMD = -0.81, 95%CI: - 1.12; - 0.49; P-value:< 0.001) which were found to be significantly lower in severe patients with other biomarker such as thrombocytes (SMD = -0.26, 95%CI: - 0.48; - 0.04; P-value:0.02), eosinophils (SMD = - 0.28, 95%CI:-0.50; - 0.06; P-value:0.01), haemoglobin (SMD = -0.20, 95%CI: - 0.37,-0.03; P-value:0.02), albuminemia (SMD-1.67,95%CI -2.40; - 0.94; P-value:< 0.001), which were also lower. Furthermore, severe COVID-19 cases had a higher risk to have lymphopenia (RR =1.66, 95%CI: 1.26;2.20; P-value:0.002), thrombocytopenia (RR = 1.86, 95%CI: 1.59;2.17; P-value: < 0.001), elevated procalcitonin level (RR = 2.94, 95%CI: 2.09-4.15; P-value:< 0.001), CRP (RR =1.41,95%CI: 1.17-1.70; P-value:0.003), ASAT(RR =2.27, 95%CI: 1.76;2.94; P-value:< 0.001), CK(RR = 2.61, 95%CI: 1.35;5.05; P-value: 0.01), Creatininemia (RR = 3.66, 95%CI: 1.53;8.81; P-value: 0.02) and LDH blood level (RR = 2.03, 95%CI: 1.42;290; P-value: 0.003).ConclusionSome inflammatory (procalcitonin, CRP), haematologic (lymphocyte, Thrombocytes), and biochemical (CK-MB, Troponin I, D-dimer, ASAT, ALAT, LDH, γ-GT) biomarkers are significantly associated with severe COVID-19. These biomarkers might help in prognostic risk stratification of patients with COVID-19.

Dataset Information

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

Purpose

Experimental design

Results

Conclusions and clinical relevance

Publications

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

Similar Datasets

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