Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Feb 10;24(4):3570.
doi: 10.3390/ijms24043570.

Plasma Proteomic Variables Related to COVID-19 Severity: An Untargeted nLC-MS/MS Investigation

Lisa Pagani  1 , Clizia Chinello  1 , Giulia Risca  2 , Giulia Capitoli  2 , Lucrezia Criscuolo  1 , Andrea Lombardi  3   4 , Riccardo Ungaro  4 , Davide Mangioni  3   4 , Isabella Piga  1 , Antonio Muscatello  4 , Francesco Blasi  3   5 , Andrea Favalli  6 , Martina Martinovic  6 , Andrea Gori  3   4 , Alessandra Bandera  3   4 , Renata Grifantini  6 , Fulvio Magni  1
Affiliations

Plasma Proteomic Variables Related to COVID-19 Severity: An Untargeted nLC-MS/MS Investigation

Lisa Pagani et al. Int J Mol Sci. .

Abstract

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection leads to a wide range of clinical manifestations and determines the need for personalized and precision medicine. To better understand the biological determinants of this heterogeneity, we explored the plasma proteome of 43 COVID-19 patients with different outcomes by an untargeted liquid chromatography-mass spectrometry approach. The comparison between asymptomatic or pauci-symptomatic subjects (MILDs), and hospitalised patients in need of oxygen support therapy (SEVEREs) highlighted 29 proteins emerged as differentially expressed: 12 overexpressed in MILDs and 17 in SEVEREs. Moreover, a supervised analysis based on a decision-tree recognised three proteins (Fetuin-A, Ig lambda-2chain-C-region, Vitronectin) that are able to robustly discriminate between the two classes independently from the infection stage. In silico functional annotation of the 29 deregulated proteins pinpointed several functions possibly related to the severity; no pathway was associated exclusively to MILDs, while several only to SEVEREs, and some associated to both MILDs and SEVEREs; SARS-CoV-2 signalling pathway was significantly enriched by proteins up-expressed in SEVEREs (SAA1/2, CRP, HP, LRG1) and in MILDs (GSN, HRG). In conclusion, our analysis could provide key information for 'proteomically' defining possible upstream mechanisms and mediators triggering or limiting the domino effect of the immune-related response and characterizing severe exacerbations.

Keywords: COVID-19; SARS-CoV-2; blood; mass spectrometry; plasma; proteomics; severe.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Volcano plot. Volcano plot showing which proteins are significantly deregulated between MILDs and SEVEREs considering a p-value (p) ≤ 0.05 and a fold change threshold of ±1.5. 17 proteins, in green, are upregulated in SEVEREs (Fold change ≤ −1.5); while 12 proteins, in red, are upregulated in MILDs (Fold change ≥ 1.5). Proteins are reported as UNIPROT_ID.
Figure 2
Figure 2
Heatmap-cluster analysis. The heatmap shows the different protein expression levels in correlation to each patient (reported as proteomic IDs). The green box highlights the proteins that are up-expressed in Severe patients, while proteins up-expressed in Mild patients are highlighted by a red box; it can be observed that the two groups of proteins differentially clusterise. Patients belonging to the MILDs group are shown in aquamarine, while SEVEREs are shown in orange; the colour scale from pink to blue indicates the level of expression of each protein, from a low expression to a high expression.
Figure 3
Figure 3
Decision tree of all 43 patients. The decision tree (DT) selects the most impactful proteins with the relative cut-off able to discriminate between the two classes. In each box are reported: the more frequent class indicated by the caption “Mild” or “Severe,” the number of cases belonging to the two classes and the percentage of patients in that box. The boxplot of the three selected proteins by the DT are reported for Mild and Severe patients.
Figure 4
Figure 4
Decision tree of patients during the acute phase. The picture illustrates the supervised decision tree (DT) analysis related only to the patients whose samples were collected in the acute phase or at least within 21 days from the first positive test (patients with negative test excluded). In each box are reported: the more frequent class indicated by the caption “Mild” or “Severe”, the number of cases belonging to the two classes and the percentage of patients in that box. The boxplot of the three selected proteins by the DT are reported for Mild and Severe patients.
Figure 5
Figure 5
Network of the enriched pathways. Overview of the main significant pathways resulting from enrichment based on the 29 DEPs in Mild and Severe SARS-CoV-2 patients (g:Profiler [17]). The network is illustrated following the hierarchical structure of the Reactome database. Pathways which are enriched only in SEVEREs are in green, while those expressed in both MILDs and SEVEREs are in blue.
Figure 6
Figure 6
Experimental design. Brief illustration of the workflow, from the collection of the samples from COVID-19 patients with different outcomes to the statistical and functional analysis. After the collection, samples undergo an inactivation process of SARS-CoV-2 virus, then, proteins are deglycosylated and enzymatically digested, the peptide mixture is analysed by LC-MS/MS and data are finally processed by specific software.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Samadizadeh S., Masoudi M., Rastegar M., Salimi V., Shahbaz M.B., Tahamtan A. COVID-19: Why does disease severity vary among individuals? Respir. Med. 2021;180:106356. doi: 10.1016/j.rmed.2021.106356. - DOI - PMC - PubMed
    1. Merino J., Joshi A.D., Nguyen L.H., Leeming E.R., Mazidi M., Drew D., Gibson R., Graham M.S., Lo C.-H., Capdevila J., et al. Diet quality and risk and severity of COVID-19: A prospective cohort study. Gut. 2021;70:2096–2104. doi: 10.1136/gutjnl-2021-325353. - DOI - PMC - PubMed
    1. Monticelli M., Mele B.H., Andreotti G., Cubellis M.V., Riccio G. Why does SARS-CoV-2 hit in different ways? Host genetic factors can influence the acquisition or the course of COVID-19. Eur. J. Med. Genet. 2021;64:104227. doi: 10.1016/j.ejmg.2021.104227. - DOI - PMC - PubMed
    1. Ciccosanti F., Antonioli M., Sacchi A., Notari S., Farina A., Beccacece A., Fusto M., Vergori A., D’Offizi G., Taglietti F., et al. Proteomic analysis identifies a signature of disease severity in the plasma of COVID-19 pneumonia patients associated to neutrophil, platelet and complement activation. Clin. Proteom. 2022;19:38. doi: 10.1186/s12014-022-09377-7. - DOI - PMC - PubMed
    1. Cosgriff C.V., Miano T.A., Mathew D., Huang A.C., Giannini H.M., Kuri-Cervantes L., Pampena M.B., Ittner C.A.G., Weisman A.R., Agyekum R.S., et al. Validating a Proteomic Signature of Severe COVID-19. Crit. Care Explor. 2022;4:e0800. doi: 10.1097/CCE.0000000000000800. - DOI - PMC - PubMed