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Altered IgG glycosylation at COVID-19 diagnosis predicts disease severity

Title
Altered IgG glycosylation at COVID-19 diagnosis predicts disease severity
Type
Article in International Scientific Journal
Year
2022
Authors
Vicente, MM
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Alves, I
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Gaifem, J
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Rodrigues, CS
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Fernandes, A
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Dias, AM
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Stambuk, J
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Petrovic, T
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oliveira, p
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Ferreira-da-Silva, F
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Soares, A
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Seixas, N
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Teixeira, T
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Malheiro, L
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Abreu, MM
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Lauc, G
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Castro, RSE
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Pinho, SS
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Journal
Vol. 52
Pages: 946-957
ISSN: 0014-2980
Publisher: Wiley-Blackwell
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Publicação em ISI Web of Knowledge ISI Web of Knowledge - 0 Citations
Publicação em Scopus Scopus - 0 Citations
Other information
Authenticus ID: P-00W-CPM
Abstract (EN): The nature of the immune responses associated with COVID-19 pathogenesis and disease severity, as well as the breadth of vaccine coverage and duration of immunity, is still unclear. Given the unpredictability for developing a severe/complicated disease, there is an urgent need in the field for predictive biomarkers of COVID-19. We have analyzed IgG Fc N-glycan traits of 82 SARS-CoV-2+ unvaccinated patients, at diagnosis, by nano-LC-ESI-MS. We determined the impact of IgG Fc glyco-variations in the induction of NK cells activation, further evaluating the association between IgG Fc N-glycans and disease severity/prognosis. We found that SARS-CoV-2+ individuals display, at diagnosis, variations in the glycans composition of circulating IgGs. Importantly, levels of galactose and sialic acid structures on IgGs are able to predict the development of a poor COVID-19 disease. Mechanistically, we demonstrated that a deficiency on galactose structures on IgG Fc in COVID-19 patients appears to induce NK cells activation associated with increased release of IFN-gamma and TNF-alpha, which indicates the presence of pro-inflammatory immunoglobulins and higher immune activation, associated with a poor disease course. This study brings to light a novel blood biomarker based on IgG Fc glycome composition with capacity to stratify patients at diagnosis.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 12
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