The coronavirus disease 2019 (COVID-19) is related to enhanced production of NETs, and autoimmune/autoinflammatory phenomena. We evaluated the proportion of low-density granulocytes (LDG) by flow cytometry, and their capacity to produce NETs was compared with that of conventional neutrophils. NETs and their protein cargo were quantified by confocal microscopy and ELISA. Antinuclear antibodies (ANA), anti-neutrophil cytoplasmic antibodies (ANCA) and the degradation capacity of NETs were addressed in serum. MILLIPLEX assay was used to assess the cytokine levels in macrophages’ supernatant and serum. We found a higher proportion of LDG in severe and critical COVID-19 which correlated with severity and inflammatory markers. Severe/critical COVID-19 patients had higher plasmatic NE, LL-37 and HMGB1-DNA complexes, whilst ISG-15-DNA complexes were lower in severe patients. Sera from severe/critical COVID-19 patients had lower degradation capacity of NETs, which was reverted after adding hrDNase. Anti-NET antibodies were found in COVID-19, which correlated with ANA and ANCA positivity. NET stimuli enhanced the secretion of cytokines in macrophages. This study unveils the role of COVID-19 NETs as inducers of pro-inflammatory and autoimmune responses. The deficient degradation capacity of NETs may contribute to the accumulation of these structures and anti-NET antibodies are related to the presence of autoantibodies.

BackgroundMost of the explanatory and prognostic models of COVID-19 lack of a comprehensive assessment of the wide COVID-19 spectrum of abnormalities. The aim of this study was to unveil novel biological features to explain COVID-19 severity and prognosis (death and disease progression).MethodsA predictive model for COVID-19 severity in 121 patients was constructed by ordinal logistic regression calculating odds ratio (OR) with 95% confidence intervals (95% CI) for a set of clinical, immunological, metabolomic, and other biological traits. The accuracy and calibration of the model was tested with the area under the curve (AUC), Somer’s D, and calibration plot. Hazard ratios with 95% CI for adverse outcomes were calculated with a Cox proportional-hazards model.ResultsThe explanatory variables for COVID-19 severity were the body mass index (BMI), hemoglobin, albumin, 3-Hydroxyisovaleric acid, CD8+ effector memory T cells, Th1 cells, low-density granulocytes, monocyte chemoattractant protein-1, plasma TRIM63, and circulating neutrophil extracellular traps. The model showed an outstanding performance with an optimism-adjusted AUC of 0.999, and Somer’s D of 0.999. The predictive variables for adverse outcomes in COVID-19 were severe and critical disease diagnosis, BMI, lactate dehydrogenase, Troponin I, neutrophil/lymphocyte ratio, serum levels of IP-10, malic acid, 3, 4 di-hydroxybutanoic acid, citric acid, myoinositol, and cystine.ConclusionsHerein, we unveil novel immunological and metabolomic features associated with COVID-19 severity and prognosis. Our models encompass the interplay among innate and adaptive immunity, inflammation-induced muscle atrophy and hypoxia as the main drivers of COVID-19 severity.

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