Serum proteomics hint at an early T-cell response and modulation of SARS-CoV-2-related pathogenic pathways in COVID-19-ARDS treated with Ruxolitinib

Similarity of immune-associated markers in COVID-19 and Kawasaki disease: analyses from bioinformatics and machine learning

BACKGROUND

Infection by the SARS-CoV-2 virus can cause coronavirus disease 2019 (COVID-19) and can also exacerbate the symptoms of Kawasaki disease (KD), an acute vasculitis that mostly affects children. This study used bioinformatics and machine learning to examine similarities in the molecular pathogenesis of COVID-19 and KD.

METHODS

We first identified disease-associated modules in KD using weighted gene co-expression network analysis. Then, we determined shared differentially expressed genes (DEGs) in training datasets for KD (GSE100154) and COVID-19 (GSE225220), performed functional annotation of these shared DEGs, and used Cytoscape plug-ins (MCODE and Cytohubba) to characterize the protein-protein interaction (PPI) network and identify the hub genes. We performed Least Absolute Shrinkage and Selection Operator(LASSO) regression and receiver operating characteristic (ROC) curve analysis to identify the most robust markers, validated these results by analysis of two other datasets (GSE73461 and GSE18606), and then calculated the correlations of these key genes with immune cells.

RESULTS

This analysis identified 26 shared DEGs in COVID-19 and KD. The results from functional annotation showed that the shared DEGs primarily functioned in immune responses, the formation of neutrophil extracellular traps, and NOD-like receptor signaling pathways. There were three key genes (PGLYRP1, DEFA4, RETN), and they had positive correlations with monocytes, M0 macrophages, and dendritic cells, which function as immune infiltrating cells in KD.

CONCLUSION

The potential immune-associated biomarkers (PGLYRP1, DEFA4, RETN) along with their shared pathways, hold promise for advancing investigations into the underlying pathogenesis of KD and COVID-19.

Plasma proteomic profiles correlate with organ dysfunction in COVID-19 ARDS

Severe COVID-19 is often complicated by hypoxemic respiratory failure and acute respiratory distress syndrome (ARDS). Mechanisms governing lung injury and repair in ARDS remain poorly understood. We hypothesized that plasma proteomics may uncover protein biomarkers correlated with COVID-19 ARDS severity. We analyzed the plasma proteome from 32 patients with ARDS and COVID-19 using an aptamer-based platform of 7289 proteins, and correlated protein measurements with sequential organ failure assessment (SOFA) scores at days 1 and 7 of ICU admission. We identified 184 differentially abundant proteins correlated with SOFA at day 1 and 46 proteins at day 7. In a longitudinal analysis, we correlated dynamic changes in protein abundance and SOFA between days 1 and 7 and identified 40 significant proteins. Pathway analysis of significant proteins identified increased ephrin signaling and acute phase response signaling correlated with increased SOFA scores between days 1 and 7, while pathways related to pulmonary fibrosis signaling and wound healing had a negative correlation. These findings suggest that persistent inflammation may drive disease severity, while repair processes correlate with improvements in organ dysfunction. This approach is generalizable to future ARDS cohorts for identification of biomarkers and disease mechanisms as we strive towards targeted therapies in ARDS.

When Cell-Mediated Immunity after Vaccination Is Important

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