2 research outputs found
Gasdermin-D activation by SARS-CoV-2 triggers NET and mediate COVID-19 immunopathology
Abstract:
Background:
The release of neutrophil extracellular traps (NETs) is associated with inflammation, coagulopathy, and organ damage found in severe cases of COVID-19. However, the molecular mechanisms underlying the release of NETs in COVID-19 remain unclear.
Objectives:
We aim to investigate the role of the Gasdermin-D (GSDMD) pathway on NETs release and the development of organ damage during COVID-19.
Methods:
We performed a single-cell transcriptome analysis in public data of bronchoalveolar lavage. Then, we enrolled 63 hospitalized patients with moderate and severe COVID-19. We analyze in blood and lung tissue samples the expression of GSDMD, presence of NETs, and signaling pathways upstreaming. Furthermore, we analyzed the treatment with disulfiram in a mouse model of SARS-CoV-2 infection.
Results:
We found that the SARS-CoV-2 virus directly activates the pore-forming protein GSDMD that triggers NET production and organ damage in COVID-19. Single-cell transcriptome analysis revealed that the expression of GSDMD and inflammasome-related genes were increased in COVID-19 patients. High expression of active GSDMD associated with NETs structures was found in the lung tissue of COVID-19 patients. Furthermore, we showed that activation of GSDMD in neutrophils requires active caspase1/4 and live SARS-CoV-2, which infects neutrophils. In a mouse model of SARS-CoV-2 infection, the treatment with disulfiram inhibited NETs release and reduced organ damage.
Conclusion:
These results demonstrated that GSDMD-dependent NETosis plays a critical role in COVID-19 immunopathology and suggests GSDMD as a novel potential target for improving the COVID-19 therapeutic strategy
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Enhancing untargeted metabolomics using metadata-based source annotation
Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data