In severe cases of coronavirus disease 2019 (COVID-19), viral pneumonia progresses to respiratory failure. Neutrophil extracellular traps (NETs) are extracellular webs of chromatin, microbicidal proteins, and oxidant enzymes that are released by neutrophils to contain infections. However, when not properly regulated, NETs have the potential to propagate inflammation and microvascular thrombosis — including in the lungs of patients with acute respiratory distress syndrome. We now report that sera from patients with COVID-19 have elevated levels of cell-free DNA, myeloperoxidase-DNA (MPO-DNA), and citrullinated histone H3 (Cit-H3); the latter 2 are specific markers of NETs. Highlighting the potential clinical relevance of these findings, cell-free DNA strongly correlated with acute-phase reactants, including C-reactive protein, D-dimer, and lactate dehydrogenase, as well as absolute neutrophil count. MPO-DNA associated with both cell-free DNA and absolute neutrophil count, while Cit-H3 correlated with platelet levels. Importantly, both cell-free DNA and MPO-DNA were higher in hospitalized patients receiving mechanical ventilation as compared with hospitalized patients breathing room air. Finally, sera from individuals with COVID-19 triggered NET release from control neutrophils in vitro. Future studies should investigate the predictive power of circulating NETs in longitudinal cohorts and determine the extent to which NETs may be novel therapeutic targets in severe COVID-19.
Yu Zuo, Srilakshmi Yalavarthi, Hui Shi, Kelsey Gockman, Melanie Zuo, Jacqueline A. Madison, Christopher Blair, Andrew Weber, Betsy J. Barnes, Mikala Egeblad, Robert J. Woods, Yogendra Kanthi, Jason S. Knight
Usage data is cumulative from October 2022 through October 2023.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.