Neutrophil granulocytes, also called polymorphonuclear leukocytes (PMNs), extrude molecular lattices of decondensed chromatin studded with histones, granule enzymes, and antimicrobial peptides that are referred to as neutrophil extracellular traps (NETs). NETs capture and contain bacteria, viruses, and other pathogens. Nevertheless, experimental evidence indicates that NETs also cause inflammatory vascular and tissue damage, suggesting that identifying pathways that inhibit NET formation may have therapeutic implications. Here, we determined that neonatal NET-inhibitory factor (nNIF) is an inhibitor of NET formation in umbilical cord blood. In human neonatal and adult neutrophils, nNIF inhibits key terminal events in NET formation, including peptidyl arginine deiminase 4 (PAD4) activity, neutrophil nuclear histone citrullination, and nuclear decondensation. We also identified additional nNIF-related peptides (NRPs) that inhibit NET formation. nNIFs and NRPs blocked NET formation induced by pathogens, microbial toxins, and pharmacologic agonists in vitro and in mouse models of infection and systemic inflammation, and they improved mortality in murine models of systemic inflammation, which are associated with NET-induced collateral tissue injury. The identification of NRPs as neutrophil modulators that selectively interrupt NET generation at critical steps suggests their potential as therapeutic agents. Furthermore, our results indicate that nNIF may be an important regulator of NET formation in fetal and neonatal inflammation.
Christian C. Yost, Hansjörg Schwertz, Mark J. Cody, Jared A. Wallace, Robert A. Campbell, Adriana Vieira-de-Abreu, Claudia V. Araujo, Sebastian Schubert, Estelle S. Harris, Jesse W. Rowley, Matthew T. Rondina, James M. Fulcher, Curry L. Koening, Andrew S. Weyrich, Guy A. Zimmerman
Usage data is cumulative from January 2020 through January 2021.
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.