bioRxivCell BiologyPreprint
Heterogeneity in Neutrophil Extracellular Traps from Healthy Human Subjects
M. S. Collins, M. A. Imbrogno, E. J. Kopras, J. A. Howard, N. Zhang, E. L. Kramer, K. M. Hudock
Abstract
Neutrophil Extracellular Traps (NETs), a key component of early defense against microbial infection, are also associated with tissue injury. NET composition has been reported to vary with some disease states, but the composition and variability of NETs across many healthy subjects provides a critical comparison that has not been well investigated. We evaluated NETs from twelve healthy subjects of varying ages isolated from multiple blood draws over a three and one half-year period to delineate the variability in extracellular DNA, protein, enzymatic activities, and susceptibility to protease inhibitors. We calculated correlations for NET constituents and loss of human bronchial epithelial barrier integrity, measured by transepithelial electrical resistance, after NET exposure. We found that although there was some variability within the same subject over time, the mean numbers of neutrophils, protein, LDH, serine protease activities, and cytokines IL-8, IL-1RA, and G-CSF in isolated NETs were consistent across subjects. Total DNA and double stranded DNA content in NETs were different across donors. NETs had little or no TNF, IL-17A, or GM-CSF. NET DNA concentration correlated with increased NET neutrophil elastase activity and higher NET IL-1RA concentrations. NET serine protease activity varied considerably within the same donor from day-to-day. Mean response to protease inhibitors was significantly different across donors. NET DNA concentration correlated best with reductions in barrier integrity of human bronchial epithelia. Defining NET concentration by DNA content correlates with other NET components and reductions in NET-driven epithelial barrier dysfunction, suggesting DNA is a reasonable surrogate measurement for these complex structures in healthy subjects.
- organism: human
- assay: proteomics
- assay: metagenomics
- research focus: cell signaling & trafficking
- approach: statistical model
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