[HTML][HTML] Cystic fibrosis inflammation: hyperinflammatory, hypoinflammatory, or both?

SV Murphy, CMP Ribeiro - American Journal of Respiratory Cell and …, 2019 - atsjournals.org
SV Murphy, CMP Ribeiro
American Journal of Respiratory Cell and Molecular Biology, 2019atsjournals.org
A major focus of cystic fibrosis (CF) research has been on CF transmembrane conductance
regulator (CFTR) ion channel dysfunction in epithelial cells and its impact on dehydration of
airway surface liquid, mucus deposition, and impaired mucociliary clearance (1–4). The
resultant airway obstruction contributes to chronic infections by opportunistic bacteria such
as Pseudomonas aeruginosa (5). In patients with CF, airway infection is accompanied by
exaggerated inflammation, with evidence of an excess of proinflammatory cytokines, and …
A major focus of cystic fibrosis (CF) research has been on CF transmembrane conductance regulator (CFTR) ion channel dysfunction in epithelial cells and its impact on dehydration of airway surface liquid, mucus deposition, and impaired mucociliary clearance (1–4). The resultant airway obstruction contributes to chronic infections by opportunistic bacteria such as Pseudomonas aeruginosa (5). In patients with CF, airway infection is accompanied by exaggerated inflammation, with evidence of an excess of proinflammatory cytokines, and observed alterations in innate and adaptive immune responses (6–8). The pathophysiology of CF inflammation remains poorly understood. Intrinsic effects of mutant CFTR on the immune system have been described for neutrophils (9), macrophages (10), lymphocytes (11), and dendritic cells (12). For example, alveolar macrophages from CFTR J/J mice exhibit exaggerated inflammatory responses to bacterial LPS (13), and inhibition or mutation of CFTR enhances production of cytokines in both murine and human macrophages (14, 15). However, other studies have indicated that CF airway macrophages in primary culture are hyperinflammatory, a phenotype that is independent of defective CFTR and results from exposure to the CF airway infectious/inflammatory environment (16). Nevertheless, it is currently unclear whether extrinsic signals have a role in the regulation of the CF immune response in peripheral blood. As described in this issue of the Journal, Zhang and colleagues (pp. 301–311) developed a novel transcriptomic profiling-based immune cell analysis to test the hypothesis that the response of peripheral immune cells from patients with CF involves an extrinsic regulatory mechanism (17). To evaluate the effect of extrinsic factors on the pathophysiology of the CF immune response, the authors analyzed gene expression and microRNA (miRNA)‒mRNA networks, and performed blood transcriptomics to distinguish immune cell subsets in healthy peripheral blood mononuclear cells (PBMCs) exposed to either autologous plasma or plasma derived from the peripheral blood of patients with CF. Samples from a discovery cohort of 12 patients with CF and 12 healthy control subjects, as well as a validation cohort of 103 patients with CF and 31 healthy control subjects, were analyzed. The authors identified a significant downregulation of immune-related genes in PBMCs in response to CF plasma, including genes involved in immune cell functions such as cell binding and cell adhesion, and transcripts encoding immune receptors, cytokine receptors, and inflammatory mediators. These transcripts were related to key inflammatory signaling pathways, including the TREM1 (triggering receptor expressed on myeloid cells 1), IL-6, and IL-17F pathways, and pathways involved in cellular recognition of bacteria and viruses. Next, Zhang and colleagues sought to determine whether the transcriptional effects of CF plasma on PBMCs could be explained by shifts in immune cell subsets in their PMBC samples. For this purpose, they used a gene-based computational approach, which uses a novel marker gene matrix, to systematically infer the composition of 10 immune cell subsets. These included five lymphoid lineage subsets (total T cells, CD8 T cells, CD4 T cells, B cells, and natural killer cells) and five myeloid lineage subsets (monocytes, macrophages, M2 macrophages, general dendritic cells, and activated dendritic cells). This novel application of transcriptomics bioinformatics to distinguish immune cell subsets provides a new approach to evaluate the diversity of immune cell samples when traditional methods, such as …
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