7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      IL-1β dominates the promucin secretory cytokine profile in cystic fibrosis

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Cystic fibrosis (CF) lung disease is characterized by early and persistent mucus accumulation and neutrophilic inflammation in the distal airways. Identification of the factors in CF mucopurulent secretions that perpetuate CF mucoinflammation may provide strategies for novel CF pharmacotherapies. We show that IL-1β, with IL-1α, dominated the mucin prosecretory activities of supernatants of airway mucopurulent secretions (SAMS). Like SAMS, IL-1β alone induced MUC5B and MUC5AC protein secretion and mucus hyperconcentration in CF human bronchial epithelial (HBE) cells. Mechanistically, IL-1β induced the sterile α motif–pointed domain containing ETS transcription factor (SPDEF) and downstream endoplasmic reticulum to nucleus signaling 2 (ERN2) to upregulate mucin gene expression. Increased mRNA levels of IL1B , SPDEF , and ERN2 were associated with increased MUC5B and MUC5AC expression in the distal airways of excised CF lungs. Administration of an IL-1 receptor antagonist (IL-1Ra) blocked SAMS-induced expression of mucins and proinflammatory mediators in CF HBE cells. In conclusion, IL-1α and IL-1β are upstream components of a signaling pathway, including IL-1R1 and downstream SPDEF and ERN2, that generate a positive feedback cycle capable of producing persistent mucus hyperconcentration and IL-1α and/or IL-1β–mediated neutrophilic inflammation in the absence of infection in CF airways. Targeting this pathway therapeutically may ameliorate mucus obstruction and inflammation-induced structural damage in young CF children.

          Related collections

          Most cited references75

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Complex heatmaps reveal patterns and correlations in multidimensional genomic data.

            Parallel heatmaps with carefully designed annotation graphics are powerful for efficient visualization of patterns and relationships among high dimensional genomic data. Here we present the ComplexHeatmap package that provides rich functionalities for customizing heatmaps, arranging multiple parallel heatmaps and including user-defined annotation graphics. We demonstrate the power of ComplexHeatmap to easily reveal patterns and correlations among multiple sources of information with four real-world datasets.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

              TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.
                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                October 1 2019
                October 1 2019
                October 1 2019
                September 16 2019
                September 16 2019
                October 1 2019
                : 129
                : 10
                : 4433-4450
                Article
                10.1172/JCI125669
                6763234
                31524632
                d75638fc-8bc2-4847-873c-29dee2c04b65
                © 2019
                History

                Comments

                Comment on this article