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      IL-1-driven stromal–neutrophil interactions define a subset of patients with inflammatory bowel disease that does not respond to therapies

      research-article
      1 , 1 , 1 , 2 , 3 , 4 , 5 , 6 , 1 , 1 , 7 , 8 , 8 , 8 , 8 , 1 , 9 , 6 , 6 , 1 , 8 , 1 , 10 , Oxford IBD Cohort Investigators, Roche Fibroblast Network Consortium, 1 , 8 , 8 , 8 , 11 , 1 , 12 , 2 , 3 , 4 , 5 , 6 , 13 , 8 , 1 ,
      Nature Medicine
      Nature Publishing Group US
      Mucosal immunology, Neutrophils, Chemokines, Inflammatory bowel disease, Cytokines

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          Abstract

          Current inflammatory bowel disease (IBD) therapies are ineffective in a high proportion of patients. Combining bulk and single-cell transcriptomics, quantitative histopathology and in situ localization across three cohorts of patients with IBD (total n = 376), we identify coexpressed gene modules within the heterogeneous tissular inflammatory response in IBD that map to distinct histopathological and cellular features (pathotypes). One of these pathotypes is defined by high neutrophil infiltration, activation of fibroblasts and vascular remodeling at sites of deep ulceration. Activated fibroblasts in the ulcer bed display neutrophil-chemoattractant properties that are IL-1R, but not TNF, dependent. Pathotype-associated neutrophil and fibroblast signatures are increased in nonresponders to several therapies across four independent cohorts (total n = 343). The identification of distinct, localized, tissular pathotypes will aid precision targeting of current therapeutics and provides a biological rationale for IL-1 signaling blockade in ulcerating disease.

          Abstract

          Transcriptomic and histological profiling of gut biopsies from multiple independent cohorts of patients with inflammatory bowel disease identifies distinct histopathological, molecular and cellular features associated with treatment response, providing insights for patient stratification and precision therapy.

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          Most cited references51

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                fiona.powrie@kennedy.ox.ac.uk
                Journal
                Nat Med
                Nat Med
                Nature Medicine
                Nature Publishing Group US (New York )
                1078-8956
                1546-170X
                21 October 2021
                21 October 2021
                2021
                : 27
                : 11
                : 1970-1981
                Affiliations
                [1 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, , University of Oxford, ; Oxford, UK
                [2 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Center for Data Sciences, Brigham and Women’s Hospital, ; Boston, MA USA
                [3 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, ; Boston, MA USA
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biomedical Informatics, Harvard Medical School, ; Boston, MA USA
                [5 ]GRID grid.66859.34, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [6 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, ; Boston, MA USA
                [7 ]GRID grid.8348.7, ISNI 0000 0001 2306 7492, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, , University of Oxford, John Radcliffe Hospital, ; Oxford, UK
                [8 ]GRID grid.8348.7, ISNI 0000 0001 2306 7492, Translational Gastroenterology Unit, NIHR Oxford Biomedical Research Centre, , Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, ; Oxford, UK
                [9 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Jenner Institute, Nuffield Department of Medicine, , University of Oxford, ; Oxford, UK
                [10 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, The Wellcome Trust Centre for Human Genetics, , University of Oxford, ; Oxford, UK
                [11 ]GRID grid.8348.7, ISNI 0000 0001 2306 7492, Department of Paediatrics, John Radcliffe Hospital, ; Oxford, UK
                [12 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Old Road Campus Research Building, Department of Oncology, Medical Sciences Division, , University of Oxford, ; Oxford, UK
                [13 ]GRID grid.5379.8, ISNI 0000000121662407, Centre for Genetics and Genomics Versus Arthritis, Manchester Academic Health Science Centre, , University of Manchester, ; Manchester, UK
                [14 ]GRID grid.6572.6, ISNI 0000 0004 1936 7486, Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, , University of Birmingham, Queen Elizabeth Hospital, ; Birmingham, UK
                [15 ]GRID grid.412563.7, ISNI 0000 0004 0376 6589, NIHR Birmingham Biomedical Research Centre, , University Hospitals Birmingham NHS Foundation Trust, ; Birmingham, UK
                [16 ]GRID grid.417570.0, ISNI 0000 0004 0374 1269, Roche Pharma Research and Early Development, Immunology, Infectious Diseases and Ophthalmology (I2O) Discovery and Translational Area, Roche Innovation Center Basel, ; Basel, Switzerland
                Author information
                http://orcid.org/0000-0003-2809-9498
                http://orcid.org/0000-0003-4848-3948
                http://orcid.org/0000-0003-3899-3872
                http://orcid.org/0000-0002-1194-2429
                http://orcid.org/0000-0001-8339-2094
                http://orcid.org/0000-0002-2304-7739
                http://orcid.org/0000-0002-6111-7355
                http://orcid.org/0000-0003-4569-8513
                http://orcid.org/0000-0001-8148-5406
                http://orcid.org/0000-0002-1901-8265
                http://orcid.org/0000-0002-2690-4361
                http://orcid.org/0000-0003-3312-5929
                Article
                1520
                10.1038/s41591-021-01520-5
                8604730
                34675383
                ba398bfb-8ab0-4c70-94ca-8b9412af5e85
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 February 2021
                : 26 August 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 095688/Z/11/Z
                Award ID: 212240/Z/18/Z
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000265, RCUK | Medical Research Council (MRC);
                Award ID: MR/N02690X/1
                Award Recipient :
                Funded by: Kennedy Trust for Rheumatology Research (MSP 14 15 01)NIHR Biomedical Research Centre - Response Mode Funding
                Funded by: NIHR Biomedical Research Centre - Response Mode FundingRoche Fibroblast Network
                Funded by: Roche Fibroblast Network
                Funded by: Kennedy Trust for Rheumatology Research
                Funded by: NIHR Biomedical Research Centre - Response Mode Funding
                Funded by: NIHR Biomedical Research Centre (BRC)
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2021

                Medicine
                mucosal immunology,neutrophils,chemokines,inflammatory bowel disease,cytokines
                Medicine
                mucosal immunology, neutrophils, chemokines, inflammatory bowel disease, cytokines

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