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      Co-expression of CD21L and IL17A defines a subset of rheumatoid synovia, characterised by large lymphoid aggregates and high inflammation

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          Abstract

          Objective

          To determine whether the expression of IL17A and CD21L genes in inflamed rheumatoid synovia is associated with the neogenesis of ectopic lymphoid follicle-like structures (ELS), and if this aids the stratification of rheumatoid inflammation and thereby distinguishes patients with rheumatoid arthritis that might be responsive to specific targeted biologic therapies.

          Methods

          Expression of IL17A and CD21L genes was assessed by RT-PCR, qRT-PCR and dPCR in synovia from 54 patients with rheumatoid arthritis. A subset of synovia (n = 30) was assessed by immunohistology for the presence of CD20 + B-lymphocytes and size of CD20 + B-lymphocyte aggregates as indicated by maximum radial cell count. The molecular profiles of six IL17A + /CD21L + and six IL17A - /CD21L - synovia were determined by complementary DNA microarray analysis.

          Results

          By RT-PCR, 26% of synovia expressed IL17A and 52% expressed CD21L. This provided the basis for distinguishing four subgroups of rheumatoid synovia: IL17A + /CD21L + (18.5% of synovia), IL17A + /CD21L - (7.5%), IL17A - /CD21L + (33.3%) and IL17A - /CD21L - (40.7%). While the subgroups did not predict clinical outcome measures, comparisons between the synovial subgroups revealed the IL17A + /CD21L + subgroup had significantly larger CD20+ B-lymphocyte aggregates ( P = 0.007) and a gene expression profile skewed toward B-cell- and antibody-mediated immunity. In contrast, genes associated with bone and cartilage remodelling were prominent in IL17A - /CD21L - synovia.

          Conclusions

          Rheumatoid synovia can be subdivided on the basis of IL17A and CD21L gene expression. Ensuing molecular subgroups do not predict clinical outcome for patients but highlight high inflammation and the predominance of B-lymphocyte mediated mechanisms operating in IL17A + /CD21L + synovia. This may provide a rationale for more refined therapeutic selection due to the distinct molecular profiles associated with IL17A + /CD21L + and IL17A - /CD21L - rheumatoid synovia.

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          Most cited references39

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          The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis.

          The revised criteria for the classification of rheumatoid arthritis (RA) were formulated from a computerized analysis of 262 contemporary, consecutively studied patients with RA and 262 control subjects with rheumatic diseases other than RA (non-RA). The new criteria are as follows: 1) morning stiffness in and around joints lasting at least 1 hour before maximal improvement; 2) soft tissue swelling (arthritis) of 3 or more joint areas observed by a physician; 3) swelling (arthritis) of the proximal interphalangeal, metacarpophalangeal, or wrist joints; 4) symmetric swelling (arthritis); 5) rheumatoid nodules; 6) the presence of rheumatoid factor; and 7) radiographic erosions and/or periarticular osteopenia in hand and/or wrist joints. Criteria 1 through 4 must have been present for at least 6 weeks. Rheumatoid arthritis is defined by the presence of 4 or more criteria, and no further qualifications (classic, definite, or probable) or list of exclusions are required. In addition, a "classification tree" schema is presented which performs equally as well as the traditional (4 of 7) format. The new criteria demonstrated 91-94% sensitivity and 89% specificity for RA when compared with non-RA rheumatic disease control subjects.
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            Cluster analysis and display of genome-wide expression patterns.

            A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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              Single-Cell Genomics Unveils Critical Regulators of Th17 Cell Pathogenicity.

              Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or differentiated in vitro under either pathogenic or non-pathogenic polarization conditions. Computational analysis relates a spectrum of cellular states in vivo to in-vitro-differentiated Th17 cells and unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four new genes: Gpr65, Plzp, Toso, and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity and can identify targets for selective suppression of pathogenic Th17 cells while potentially sparing non-pathogenic tissue-protective ones.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draft
                Role: Formal analysisRole: InvestigationRole: Validation
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: ValidationRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                16 August 2018
                2018
                : 13
                : 8
                : e0202135
                Affiliations
                [1 ] Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
                [2 ] Department of Medicine, University of Otago, Christchurch, New Zealand
                Universitatsklinikum Freiburg, GERMANY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                [¤]

                Current address: Bill Walsh Translational Cancer Research, Northern Sydney Local Health District Research (Kolling Institute) and Northern Clinical School, University of Sydney, Sydney, Australia

                Author information
                http://orcid.org/0000-0002-4923-791X
                http://orcid.org/0000-0001-6972-1242
                Article
                PONE-D-18-08431
                10.1371/journal.pone.0202135
                6095528
                30114200
                14b7b04d-a2ea-4a21-89ed-4063033297ca
                © 2018 McKelvey et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 March 2018
                : 27 July 2018
                Page count
                Figures: 4, Tables: 4, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001505, Health Research Council of New Zealand;
                Award ID: 13/065
                Award Recipient :
                This work was funded by the Health Research Council of New Zealand, www.hrc.govt.nz, grant No 13/065 to LKS, PAH, and JH. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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                Custom metadata
                All synovial gene expression data are available at http://www.ncbi.nlm.nih.gov/geo/ (GEO accession: GSE38064).

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