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      Decreased Treg Cell and TCR Expansion Are Involved in Long-Lasting Graves’ Disease

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          Abstract

          Graves’ disease (GD) is a T cell-mediated organ-specific autoimmune disorder. GD patients who have taken anti-thyroid drugs (ATDs) for more than 5 years with positive anti-thyroid stimulating hormone receptor autoantibodies value were defined as persistent GD (pGD). To develop novel immunotherapies for pGD, we investigated the role of T cells in the long-lasting phase of GD. Clinical characteristics were compared between the pGD and newly diagnosed GD (nGD) (N = 20 respectively). Flow cytometric analysis was utilized to determine the proportions of Treg and Th17 cells (pGD, N = 12; nGD, N = 14). T cell receptor sequencing (TCR-seq) and RNA sequencing (RNA-seq) were also performed (pGD, N = 13; nGD, N = 20). Flow cytometric analysis identified lower proportions of Th17 and Treg cells in pGD than in nGD (P = 0.0306 and P = 0.0223). TCR-seq analysis revealed a lower diversity (P = 0.0025) in pGD. Specifically, marked clonal expansion, represented by an increased percentage of top V-J recombination, was observed in pGD patients. Interestingly, pGD patients showed more public T cell clonotypes than nGD patients (2,741 versus 966). Meanwhile, RNA-seq analysis revealed upregulation of the inflammation and chemotaxis pathways in pGD. Specifically, the expression of pro-inflammatory and chemotactic genes ( IL1B, IL13, IL8, and CCL4) was increased in pGD, whereas Th17 and Treg cells associated genes ( RORC, CARD9, STAT5A, and SATB1) decreased in pGD. Additionally, TCR diversity was negatively correlated with the expression of pro-inflammatory or chemotactic genes ( FASLG, IL18R1, CCL24, and CCL14). These results indicated that Treg dysregulation and the expansion of pathogenic T cell clones might be involved in the long-lasting phase of GD via upregulating chemotaxis or inflammation response. To improve the treatment of pGD patients, ATDs combined therapies, especially those aimed at improving Treg cell frequencies or targeting specific expanded pathogenic TCR clones, are worth exploring in the future.

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          Most cited references47

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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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              The Molecular Signatures Database (MSigDB) hallmark gene set collection.

              The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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                Author and article information

                Contributors
                Journal
                Front Endocrinol (Lausanne)
                Front Endocrinol (Lausanne)
                Front. Endocrinol.
                Frontiers in Endocrinology
                Frontiers Media S.A.
                1664-2392
                12 April 2021
                2021
                : 12
                : 632492
                Affiliations
                [1] 1Department of Endocrinology, The First Affiliated Hospital of Xi’an Jiaotong University , Xi’an, China
                [2] 2MOE Key Laboratory for Intelligent Networks & Networks Security, School of Electronic and Information Engineering, Xi’an Jiaotong University , Xi’an, China
                [3] 3Genome Institute, The First Affiliated Hospital of Xi’an Jiaotong University , Xi’an, China
                [4] 4BioBank, The First Affiliated Hospital of Xi’an Jiaotong University , Xi’an, China
                [5] 5Department of Endocrinology, Zhejiang Provincial People’s Hospital , Hangzhou, China
                [6] 6Precision Medicine Center, The First Affiliated Hospital of Xi’an Jiaotong University , Xi’an, China
                Author notes

                Edited by: Marian Elizabeth Ludgate, Cardiff University, United Kingdom

                Reviewed by: Angela Lombardi, Albert Einstein College of Medicine, United States; Syed A. Morshed, Icahn School of Medicine at Mount Sinai, United States

                *Correspondence: Bingyin Shi, shibingy@ 123456126.com ; Yue Wang, shelly1021@ 123456126.com

                This article was submitted to Thyroid Endocrinology, a section of the journal Frontiers in Endocrinology

                †These authors have contributed equally to this work

                Article
                10.3389/fendo.2021.632492
                8074859
                99202346-d689-46fa-a339-1ac310bad593
                Copyright © 2021 Chen, Liu, Hu, Zhang, Shi and Wang

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 November 2020
                : 15 February 2021
                Page count
                Figures: 6, Tables: 1, Equations: 0, References: 47, Pages: 12, Words: 5808
                Funding
                Funded by: Natural Science Foundation of Shaanxi Province 10.13039/501100007128
                Funded by: Fundamental Research Funds for the Central Universities 10.13039/501100012226
                Funded by: China Postdoctoral Science Foundation 10.13039/501100002858
                Categories
                Endocrinology
                Original Research

                Endocrinology & Diabetes
                graves’ disease,persistent graves’ disease,refractory graves’ disease,regulatory t cell,t cell receptor sequencing,rna sequencing

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