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      Bcl-6 is the nexus transcription factor of T follicular helper cells (T FH) via repressor-of-repressor circuits

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          Abstract

          T follicular helper (T FH) cells are a distinct type of CD4 + T cells that are essential for most antibody and B lymphocyte responses. T FH cell regulation and dysregulation is involved in a range of diseases. Bcl-6 is the lineage defining transcription factor of T FH cells and its activity is essential for T FH cell differentiation and function. However, how Bcl-6 controls T FH biology has largely remained unclear, at least in part due to intrinsic challenges of connecting repressors to gene upregulation in complex cell types with multiple possible differentiation fates. Multiple competing models were tested here by a series of experimental approaches to determine that Bcl-6 exhibited negative autoregulation and controlled pleiotropic attributes of T FH differentiation and function, including migration, costimulation, inhibitory receptors, and cytokines, via multiple repressor-of-repressor gene circuits.

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          Most cited references64

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Is Open Access

              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Journal
                100941354
                21750
                Nat Immunol
                Nat. Immunol.
                Nature immunology
                1529-2908
                1529-2916
                1 July 2020
                22 June 2020
                July 2020
                22 December 2020
                : 21
                : 7
                : 777-789
                Affiliations
                [1 ]Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology (LJI), La Jolla, CA, USA
                [2 ]Department of Immunology and Microbiology, The Scripps Research Institute, Jupiter, FL, USA
                [3 ]Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
                [4 ]Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego (UCSD), La Jolla, CA, USA
                [5 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                J.C. designed and performed experiments, analyzed the data, and wrote the manuscript. H.D. and M.E.P analyzed ATAC-seq data. C.E.F. developed CRISPR/Cas9-mediated gene deletion experiments. J. T., M. R., and S.B. performed experiments. B.Y. performed PageRank analysis. A.W.G and M.E.P provided advice, reagents, and computational analyses. S.C. supervised the project, designed experiments, analyzed data, and wrote the manuscript.

                [* ]Correspondence: shane@ 123456lji.org (S.C.)
                Article
                NIHMS1591987
                10.1038/s41590-020-0706-5
                7449381
                32572238
                e2633558-2c5f-4418-b33b-02ca7ba4c8b1

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                Immunology
                Immunology

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