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      DC-SIGN binding to mannosylated B-cell receptors in follicular lymphoma down-modulates receptor signaling capacity

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          Abstract

          In follicular lymphoma (FL), surface immunoglobulin (sIg) carries mandatory N-glycosylation sites in the variable regions, inserted during somatic hypermutation. These glycosylation sites are tumor-specific, indicating a critical function in FL. Added glycan unexpectedly terminates at high mannose (Mann) and confers capability for sIg-mediated interaction with local macrophage-expressed DC-SIGN lectin resulting in low-level activation of upstream B-cell receptor signaling responses. Here we show that despite being of low-level, DC-SIGN induces a similar downstream transcriptional response to anti-IgM in primary FL cells, characterized by activation of pathways associated with B-cell survival, proliferation and cell–cell communication. Lectin binding was also able to engage post-transcriptional receptor cross-talk pathways since, like anti-IgM, DC-SIGN down-modulated cell surface expression of CXCR4. Importantly, pre-exposure of a FL-derived cell line expressing sIgM-Mann or primary FL cells to DC-SIGN, which does not block anti-IgM binding, reversibly paralyzed the subsequent Ca 2+ response to anti-IgM. These novel findings indicate that modulation of sIg function occurs in FL via lectin binding to acquired mannoses. The B-cell receptor alternative engagement described here provides two advantages to lymphoma cells: (i) activation of signaling, which, albeit of low-level, is sufficient to trigger canonical lymphoma-promoting responses, and (ii) protection from exogenous antigen by paralyzing anti-IgM-induced signaling. Blockade of this alternative engagement could offer a new therapeutic strategy.

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          Most cited references32

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            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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              Relation of Gene Expression Phenotype to Immunoglobulin Mutation Genotype in B Cell Chronic Lymphocytic Leukemia

              The most common human leukemia is B cell chronic lymphocytic leukemia (CLL), a malignancy of mature B cells with a characteristic clinical presentation but a variable clinical course. The rearranged immunoglobulin (Ig) genes of CLL cells may be either germ-line in sequence or somatically mutated. Lack of Ig mutations defined a distinctly worse prognostic group of CLL patients raising the possibility that CLL comprises two distinct diseases. Using genomic-scale gene expression profiling, we show that CLL is characterized by a common gene expression “signature,” irrespective of Ig mutational status, suggesting that CLL cases share a common mechanism of transformation and/or cell of origin. Nonetheless, the expression of hundreds of other genes correlated with the Ig mutational status, including many genes that are modulated in expression during mitogenic B cell receptor signaling. These genes were used to build a CLL subtype predictor that may help in the clinical classification of patients with this disease.
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                Author and article information

                Contributors
                gpackham@soton.ac.uk
                fs@soton.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                3 June 2021
                3 June 2021
                2021
                : 11
                : 11676
                Affiliations
                [1 ]GRID grid.5491.9, ISNI 0000 0004 1936 9297, Cancer Research UK Centre, Cancer Sciences Unit, Faculty of Medicine, Southampton General Hospital, , University of Southampton, ; Southampton, UK
                [2 ]GRID grid.412750.5, ISNI 0000 0004 1936 9166, Pathology Department, , University of Rochester Medical Center, ; NY, USA
                Article
                91112
                10.1038/s41598-021-91112-7
                8175722
                34083646
                dab0783c-0ccc-4e30-9cde-e2dca3ab18cc
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 November 2020
                : 20 May 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100011713, Worldwide Cancer Research;
                Award ID: 13-0267
                Award ID: 13-0267
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000289, Cancer Research UK;
                Award ID: C2750/A23669
                Award ID: C2750/A23669
                Award ID: C2750/A23669
                Award ID: C2750/A23669
                Award ID: C42023/A29370
                Award ID: C42023/A29370
                Award ID: C2750/A23669
                Award ID: C42023/A29370
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100007903, Bloodwise;
                Award ID: 18009
                Award Recipient :
                Funded by: Southampton Experimental Cancer Medicine and Cancer Research Centers , C34999/A18087
                Award ID: C24563/A15581
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cancer,tumour immunology,gene expression,cell signalling,calcium signalling
                Uncategorized
                cancer, tumour immunology, gene expression, cell signalling, calcium signalling

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