CD20 as a gatekeeper of the resting state of human B cells

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Significance

Worldwide about one million patients are given anti-CD20 antibodies such as rituximab (RTX) for the treatment of B cell-associated diseases. Despite the success of this first therapeutic antibody, little is known about the function of its target. The role of CD20 only becomes clear in the context of the nanoscale compartmentalization of the B lymphocyte membrane. We found that CD20 is an organizer of the IgD-class nanocluster on the B cell membrane. The loss of CD20 on human B cells results in a dissolution of the IgD-class nanocluster and a transient B cell activation inducing a B cell-to-PC differentiation. Thus, CD20 is an essential gatekeeper of a membrane nanodomain and the resting state of naive B cells.

Abstract

CD20 is a B cell-specific membrane protein and represents an attractive target for therapeutic antibodies. Despite widespread usage of anti-CD20 antibodies for B cell depletion therapies, the biological function of their target remains unclear. Here, we demonstrate that CD20 controls the nanoscale organization of receptors on the surface of resting B lymphocytes. CRISPR/Cas9-mediated ablation of CD20 in resting B cells resulted in relocalization and interaction of the IgM-class B cell antigen receptor with the coreceptor CD19. This receptor rearrangement led to a transient activation of B cells, accompanied by the internalization of many B cell surface marker proteins. Reexpression of CD20 restored the expression of the B cell surface proteins and the resting state of Ramos B cells. Similarly, treatment of Ramos or naive human B cells with the anti-CD20 antibody rituximab induced nanoscale receptor rearrangements and transient B cell activation in vitro and in vivo. A departure from the resting B cell state followed by the loss of B cell identity of CD20-deficient Ramos B cells was accompanied by a PAX5 to BLIMP-1 transcriptional switch, metabolic reprogramming toward oxidative phosphorylation, and a shift toward plasma cell development. Thus, anti-CD20 engagement or the loss of CD20 disrupts membrane organization, profoundly altering the fate of human B cells.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Genetic compensation: A phenomenon in search of mechanisms

Mohamed El-Brolosy, Didier Y. R. Stainier (2017)

Several recent studies in a number of model systems including zebrafish, Arabidopsis, and mouse have revealed phenotypic differences between knockouts (i.e., mutants) and knockdowns (e.g., antisense-treated animals). These differences have been attributed to a number of reasons including off-target effects of the antisense reagents. An alternative explanation was recently proposed based on a zebrafish study reporting that genetic compensation was observed in egfl7 mutant but not knockdown animals. Dosage compensation was first reported in Drosophila in 1932, and genetic compensation in response to a gene knockout was first reported in yeast in 1969. Since then, genetic compensation has been documented many times in a number of model organisms; however, our understanding of the underlying molecular mechanisms remains limited. In this review, we revisit studies reporting genetic compensation in higher eukaryotes and outline possible molecular mechanisms, which may include both transcriptional and posttranscriptional processes.

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Protter: interactive protein feature visualization and integration with experimental proteomic data.

Ulrich Omasits, Christian H. Ahrens, Sebastian Müller … (2014)

The ability to integrate and visualize experimental proteomic evidence in the context of rich protein feature annotations represents an unmet need of the proteomics community. Here we present Protter, a web-based tool that supports interactive protein data analysis and hypothesis generation by visualizing both annotated sequence features and experimental proteomic data in the context of protein topology. Protter supports numerous proteomic file formats and automatically integrates a variety of reference protein annotation sources, which can be readily extended via modular plug-ins. A built-in export function produces publication-quality customized protein illustrations, also for large datasets. Visualizations of surfaceome datasets show the specific utility of Protter for the integrated visual analysis of membrane proteins and peptide selection for targeted proteomics. The Protter web application is available at http://wlab.ethz.ch/protter. Source code and installation instructions are available at http://ulo.github.io/Protter/. wbernd@ethz.ch Supplementary data are available at Bioinformatics online.

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Author and article information

Journal

Journal ID (nlm-ta): Proc Natl Acad Sci U S A

Journal ID (iso-abbrev): Proc Natl Acad Sci U S A

Journal ID (hwp): pnas

Journal ID (pmc): pnas

Journal ID (publisher-id): PNAS

Title: Proceedings of the National Academy of Sciences of the United States of America

Publisher: National Academy of Sciences

ISSN (Print): 0027-8424

ISSN (Electronic): 1091-6490

Publication date (Print): 16 February 2021

Publication date (Electronic): 09 February 2021

Publication date PMC-release: 09 February 2021

Volume: 118

Issue: 7

Electronic Location Identifier: e2021342118

Affiliations

[1] ^aBiology III, Faculty of Biology, University of Freiburg , 79104 Freiburg, Germany;

[2] ^bCentre for Biological Signalling Studies, University of Freiburg , 79110 Freiburg, Germany;

[3] ^cCentre for Integrative Biological Signalling Studies, University of Freiburg , 79104 Freiburg, Germany;

[20] ^dInstitut für Klinische Chemie und Pathobiochemie, Klinikum rechts der Isar, Technical University of Munich , 81675 Munich, Germany;

[4] ^elnstitute of Medical Bioinformatics and Systems Medicine, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg , 79110 Freiburg, Germany;

[5] ^fGerman Cancer Consortium (Deutsches Konsortium für Translationale Krebsforschung) Partner Site Freiburg, German Cancer Research Center (Deutsches Krebsforschungszentrum) , 69120 Heidelberg, Germany;

[6] ^gDepartment of Rheumatology and Clinical Immunology, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg , 79106 Freiburg, Germany;

[7] ^hDepartment of Health Sciences and Technology, Institute of Translational Medicine, Swiss Federal Institute of Technology, ETH Zürich , 8093 Zurich, Switzerland;

[8] ⁱWollscheid-Group, Swiss Institute of Bioinformatics , 1015 Lausanne, Switzerland;

[9] ^jDepartment of Biology, Centre for Synthetic Biology, Technical University Darmstadt , 64289 Darmstadt, Germany;

[10] ^kComprehensive Cancer Center Freiburg, Medical Center–University of Freiburg, University of Freiburg , 79106 Freiburg, Germany

Author notes

¹To whom correspondence may be addressed. Email: michael.reth@ 123456bioss.uni-freiburg.de .

This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2018.

Author contributions: K.K., J.J., and M.R. designed research; K.K., J.J., G.A., U.S., and C.B. performed research; K.K., J.J., S.N.S., M.C., B.S., R.E.V., M.B., and B.W. contributed new reagents/analytic tools; K.K., J.J., G.A., U.S., C.B., S.N.S., J.B.A., and M.R. analyzed data; and K.K., J.J., and M.R. wrote the paper.

Reviewers: R.L., Stanford University; S.L.N., The Walter and Eliza Hall Institute of Medical Research; and L.M.S., National Cancer Institute.

Contributed by Michael Reth, December 21, 2020 (sent for review October 13, 2020; reviewed by Ronald Levy, Stephen L. Nutt, and Louis M. Staudt)