Genetic alterations of the SUMO isopeptidase SENP6 drive lymphomagenesis and genetic instability in diffuse large B-cell lymphoma

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Abstract

SUMOylation is a post-translational modification of proteins that regulates these proteins’ localization, turnover or function. Aberrant SUMOylation is frequently found in cancers but its origin remains elusive. Using a genome-wide transposon mutagenesis screen in a MYC-driven B-cell lymphoma model, we here identify the SUMO isopeptidase (or deconjugase) SENP6 as a tumor suppressor that links unrestricted SUMOylation to tumor development and progression. Notably, SENP6 is recurrently deleted in human lymphomas and SENP6 deficiency results in unrestricted SUMOylation. Mechanistically, SENP6 loss triggers release of DNA repair- and genome maintenance-associated protein complexes from chromatin thereby impairing DNA repair in response to DNA damages and ultimately promoting genomic instability. In line with this hypothesis, SENP6 deficiency drives synthetic lethality to Poly-ADP-Ribose-Polymerase (PARP) inhibition. Together, our results link SENP6 loss to defective genome maintenance and reveal the potential therapeutic application of PARP inhibitors in B-cell lymphoma.

Abstract

SUMOylation is a post-translational modification that has been shown to be altered in cancer. Here, the authors show that loss of the SUMO isopeptidase SENP6 leads to unrestricted SUMOylation and genomic instability promoting lymphomagenesis and generating vulnerability to PARP inhibition.

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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

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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STAR: ultrafast universal RNA-seq aligner.

Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)

Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

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

A. Subramanian, P. Tamayo, V. K. Mootha … (2005)

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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Author and article information

Contributors

Stefan Müller: ste.mueller@em.uni-frankfurt.de

Ulrich Keller:

ORCID: http://orcid.org/0000-0002-8485-1958

ulrich.keller@charite.de

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 12 January 2022

Publication date PMC-release: 12 January 2022

Publication date Collection: 2022

Volume: 13

Electronic Location Identifier: 281

Affiliations

[1 ]GRID grid.7468.d, ISNI 0000 0001 2248 7639, Department of Hematology, Oncology and Cancer Immunology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, , corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ; 12203 Berlin, Germany

[2 ]GRID grid.6936.a, ISNI 0000000123222966, Internal Medicine III, School of Medicine, , Technische Universität München, ; 81675 Munich, Germany

[3 ]GRID grid.6936.a, ISNI 0000000123222966, Internal Medicine II, School of Medicine, , Technische Universität München, ; 81675 Munich, Germany

[4 ]GRID grid.11749.3a, ISNI 0000 0001 2167 7588, Center for Bioinformatics, Saarland Informatics Campus, , Saarland University, ; 66123 Saarbrücken, Germany

[5 ]GRID grid.7839.5, ISNI 0000 0004 1936 9721, Institute of Biochemistry II, , Goethe University, Medical School, ; 60590 Frankfurt, Germany

[6 ]GRID grid.8379.5, ISNI 0000 0001 1958 8658, Cancer Systems Biology Group, Theodor Boveri Institute, , University of Würzburg, ; 97074 Würzburg, Germany

[7 ]GRID grid.6936.a, ISNI 0000000123222966, Institute of Pathology, School of Medicine, , Technische Universität München, ; 81675 Munich, Germany

[8 ]GRID grid.6936.a, ISNI 0000000123222966, Institute of Molecular Oncology and Functional Genomics, TUM School of Medicine, , Technische Universität München, ; 81675 Munich, Germany

[9 ]GRID grid.6936.a, ISNI 0000000123222966, Center for Translational Cancer Research (TranslaTUM), , Technische Universität München, ; 81675 Munich, Germany

[10 ]GRID grid.414279.d, ISNI 0000 0001 0349 2029, Bavarian Health and Food Safety Authority, ; 85764 Oberschleißheim, Germany

[11 ]GRID grid.7708.8, ISNI 0000 0000 9428 7911, Department of Hematology, Oncology, Stem Cell Transplantation, , University Medical Center Freiburg, ; 79106 Freiburg, Germany

[12 ]GRID grid.411984.1, ISNI 0000 0001 0482 5331, Department of Hematology and Oncology, , University Medical Center Göttingen, ; 37075 Göttingen, Germany

[13 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, German Cancer Consortium (DKTK), , German Cancer Research Center (DKFZ), ; 69120 Heidelberg, Germany

[14 ]GRID grid.419491.0, ISNI 0000 0001 1014 0849, Max-Delbrück-Center for Molecular Medicine, ; 13125 Berlin, Germany

Author information

Julia Hofstetter http://orcid.org/0000-0003-4496-2394

Apoorva Baluapuri http://orcid.org/0000-0002-3723-7891

Julia Weber http://orcid.org/0000-0002-0183-8994

Roman Maresch http://orcid.org/0000-0003-3393-8882

Richard Lewis http://orcid.org/0000-0002-2404-6703

Stefan Habringer http://orcid.org/0000-0002-3976-9592

Katja Steiger http://orcid.org/0000-0002-7269-5433

Robert A. J. Oostendorp http://orcid.org/0000-0002-4947-0412

Cornelius Miething http://orcid.org/0000-0003-4699-3805

Björn Chapuy http://orcid.org/0000-0002-6485-8773

Matthias Wirth http://orcid.org/0000-0002-8340-0872

Elmar Wolf http://orcid.org/0000-0002-5299-6335

Roland Rad http://orcid.org/0000-0002-6849-9659

Ulrich Keller http://orcid.org/0000-0002-8485-1958

Article

Publisher ID: 27704

DOI: 10.1038/s41467-021-27704-8

PMC ID: 8755833

PubMed ID: 35022408

SO-VID: cab12169-60a6-4db0-a611-3cc2773f54e4

License:

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 26 August 2020

Date accepted : 7 December 2021

Funding

Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);

Award ID: SFB824/C3

Award Recipient : Ulrich Keller

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ScienceOpen disciplines: Uncategorized

Keywords: b-cell lymphoma,tumour biomarkers,sumoylation

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ScienceOpen disciplines: Uncategorized

Keywords: b-cell lymphoma, tumour biomarkers, sumoylation

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Genetic alterations of the SUMO isopeptidase SENP6 drive lymphomagenesis and genetic instability in diffuse large B-cell lymphoma

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Most cited references 64

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

STAR: ultrafast universal RNA-seq aligner.

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

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Cited by 11

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