159 research outputs found
Subtyping Burkitt Lymphoma by DNA Methylation
No full textABSTRACT Burkitt lymphoma (BL) is an aggressive germinal center B‐cell‐derived malignancy. Historically, sporadic, endemic, and immunodeficiency‐associated variants were distinguished, which differ in the frequency of Epstein–Barr virus (EBV) positivity. Aiming to identify subgroups based on DNA methylation patterns, we here profiled 96 BL cases, 17 BL cell lines, and six EBV‐transformed lymphoblastoid cell lines using Illumina BeadChip arrays. DNA methylation analyses clustered the cases into four subgroups: two containing mostly EBV‐positive cases (BL‐mC1, BL‐mC2) and two containing mostly EBV‐negative cases (BL‐mC3, BL‐mC4). The subgroups BL‐mC1/2, enriched for EBV‐positive cases, showed increased DNA methylation, epigenetic age, and, in part, proliferation history compared to BL‐mC3/4. CpGs hypermethylated in EBV‐positive BLs were enriched for polycomb repressive complex 2 marks, while the CpGs hypomethylated in EBV‐negative BLs were linked to, for example, B‐cell receptor signaling. EBV‐associated hypermethylation affected regulatory regions of genes frequently mutated in BL (e.g., CCND3 , TP53 ) and impacted superenhancers. This finding suggests that hypermethylation may compensate for the lower mutational burden of pathogenic drivers in EBV‐positive BLs. Though minor, significant differences were also observed between EBV‐positive endemic and sporadic cases (e.g., at the SOX11 and RUNX1 loci). Our findings suggest that EBV status, rather than epidemiological variants, drives the DNA methylation‐based subgrouping of BL.Intramural Research Program https://doi.org/10.13039/10003069
Differential expression analysis for sequence count data
No full text*Motivation:* High-throughput nucleotide sequencing provides quantitative readouts in assays for RNA expression (RNA-Seq), protein-DNA binding (ChIP-Seq) or cell counting (barcode sequencing). Statistical inference of differential signal in such data requires estimation of their variability throughout the dynamic range. When the number of replicates is small, error modelling is needed to achieve statistical power.

*Results:* We propose an error model that uses the negative binomial distribution, with variance and mean linked by local regression, to model the null distribution of the count data. The method controls type-I error and provides good detection power. 

*Availability:* A free open-source R software package, _DESeq_, is available from the Bioconductor project and from "http://www-huber.embl.de/users/anders/DESeq":http://www-huber.embl.de/users/anders/DESeq
Nuclear RNA sequencing of the mouse erythroid cell transcriptome
No full textCopyright @ 2012 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.In addition to protein coding genes a substantial proportion of mammalian genomes are transcribed. However, most transcriptome studies investigate steady-state mRNA levels, ignoring a considerable fraction of the transcribed genome. In addition, steady-state mRNA levels are influenced by both transcriptional and posttranscriptional mechanisms, and thus do not provide a clear picture of transcriptional output. Here, using deep sequencing of nuclear RNAs (nucRNA-Seq) in parallel with chromatin immunoprecipitation sequencing (ChIP-Seq) of active RNA polymerase II, we compared the nuclear transcriptome of mouse anemic spleen erythroid cells with polymerase occupancy on a genome-wide scale. We demonstrate that unspliced transcripts quantified by nucRNA-seq correlate with primary transcript frequencies measured by RNA FISH, but differ from steady-state mRNA levels measured by poly(A)-enriched RNA-seq. Highly expressed protein coding genes showed good correlation between RNAPII occupancy and transcriptional output; however, genome-wide we observed a poor correlation between transcriptional output and RNAPII association. This poor correlation is due to intergenic regions associated with RNAPII which correspond with transcription factor bound regulatory regions and a group of stable, nuclear-retained long non-coding transcripts. In conclusion, sequencing the nuclear transcriptome provides an opportunity to investigate the transcriptional landscape in a given cell type through quantification of unspliced primary transcripts and the identification of nuclear-retained long non-coding RNAs.This work was supported by the Medical Research Council and the Biotechnology and Biological Sciences Research Council, UK (operating grants held by PF) and the Natural Sciences and Engineering Research Council of Canada (Discovery Grant held by JAM). DU was supported by an EMBO fellowship, and CYC was supported in part by an Ontario Graduate Scholarship. The ENCODE project is funded by the National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
Focal structural variants revealed by whole genome sequencing disrupt the histone demethylase KDM4C in B cell lymphomas
No full textHistone methylation-modifiers, such as EZH2 and KMT2D, are recurrently altered in B-cell lymphomas. To comprehensively describe the landscape of alterations affecting genes encoding histone methylation-modifiers in lymphomagenesis we investigated whole genome and transcriptome data of 186 mature B-cell lymphomas sequenced in the ICGC MMML-Seq project. Besides confirming common alterations of KMT2D (47% of cases), EZH2 (17%), SETD1B (5%), PRDM9 (4%), KMT2C (4%), and SETD2 (4%), also identified by prior exome or RNA-sequencing studies, we here found recurrent alterations to KDM4C in chromosome 9p24, encoding a histone demethylase. Focal structural variation was the main mechanism of KDM4C alterations, and was independent from 9p24 amplification. We also identified KDM4C alterations in lymphoma cell lines including a focal homozygous deletion in a classical Hodgkin lymphoma cell line. By integrating RNA-sequencing and genome sequencing data we predict that KDM4C structural variants result in loss-offunction. By functional reconstitution studies in cell lines, we provide evidence that KDM4C can act as a tumor suppressor. Thus, we show that identification of structural variants in whole genome sequencing data adds to the comprehensive description of the mutational landscape of lymphomas and, moreover, establish KDM4C as a putative tumor suppressive gene recurrently altered in subsets of B-cell derived lymphomas
Mutational mechanisms shaping the coding and noncoding genome of germinal center derived B-cell lymphomas
No full textB cells have the unique property to somatically alter their immunoglobulin (IG) genes by V(D)J recombination, somatic hypermutation (SHM) and class-switch recombination (CSR). Aberrant targeting of these mechanisms is implicated in lymphomagenesis, but the mutational processes are poorly understood. By performing whole genome and transcriptome sequencing of 181 germinal center derived B-cell lymphomas (gcBCL) we identified distinct mutational signatures linked to SHM and CSR. We show that not only SHM, but presumably also CSR causes off-target mutations in non-IG genes. Kataegis clusters with high mutational density mainly affected early replicating regions and were enriched for SHM- and CSR-mediated off-target mutations. Moreover, they often co-occurred in loci physically interacting in the nucleus, suggesting that mutation hotspots promote increased mutation targeting of spatially co-localized loci (termed hypermutation by proxy). Only around 1% of somatic small variants were in protein coding sequences, but in about half of the driver genes, a contribution of B-cell specific mutational processes to their mutations was found. The B-cell-specific mutational processes contribute to both lymphoma initiation and intratumoral heterogeneity. Overall, we demonstrate that mutational processes involved in the development of gcBCL are more complex than previously appreciated, and that B cell-specific mutational processes contribute via diverse mechanisms to lymphomagenesis.This study has been supported by the German Ministry of Science and Education (BMBF) in the framework of the ICGC MMML-Seq project (01KU1002A-J) the MMML-MYC-SYS project (036166B) and the project ICGC DE-MINING (01KU1505E), the European Union in the framework of the BLUEPRINT Project (HEALTH-F5-2011-282510) and the KinderKrebsInitiative Buchholz/Holm-Seppensen. This work was supported by the BMBF-funded Heidelberg Center for Human Bioinformatics (HD-HuB) within the German Network for Bioinformatics Infrastructure (de.NBI) (#031A537A, #031A537C). Former grant support of MMML by the Deutsche Krebshilfe (2003–2011) is gratefully acknowledged. We acknowledge COSMIC and use of Cancer Gene Census. Part of the work was performed in association with SFB1074 (particularly subproject B1) funded by DFG. We wish to thank Barbara Hutter, Ivo Buchhalter, Zuguang Gu, and Natalie Jäger for skillful technical assistance. We thank the High-Throughput Sequencing Unit of the Genome and Proteome Core Facility and the Omics IT and Data Management Core Facility of the German Cancer Research Center (DKFZ, Heidelberg) as well as the Institute of Clinical Molecular Biology (IKMB, Christian-Albrechts-University Kiel) for excellent technical support and expertise. DH is a member of the Hartmut Hoffmann-Berling International Graduate School of Molecular and Cellular Biology (HBIGS) and of the MD/PhD-program of the University of Heidelberg. KK and UHT are funded by the Helmholtz International Graduate School for Cancer Research at the German Cancer Research Center. SHB, HK, and SH acknowledge support by LIFE (Leipzig Research Center for Civilization Diseases), Leipzig University. LIFE is funded by the European Union, the European Regional Development Fund (ERDF), the European Social Fund (ESF), and the Free State of Saxony. This work has been carried out with the help of the Interdisciplinary Bank of Biomaterials and Data of the University Hospital of Würzburg and the Julius Maximilian University of Würzburg (idbw).Peer Reviewed"Article signat per 70 autors/es:Daniel Hübschmann, Kortine Kleinheinz, Rabea Wagener, Stephan H. Bernhart, Cristina López, Umut H. Toprak, Stephanie Sungalee, Naveed Ishaque, Helene Kretzmer, Markus Kreuz, Sebastian M. Waszak, Nagarajan Paramasivam, Ole Ammerpohl, Sietse M. Aukema, Renée Beekman, Anke K. Bergmann, Matthias Bieg, Hans Binder, Arndt Borkhardt, Christoph Borst, Benedikt Brors, Philipp Bruns, Enrique Carrillo de Santa Pau, Alexander Claviez, Gero Doose, Andrea Haake, Dennis Karsch, Siegfried Haas, Martin-Leo Hansmann, Jessica I. Hoell, Volker Hovestadt, Bingding Huang, Michael Hummel, Christina Jäger-Schmidt, Jules N. A. Kerssemakers, Jan O. Korbel, Dieter Kube, Chris Lawerenz, Dido Lenze, Joost H. A. Martens, German Ott, Bernhard Radlwimmer, Eva Reisinger, Julia Richter, Daniel Rico, Philip Rosenstiel, Andreas Rosenwald, Markus Schillhabel, Stephan Stilgenbauer, Peter F. Stadler, José I. Martín-Subero, Monika Szczepanowski, Gregor Warsow, Marc A. Weniger, Marc Zapatka, Alfonso Valencia, Hendrik G. Stunnenberg, Peter Lichter, Peter Möller, Markus Loeffler, Roland Eils, Wolfram Klapper, Steve Hoffmann, Lorenz Trümper, ICGC MMML-Seq consortium, ICGC DE-Mining consortium, BLUEPRINT consortium, Ralf Küppers, Matthias Schlesner & Reiner Siebert"Postprint (published version
Alterations of miRNAs and miRNA-regulated mRNA expression in GC B cell lymphomas determined by integrative sequencing analysis.
No full textMicroRNAs are well-established players in posttranscriptional gene regulation. However, information on the effects of microRNA deregulation mainly relies on bioinformatic prediction of potential targets, whereas proof of the direct physical microRNAs/target mRNAs interaction is mostly lacking. Within the International Cancer Genome Consortium Project Determining Molecular Mechanisms in Malignant Lymphoma by Sequencing (ICGC MMML-Seq), we performed miRnome sequencing from 16 Burkitt lymphomas, 19 diffuse large B-cell lymphomas, and 21 follicular lymphomas. Twenty-two miRNAs separated Burkitt lymphomas from diffuse large B-cell lymphomas/follicular lymphomas, of which 13 have shown regulation by MYC. Moreover, we show expression of three hitherto unreported microRNAs. Additionally, we detect recurrent mutations of hsa-miR-142 in diffuse large B-cell lymphomas and follicular lymphomas, and editing of the hsa-miR-376 cluster, providing evidence for microRNA editing in lymphomagenesis. To interrogate the direct physical interactions of microRNAs with mRNAs, we performed Argonaute-2 photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation experiments. MicroRNAs directly targeted 208 mRNAs in the Burkitt lymphomas and 328 mRNAs in the non-Burkitt lymphoma models. This integrative analysis discovered several regulatory pathways of relevance in lymphomagenesis including Ras, PI3K-Akt and MAPK signaling pathways, also recurrently deregulated in lymphomas by mutations. Our dataset uncovers in detail the mRNA deregulation through microRNAs as a highly relevant mechanism in lymphomagenesis
Focal structural variants revealed by whole genome sequencing disrupt the histone demethylase <i>KDM4C</i> in B cell lymphomas
No full textHistone methylation-modifiers, like EZH2 and KMT2D, are recurrently altered in B-cell lymphomas. To comprehensively describe the landscape of alterations affecting genes encoding histone methylation-modifiers in lymphomagenesis we investigated whole genome and transcriptome data of 186 mature B-cell lymphomas sequenced in the ICGC MMML-Seq project. Besides confirming common alterations of KMT2D (47% of cases), EZH2 (17%), SETD1B (5%), PRDM9 (4%), KMT2C (4%), and SETD2 (4%) also identified by prior exome or RNAseq studies, we here unravel KDM4C in chromosome 9p24, encoding a histone demethylase, to be recurrently altered. Focal structural variation was the main mechanism of KDM4C alterations, which was independent from 9p24 amplification. We identified KDM4C alterations also in lymphoma cell lines including a focal homozygous deletion in a classical Hodgkin lymphoma cell line. By integrating RNAseq and genome sequencing data we predict KDM4C structural variants to result in loss-of-function. By functional reconstitution studies in cell lines, we provide evidence that KDM4C can act as tumor suppressor. Thus, we show that identification of structural variants in whole genome sequencing data adds to the comprehensive description of the mutational landscape of lymphomas and, moreover, establish KDM4C as putative tumor suppressive gene recurrently altered in subsets of B-cell derived lymphomas
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