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1.
Oncotarget. 2018 Aug 28;9(67):32855-32867. doi: 10.18632/oncotarget.26023. eCollection 2018 Aug 28.

Beyond the 3'UTR binding-microRNA-induced protein truncation via DNA binding.

Author information

1
Department of Urology, University Hospital of Cologne, Cologne, Germany.
2
Bioinformatics Group, Department of Computer Science and Interdisciplinary Center of Bioinformatics, Leipzig University, Leipzig, Germany.
3
Department of Natural Sciences, University of Applied Sciences Bonn-Rhein-Sieg, Rheinbach, Germany.
4
Institute of Pathology, University Hospital of Cologne, Cologne, Germany.
5
Institute of Neuropathology, University Hospital of Cologne, Cologne, Germany.

Abstract

Here, we present a miR mechanism which is active in the nucleus and is essential for the production of intron included, C-terminal truncated and biologically active proteins, like e.g. Vim3. We exemplified this mechanism by miRs, miR-15a and miR-498, which are overexpressed in clear cell renal carcinoma or oncocytoma. Both miRs directly interact with DNA in an intronic region, leading to transcriptional stop, and therefore repress the full length version of the pre-mRNA, resulting in intron included truncated proteins (Mxi-2 and Vim3). A computational survey shows that this miR:DNA interactions mechanism may be generally involved in regulating the human transcriptome, with putative interaction sites in intronic regions for over 1000 genes. In this work, an entirely new mechanism is revealed how miRs can repress full length protein translation, resulting in C-terminal truncated proteins.

KEYWORDS:

DNA interaction; Mxi-2; Vim3; miR-15; miR-498

Conflict of interest statement

CONFLICTS OF INTEREST The authors declare no conflicts of interest.

2.
Stem Cells Int. 2018 Aug 19;2018:5692840. doi: 10.1155/2018/5692840. eCollection 2018.

Noncoding RNA Transcripts during Differentiation of Induced Pluripotent Stem Cells into Hepatocytes.

Author information

1
Applied Stem Cell Biology and Cell Technology, Biomedical and Biotechnological Center, Leipzig University, Deutscher Platz 5, 04103 Leipzig, Germany.
2
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Centre for Bioinformatics, Leipzig University, Härtelstr. 1618, 04107 Leipzig, Germany.
3
Transcriptome Bioinformatics Group at the Interdisciplinary Centre for Bioinformatics, Leipzig University, Härtelstr. 1618, 04107 Leipzig, Germany.
4
Department of Plastic Surgery and Hand Surgery, University Hospital Rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 Munich, Germany.
5
Competence Center for Scalable Data Services and Solutions Dresden/Leipzig, German Centre for Integrative Biodiversity Research (iDiv), and Leipzig Research Center for Civilization Diseases, Universität Leipzig, Ritterstrasse 9-13, 04109 Leipzig, Germany.
6
Max Planck Institute for Mathematics in the Sciences, Insel Strasse 22, 04103 Leipzig, Germany.
7
Fraunhofer Institute for Cell Therapy and Immunology, Perlickstrasse 1, 04103 Leipzig, Germany.
8
Department of Theoretical Chemistry, University of Vienna, Wahringerstrasse 17, 1090 Vienna, Austria.
9
Center for RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg C, Denmark.
10
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe NM 87501, USA.

Abstract

Recent advances in the stem cell field allow to obtain many human tissues in vitro. However, hepatic differentiation of induced pluripotent stem cells (iPSCs) still remains challenging. Hepatocyte-like cells (HLCs) obtained after differentiation resemble more fetal liver hepatocytes. MicroRNAs (miRNA) play an important role in the differentiation process. Here, we analysed noncoding RNA profiles from the last stages of differentiation and compare them to hepatocytes. Our results show that HLCs maintain an epithelial character and express miRNA which can block hepatocyte maturation by inhibiting the epithelial-mesenchymal transition (EMT). Additionally, we identified differentially expressed small nucleolar RNAs (snoRNAs) and discovered novel noncoding RNA (ncRNA) genes.

3.
Blood Cancer J. 2018 Aug 10;8(8):77. doi: 10.1038/s41408-018-0114-3.

RBFOX2 and alternative splicing in B-cell lymphoma.

Author information

1
Department of Human and Animal Cell Lines, Leibniz-Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany. hqu@dsmz.de.
2
Department of Human and Animal Cell Lines, Leibniz-Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
3
Transcriptome Bioinformatics Group - Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany.
4
Computational Biology, Leibniz Institute on Aging - Fritz Lipmann Institute and Friedrich Schiller University Jena, Jena, Germany.
5
Institute of Human Genetics, Ulm University and Ulm University Medical Center, Ulm, Germany.

Publication type

Publication type

4.
Nature. 2018 Jul;559(7714):E10. doi: 10.1038/s41586-018-0167-2.

Author Correction: The landscape of genomic alterations across childhood cancers.

Author information

1
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.
2
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
3
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
4
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany.
5
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
6
The Finsen Laboratory, Rigshospitalet, Biotech Research and Innovation Centre (BRIC), Copenhagen University, Copenhagen, Denmark.
7
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
8
Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
9
Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
10
Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University and BioQuant Center, 69120, Heidelberg, Germany.
11
Klinikum Stuttgart - Olgahospital, Zentrum für Kinder-, Jugend- und Frauenmedizin, Pädiatrie, Stuttgart, Germany.
12
Department of Pediatric Oncology, Hematology & Clinical Immunology, University Children's Hospital, Heinrich Heine University, Düsseldorf, Germany.
13
Department of Pediatric Oncology/Hematology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
14
Institute for Experimental Cancer Research in Pediatrics, University Hospital Frankfurt, Frankfurt am Main, Germany.
15
Theodor-Boveri-Institute/Biocenter, Developmental Biochemistry, and Comprehensive Cancer Center Mainfranken, Wuerzburg University, Würzburg, Germany.
16
Department of Pediatric Surgery, Research Laboratories, Dr. von Hauner Children's Hospital, Ludwig Maximilians University Munich, Munich, Germany.
17
Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel and University of Basel, Basel, Switzerland.
18
Children's Cancer Research Centre and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
19
Division of Pediatric Hematology and Oncology, University Medical Center Aachen, Aachen, Germany.
20
Department of Human Genetics, University Hospital Essen, Essen, Germany.
21
Division of Pediatric Hematology and Oncology, Department of Pediatrics, University Medical Center Freiburg, Freiburg, Germany.
22
Department of Pediatric Oncology, Klinikum Kassel, Kassel, Germany.
23
Institute of Human Genetics, University of Ulm & University Hospital of Ulm, Ulm, Germany.
24
Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
25
Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
26
Innovative Therapies for Children with Cancer Consortium and Department of Clinical Research, Gustave Roussy, Université Paris-Saclay, Villejuif, France.
27
Pediatric Hematology and Oncology, University Hospital Münster, Muenster, Germany.
28
Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany.
29
Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
30
Center for Individualized Pediatric Oncology (ZIPO) and Brain Tumors, University Hospital and German Cancer Research Center (DKFZ), Heidelberg, Germany.
31
Division of Pediatric Hematology and Oncology, University Medical Center Göttingen, Göttingen, Germany.
32
Pediatric Oncology & Hematology, Pediatrics III, University Hospital of Essen, Essen, Germany.
33
Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
34
Swabian Children's Cancer Center, Children's Hospital, Klinikum Augsburg, Augsburg, Germany.
35
Genomics and Proteomics Core Facility, High Throughput Sequencing Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany.
36
Hospital for Children and Adolescents, University Hospital Frankfurt, Frankfurt, Germany.
37
University Hospital Cologne, Klinik und Poliklinik für Kinder- und Jugendmedizin, Cologne, Germany.
38
Department of Oncogenomics, Academic Medical Center, Amsterdam, The Netherlands.
39
Division of Neurosurgery, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
40
Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
41
Division of Oncology, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, USA.
42
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
43
Institute of Computer Science, Freie Universität Berlin, Berlin, Germany.
44
Institute of Medical Genetics and Human Genetics, Charité University Hospital, Berlin, Germany.
45
Bioinformatics and Omics Data Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.
46
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany. s.pfister@dkfz.de.
47
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany. s.pfister@dkfz.de.
48
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. s.pfister@dkfz.de.
49
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany. s.pfister@dkfz.de.

Abstract

In this Article, author Benedikt Brors was erroneously associated with affiliation number '8' (Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA); the author's two other affiliations (affiliations '3' and '7', both at the German Cancer Research Center (DKFZ)) were correct. This has been corrected online.

5.
Nature. 2018 Mar 15;555(7696):321-327. doi: 10.1038/nature25480. Epub 2018 Feb 28.

The landscape of genomic alterations across childhood cancers.

Author information

1
Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.
2
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
3
German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
4
Department of Pediatric Oncology, Hematology & Immunology, Heidelberg University Hospital, Heidelberg, Germany.
5
European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
6
The Finsen Laboratory, Rigshospitalet, Biotech Research and Innovation Centre (BRIC), Copenhagen University, Copenhagen, Denmark.
7
Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
8
Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
9
Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
10
Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, Heidelberg University and BioQuant Center, 69120, Heidelberg, Germany.
11
Klinikum Stuttgart - Olgahospital, Zentrum für Kinder-, Jugend- und Frauenmedizin, Pädiatrie, Stuttgart, Germany.
12
Department of Pediatric Oncology, Hematology & Clinical Immunology, University Children's Hospital, Heinrich Heine University, Düsseldorf, Germany.
13
Department of Pediatric Oncology/Hematology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
14
Institute for Experimental Cancer Research in Pediatrics, University Hospital Frankfurt, Frankfurt am Main, Germany.
15
Theodor-Boveri-Institute/Biocenter, Developmental Biochemistry, and Comprehensive Cancer Center Mainfranken, University of Würzburg, Würzburg, Germany.
16
Department of Pediatric Surgery, Research Laboratories, Dr von Hauner Children's Hospital, Ludwig Maximilians University Munich, Munich, Germany.
17
Bone Tumor Reference Center at the Institute of Pathology, University Hospital Basel and University of Basel, Basel, Switzerland.
18
Children's Cancer Research Centre and Department of Pediatrics, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
19
Division of Pediatric Hematology and Oncology, University Medical Center Aachen, Aachen, Germany.
20
Department of Human Genetics, University Hospital Essen, Essen, Germany.
21
Division of Pediatric Hematology and Oncology, Department of Pediatrics, University Medical Center Freiburg, Freiburg, Germany.
22
Department of Pediatric Oncology, Klinikum Kassel, Kassel, Germany.
23
Institute of Human Genetics, University of Ulm & University Hospital of Ulm, Ulm, Germany.
24
Division of Neuroblastoma Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
25
Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
26
Innovative Therapies for Children with Cancer Consortium and Department of Clinical Research, Gustave Roussy, Université Paris-Saclay, Villejuif, France.
27
Pediatric Hematology and Oncology, University Hospital Münster, Münster, Germany.
28
Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany.
29
Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
30
Center for Individualized Pediatric Oncology (ZIPO) and Brain Tumors, University Hospital and German Cancer Research Center (DKFZ), Heidelberg, Germany.
31
Division of Pediatric Hematology and Oncology, University Medical Center Göttingen, Göttingen, Germany.
32
Pediatric Oncology & Hematology, Pediatrics III, University Hospital of Essen, Essen, Germany.
33
Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
34
Swabian Children's Cancer Center, Children's Hospital, Klinikum Augsburg, Augsburg, Germany.
35
Genomics and Proteomics Core Facility, High Throughput Sequencing Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany.
36
Hospital for Children and Adolescents, University Hospital Frankfurt, Frankfurt, Germany.
37
University Hospital Cologne, Klinik und Poliklinik für Kinder- und Jugendmedizin, Cologne, Germany.
38
Department of Oncogenomics, Academic Medical Center, Amsterdam, The Netherlands.
39
Division of Neurosurgery, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
40
Department of Computational Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA.
41
Division of Oncology, Center for Childhood Cancer Research, Department of Biomedical and Health Informatics and Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, USA.
42
Division of Molecular Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
43
Institute of Computer Science, Freie Universität Berlin, Berlin, Germany.
44
Institute of Medical Genetics and Human Genetics, Charité University Hospital, Berlin, Germany.
45
Bioinformatics and Omics Data Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Abstract

Pan-cancer analyses that examine commonalities and differences among various cancer types have emerged as a powerful way to obtain novel insights into cancer biology. Here we present a comprehensive analysis of genetic alterations in a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer. Using a standardized workflow, we identified marked differences in terms of mutation frequency and significantly mutated genes in comparison to previously analysed adult cancers. Genetic alterations in 149 putative cancer driver genes separate the tumours into two classes: small mutation and structural/copy-number variant (correlating with germline variants). Structural variants, hyperdiploidy, and chromothripsis are linked to TP53 mutation status and mutational signatures. Our data suggest that 7-8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.

Comment in

PMID:
29489754
DOI:
10.1038/nature25480
[Indexed for MEDLINE]
Icon for Nature Publishing Group
6.
Cardiol J. 2018;25(6):714-721. doi: 10.5603/CJ.a2018.0001. Epub 2018 Jan 17.

Pericardial effusion unrelated to surgery is a predictor of mortality in heart transplant patients.

Author information

1
University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland. simon.staempfli@usz.ch.

Abstract

BACKGROUND:

Hemodynamically irrelevant pericardial effusion (PeEf) is a predictor of adverse outcome in heart failure patients. The clinical relevance of a PeEf unrelated to surgery in heart transplant patients remains unknown. This study assesses the prognostic value of PeEf occurring later than 1 year after transplantation.

METHODS:

All patients undergoing heart transplantation in Zurich between 1989 and 2012 were screened. Cox proportional hazard models were used to analyze mortality (primary) and hospitalization (secondary endpoint). PeEf time points were compared to baseline for rejection, immunosuppressants, tumors, inflam-mation, heart failure, kidney function, hemodynamic, and echocardiographic parameters.

RESULTS:

Of 152 patients (mean age 48.3 ± 11.9), 25 developed PeEf. Median follow-up period was 11.9 (IQR 5.8-17) years. The number of deaths was 6 in the PeEf group and 46 in the non-PeEf group. The occurrence of PeEf was associated with a 2.5-fold increased risk of death (HR 2.49, 95% CI 1.02-6.13, p = 0.046) and hospitalization (HR 2.53, 95% CI 1.57-4.1, p = 0.0002).

CONCLUSIONS:

This study reveals that the finding of hemodynamically irrelevant PeEf in heart trans-plant patients is a predictor of adverse outcome, suggesting that a careful clinical assessment is war-ranted in heart transplant patients exhibiting small PeEf.

KEYWORDS:

cancer; echocardiography; hospitalization; prognosis; survival

PMID:
29341061
DOI:
10.5603/CJ.a2018.0001
Free full text
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7.
Bioinformatics. 2018 Mar 15;34(6):1066-1068. doi: 10.1093/bioinformatics/btx690.

DIEGO: detection of differential alternative splicing using Aitchison's geometry.

Author information

1
Transcriptome Bioinformatics Group, Interdisciplinary Center for Bioinformatics, Leipzig University, 04107 Leipzig.
2
Chair of Bioinformatics, Faculty of Mathematics and Computer Science, Leipzig University, 04107 Leipzig, Germany.
3
ecSeq Bioinformatics, 04103 Leipzig, Germany.
4
Institute of Human Genetics, University of Ulm and University of Ulm Medical Center, 89081 Ulm, Germany.
5
Computational Biology Group, Leibniz Institute on Ageing - Fritz Lipmann Institute (FLI) and Friedrich-Schiller-University Jena, 07745 Jena, Germany.

Abstract

Motivation:

Alternative splicing is a biological process of fundamental importance in most eukaryotes. It plays a pivotal role in cell differentiation and gene regulation and has been associated with a number of different diseases. The widespread availability of RNA-Sequencing capacities allows an ever closer investigation of differentially expressed isoforms. However, most tools for differential alternative splicing (DAS) analysis do not take split reads, i.e. the most direct evidence for a splice event, into account. Here, we present DIEGO, a compositional data analysis method able to detect DAS between two sets of RNA-Seq samples based on split reads.

Results:

The python tool DIEGO works without isoform annotations and is fast enough to analyze large experiments while being robust and accurate. We provide python and perl parsers for common formats.

Availability and implementation:

The software is available at: www.bioinf.uni-leipzig.de/Software/DIEGO.

Contact:

steve@bioinf.uni-leipzig.de.

Supplementary information:

Supplementary data are available at Bioinformatics online.

8.
Elife. 2017 Sep 18;6. pii: e26876. doi: 10.7554/eLife.26876.

The DREAM complex through its subunit Lin37 cooperates with Rb to initiate quiescence.

Author information

1
Molecular Oncology, Medical School, University of Leipzig, Leipzig, Germany.
2
Computational EvoDevo Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.
3
Transcriptome Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany.
4
Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.

Abstract

The retinoblastoma Rb protein is an important factor controlling the cell cycle. Yet, mammalian cells carrying Rb deletions are still able to arrest under growth-limiting conditions. The Rb-related proteins p107 and p130, which are components of the DREAM complex, had been suggested to be responsible for a continued ability to arrest by inhibiting E2f activity and by recruiting chromatin-modifying enzymes. Here, we show that p130 and p107 are not sufficient for DREAM-dependent repression. We identify the MuvB protein Lin37 as an essential factor for DREAM function. Cells not expressing Lin37 proliferate normally, but DREAM completely loses its ability to repress genes in G0/G1 while all remaining subunits, including p130/p107, still bind to target gene promoters. Furthermore, cells lacking both Rb and Lin37 are incapable of exiting the cell cycle. Thus, Lin37 is an essential component of DREAM that cooperates with Rb to induce quiescence.

KEYWORDS:

biochemistry; cancer; cancer biology; cell cycle exit; cell cycle regulation; cell proliferation; gene expression; human; mouse; transcription factors

PMID:
28920576
PMCID:
PMC5602299
DOI:
10.7554/eLife.26876
[Indexed for MEDLINE]
Free PMC Article
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10.
Sci Rep. 2016 Nov 23;6:37393. doi: 10.1038/srep37393.

Changes of bivalent chromatin coincide with increased expression of developmental genes in cancer.

Author information

1
Leipzig University, Chair of Bioinformatics, Leipzig, 04107, Germany.
2
Leipzig University, Transcriptome Bioinformatics Group - Interdisciplinary Center for Bioinformatics, Leipzig, 04107, Germany.
3
Ludwig-Maximilians-University, Institute of Laboratory Medicine, Munich, 81377, Germany.
4
Inserm, U1110 - Institut de Recherche sur les Maladies Virales et Hépatiques, Strasbourg, 67000, France.
5
Université de Strasbourg, Strasbourg, 67000, France.
6
Christian Albrechts University &University Hospital Schleswig-Holstein - Campus Kiel, Institute of Human Genetics, Kiel, 24105, Germany.
7
Christian Albrechts University Kiel &University Hospital Schleswig-Holstein - Campus Kiel, Department of Pediatrics, Kiel, 24105, Germany.
8
Ulm University &Ulm University Medical Center, Institute for Human Genetics, Ulm, 89081, Germany.
9
Leipzig University, LIFE - Leipzig Research Center for Civilization Diseases, Leipzig, 04107, Germany.
10
University of Vienna, Department of Theoretical Chemistry, Vienna, 1090, Austria.
11
Max-Planck-Institute for Mathematics in Sciences, Leipzig, 04103, Germany.
12
Santa Fe Institute, Santa Fe, NM 87501, USA.

Abstract

Bivalent (poised or paused) chromatin comprises activating and repressing histone modifications at the same location. This combination of epigenetic marks at promoter or enhancer regions keeps genes expressed at low levels but poised for rapid activation. Typically, DNA at bivalent promoters is only lowly methylated in normal cells, but frequently shows elevated methylation levels in cancer samples. Here, we developed a universal classifier built from chromatin data that can identify cancer samples solely from hypermethylation of bivalent chromatin. Tested on over 7,000 DNA methylation data sets from several cancer types, it reaches an AUC of 0.92. Although higher levels of DNA methylation are often associated with transcriptional silencing, counter-intuitive positive statistical dependencies between DNA methylation and expression levels have been recently reported for two cancer types. Here, we re-analyze combined expression and DNA methylation data sets, comprising over 5,000 samples, and demonstrate that the conjunction of hypermethylation of bivalent chromatin and up-regulation of the corresponding genes is a general phenomenon in cancer. This up-regulation affects many developmental genes and transcription factors, including dozens of homeobox genes and other genes implicated in cancer. Thus, we reason that the disturbance of bivalent chromatin may be intimately linked to tumorigenesis.

PMID:
27876760
PMCID:
PMC5120258
DOI:
10.1038/srep37393
[Indexed for MEDLINE]
Free PMC Article
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11.
Haematologica. 2016 Nov;101(11):1380-1389. Epub 2016 Jul 6.

Alterations of microRNA and microRNA-regulated messenger RNA expression in germinal center B-cell lymphomas determined by integrative sequencing analysis.

Author information

1
Department of Pediatric Oncology, Hematology and Clinical Immunology, Heinrich-Heine-University, Medical Faculty, Düsseldorf, Germany.
2
Department of Algorithmic Bioinformatics, Heinrich-Heine University, Duesseldorf, Germany.
3
Transcriptome Bioinformatics Group, LIFE Research Center for Civilization Diseases, University of Leipzig, Germany.
4
Bioinformatics Group, Department of Computer Science, University of Leipzig, Germany.
5
Interdisciplinary Center for Bioinformatics, University of Leipzig, Germany.
6
Institute of Pathology, Charité - University Medicine Berlin, Germany.
7
Institute of Human Genetics, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Germany.
8
Division Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
9
National Center for Tumor Diseases (NCT), Heidelberg, Germany.
10
German Cancer Consortium (DKTK), Heidelberg, Germany.
11
Department of Pediatric Hematology and Oncology, University Hospital Münster, Germany.
12
Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Germany.
13
Department of Human and Animal Cell Cultures, German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
14
Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Heidelberg, Germany.
15
Department of Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology and Bioquant, Heidelberg University, Germany.
16
Friedrich-Ebert Hospital Neumünster, Clinics for Hematology, Oncology and Nephrology, Neumünster, Germany.
17
Department of Internal Medicine II: Hematology and Oncology, University Medical Centre, Campus Kiel, Germany.
18
Hematopathology Section, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Germany.
19
EMBL Heidelberg, Genome Biology, Heidelberg, Germany.
20
Institute for Medical Informatics Statistics and Epidemiology, Leipzig, Germany.
21
Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, Essen, Germany.
22
Institute of Pathology, University of Würzburg, and Comprehensive Cancer Center Mainfranken, Würzburg, Germany.
23
Hospital of Internal Medicine II, Hematology and Oncology, St-Georg Hospital Leipzig, Germany.
24
Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany.
25
Institute of Pathology, Medical Faculty of the Ulm University, Germany.
26
Department of Pediatric Hematology and Oncology University Hospital Giessen, Germany.
27
Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Germany.
28
RNomics Group, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany.
29
Max-Planck-Institute for Mathematics in Sciences, Leipzig, Germany.
30
Santa Fe Institute, NM, USA.
31
Department of Internal Medicine III, University of Ulm, Germany.
32
Department of Hematology and Oncology, Georg-August-University of Göttingen, Germany.

Abstract

MicroRNA are well-established players in post-transcriptional gene regulation. However, information on the effects of microRNA deregulation mainly relies on bioinformatic prediction of potential targets, whereas proof of the direct physical microRNA/target messenger RNA interaction is mostly lacking. Within the International Cancer Genome Consortium Project "Determining Molecular Mechanisms in Malignant Lymphoma by Sequencing", we performed miRnome sequencing from 16 Burkitt lymphomas, 19 diffuse large B-cell lymphomas, and 21 follicular lymphomas. Twenty-two miRNA separated Burkitt lymphomas from diffuse large B-cell lymphomas/follicular lymphomas, of which 13 have shown regulation by MYC. Moreover, we found expression of three hitherto unreported microRNA. Additionally, we detected 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 microRNA with messenger RNA, we performed Argonaute-2 photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation experiments. MicroRNA directly targeted 208 messsenger RNA in the Burkitt lymphomas and 328 messenger RNA 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 reveals that messenger RNA deregulation through microRNA is a highly relevant mechanism in lymphomagenesis.

PMID:
27390358
PMCID:
PMC5394868
DOI:
10.3324/haematol.2016.143891
[Indexed for MEDLINE]
Free PMC Article
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12.
Sci Rep. 2016 Oct 7;6:34589. doi: 10.1038/srep34589.

Differential transcriptional responses to Ebola and Marburg virus infection in bat and human cells.

Author information

1
RNA Bioinformatics and High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Leutragraben 1, 07743, Jena, Germany.
2
Institute of Virology, Philipps University Marburg, Hans-Meerwein-Str. 2, 35043 Marburg, Germany.
3
German Center for Infection Research (DZIF), partner site Gießen-Marburg-Langen, Hans-Meerwein Str. 2, 35043, Marburg, Germany.
4
Bioinformatics Group, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
5
FLI Leibniz Institute for Age Research, Beutenbergstraße 11, 07745 Jena, Germany.
6
Transcriptome Bioinformatics, Junior Research Group, Leipzig Research Center for Civilization Diseases, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
7
Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark.
8
Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, 1870, Frederiksberg C, Denmark.
9
Theoretical Biochemistry Group, Institute of Theoretical Chemistry, University of Vienna, Währingerstraße 17, 1090, Vienna, Austria.
10
Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110, Freiburg, Germany.
11
Research Group Theoretical Systems Biology, Department of Bioinformatics, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
12
Institute of Computer Science, Martin-Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120, Halle/Saale, Germany.
13
Department of Soil Ecology, UFZ - Helmholtz Centre for Environmental Research, Theodor-Lieser-Str. 4, 06120, Halle/Saale, Germany.
14
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany.
15
Biozentrum, University of Basel, Klingelbergstraße 50/70, CH-4056, Basel, Switzerland.
16
Chair of Bioinformatics, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany.
17
Junior Professorship for Computational EvoDevo, Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
18
TFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, Department of Computer Science, University of Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
19
Paul-Flechsig-Institute for Brain Research, University of Leipzig, Jahnallee 54, 04109, Leipzig, Germany.
20
Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute (HKI), Systems Biology and Bioinformatics, Beutenbergstraße 11a, 07745, Jena, Germany.
21
Department of Bioanalytical Ecotoxicology, UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany.
22
Doctoral School of Science and Technology, AZM Center for Biotechnology Research, Lebanese University, Tripoli, Lebanon.
23
TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University Mainz gGmbH, Mainz, Germany.
24
Institute of Aquaculture, University of Stirling, Stirling FK9 4LA, Scotland, U.K.
25
Medical University of Vienna, Center for Anatomy and Cell Biology, Währingerstraße 13, 1090, Vienna, Austria.
26
Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany.
27
Research group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Währingerstraße 29, 1090, Vienna, Austria.
28
Research Group Medical Systems Biology, Institute for Experimental Medicine, Christian-Albrechts-University Kiel, Brunswiker Str. 10, 24105, Kiel, Germany.

Abstract

The unprecedented outbreak of Ebola in West Africa resulted in over 28,000 cases and 11,000 deaths, underlining the need for a better understanding of the biology of this highly pathogenic virus to develop specific counter strategies. Two filoviruses, the Ebola and Marburg viruses, result in a severe and often fatal infection in humans. However, bats are natural hosts and survive filovirus infections without obvious symptoms. The molecular basis of this striking difference in the response to filovirus infections is not well understood. We report a systematic overview of differentially expressed genes, activity motifs and pathways in human and bat cells infected with the Ebola and Marburg viruses, and we demonstrate that the replication of filoviruses is more rapid in human cells than in bat cells. We also found that the most strongly regulated genes upon filovirus infection are chemokine ligands and transcription factors. We observed a strong induction of the JAK/STAT pathway, of several genes encoding inhibitors of MAP kinases (DUSP genes) and of PPP1R15A, which is involved in ER stress-induced cell death. We used comparative transcriptomics to provide a data resource that can be used to identify cellular responses that might allow bats to survive filovirus infections.

PMID:
27713552
PMCID:
PMC5054393
DOI:
10.1038/srep34589
[Indexed for MEDLINE]
Free PMC Article
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13.
Nat Commun. 2016 Jun 24;7:11807. doi: 10.1038/ncomms11807.

MYC/MIZ1-dependent gene repression inversely coordinates the circadian clock with cell cycle and proliferation.

Author information

1
Heidelberg University, Biochemistry Center, Im Neuenheimer Feld 328, D-69120 Heidelberg, Germany.
2
Division Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany.
3
Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Im Neuenheimer Feld 364, D-69120 Heidelberg, Germany.

Abstract

The circadian clock and the cell cycle are major cellular systems that organize global physiology in temporal fashion. It seems conceivable that the potentially conflicting programs are coordinated. We show here that overexpression of MYC in U2OS cells attenuates the clock and conversely promotes cell proliferation while downregulation of MYC strengthens the clock and reduces proliferation. Inhibition of the circadian clock is crucially dependent on the formation of repressive complexes of MYC with MIZ1 and subsequent downregulation of the core clock genes BMAL1 (ARNTL), CLOCK and NPAS2. We show furthermore that BMAL1 expression levels correlate inversely with MYC levels in 102 human lymphomas. Our data suggest that MYC acts as a master coordinator that inversely modulates the impact of cell cycle and circadian clock on gene expression.

PMID:
27339797
PMCID:
PMC4931031
DOI:
10.1038/ncomms11807
[Indexed for MEDLINE]
Free PMC Article
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14.
Proc Natl Acad Sci U S A. 2016 Jun 28;113(26):7237-42. doi: 10.1073/pnas.1523004113. Epub 2016 Jun 13.

Temperature-responsive in vitro RNA structurome of Yersinia pseudotuberculosis.

Author information

1
Department of Microbial Biology, Ruhr University Bochum, 44801 Bochum, Germany;
2
Department of Molecular Infection Biology, Helmholtz Centre for Infection Research, 38124 Braunschweig, Germany;
3
Department of Computer Science, University of Leipzig, 04107 Leipzig, Germany; Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany;
4
Department of Computer Science, University of Leipzig, 04107 Leipzig, Germany; Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany; Max Planck Institute Mathematics in the Sciences, 04103 Leipzig, Germany; Santa Fe Institute, Santa Fe, NM 87501; Fraunhofer Institute Cell Therapy and Immunology, 04103 Leipzig, Germany.
5
Department of Microbial Biology, Ruhr University Bochum, 44801 Bochum, Germany; franz.narberhaus@rub.de.

Abstract

RNA structures are fundamentally important for RNA function. Dynamic, condition-dependent structural changes are able to modulate gene expression as shown for riboswitches and RNA thermometers. By parallel analysis of RNA structures, we mapped the RNA structurome of Yersinia pseudotuberculosis at three different temperatures. This human pathogen is exquisitely responsive to host body temperature (37 °C), which induces a major metabolic transition. Our analysis profiles the structure of more than 1,750 RNAs at 25 °C, 37 °C, and 42 °C. Average mRNAs tend to be unstructured around the ribosome binding site. We searched for 5'-UTRs that are folded at low temperature and identified novel thermoresponsive RNA structures from diverse gene categories. The regulatory potential of 16 candidates was validated. In summary, we present a dynamic bacterial RNA structurome and find that the expression of virulence-relevant functions in Y. pseudotuberculosis and reprogramming of its metabolism in response to temperature is associated with a restructuring of numerous mRNAs.

KEYWORDS:

RNA structure; RNA thermometer; temperature; translational control; virulence

PMID:
27298343
PMCID:
PMC4932938
DOI:
10.1073/pnas.1523004113
[Indexed for MEDLINE]
Free PMC Article
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16.
Nat Genet. 2016 Feb;48(2):183-8. doi: 10.1038/ng.3473. Epub 2015 Dec 21.

Recurrent mTORC1-activating RRAGC mutations in follicular lymphoma.

Author information

1
Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK.
2
Whitehead Institute for Biomedical Research and Massachusetts Institute of Technology, Department of Biology, Cambridge, Massachusetts, USA.
3
Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
4
Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, London, UK.
5
Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, USA.
6
Division of Molecular Histopathology, Department of Pathology, University of Cambridge, Cambridge, UK.
7
Institute of Human Genetics, University Hospital Schleswig-Holstein Campus Kiel and Christian Albrechts University Kiel, Kiel, Germany.
8
Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, Leipzig, Germany.
9
Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.
10
Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany.
11
MTA-SE Lendulet Molecular Oncohematology Research Group, 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest, Hungary.
12
Leibniz Institute DSMZ, German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
13
Cancer Sciences Unit, Faculty of Medicine, University of Southampton, Southampton, UK.
14
Haematological Malignancy Diagnostic Service, St. James's Institute of Oncology, Leeds, UK.
15
Koch Institute for Integrative Cancer Research, Cambridge, Massachusetts, USA.
16
Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

Abstract

Follicular lymphoma is an incurable B cell malignancy characterized by the t(14;18) translocation and mutations affecting the epigenome. Although frequent gene mutations in key signaling pathways, including JAK-STAT, NOTCH and NF-κB, have also been defined, the spectrum of these mutations typically overlaps with that in the closely related diffuse large B cell lymphoma (DLBCL). Using a combination of discovery exome and extended targeted sequencing, we identified recurrent somatic mutations in RRAGC uniquely enriched in patients with follicular lymphoma (17%). More than half of the mutations preferentially co-occurred with mutations in ATP6V1B2 and ATP6AP1, which encode components of the vacuolar H(+)-ATP ATPase (V-ATPase) known to be necessary for amino acid-induced activation of mTORC1. The RagC variants increased raptor binding while rendering mTORC1 signaling resistant to amino acid deprivation. The activating nature of the RRAGC mutations, their existence in the dominant clone and their stability during disease progression support their potential as an excellent candidate for therapeutic targeting.

PMID:
26691987
PMCID:
PMC4731318
DOI:
10.1038/ng.3473
[Indexed for MEDLINE]
Free PMC Article
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17.
Genome Res. 2016 Feb;26(2):256-62. doi: 10.1101/gr.196394.115. Epub 2015 Dec 2.

metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data.

Author information

1
Transcriptome Bioinformatics Group, LIFE - Leipzig Research Center for Civilization Diseases, University of Leipzig, 04107 Leipzig, Germany; Interdisciplinary Center for Bioinformatics and Bioinformatics Group, Faculty of Computer Science, University of Leipzig, 04107 Leipzig, Germany;
2
Interdisciplinary Center for Bioinformatics and Bioinformatics Group, Faculty of Computer Science, University of Leipzig, 04107 Leipzig, Germany; RNomics Group, Fraunhofer Institute for Cell Therapy and Immunology - IZI, 04103 Leipzig, Germany; Santa Fe Institute, Santa Fe, New Mexico 87501, USA; Department of Theoretical Chemistry, University of Vienna, 1090 Vienna, Austria; Max Planck Institute for Mathematics in Sciences, 04103 Leipzig, Germany.

Abstract

The detection of differentially methylated regions (DMRs) is a necessary prerequisite for characterizing different epigenetic states. We present a novel program, metilene, to identify DMRs within whole-genome and targeted data with unrivaled specificity and sensitivity. A binary segmentation algorithm combined with a two-dimensional statistical test allows the detection of DMRs in large methylation experiments with multiple groups of samples in minutes rather than days using off-the-shelf hardware. metilene outperforms other state-of-the-art tools for low coverage data and can estimate missing data. Hence, metilene is a versatile tool to study the effect of epigenetic modifications in differentiation/development, tumorigenesis, and systems biology on a global, genome-wide level. Whether in the framework of international consortia with dozens of samples per group, or even without biological replicates, it produces highly significant and reliable results.

PMID:
26631489
PMCID:
PMC4728377
DOI:
10.1101/gr.196394.115
[Indexed for MEDLINE]
Free PMC Article
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18.
Nat Genet. 2015 Nov;47(11):1316-1325. doi: 10.1038/ng.3413. Epub 2015 Oct 5.

DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control.

Author information

1
Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.
2
Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany.
3
Bioinformatics Group, Department of Computer Science, University of Leipzig, Leipzig, Germany.
4
German ICGC MMML-Seq-project.
5
German Cancer Research Center (DKFZ), Division Molecular Genetics, Heidelberg, Germany.
6
Institute of Human Genetics, Christian-Albrechts-University, Kiel, Germany.
7
Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, Essen, Germany.
8
Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
9
BLUEPRINT project.
10
Cell Networks, Bioquant, University of Heidelberg, Heidelberg, Germany.
11
Structural Biology and BioComputing Programme, Spanish National Cancer Research Center (CNIO), Madrid, Spain.
12
Deutsches Krebsforschungszentrum Heidelberg (DKFZ), Division Theoretical Bioinformatics, Heidelberg, Germany.
13
Department of Otorhinolaryngology, University of Duisburg-Essen, Essen, Germany.
14
University Hospital Muenster - Pediatric Hematology and Oncology, Münster Germany.
15
Leibniz-Institut DSMZ, German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
16
Department of Hematology and Oncology, Georg-Augusts-University of Göttingen, Göttingen, Germany.
17
Institute of Pharmacy and Molecular Biotechnology, Bioquant, University of Heidelberg, Heidelberg, Germany.
18
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
19
Friedrich-Ebert Hospital Neumuenster, Clinics for Haematology, Oncology and Nephrology, Neumünster, Germany.
20
Institute of Pathology, Charité - University Medicine Berlin, Berlin, Germany.
21
Department of Internal Medicine II: Hematology and Oncology, University Medical Centre, Campus Kiel, Kiel, Germany.
22
Radboud University, Department of Molecular Biology, Faculty of Science, Nijmegen, Netherlands.
23
Hematopathology Section, Christian-Albrechts-University, Kiel, Germany.
24
Institute for Medical Informatics Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
25
Departamento de Anatomía Patológica, Farmacología y Microbiología, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
26
Institute of Pathology, Medical Faculty of the Ulm University, Ulm, Germany.
27
University Hospital Giessen, Pediatric Hematology and Oncology, Giessen, Germany.
28
Institute of Clinical Molecular Biology, Christian-Albrechts-University, Kiel, Germany.
29
RNomics Group, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany.
30
Santa Fe Institute, Santa Fe, New Mexico, United States of America.
31
Max-Planck-Institute for Mathematics in Sciences, Leipzig, Germany.
#
Contributed equally

Abstract

Although Burkitt lymphomas and follicular lymphomas both have features of germinal center B cells, they are biologically and clinically quite distinct. Here we performed whole-genome bisulfite, genome and transcriptome sequencing in 13 IG-MYC translocation-positive Burkitt lymphoma, nine BCL2 translocation-positive follicular lymphoma and four normal germinal center B cell samples. Comparison of Burkitt and follicular lymphoma samples showed differential methylation of intragenic regions that strongly correlated with expression of associated genes, for example, genes active in germinal center dark-zone and light-zone B cells. Integrative pathway analyses of regions differentially methylated in Burkitt and follicular lymphomas implicated DNA methylation as cooperating with somatic mutation of sphingosine phosphate signaling, as well as the TCF3-ID3 and SWI/SNF complexes, in a large fraction of Burkitt lymphomas. Taken together, our results demonstrate a tight connection between somatic mutation, DNA methylation and transcriptional control in key B cell pathways deregulated differentially in Burkitt lymphoma and other germinal center B cell lymphomas.

PMID:
26437030
PMCID:
PMC5444523
DOI:
10.1038/ng.3413
[Indexed for MEDLINE]
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19.
Proc Natl Acad Sci U S A. 2015 Sep 22;112(38):E5261-70. doi: 10.1073/pnas.1505753112. Epub 2015 Sep 8.

MINCR is a MYC-induced lncRNA able to modulate MYC's transcriptional network in Burkitt lymphoma cells.

Author information

1
Transcriptome Bioinformatics, Leipzig Research Center for Civilization Diseases, University of Leipzig, D-04107 Leipzig, Germany;
2
Institute of Human Genetics, University Hospital Schleswig-Holstein, Christian Albrechts University, D-24105 Kiel, Germany;
3
Department of Pediatric Oncology, Hematology and Clinical Immunology, University Children's Hospital, Heinrich Heine University, Medical Faculty, D-40225 Düsseldorf, Germany;
4
Institute of Human Genetics, University Hospital Schleswig-Holstein, Christian Albrechts University, D-24105 Kiel, Germany; Department of Pediatrics, University Hospital Schleswig-Holstein, D-24105 Kiel, Germany;
5
Department of Pediatric Hematology and Oncology, University Hospital Münster, D-48149 Munster, Germany; Department of Pediatric Hematology and Oncology, University Hospital Giessen, D-35392 Giessen, Germany;
6
Department of Pediatrics, University Hospital Schleswig-Holstein, D-24105 Kiel, Germany;
7
Institute of Pathology, Charité University Medicine Berlin, D-12200 Berlin, Germany;
8
Friedrich-Ebert Hospital Neumünster, Clinics for Hematology, Oncology and Nephrology, D-24534 Neumünster, Germany;
9
Department of Internal Medicine II: Hematology and Oncology, University Medical Centre, D-24105 Kiel, Germany;
10
Hematopathology Section, University Hospital Schleswig-Holstein, Christian Albrechts University, D-24105 Kiel, Germany;
11
Institute for Medical Informatics Statistics and Epidemiology, University of Leipzig, D-04107 Leipzig, Germany;
12
Institute of Cell Biology (Cancer Research), University of Duisburg-Essen, D-45122 Essen, Germany;
13
Division Theoretical Bioinformatics, German Cancer Research Center, D-69120 Heidelberg, Germany;
14
Hospital of Internal Medicine II, Hematology and Oncology, St. Georg Hospital Leipzig, D-04129 Leipzig, Germany;
15
Institute of Pathology, Ulm University, D-89070 Ulm, Germany;
16
Department of Clinical Pathology, Robert Bosch Hospital and Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, D-70376 Stuttgart, Germany;
17
Department of Pediatric Hematology and Oncology, University Hospital Giessen, D-35392 Giessen, Germany;
18
Institute of Clinical Molecular Biology, Christian Albrechts University, D-24105 Kiel, Germany;
19
Institute of Pathology, University of Würzburg and Comprehensive Cancer Center Mainfranken, D-97080 Würzburg, Germany;
20
Department of Internal Medicine III, University of Ulm, D-89081 Ulm, Germany;
21
Department of Molecular Biology, Radboud Institute for Molecular Life Sciences, Radboud University, 6525 Nijmegen, The Netherlands;
22
Department of Hematology and Oncology, Georg August University of Göttingen, D-37075 Göttingen, Germany;
23
Institute of Human Genetics, University Hospital Schleswig-Holstein, Christian Albrechts University, D-24105 Kiel, Germany; Institute of Genetics and Biophysics "A.Buzzati-Traverso," Consiglio Nazionale delle Ricerche, I-80131 Naples, Italy iiaccarino@medgen.uni-kiel.de.

Abstract

Despite the established role of the transcription factor MYC in cancer, little is known about the impact of a new class of transcriptional regulators, the long noncoding RNAs (lncRNAs), on MYC ability to influence the cellular transcriptome. Here, we have intersected RNA-sequencing data from two MYC-inducible cell lines and a cohort of 91 B-cell lymphomas with or without genetic variants resulting in MYC overexpression. We identified 13 lncRNAs differentially expressed in IG-MYC-positive Burkitt lymphoma and regulated in the same direction by MYC in the model cell lines. Among them, we focused on a lncRNA that we named MYC-induced long noncoding RNA (MINCR), showing a strong correlation with MYC expression in MYC-positive lymphomas. To understand its cellular role, we performed RNAi and found that MINCR knockdown is associated with an impairment in cell cycle progression. Differential gene expression analysis after RNAi showed a significant enrichment of cell cycle genes among the genes down-regulated after MINCR knockdown. Interestingly, these genes are enriched in MYC binding sites in their promoters, suggesting that MINCR acts as a modulator of the MYC transcriptional program. Accordingly, MINCR knockdown was associated with a reduction in MYC binding to the promoters of selected cell cycle genes. Finally, we show that down-regulation of Aurora kinases A and B and chromatin licensing and DNA replication factor 1 may explain the reduction in cellular proliferation observed on MINCR knockdown. We, therefore, suggest that MINCR is a newly identified player in the MYC transcriptional network able to control the expression of cell cycle genes.

KEYWORDS:

B-cell lymphoma; MYC; cell cycle; lncRNA

Comment in

PMID:
26351698
PMCID:
PMC4586867
DOI:
10.1073/pnas.1505753112
[Indexed for MEDLINE]
Free PMC Article
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20.
Genes Chromosomes Cancer. 2015 Sep;54(9):555-64. doi: 10.1002/gcc.22268. Epub 2015 Jul 14.

The PCBP1 gene encoding poly(rC) binding protein I is recurrently mutated in Burkitt lymphoma.

Collaborators (131)

Richter G, Siebert R, Wagner S, Haake A, Richter J, Eils R, Lawerenz C, Radomski S, Scholz I, Borst C, Burkhardt B, Claviez A, Dreyling M, Eberth S, Einsele H, Frickhofen N, Haas S, Hansmann ML, Karsch D, Kneba M, Lisfeld J, Mantovani-Löffler L, Rohde M, Stadler C, Staib P, Stilgenbauer S, Ott G, Trümper L, Zenz T, Hansmann ML, Kube D, Küppers R, Weniger M, Haas S, Hummel M, Klapper W, Kostezka U, Lenze D, Möller P, Rosenwald A, Szczepanowski M, Ammerpohl O, Aukema SM, Binder V, Borkhardt A, Haake A, Hezaveh K, Hoell J, Leich E, Lichter P, Lopez C, Nagel I, Pischimariov J, Radlwimmer B, Richter J, Rosenstiel P, Rosenwald A, Schilhabel M, Schreiber S, Vater I, Wagner R, Siebert R, Bernhart SH, Binder H, Brors B, Doose G, Eils J, Eils R, Hoffmann S, Hopp L, Kretzmer H, Kreuz M, Korbel J, Langenberger D, Loeffler M, Radomski S, Rosolowski M, Schlesner M, Stadler PF, Sungalee S, Barth TF, Bernd HW, Cogliatti SB, Feller AC, Hansmann ML, Hummel M, Klapper W, Lenze D, Möller P, Müller-Hermelink HK, Ott G, Rosenwald A, Stein H, Szczepanowski M, Wacker HH, Barth TF, Behrmann P, Daniel P, Dierlammm J, Haralambieva E, Harder L, Holterhus PM, Küppers R, Kube D, Lichter P, Martín-Subero JI, Möller P, Murga-Peñas EM, Ott G, Pott C, Pscherer A, Rosenwald A, Schwaenen C, Siebert R, Trautmann H, Vockerodt M, Wessendorf S, Bentink S, Berger H, Hasenclever D, Kreuz M, Loeffler M, Rosolowski M, Spang R, Stürzenhofecker B, Trümper L, Wehner M, Loeffler M, Siebert R, Stein H, Trümper L.

Author information

1
Institute of Human Genetics, Christian-Albrechts-University Kiel and University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany.
2
Deutsches Krebsforschungszentrum Heidelberg (DKFZ), Division Theoretical Bioinformatics, Heidelberg, Germany.
3
Non-Hodgkin Lymphoma Berlin-Frankfurt-Münster Group Study Center, Department of Pediatric Hematology and Oncology, University Children's Hospital, Münster, Germany.
4
Department of Pediatrics, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University, Kiel, Germany.
5
Leibniz-Institute DSMZ- German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany.
6
Institute of Pathology, Campus Benjamin Franklin, Charité-Universitätsmedizin, Berlin, Germany.
7
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Germany.
8
Institute of Pathology, Universitätsklinikum Ulm, Ulm, Germany.
9
Department of Pediatric Hematology and Oncology, Justus Liebig University, Giessen, Germany.
10
Cell Networks, Bioquant, University of Heidelberg, Heidelberg, Germany.
11
Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany.
12
Institute of Clinical Molecular Biology, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Kiel, Germany.
13
Institute of Hematopathology, University Hospital Schleswig-Holstein Campus Kiel/Christian-Albrechts University Kiel, Germany.
14
Department of Hematology and Oncology, Georg-August University of Göttingen, Germany.

Abstract

The genetic hallmark of Burkitt lymphoma is the translocation t(8;14)(q24;q32), or one of its light chain variants, resulting in IG-MYC juxtaposition. However, these translocations alone are insufficient to drive lymphomagenesis, which requires additional genetic changes for malignant transformation. Recent studies of Burkitt lymphoma using next generation sequencing approaches have identified various recurrently mutated genes including ID3, TCF3, CCND3, and TP53. Here, by using similar approaches, we show that PCBP1 is a recurrently mutated gene in Burkitt lymphoma. By whole-genome sequencing, we identified somatic mutations in PCBP1 in 3/17 (18%) Burkitt lymphomas. We confirmed the recurrence of PCBP1 mutations by Sanger sequencing in an independent validation cohort, finding mutations in 3/28 (11%) Burkitt lymphomas and in 6/16 (38%) Burkitt lymphoma cell lines. PCBP1 is an intron-less gene encoding the 356 amino acid poly(rC) binding protein 1, which contains three K-Homology (KH) domains and two nuclear localization signals. The mutations predominantly (10/12, 83%) affect the KH III domain, either by complete domain loss or amino acid changes. Thus, these changes are predicted to alter the various functions of PCBP1, including nuclear trafficking and pre-mRNA splicing. Remarkably, all six primary Burkitt lymphomas with a PCBP1 mutation expressed MUM1/IRF4, which is otherwise detected in around 20-40% of Burkitt lymphomas. We conclude that PCBP1 mutations are recurrent in Burkitt lymphomas and might contribute, in cooperation with other mutations, to its pathogenesis.

PMID:
26173642
DOI:
10.1002/gcc.22268
[Indexed for MEDLINE]
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