Format
Sort by
Items per page

Send to

Choose Destination

Search results

Items: 1 to 20 of 161

1.
RNA Biol. 2018 Aug 15:1-12. doi: 10.1080/15476286.2018.1493330. [Epub ahead of print]

CRISPR-Cas systems in multicellular cyanobacteria.

Author information

1
a Faculty of Biology, Genetics and Experimental Bioinformatics , University of Freiburg , Freiburg , Germany.
2
b Instituto de Bioquímica Vegetal y Fotosíntesis , Consejo Superior de Investigaciones Científicas and Universidad de Sevilla , Seville , Spain.
3
c Bioinformatics group, Department of Computer Science , University of Freiburg , Freiburg , Germany.
4
d Center for Biological Systems Analysis (ZBSA) , University of Freiburg , Freiburg , Germany.
5
e BIOSS Centre for Biological Signaling Studies , University of Freiburg , Freiburg , Germany.
6
f Freiburg Institute for Advanced Studies,University of Freiburg, Freiburg, Germany.

Abstract

Novel CRISPR-Cas systems possess substantial potential for genome editing and manipulation of gene expression. The types and numbers of CRISPR-Cas systems vary substantially between different organisms. Some filamentous cyanobacteria harbor > 40 different putative CRISPR repeat-spacer cassettes, while the number of cas gene instances is much lower. Here we addressed the types and diversity of CRISPR-Cas systems and of CRISPR-like repeat-spacer arrays in 171 publicly available genomes of multicellular cyanobacteria. The number of 1328 repeat-spacer arrays exceeded the total of 391 encoded Cas1 proteins suggesting a tendency for fragmentation or the involvement of alternative adaptation factors. The model cyanobacterium Anabaena sp. PCC 7120 contains only three cas1 genes but hosts three Class 1, possibly one Class 2 and five orphan repeat-spacer arrays, all of which exhibit crRNA-typical expression patterns suggesting active transcription, maturation and incorporation into CRISPR complexes. The CRISPR-Cas system within the element interrupting the Anabaena sp. PCC 7120 fdxN gene, as well as analogous arrangements in other strains, occupy the genetic elements that become excised during the differentiation-related programmed site-specific recombination. This fact indicates the propensity of these elements for the integration of CRISPR-cas systems and points to a previously not recognized connection. The gene all3613 resembling a possible Class 2 effector protein is linked to a short repeat-spacer array and a single tRNA gene, similar to its homologs in other cyanobacteria. The diversity and presence of numerous CRISPR-Cas systems in DNA elements that are programmed for homologous recombination make filamentous cyanobacteria a prolific resource for their study.

ABBREVIATIONS:

Cas: CRISPR associated sequences; CRISPR: Clustered Regularly Interspaced Short Palindromic Repeats; C2c: Class 2 candidate; SDR: small dispersed repeat; TSS: transcriptional start site; UTR: untranslated region.

KEYWORDS:

CRISPR; cyanobacteria; heterocyst; nitrogen fixation; programmed DNA recombination

2.
Cell Syst. 2018 Jun 27;6(6):752-758.e1. doi: 10.1016/j.cels.2018.05.012.

Community-Driven Data Analysis Training for Biology.

Author information

1
Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany.
2
Erasmus Medical Centre, Wytemaweg 80, Rotterdam 3015 CN, the Netherlands.
3
Department of Systems Biology and Bioinformatics, University of Rostock, Ulmenstraße 69, Rostock 18051, Germany.
4
Johns Hopkins University, 3400 N Charles Street, Mudd Hall 144, Baltimore 21218, MD, USA.
5
Department of Biology, Albert-Ludwigs-University, Schänzlestraße 1, Freiburg 79104, Germany.
6
INRA, UMR IGEPP, BIPAA/GenOuest, INRIA/Irisa - Campus de Beaulieu, 35042 RENNES Cedex, France.
7
CNRS, UMPC, FR2424, ABiMS, Station Biologique, Roscoff, France.
8
The Pennsylvania State University, 505 Wartik Lab, University Park, PA 16802, USA.
9
Bioinformatics and Biostatistics HUB, Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI, USR 3756 Institut Pasteur et CNRS), Institut Pasteur, 25-28 Rue du Docteur Roux, 75015 Paris, France.
10
European Bioinformatics Institute, Hinxton, Cambridge, UK.
11
Melbourne Bioinformatics, The University of Melbourne, Melbourne, VIC 3010, Australia.
12
Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, Basel 4058, Switzerland.
13
Biochemistry, Molecular Biology and Biophysics, University of Minnesota Medical School, 420 Delaware Street SE, Minneapolis, MN 55455, USA.
14
PMC, CNRS, FR2424, ABiMS, Station Biologique, Place Georges Teissier, Roscoff 29680, France.
15
Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51, Freiburg 79108, Germany.
16
Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK.
17
Leibniz Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, Dummerstorf 18196, Germany.
18
Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany. Electronic address: backofen@informatik.uni-freiburg.de.
19
The Pennsylvania State University, 505 Wartik Lab, University Park, PA 16802, USA. Electronic address: anton@nekrut.org.
20
Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee 106, Freiburg 79110, Germany. Electronic address: gruening@informatik.uni-freiburg.de.

Abstract

The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org.

KEYWORDS:

data analysis; genomics; next-generation sequencing; proteomics; training

3.
Cell Syst. 2018 Jun 27;6(6):631-635. doi: 10.1016/j.cels.2018.03.014.

Practical Computational Reproducibility in the Life Sciences.

Author information

1
Albert Ludwigs University, Freiburg, Germany.
2
The Pennsylvania State University, University Park, PA, USA.
3
University of Duisburg-Essen, Essen, Germany.
4
National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA.
5
Earlham Institute, Norwich, UK.
6
Institut Curie, Paris, France.
7
Oregon Health & Sciences University, Portland, OR, USA.
8
Albert Ludwigs University, Freiburg, Germany. Electronic address: backofen@informatik.uni-freiburg.de.
9
The Pennsylvania State University, University Park, PA, USA. Electronic address: anton@nekrut.org.
10
Johns Hopkins University, Baltimore, MD, USA. Electronic address: james@taylorlab.org.

Abstract

Many areas of research suffer from poor reproducibility, particularly in computationally intensive domains where results rely on a series of complex methodological decisions that are not well captured by traditional publication approaches. Various guidelines have emerged for achieving reproducibility, but implementation of these practices remains difficult due to the challenge of assembling software tools plus associated libraries, connecting tools together into pipelines, and specifying parameters. Here, we discuss a suite of cutting-edge technologies that make computational reproducibility not just possible, but practical in both time and effort. This suite combines three well-tested components-a system for building highly portable packages of bioinformatics software, containerization and virtualization technologies for isolating reusable execution environments for these packages, and workflow systems that automatically orchestrate the composition of these packages for entire pipelines-to achieve an unprecedented level of computational reproducibility. We also provide a practical implementation and five recommendations to help set a typical researcher on the path to performing data analyses reproducibly.

PMID:
29953862
DOI:
10.1016/j.cels.2018.03.014
Free full text
Icon for Elsevier Science
4.
RNA Biol. 2018 Jun 19:1-13. doi: 10.1080/15476286.2018.1483685. [Epub ahead of print]

Comprehensive search for accessory proteins encoded with archaeal and bacterial type III CRISPR-cas gene cassettes reveals 39 new cas gene families.

Author information

1
a Copenhagen Prospective Studies on Asthma in Childhood , Herlev and Gentofte Hospital, University of Copenhagen , Denmark.
2
d Danish Archaea Centre, Department of Biology , University of Copenhagen , Copenhagen N , Denmark.
3
b Freiburg Bioinformatics Group, Department of Computer Science , University of Freiburg , Freiburg , Germany.
4
c Genetics and Experimental Bioinformatics, Faculty of Biology , University of Freiburg, Freiburg , Germany.
5
e Freiburg Institute for Advanced Studies , University of Freiburg , Freiburg , Germany.
6
f BIOSS Centre for Biological Signaling Studies , University of Freiburg , Freiburg , Germany.

Abstract

A study was undertaken to identify conserved proteins that are encoded adjacent to cas gene cassettes of Type III CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats - CRISPR associated) interference modules. Type III modules have been shown to target and degrade dsDNA, ssDNA and ssRNA and are frequently intertwined with cofunctional accessory genes, including genes encoding CRISPR-associated Rossman Fold (CARF) domains. Using a comparative genomics approach, and defining a Type III association score accounting for coevolution and specificity of flanking genes, we identified and classified 39 new Type III associated gene families. Most archaeal and bacterial Type III modules were seen to be flanked by several accessory genes, around half of which did not encode CARF domains and remain of unknown function. Northern blotting and interference assays in Synechocystis confirmed that one particular non-CARF accessory protein family was involved in crRNA maturation. Non-CARF accessory genes were generally diverse, encoding nuclease, helicase, protease, ATPase, transporter and transmembrane domains with some encoding no known domains. We infer that additional families of non-CARF accessory proteins remain to be found. The method employed is scalable for potential application to metagenomic data once automated pipelines for annotation of CRISPR-Cas systems have been developed. All accessory genes found in this study are presented online in a readily accessible and searchable format for researchers to audit their model organism of choice: http://accessory.crispr.dk .

KEYWORDS:

CARF; CRISPR; accessory; ancillary; archaea; auxillary; bacteria; cas; csx1; csx3; helicase; nuclease; protease; type III

5.
Nucleic Acids Res. 2018 Jul 2;46(W1):W11-W16. doi: 10.1093/nar/gky504.

Galaxy HiCExplorer: a web server for reproducible Hi-C data analysis, quality control and visualization.

Author information

1
Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.
2
Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51, 79108 Freiburg im Breisgau.
3
Faculty of Biology, University of Freiburg, Schänzlestr. 1, 79104 Freiburg, Germany.
4
Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Albertstr. 25, 79104 Freiburg, Germany.
5
Hermann Staudinger Graduate School, University of Freiburg, Hebelstrasse 27, 79104 Freiburg, Germany.
6
IGEPP, INRA, Agrocampus Ouest, Univ Rennes, 35600 Le Rheu, France.
7
Center for Biological Systems Analysis (ZBSA), University of Freiburg, Habsburgerstr. 49, 79104 Freiburg, Germany.
8
BIOSS Centre for Biological Signaling Studies, University of Freiburg, Schänzlestr. 18, 79104 Freiburg, Germany.

Abstract

Galaxy HiCExplorer is a web server that facilitates the study of the 3D conformation of chromatin by allowing Hi-C data processing, analysis and visualization. With the Galaxy HiCExplorer web server, users with little bioinformatic background can perform every step of the analysis in one workflow: mapping of the raw sequence data, creation of Hi-C contact matrices, quality assessment, correction of contact matrices and identification of topological associated domains (TADs) and A/B compartments. Users can create publication ready plots of the contact matrix, A/B compartments, and TADs on a selected genomic locus, along with additional information like gene tracks or ChIP-seq signals. Galaxy HiCExplorer is freely usable at: https://hicexplorer.usegalaxy.eu and is available as a Docker container: https://github.com/deeptools/docker-galaxy-hicexplorer.

6.
Mol Ther Nucleic Acids. 2018 Jun 1;11:515-517. doi: 10.1016/j.omtn.2018.04.006. Epub 2018 Apr 22.

AptaSUITE: A Full-Featured Bioinformatics Framework for the Comprehensive Analysis of Aptamers from HT-SELEX Experiments.

Author information

1
National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA. Electronic address: jan.hoinka@nih.gov.
2
Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg 79110, Germany.
3
National Center of Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20894, USA.

Publication type

Publication type

7.
Nat Genet. 2018 Jun;50(6):814-824. doi: 10.1038/s41588-018-0120-1. Epub 2018 May 28.

Analysis of the androgen receptor-regulated lncRNA landscape identifies a role for ARLNC1 in prostate cancer progression.

Zhang Y1,2,3,4, Pitchiaya S1,2, Cieślik M1,2, Niknafs YS1,5, Tien JC1,2, Hosono Y1, Iyer MK1,4, Yazdani S1, Subramaniam S1, Shukla SK1,6, Jiang X1, Wang L1, Liu TY7, Uhl M8, Gawronski AR9, Qiao Y1,2,10, Xiao L1, Dhanasekaran SM1,2, Juckette KM1, Kunju LP1,2,10, Cao X1,11, Patel U12, Batish M12,13, Shukla GC14, Paulsen MT10,15, Ljungman M10,15, Jiang H7,10, Mehra R2,10,16, Backofen R8, Sahinalp CS17,18, Freier SM19, Watt AT19, Guo S19, Wei JT16, Feng FY1,10,15,20,21, Malik R1,22, Chinnaiyan AM23,24,25,26,27,28,29.

Author information

1
Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA.
2
Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
3
Molecular and Cellular Pathology Program, University of Michigan, Ann Arbor, MI, USA.
4
Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA.
5
Department of Cellular and Molecular Biology, University of Michigan, Ann Arbor, MI, USA.
6
Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad, Dharwad, India.
7
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
8
Department of Computer Science and Centre for Biological Signaling Studies (BIOSS), University of Freiburg, Freiburg, Germany.
9
School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.
10
Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA.
11
Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI, USA.
12
New Jersey Medical School, Rutgers University, Newark, NJ, USA.
13
Department of Medical Laboratory Sciences, University of Delaware, Newark, DE, USA.
14
Department of Biological, Geological and Environmental Sciences, Center for Gene Regulation in Health and Disease, Cleveland State Univesity, Cleveland, OH, USA.
15
Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
16
Department of Urology, University of Michigan, Ann Arbor, MI, USA.
17
School of Informatics and Computing, Indiana University, Bloomington, IN, USA.
18
Vancouver Prostate Centre, Vancouver, British Columbia, Canada.
19
Ionis Pharmaceuticals, Carlsbad, CA, USA.
20
Breast Oncology Program, University of Michigan, Ann Arbor, MI, USA.
21
Departments of Radiation Oncology, Urology, and Medicine, Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, CA, USA.
22
Bristol-Myers Squibb, Princeton, NJ, USA.
23
Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA. arul@umich.edu.
24
Department of Pathology, University of Michigan, Ann Arbor, MI, USA. arul@umich.edu.
25
Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA. arul@umich.edu.
26
Department of Cellular and Molecular Biology, University of Michigan, Ann Arbor, MI, USA. arul@umich.edu.
27
Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA. arul@umich.edu.
28
Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI, USA. arul@umich.edu.
29
Department of Urology, University of Michigan, Ann Arbor, MI, USA. arul@umich.edu.

Abstract

The androgen receptor (AR) plays a critical role in the development of the normal prostate as well as prostate cancer. Using an integrative transcriptomic analysis of prostate cancer cell lines and tissues, we identified ARLNC1 (AR-regulated long noncoding RNA 1) as an important long noncoding RNA that is strongly associated with AR signaling in prostate cancer progression. Not only was ARLNC1 induced by the AR protein, but ARLNC1 stabilized the AR transcript via RNA-RNA interaction. ARLNC1 knockdown suppressed AR expression, global AR signaling and prostate cancer growth in vitro and in vivo. Taken together, these data support a role for ARLNC1 in maintaining a positive feedback loop that potentiates AR signaling during prostate cancer progression and identify ARLNC1 as a novel therapeutic target.

PMID:
29808028
PMCID:
PMC5980762
[Available on 2018-11-28]
DOI:
10.1038/s41588-018-0120-1
Icon for Nature Publishing Group
8.
Nucleic Acids Res. 2018 Jul 2;46(W1):W25-W29. doi: 10.1093/nar/gky329.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching.

Author information

1
Bioinformatics, Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany.
2
Institute of Molecular Biology (IMB), Ackermannweg 4, 55128 Mainz, Germany.
3
Department of Computer Science, University of Exeter, Exeter EX4 4QF, UK.
4
Coreva Scientific, Kaiser-Joseph-Str 198-200, 79098 Freiburg, Germany.
5
Genetics and Experimental Bioinformatics, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany.
6
Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51, 79108 Freiburg, Germany.
7
Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
8
Max Planck Institute for the Physics of Complex Systems, Dresden, Germany.
9
Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Long Road, Cambridge CB2 0PT, UK.
10
Department of Human Genetics, The Wellcome Trust Sanger Institute, Hinxton Cambridge CB10 1HH, UK.
11
Genedata AG, Margarethenstrasse 38, 4053 Basel, Switzerland.
12
Theoretical Biochemistry Group, University of Vienna, Währingerstraße 17, 1090 Vienna, Austria.
13
Department of Clinical Research, Clinical Trial Unit, University of Basel Hospital, Schanzenstrasse 55, 4031 Basel, Switzerland.
14
Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany.

Abstract

The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.

9.
Front Genet. 2018 Apr 17;9:124. doi: 10.3389/fgene.2018.00124. eCollection 2018.

GLASSgo - Automated and Reliable Detection of sRNA Homologs From a Single Input Sequence.

Author information

1
Genetics and Experimental Bioinformatics, Faculty of Biology, University of Freiburg, Freiburg, Germany.
2
Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany.
3
Bioinformatics Group, Faculty of Computer Science, University of Freiburg, Freiburg, Germany.
4
Forest Growth and Dendroecology, Institute of Forest Sciences, University of Freiburg, Freiburg, Germany.
5
ZBSA Center for Biological Systems Analysis, University of Freiburg, Freiburg, Germany.
6
BIOSS Centre for Biological Signalling Studies, Cluster of Excellence, University of Freiburg, Freiburg, Germany.
7
Center for Non-coding RNA in Technology and Health, University of Copenhagen, Frederiksberg, Denmark.
8
Freiburg Institute for Advanced Studies, University of Freiburg, Freiburg, Germany.

Abstract

Bacterial small RNAs (sRNAs) are important post-transcriptional regulators of gene expression. The functional and evolutionary characterization of sRNAs requires the identification of homologs, which is frequently challenging due to their heterogeneity, short length and partly, little sequence conservation. We developed the GLobal Automatic Small RNA Search go (GLASSgo) algorithm to identify sRNA homologs in complex genomic databases starting from a single sequence. GLASSgo combines an iterative BLAST strategy with pairwise identity filtering and a graph-based clustering method that utilizes RNA secondary structure information. We tested the specificity, sensitivity and runtime of GLASSgo, BLAST and the combination RNAlien/cmsearch in a typical use case scenario on 40 bacterial sRNA families. The sensitivity of the tested methods was similar, while the specificity of GLASSgo and RNAlien/cmsearch was significantly higher than that of BLAST. GLASSgo was on average ∼87 times faster than RNAlien/cmsearch, and only ∼7.5 times slower than BLAST, which shows that GLASSgo optimizes the trade-off between speed and accuracy in the task of finding sRNA homologs. GLASSgo is fully automated, whereas BLAST often recovers only parts of homologs and RNAlien/cmsearch requires extensive additional bioinformatic work to get a comprehensive set of homologs. GLASSgo is available as an easy-to-use web server to find homologous sRNAs in large databases.

KEYWORDS:

Rfam; bacteria; comparative genomics; graph-based clustering; homology search; ncRNA; prediction; sRNA

10.
Microbiol Spectr. 2018 Apr;6(2). doi: 10.1128/microbiolspec.RWR-0001-2017.

Structure and Interaction Prediction in Prokaryotic RNA Biology.

Author information

1
Bioinformatics Group.

Abstract

Many years of research in RNA biology have soundly established the importance of RNA-based regulation far beyond most early traditional presumptions. Importantly, the advances in "wet" laboratory techniques have produced unprecedented amounts of data that require efficient and precise computational analysis schemes and algorithms. Hence, many in silico methods that attempt topological and functional classification of novel putative RNA-based regulators are available. In this review, we technically outline thermodynamics-based standard RNA secondary structure and RNA-RNA interaction prediction approaches that have proven valuable to the RNA research community in the past and present. For these, we highlight their usability with a special focus on prokaryotic organisms and also briefly mention recent advances in whole-genome interactomics and how this may influence the field of predictive RNA research.

11.
RNA Biol. 2018 May 21:1-12. doi: 10.1080/15476286.2018.1460994. [Epub ahead of print]

The nuts and bolts of the Haloferax CRISPR-Cas system I-B.

Author information

1
a Biology II, Ulm University , Ulm , Germany.
2
b Microbiology and Biotechnology, Ulm University , Ulm , Germany.
3
c Freiburg Bioinformatics Group, Department of Computer Science , University of Freiburg , Georges-Köhler-Allee 106, Freiburg , Germany.
4
e Max Planck Institute of Biophysical Chemistry , Am Faßberg 11, Göttingen , Germany.
5
f Ludwig Institute for Cancer Research, University of Oxford , Oxford , United Kingdom.
6
g Institute for Clinical Chemistry, University Medical Center Göttingen , Robert Koch Straße 10, Göttingen , Germany.
7
d Centre for Biological Signalling Studies (BIOSS), Cluster of Excellence, University of Freiburg , Germany.
8
h School of Molecular Cell Biology & Biotechnology, George S. Wise, Faculty of Life Sciences, Tel Aviv University , Tel Aviv , Israel.

Abstract

Invading genetic elements pose a constant threat to prokaryotic survival, requiring an effective defence. Eleven years ago, the arsenal of known defence mechanisms was expanded by the discovery of the CRISPR-Cas system. Although CRISPR-Cas is present in the majority of archaea, research often focuses on bacterial models. Here, we provide a perspective based on insights gained studying CRISPR-Cas system I-B of the archaeon Haloferax volcanii. The system relies on more than 50 different crRNAs, whose stability and maintenance critically depend on the proteins Cas5 and Cas7, which bind the crRNA and form the Cascade complex. The interference machinery requires a seed sequence and can interact with multiple PAM sequences. H. volcanii stands out as the first example of an organism that can tolerate autoimmunity via the CRISPR-Cas system while maintaining a constitutively active system. In addition, the H. volcanii system was successfully developed into a tool for gene regulation.

KEYWORDS:

Archaea; CRISPR-Cas; CRISPRi; Haloarchaea; self-targeting; type I-B

12.
Genome Res. 2018 May;28(5):699-713. doi: 10.1101/gr.229757.117. Epub 2018 Apr 11.

In vitro iCLIP-based modeling uncovers how the splicing factor U2AF2 relies on regulation by cofactors.

Author information

1
Institute of Molecular Biology (IMB) gGmbH, 55128 Mainz, Germany.
2
Institute of Structural Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany.
3
Biomolecular NMR and Center for Integrated Protein Science Munich at Department of Chemistry, Technical University of Munich, 85747 Garching, Germany.
4
Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107 Leipzig, Germany.
5
Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany.
6
Centre for Biological Signalling Studies (BIOSS), University of Freiburg, 79104 Freiburg, Germany.
7
Buchmann Institute for Molecular Life Sciences (BMLS), Goethe University Frankfurt, 60438 Frankfurt a.M., Germany.
#
Contributed equally

Abstract

Alternative splicing generates distinct mRNA isoforms and is crucial for proteome diversity in eukaryotes. The RNA-binding protein (RBP) U2AF2 is central to splicing decisions, as it recognizes 3' splice sites and recruits the spliceosome. We establish "in vitro iCLIP" experiments, in which recombinant RBPs are incubated with long transcripts, to study how U2AF2 recognizes RNA sequences and how this is modulated by trans-acting RBPs. We measure U2AF2 affinities at hundreds of binding sites and compare in vitro and in vivo binding landscapes by mathematical modeling. We find that trans-acting RBPs extensively regulate U2AF2 binding in vivo, including enhanced recruitment to 3' splice sites and clearance of introns. Using machine learning, we identify and experimentally validate novel trans-acting RBPs (including FUBP1, CELF6, and PCBP1) that modulate U2AF2 binding and affect splicing outcomes. Our study offers a blueprint for the high-throughput characterization of in vitro mRNP assembly and in vivo splicing regulation.

PMID:
29643205
PMCID:
PMC5932610
[Available on 2018-11-01]
DOI:
10.1101/gr.229757.117
Icon for HighWire
13.
Bioinformatics. 2018 Apr 3. doi: 10.1093/bioinformatics/bty208. [Epub ahead of print]

MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions.

Author information

1
Computing Science, Simon Fraser University, Burnaby, Canada.
2
Institut für Informatik, University of Freiburg, Freiburg im Breisgau, Germany.
3
Centre for Biological Signalling Studies, University of Freiburg, Freiburg im Breisgau, Germany.
4
Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.
5
Department of Computational Medicine and Bioinformatics, Ann Arbor, Michigan 48109, USA.
6
Vancouver Prostate Centre, Vancouver, BC, Canada.
7
Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, Michigan 48109, USA.
8
Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109, USA.
9
Howard Hughes Medical Institute, University of Michigan, Ann Arbor, Michigan 48109, USA.
10
Department of Computer Science, Indiana University, Bloomington, USA.

Abstract

Motivation:

Long non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nucleotides that do not get translated into proteins. Often these transcripts are processed (spliced, capped, polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA.

Approach:

Here we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined p-value.

Results:

We applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29, ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays.

Availability:

MechRNA is available for download at https://bitbucket.org/compbio/mechrna.

Contact:

backofen@informatik.uni-freiburg.de, cenksahi@indiana.edu.

Supplementary information:

Supplementary data are available at Bioinformatics online.

14.
Nat Commun. 2018 Mar 26;9(1):1235. doi: 10.1038/s41467-018-03681-3.

The RNA-binding protein ARPP21 controls dendritic branching by functionally opposing the miRNA it hosts.

Author information

1
Institute for Cell Biology and Neurobiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
2
Department of Computer Science, Albert-Ludwigs-Universität Freiburg, Georges-Köhler-Allee 106, 79110, Freiburg im Breisgau, Germany.
3
Institute for Integrative Neuroanatomy, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
4
Institute for Chemistry and Biochemistry, Freie Universität Berlin, Thielallee 63, 14195, Berlin, Germany.
5
Institute for Cell Biology and Neurobiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany. gregory.wulczyn@charite.de.

Abstract

About half of mammalian miRNA genes lie within introns of protein-coding genes, yet little is known about functional interactions between miRNAs and their host genes. The intronic miRNA miR-128 regulates neuronal excitability and dendritic morphology of principal neurons during mouse cerebral cortex development. Its conserved host genes, R3hdm1 and Arpp21, are predicted RNA-binding proteins. Here we use iCLIP to characterize ARPP21 recognition of uridine-rich sequences with high specificity for 3'UTRs. ARPP21 antagonizes miR-128 activity by co-regulating a subset of miR-128 target mRNAs enriched for neurodevelopmental functions. Protein-protein interaction data and functional assays suggest that ARPP21 acts as a positive post-transcriptional regulator by interacting with the translation initiation complex eIF4F. This molecular antagonism is reflected in inverse activities during dendritogenesis: miR-128 overexpression or knockdown of ARPP21 reduces dendritic complexity; ectopic ARPP21 leads to an increase. Thus, we describe a unique example of convergent function by two products of a single gene.

15.
Nat Commun. 2018 Mar 20;9(1):1142. doi: 10.1038/s41467-018-03575-4.

uvCLAP is a fast and non-radioactive method to identify in vivo targets of RNA-binding proteins.

Author information

1
Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110, Freiburg, Germany.
2
Max Planck Institute of Immunobiology and Epigenetics, Stuebeweg 51, 79108, Freiburg, Germany.
3
Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110, Freiburg, Germany. backofen@informatik.uni-freiburg.de.
4
Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Schaenzlestr. 18, 79104, Freiburg, Germany. backofen@informatik.uni-freiburg.de.
5
Max Planck Institute of Immunobiology and Epigenetics, Stuebeweg 51, 79108, Freiburg, Germany. akhtar@ie-freiburg.mpg.de.

Abstract

RNA-binding proteins (RBPs) play important and essential roles in eukaryotic gene expression regulating splicing, localization, translation, and stability of mRNAs. We describe ultraviolet crosslinking and affinity purification (uvCLAP), an easy-to-use, robust, reproducible, and high-throughput method to determine in vivo targets of RBPs. uvCLAP is fast and does not rely on radioactive labeling of RNA. We investigate binding of 15 RBPs from fly, mouse, and human cells to test the method's performance and applicability. Multiplexing of signal and control libraries enables straightforward comparison of samples. Experiments for most proteins achieve high enrichment of signal over background. A point mutation and a natural splice isoform that change the RBP subcellular localization dramatically alter target selection without changing the targeted RNA motif, showing that compartmentalization of RBPs can be used as an elegant means to generate RNA target specificity.

16.
Bioinformatics. 2018 Aug 1;34(15):2676-2678. doi: 10.1093/bioinformatics/bty158.

CMV: visualization for RNA and protein family models and their comparisons.

Author information

1
Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.
2
Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria.
3
Bioinformatics and Computational Biology Research Group, University of Vienna, Vienna, Austria.
4
Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany.
5
Bioinformatics Group, Department of Computer Science, University of Leipzig, D-04107 Leipzig, Germany.
6
Interdisciplinary Center for Bioinformatics, University of Leipzig, D-04107 Leipzig, Germany.

Abstract

Summary:

A standard method for the identification of novel RNAs or proteins is homology search via probabilistic models. One approach relies on the definition of families, which can be encoded as covariance models (CMs) or Hidden Markov Models (HMMs). While being powerful tools, their complexity makes it tedious to investigate them in their (default) tabulated form. This specifically applies to the interpretation of comparisons between multiple models as in family clans. The Covariance model visualization tools (CMV) visualize CMs or HMMs to: I) Obtain an easily interpretable representation of HMMs and CMs; II) Put them in context with the structural sequence alignments they have been created from; III) Investigate results of model comparisons and highlight regions of interest.

Availability and implementation:

Source code (http://www.github.com/eggzilla/cmv), web-service (http://rna.informatik.uni-freiburg.de/CMVS).

Supplementary information:

Supplementary data are available at Bioinformatics online.

17.
Proc Natl Acad Sci U S A. 2018 Mar 20;115(12):E2859-E2868. doi: 10.1073/pnas.1721670115. Epub 2018 Mar 5.

hnRNP R and its main interactor, the noncoding RNA 7SK, coregulate the axonal transcriptome of motoneurons.

Author information

1
Institute for Clinical Neurobiology, University of Wuerzburg, 97078 Wuerzburg, Germany.
2
Department of Computer Science, Albert-Ludwigs-Universität Freiburg, 79110 Freiburg, Germany.
3
Centre for Biological Signalling Studies, Albert-Ludwigs-Universität Freiburg, 79104 Freiburg, Germany.
4
Core Unit Systems Medicine, University of Wuerzburg, 97078 Wuerzburg, Germany.
5
Comprehensive Cancer Center Mainfranken, University of Wuerzburg, 97080 Wuerzburg, Germany.
6
Institute for Clinical Neurobiology, University of Wuerzburg, 97078 Wuerzburg, Germany; Sendtner_M@ukw.de.

Abstract

Disturbed RNA processing and subcellular transport contribute to the pathomechanisms of motoneuron diseases such as amyotrophic lateral sclerosis and spinal muscular atrophy. RNA-binding proteins are involved in these processes, but the mechanisms by which they regulate the subcellular diversity of transcriptomes, particularly in axons, are not understood. Heterogeneous nuclear ribonucleoprotein R (hnRNP R) interacts with several proteins involved in motoneuron diseases. It is located in axons of developing motoneurons, and its depletion causes defects in axon growth. Here, we used individual nucleotide-resolution cross-linking and immunoprecipitation (iCLIP) to determine the RNA interactome of hnRNP R in motoneurons. We identified ∼3,500 RNA targets, predominantly with functions in synaptic transmission and axon guidance. Among the RNA targets identified by iCLIP, the noncoding RNA 7SK was the top interactor of hnRNP R. We detected 7SK in the nucleus and also in the cytosol of motoneurons. In axons, 7SK localized in close proximity to hnRNP R, and depletion of hnRNP R reduced axonal 7SK. Furthermore, suppression of 7SK led to defective axon growth that was accompanied by axonal transcriptome alterations similar to those caused by hnRNP R depletion. Using a series of 7SK-deletion mutants, we show that the function of 7SK in axon elongation depends on its interaction with hnRNP R but not with the PTEF-B complex involved in transcriptional regulation. These results propose a role for 7SK as an essential interactor of hnRNP R to regulate its function in axon maintenance.

KEYWORDS:

7SK; axon; hnRNP R; iCLIP; motoneuron

Conflict of interest statement

The authors declare no conflict of interest.

18.
Nat Commun. 2018 Jan 26;9(1):391. doi: 10.1038/s41467-017-02762-z.

Distinct epigenetic programs regulate cardiac myocyte development and disease in the human heart in vivo.

Author information

1
Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, 79104, Freiburg, Germany.
2
Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110, Freiburg, Germany.
3
Department for Cardiology und Angiology II, University Heart Center Freiburg • Bad Krozingen, 79189, Bad Krozingen, Germany.
4
The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029-6542, USA.
5
Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.
6
Forensic Institute, Ludwig-Maximilians-University, 80046, Munich, Germany.
7
Department of Cardiovascular Surgery, German Heart Center, Technische Universität München, 80636, Munich, Germany.
8
Insure (Institute for Translational Cardiac Surgery), Department of Cardiovascular Surgery, German Heart Center, Technische Universität München, 80636, Munich, Germany.
9
Department of Obstetrics, Gynecology and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
10
Department of Cardiothoracic Surgery, Jena University Hospital, Friedrich-Schiller-University, 07740, Jena, Germany.
11
DZHK (German Center for Cardiovascular Research) - Partner Site Munich Heart Alliance, Munich, 60046, Germany.
12
Department of Genetics and Genomic Sciences & Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029-6574, USA.
13
Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, 79104, Freiburg, Germany. lutz.hein@pharmakol.uni-freiburg.de.
14
BIOSS Centre for Biological Signalling Studies, University of Freiburg, 79104, Freiburg, Germany. lutz.hein@pharmakol.uni-freiburg.de.

Abstract

Epigenetic mechanisms and transcription factor networks essential for differentiation of cardiac myocytes have been uncovered. However, reshaping of the epigenome of these terminally differentiated cells during fetal development, postnatal maturation, and in disease remains unknown. Here, we investigate the dynamics of the cardiac myocyte epigenome during development and in chronic heart failure. We find that prenatal development and postnatal maturation are characterized by a cooperation of active CpG methylation and histone marks at cis-regulatory and genic regions to shape the cardiac myocyte transcriptome. In contrast, pathological gene expression in terminal heart failure is accompanied by changes in active histone marks without major alterations in CpG methylation and repressive chromatin marks. Notably, cis-regulatory regions in cardiac myocytes are significantly enriched for cardiovascular disease-associated variants. This study uncovers distinct layers of epigenetic regulation not only during prenatal development and postnatal maturation but also in diseased human cardiac myocytes.

PMID:
29374152
PMCID:
PMC5786002
DOI:
10.1038/s41467-017-02762-z
[Indexed for MEDLINE]
Free PMC Article
Icon for Nature Publishing Group Icon for PubMed Central
19.
Mol Cell Proteomics. 2018 Apr;17(4):565-579. doi: 10.1074/mcp.RA117.000437. Epub 2018 Jan 11.

Combinatorial Omics Analysis Reveals Perturbed Lysosomal Homeostasis in Collagen VII-deficient Keratinocytes.

Author information

1
From the ‡Department of Dermatology, Medical Center - University of Freiburg, Germany.
2
§Centre for Biological Systems Analysis (ZBSA), University of Freiburg, Germany.
3
¶Department of Computer Science, University of Freiburg, Germany.
4
‖Institute of Physiological Chemistry, Medical Faculty, Martin Luther University Halle-Wittenberg, Germany.
5
**Institute of Molecular Medicine and Cell Research, Faculty of Medicine, University of Freiburg, Germany.
6
‡‡Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Germany.
7
From the ‡Department of Dermatology, Medical Center - University of Freiburg, Germany; joern.dengjel@unifr.ch bruckner-tuderman@uniklinik-freiburg.de.
8
§§Department of Biology, University of Fribourg, Switzerland.

Abstract

The extracellular matrix protein collagen VII is part of the microenvironment of stratified epithelia and critical in organismal homeostasis. Mutations in the encoding gene COL7A1 lead to the skin disorder dystrophic epidermolysis bullosa (DEB), are linked to skin fragility and progressive inflammation-driven fibrosis that facilitates aggressive skin cancer. So far, these changes have been linked to mesenchymal alterations, the epithelial consequences of collagen VII loss remaining under-addressed. As epithelial dysfunction is a principal initiator of fibrosis, we performed a comprehensive transcriptome and proteome profiling of primary human keratinocytes from DEB and control subjects to generate global and detailed images of dysregulated epidermal molecular pathways linked to loss of collagen VII. These revealed downregulation of interaction partners of collagen VII on mRNA and protein level, but also increased abundance of S100 pro-inflammatory proteins in primary DEB keratinocytes. Increased TGF-β signaling because of loss of collagen VII was associated with enhanced activity of lysosomal proteases in both keratinocytes and skin of collagen VII-deficient individuals. Thus, loss of a single structural protein, collagen VII, has extra- and intracellular consequences, resulting in inflammatory processes that enable tissue destabilization and promote keratinocyte-driven, progressive fibrosis.

PMID:
29326176
PMCID:
PMC5880109
[Available on 2019-04-01]
DOI:
10.1074/mcp.RA117.000437
Icon for HighWire
20.
Methods Mol Biol. 2018;1704:363-400. doi: 10.1007/978-1-4939-7463-4_14.

Comparative RNA Genomics.

Backofen R1,2, Gorodkin J2, Hofacker IL2,3,4, Stadler PF5,6,7,8,9,10.

Author information

1
Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, D-79110 Freiburg, Germany.
2
Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark.
3
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria.
4
Bioinformatics and Computational Biology Research Group, University of Vienna, Währingerstraße 17, A-1090 Vienna, Austria.
5
Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark. studla@bioinf.uni-leipzig.de.
6
Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria. studla@bioinf.uni-leipzig.de.
7
Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
8
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
9
Fraunhofer Institute for Cell Therapy and Immunology, Perlickstraße 1, D-04103 Leipzig, Germany. studla@bioinf.uni-leipzig.de.
10
Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA. studla@bioinf.uni-leipzig.de.

Abstract

Over the last two decades it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible noncoding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of noncoding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.

KEYWORDS:

Alternative splicing; Chromatin; Evolution; Long noncoding RNA; RNA secondary structure

Supplemental Content

Loading ...
Support Center