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  • Annotation of genomics data using bidirectional hidden Markov models unveils variations in Pol II transcription cycle
    1. Benedikt Zacher1,2,
    2. Michael Lidschreiber1,3,
    3. Patrick Cramer1,3,
    4. Julien Gagneur*,1 and
    5. Achim Tresch*,1,2,4
    1. 1Gene Center and Department of Biochemistry, Center for Integrated Protein Science CIPSM, Ludwig‐Maximilians‐Universität München, Munich, Germany
    2. 2Institute for Genetics, University of Cologne, Cologne, Germany
    3. 3Department of Molecular Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
    4. 4Max Planck Institute for Plant Breeding Research, Cologne, Germany
    1. * Corresponding author. Tel: +49 221 5062 161; Fax: +49 221 5062 163; E‐mail: tresch{at}mpipz.mpg.de

      Corresponding author. Tel: +49 89 2180 76742; Fax: +49 89 2180 76797; E‐mail: gagneur{at}genzentrum.lmu.de

    Bidirectional hidden Markov models improve the annotation of DNA‐associated processes from genomics data, reveal variations in the yeast Pol II transcription cycle and identify directed chromatin state patterns at transcribed regions in the human genome.

    Synopsis

    Bidirectional hidden Markov models improve the annotation of DNA‐associated processes from genomics data, reveal variations in the yeast Pol II transcription cycle and identify directed chromatin state patterns at transcribed regions in the human genome.

    • Genomic feature annotations derived from bidirectional hidden Markov models are up to twice as accurate compared to those from standard hidden Markov models.

    • Variations in the yeast Pol II transcription cycle fall into clusters of co‐regulated genes, whose functional categories range from housekeeping and cell cycle to stress response.

    • New insights into transcriptional regulation are obtained, indicating a regulated initiation–elongation transition and a distinct transcription mechanism for highly expressed genes.

    • An implementation of bidirectional hidden Markov models is freely available at the Bioconductor website: http://www.bioconductor.org/packages/devel/bioc/html/STAN.html.

    • bidirectional hidden Markov model
    • chromatin marks
    • genome annotation
    • RNA transcription cycle

    Mol Syst Biol. (2014) 10: 768

    • Received August 4, 2014.
    • Revision received November 19, 2014.
    • Accepted November 21, 2014.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Benedikt Zacher, Michael Lidschreiber, Patrick Cramer, Julien Gagneur, Achim Tresch
  • Phosphoproteomic analyses reveal novel cross‐modulation mechanisms between two signaling pathways in yeast
    1. Stefania Vaga1,
    2. Marti Bernardo‐Faura2,
    3. Thomas Cokelaer2,
    4. Alessio Maiolica1,
    5. Christopher A Barnes1,3,
    6. Ludovic C Gillet1,
    7. Björn Hegemann3,
    8. Frank van Drogen3,
    9. Hoda Sharifian3,
    10. Edda Klipp4,
    11. Matthias Peter3,
    12. Julio Saez‐Rodriguez2 and
    13. Ruedi Aebersold*,1,5
    1. 1Department of Biology, Institute of Molecular Systems Biology ETH Zürich, Zürich, Switzerland
    2. 2European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EBI), Cambridge, UK
    3. 3Department of Biology, Institute of Biochemistry, ETH Zürich, Zürich, Switzerland
    4. 4Department of Biology, Theoretical Biophysics, Humboldt‐Universität zu Berlin, Berlin, Germany
    5. 5Faculty of Science, University of Zurich, Zurich, Switzerland
    1. *Corresponding author. Tel: +41 44 633 3191; Fax: +41 44 633 1051; E‐mail: aebersold{at}imsb.biol.ethz.ch

    A quantitative analysis of phosphoproteome dynamics by shotgun mass spectrometry, combined with mathematical modeling reveals complex crosstalk between the Hog1 and the pheromone signaling pathways in budding yeast.

    Synopsis

    A quantitative analysis of phosphoproteome dynamics by shotgun mass spectrometry, combined with mathematical modeling reveals complex crosstalk between the Hog1 and the pheromone signaling pathways in budding yeast.

    • Signal integration occurs at different levels within the hyper‐osmotic stress and the pheromone pathways in Saccharomyces cerevisiae.

    • Pheromone induces a short but strong down‐regulation of Hog1 phosphorylation.

    • A set of feedback loops regulate the activation of Hog1 and Fus3.

    • Ste20 and Pbs2 are key crosstalk mediators: their different phosphosites respond differently to the two stimuli.

    • cell signaling network
    • crosstalk
    • HOG pathway
    • pheromone pathway
    • phosphoproteomics

    Mol Syst Biol. (2014) 10: 767

    • Received March 18, 2014.
    • Revision received October 31, 2014.
    • Accepted November 7, 2014.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Stefania Vaga, Marti Bernardo‐Faura, Thomas Cokelaer, Alessio Maiolica, Christopher A Barnes, Ludovic C Gillet, Björn Hegemann, Frank van Drogen, Hoda Sharifian, Edda Klipp, Matthias Peter, Julio Saez‐Rodriguez, Ruedi Aebersold
  • Natural genetic variation impacts expression levels of coding, non‐coding, and antisense transcripts in fission yeast
    1. Mathieu Clément‐Ziza1,2,,
    2. Francesc X Marsellach3,,
    3. Sandra Codlin3,,
    4. Manos A Papadakis4,
    5. Susanne Reinhardt1,
    6. María Rodríguez‐López3,
    7. Stuart Martin3,
    8. Samuel Marguerat36,
    9. Alexander Schmidt5,
    10. Eunhye Lee3,
    11. Christopher T Workman4,
    12. Jürg Bähler*,3 and
    13. Andreas Beyer*,1,2
    1. 1Biotechnology Centre, Technische Universität Dresden, Dresden, Germany
    2. 2Cologne Cluster of Excellence in Cellular Stress Responses in Aging‐associated Diseases (CECAD), University of Cologne, Cologne, Germany
    3. 3Department of Genetics, Evolution & Environment and UCL Genetics Institute, University College London, London, UK
    4. 4Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
    5. 5Biozentrum, University of Basel, Basel, Switzerland
    6. 6MRC Clinical Sciences Centre, Imperial College London, London, UK
    1. * Corresponding author. Tel: +44 203 108 1602; E‐mail: j.bahler{at}ucl.ac.uk

      Corresponding author. Tel: +49 221 478 84 429; E‐mail: andreas.beyer{at}uni-koeln.de

    1. These authors contributed equally to this work

    A large‐scale eQTL study of coding, non‐coding, and antisense transcripts, performed using a recombinant fission yeast strain library, reveals the prevalence of trans‐eQTLs and identifies a variant affecting thousands of expression traits, presumably via epigenetic modification.

    Synopsis

    A large‐scale eQTL study of coding, non‐coding, and antisense transcripts, performed using a recombinant fission yeast strain library, reveals the prevalence of trans‐eQTLs and identifies a variant affecting thousands of expression traits, presumably via epigenetic modification.

    • A fission yeast strain library suitable for quantitative trait locus (QTL) analyses is generated and characterized.

    • A QTL analysis of sense, antisense, coding, and non‐coding expression as well as growth traits is performed.

    • Non‐coding genes are subject to eQTL control as much as coding genes.

    • Trans‐eQTLs are far more abundant than cis‐eQTLs.

    • A swc5 variant impacting thousands of sense and antisense traits, presumably through epigenetic modification (H2A.Z occupancy), is identified.

    • antisense transcription
    • histone variant
    • non‐coding RNA
    • QTL
    • Schizosaccharomyces pombe

    Mol Syst Biol. (2014) 10: 764

    • Received January 14, 2014.
    • Revision received October 27, 2014.
    • Accepted November 3, 2014.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Mathieu Clément‐Ziza, Francesc X Marsellach, Sandra Codlin, Manos A Papadakis, Susanne Reinhardt, María Rodríguez‐López, Stuart Martin, Samuel Marguerat, Alexander Schmidt, Eunhye Lee, Christopher T Workman, Jürg Bähler, Andreas Beyer
  • Potential of fecal microbiota for early‐stage detection of colorectal cancer
    1. Georg Zeller1,,
    2. Julien Tap1,2,,
    3. Anita Y Voigt1,3,4,5,,
    4. Shinichi Sunagawa1,
    5. Jens Roat Kultima1,
    6. Paul I Costea1,
    7. Aurélien Amiot2,
    8. Jürgen Böhm6,7,
    9. Francesco Brunetti8,
    10. Nina Habermann6,7,
    11. Rajna Hercog9,
    12. Moritz Koch1017,
    13. Alain Luciani11,
    14. Daniel R Mende1,
    15. Martin A Schneider10,
    16. Petra Schrotz‐King6,7,
    17. Christophe Tournigand12,
    18. Jeanne Tran Van Nhieu13,
    19. Takuji Yamada14,
    20. Jürgen Zimmermann9,
    21. Vladimir Benes9,
    22. Matthias Kloor3,4,5,
    23. Cornelia M Ulrich6,7,15,
    24. Magnus von Knebel Doeberitz3,4,5,
    25. Iradj Sobhani*,2 and
    26. Peer Bork*,1,5,16
    1. 1Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    2. 2Department of Gastroenterology and LIC‐EA4393‐EC2M3, APHP and UPEC Université Paris‐Est Créteil, Créteil, France
    3. 3Department of Applied Tumor Biology, Institute of Pathology University Hospital Heidelberg, Heidelberg, Germany
    4. 4Clinical Cooperation Unit Applied Tumor Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
    5. 5Molecular Medicine Partnership Unit (MMPU), University Hospital Heidelberg and European Molecular Biology Laboratory, Heidelberg, Germany
    6. 6Division of Preventive Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany
    7. 7German Cancer Research Center (DKFZ), Heidelberg, Germany
    8. 8Department of Surgery, APHP and UPEC Université Paris‐Est Créteil, Créteil, France
    9. 9Genomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
    10. 10Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, Heidelberg, Germany
    11. 11Department of Radiology, APHP and UPEC Université Paris‐Est Créteil, Créteil, France
    12. 12Department of Medical Oncology, APHP and UPEC Université Paris‐Est Créteil, Créteil, France
    13. 13Department of Pathology and LIC‐EA4393‐EC2M3, APHP and UPEC Université Paris‐Est Créteil, Créteil, France
    14. 14Department of Biological Information, Tokyo Institute of Technology, Tokyo, Japan
    15. 15Fred Hutchinson Cancer Research Center (FHCRC), Seattle, WA, USA
    16. 16Max Delbrück Centre for Molecular Medicine, Berlin, Germany
    17. 17Department of Abdominal, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus Technical University Dresden, Dresden, Germany
    1. * Corresponding author. Tel: +33 1 49814358; E‐mail: iradj.sobhani{at}hmn.aphp.fr

      Corresponding author. Tel: +49 6221 3878361; E‐mail: bork{at}embl.de

    1. These authors contributed equally to this work

    Metagenomic profiling of fecal samples from colorectal cancer (CRC) patients in comparison with tumor‐free controls reveals strong associations between the gut microbiota and cancer. Their potential for noninvasive cancer screening is explored systematically.

    Synopsis

    Metagenomic profiling of fecal samples from colorectal cancer (CRC) patients in comparison with tumor‐free controls reveals strong associations between the gut microbiota and cancer. Their potential for noninvasive cancer screening is explored systematically.

    • A classification model based on gut microbial marker species distinguishes CRC patients from controls with similar accuracy as the fecal occult blood test (FOBT), routinely used for clinical screening.

    • Combining metagenomic data with the FOBT leads to a relative improvement in sensitivity of > 45% over the FOBT alone at identical specificity.

    • Detection accuracy of the metagenomic test is maintained in an independent study population and is still high for alternative microbiome readouts, such as the abundance of 16S rRNA OTUs or families of functionally related genes.

    • Functional metagenomic analysis indicates an increased potential of CRC‐associated microbiota for degradation of host glycans and amino acids and for pro‐inflammatory lipopolysaccharide metabolism.

    • cancer screening
    • colorectal cancer
    • fecal biomarkers
    • human gut microbiome
    • metagenomics

    Mol Syst Biol. (2014) 10: 766

    • Received August 1, 2014.
    • Revision received November 12, 2014.
    • Accepted November 12, 2014.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Georg Zeller, Julien Tap, Anita Y Voigt, Shinichi Sunagawa, Jens Roat Kultima, Paul I Costea, Aurélien Amiot, Jürgen Böhm, Francesco Brunetti, Nina Habermann, Rajna Hercog, Moritz Koch, Alain Luciani, Daniel R Mende, Martin A Schneider, Petra Schrotz‐King, Christophe Tournigand, Jeanne Tran Van Nhieu, Takuji Yamada, Jürgen Zimmermann, Vladimir Benes, Matthias Kloor, Cornelia M Ulrich, Magnus von Knebel Doeberitz, Iradj Sobhani, Peer Bork
  • A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration
    Keren Yizhak, Sylvia E Le Dévédec, Vasiliki Maria Rogkoti, Franziska Baenke, Vincent C de Boer, Christian Frezza, Almut Schulze, Bob van de Water, Eytan Ruppin
  • Protein acetylation affects acetate metabolism, motility and acid stress response in Escherichia coli
    1. Sara Castaño‐Cerezo1,
    2. Vicente Bernal*,1,
    3. Harm Post2,3,
    4. Tobias Fuhrer4,
    5. Salvatore Cappadona2,
    6. Nerea C Sánchez‐Díaz1,
    7. Uwe Sauer4,
    8. Albert JR Heck2,3,
    9. AF Maarten Altelaar2,3 and
    10. Manuel Cánovas*,1
    1. 1Departamento de Bioquímica y Biología Molecular B e Inmunología, Facultad de Química, Universidad de Murcia Campus de Excelencia Mare Nostrum, Murcia, Spain
    2. 2Biomolecular Mass Spectrometry and Proteomics Group, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
    3. 3Netherlands Proteomics Center, Utrecht, The Netherlands
    4. 4Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
    1. * Corresponding author. Tel: +34 620 308626; E‐mail: vicente.bernal{at}gmail.com

      Corresponding author. Tel. +34 868 887393; E‐mail: mcanovas{at}um.es

    An integrated analysis of proteomic, transcriptomic and metabolic flux data reveals functional roles of protein acetylation in E. coli. Acetylation regulates protein function directly, by modulating metabolic enzyme activity, or indirectly by affecting transcriptional regulators.

    Synopsis

    An integrated analysis of proteomic, transcriptomic and metabolic flux data reveals functional roles of protein acetylation in E. coli. Acetylation regulates protein function directly, by modulating metabolic enzyme activity, or indirectly by affecting transcriptional regulators.

    • Protein acetylation is analyzed under four different growth conditions and is found to be highly context‐dependent.

    • The global activity of the lysine deacetylase CobB contributes to the deacetylation of a large number of substrates and affects physiology and metabolism.

    • Acetylation of the transcription factor RcsB prevents DNA binding, impairs flagella biosynthesis and motility and increases acid stress susceptibility.

    • Deletion of the lysine acetyltransferase patZ increases acetylation in acetate cultures, suggesting that PatZ regulates the levels of acetylating agents.

    • flagella biosynthesis
    • isocitrate lyase
    • metabolic regulation
    • sirtuin

    Mol Syst Biol. (2014) 10: 762

    • Received February 28, 2014.
    • Revision received October 14, 2014.
    • Accepted October 23, 2014.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Sara Castaño‐Cerezo, Vicente Bernal, Harm Post, Tobias Fuhrer, Salvatore Cappadona, Nerea C Sánchez‐Díaz, Uwe Sauer, Albert JR Heck, AF Maarten Altelaar, Manuel Cánovas
  • Multi‐input CRISPR/Cas genetic circuits that interface host regulatory networks
    1. Alec AK Nielsen1 and
    2. Christopher A Voigt*,1
    1. 1Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
    1. *Corresponding author. Tel: +1 617 324 4851; E‐mail: cavoigt{at}gmail.com

    Genetic gates are assembled based on dCas9 and engineered small guide RNAs (sgRNAs) that drive Cas9 to target promoters. These transcriptional gates are linked to build larger genetic circuits that are connected to the natural regulatory network of the cell.

    Synopsis

    Genetic gates are assembled based on dCas9 and engineered small guide RNAs (sgRNAs) that drive Cas9 to target promoters. These transcriptional gates are linked to build larger genetic circuits that are connected to the natural regulatory network of the cell.

    • Synthetic promoters and cognate sgRNAs exhibit large dynamic range and negligible crosstalk.

    • sgRNA‐based NOT gate response functions are non‐cooperative and quantitatively different from those based on transcriptional repressors.

    • Multi‐input logic gates are constructed by layering orthogonal sgRNAs.

    • Host regulatory networks can be interfaced through the design of sgRNA circuit outputs targeted to native transcription factors.

    • CRISPR
    • genetic compiler
    • synthetic biology
    • TALE
    • TetR homologue

    Mol Syst Biol. (2014) 10: 763

    • Received September 1, 2014.
    • Revision received October 26, 2014.
    • Accepted October 29, 2014.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Alec AK Nielsen, Christopher A Voigt

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