Methods & Resources
- Strand‐specific, high‐resolution mapping of modified RNA polymerase II
- Laura Milligan1,
- Vân A Huynh‐Thu2,3,
- Clémentine Delan‐Forino1,
- Alex Tuck1,4,5,
- Elisabeth Petfalski1,
- Rodrigo Lombraña6,
- Guido Sanguinetti*,2,
- Grzegorz Kudla*,6 and
- David Tollervey*,1
- 1Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, UK
- 2School of Informatics, University of Edinburgh, Edinburgh, UK
- 3Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium
- 4Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
- 5European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL‐EBI) Wellcome Trust Genome Campus, Cambridge, UK
- 6MRC Human Genetics Unit, IGMM, University of Edinburgh, Edinburgh, UK
- ↵*
Corresponding author. Tel: +44 131 650 7092; E‐mail: d.tollervey{at}ed.ac.uk
Corresponding author. Tel: +44 131 651 8628; E‐mail: gkudla{at}gmail.com
Corresponding author. Tel: +44 131 650 5136; E‐mail: gsanguin{at}inf.ed.ac.uk
A new technique for transcriptome‐wide mapping of RNAPII carrying the five types of CTD phosphorylation is presented. Distinct modification states associated with initiating, early and late elongating RNAPII are identified using a hidden Markov model.
Synopsis
A new technique for transcriptome‐wide mapping of RNAPII carrying the five types of CTD phosphorylation is presented. Distinct modification states associated with initiating, early and late elongating RNAPII are identified using a hidden Markov model.
Multiple modified forms of RNAPII can be mapped with nucleotide resolution.
Machine learning can be used to extract biological insights from these datasets.
Initiating RNAPII is associated with a distinct, surveillance‐prone state.
Unstable ncRNAs fail to exit this state, potentially linked to rapid degradation.
Mol Syst Biol. (2016) 12: 874
- Received February 8, 2016.
- Revision received May 13, 2016.
- Accepted May 18, 2016.
- © 2016 The Authors. Published under the terms of the CC BY 4.0 license
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.
- Chemogenomics and orthology‐based design of antibiotic combination therapies
- Sriram Chandrasekaran*,1,2,3,
- Melike Cokol‐Cakmak4,
- Nil Sahin4,
- Kaan Yilancioglu5,
- Hilal Kazan6,
- James J Collins2,3,7,8,9 and
- Murat Cokol*,4,10,11
- 1Harvard Society of Fellows, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
- 2Broad Institute of MIT and Harvard, Cambridge, MA, USA
- 3Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, USA
- 4Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
- 5Department of Molecular Biology and Genetics, Uskudar University, Istanbul, Turkey
- 6Department of Computer Engineering, Antalya International University, Antalya, Turkey
- 7Department of Biological Engineering, Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- 8Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
- 9Harvard‐MIT Program in Health Sciences and Technology, Cambridge, MA, USA
- 10Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, USA
- 11Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- ↵*
Corresponding author. Tel: +1 617 496 0048; E‐mail: chandrasekaran{at}fas.harvard.edu
Corresponding author. Tel: +1 617 432 6164; E‐mail: murat_cokol{at}hms.harvard.edu
Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms.
Synopsis
Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms.
INDIGO approach identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics.
The analysis reveals a small set of genes in E. coli that are predictive of interaction outcomes and are surprisingly conserved between distant bacterial species.
INDIGO can estimate the interaction outcomes in pathogens such as M. tuberculosis and S. aureus, based on the conservation of drug‐interaction‐related genes in E. coli.
Novel predictions were experimentally validated in E. coli (66 combinations) and S. aureus (45 combinations).
Mol Syst Biol. (2016) 12: 872
- Received December 31, 2015.
- Revision received April 20, 2016.
- Accepted April 21, 2016.
- © 2016 The Authors. Published under the terms of the CC BY 4.0 license
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.
- Tristetraprolin binding site atlas in the macrophage transcriptome reveals a switch for inflammation resolution
- Vitaly Sedlyarov1,†,
- Jörg Fallmann2,†,
- Florian Ebner1,
- Jakob Huemer1,
- Lucy Sneezum1,
- Masa Ivin1,
- Kristina Kreiner1,
- Andrea Tanzer2,
- Claus Vogl3,
- Ivo Hofacker*,2,4,5 and
- Pavel Kovarik*,1
- 1Max F. Perutz Laboratories, University of Vienna, Vienna, Austria
- 2Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria
- 3Institute of Animal Breeding and Genetics, University of Veterinary Medicine Vienna, Vienna, Austria
- 4Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria
- 5Center for non‐coding RNA in Technology and Health, University of Copenhagen, Frederiksberg C, Denmark
- ↵*
Corresponding author. Tel: +43 1427754608; E‐mail: ivo.hofacker{at}univie.ac.at
Corresponding author. Tel: +43 1427752738; E‐mail: pavel.kovarik{at}univie.ac.at
↵† These authors contributed equally to this work
A time‐resolved quantitative analysis of TTP binding sites, mRNA abundance, and mRNA stability in mouse macrophages generates a transcriptome‐wide atlas of cis‐acting elements controlling mRNA decay in inflammation.
Synopsis
A time‐resolved quantitative analysis of TTP binding sites, mRNA abundance, and mRNA stability in mouse macrophages generates a transcriptome‐wide atlas of cis‐acting elements controlling mRNA decay in inflammation.
A genome‐wide, high‐resolution, and time‐resolved analysis of TTP binding sites is performed in immunostimulated macrophages.
“TTP atlas”, a functionally annotated collection of mapped TTP binding sites relating TTP binding to transcriptome‐wide differential mRNA decay rates and differential mRNA expression in WT and TTP‐deficient macrophages, is generated (http://ttp-atlas.univie.ac.at).
Transcriptome‐wide analysis of mRNA stability and mRNA expression identifies a TTP‐driven switch initiating resolution of inflammation and indicates a limited co‐regulation of inflammatory mRNA decay by TTP and HuR.
Mol Syst Biol. (2016) 12: 868
- Received October 9, 2015.
- Revision received April 7, 2016.
- Accepted April 13, 2016.
- © 2016 The Authors. Published under the terms of the CC BY 4.0 license
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.
- Pooled‐matrix protein interaction screens using Barcode Fusion Genetics
- Nozomu Yachie*,1,2,3,4,5,†,
- Evangelia Petsalaki1,2,†,
- Joseph C Mellor1,2,
- Jochen Weile1,2,6,
- Yves Jacob7,
- Marta Verby1,2,
- Sedide B Ozturk1,2,
- Siyang Li1,2,
- Atina G Cote1,2,
- Roberto Mosca8,
- Jennifer J Knapp1,2,
- Minjeong Ko1,2,
- Analyn Yu1,2,
- Marinella Gebbia1,2,
- Nidhi Sahni9,10,16,
- Song Yi9,10,
- Tanya Tyagi1,2,
- Dayag Sheykhkarimli1,2,6,
- Jonathan F Roth1,2,6,
- Cassandra Wong1,2,
- Louai Musa1,2,
- Jamie Snider1,
- Yi‐Chun Liu1,
- Haiyuan Yu11,
- Pascal Braun9,10,12,
- Igor Stagljar1,6,
- Tong Hao9,10,
- Michael A Calderwood9,10,
- Laurence Pelletier2,6,
- Patrick Aloy8,13,
- David E Hill9,10,
- Marc Vidal9,10 and
- Frederick P Roth*,1,2,6,9,14,15
- 1Donnelly Centre, University of Toronto, Toronto, ON, Canada
- 2Lunenfeld‐Tanenbaum Research Institute Mt. Sinai Hospital, Toronto, ON, Canada
- 3Synthetic Biology Division, Research Center for Advanced Science and Technology The University of Tokyo, Tokyo, Japan
- 4Institute for Advanced Bioscience, Keio University, Tsuruoka, Yamagata, Japan
- 5PRESTO, Japan Science and Technology Agency (JST), Tokyo, Japan
- 6Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- 7Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN Institut Pasteur, Paris, France
- 8Joint IRB‐BSC Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), Barcelona, Spain
- 9Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana‐Farber Cancer Institute, Boston, MA, USA
- 10Department of Genetics, Harvard Medical School, Boston, MA, USA
- 11Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
- 12Department of Plant Systems Biology, Technische Universität München Wissenschaftszentrum Weihenstephan, Freising, Germany
- 13Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- 14Canadian Institute for Advanced Research, Toronto, ON, Canada
- 15Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- 16Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- ↵*
Corresponding author. Tel: +81 3 5452 5242; E‐mail: yachie{at}synbiol.rcast.u-tokyo.ac.jp
Corresponding author. Tel: +1 416 946 5130; E‐mail: fritz.roth{at}utoronto.ca
↵† These authors contributed equally to this work
Barcode Fusion Genetics‐Yeast Two‐Hybrid, a new technology allowing many‐by‐many screening of protein interactions in a single pooled assay, is presented. High‐quality interactions are identified in search matrices up to ~2.5 million protein pairs in scale.
Synopsis
Barcode Fusion Genetics‐Yeast Two‐Hybrid, a new technology allowing many‐by‐many screening of protein interactions in a single pooled assay, is presented. High‐quality interactions are identified in search matrices up to ~2.5 million protein pairs in scale.
Barcode Fusion Genetics (BFG) enables phenotypic analysis of millions of strains, each carrying two engineered loci.
BFG is applied to perform highly multiplexed yeast two‐hybrid protein interaction assays (BFG‐Y2H).
Protein interactions for ˜2.5 million protein pairs are tested in a single BFG‐Y2H run.
The quality of BFG‐Y2H results is on par with current state‐of‐the‐art Y2H methods.
Mol Syst Biol. (2016) 12: 863
- Received October 27, 2015.
- Revision received March 11, 2016.
- Accepted March 18, 2016.
- © 2016 The Authors. Published under the terms of the CC BY 4.0 license
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.
- Transcriptomics resources of human tissues and organs
- Mathias Uhlén*,1,2,3,
- Björn M Hallström1,2,
- Cecilia Lindskog4,
- Adil Mardinoglu1,5,
- Fredrik Pontén4 and
- Jens Nielsen1,3,5
- 1Science for Life Laboratory, KTH ‐ Royal Institute of Technology, Stockholm, Sweden
- 2Department of Proteomics, KTH ‐ Royal Institute of Technology, Stockholm, Sweden
- 3Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
- 4Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- 5Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- ↵*Corresponding author: Tel: +46 8 5537 8325; E‐mail: mathias.uhlen{at}scilifelab.se
Quantifying gene expression in human organs, tissues and cell types is vital to understand physiology and disease. Here we discuss publically available human transcriptome resources and their applications in combination with genome‐scale metabolic models.
Mol Syst Biol. (2016) 12: 862
- Received September 28, 2015.
- Revision received February 15, 2016.
- Accepted February 17, 2016.
- © 2016 The Authors. Published under the terms of the CC BY 4.0 license
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.
- The BioStudies database
The BioStudies database is a new EMBL‐EBI resource that holds descriptions of biological studies, links to supporting data in other databases, and archives data files that do not fit in existing public structured archives.
Mol Syst Biol. (2015) 11: 847
- © 2015 The Authors. Published under the terms of the CC BY 4.0 license
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.
- The evolution of standards and data management practices in systems biology
- Natalie J Stanford*,1,2,
- Katherine Wolstencroft3,
- Martin Golebiewski4,
- Renate Kania4,
- Nick Juty5,
- Christopher Tomlinson6,
- Stuart Owen2,
- Sarah Butcher6,
- Henning Hermjakob5,
- Nicolas Le Novère7,
- Wolfgang Mueller4,
- Jacky Snoep8,9 and
- Carole Goble2
- 1Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK
- 2School of Computer Science, University of Manchester, Manchester, UK
- 3Leiden Institute of Advanced Computer Science, Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands
- 4Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
- 5European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL‐EBI), Cambridge, UK
- 6Department of Surgery and Cancer, Imperial College London, London, UK
- 7Babraham Institute, Cambridge, UK
- 8Department of Biochemistry, University of Stellenbosch, Matieland, South Africa
- 9School of Chemical Engineering & Analytical Science, The University of Manchester, Manchester, UK
- ↵*Corresponding author. Tel: +44 161 275 0145; E‐mail: natalie.stanford{at}manchester.ac.uk
A recent community survey conducted by Infrastructure for Systems Biology Europe (ISBE) informs requirements for developing an efficient infrastructure for systems biology standards, data and model management.
Mol Syst Biol. (2015) 11: 851
- © 2015 The Authors. Published under the terms of the CC BY 4.0 license
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.
- Cytometry‐based single‐cell analysis of intact epithelial signaling reveals MAPK activation divergent from TNF‐α‐induced apoptosis in vivo
- Alan J Simmons1,2,†,
- Amrita Banerjee1,2,†,
- Eliot T McKinley1,3,
- Cherie' R Scurrah1,2,
- Charles A Herring1,4,
- Leslie S Gewin2,3,5,
- Ryota Masuzaki6,
- Seth J Karp6,
- Jeffrey L Franklin1,2,
- Michael J Gerdes7,
- Jonathan M Irish8,
- Robert J Coffey1,2,3,5 and
- Ken S Lau*,1,2,4
- 1Epithelial Biology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- 2Department of Cell and Developmental Biology, Vanderbilt University Medical Center, Nashville, TN, USA
- 3Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- 4Department of Chemical and Physical Biology, Vanderbilt University Medical Center, Nashville, TN, USA
- 5Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, TN, USA
- 6The Transplant Center and Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- 7Life Sciences Division, GE Global Research, Niskayuna, NY, USA
- 8Departments of Cancer Biology, and Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
- ↵*Corresponding author. Tel: +1 615 936 6859; E‐mail: ken.s.lau{at}vanderbilt.edu
↵† These authors contributed equally to this work
“DISSECT”, a novel approach for cytometry‐based single‐cell analysis of epithelial signaling, reveals divergent regulation of p‐ERK and apoptosis and indicates a “bystander” survival program during TNF‐α‐induced apoptosis of the in vivo intestinal epithelium.
Synopsis
“DISSECT”, a novel approach for cytometry‐based single‐cell analysis of epithelial signaling, reveals divergent regulation of p‐ERK and apoptosis and indicates a “bystander” survival program during TNF‐α‐induced apoptosis of the in vivo intestinal epithelium.
A novel method, DISSECT, generates single‐cell suspensions that preserve native cell morphology, cell surface markers and intact in vivo signaling when rigorously compared to bulk approaches.
DISSECT coupled to multiplex cytometry robustly characterizes the functional state of individual epithelial cells and can generate cell type‐ or “single cell”‐specific biological insights.
TNF‐α induces apoptosis almost exclusively in enterocytes and not in other epithelial cell types. Single‐cell signaling analysis identifies a non‐cell autonomous, p‐ERK‐dependent survival program in the direct neighbors of the dying cell. This may represent a mechanism for preventing contiguous barrier defects in an intact epithelium.
Mol Syst Biol. (2015) 11: 835
- Received May 7, 2015.
- Revision received September 25, 2015.
- Accepted September 29, 2015.
- © 2015 The Authors. Published under the terms of the CC BY 4.0 license
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.
- Reply to “Do genome‐scale models need exact solvers or clearer standards?”
Chindelevitch et al address the issues raised by Ebrahim et al and advocate that both improved standards and exact arithmetic are needed to advance the field.
Mol Syst Biol. (2015) 11: 830
- © 2015 The Authors. Published under the terms of the CC BY 4.0 license
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.
- Do genome‐scale models need exact solvers or clearer standards?
- Ali Ebrahim (aebrahim{at}ucsd.edu)1,
- Eivind Almaas2,
- Eugen Bauer3,
- Aarash Bordbar4,
- Anthony P Burgard5,
- Roger L Chang6,
- Andreas Dräger1,7,
- Iman Famili8,
- Adam M Feist1,
- Ronan MT Fleming3,
- Stephen S Fong9,
- Vassily Hatzimanikatis10,
- Markus J Herrgård11,
- Allen Holder12,
- Michael Hucka13,
- Daniel Hyduke14,
- Neema Jamshidi15,16,
- Sang Yup Lee11,17,
- Nicolas Le Novère18,
- Joshua A Lerman1,
- Nathan E Lewis19,
- Ding Ma20,
- Radhakrishnan Mahadevan21,
- Costas Maranas22,
- Harish Nagarajan5,
- Ali Navid23,
- Jens Nielsen11,24,
- Lars K Nielsen25,
- Juan Nogales26,
- Alberto Noronha3,
- Csaba Pal27,
- Bernhard O Palsson1,
- Jason A Papin28,
- Kiran R Patil29,
- Nathan D Price30,
- Jennifer L Reed31,
- Michael Saunders20,
- Ryan S Senger32,
- Nikolaus Sonnenschein11,
- Yuekai Sun33 and
- Ines Thiele3
- 1Department of Bioengineering, University of California, San Diego, CA, USA
- 2Department of Biotechnology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- 3Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
- 4Sinopia Biosciences Inc., San Diego, CA, USA
- 5Genomatica, Inc., San Diego, CA, USA
- 6Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- 7Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany
- 8Intrexon, Inc., San Diego, CA, USA
- 9Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, USA
- 10Laboratory of Computational Systems Biotechnology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- 11The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
- 12Department of Mathematics, Rose‐Hulman Institute of Technology, Terre Haute, IN, USA
- 13Department of Computing and Mathematical Science, California Institute of Technology, Pasadena, CA, USA
- 14Department of Biological Engineering, Utah State University, Logan, UT, USA
- 15Department of Radiology, University of California, Los Angeles, CA, USA
- 16Institute of Engineering in Medicine, University of California, San Diego, CA, USA
- 17Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
- 18Babraham Institute, Cambridge, UK
- 19Department of Pediatrics, University of California, San Diego, CA, USA
- 20Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
- 21Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
- 22Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA
- 23Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, USA
- 24Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- 25Australian Institute for Bioengineering & Nanotechnology (AIBN), The University of Queensland, Brisbane, Queensland, Australia
- 26Department of Environmental Biology, Centro de Investigaciones Biológicas (CSIC), Madrid, Spain
- 27Synthetic and Systems Biology Unit, Biological Research Center, Szeged, Hungary
- 28Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
- 29European Molecular Biology Laboratory, Heidelberg, Germany
- 30Institute for Systems Biology, Seattle, WA, USA
- 31Department of Chemical and Biological Engineering, University of Wisconsin‐Madison, Madison, WI, USA
- 32Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA
- 33Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
Recent results obtained by Chindelevitch et al (2014) are challenged by Ebrahim et al, illustrating several issues in the reproducibility of computational biology methods.
Mol Syst Biol. (2015) 11: 831
- © 2015 The Authors. Published under the terms of the CC BY 4.0 license
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.
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