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Table of Contents

01 January 2013; volume 9, issue 1

Article

  • Deconvolving the roles of Wnt ligands and receptors in sensing and amplification
    1. Rui Zhen Tan1,,
    2. Ni Ji2,
    3. Remco A Mentink3,
    4. Hendrik C Korswagen3 and
    5. Alexander van Oudenaarden*,3,4,5
    1. 1 Harvard University Graduate Biophysics Program, Harvard Medical School, Boston, MA, USA
    2. 2 Department of Brian and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
    3. 3 Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and University Medical Center Utrecht, Utrecht, The Netherlands
    4. 4 Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
    5. 5 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
    1. *Corresponding author. Department of Physics/Biology, Massachusetts Institute of Technology, 31 Ames Street, Room 68‐371B, Cambridge, MA 02139, USA. Tel.:+1 617 253 4446; Fax:+1 617 258 6883; E‐mail: avano{at}mit.edu
    • Present address: Bioinformatics Institute, A*STAR, Singapore 138671, Singapore.

    Establishment of cell polarity involves sensing of external cues followed by signal amplification. Analysis of Caenorhabditis elegans P‐cell polarity in Wnt ligand and receptor mutants is used to separate the contribution of ligands and receptors to the sensing and amplification processes.

    Synopsis

    Establishment of cell polarity involves sensing of external cues followed by signal amplification. Analysis of Caenorhabditis elegans P‐cell polarity in Wnt ligand and receptor mutants is used to separate the contribution of ligands and receptors to the sensing and amplification processes.

    • By combining quantitative single molecule transcript counting with phenomenological modeling, we studied the effects of ligand and receptor loss on P cells’ division in Caenorhabditis elegans.

    • We found that loss of ligands leads to polarity reversals whereas polarity loss is observed in the receptor mutants.

    • These results suggest that ligands affect primarily the sensing process whereas receptors are needed for both sensing and amplification.

    • Our integrated approach is generally applicable to other systems and will facilitate decoupling of the different layers of signal sensing and amplification.

    • Caenorhabditis elegans
    • cell polarity
    • phenomenological modeling
    • Wnt signaling

    Mol Syst Biol. 9: 631

    • Received February 13, 2012.
    • Accepted November 16, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Rui Zhen Tan, Ni Ji, Remco A Mentink, Hendrik C Korswagen, Alexander van Oudenaarden
  • An Oct4‐Sall4‐Nanog network controls developmental progression in the pre‐implantation mouse embryo
    1. Meng How Tan1,||,
    2. Kin Fai Au2,||,
    3. Denise E Leong1,
    4. Kira Foygel1,,
    5. Wing H Wong*,2,3 and
    6. Mylene WM Yao*,1,§
    1. 1 Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA
    2. 2 Department of Statistics, School of Humanities and Sciences, Stanford University, Stanford, CA, USA
    3. 3 Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA
    1. *Corresponding authors. Department of Statistics, School of Humanities and Sciences, Stanford University, 390 Serra Mall, Stanford, CA 94305, USA. Tel.:+1 650 725 2915; Fax:+1 650 725 8977; E‐mail: whwong{at}stanford.edu or Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA. Tel.:+1 650 799 8003; Fax:+1 650 961 1377; E‐mail: mylene.yao{at}gmail.com
    1. || These authors contributed equally to this work

    • Present address: Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA

    • Present address: Department of Radiology, Molecular Imaging Program, Stanford University School of Medicine, Stanford, CA 94305, USA

    • § Present address: Univfy Inc., Los Altos, CA 94022, USA

    Coordination of many biological processes is necessary for mammalian pre‐implantation embryo development. The underlying regulatory network was mapped through mathematical modeling, gene‐specific knockdowns, and profiling of pooled embryos, single embryos, and single cells.

    Synopsis

    Coordination of many biological processes is necessary for mammalian pre‐implantation embryo development. The underlying regulatory network was mapped through mathematical modeling, gene‐specific knockdowns, and profiling of pooled embryos, single embryos, and single cells.

    • An integrated Oct4‐Sall4‐Nanog regulatory network of protein‐coding genes and microRNAs governs developmental progression in pre‐implantation mouse embryos.

    • While many target genes are common between embryos and embryonic stem cells (ESCs), pluripotency factors regulate the expression of many metabolism‐ and transport‐related genes only in embryos but not in stem cells.

    • The expression of some genes, including the DNA methyltransferase Dnmt3b, correlates strongly with the extent to which an embryo depleted of Oct4, Sall4, or Nanog can develop.

    • In wild‐type embryos and ESCs, a coherent feed‐forward loop buffers the expression of Dnmt3b against intrinsic fluctuations in the levels of the pluripotency factors.

    • pluripotency factors
    • pre‐implantation development
    • transcriptional networks

    Mol Syst Biol. 9: 632

    • Received July 28, 2011.
    • Accepted November 30, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Meng How Tan, Kin Fai Au, Denise E Leong, Kira Foygel, Wing H Wong, Mylene WM Yao
  • Widespread splicing changes in human brain development and aging
    1. Pavel Mazin1,2,,
    2. Jieyi Xiong1,,
    3. Xiling Liu1,,
    4. Zheng Yan1,
    5. Xiaoyu Zhang1,3,
    6. Mingshuang Li1,3,
    7. Liu He1,
    8. Mehmet Somel1,4,
    9. Yuan Yuan1,5,
    10. Yi‐Ping Phoebe Chen5,
    11. Na Li6,
    12. Yuhui Hu6,
    13. Ning Fu7,
    14. Zhibin Ning7,
    15. Rong Zeng7,
    16. Hongyi Yang3,
    17. Wei Chen*,6,8,
    18. Mikhail Gelfand*,2,9 and
    19. Philipp Khaitovich*,1,4
    1. 1 Key Laboratory of Computational Biology, CAS‐MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, China
    2. 2 Department of Bioengineering and Bioinformatics, Moscow State University, Moscow, Russia
    3. 3 College of Life Science, Northeast Forestry University, Harbin, China
    4. 4 Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
    5. 5 Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria, Australia
    6. 6 Max Delbrück Center for Molecular Medicine, Berlin Institute for Medical Systems Biology, Berlin‐Buch, Germany
    7. 7 Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai, China
    8. 8 Max Planck Institute for Molecular Genetics, Berlin, Germany
    9. 9 Institute for Information Transmission Problems RAS, Moscow, Russia
    1. *Corresponding authors. Max‐Delbrück‐Center for Molecular Medicine, Berlin Institute for Medical Systems Biology, Robert Rössle Straße 10, Berlin 13125, Germany. Tel.:+49 30 9406 2995; Fax:+49 30 9406 3068; E‐mail: wei.chen{at}mdc-berlin.de or Institute for Information Transmission Problems, Bolshoy Karetny per. 19, Moscow 127994, Russia. Tel.:+7 495 650 4225; Fax:+7 495 650 0579; E‐mail: gelfand{at}iitp.ru or CAS‐MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China. Tel.:+86 21 5492 0454; Fax:+86 21 5492 0451; E‐mail: khaitovich{at}eva.mpg.de
    1. These authors contributed equally to this work.

    Human brain transcriptome analysis revealed widespread age‐related splicing changes in the prefrontal cortex and cerebellum. While most of the splicing changes take place in development, approximately one‐third of them extends into aging.

    Synopsis

    Human brain transcriptome analysis revealed widespread age‐related splicing changes in the prefrontal cortex and cerebellum. While most of the splicing changes take place in development, approximately one‐third of them extends into aging.

    • More than one‐third of genes expressed in the human brain change splicing with age.

    • Approximately 30% of observed splicing changes occur in aging.

    • Age‐related splicing patterns are largely conserved between the human and macaque brains.

    • High frequency of intron retention events suggests the role of nonsense‐mediated decay in age‐related gene expression regulation.

    • alternative splicing
    • brain
    • development
    • human
    • RNA‐seq

    Mol Syst Biol. 9: 633

    • Received March 6, 2012.
    • Revision received November 14, 2012.
    • Accepted December 16, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Pavel Mazin, Jieyi Xiong, Xiling Liu, Zheng Yan, Xiaoyu Zhang, Mingshuang Li, Liu He, Mehmet Somel, Yuan Yuan, Yi‐Ping Phoebe Chen, Na Li, Yuhui Hu, Ning Fu, Zhibin Ning, Rong Zeng, Hongyi Yang, Wei Chen, Mikhail Gelfand, Philipp Khaitovich
  • Shared control of gene expression in bacteria by transcription factors and global physiology of the cell
    1. Sara Berthoumieux1,,
    2. Hidde de Jong*,1,,
    3. Guillaume Baptist1,2,
    4. Corinne Pinel1,2,
    5. Caroline Ranquet1,2,
    6. Delphine Ropers1 and
    7. Johannes Geiselmann1,2
    1. 1 INRIA Grenoble—Rhône‐Alpes, Saint Ismier Cedex, France
    2. 2 Laboratoire Adaptation et Pathogénie des Microorganismes (CNRS UMR 5163), Université Joseph Fourier, La Tronche, France
    1. *Corresponding author. INRIA Grenoble—Rhône‐Alpes, 655 avenue de l'Europe, Montbonnot, Saint Ismier Cedex 38334, France, Tel.:+33 476615335; Fax:+33 456527120; E‐mail: hidde.de-jong{at}inria.fr
    1. These authors contributed equally to this work

    A simple, parameterless mathematical model, in combination with real‐time monitoring of promoter activities, shows how control of gene expression in bacteria is shared between transcription factors and global physiological effects.

    Synopsis

    A simple, parameterless mathematical model, in combination with real‐time monitoring of promoter activities, shows how control of gene expression in bacteria is shared between transcription factors and global physiological effects.

    • We present an approach based on a simple, paramaterless mathematical model to analyze the control of gene expression by transcription factors and the global physiological state of the cell.

    • We illustrate the strength of this approach by means of time‐resolved measurements of the transcriptional activities of genes in a central regulatory circuit in Escherichia coli.

    • We conclude that global physiological effects rather than transcription factors dominate the control of gene expression during a growth transition.

    • Our results call for a reappraisal of the role of transcription factors, which may be most appropriately viewed as complementing and finetuning global control exerted by the physiological state of the cell.

    • bacterial physiology
    • carbon metabolism
    • E. coli
    • gene regulatory networks
    • systems biology

    Mol Syst Biol. 9: 634

    • Received July 10, 2012.
    • Accepted December 8, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Sara Berthoumieux, Hidde de Jong, Guillaume Baptist, Corinne Pinel, Caroline Ranquet, Delphine Ropers, Johannes Geiselmann
  • An in vivo control map for the eukaryotic mRNA translation machinery
    1. Helena Firczuk1,
    2. Shichina Kannambath1,
    3. Jürgen Pahle2,3,
    4. Amy Claydon4,
    5. Robert Beynon4,
    6. John Duncan1,
    7. Hans Westerhoff2,3,
    8. Pedro Mendes2,3 and
    9. John EG McCarthy*,1
    1. 1 School of Life Sciences, University of Warwick, Coventry, UK
    2. 2 Manchester Interdisciplinary Biocentre, University of Manchester, Manchester, UK
    3. 3 Virginia Bioinformatics Institute, Blacksburg, VA, USA
    4. 4 Institute of Integrative Biology, University of Liverpool, Liverpool, UK
    1. *Corresponding author. School of Life Sciences, University of Warwick, Gibbet Hill Campus, Coventry CV4 7AL, UK. Tel.: +44 (0)2476 528380; Fax: +44 (0)2476 522052; E‐mail: john.mccarthy{at}warwick.ac.uk

    A new quantitative strategy has generated a comprehensive rate control map for protein synthesis in exponentially growing yeast cells. This analysis reveals the modularity of the system as well as highly non‐stoichiometric relationships between components.

    Synopsis

    A new quantitative strategy has generated a comprehensive rate control map for protein synthesis in exponentially growing yeast cells. This analysis reveals the modularity of the system as well as highly non‐stoichiometric relationships between components.

    • A ‘genetic titration’ method has generated a map of the in vivo rate control properties of components of the protein synthesis machinery in Saccharomyces cerevisiae and has been used to parameterize a new comprehensive model of the translation pathway.

    • The translation machinery is found to be a highly modular system in functional terms yet the intracellular concentrations of its components range from a few thousand to one million molecules per cell.

    • This approach identifies non‐intuitive features of the system such as the strongest rate control being exercised by high abundance elongation factors.

    • The rate control analysis allows us to identify a surprising fine‐control function for duplicated translation factor genes.

    • eukaryotic translation machinery
    • gene duplication
    • in vivo rate control
    • post‐transcriptional gene expression
    • system modularity

    Mol Syst Biol. 9: 635

    • Received September 25, 2012.
    • Accepted December 16, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Helena Firczuk, Shichina Kannambath, Jürgen Pahle, Amy Claydon, Robert Beynon, John Duncan, Hans Westerhoff, Pedro Mendes, John EG McCarthy
  • Autonomous bacterial localization and gene expression based on nearby cell receptor density
    1. Hsuan‐Chen Wu1,2,
    2. Chen‐Yu Tsao1,2,
    3. David N Quan1,2,
    4. Yi Cheng3,
    5. Matthew D Servinsky4,
    6. Karen K Carter2,5,
    7. Kathleen J Jee1,
    8. Jessica L Terrell1,2,
    9. Amin Zargar2,5,
    10. Gary W Rubloff3,
    11. Gregory F Payne1,2,
    12. James J Valdes6 and
    13. William E Bentley*,1,2,5
    1. 1 Fischell Department of Bioengineering, University of Maryland, College Park, MD, USA
    2. 2 Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD, USA
    3. 3 Department of Material Science and Engineering, University of Maryland, College Park, MD, USA
    4. 4 Sensors and Electron Devices Directorate, US Army Research Laboratory, Adelphi, MD, USA
    5. 5 Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA
    6. 6 US Army Edgewood Chemical Biological Center, Aberdeen Proving Ground, MD, USA
    1. *Corresponding author. Institute for Bioscience and Biotechnology Research, University of Maryland, 5115 Plant Sciences Building, College Park, MD 20742, USA. Tel.:+1 301 405 4321; Fax:+1 301 405 9953; E‐mail: bentley{at}umd.edu

    Escherichia coli were engineered to enable programmed motility, sensing and phenotypic response to the density of epidermal growth factor receptor expressed on the surface of cancer cells.

    Synopsis

    Escherichia coli were engineered to enable programmed motility, sensing and phenotypic response to the density of epidermal growth factor receptor expressed on the surface of cancer cells.

    • Bacteria were engineered to display targeted motility through AI‐2‐mediated chemotaxis.

    • Recruitment of motile bacteria was achieved by site‐specific synthesis of quorum sensing autoinducers using anti‐EGFR nanofactories.

    • Threshold‐based switching of bacterial gene expression was controlled by AI‐2 quorum sensing.

    • The engineered ‘bacterial dirigible’ represents a new means for targeted drug delivery and may have multiple applications wherein bacterial cells are designed to carry out specified tasks.

    • cancer
    • EGFR
    • Escherichia coli
    • quorum sensing
    • synthetic biology

    Mol Syst Biol. 9: 636

    • Received July 16, 2012.
    • Accepted December 8, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Hsuan‐Chen Wu, Chen‐Yu Tsao, David N Quan, Yi Cheng, Matthew D Servinsky, Karen K Carter, Kathleen J Jee, Jessica L Terrell, Amin Zargar, Gary W Rubloff, Gregory F Payne, James J Valdes, William E Bentley
  • Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers
    1. Jüri Reimand*,1 and
    2. Gary D Bader*,1
    1. 1 The Donnelly Centre, University of Toronto, Toronto, Canada
    1. *Corresponding authors. The Donnelly Centre, University of Toronto, 160 College Street, Toronto, Canada M5S 3E1. Tel.:+1 416 978 3935; Fax:+1 416 978 8287; E‐mail: Juri.Reimand{at}utoronto.ca or E‐mail: Gary.Bader{at}utoronto.ca

    Phosphorylation sites of human proteins are frequently mutated in cancer. Statistical analysis of phosphorylation‐associated single nucleotide variants (pSNVs) predicts novel cancer drivers and phospho‐mutation mechanisms in known cancer genes.

    Synopsis

    Phosphorylation sites of human proteins are frequently mutated in cancer. Statistical analysis of phosphorylation‐associated single nucleotide variants (pSNVs) predicts novel cancer drivers and phospho‐mutation mechanisms in known cancer genes.

    • We designed the ActiveDriver method to identify significantly mutated signaling regions in proteins. ActiveDriver is complementary to standard frequency‐based methods of mutation significance and helps interpret rare, but site‐specific mutations.

    • Analysis of somatic mutations in 800 cancer genomes reveals dozens of known and novel cancer genes, including potential drivers that are apparent only when integrating multiple cancer types.

    • Pathway and network analysis identifies systems with significantly enriched pSNVs, including kinase modules and protein complexes.

    • Clinical data analysis identifies phospho‐mutations of TP53 that correlate with prolonged patient survival in ovarian and brain cancer. Kinase network analysis highlights multiple survival‐associated signaling modules with pSNVs.

    • cancer drivers
    • phosphorylation
    • somatic mutations

    Mol Syst Biol. 9: 637

    • Received May 4, 2012.
    • Accepted December 6, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Jüri Reimand, Gary D Bader
  • Genome‐wide analysis of FOXO3 mediated transcription regulation through RNA polymerase II profiling
    1. Astrid Eijkelenboom1,
    2. Michal Mokry2,,
    3. Elzo de Wit2,
    4. Lydia M Smits1,
    5. Paulien E Polderman1,
    6. Miranda H van Triest1,
    7. Ruben van Boxtel3,
    8. Almut Schulze4,
    9. Wouter de Laat2,
    10. Edwin Cuppen2, and
    11. Boudewijn M T Burgering*,1
    1. 1 Department of Molecular Cancer Research, University Medical Centre, Utrecht, The Netherlands
    2. 2 Hubrecht Institute for Developmental Biology and Stem Cell Research, KNAW and University Medical Centre, Utrecht, The Netherlands
    3. 3 Department of Cell Biology, University Medical Centre, Utrecht, The Netherlands
    4. 4 Gene Expression Analysis Laboratory, Cancer Research UK London Research Institute, London, UK
    1. *Corresponding author. Department of Molecular Cancer Research, University Medical Center, Universiteitsweg 100, Utrecht 3584 CG, The Netherlands. Tel.:+31 88 7568918; Fax:+31 88 7568101; E‐mail: B.M.T.Burgering{at}umcutrecht.nl
    • Present address: Laboratory of Pediatric Gastroenterology, Wilhelmina Children's Hospital, University Medical Centre, Lundlaan 6, 3584 EA Utrecht, The Netherlands

    • Present address: Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands

    By comparative analysis of RNA polymerase II and FOXO3 ChIP‐sequencing, combined with 4C‐sequencing and ChIPs on histone modifications, general mechanisms of FOXO3‐mediated target gene regulation are identified.

    Synopsis

    By comparative analysis of RNA polymerase II and FOXO3 ChIP‐sequencing, combined with 4C‐sequencing and ChIPs on histone modifications, general mechanisms of FOXO3‐mediated target gene regulation are identified.

    • FOXO3 acts as a transcriptional activator, inducing target gene expression through RNA polymerase II recruitment.

    • FOXO3 binds and activates a pre‐existing network of distal enhancers.

    • FOXO3 bound distant regulatory regions contribute to target gene regulation.

    • Chromatin architecture could determine the cell type‐specific effects of FOXO3 target gene regulation.

    • enhancer
    • FOXO
    • initiation
    • RNA pol II
    • transcription

    Mol Syst Biol. 9: 638

    • Received October 10, 2012.
    • Accepted December 10, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Astrid Eijkelenboom, Michal Mokry, Elzo de Wit, Lydia M Smits, Paulien E Polderman, Miranda H van Triest, Ruben van Boxtel, Almut Schulze, Wouter de Laat, Edwin Cuppen, Boudewijn M T Burgering
  • Accurate measurements of dynamics and reproducibility in small genetic networks
    1. Julien O Dubuis1,2,
    2. Reba Samanta3 and
    3. Thomas Gregor*,1,2
    1. 1 Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ, USA
    2. 2 Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
    3. 3 Howard Hughes Medical Institute, Princeton University, Princeton, NJ, USA
    1. *Corresponding author. Joseph Henry Laboratories of Physics, Lewis Sigler Institute for Integrative Genomics, Princeton University, Washington Road, Princeton, NJ 08544, USA. Tel.:+1 609 258 4335; Fax:+1 609 258 6360; E‐mail: tg2{at}princeton.edu

    Precise analysis of systematic errors shows suitability of immunofluorescence protocols to quantify gene expression means, variances, and cross‐correlations. Application to Drosophila gap genes enables reconstructing expression level dynamics and the progression of positional accuracy.

    Synopsis

    Precise analysis of systematic errors shows suitability of immunofluorescence protocols to quantify gene expression means, variances, and cross‐correlations. Application to Drosophila gap genes enables reconstructing expression level dynamics and the progression of positional accuracy.

    • A careful analysis of the contribution of multiple sources of measurement errors shows that <20% of the observed embryo‐to‐embryo fluctuations stem from experimental error.

    • Intensities and slopes of the borders of gap gene expression patterns simultaneously reach a maximum around 15 min before gastrulation in a precisely coordinated fashion, hinting at an intrinsically collective organization of the gap gene network.

    • The reproducibility of gap gene expression levels increases two‐fold before reaching a maximum when the overall network dynamics peak. At the same time, the positional accuracy of determining cell fates is half an internuclear distance and uniform along the entire embryo length.

    • Drosophila gap genes
    • dynamics
    • error analysis
    • immunofluorescence
    • reproducibility

    Mol Syst Biol. 9: 639

    • Received September 10, 2012.
    • Accepted December 10, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Julien O Dubuis, Reba Samanta, Thomas Gregor
  • Evolutionary potential, cross‐stress behavior and the genetic basis of acquired stress resistance in Escherichia coli
    1. Martin Dragosits1,,
    2. Vadim Mozhayskiy1,2,,
    3. Semarhy Quinones‐Soto1,
    4. Jiyeon Park1 and
    5. Ilias Tagkopoulos*,1,2
    1. 1 UC Davis Genome Center, University of California‐Davis, Davis, CA, USA
    2. 2 Department of Computer Science, University of California‐Davis, Davis, CA, USA
    1. *Corresponding author. UC Davis Department of Computer Science and Genome Center, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA. Tel.:+1 530 752 7707; Fax:+1 530 752 4767; E‐mail: iliast{at}ucdavis.edu
    1. These authors contributed equally to this work

    • Present address: University of Natural Resources and Life Sciences, Vienna, Department of Chemistry, Vienna, Austria

    Escherichia coli cells were evolved over 500 generations and profiled in four abiotic stressors to observe several cases of emerging cross‐stress behavior whereby adaptation to one stressful environment provided fitness advantage when exposed to a second stressor.

    Synopsis

    Escherichia coli cells were evolved over 500 generations and profiled in four abiotic stressors to observe several cases of emerging cross‐stress behavior whereby adaptation to one stressful environment provided fitness advantage when exposed to a second stressor.

    • Cross‐stress dependencies were found to be ubiquitous, highly interconnected and can emerge within short timeframes.

    • Several targets were implicated in adaptation and cross‐stress protection, including genes related to iron transport and flagella.

    • Adaptation in a first stress can lead to higher fitness to a second stress when compared with cells adapted only in the latter environment.

    • Adaptation to any specific stress and the growth media was found to be generally independent.

    • cross‐stress protection
    • evolutionary trade‐offs
    • microbial evolution
    • stress adaptation

    Mol Syst Biol. 9: 643

    • Received August 6, 2012.
    • Accepted December 8, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Martin Dragosits, Vadim Mozhayskiy, Semarhy Quinones‐Soto, Jiyeon Park, Ilias Tagkopoulos
  • Properties of cell death models calibrated and compared using Bayesian approaches
    1. Hoda Eydgahi1,2,,
    2. William W Chen1,,
    3. Jeremy L Muhlich1,
    4. Dennis Vitkup3,
    5. John N Tsitsiklis2 and
    6. Peter K Sorger*,1
    1. 1 Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
    2. 2 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
    3. 3 Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA
    1. *Corresponding author. Center for Cell Decision Processes, Department of Systems Biology, Harvard Medical School, WAB Room 438, 200 Longwood Avenue, Boston, MA 02115, USA. Tel.:+1 617 432 6901/6902; Fax:+1 617 432 5012; E‐mail: peter_sorger{at}hms.harvard.edu
    1. These authors contributed equally to this work

    A Bayesian framework is used to calibrate a mass‐action model of receptor‐mediated apoptosis. Despite parameter non‐identifiability and model ‘sloppiness’, Bayes factor analysis discriminates between two alternative models of mitochondrial outer membrane permeabilization.

    Synopsis

    A Bayesian framework is used to calibrate a mass‐action model of receptor‐mediated apoptosis. Despite parameter non‐identifiability and model ‘sloppiness’, Bayes factor analysis discriminates between two alternative models of mitochondrial outer membrane permeabilization.

    • Bayesian estimation returns statistically complete joint parameter distribution for mass‐action models of receptor‐mediated apoptosis calibrated to dynamic, live‐cell data.

    • Analysis of joint distributions reveals strong, non‐linear correlations between parameters that are poorly captured by a conventional table of mean values and covariances; a high‐dimensional distribution must therefore be reported as the true estimate of parameter values.

    • Despite non‐identifiablility and model ‘sloppiness,’ a Bayesian framework returns probabilistic predictions for cell death dynamics that have tight confidence intervals and match experimental data.

    • Use of a Bayesian framework to discriminate between two competing models of mitochondrial outer membrane permeabilization shows that a ‘direct’ mechanism has ∼20‐fold greater plausibility than an ‘indirect’ mechanism, even though both models exhibit equally good fits to data for some parameters.

    • apoptosis
    • Bayesian estimation
    • biochemical networks
    • modeling

    Mol Syst Biol. 9: 644

    • Received June 22, 2012.
    • Accepted December 17, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Hoda Eydgahi, William W Chen, Jeremy L Muhlich, Dennis Vitkup, John N Tsitsiklis, Peter K Sorger
  • Programming biological models in Python using PySB
    1. Carlos F Lopez1,,
    2. Jeremy L Muhlich2,,
    3. John A Bachman2, and
    4. Peter K Sorger*,2
    1. 1 Department of Cancer Biology, Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA
    2. 2 Department of Systems Biology, Center for Cell Decision Processes, Harvard Medical School, Boston, MA, USA
    1. *Corresponding author. Department of Systems Biology, Center for Cell Decision Processes, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA. Tel.:+1 617 432 6901; Fax:+1 617 432 5012; E‐mail: peter_sorger{at}hms.harvard.edu
    1. These authors contributed equally to this work

    PySB is a framework for creating biological models as Python programs using a high‐level, action‐oriented vocabulary that promotes transparency, extensibility and reusability. PySB interoperates with many existing modeling tools and supports distributed model development.

    Synopsis

    PySB is a framework for creating biological models as Python programs using a high‐level, action‐oriented vocabulary that promotes transparency, extensibility and reusability. PySB interoperates with many existing modeling tools and supports distributed model development.

    • PySB models are programs and leverage existing programming tools for documentation, testing, and collaborative development.

    • Reusable functions can encode common low‐level biochemical processes as well as high‐level modules, making models transparent and concise.

    • Modeling workflow is accelerated through close integration with Python numerical tools and interoperability with existing modeling software.

    • We demonstrate the use of PySB to encode 15 alternative hypotheses for the mitochondrial regulation of apoptosis, including a new ‘Embedded Together’ model based on recent biochemical findings.

    • apoptosis
    • modeling
    • rule‐based
    • software engineering

    Mol Syst Biol. 9: 646

    • Received August 31, 2012.
    • Accepted January 7, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Carlos F Lopez, Jeremy L Muhlich, John A Bachman, Peter K Sorger
  • A fluorescent reporter for mapping cellular protein‐protein interactions in time and space
    1. Daniel Moreno1,
    2. Joachim Neller1,
    3. Hans A Kestler2,
    4. Johann Kraus2,
    5. Alexander Dünkler1 and
    6. Nils Johnsson*,1
    1. 1 Department of Biology, Institute of Molecular Genetics and Cell Biology, Ulm University, Ulm, Germany
    2. 2 Research Group for Bioinformatics and Systems Biology, Institute of Neural Information Processing, Ulm University, Ulm, Germany
    1. *Corresponding author. Department of Biology, Institute of Molecular Genetics and Cell Biology, Ulm University, James Franck Ring N27, 89081 Ulm, Germany. Tel.:+49 731 50 36300; Fax:+49 731 50 36302; E‐mail: nils.johnsson{at}uni-ulm.de

    A method based on a combination of the Split‐Ubiquitin system with two spectrally different fluorescent proteins (SPLIFF) is shown to enable measurement of protein interactions in vivo with high spatial and temporal resolution in yeast.

    Synopsis

    A method based on a combination of the Split‐Ubiquitin system with two spectrally different fluorescent proteins (SPLIFF) is shown to enable measurement of protein interactions in vivo with high spatial and temporal resolution in yeast.

    • SPLIFF visualizes protein interactions with high spatial and temporal resolution.

    • Spc72p and Kar9p interact with the MAP Stu2p at opposite poles of microtubules.

    • Histone chaperone Nap1p and Kcc4 kinase interact preferentially at the bud site.

    • F‐BAR protein Hof1p associates with the polarisome during cell fusion and cytokinesis.

    • fluorescent reporter
    • protein interaction
    • protein interaction networks
    • single‐cell analysis
    • Split‐Ubiquitin

    Mol Syst Biol. 9: 647

    • Received November 22, 2012.
    • Accepted January 28, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Daniel Moreno, Joachim Neller, Hans A Kestler, Johann Kraus, Alexander Dünkler, Nils Johnsson
  • Cell type‐specific nuclear pores: a case in point for context‐dependent stoichiometry of molecular machines
    1. Alessandro Ori1,
    2. Niccolò Banterle1,,
    3. Murat Iskar1,,
    4. Amparo Andrés‐Pons1,,
    5. Claudia Escher2,
    6. Huy Khanh Bui1,
    7. Lenore Sparks1,
    8. Victor Solis‐Mezarino1,
    9. Oliver Rinner2,
    10. Peer Bork*,1,
    11. Edward A Lemke*,1 and
    12. Martin Beck*,1
    1. 1 Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    2. 2 Biognosys AG, Schlieren, Switzerland
    1. *Corresponding authors. Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg 69117, Germany. Tel.:+49 6221 387 8526; Fax:+ 49 6221 387 8517; E‐mail: bork{at}embl.de or Tel.:+49 6221 387 8536; Fax:+49 6221 387 8519; E‐mail: lemke{at}embl.de or Tel.:+49 6221 387 8267; Fax:+49 6221 387 8519; E‐mail: mbeck{at}embl.de
    1. These authors contributed equally to this work.

    The stoichiometry of the human nuclear pore complex is revealed by targeted mass spectrometry and super‐resolution microscopy. The analysis reveals that the composition of the nuclear pore and other nuclear protein complexes is remodeled as a function of the cell type.

    Synopsis

    The stoichiometry of the human nuclear pore complex is revealed by targeted mass spectrometry and super‐resolution microscopy. The analysis reveals that the composition of the nuclear pore and other nuclear protein complexes is remodeled as a function of the cell type.

    • The human NPC has a previously unanticipated stoichiometry that varies across cell types.

    • Primarily functional Nups are dynamic, while the NPC scaffold is static.

    • Stoichiometries of many complexes are fine‐tuned toward cell type‐specific needs.

    • fluorophore counting
    • nucleoporin
    • protein complex‐based analysis
    • super‐resolution microscopy
    • targeted proteomics

    Mol Syst Biol. 9: 648

    • Received December 13, 2012.
    • Accepted February 17, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Alessandro Ori, Niccolò Banterle, Murat Iskar, Amparo Andrés‐Pons, Claudia Escher, Huy Khanh Bui, Lenore Sparks, Victor Solis‐Mezarino, Oliver Rinner, Peer Bork, Edward A Lemke, Martin Beck
  • Integration of clinical data with a genome‐scale metabolic model of the human adipocyte
    1. Adil Mardinoglu1,
    2. Rasmus Agren1,
    3. Caroline Kampf2,
    4. Anna Asplund2,
    5. Intawat Nookaew1,
    6. Peter Jacobson3,
    7. Andrew J Walley4,
    8. Philippe Froguel4,5,
    9. Lena M Carlsson3,
    10. Mathias Uhlen6 and
    11. Jens Nielsen*,1
    1. 1 Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
    2. 2 Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
    3. 3 Department of Molecular and Clinical Medicine and Center for Cardiovascular and Metabolic Research, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
    4. 4 Department of Genomics of Common Diseases, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK
    5. 5 Unité Mixte de Recherche 8199, Centre National de Recherche Scientifique (CNRS) and Pasteur Institute, Lille, France
    6. 6 Department of Proteomics, School of Biotechnology, AlbaNova University Center, Royal Institute of Technology (KTH), Stockholm, Sweden
    1. *Corresponding author. Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg 41128, Sweden. Tel.:+46 3 1772 3804; Fax:+46 3 1772 3801; E‐mail: nielsenj{at}chalmers.se

    Combining large‐scale immunohistochemical analysis and proteomics data, 7340 gene products are identified in human adipocytes. Based on this data, a genome‐scale metabolic model is reconstructed and used to integrate clinical and transcriptome data from lean and obese subjects.

    Synopsis

    Combining large‐scale immunohistochemical analysis and proteomics data, 7340 gene products are identified in human adipocytes. Based on this data, a genome‐scale metabolic model is reconstructed and used to integrate clinical and transcriptome data from lean and obese subjects.

    • We simulated the metabolic differences between the individuals with different body mass indexes (BMIs) using transcriptome and fluxome data.

    • An increase in the metabolic activity around androsterone, ganglioside GM2 and degradation products of heparan sulfate and keratan sulfate, and a decrease in mitochondrial metabolic activities are found in obese subjects compared with lean subjects.

    • We simulated the change in lipid droplet (LD) size and found that lean subjects have large dynamic changes in LD formation compared with obese subjects.

    • Besides enabling patient stratification, our study allows the identification of novel therapeutic targets for obesity.

    • adipocyte
    • flux balance analysis
    • genome‐scale metabolic model
    • obesity
    • proteome

    Mol Syst Biol. 9: 649

    • Received October 24, 2012.
    • Accepted February 11, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Adil Mardinoglu, Rasmus Agren, Caroline Kampf, Anna Asplund, Intawat Nookaew, Peter Jacobson, Andrew J Walley, Philippe Froguel, Lena M Carlsson, Mathias Uhlen, Jens Nielsen
  • Network balance via CRY signalling controls the Arabidopsis circadian clock over ambient temperatures
    1. Peter D Gould1,,
    2. Nicolas Ugarte1,,
    3. Mirela Domijan2,,
    4. Maria Costa2,
    5. Julia Foreman3,
    6. Dana MacGregor4,
    7. Ken Rose3,
    8. Jayne Griffiths3,
    9. Andrew J Millar3,5,
    10. Bärbel Finkenstädt2,
    11. Steven Penfield4,
    12. David A Rand2,
    13. Karen J Halliday3,5 and
    14. Anthony J W Hall*,1
    1. 1 Institute of Integrative Biology, University of Liverpool, Liverpool, UK
    2. 2 Warwick Systems Biology and Mathematics Institute, Coventry House, University of Warwick, Coventry, UK
    3. 3 SynthSys, Edinburgh, UK
    4. 4 School of Life Sciences, University of Exeter, Exeter, UK
    5. 5 School of Biological Sciences, University of Edinburgh, Edinburgh, UK
    1. *Corresponding author. Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK. Tel.:+44 151 795 4565; Fax:+44 151 795 4403; E‐mail: Anthony.hall{at}liverpool.ac.uk
    1. These authors contributed equally to this work.

    Temperature compensation of the Arabidopsis circadian clock is shown to be mediated by the interaction of light and temperature at the level of the crytochrome photoreceptors. These findings reveal that light and temperature share common input mechanisms to the circadian network.

    Synopsis

    Temperature compensation of the Arabidopsis circadian clock is shown to be mediated by the interaction of light and temperature at the level of the crytochrome photoreceptors. These findings reveal that light and temperature share common input mechanisms to the circadian network.

    • We provide evidence that blue light signalling via the cryptochromes is important for the temperature‐dependent control of circadian period in plants.

    • Light and temperature converge upon common targets in the circadian network.

    • We have constructed a temperature‐compensated model of the plant circadian clock by adding a temperature effect to a subset of light‐sensitive processes.

    • The model matches experimental data and predicted a temperature‐dependent change in the protein level of a key clock gene.

    • circadian rhythm
    • genetic network
    • mathematical model
    • systems biology

    Mol Syst Biol. 9: 650

    • Received June 27, 2012.
    • Accepted January 28, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Peter D Gould, Nicolas Ugarte, Mirela Domijan, Maria Costa, Julia Foreman, Dana MacGregor, Ken Rose, Jayne Griffiths, Andrew J Millar, Bärbel Finkenstädt, Steven Penfield, David A Rand, Karen J Halliday, Anthony J W Hall
  • Temporal system‐level organization of the switch from glycolytic to gluconeogenic operation in yeast
    1. Guillermo G Zampar1,
    2. Anne Kümmel2,
    3. Jennifer Ewald2,
    4. Stefan Jol2,
    5. Bastian Niebel1,
    6. Paola Picotti2,
    7. Ruedi Aebersold2,3,
    8. Uwe Sauer2,
    9. Nicola Zamboni2 and
    10. Matthias Heinemann*,1,2
    1. 1 Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands
    2. 2 ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland
    3. 3 Faculty of Science, University of Zurich, Zurich, Switzerland
    1. *Corresponding author. Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands. Tel.:+31 50 363 8146; Fax:+31 50 363 4165; E‐mail: m.heinemann{at}rug.nl

    Metabolome, proteome and physiology measurements were combined with mathematical modeling to unravel the temporal regulation of the metabolic fluxes during the diauxic shift in Saccharomyces cerevisiae.

    Synopsis

    Metabolome, proteome and physiology measurements were combined with mathematical modeling to unravel the temporal regulation of the metabolic fluxes during the diauxic shift in Saccharomyces cerevisiae.

    • The diauxic shift involves three main events: a reduction in the glycolytic flux and the production of storage compounds before glucose depletion; the reversion of carbon flow through glycolysis and onset of the glyoxylate cycle operation upon glucose exhaustion; and the shutting down of the pentose phosphate (PP) pathway with a change in the source of NADPH regeneration.

    • The redistribution of fluxes toward the production of storage compounds prior glucose depletion drives glycolytic reactions closer to equilibrium, which is essential for the reversion of fluxes upon glucose exhaustion.

    • The onset of the glyoxylate cycle is quantitatively more important than the activation of the tricarboxylic acid cycle for growth on ethanol.

    • Flux through the PP pathway is halted in the later stages of the adaptation and NADPH regeneration is taken over by NADP‐dependent enzymes in the glyoxylate cycle and ethanol metabolism.

    • diauxic shift
    • fluxome
    • metabolome
    • proteome
    • Saccharomyces cerevisiae

    Mol Syst Biol. 9: 651

    • Received August 16, 2012.
    • Accepted February 21, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Guillermo G Zampar, Anne Kümmel, Jennifer Ewald, Stefan Jol, Bastian Niebel, Paola Picotti, Ruedi Aebersold, Uwe Sauer, Nicola Zamboni, Matthias Heinemann
  • SH3 interactome conserves general function over specific form
    1. Xiaofeng Xin1,2,,
    2. David Gfeller1,§,
    3. Jackie Cheng3,||,
    4. Raffi Tonikian1,2,,
    5. Lin Sun4,††,
    6. Ailan Guo5,
    7. Lianet Lopez1,
    8. Alevtina Pavlenco1,
    9. Adenrele Akintobi4,
    10. Yingnan Zhang6,
    11. Jean‐François Rual7,8,‡‡,
    12. Bridget Currell9,
    13. Somasekar Seshagiri9,
    14. Tong Hao7,8,
    15. Xinping Yang7,8,
    16. Yun A Shen7,8,
    17. Kourosh Salehi‐Ashtiani7,8,§§,
    18. Jingjing Li1,2,
    19. Aaron T Cheng3,
    20. Dryden Bouamalay3,
    21. Adrien Lugari10,
    22. David E Hill7,8,
    23. Mark L Grimes11,
    24. David G Drubin3,
    25. Barth D Grant4,
    26. Marc Vidal7,8,
    27. Charles Boone*,1,2,
    28. Sachdev S Sidhu*,1,2 and
    29. Gary D Bader*,1,2,12
    1. 1 The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
    2. 2 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
    3. 3 Department of Molecular and Cell Biology, University of California Berkeley, Berkeley, CA, USA
    4. 4 Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ, USA
    5. 5 Cell Signaling Technology, Danvers, MA, USA
    6. 6 Department of Early Discovery Biochemistry, Genentech, South San Francisco, CA, USA
    7. 7 Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana‐Farber Cancer Institute, Boston, MA, USA
    8. 8 Department of Genetics, Harvard Medical School, Boston, MA, USA
    9. 9 Department of Molecular Biology, Genentech, South San Francisco, CA, USA
    10. 10  IMR Laboratory, UPR 3243, Institut de Microbiologie de la Méditérannée, CNRS and Aix‐Marseille Université, Marseille Cedex 20, France
    11. 11  Division of Biological Sciences, Center for Structural and Functional Neuroscience, The University of Montana, Missoula, MT, USA
    12. 12  Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
    1. *Corresponding authors. The Donnelly Centre, University of Toronto, 160 College Street, #602, Toronto, Ontario, Canada M5S 3E1. E‐mail: charlie.boone{at}utoronto.ca or E‐mail: sachdev.sidhu{at}utoronto.ca or Tel.:+416 978 3935; E‐mail: gary.bader{at}utoronto.ca
    1. These authors contributed equally to this work.

    • Present address: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

    • § Present address: Swiss Institute of Bioinformatics, Molecular Modelling, Génopode, 1015 Lausanne, Switzerland.

    • || Present address: MedImmune, 24500 Clawiter Road, Hayward, CA 94541, USA.

    • Present address: Department of Translational Sciences, Biogen Idec, Cambridge, MA 02142, USA.

    • †† Present address: Department of Physiology and Biophysics, Boston University School of Medicine, Boston, MA 02118, USA.

    • ‡‡ Present address: Department of Pathology, University of Michigan, Ann Arbor, MI, USA.

    • §§ Present address: Division of Science and Math, Center for Genomics and Systems Biology, New York University Abu Dhabi, PO Box 129188, Abu Dhabi, UAE.

    The Caenorhabditis elegans SH3 domain interactome was mapped and compared with the yeast SH3 interactome. Orthologous SH3 domain‐mediated interactions are highly rewired, but the general function of the SH3 domain network is conserved between the two species

    Synopsis

    The Caenorhabditis elegans SH3 domain interactome was mapped and compared with the yeast SH3 interactome. Orthologous SH3 domain‐mediated interactions are highly rewired, but the general function of the SH3 domain network is conserved between the two species

    • C. elegans Src homology 3 (SH3) domain interactome was mapped using stringent yeast two‐hybrid, resulting in a total of 1070 interactions among 79 out of 84 worm SH3 domains and 475 proteins.

    • SH3 domain binding specificities were profiled for 36 worm SH3 domains using peptide phage display.

    • The yeast and worm SH3 domain interactomes are significantly enriched in endocytosis proteins, but the specific interactions mediated by orthologous SH3 domains are highly rewired.

    • Using the worm SH3 interactome, we identified new endocytosis proteins in worm and human.

    • network evolution
    • phage display
    • protein interaction conservation
    • SH3 domains
    • yeast two‐hybrid

    Mol Syst Biol. 9: 652

    • Received September 27, 2012.
    • Accepted February 20, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Xiaofeng Xin, David Gfeller, Jackie Cheng, Raffi Tonikian, Lin Sun, Ailan Guo, Lianet Lopez, Alevtina Pavlenco, Adenrele Akintobi, Yingnan Zhang, Jean‐François Rual, Bridget Currell, Somasekar Seshagiri, Tong Hao, Xinping Yang, Yun A Shen, Kourosh Salehi‐Ashtiani, Jingjing Li, Aaron T Cheng, Dryden Bouamalay, Adrien Lugari, David E Hill, Mark L Grimes, David G Drubin, Barth D Grant, Marc Vidal, Charles Boone, Sachdev S Sidhu, Gary D Bader
  • Dissecting the energy metabolism in Mycoplasma pneumoniae through genome‐scale metabolic modeling
    1. Judith A H Wodke1,2,3,
    2. Jacek Puchałka4,,
    3. Maria Lluch‐Senar1,2,
    4. Josep Marcos5,6,
    5. Eva Yus1,2,
    6. Miguel Godinho4,7,
    7. Ricardo Gutiérrez‐Gallego5,6,
    8. Vitor A P Martins dos Santos4,8,9,
    9. Luis Serrano*,1,2,10,
    10. Edda Klipp*,3 and
    11. Tobias Maier*,1,2
    1. 1 EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Barcelona, Spain
    2. 2 Universitat Pompeu Fabra, Barcelona, Spain
    3. 3 Theoretical Biophysics, Humboldt‐Universität zu Berlin, Berlin, Germany
    4. 4 Synthetic and Systems Biology Group, Helmholtz Center for Infection Research (HZI), Braunschweig, Germany
    5. 5 Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain
    6. 6 Bio‐analysis Group, Neuroscience Research Program, IMIM‐Parc Salut Mar, Barcelona, Spain
    7. 7 Lifewizz Lda, Porto, Portugal
    8. 8 Systems and Synthetic Biology, Wageningen University, The Netherlands
    9. 9 LifeGlimmer GMBH, Berlin, Germany
    10. 10  Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
    1. *Corresponding authors. EMBL/CRG Systems Biology Research Unit Centre for Genomic Regulation (CRG), Dr Aiguader 88, Barcelona 08003, Spain. Tel.:+34 933 160 101; Fax:+34 933 160 099; E‐mail: luis.serrano{at}crg.eu or E‐mail: tobias.maier{at}crg.eu or Theoretical Biophysics, Humboldt‐Universität zu Berlin, Berlin, Germany. Tel.:+49 30 20939040; Fax:+49 30 20938813; E‐mail: edda.klipp{at}biologie.hu-berlin.de
    • Present address: Dr. von Hauner Children's Hospital, Department of Pediatrics, Ludwig Maximilian University, Munich, Germany

    A new genome‐scale metabolic reconstruction of M. pneumonia is used in combination with external metabolite measurement and protein abundance measurements to quantitatively explore the energy metabolism of this genome‐reduce human pathogen.

    Synopsis

    A new genome‐scale metabolic reconstruction of M. pneumonia is used in combination with external metabolite measurement and protein abundance measurements to quantitatively explore the energy metabolism of this genome‐reduce human pathogen.

    • We established a detailed biomass composition for M. pneumoniae, thus allowing for growth simulations.

    • Using our metabolic model, we corrected the metabolic network topology and the functional annotation of key metabolic enzymes.

    • M. pneumoniae, unlike other laboratory‐grown bacteria, uses a high fraction of energy (up to 89%) for cellular maintenance and not for growth.

    • Simulating different growth conditions as well as single and double mutant phenotypes, we analyzed pathway connectivity and the impact of gene deletions on the growth performance of M. pneumoniae, highlighting the limited adaptive capabilities of this minimal model organism.

    • biomass composition
    • energy metabolism
    • in silico knock‐outs
    • metabolic modeling
    • Mycoplasma pneumonia

    Mol Syst Biol. 9: 653

    • Received June 12, 2012.
    • Accepted February 20, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Judith A H Wodke, Jacek Puchałka, Maria Lluch‐Senar, Josep Marcos, Eva Yus, Miguel Godinho, Ricardo Gutiérrez‐Gallego, Vitor A P Martins dos Santos, Luis Serrano, Edda Klipp, Tobias Maier
  • Plant stem cell maintenance involves direct transcriptional repression of differentiation program
    1. Ram Kishor Yadav1,2,,
    2. Mariano Perales1,,
    3. Jérémy Gruel3,4,,
    4. Carolyn Ohno5,6,
    5. Marcus Heisler5,6,
    6. Thomas Girke1,
    7. Henrik Jönsson*,3,4 and
    8. G Venugopala Reddy*,1
    1. 1 Department of Botany and Plant Sciences, Center for Plant Cell Biology (CEPCEB), Institute of Integrative Genome Biology (IIGB), University of California, Riverside, CA, USA
    2. 2 Indian Institute of Science Education and Research, Mohali, India
    3. 3 Computational Biology and Biological Physics Group, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden
    4. 4 Sainsbury Laboratory, University of Cambridge, Cambridge, UK
    5. 5 European Molecular Biology Laboratory, Heidelberg, Germany
    6. 6 University of Sydney, Sydney, Australia
    1. *Corresponding authors. Department of Botany and Plant Sciences, Center for Plant Cell Biology (CEPCEB), Institute of Integrative Genome Biology (IIGB), University of California, Riverside, CA 92521, USA. Tel.:+1 951 8273482; Fax:+1 951 8274437; E‐mail: venug{at}ucr.edu or Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK. Tel.:+44 (0)1223 761128; Fax:+44 (0)1223 350422; E‐mail: henrik{at}thep.lu.se
    1. These authors contributed equally to this work.

    The plant stem cell regulator WUSCHEL is shown to repress differentiation‐promoting transcription factors. This regulatory network is analyzed with a computational model of the three‐dimensional shoot stem cell niche and a combination of genetic perturbation and live imaging.

    Synopsis

    The plant stem cell regulator WUSCHEL is shown to repress differentiation‐promoting transcription factors. This regulatory network is analyzed with a computational model of the three‐dimensional shoot stem cell niche and a combination of genetic perturbation and live imaging.

    • We find that the transcription factor (TF) WUSCHEL (WUS) directly binds to the promoters and represses a group of genes including key TFs involved in differentiation thus keeping them repressed in the stem cells of the plant shoot, a mechanistic logic that is similar to animal stem cell regulation.

    • We use a three‐dimensional computational model of the plant shoot stem cell niche to show that the WUS‐mediated repression of the differentiation program along with the previously reported activation of its own negative regulator leads to a robust stem cell homeostasis in a dynamic growth environment.

    • Live imaging of target genes upon transient manipulation of WUS levels is combined with model perturbations to validate the proposed network and to connect it with a large body of previous experimental work.

    • central zone
    • CLAVATA3
    • shoot apical meristem
    • stem cell niche
    • WUSCHEL

    Mol Syst Biol. 9: 654

    • Received July 25, 2012.
    • Accepted February 18, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Ram Kishor Yadav, Mariano Perales, Jérémy Gruel, Carolyn Ohno, Marcus Heisler, Thomas Girke, Henrik Jönsson, G Venugopala Reddy
  • Construction of human activity‐based phosphorylation networks
    1. Robert H Newman1,2,,
    2. Jianfei Hu3,,
    3. Hee‐Sool Rho1,4,,
    4. Zhi Xie3,,
    5. Crystal Woodard1,4,
    6. John Neiswinger1,4,
    7. Christopher Cooper5,
    8. Matthew Shirley1,
    9. Hillary M Clark1,
    10. Shaohui Hu1,4,
    11. Woochang Hwang3,
    12. Jun Seop Jeong1,4,
    13. George Wu6,
    14. Jimmy Lin7,
    15. Xinxin Gao1,
    16. Qiang Ni1,
    17. Renu Goel8,
    18. Shuli Xia9,
    19. Hongkai Ji6,
    20. Kevin N Dalby10,
    21. Morris J Birnbaum11,
    22. Philip A Cole1,
    23. Stefan Knapp12,
    24. Alexey G Ryazanov13,
    25. Donald J Zack3,5,14,15,
    26. Seth Blackshaw4,9,16,17,
    27. Tony Pawson18,19,
    28. Anne‐Claude Gingras18,19,
    29. Stephen Desiderio5,17,
    30. Akhilesh Pandey5,17,
    31. Benjamin E Turk20,
    32. Jin Zhang*,1,7,14,
    33. Heng Zhu*,1,4,7 and
    34. Jiang Qian*,3,7
    1. 1 Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
    2. 2 Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC, USA
    3. 3 Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD, USA
    4. 4 Center for High‐Throughput Biology, Johns Hopkins School of Medicine, Baltimore, MD, USA
    5. 5 Department of Molecular Biology and Genetics, Johns Hopkins School of Medicine, Baltimore, MD, USA
    6. 6 Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
    7. 7 The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
    8. 8 Institute of Bioinformatics, International Tech Park, Bangalore, India
    9. 9 Hugo W. Moser Kennedy Krieger Institute, Baltimore, MD, USA
    10. 10 Division of Medicinal Chemistry, College of Pharmacy, University of Texas at Austin, Austin, TX, USA
    11. 11 Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
    12. 12 Nuffield Department of Clinical Medicine, Structural Genomics Consortium, University of Oxford, Oxford, UK
    13. 13 Department of Pharmacology, University of Medicine and Dentistry of New Jersey, Piscataway, NJ, USA
    14. 14 Sol H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
    15. 15 The McKusick‐Nathans Institute of Genetic Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
    16. 16 Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
    17. 17 Institute of Cell Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
    18. 18 Centre for Systems Biology, Samuel Lunenfeld Research Institute, Mount Sinai Hospital Toronto, ON, Canada
    19. 19 Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
    20. 20 Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
    1. *Corresponding authors. Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA. Tel.:+410 502 0713; Fax:+410 955 3023; E‐mail: jzhang32{at}jhmi.edu or Tel.:+410 502 1872; Fax:+410 502 1872; E‐mail: hzhu4{at}jhmi.edu or Department of Ophthalomolgy, Johns Hopkins School of Medicine, 600 North Wolfe Street, Baltimore, MD 21205, USA. Tel.:+1 443 287 3882; Fax:+1 410 502 5382; E‐mail: jiang.qian{at}jhmi.edu
    1. These authors contributed equally to this work.

    A high‐resolution map of human phosphorylation networks was constructed by integrating experimentally determined kinase‐substrate relationships with other resources, such as in vivo phosphorylation sites.

    Synopsis

    A high‐resolution map of human phosphorylation networks was constructed by integrating experimentally determined kinase‐substrate relationships with other resources, such as in vivo phosphorylation sites.

    • High‐quality kinase‐substrate relationships (KSRs) were determined using an integrated approach that combines protein microarray technology and bioinformatics analysis.

    • Phosphorylation motifs were predicted for 284 human kinases, representing 55% of the human kinome.

    • A high‐resolution map of human phosphorylation networks was constructed that connects 230 kinases to 2591 in vivo phosphorylation sites in 652 substrates.

    • A new role for PKA downstream of Btk (Bruton's tyrosine kinase) during B‐cell receptor signaling was discovered based on KSRs identified in the phosphorylation networks.

    • phosphorylation
    • signaling networks
    • systems biology

    Mol Syst Biol. 9: 655

    • Received August 20, 2012.
    • Accepted March 1, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Robert H Newman, Jianfei Hu, Hee‐Sool Rho, Zhi Xie, Crystal Woodard, John Neiswinger, Christopher Cooper, Matthew Shirley, Hillary M Clark, Shaohui Hu, Woochang Hwang, Jun Seop Jeong, George Wu, Jimmy Lin, Xinxin Gao, Qiang Ni, Renu Goel, Shuli Xia, Hongkai Ji, Kevin N Dalby, Morris J Birnbaum, Philip A Cole, Stefan Knapp, Alexey G Ryazanov, Donald J Zack, Seth Blackshaw, Tony Pawson, Anne‐Claude Gingras, Stephen Desiderio, Akhilesh Pandey, Benjamin E Turk, Jin Zhang, Heng Zhu, Jiang Qian
  • Increasing population growth by asymmetric segregation of a limiting resource during cell division
    1. Nurit Avraham1,
    2. Ilya Soifer1,
    3. Miri Carmi1 and
    4. Naama Barkai*,1
    1. 1 Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
    1. *Corresponding author. Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel. Tel:+972 8 934 4429; Fax:+972 8 934 4108; E‐mail: naama.barkai{at}weizmann.ac.il

    When stressed by metal depletion, budding yeast adopt an asymmetric division pattern whereby vacuoles are maintained within dividing mother cells while the vacuoles‐deprived daughter cells arrest division. This linear growth mode represents a bet‐hedging strategy beneficial at the population level.

    Synopsis

    When stressed by metal depletion, budding yeast adopt an asymmetric division pattern whereby vacuoles are maintained within dividing mother cells while the vacuoles‐deprived daughter cells arrest division. This linear growth mode represents a bet‐hedging strategy beneficial at the population level.

    • Budding yeast restricts division to a subpopulation of mother cells when metal is depleted.

    • This population splitting into dividing mothers and arrested daughters implements a bet‐hedging strategy beneficial for population long‐term survival.

    • Proliferation is limited by the availability of vacuoles, which are asymmetrically segregated to mother cells in a WHI5‐dependent manner.

    • Asymmetric resource distribution increases population growth under limiting conditions, defining a novel stress‐response strategy.

    • budding yeast
    • nutrients limitation
    • phenotypic diversity
    • zinc

    Mol Syst Biol. 9: 656

    • Received September 26, 2012.
    • Accepted March 1, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Nurit Avraham, Ilya Soifer, Miri Carmi, Naama Barkai
  • Global remodelling of cellular microenvironment due to loss of collagen VII
    1. Victoria Küttner1,2,3,4,
    2. Claudia Mack3,
    3. Kristoffer TG Rigbolt1,2,
    4. Johannes S Kern3,
    5. Oliver Schilling5,6,
    6. Hauke Busch1,2,5,
    7. Leena Bruckner‐Tuderman*,1,2,3,6 and
    8. Jörn Dengjel*,1,2,6
    1. 1 School of Life Science‐LifeNet, Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Freiburg, Germany
    2. 2 ZBSA Center for Biological Systems Analysis, University of Freiburg, Freiburg, Germany
    3. 3 Department of Dermatology, University Freiburg Medical Center, Freiburg, Germany
    4. 4 Faculty of Biology, University of Freiburg, Freiburg, Germany
    5. 5 Institute for Molecular Medicine and Cell Research, University of Freiburg, Freiburg, Germany
    6. 6 BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany
    1. *Corresponding authors. School of Life Science‐LifeNet, Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Albertstrasse 19, Freiburg 79104, Germany. Tel.:+49 761 270 67160; Fax:+49 761 270 69360; E‐mail: bruckner-tuderman{at}uniklinik-freiburg.de or Tel.:+49 761 203 97208; Fax:+49 761 203 97241; E‐mail: joern.dengjel{at}frias.uni-freiburg.de

    Loss of collagen VII causes recessive dystrophic epidermolysis bullosa. Quantitative proteomics analysis of the extracellular matrix and secretome of human fibroblasts derived from pathologically altered skin reveals a global remodelling of the cellular microenvironment.

    Synopsis

    Loss of collagen VII causes recessive dystrophic epidermolysis bullosa. Quantitative proteomics analysis of the extracellular matrix and secretome of human fibroblasts derived from pathologically altered skin reveals a global remodelling of the cellular microenvironment.

    • A global analysis of the microenvironment of human skin fibroblasts was carried out to reveal disease‐related alterations in the extracellular proteome.

    • The loss of collagen VII causes a deregulation of the basement membrane and dermal matrix proteome.

    • Post‐translational modifications of secreted proteins were altered in fibroblasts from recessive dystrophic epidermolysis bullosa samples.

    • Metalloproteases displayed reduced activity and turnover in collagen VII‐deficient cells.

    • disease proteomics
    • extracellular matrix (ECM)
    • mass spectrometry
    • MMP14
    • primary human fibroblasts

    Mol Syst Biol. 9: 657

    • Received December 6, 2012.
    • Accepted March 13, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Victoria Küttner, Claudia Mack, Kristoffer TG Rigbolt, Johannes S Kern, Oliver Schilling, Hauke Busch, Leena Bruckner‐Tuderman, Jörn Dengjel
  • Dissecting specific and global transcriptional regulation of bacterial gene expression
    1. Luca Gerosa1,2,,
    2. Karl Kochanowski1,3,,
    3. Matthias Heinemann1,4 and
    4. Uwe Sauer*,1,3
    1. 1 Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
    2. 2 Life Science Zurich PhD Program on Systems Biology of Complex Diseases, Zurich, Switzerland
    3. 3 Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland
    4. 4 Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands
    1. *Corresponding author. Institute of Molecular Systems Biology, ETH Zurich, Wolfgang Pauli Strasse 16, Zurich 8093, Switzerland. Tel.:+41 44633 3672; Fax:+41 44633 1051; E‐mail: sauer{at}imsb.biol.ethz.ch
    1. These authors contributed equally to this work.

    An experimental‐computational approach is applied to dissect the contribution of specific transcription factor‐mediated versus global growth‐dependent regulation to bacterial gene expression, and obtain a quantitative understanding of dynamic adaptations in arginine biosynthesis of E. coli.

    Synopsis

    An experimental‐computational approach is applied to dissect the contribution of specific transcription factor‐mediated versus global growth‐dependent regulation to bacterial gene expression, and obtain a quantitative understanding of dynamic adaptations in arginine biosynthesis of E. coli.

    • We present a model‐based approach to quantitatively dissect simultaneous contributions from specific transcription factors and the global growth status to bacterial gene expression, based on parameter inference from GFP‐based promoter activity measurements.

    • We show that growth rate can be used to predict the unregulated expression baseline of a gene, since growth rate dependence of global regulation occurs both in steady state and during transient changes in growth rate.

    • We obtain a quantitative understanding of both specific and global regulation in arginine biosynthesis, as demonstrated by accurate model‐based predictions of complex transient gene‐expression responses to simultaneous perturbation in growth rate and arginine availability.

    • We uncover two principles of joint regulation of the arginine biosynthesis pathway: (i) specific regulation by repression dominates in steady metabolic states and (ii) global regulation sets the maximal expression reachable during transition between steady metabolic states.

    • expression machinery
    • modelling
    • synthetic biology
    • transcriptional circuit
    • transcriptional regulation

    Mol Syst Biol. 9: 658

    • Received December 18, 2012.
    • Accepted March 6, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Luca Gerosa, Karl Kochanowski, Matthias Heinemann, Uwe Sauer
  • Apoptosis and other immune biomarkers predict influenza vaccine responsiveness
    1. David Furman1,,
    2. Vladimir Jojic2,§,
    3. Brian Kidd3,
    4. Shai Shen‐Orr4,
    5. Jordan Price1,
    6. Justin Jarrell5,
    7. Tiffany Tse3,
    8. Huang Huang1,
    9. Peder Lund1,
    10. Holden T Maecker3,
    11. Paul J Utz3,5,
    12. Cornelia L Dekker3,6,
    13. Daphne Koller2 and
    14. Mark M Davis*,1,3,7
    1. 1 Department of Microbiology and Immunology, School of Medicine, Stanford University, Palo Alto, CA, USA
    2. 2 Department of Computer Science, School of Medicine, Stanford University, Palo Alto, CA, USA
    3. 3 Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Palo Alto, CA, USA
    4. 4 Department of Immunology, Faculty of Medicine, Technion, Technion City, Haifa, Israel
    5. 5 Division of Immunology and Rheumatology, Department of Medicine, School of Medicine, Stanford University, Palo Alto, CA, USA
    6. 6 Department of Pediatrics, Division of Infectious Diseases, School of Medicine, Stanford University, Palo Alto, CA, USA
    7. 7 The Howard Hughes Medical Institute, Chevy Chase, MD, USA
    1. *Corresponding author. Department of Microbiology and Immunology, School of Medicine, Stanford University, 279 Campus Drive, Beckman Building B219, Palo Alto, CA 94305, USA. Tel.:+1 650 725 4755; Fax:+1 650 498 7771; E‐mail: mmdavis{at}stanford.edu
    1. These authors contributed equally to this work

    • Present address: CNRS‐UMR 5164, University of Bordeaux, Bordeaux 33076, France

    • § Present address: Department of Computer Science, University of North Carolina, Chapel Hill, NC 27514, USA

    A systems analysis of immune biomarkers in 89 young and older adults revealed age‐dependent and age‐independent features, including markers of apoptosis that correlated with antibody responses to a seasonal influenza vaccine.

    Synopsis

    A systems analysis of immune biomarkers in 89 young and older adults revealed age‐dependent and age‐independent features, including markers of apoptosis that correlated with antibody responses to a seasonal influenza vaccine.

    • Influenza hemagglutinin peptide arrays reveal age‐associated effects that correlate with both pre‐existing and vaccine‐induced antibody titers.

    • Age‐dependent and age‐independent baseline immune parameters correlate with and substantially predict the serological response to a seasonal influenza vaccine.

    • Soluble FasL and gene modules associated with apoptosis are predictors of the serological response to an influenza vaccine, which was abrogated in Fas‐deficient mice.

    • aging
    • apoptosis
    • influenza
    • systems immunology
    • vaccinology

    Mol Syst Biol. 9: 659

    • Received November 28, 2012.
    • Accepted March 7, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    David Furman, Vladimir Jojic, Brian Kidd, Shai Shen‐Orr, Jordan Price, Justin Jarrell, Tiffany Tse, Huang Huang, Peder Lund, Holden T Maecker, Paul J Utz, Cornelia L Dekker, Daphne Koller, Mark M Davis
  • Indirect and suboptimal control of gene expression is widespread in bacteria
    1. Morgan N Price*,1,
    2. Adam M Deutschbauer1,
    3. Jeffrey M Skerker2,3,
    4. Kelly M Wetmore1,3,
    5. Troy Ruths1,
    6. Jordan S Mar2,3,
    7. Jennifer V Kuehl1,
    8. Wenjun Shao4 and
    9. Adam P Arkin*,1,2,3
    1. 1 Physical Biosciences Division, Lawrence Berkeley National Lab, Berkeley, CA, USA
    2. 2 Department of Bioengineering, University of California, Berkeley, CA, USA
    3. 3 Energy Biosciences Institute, University of California, Berkeley, CA, USA
    4. 4 Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
    1. *Corresponding authors. Physical Biosciences Division, Lawrence Berkeley National Lab, 1 Cyclotron Road, Mailstop 955‐512L, Berkeley, CA 94720, USA. Tel.:+1 510 643 3722; Fax:+1 510 486 6219; E‐mail: morgannprice{at}yahoo.com or E‐mail: aparkin{at}lbl.gov

    This study shows that, in bacteria grown in the laboratory, there is little correlation between when genes are important for fitness and when they are more highly expressed. Most genes thus appear to be regulated by signals that are not related to their function.

    Synopsis

    This study shows that, in bacteria grown in the laboratory, there is little correlation between when genes are important for fitness and when they are more highly expressed. Most genes thus appear to be regulated by signals that are not related to their function.

    • Many bacterial genes are expressed when they are detrimental to fitness.

    • Most genes are not upregulated when they are important for fitness.

    • Even biosynthetic genes are often not downregulated when they are not needed (except in E. coli).

    • Genes with closely related functions often have different expression patterns.

    • bacterial evolution
    • gene regulation
    • optimal regulation

    Mol Syst Biol. 9: 660

    • Received November 27, 2012.
    • Accepted March 13, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Morgan N Price, Adam M Deutschbauer, Jeffrey M Skerker, Kelly M Wetmore, Troy Ruths, Jordan S Mar, Jennifer V Kuehl, Wenjun Shao, Adam P Arkin
  • Characterization of drug‐induced transcriptional modules: towards drug repositioning and functional understanding
    1. Murat Iskar1,
    2. Georg Zeller1,
    3. Peter Blattmann2,3,
    4. Monica Campillos4,5,
    5. Michael Kuhn6,
    6. Katarzyna H Kaminska1,,
    7. Heiko Runz3,7,
    8. Anne‐Claude Gavin1,
    9. Rainer Pepperkok2,3,
    10. Vera van Noort1 and
    11. Peer Bork*,1,8
    1. 1 Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
    2. 2 Cell Biology/Biophysics Unit, EMBL, Heidelberg, Germany
    3. 3 Molecular Medicine Partnership Unit (MMPU), EMBL, University of Heidelberg, Heidelberg, Germany
    4. 4 Institute for Bioinformatics and Systems Biology, Helmholtz Center Munich–German Research Center for Environmental Health (GmbH), Neuherberg, Germany
    5. 5 German Center for Diabetes Research (DZD), Neuherberg, Germany
    6. 6 Biotechnology Center, TU Dresden, Dresden, Germany
    7. 7 Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany
    8. 8 Max‐Delbrück‐Centre for Molecular Medicine, Berlin, Germany
    1. *Corresponding author. Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg, Germany. Tel.:+49 6221 387 8526; Fax:+49 6221 387 8517; E‐mail: bork{at}embl.de
    • Present address: International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, 02‐109 Warsaw, Poland

    Drug‐induced transcriptional modules (biclusters) were identified and annotated in three human cell lines and rat liver. These were used to assess conservation across systems and to infer and experimentally validate novel drug effects and gene functions.

    Synopsis

    Drug‐induced transcriptional modules (biclusters) were identified and annotated in three human cell lines and rat liver. These were used to assess conservation across systems and to infer and experimentally validate novel drug effects and gene functions.

    • Biclustering of drug‐induced gene expression profiles resulted in modules of drugs and genes, which were enriched in both drug and gene annotations.

    • Identifying drug‐induced transcriptional modules separately in three human cell lines and rat liver allows assessment of their conservation across model systems. About 70% of modules are conserved across cell lines, a lower bound of 15% was estimated for their conservation across organisms, and between the in vitro and in vivo systems.

    • Drug‐induced transcriptional modules can predict novel gene functions. A conserved module associated with (chole)sterol metabolism revealed novel regulators of cellular cholesterol homeostasis; 10 of them were validated in functional imaging assays.

    • Analysis of drugs clustered into modules can give new insights into their mechanisms of action and provide leads for drug repositioning. We predicted and experimentally validated novel cell cycle inhibitors and modulators of PPARγ, estrogen and adrenergic receptors, with potential for developing new therapies against diabetes and cancer.

    • cell line models in drug discovery
    • drug‐induced transcriptional modules
    • drug repositioning
    • gene function prediction
    • transcriptome conservation across cell types and organisms

    Mol Syst Biol. 9: 662

    • Received January 21, 2013.
    • Accepted March 28, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Murat Iskar, Georg Zeller, Peter Blattmann, Monica Campillos, Michael Kuhn, Katarzyna H Kaminska, Heiko Runz, Anne‐Claude Gavin, Rainer Pepperkok, Vera van Noort, Peer Bork
  • Systematic identification of proteins that elicit drug side effects
    1. Michael Kuhn1,,
    2. Mumna Al Banchaabouchi2,,
    3. Monica Campillos1,§,
    4. Lars Juhl Jensen1,||,
    5. Cornelius Gross2,
    6. Anne‐Claude Gavin1 and
    7. Peer Bork*,1,3
    1. 1 Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    2. 2 Mouse Biology Unit, European Molecular Biology Laboratory, Monterotondo, Italy
    3. 3 Max‐Delbrück‐Centre for Molecular Medicine, Berlin, Germany
    1. *Corresponding author. Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, Heidelberg 69117, Germany. Tel.:+49 6221 387 8526; Fax:+49 6221 387 517; E‐mail: bork{at}embl.de
    • Present address: Biotechnology Center, TU Dresden, 01062 Dresden, Germany

    • Present address: Preclinical Phenotyping Facility, Campus Science Support Facilities GmbH, Dr Bohr Gasse 3, 1030 Vienna, Austria

    • § Present address: Institute for Bioinformatics and Systems Biology (MIPS), Helmholtz Center Munich—German Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, 85764 Neuherberg, Germany

    • || Present address: Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3b, 2200 Copenhagen, Denmark

    Protein–side effects associations are identified by integrating drug–target data with side effects information from drug labels. Benchmarking against the literature and validation with an in vivo mouse model shows that these pairs correspond to causal relations.

    Synopsis

    Protein–side effects associations are identified by integrating drug–target data with side effects information from drug labels. Benchmarking against the literature and validation with an in vivo mouse model shows that these pairs correspond to causal relations.

    • For more than half of the investigated side effects, we can predict causal proteins.

    • Off‐targets contribute slightly more to the explained side effects than main targets.

    • With the current data, we are most successful in explaining the side effects of drugs that target G protein‐coupled receptors.

    • Activation of HTR7 causes hyperesthesia in mice, explaining a side effect of triptan drugs.

    • computational biology
    • drug targets
    • side effects

    Mol Syst Biol. 9: 663

    • Received November 20, 2012.
    • Accepted February 17, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Michael Kuhn, Mumna Al Banchaabouchi, Monica Campillos, Lars Juhl Jensen, Cornelius Gross, Anne‐Claude Gavin, Peer Bork
  • The selective control of glycolysis, gluconeogenesis and glycogenesis by temporal insulin patterns
    1. Rei Noguchi1,
    2. Hiroyuki Kubota2,
    3. Katsuyuki Yugi2,
    4. Yu Toyoshima2,
    5. Yasunori Komori2,
    6. Tomoyoshi Soga3 and
    7. Shinya Kuroda*,1,2,4
    1. 1 Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
    2. 2 Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo‐ku, Tokyo, Japan
    3. 3 Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
    4. 4 CREST, Japan Science and Technology Corporation, Bunkyo‐ku, Tokyo, Japan
    1. *Corresponding author. Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, 7‐3‐1 Hongo, Bunkyo‐ku, Tokyo 113 0033, Japan. Tel.:+81 3 5841 4697; Fax:+81 3 5841 4698; E‐mail: skuroda{at}bi.s.u-tokyo.ac.jp

    The regulation of glucose metabolism by pulse stimulations of insulin is compared with the effect of ramp stimulations. Specific network motifs mediate the differential response to these temporal patterns of stimulations that mimic in vivo patterns of insulin secretion.

    Synopsis

    The regulation of glucose metabolism by pulse stimulations of insulin is compared with the effect of ramp stimulations. Specific network motifs mediate the differential response to these temporal patterns of stimulations that mimic in vivo patterns of insulin secretion.

    • Temporal patterns and absolute concentration of insulin selectively control glycolysis, gluconeogenesis and glycogenesis through the different network motif in FAO hepatoma cells.

    • Step stimulation of insulin induces the transient responses and adaptations of glycolysis (via F16P) and glycogenesis through a feedforward with substrate depletion and though an incoherent feedforward loop, respectively, and induces the sustained response of gluconeogenesis (via PEPCK) through a feedforward inhibition.

    • Pulse stimulation of insulin, like additional secretory pattern in vivo, induces responses of glycolysis (via F16P), gluconeogenesis (via PEPCK) and glycogenesis.

    • Ramp stimulation of insulin, like basal secretory pattern in vivo, induces only the response of gluconeogenesis (via PEPCK), but not the responses of glycolysis (via F16P) and glycogenesis.

    • computational model
    • glucose metabolism
    • insulin
    • network motif
    • temporal coding

    Mol Syst Biol. 9: 664

    • Received October 29, 2012.
    • Accepted March 28, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Rei Noguchi, Hiroyuki Kubota, Katsuyuki Yugi, Yu Toyoshima, Yasunori Komori, Tomoyoshi Soga, Shinya Kuroda
  • Nucleotide degradation and ribose salvage in yeast
    1. Yi‐Fan Xu1,2,
    2. Fabien Létisse3,
    3. Farnaz Absalan4,
    4. Wenyun Lu1,
    5. Ekaterina Kuznetsova5,
    6. Greg Brown5,
    7. Amy A Caudy6,
    8. Alexander F Yakunin5,
    9. James R Broach4 and
    10. Joshua D Rabinowitz*,1,2
    1. 1 Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
    2. 2 Department of Chemistry, Princeton University, Princeton, NJ, USA
    3. 3 Université de Toulouse, INSA, UPS, INP; LISBP, Toulouse, France
    4. 4 Department of Molecular Biology, Princeton University, Princeton, NJ, USA
    5. 5 Department of Chemical Engineering and Applied Chemistry, Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada
    6. 6 Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
    1. *Corresponding author. Chemistry and Genomics, Princeton University, 241 Carl Icahn Laboratory, Princeton, NJ 08540, USA. Tel.:+1 609 258 8985; Fax:+1 609 258 3565; E‐mail: joshr{at}princeton.edu

    Metabolomics, genetics and biochemistry were combined to obtain the first complete map of the nucleotide degradation and ribose salvage pathway in yeast. This pathway promotes yeast survival in starvation and oxidative stress.

    Synopsis

    Metabolomics, genetics and biochemistry were combined to obtain the first complete map of the nucleotide degradation and ribose salvage pathway in yeast. This pathway promotes yeast survival in starvation and oxidative stress.

    • During carbon starvation, ribose salvage from nucleotides promotes yeast survival.

    • The salvage pathway requires the previously misannotated nucleotidase Phm8.

    • Ribose‐derived carbon accumulates as sedoheptulose‐7‐phosphate.

    • This carbon reserve enables rapid NADPH production in oxidative stress.

    • autophagy
    • mass spectrometry
    • metabolism
    • nutrient starvation
    • Saccharomyces cerevisiae

    Mol Syst Biol. 9: 665

    • Received February 13, 2013.
    • Accepted April 8, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Yi‐Fan Xu, Fabien Létisse, Farnaz Absalan, Wenyun Lu, Ekaterina Kuznetsova, Greg Brown, Amy A Caudy, Alexander F Yakunin, James R Broach, Joshua D Rabinowitz
  • The Neurospora photoreceptor VIVID exerts negative and positive control on light sensing to achieve adaptation
    1. Elan Gin1,2,
    2. Axel C R Diernfellner3,
    3. Michael Brunner*,3 and
    4. Thomas Höfer*,1,2
    1. 1 Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
    2. 2 Bioquant Center, University of Heidelberg, Germany
    3. 3 University of Heidelberg Biochemistry Center (BZH), Heidelberg, Germany
    1. *Corresponding authors. University of Heidelberg Biochemistry Center (BZH), Im Neuenheimer Feld 328, Heidelberg 69120, Germany. Tel.:+49 6221 544207; Fax:+49 6221 544769; E‐mail: michael.brunner{at}bzh.uni-heidelberg.de or Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany. Tel.:+49 6221 5451380; Fax:+49 6221 5451487; E‐mail: t.hoefer{at}dkfz-heidelberg.de

    Light adaptation in Neurospora is mediated by the photoreceptor VIVID, which exerts both a negative and positive effect on light sensing. These apparently paradoxical roles of VIVID are explained by the dynamics of a network motif that utilizes futile cycling.

    Synopsis

    Light adaptation in Neurospora is mediated by the photoreceptor VIVID, which exerts both a negative and positive effect on light sensing. These apparently paradoxical roles of VIVID are explained by the dynamics of a network motif that utilizes futile cycling.

    • The fungus Neurospora detects relative changes in light intensity by adapting to the ambient light level and remaining responsive to increases in light intensity.

    • Both the downregulation of the acute light response and maintained responsiveness are mediated by the photoreceptor VIVID (VVD).

    • Data‐based mathematical modeling shows that this paradoxical function of VVD can be realized by a futile‐cycle network motif that turns feedback inhibition into sensory adaptation.

    • adaptation
    • mathematical model
    • Neurospora
    • protein–protein interaction
    • VVD

    Mol Syst Biol. 9: 667

    • Received October 26, 2012.
    • Accepted April 18, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Elan Gin, Axel C R Diernfellner, Michael Brunner, Thomas Höfer
  • Phosphoproteome dynamics reveal novel ERK1/2 MAP kinase substrates with broad spectrum of functions
    1. Mathieu Courcelles1,2,,
    2. Christophe Frémin1,,
    3. Laure Voisin1,,
    4. Sébastien Lemieux1,3,
    5. Sylvain Meloche*,1,4 and
    6. Pierre Thibault*,1,2,5
    1. 1 Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Quebec, Canada
    2. 2 Department of Biochemistry, Université de Montréal, Montreal, Quebec, Canada
    3. 3 Department of Informatics and Operational Research, Université de Montréal, Montreal, Quebec, Canada
    4. 4 Department of Pharmacology, Université de Montréal, Montreal, Quebec, Canada
    5. 5 Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada
    1. *Corresponding authors. Institute for Research in Immunology and Cancer, Université de Montreal, P.O. Box 6128, Station Centre‐Ville, Montréal, Quebec, Canada H3C 3J7. Tel.:+1 514 3436966; Fax.: +1 514 3436843; E‐mail: sylvain.meloche{at}umontreal.ca or Tel.:+1 514 3436910; Fax: +1 514 3436843; E‐mail: pierre.thibault{at}umontreal.ca
    1. These authors contributed equally to this work.

    Quantitative phosphoproteomics was used to measure the dynamic changes in phosphorylation of ERK1/2 MAP kinases consensus sequences in epithelial cells and to identify 128 novel candidate substrates involved in a broad spectrum of biological functions.

    Synopsis

    Quantitative phosphoproteomics was used to measure the dynamic changes in phosphorylation of ERK1/2 MAP kinases consensus sequences in epithelial cells and to identify 128 novel candidate substrates involved in a broad spectrum of biological functions.

    • Global proteomic analysis of dynamic phosphorylation profiles identifies 7936 phosphorylation sites in 1861 proteins.

    • A biological filtering strategy identifies 128 novel candidate ERK1/2 MAP kinases substrates.

    • Candidate ERK1/2 substrates are involved in a broad spectrum of cellular processes including transcription, RNA splicing, cytoskeleton dynamics and signal transduction.

    • ERK1/2 phosphorylation of JunB potentiates DNA binding of c‐Fos/JunB heterodimers.

    • bioinformatics
    • cell signaling
    • MAP kinases
    • phosphoproteomics
    • phosphorylation dynamics

    Mol Syst Biol. 9: 669

    • Received December 22, 2012.
    • Accepted April 18, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Mathieu Courcelles, Christophe Frémin, Laure Voisin, Sébastien Lemieux, Sylvain Meloche, Pierre Thibault
  • The functional interactome landscape of the human histone deacetylase family
    1. Preeti Joshi1,,
    2. Todd M Greco1,,
    3. Amanda J Guise1,
    4. Yang Luo1,
    5. Fang Yu1,
    6. Alexey I Nesvizhskii2 and
    7. Ileana M Cristea*,1
    1. 1 Department of Molecular Biology, Princeton University, Princeton, NJ, USA
    2. 2 Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
    1. *Corresponding author. 210 Lewis Thomas Laboratory, Department of Molecular Biology, Princeton University, Washington Road, Princeton, NJ 08544, USA. Tel.:+1 609 258 9417; Fax:+1 609 258 4575; E‐mail: icristea{at}princeton.edu
    1. These authors contributed equally to this work.

    This study presents the first global protein interaction network for all 11 human HDACs in T cells and an integrative mass spectrometry approach for profiling relative interaction stability within isolated protein complexes.

    Synopsis

    This study presents the first global protein interaction network for all 11 human HDACs in T cells and an integrative mass spectrometry approach for profiling relative interaction stability within isolated protein complexes.

    • T‐cell lines stably expressing each of the human HDACs (1 ‐ 11), C‐terminally tagged with both EGFP and FLAG, were generated using retroviral transduction.

    • Affinity purification coupled to mass spectrometry‐based proteomics (AP‐MS) was used to build the first global protein interaction network for all eleven human HDACs in T cells.

    • An optimized label free AP‐MS and computational workflow was developed for profiling relative interaction stability among isolated protein complexes.

    • HDAC11 is a member of the “survival of motor neuron” protein complex with a functional role in mRNA splicing.

    • HDAC
    • I‐DIRT
    • interactions
    • proteomics
    • SAINT

    Mol Syst Biol. 9: 672

    • Received January 30, 2013.
    • Accepted April 29, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Preeti Joshi, Todd M Greco, Amanda J Guise, Yang Luo, Fang Yu, Alexey I Nesvizhskii, Ileana M Cristea
  • Network quantification of EGFR signaling unveils potential for targeted combination therapy
    1. Bertram Klinger1,2,,
    2. Anja Sieber1,,
    3. Raphaela Fritsche‐Guenther1,,
    4. Franziska Witzel1,2,
    5. Leanne Berry3,
    6. Dirk Schumacher1,
    7. Yibing Yan4,
    8. Pawel Durek1,2,5,
    9. Mark Merchant3,
    10. Reinhold Schäfer1,5,
    11. Christine Sers1 and
    12. Nils Blüthgen*,1,2
    1. 1 Laboratory of Molecular Tumour Pathology, Institute of Pathology, Charité ‐ Universitätsmedizin Berlin, Berlin, Germany
    2. 2 Institute for Theoretical Biology, Humboldt University Berlin, Berlin, Germany
    3. 3 Department of Translation Oncology, Genentech, Inc., South San Francisco, CA, USA
    4. 4 Oncology Biomarker Development, Genentech, Inc., South San Francisco, CA, USA
    5. 5 German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
    1. *Corresponding author. Institute of Pathology, Charité ‐ Universitätsmedizin Berlin, Charitéplatz 1, Berlin 10115, Germany. Tel.:+49 30 2093 8924; Fax:+49 30 2093 8801; E‐mail: nils.bluethgen{at}charite.de
    1. These authors are the joint first authors

    Analysis of the signaling response of colon cancer cells to systematic perturbations reveals an EGF receptor‐mediated cross‐talk between the MAPK and AKT pathways. Accordingly, the predicted combinatorial treatment is shown to inhibit tumor growth in vivo.

    Synopsis

    Analysis of the signaling response of colon cancer cells to systematic perturbations reveals an EGF receptor‐mediated cross‐talk between the MAPK and AKT pathways. Accordingly, the predicted combinatorial treatment is shown to inhibit tumor growth in vivo.

    • A modular response analysis model trained on perturbation data in a panel of colon cancer cells revealed a negative feedback from MAPK signaling to the EGF receptor, which leads to AKT cross‐activation after MEK inhibition.

    • The model predicted that successful inhibition of growth‐factor signaling in colon cancer requires combined inhibition of MEK and EGF receptor to prevent AKT activation and tumor cell survival, which was confirmed by growth assays.

    • A xenograft tumor model of KRAS‐mutant colon cancer showed that EGFR receptor inhibition alone has no effect, but in combination with a MEK inhibitor successfully reduces tumor growth.

    • cancer
    • EGFR signaling
    • mathematical modeling
    • modular response analysis
    • signal transduction

    Mol Syst Biol. 9: 673

    • Received November 22, 2012.
    • Accepted May 8, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Bertram Klinger, Anja Sieber, Raphaela Fritsche‐Guenther, Franziska Witzel, Leanne Berry, Dirk Schumacher, Yibing Yan, Pawel Durek, Mark Merchant, Reinhold Schäfer, Christine Sers, Nils Blüthgen
  • Dissecting a complex chemical stress: chemogenomic profiling of plant hydrolysates
    1. Jeffrey M Skerker1,2,3,,
    2. Dacia Leon1,3,,
    3. Morgan N Price3,
    4. Jordan S Mar1,4,
    5. Daniel R Tarjan1,4,
    6. Kelly M Wetmore3,
    7. Adam M Deutschbauer3,
    8. Jason K Baumohl3,
    9. Stefan Bauer1,
    10. Ana B Ibáñez1,
    11. Valerie D Mitchell1,
    12. Cindy H Wu4,
    13. Ping Hu4,
    14. Terry Hazen4 and
    15. Adam P Arkin*,1,2,3
    1. 1 Energy Biosciences Institute, University of California, Berkeley, CA, USA
    2. 2 Department of Bioengineering, University of California, Berkeley, CA, USA
    3. 3 Physical Biosciences Division, LBNL, Berkeley, CA, USA
    4. 4 Earth Sciences Division, LBNL, Berkeley, CA, USA
    1. *Corresponding author. Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mailstop 955‐512L, Berkeley, CA 94720, USA. Tel.:+1 510 495 2116; Fax:+1 510 486 6219; E‐mail: aparkin{at}lbl.gov
    1. These authors contributed equally to this work.

    Complex chemical stress arises during the production of biofuels. Large‐scale mutant fitness profiling was used to identify bacterial and yeast tolerance genes and to model fitness in a complex hydrolysate mixture. The resulting model can be used to engineer more tolerant strains.

    Synopsis

    Complex chemical stress arises during the production of biofuels. Large‐scale mutant fitness profiling was used to identify bacterial and yeast tolerance genes and to model fitness in a complex hydrolysate mixture. The resulting model can be used to engineer more tolerant strains.

    • Genome‐wide fitness profiling was used to identify plant hydrolysate tolerance genes in Zymomonas mobilis and Saccharomyces cerevisiae.

    • We modeled fitness in hydrolysate as a mixture of fitness in its components.

    • Outliers in our model led to the identification of a previously unknown component of hydrolysate.

    • Overexpression of a Z. mobilis tolerance gene of unknown function improved ethanol productivity in plant hydrolysate.

    • biofuels
    • chemogenomics
    • plant hydrolysate
    • systems biology
    • tolerance

    Mol Syst Biol. 9: 674

    • Received November 30, 2012.
    • Accepted May 12, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Jeffrey M Skerker, Dacia Leon, Morgan N Price, Jordan S Mar, Daniel R Tarjan, Kelly M Wetmore, Adam M Deutschbauer, Jason K Baumohl, Stefan Bauer, Ana B Ibáñez, Valerie D Mitchell, Cindy H Wu, Ping Hu, Terry Hazen, Adam P Arkin
  • Efficient translation initiation dictates codon usage at gene start
    1. Kajetan Bentele1,3,
    2. Paul Saffert2,
    3. Robert Rauscher2,
    4. Zoya Ignatova2 and
    5. Nils Blüthgen*,1,3
    1. 1 Institute for Theoretical Biology, Humboldt Universität zu Berlin, Berlin, Germany
    2. 2 Insitute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
    3. 3 Institute of Pathology, Charite—Universitätsmedizin Berlin, Berlin, Germany
    1. *Corresponding author. Institute of Pathology, Charite—Universitätsmedizin Berlin, Chariteplatz 1, Berlin D‐10115, Germany. Tel.:+49 30 2093 8924; Fax:+49 30 2093 8801; E‐mail: nils.bluethgen{at}charite.de

    Rare codons are enriched at gene start in many genomes. Genome analysis and experimental testing show that this enrichment evolved to keep the ribosome binding site free from stable mRNA structures, in order to facilitate efficient translation initiation.

    Synopsis

    Rare codons are enriched at gene start in many genomes. Genome analysis and experimental testing show that this enrichment evolved to keep the ribosome binding site free from stable mRNA structures, in order to facilitate efficient translation initiation.

    • The use of rare codons coincides with suppression of mRNA structures at the ribosome binding site across genomes.

    • There is preferential selection for synonymous codons that reduce GC‐content at the beginning of genes and a stronger pressure for rare codon usage in GC‐rich organisms.

    • Amino acids encoded by AU‐rich codons are preferred at gene start.

    • Experimental results show that mRNA structure at translation start strongly influences protein yield.

    • codon usage
    • mRNA structure
    • translation

    Mol Syst Biol. 9: 675

    • Received February 26, 2013.
    • Accepted May 14, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Kajetan Bentele, Paul Saffert, Robert Rauscher, Zoya Ignatova, Nils Blüthgen
  • Integrative genomics of gene and metabolic regulation by estrogen receptors α and β, and their coregulators
    1. Zeynep Madak‐Erdogan1,,
    2. Tze‐Howe Charn2,,
    3. Yan Jiang1,
    4. Edison T Liu3,
    5. John A Katzenellenbogen4 and
    6. Benita S Katzenellenbogen*,1
    1. 1 Department of Molecular and Integrative Physiology, and Cell and Developmental Biology, University of Illinois at Urbana‐Champaign, Urbana, IL, USA
    2. 2 Department of Bioengineering, University of Illinois at Urbana‐Champaign, Urbana, IL, USA
    3. 3 The Genome Institute of Singapore, Singapore, Singapore
    4. 4 Department of Chemistry, University of Illinois at Urbana‐Champaign, Urbana, IL, USA
    1. *Corresponding author. Department of Molecular and Integrative Physiology, University of Illinois at Urbana‐Champaign, 524 Burrill Hall, 407 South Goodwin Avenue, Urbana, IL 61801, USA. Tel.:+1 217 333 9769; Fax:+1 217 244 9906; E‐mail: katzenel{at}illinois.edu
    1. These authors contributed equally to this work.

    To define how the estrogen receptors α and β control specific responses in breast cancer cells, genome‐wide patterns of chromatin binding of the ERα and ERβ receptors and their coregulators, SRC3 and RIP140, were determined and integrated with gene expression data and functional analyses.

    Synopsis

    To define how the estrogen receptors α and β control specific responses in breast cancer cells, genome‐wide patterns of chromatin binding of the ERα and ERβ receptors and their coregulators, SRC3 and RIP140, were determined and integrated with gene expression data and functional analyses.

    • The closely related transcription factors, estrogen receptors ERα and ERβ, can elicit differential cellular responses.

    • To understand the basis of this specificity, chromatin binding of ERs and key coregulators, and gene expression, were analyzed genome wide in human breast cancer cells containing ERα only, ERα+ERβ, and ERβ only.

    • A clustering‐based combinatorial analysis of ChIP‐Seq and gene expression data was used to parse genes into groups, specifying their mode of functional regulation in a particular cell background.

    • Through this analysis, RIP140 was identified as an ERβ‐preferential cofactor regulating cell proliferation, apoptosis, and adipogenesis programs.

    • A 20‐gene ERβ and RIP140 signature was developed, which predicted outcome and disease‐free survival in breast cancer patients.

    • coregulator usage
    • estrogen receptors α and β
    • gene regulation
    • metabolism
    • proliferation

    Mol Syst Biol. 9: 676

    • Received November 12, 2012.
    • Accepted May 3, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Zeynep Madak‐Erdogan, Tze‐Howe Charn, Yan Jiang, Edison T Liu, John A Katzenellenbogen, Benita S Katzenellenbogen
  • Insulin/IGF‐1‐mediated longevity is marked by reduced protein metabolism
    1. Gerdine J Stout1,,
    2. Edwin C A Stigter1,,
    3. Paul B Essers3,,
    4. Klaas W Mulder2,
    5. Annemieke Kolkman1,,
    6. Dorien S Snijders1,
    7. Niels J F van den Broek1,
    8. Marco C Betist3,
    9. Hendrik C Korswagen3,
    10. Alyson W MacInnes3 and
    11. Arjan B Brenkman*,1
    1. 1 University Medical Center Utrecht, Wilhelmina Children's Hospital, Department of Molecular Cancer Research, Section Metabolic Diseases, Utrecht, The Netherlands and Netherlands Metabolomics Centre, Leiden, The Netherlands
    2. 2 Nijmegen Centre for Molecular Life Sciences, Molecular Developmental Biology 274, Nijmegen, The Netherlands
    3. 3 Hubrecht Institute, KNAW and University Medical Center Utrecht, Utrecht, The Netherlands
    1. *Corresponding author. University Medical Center Utrecht, Wilhelmina Children's Hospital, Department of Molecular Cancer Research, Section of Metabolic diseases, and Netherlands Metabolomics Centre, Lundlaan 6, Huispostnummer: KC.02.069.1, 3508 AB, Utrecht, The Netherlands; Tel.:+31 8875 55318; Fax:+31 8875 54295; E‐mail: a.b.brenkman{at}umcutrecht.nl
    1. These authors contributed equally to this work.

    • Present address: KWR Watercycle Research Institute, Nieuwegein, The Netherlands.

    Quantitative proteomics, lifespan analysis, and biochemical assays were utilized to show that Insulin/IGF‐1‐mediated longevity in C. elegans is strongly associated with a daf‐16 dependent global reduction in protein metabolism.

    Synopsis

    Quantitative proteomics, lifespan analysis, and biochemical assays were utilized to show that Insulin/IGF‐1‐mediated longevity in C. elegans is strongly associated with a daf‐16 dependent global reduction in protein metabolism.

    • A daf‐16 dependent global reduction in protein translation is observed in daf‐2 long‐lived mutant.

    • The reduction in active translation is independent of germline activity

    • A role for protein metabolism is identified in the Insulin/IGF‐1‐mediated extension of life.

    • ageing
    • high‐throughput analysis
    • metabolism
    • protein metabolism
    • translation

    Mol Syst Biol. 9: 679

    • Received July 31, 2012.
    • Accepted May 27, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Gerdine J Stout, Edwin C A Stigter, Paul B Essers, Klaas W Mulder, Annemieke Kolkman, Dorien S Snijders, Niels J F van den Broek, Marco C Betist, Hendrik C Korswagen, Alyson W MacInnes, Arjan B Brenkman
  • Targeted proteomics reveals strain‐specific changes in the mouse insulin and central metabolic pathways after a sustained high‐fat diet
    1. Eduard Sabidó1,
    2. Yibo Wu1,
    3. Lucia Bautista1,
    4. Thomas Porstmann1,
    5. Ching‐Yun Chang2,
    6. Olga Vitek2,3,
    7. Markus Stoffel*,1 and
    8. Ruedi Aebersold*,1,4
    1. 1 Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
    2. 2 Department of Statistics, Purdue University, West Lafayette, IN, USA
    3. 3 Department of Computer Science, Purdue University, West Lafayette, IN, USA
    4. 4 Department of Science, Faculty of Science, University of Zürich, Zürich, Switzerland
    1. *Corresponding authors. Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Wolfgang‐Pauli‐Strasse 16, 8093 Zürich, Switzerland. Tel.:+41 44 633 4560; Fax:+41 44 633 1362; E‐mail: stoffel{at}imsb.biol.ethz.ch or Tel.:+41 44 633 1071; Fax:+41 44 633 1051; E‐mail: aebersold{at}imsb.biol.ethz.ch

    Quantitative measurement of proteins involved in insulin signaling and central metabolism in C57BL/6J and 129Sv mice subjected to a sustained high‐fat diet reveals that the two strains diverge early in their response to the feeding regimen.

    Synopsis

    Quantitative measurement of proteins involved in insulin signaling and central metabolism in C57BL/6J and 129Sv mice subjected to a sustained high‐fat diet reveals that the two strains diverge early in their response to the feeding regimen.

    • Quantitative targeted protein measurements were designed to quantify murine proteins covering the insulin‐signaling pathwayand the lipid and carbohydrate metabolism and used to compare the differential effect of a persistent high‐fat diet in C57BL/6Jand 129Sv mouse strains.

    • Differential effect of a persistent high‐fat diet were compared in C57BL/6J and 129Sv mouse strains.

    • Differences in protein abundances suggest that peroxisomal β‐oxidation is actively promoted in fatty C57BL/6J mice whereaslipogenesis activation dominates the response of 129Sv mice.

    • Most strain‐specific changes were apparent early in the regimen when phenotypic changes were already set, but not yet verypronounced and they allow a clear discrimination of the mouse strains at an early stage during the long‐term high‐fat diet.

    • Persistent high‐fat diet also alters the transient changes that normally occur in C57BL/6J and 129Sv mice in response to fastingor food intake.

    • liver
    • metabolic syndrome
    • NAFLD
    • proteomics
    • SRM

    Mol Syst Biol. 9: 681

    • Received October 18, 2012.
    • Accepted June 1, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Eduard Sabidó, Yibo Wu, Lucia Bautista, Thomas Porstmann, Ching‐Yun Chang, Olga Vitek, Markus Stoffel, Ruedi Aebersold
  • A yeast one‐hybrid and microfluidics‐based pipeline to map mammalian gene regulatory networks
    1. Carine Gubelmann1,
    2. Sebastian M Waszak1,
    3. Alina Isakova1,
    4. Wiebke Holcombe1,
    5. Korneel Hens1,
    6. Antonina Iagovitina1,
    7. Jean‐Daniel Feuz1,
    8. Sunil K Raghav1,
    9. Jovan Simicevic1 and
    10. Bart Deplancke*,1
    1. 1 Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
    1. *Corresponding author. Laboratory of Systems Biology and Genetics (LSBG), EPFL‐SV‐IBI‐LSBG, Station 19, 1015 Lausanne, Switzerland. Tel.: +41 21 693 18 21; Fax: +41 21 693 09 80; E‐mail: bart.deplancke{at}epfl.ch

    A combined cross‐platform approach is presented to experimentally identify and characterize interactions between mouse transcription factors and regulatory elements at unprecedented resolution and throughput.

    Synopsis

    A combined cross‐platform approach is presented to experimentally identify and characterize interactions between mouse transcription factors and regulatory elements at unprecedented resolution and throughput.

    • We generated a mouse‐specific transcription factor (TF) library consisting of 750 full‐length sequence‐verified open‐reading frame clones.

    • We used this resource to develop a cross‐platform pipeline to experimentally characterize mammalian regulatory elements of interest for interacting TFs at unprecedented throughput and resolution.

    • Using well‐described regulatory elements as well as orphan enhancers, we show that this cross‐platform pipeline characterizes known and uncovers novel TF–DNA interactions that are relevant in vivo.

    • gene regulatory networks
    • microfluidics
    • mouse open‐reading frame (ORF) clone collection
    • transcription factor
    • yeast one‐hybrid

    Mol Syst Biol. 9: 682

    • Received January 10, 2013.
    • Accepted June 28, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Carine Gubelmann, Sebastian M Waszak, Alina Isakova, Wiebke Holcombe, Korneel Hens, Antonina Iagovitina, Jean‐Daniel Feuz, Sunil K Raghav, Jovan Simicevic, Bart Deplancke
  • Bacterial cheating drives the population dynamics of cooperative antibiotic resistance plasmids
    1. Eugene A Yurtsev1,,
    2. Hui Xiao Chao1,,
    3. Manoshi S Datta2,
    4. Tatiana Artemova1 and
    5. Jeff Gore*,1
    1. 1 Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
    2. 2 Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
    1. *Corresponding author. Department of Physics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA. Tel.:+1 617 715 4251; Fax:+1 617 258 6883; E‐mail: gore{at}mit.edu
    1. These authors contributed equally to this work.

    Analysis of the cooperative nature of antibiotic inactivation reveals factors enabling coexistence of resistant and sensitive cells, showing that social interactions affect the spread of antibiotic resistance.

    Synopsis

    Analysis of the cooperative nature of antibiotic inactivation reveals factors enabling coexistence of resistant and sensitive cells, showing that social interactions affect the spread of antibiotic resistance.

    • Inactivation of β‐lactam antibiotics by resistant bacteria is a cooperative behavior that enables sensitive bacteria to survive antibiotic treatment.

    • At high cell densities, resistant cells protect sensitive cells against antibiotic concentrations that are 100‐fold higher than the minimum inhibitory concentration of sensitive cells.

    • Eventually, the fraction of resistant cells in a bacterial population reaches an equilibrium fraction that depends on the initial cell density and antibiotic concentration.

    • The addition of a commonly used β‐lactamase inhibitor can lead to the spread of resistance in the population.

    • antibiotic inactivation
    • antibiotic resistance
    • cooperation and cheating
    • β‐lactam
    • population dynamics

    Mol Syst Biol. 9: 683

    • Received February 10, 2013.
    • Accepted July 7, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Eugene A Yurtsev, Hui Xiao Chao, Manoshi S Datta, Tatiana Artemova, Jeff Gore
  • Multilevel selection analysis of a microbial social trait
    1. Laura de Vargas Roditi1,
    2. Kerry E Boyle1 and
    3. Joao B Xavier*,1
    1. 1 Program in Computational Biology, Memorial Sloan‐Kettering Cancer Center, New York, NY, USA
    1. *Corresponding author. Program in Computational Biology, Memorial Sloan‐Kettering Cancer Center, 1275 York Avenue, Box 460, New York, NY 10065, USA. Tel.:+1 646 888 3195; Fax:+1 646 422 0717; E‐mail: xavierj{at}mskcc.org

    The evolution of cooperation in colonies of swarming bacteria is analyzed by manipulating the cost‐to‐benefit ratio of cooperation to show that ‘constitutive’ cooperation is favored only when relatedness is high, in contrast to ‘prudent’ cooperation.

    Synopsis

    The evolution of cooperation in colonies of swarming bacteria is analyzed by manipulating the cost‐to‐benefit ratio of cooperation to show that ‘constitutive’ cooperation is favored only when relatedness is high, in contrast to ‘prudent’ cooperation.

    • Swarming in the bacterium Pseudomonas aeruginosa is a cooperative trait that is beneficial for the group, as it allows colony expansion.

    • Constitutive swarming cooperation is costly to cooperating individuals and has diminishing returns, but can still be favored by multilevel selection if relatedness is high.

    • Swarming cooperation is favored in a wider range of conditions when regulated by metabolic prudence.

    • conflict
    • cooperation
    • metabolic prudence
    • Pseudomonas aeruginosa
    • swarming

    Mol Syst Biol. 9: 684

    • Received May 17, 2013.
    • Accepted July 24, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Laura de Vargas Roditi, Kerry E Boyle, Joao B Xavier
  • Generalized bacterial genome editing using mobile group II introns and Cre‐lox
    1. Peter J Enyeart1,
    2. Steven M Chirieleison2,,
    3. Mai N Dao3,4,
    4. Jiri Perutka1,3,
    5. Erik M Quandt1,
    6. Jun Yao1,3,4,
    7. Jacob T Whitt1,3,4,
    8. Adrian T Keatinge‐Clay1,3,
    9. Alan M Lambowitz1,3,4 and
    10. Andrew D Ellington*,1,3
    1. 1 Institute for Cell and Molecular Biology, University of Texas at Austin, Austin, TX, USA
    2. 2 Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA
    3. 3 Department of Chemistry and Biochemistry, University of Texas at Austin, Austin, TX, USA
    4. 4 Section of Molecular Genetics and Microbiology, School of Biological Sciences, University of Texas at Austin, Austin, TX, USA
    1. *Corresponding author. Institute for Cell and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA. Tel.:+1 512 232 3424; Fax:+1 512 471 7014; E‐mail: andy.ellington{at}mail.utexas.edu
    • Present address: School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.

    A general bacterial genome engineering framework, ‘Genome Editing via Targetrons and Recombinases’ (GETR), is presented. GETR combines mobile group II introns (targetrons) and the Cre/lox system to allow genomic manipulations at a large scale.

    Synopsis

    A general bacterial genome engineering framework, ‘Genome Editing via Targetrons and Recombinases’ (GETR), is presented. GETR combines mobile group II introns (targetrons) and the Cre/lox system to allow genomic manipulations at a large scale.

    • The combination of targetrons and Cre/lox represents a broad‐host range solution to genome editing.

    • Engineered targetrons were used to deliver lox sites site‐specifically into the bacterial genome.

    • Targetrons carrying lox sites were used to generate large‐scale insertions, deletions, inversions, and unique cut‐and‐paste operations in bacterial genomes.

    • bacterial genome engineering
    • Cre‐lox
    • mobile group II introns
    • Staphylococcus aureus
    • Shewanella oneidensis

    Mol Syst Biol. 9: 685

    • Received May 31, 2013.
    • Accepted July 23, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Peter J Enyeart, Steven M Chirieleison, Mai N Dao, Jiri Perutka, Erik M Quandt, Jun Yao, Jacob T Whitt, Adrian T Keatinge‐Clay, Alan M Lambowitz, Andrew D Ellington
  • Chromosome segregation by the Escherichia coli Min system
    1. Barbara Di Ventura*,1,,
    2. Benoît Knecht2,
    3. Helena Andreas3,
    4. William J Godinez4,
    5. Miriam Fritsche2,
    6. Karl Rohr4,
    7. Walter Nickel3,
    8. Dieter W Heermann2 and
    9. Victor Sourjik*,1
    1. 1 Zentrum für Molekulare Biologie der Universität Heidelberg, DKFZ‐ZMBH Alliance, Heidelberg, Germany
    2. 2 Institute for Theoretical Physics, University of Heidelberg, Heidelberg, Germany
    3. 3 Heidelberg University Biochemistry Center, University of Heidelberg, Heidelberg, Germany
    4. 4 Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Institute for Pharmacy and Molecular Biotechnology (IPMB), BioQuant and DKFZ, University of Heidelberg, Heidelberg, Germany
    1. *Corresponding authors. BioQuant, University of Heidelberg, Im Neuenheimer Feld 267, Heidelberg 69120, Germany. Tel.:+49 6221 54 51283; Fax:+49 6221 54 51488; E‐mail: barbara.diventura{at}bioquant.uni-heidelberg.de or Zentrum für Molekulare Biologie der Universität Heidelberg, DKFZ‐ZMBH Alliance, Heidelberg, Germany. Tel.:+49 6221 54 6958; Fax:+49 6221 54 5892; E‐mail: v.sourjik{at}zmbh.uni-heidelberg.de
    • Present address: Department of Bioinformatics and Functional Genomics, Synthetic Biology Group, Institute for Pharmacy and Biotechnology (IPMB) and BioQuant, University of Heidelberg, Heidelberg, Germany

    The existence and nature of an active chromosome segregation apparatus in bacteria has been a long‐standing debate. A novel Brownian ratchet‐type mechanism of chromosome segregation mediated by the Min system is identified in E. coli.

    Synopsis

    The existence and nature of an active chromosome segregation apparatus in bacteria has been a long‐standing debate. A novel Brownian ratchet‐type mechanism of chromosome segregation mediated by the Min system is identified in E. coli.

    • Numerical simulations show that entropy alone is not sufficient to complete segregation of bacterial chromosomes.

    • Chromosome segregation can be enhanced by a polar gradient of DNA tethering sites on the membrane.

    • The cell‐division regulator MinD forms a polar gradient on the membrane and binds DNA in an ATP‐dependent manner.

    • The bacterial Min system coordinates cell division and chromosome segregation.

    • computer simulations
    • chromosome segregation
    • DNA binding
    • MinD
    • Min system

    Mol Syst Biol. 9: 686

    • Received April 19, 2013.
    • Accepted August 7, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Barbara Di Ventura, Benoît Knecht, Helena Andreas, William J Godinez, Miriam Fritsche, Karl Rohr, Walter Nickel, Dieter W Heermann, Victor Sourjik
  • Barriers to transmission of transcriptional noise in a c‐fos c‐jun pathway
    1. Khyati Shah1,2 and
    2. Sanjay Tyagi*,1
    1. 1 Public Health Research Institute, New Jersey Medical School, Rutgers University, Newark, NJ, USA
    2. 2 Department of Biological Chemistry, New Jersey Institute of Technology, Newark, NJ, USA
    1. *Corresponding author. Public Health Research Institute, New Jersey Medical School, Rutgers University, 225 Warren Street, Newark, NJ 07103, USA. Tel.:+1 973 854 3372; Fax:+1 973 854 3374; E‐mail: sanjay.tyagi{at}rutgers.edu

    In higher eukaryotes, chromatin limits the transmission of transcriptional noise by insulating downstream genes from cell‐to‐cell variations in transcription factor heterodimers. In addition, heterodimers are shown to exhibit reduced cell‐to‐cell variation compared to their parent mRNAs.

    Synopsis

    In higher eukaryotes, chromatin limits the transmission of transcriptional noise by insulating downstream genes from cell‐to‐cell variations in transcription factor heterodimers. In addition, heterodimers are shown to exhibit reduced cell‐to‐cell variation compared to their parent mRNAs.

    • The numbers of mRNA molecules encoding c‐fos and c‐jun do not correlate with each other in individual cells.

    • The numbers of c‐fos and c‐jun heterodimers vary little compared to their parent mRNAs.

    • Transcription of downstream genes is intrinsically noisy but there is little or no transmission of noise from the upstream steps.

    • cellular heterogeneity
    • noise in gene expression
    • transcription

    Mol Syst Biol. 9: 687

    • Received December 21, 2012.
    • Accepted August 8, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Khyati Shah, Sanjay Tyagi
  • A map of cell type‐specific auxin responses
    1. Bastiaan O R Bargmann1,2,
    2. Steffen Vanneste3,4,
    3. Gabriel Krouk5,
    4. Tal Nawy1,
    5. Idan Efroni1,
    6. Eilon Shani2,
    7. Goh Choe2,
    8. Jiří Friml3,4,6,
    9. Dominique C Bergmann7,
    10. Mark Estelle2 and
    11. Kenneth D Birnbaum*,1
    1. 1 Biology Department, Center for Genomics and Systems Biology, New York University, New York, NY, USA
    2. 2 Department of Cell and Developmental Biology, UCSD, La Jolla, CA, USA
    3. 3 Department of Plant Systems Biology, VIB, Ghent, Belgium
    4. 4 Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
    5. 5 Laboratoire de Biochimie et Physiologie Moléculaire des Plantes, Institut de Biologie Intégrative des Plantes—Claude Grignon, Montpellier, France
    6. 6 Institute of Science and Technology Austria (IST Austria), Klosterneuburg, Austria
    7. 7 Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
    1. *Corresponding author. Biology Department, Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA. Tel.:+1 212 998 8257; Fax:+1 212 995 4015; E‐mail: ken.birnbaum{at}nyu.edu

    The transcriptional response to auxin was analyzed in four root cell types. The newly obtained data were cross‐referenced with spatial expression maps to examine auxin's role in regulating gene expression in the root meristem.

    Synopsis

    The transcriptional response to auxin was analyzed in four root cell types. The newly obtained data were cross‐referenced with spatial expression maps to examine auxin's role in regulating gene expression in the root meristem.

    • The majority of the thousands of auxin‐responsive genes in the Arabidopsis thaliana root show a spatial bias in their induction or repression by auxin treatment.

    • Auxin promotes the expression of cell‐identity markers for the developing xylem and quiescent center, whereas it inhibits markers for the maturing xylem, cortex and trichoblasts.

    • Relative induction or repression by auxin predicts expression along the longitudinal axis of the root.

    • Arabidopsis
    • development
    • root apical meristem
    • signaling gradient

    Mol Syst Biol. 9: 688

    • Received March 13, 2013.
    • Accepted July 23, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Bastiaan O R Bargmann, Steffen Vanneste, Gabriel Krouk, Tal Nawy, Idan Efroni, Eilon Shani, Goh Choe, Jiří Friml, Dominique C Bergmann, Mark Estelle, Kenneth D Birnbaum
  • Protein synthesis rate is the predominant regulator of protein expression during differentiation
    1. Anders R Kristensen1,
    2. Joerg Gsponer1 and
    3. Leonard J Foster*,1
    1. 1 Department of Biochemistry and Molecular Biology, Centre for High‐Throughput Biology, University of British Columbia, Vancouver, British Columbia, Canada
    1. *Corresponding author. Department of Biochemistry and Molecular Biology, University of British Columbia, 2125 East Mall, Vancouver, British Columbia, Canada V6T 1Z4. Tel.:+1 604 822 8311; E‐mail: foster{at}chibi.ubc.ca

    The contribution of transcription, protein synthesis and degradation rates to the control of protein expression during differentiation was analyzed using quantitative proteomics and transcriptomics data. Protein synthesis rate was identified as the main determinant of protein expression.

    Synopsis

    The contribution of transcription, protein synthesis and degradation rates to the control of protein expression during differentiation was analyzed using quantitative proteomics and transcriptomics data. Protein synthesis rate was identified as the main determinant of protein expression.

    • The lack of correlation usually observed between transcript and protein levels can be fully explained when correcting for the synthesis and degradation rates of the individual proteins.

    • Synthesis rates for individual proteins are extensively regulated, in contrast to degradation rates that mostly remain constant in response to differentiation.

    • The modularity of macromolecular complexes is maintained during synthesis and degradation of the complexes.

    • differentiation
    • macromolecular complexes
    • protein turnover
    • proteomics
    • systems biology

    Mol Syst Biol. 9: 689

    • Received April 24, 2013.
    • Accepted August 21, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Anders R Kristensen, Joerg Gsponer, Leonard J Foster
  • Dissection of a Krox20 positive feedback loop driving cell fate choices in hindbrain patterning
    1. Yassine X Bouchoucha1,2,3,,
    2. Jürgen Reingruber1,2,3,4,,
    3. Charlotte Labalette1,2,3,
    4. Michel A Wassef1,2,3,,
    5. Elodie Thierion1,2,3,
    6. Carole Desmarquet‐Trin Dinh1,2,3,
    7. David Holcman*,1,2,3,4,,
    8. Pascale Gilardi‐Hebenstreit1,2,3, and
    9. Patrick Charnay*,1,2,3
    1. 1 Ecole Normale Supérieure, IBENS, Paris, France
    2. 2 INSERM, U1024, Paris, France
    3. 3 CNRS, UMR 8197, Paris, France
    4. 4 Group of Computational Biology and Applied Mathematics, IBENS, Paris, France
    1. *Corresponding authors. Ecole Normale Supérieure; IBENS, 46 rue d’Ulm, 75005 Paris, France. Tel.:+33 1 4432 3607; Fax:+33 1 4432 3988; E‐mail: patrick.charnay{at}ens.fr or E‐mail: david.holcman{at}ens.fr
    1. These authors contributed equally to this work.

    • Present address: Institut Curie, Unité de Génétique et de Biologie du Développement, 75005 Paris, France.

    A positive autoregulatory loop required for the expression of the transcription factor Krox20 was dissected using in vivo quantitative data and biophysical modelling to demonstrate how Krox20 controls cell fate decision and rhombomere size in the hindbrain.

    Synopsis

    A positive autoregulatory loop required for the expression of the transcription factor Krox20 was dissected using in vivo quantitative data and biophysical modelling to demonstrate how Krox20 controls cell fate decision and rhombomere size in the hindbrain.

    • Positive autoregulation of Krox20 underpins a bistable switch that turns a transient input signal into cell fate commitment, as demonstrated in single cell analyses.

    • The duration and strength of the input signal control the size of the hindbrain segments by modulating the distribution between two cell fates.

    • The progressive extinction of Krox20 expression involves a destabilization of the loop by repressor molecules.

    • Fgf
    • Krox20
    • rhombomere
    • stochastic model
    • transcriptional enhancer

    Mol Syst Biol. 9: 690

    • Received March 13, 2013.
    • Accepted August 21, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Yassine X Bouchoucha, Jürgen Reingruber, Charlotte Labalette, Michel A Wassef, Elodie Thierion, Carole Desmarquet‐Trin Dinh, David Holcman, Pascale Gilardi‐Hebenstreit, Patrick Charnay
  • Human disease locus discovery and mapping to molecular pathways through phylogenetic profiling
    1. Yuval Tabach*,1,2,
    2. Tamar Golan3,
    3. Abrahan Hernández‐Hernández4,
    4. Arielle R Messer3,
    5. Tomoyuki Fukuda4,
    6. Anna Kouznetsova4,
    7. Jian‐Guo Liu4,
    8. Ingrid Lilienthal4,
    9. Carmit Levy*,3, and
    10. Gary Ruvkun*,1,2,
    1. 1 Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
    2. 2 Department of Genetics, Harvard Medical School, Boston, MA, USA
    3. 3 Department of Human Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
    4. 4 Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
    1. *Corresponding authors. Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA. Tel.:+1 917 755 7233; Fax:+1 617 726 5949; E‐mail: tabach{at}molbio.mgh.harvard.edu or Department of Human Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel. Tel.:+972 3 6409900, Fax:+972 3 6405168; E‐mail: carmitlevy{at}post.tau.ac.il or Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA. Tel.:+1 617 726 5959; E‐mail: ruvkun{at}molbio.mgh.harvard.edu
    1. These authors contributed equally to this work

    By analyzing the conservation of human proteins across 87 species, we sorted proteins into clusters of coevolution. Some clusters are enriched for genes assigned to particular human diseases or molecular pathways; the other genes in the same cluster may function in related pathways and diseases.

    Synopsis

    By analyzing the conservation of human proteins across 87 species, we sorted proteins into clusters of coevolution. Some clusters are enriched for genes assigned to particular human diseases or molecular pathways; the other genes in the same cluster may function in related pathways and diseases.

    • Many genes that were thought to map to different diseases are actually coevolved together and mapped into the same phylogenetic clusters.

    • Many molecular pathways map to the same phylogenetic clusters as genes associated with specific human diseases.

    • Focusing on proteins coevolved with the microphthalmia‐associated transcription factor (MITF), we identified the Notch pathway suppressor of hairless (RBP‐Jk/SuH) transcription factor, and showed that RBP‐Jk functions as an MITF cofactor.

    • Our analysis thus establishes a connectivity between different diseases and pathways, linking diseases phenotypes and functional gene groups.

    • HPO
    • MSigDB
    • Heme
    • synaptonemal complex

    Mol Syst Biol. 9: 692

    • Received June 21, 2013.
    • Accepted August 29, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Yuval Tabach, Tamar Golan, Abrahan Hernández‐Hernández, Arielle R Messer, Tomoyuki Fukuda, Anna Kouznetsova, Jian‐Guo Liu, Ingrid Lilienthal, Carmit Levy, Gary Ruvkun
  • Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction
    1. Edward J O'Brien1,,
    2. Joshua A Lerman1,,
    3. Roger L Chang1,
    4. Daniel R Hyduke1 and
    5. Bernhard Ø Palsson*,1
    1. 1 Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
    1. *Corresponding author. Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, Mail Code 0412, PFBH Room 419, La Jolla, CA 92093‐0412, USA. Tel.:+1 858 534 5668; Fax:+1 858 822 3120; E‐mail: palsson{at}ucsd.edu
    1. These authors contributed equally to this work.

    A constraint‐based approach for integrative modeling of metabolism and gene expression is developed. New constraints on molecular catalysis increase both the accuracy and scope of computable phenotypes corresponding to optimal microbial growth.

    Synopsis

    A constraint‐based approach for integrative modeling of metabolism and gene expression is developed. New constraints on molecular catalysis increase both the accuracy and scope of computable phenotypes corresponding to optimal microbial growth.

    • An integrated network of metabolic and gene expression pathways is built for E. coli.

    • A growth model is developed by adding demands and constraints on molecular catalysis.

    • Model yields accurate predictions of growth phenotypes from molecules to whole cell.

    • A few basic principles underlie growth rate optimization at the systems level.

    • gene expression
    • genome‐scale
    • metabolism
    • molecular efficiency
    • optimality

    Mol Syst Biol. 9: 693

    • Received April 22, 2013.
    • Accepted September 5, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Edward J O'Brien, Joshua A Lerman, Roger L Chang, Daniel R Hyduke, Bernhard Ø Palsson
  • A competitive protein interaction network buffers Oct4‐mediated differentiation to promote pluripotency in embryonic stem cells
    1. Silvia Muñoz Descalzo1,2,,
    2. Pau Rué3,4,,
    3. Fernando Faunes1,§,
    4. Penelope Hayward1,
    5. Lars Martin Jakt5,||,
    6. Tina Balayo1,
    7. Jordi Garcia‐Ojalvo3,4 and
    8. Alfonso Martinez Arias*,1
    1. 1 Department of Genetics, University of Cambridge, Cambridge, UK
    2. 2 Biology and Biochemistry Department, University of Bath, Bath, UK
    3. 3 Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Barcelona, Spain
    4. 4 Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Terrassa, Spain
    5. 5 Stem Cell Biology Group, Riken Center for Developmental Biology, Kobe, Japan
    1. *Corresponding author. Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK. Tel.:+44 (0)1223 766742; Fax:+44 (0)1223 333992; E‐mail: ama11{at}hermes.cam.ac.uk
    1. These authors contributed equally to this work

    • Present address: Department of Genetics, University of Cambridge, Cambridge, UK

    • § Present address: Facultad de Ciencias Biologicas, Pontificia Universidad Católica de Chile, Avda. Libertador Bernardo OHiggins 340, Santiago, Chile

    • || Present address: Department of Systems Medicine, Mitsunada Sakaguchi Laboratory, Keio University School of Medicine, Tokyo, Japan.

    The dynamic competition for complex formation between the pluripotency network components Oct4, Nanog, Tcf3, and β‐catenin prevents embryonic stem cell differentiation by controlling the levels of free Oct4.

    Synopsis

    The dynamic competition for complex formation between the pluripotency network components Oct4, Nanog, Tcf3, and β‐catenin prevents embryonic stem cell differentiation by controlling the levels of free Oct4.

    • Pluripotency is defined by the ratios between the levels of pluripotency factors rather than by their absolute levels.

    • Competition between different protein complexes involving Nanog, Oct4, Tcf3, and β‐catenin can account for the ratios associated with pluripotency.

    • The unstable pluripotency of Nanog mutant cells was shown to depend on the interactions between Oct4 and β‐catenin.

    • The function of the protein competition network is to control the levels of free Oct4, which are balanced by Nanog and β‐catenin in embryonic stem cells.

    • β‐catenin
    • mathematical modelling
    • Oct4
    • pluripotency
    • protein network

    Mol Syst Biol. 9: 694

    • Received April 23, 2013.
    • Accepted August 23, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Silvia Muñoz Descalzo, Pau Rué, Fernando Faunes, Penelope Hayward, Lars Martin Jakt, Tina Balayo, Jordi Garcia‐Ojalvo, Alfonso Martinez Arias
  • Natural sequence variants of yeast environmental sensors confer cell‐to‐cell expression variability
    1. Steffen Fehrmann1,
    2. Hélène Bottin‐Duplus1,
    3. Andri Leonidou1,
    4. Esther Mollereau1,
    5. Audrey Barthelaix1,
    6. Wu Wei2,
    7. Lars M Steinmetz2 and
    8. Gaël Yvert*,1
    1. 1 Laboratoire de Biologie Moléculaire de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1, Lyon, France
    2. 2 Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    1. *Corresponding author. Laboratoire de Biologie Moléculaire de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, 46 Allée d'Italie, Lyon F‐69007, France. Tel.:+33 4 72 72 87 17; Fax:+33 4 72 72 80 80; E‐mail: Gael.Yvert{at}ens-lyon.fr

    DNA polymorphisms that change cell‐to‐cell variability in gene expression are identified in a screen for ‘Probabilistic Trait Loci’ in yeast. By modifying transmembrane transporter genes, these natural variants modulate intraclonal phenotypic diversification.

    Synopsis

    DNA polymorphisms that change cell‐to‐cell variability in gene expression are identified in a screen for ‘Probabilistic Trait Loci’ in yeast. By modifying transmembrane transporter genes, these natural variants modulate intraclonal phenotypic diversification.

    • We mapped genetic loci affecting cell–cell variability in gene expression.

    • One variant enhanced both expression of a transporter and variability in a metabolic pathway.

    • A sequence change in another transporter also increased pathway variability.

    • The study invites to apprehend complex traits from a nondeterministic angle.

    • bet hedging
    • complex trait
    • methionine
    • noise in gene expression
    • QTL

    Mol Syst Biol. 9: 695

    • Received February 14, 2013.
    • Accepted September 6, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Steffen Fehrmann, Hélène Bottin‐Duplus, Andri Leonidou, Esther Mollereau, Audrey Barthelaix, Wu Wei, Lars M Steinmetz, Gaël Yvert
  • A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities
    1. Franco J Vizeacoumar1,2,,
    2. Roland Arnold1,,
    3. Frederick S Vizeacoumar3,
    4. Megha Chandrashekhar1,
    5. Alla Buzina1,
    6. Jordan T F Young3,4,
    7. Julian H M Kwan1,4,
    8. Azin Sayad1,
    9. Patricia Mero1,
    10. Steffen Lawo3,4,
    11. Hiromasa Tanaka1,
    12. Kevin R Brown1,
    13. Anastasia Baryshnikova1,4,
    14. Anthony B Mak1,
    15. Yaroslav Fedyshyn1,
    16. Yadong Wang5,
    17. Glauber C Brito1,
    18. Dahlia Kasimer1,
    19. Taras Makhnevych1,
    20. Troy Ketela1,
    21. Alessandro Datti3,
    22. Mohan Babu6,
    23. Andrew Emili1,4,
    24. Laurence Pelletier3,4,
    25. Jeff Wrana3,4,
    26. Zev Wainberg7,
    27. Philip M Kim1,4,8,
    28. Robert Rottapel5,9,10,
    29. Catherine A O'Brien5,11,12,
    30. Brenda Andrews1,4,
    31. Charles Boone1,4 and
    32. Jason Moffat*,1,4
    1. 1 Donnelly Centre and Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada
    2. 2 Saskatchewan Cancer Agency, Department of Biochemistry, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
    3. 3 Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
    4. 4 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
    5. 5 Campbell Family Institute, Ontario Cancer Institute, Princess Margaret Hospital, University Health Network, Toronto, Ontario, Canada
    6. 6 Department of Biochemistry, Research and Innovation Centre, University of Regina, Regina, Saskatchewan, Canada
    7. 7 Jonnson Comprehensive Cancer Center, Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, USA
    8. 8 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
    9. 9 Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
    10. 10 Division of Rheumatology, Department of Medicine, St. Michael's Hospital, Toronto, Ontario, Canada
    11. 11 Department of Laboratory Medicine and Pathology, and Department of Surgery, University of Toronto, Toronto, Ontario, Canada
    12. 12 Department of Surgery, University Health Network, Toronto, Ontario, Canada
    1. *Corresponding author. Donnelly Centre and Banting and Best Department of Medical Research, University of Toronto, 160 College Street, Toronto, Ontario, Canada M5S 3E1. Tel.:+1 416 978 0336; Fax:+1 416 946 8253; E‐mail: j.moffat{at}utoronto.ca
    1. These authors contributed equally to this work

    This study defines a network of synthetic sick/lethal interactions with a set of query genes in a series of isogenic cancer cell lines. Analysis of differential essentiality reveals general properties in genetic interaction networks derived from studies on model organisms.

    Synopsis

    This study defines a network of synthetic sick/lethal interactions with a set of query genes in a series of isogenic cancer cell lines. Analysis of differential essentiality reveals general properties in genetic interaction networks derived from studies on model organisms.

    • This study defined about 200 negative genetic interactions in the isogenic cancer cell line background.

    • Mapping of negative genetic interactions in a systematic fashion in isogenic cancer cell lines has revealed novel functions for several uncharacterized genes.

    • This study demonstrates that differential essentiality profiles derived from isogenic cancer cell lines can be used to classify genetic dependencies in non‐isogenic cancer cell lines.

    • genetic interaction
    • genome stability
    • mitotic stress
    • pooled shRNA screening

    Mol Syst Biol. 9: 696

    • Received May 24, 2013.
    • Accepted September 3, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Franco J Vizeacoumar, Roland Arnold, Frederick S Vizeacoumar, Megha Chandrashekhar, Alla Buzina, Jordan T F Young, Julian H M Kwan, Azin Sayad, Patricia Mero, Steffen Lawo, Hiromasa Tanaka, Kevin R Brown, Anastasia Baryshnikova, Anthony B Mak, Yaroslav Fedyshyn, Yadong Wang, Glauber C Brito, Dahlia Kasimer, Taras Makhnevych, Troy Ketela, Alessandro Datti, Mohan Babu, Andrew Emili, Laurence Pelletier, Jeff Wrana, Zev Wainberg, Philip M Kim, Robert Rottapel, Catherine A O'Brien, Brenda Andrews, Charles Boone, Jason Moffat
  • Temporal control of self‐organized pattern formation without morphogen gradients in bacteria
    1. Stephen Payne1,,
    2. Bochong Li1,,
    3. Yangxiaolu Cao1,
    4. David Schaeffer2,
    5. Marc D Ryser2 and
    6. Lingchong You*,1,3,4
    1. 1 Department of Biomedical Engineering, Duke University, Durham, NC, USA
    2. 2 Department of Mathematics, Duke University, Durham, NC, USA
    3. 3 Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA
    4. 4 Duke Center for Systems Biology, Duke University, Durham, NC, USA
    1. *Corresponding author. Department of Biomedical Engineering, Duke University, CIEMAS 2355 101 Science Drive, Box 3382, Durham, NC 27708, USA. Tel.:+1 919 660 8408; Fax:+1 919 668 0795; E‐mail: you{at}duke.edu
    1. These authors contributed equally to this work

    The generation of self‐organized ring patterns of gene expression in the absence of a morphogen gradient was demonstrated using bacteria programmed by a synthetic gene circuit. This work presents a timing mechanism of pattern formation.

    Synopsis

    The generation of self‐organized ring patterns of gene expression in the absence of a morphogen gradient was demonstrated using bacteria programmed by a synthetic gene circuit. This work presents a timing mechanism of pattern formation.

    • Using Escherichia coli programmed by a synthetic gene circuit, we demonstrate the generation of robust, self‐organized ring patterns of gene expression in the absence of an apparent morphogen gradient.

    • Instead of being a spatial cue, the morphogen serves as a timing cue to trigger the formation and maintenance of the ring patterns.

    • The timing mechanism enables the system to sense the domain size of the environment and generate patterns that scale accordingly.

    • morphogen
    • pattern formation
    • synthetic biology
    • systems biology
    • temporal control

    Mol Syst Biol. 9: 697

    • Received April 3, 2013.
    • Accepted September 6, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Stephen Payne, Bochong Li, Yangxiaolu Cao, David Schaeffer, Marc D Ryser, Lingchong You
  • Sequential induction of auxin efflux and influx carriers regulates lateral root emergence
    1. Benjamin Péret1,2,3,,
    2. Alistair M Middleton1,2,4,,
    3. Andrew P French1,2,
    4. Antoine Larrieu1,2,
    5. Anthony Bishopp1,2,5,
    6. Maria Njo6,7,
    7. Darren M Wells1,2,
    8. Silvana Porco1,2,
    9. Nathan Mellor1,2,
    10. Leah R Band1,2,4,
    11. Ilda Casimiro8,
    12. Jürgen Kleine‐Vehn6,7,
    13. Steffen Vanneste6,7,
    14. Ilkka Sairanen9,
    15. Romain Mallet1,2,
    16. Göran Sandberg10,
    17. Karin Ljung9,
    18. Tom Beeckman6,7,
    19. Eva Benkova6,7,
    20. Jiří Friml6,7,
    21. Eric Kramer11,
    22. John R King1,4,
    23. Ive De Smet2,6,7,
    24. Tony Pridmore1,
    25. Markus Owen1,4 and
    26. Malcolm J Bennett*,1,2
    1. 1 Centre for Plant Integrative Biology, University of Nottingham, Loughborough, UK
    2. 2 Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Loughborough, UK
    3. 3 Unité Mixte de Recherche 7265, Commissariat à l'Energie Atomique et aux Energies Alternatives, Centre National de la Recherche Scientifique, Aix‐Marseille Université, Laboratoire de Biologie du Développement des Plantes, Saint‐Paul‐lez‐Durance, France
    4. 4 Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
    5. 5 Department of Biosciences, Institute of Biotechnology, University of Helsinki, Helsinki, Finland
    6. 6 Department of Plant Systems Biology, Flanders Institute for Biotechnology, Ghent, Belgium
    7. 7 Department of Plant Biotechnology and Genetics, Ghent University, Ghent, Belgium
    8. 8 Universidad de Extremadura, Facultad de Ciencias, Badajoz, Spain
    9. 9 Department of Forest Genetics and Plant Physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences, Umeå, Sweden
    10. 10 Department of Plant Physiology, Umeå Plant Science Centre, Umeå University, Umeå, Sweden
    11. 11 Physics Department, Simon's Rock College, Great Barrington, MA, USA
    1. *Corresponding author. Centre for Plant Integrative Biology, University of Nottingham, Sutton Bonington Campus, Loughborough, Leics LE12 5RD, UK. Tel.:+44 115 951 3255; Fax:+44 115 951 6334; E‐mail: malcolm.bennett{at}nottingham.ac.uk
    1. These authors contributed equally to this work.

    • Present address: University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.

    Emergence of a new lateral root primordium through the outer layers of the parental root requires the sequential auxin‐mediated induction of two auxin transporters. This positive feedback regulatory loop coordinates patterned gene expression in outer tissues.

    Synopsis

    Emergence of a new lateral root primordium through the outer layers of the parental root requires the sequential auxin‐mediated induction of two auxin transporters. This positive feedback regulatory loop coordinates patterned gene expression in outer tissues.

    • The emergence of lateral roots through several tissues requires the precise regulation of gene expression in overlaying cells to trigger cell separation.

    • Auxin derived from new lateral root primordia induces a positive feedback loop in the outer tissues by promoting the expression of the auxin influx transporter LAX3.

    • A mathematical model based on realistic 3D geometries predicted the involvement of an auxin efflux carrier that was later identified to be PIN3.

    • The model also revealed that PIN3 must be expressed before LAX3 to ensure a ‘robust’ pattern of LAX3 induction in just two overlaying cortical cell files, thereby delimiting cell separation.

    • 3D modelling
    • auxin transport
    • lateral root emergence
    • ODE

    Mol Syst Biol. 9: 699

    • Received March 15, 2013.
    • Accepted August 6, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Benjamin Péret, Alistair M Middleton, Andrew P French, Antoine Larrieu, Anthony Bishopp, Maria Njo, Darren M Wells, Silvana Porco, Nathan Mellor, Leah R Band, Ilda Casimiro, Jürgen Kleine‐Vehn, Steffen Vanneste, Ilkka Sairanen, Romain Mallet, Göran Sandberg, Karin Ljung, Tom Beeckman, Eva Benkova, Jiří Friml, Eric Kramer, John R King, Ive De Smet, Tony Pridmore, Markus Owen, Malcolm J Bennett
  • Bacterial evolution of antibiotic hypersensitivity
    1. Viktória Lázár1,
    2. Gajinder Pal Singh1,
    3. Réka Spohn1,
    4. István Nagy2,
    5. Balázs Horváth2,
    6. Mónika Hrtyan1,
    7. Róbert Busa‐Fekete3,
    8. Balázs Bogos1,
    9. Orsolya Méhi1,
    10. Bálint Csörgő1,
    11. György Pósfai1,
    12. Gergely Fekete1,
    13. Balázs Szappanos1,
    14. Balázs Kégl3,
    15. Balázs Papp*,1 and
    16. Csaba Pál*,1
    1. 1 Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center, Szeged, Hungary
    2. 2 Genomics Unit, Institute of Biochemistry, Biological Research Center, Szeged, Hungary
    3. 3 Linear Accelerator Laboratory, University of Paris‐Sud, CNRS, Orsay, France
    1. *Corresponding authors. Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center, Temesvari krt 62, Szeged 6726, Hungary. Tel.:+36 62 599 661; Fax:+36 62 433 506; E‐mail: cpal{at}brc.hu or E‐mail: pappb{at}brc.hu

    Understanding how adaptation to a given antibiotic increases the sensitivity to other antibiotics is of great medical importance for the understanding of evolutionary trade‐offs. Here, the first experimental map of such collateral sensitivity is presented, along with insights into the underlying mechanisms.

    Synopsis

    Understanding how adaptation to a given antibiotic increases the sensitivity to other antibiotics is of great medical importance for the understanding of evolutionary trade‐offs. Here, the first experimental map of such collateral sensitivity is presented, along with insights into the underlying mechanisms.

    • Large‐scale laboratory evolution experiments revealed that evolution of resistance to a single antibiotic frequently yields enhanced sensitivity to other antibiotics (collateral sensitivity).

    • Specifically, genetic adaptation to aminoglycosides increased the sensitivity to many other classes of antibiotics.

    • Whole‐genome sequencing of laboratory‐evolved strains demonstrated that aminoglycoside resistance is partly achieved through reduction in the proton‐motive force (PMF). As a side effect, the corresponding mutations diminish the activity of PMF‐dependent major efflux pumps, leading to antibiotic hypersensitivity.

    • antibiotic resistance
    • collateral sensitivity network
    • evolutionary experiment
    • trade off

    Mol Syst Biol. 9: 700

    • Received May 27, 2013.
    • Accepted September 25, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Viktória Lázár, Gajinder Pal Singh, Réka Spohn, István Nagy, Balázs Horváth, Mónika Hrtyan, Róbert Busa‐Fekete, Balázs Bogos, Orsolya Méhi, Bálint Csörgő, György Pósfai, Gergely Fekete, Balázs Szappanos, Balázs Kégl, Balázs Papp, Csaba Pál
  • Promoters maintain their relative activity levels under different growth conditions
    1. Leeat Keren1,2,3,
    2. Ora Zackay1,2,
    3. Maya Lotan‐Pompan1,2,
    4. Uri Barenholz3,
    5. Erez Dekel2,
    6. Vered Sasson2,
    7. Guy Aidelberg2,
    8. Anat Bren2,
    9. Danny Zeevi1,2,
    10. Adina Weinberger1,2,
    11. Uri Alon2,
    12. Ron Milo3 and
    13. Eran Segal*,1,2
    1. 1 Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
    2. 2 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
    3. 3 Department of Plant Sciences, Weizmann Institute of Science, Rehovot, Israel
    1. *Corresponding author. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 234 Herzl Street, Rehovot 76100, Israel. Tel.:+972 89346488; Fax:+972 89346488; E‐mail: eran.segal{at}weizmann.ac.il

    Libraries of S. cerevisiae and E. coli promoter reporters measured under different conditions reveal scaling relationships between expression profiles across conditions and suggest that most changes in activity are due to global effects.

    Synopsis

    Libraries of S. cerevisiae and E. coli promoter reporters measured under different conditions reveal scaling relationships between expression profiles across conditions and suggest that most changes in activity are due to global effects.

    • Between any two conditions, the activity of most promoters changes by a constant global scaling factor that depends only on the conditions and not on the promoter's identity.

    • The value of the global scaling factor between any two conditions corresponds to the change in growth rate and magnitude of the condition‐specific response.

    • When specific groups of genes are activated, they also tend to change according to scaling factors, changing the degree to which the entire group is activated, while preserving the ratios between genes within the group.

    • Altogether, a handful of scaling factors are sufficient for quantitatively describing genome‐wide expression profiles across conditions.

    • gene expression
    • growth rate
    • modeling
    • promoter activity
    • transcription regulation

    Mol Syst Biol. 9: 701

    • Received August 5, 2013.
    • Accepted September 27, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Leeat Keren, Ora Zackay, Maya Lotan‐Pompan, Uri Barenholz, Erez Dekel, Vered Sasson, Guy Aidelberg, Anat Bren, Danny Zeevi, Adina Weinberger, Uri Alon, Ron Milo, Eran Segal
  • Design of orthogonal genetic switches based on a crosstalk map of σs, anti‐σs, and promoters
    1. Virgil A Rhodius1,,
    2. Thomas H Segall‐Shapiro2,,
    3. Brian D Sharon3,
    4. Amar Ghodasara2,
    5. Ekaterina Orlova1,
    6. Hannah Tabakh1,
    7. David H Burkhardt3,
    8. Kevin Clancy4,
    9. Todd C Peterson4,
    10. Carol A Gross*,1,5 and
    11. Christopher A Voigt*,2
    1. 1 Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA, USA
    2. 2 Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
    3. 3 Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA, USA
    4. 4 Synthetic Biology Research and Development, Life Technologies, Carlsbad, CA, USA
    5. 5 Department of Cell and Tissue Biology, University of California San Francisco, San Francisco, CA, USA
    1. *Corresponding authors. Department of Microbiology and Immunology, University of California San Francisco, 600 16th Street, San Francisco, CA 94158, USA. Tel.:+1 415 476 4161; Fax:+1 415 514 4080; E‐mail: cgrossucsf{at}gmail.com or Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, 500 Technology Square NE47‐277, Cambridge, MA 02139, USA. Tel.:+1 617 324 4851; E‐mail: cavoigt{at}gmail.com
    1. These authors contributed equally to this work

    The interaction specificities of extracytoplasmic function (ECF) sigma (σ) factors with promoters and their negative regulators (anti‐σs) were mapped to identify non‐crossreacting parts. These orthogonal sets represent a synthetic biology toolbox of genetic switches.

    Synopsis

    The interaction specificities of extracytoplasmic function (ECF) sigma (σ) factors with promoters and their negative regulators (anti‐σs) were mapped to identify non‐crossreacting parts. These orthogonal sets represent a synthetic biology toolbox of genetic switches.

    • Part mining was applied to characterize 86 extracytoplasmic function (ECF) σs, their promoters, and 62 anti‐σs identified from the genomes of diverse bacteria.

    • A subset of 20 σs and promoters were found to be highly orthogonal to each other and can be used to build non‐crossreacting switches in single cells.

    • The N‐ and C‐terminal domains from σs from different subgroups can be recombined and recognize the corresponding chimeric promoter.

    • These parts functioned off‐the‐shelf in an E. coli host with minimal re‐engineering and minimally affected host growth and gene expression.

    • compiler
    • genetic circuit
    • part mining
    • synthetic biology
    • systems biology

    Mol Syst Biol. 9: 702

    • Received May 6, 2013.
    • Accepted September 26, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Virgil A Rhodius, Thomas H Segall‐Shapiro, Brian D Sharon, Amar Ghodasara, Ekaterina Orlova, Hannah Tabakh, David H Burkhardt, Kevin Clancy, Todd C Peterson, Carol A Gross, Christopher A Voigt
  • Promoter decoding of transcription factor dynamics involves a trade‐off between noise and control of gene expression
    1. Anders S Hansen1,2,3 and
    2. Erin K O'Shea*,1,2,3,4
    1. 1 Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
    2. 2 Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA
    3. 3 Faculty of Arts and Sciences Center for Systems Biology, Northwest Laboratory, Harvard University, Cambridge, MA, USA
    4. 4 Department of Molecular and Cellular Biology, Harvard University, Northwest Laboratory, Cambridge, MA, USA
    1. *Corresponding author. Northwest Laboratory, Faculty of Arts and Sciences, Center for Systems Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA. Tel.:+1 617 495 4328; Fax:+1 617 496 5425; E‐mail: erin_oshea{at}harvard.edu

    The relationship between the dynamics of transcription factor activity, differential gene expression and noise in gene expression is analyzed in yeast to understand how different genetic programs can be activated by controlling the dynamics of a single transcription factor.

    Synopsis

    The relationship between the dynamics of transcription factor activity, differential gene expression and noise in gene expression is analyzed in yeast to understand how different genetic programs can be activated by controlling the dynamics of a single transcription factor.

    • The general principles that govern how promoters decode transcription factor (TF) dynamics are investigated.

    • Multiple gene expression programs can be encoded in the dynamics of a single TF.

    • Oscillatory TF dynamics leads to higher noise in gene expression.

    • Promoters with slow activation inherently suffer from high noise in gene expression.

    • gene regulation
    • gene expression noise
    • microfluidics
    • Msn2
    • transcription factor dynamics

    Mol Syst Biol. 9: 704

    • Received August 7, 2013.
    • Accepted September 24, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Anders S Hansen, Erin K O'Shea
  • Perturbation of the mutated EGFR interactome identifies vulnerabilities and resistance mechanisms
    1. Jiannong Li1,
    2. Keiryn Bennett2,
    3. Alexey Stukalov2,
    4. Bin Fang3,
    5. Guolin Zhang1,
    6. Takeshi Yoshida4,
    7. Isamu Okamoto4,
    8. Jae‐Young Kim1,
    9. Lanxi Song1,
    10. Yun Bai1,
    11. Xiaoning Qian5,
    12. Bhupendra Rawal6,
    13. Michael Schell6,
    14. Florian Grebien2,
    15. Georg Winter2,
    16. Uwe Rix7,
    17. Steven Eschrich8,
    18. Jacques Colinge2,
    19. John Koomen3,
    20. Giulio Superti‐Furga*,2 and
    21. Eric B Haura*,1
    1. 1 Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
    2. 2 CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
    3. 3 Proteomics and Molecular Oncology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
    4. 4 Center for Clinical and Translational Research, Kyushu University Hospital, Fukuoka, Japan
    5. 5 Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA
    6. 6 Biostatistics Departments, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
    7. 7 Drug Discovery Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
    8. 8 Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
    1. *Corresponding authors. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT25.2, 1090 Vienna, Austria. Tel.:+43 1 40 160 70001; Fax:+43 1 40 160 970000; gsuperti{at}cemm.oeaw.ac.at or Department of Thoracic Oncology, Chemical Biology and Molecular Medicine Program, H. Lee Moffitt Cancer Center and Research Institute, MRC3 East, Room 3056F, 12902 Magnolia Drive, Tampa, FL 33612‐9497, USA. Tel.:+1 813 903 6827; Fax:+1 813 903 6817; E‐mail: eric.haura{at}moffitt.org

    A ‘lung cancer’‐specific mutant EGFR interactome was generated by a global analysis of protein–protein interactions and phosphorylation. After functional screening, nine proteins were identified as essential for the viability of EGFR‐mutant lung cancer cells.

    Synopsis

    A ‘lung cancer’‐specific mutant EGFR interactome was generated by a global analysis of protein–protein interactions and phosphorylation. After functional screening, nine proteins were identified as essential for the viability of EGFR‐mutant lung cancer cells.

    • The interactome of lung cancer‐associated mutant forms of epidermal growth factor receptor (EGFR), consisting of 263 proteins, was built by integrating protein–protein interactions and tyrosine phosphorylation.

    • Systematic perturbations of the network nodes revealed a core network of 14 proteins, 9 of which were shown to be specifically associated with survival of EGFR‐mutant lung cancer cells.

    • Cells with acquired resistance to EGFR tyrosine kinase inhibitors showed differential dependence on the core network proteins.

    • A drug network associated with the core network proteins led to the identification of two compounds, midostaurin and lestaurtinib, that could overcome drug resistance through direct EGFR inhibition when combined with erlotinib.

    • epidermal growth factor receptor
    • interactome
    • lung cancer
    • proteomics
    • tyrosine kinase inhibitor

    Mol Syst Biol. 9: 705

    • Received February 19, 2013.
    • Accepted October 2, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Jiannong Li, Keiryn Bennett, Alexey Stukalov, Bin Fang, Guolin Zhang, Takeshi Yoshida, Isamu Okamoto, Jae‐Young Kim, Lanxi Song, Yun Bai, Xiaoning Qian, Bhupendra Rawal, Michael Schell, Florian Grebien, Georg Winter, Uwe Rix, Steven Eschrich, Jacques Colinge, John Koomen, Giulio Superti‐Furga, Eric B Haura
  • A pharmaco‐epistasis strategy reveals a new cell size controlling pathway in yeast
    1. Fabien Moretto1,2,
    2. Isabelle Sagot1,2,
    3. Bertrand Daignan‐Fornier*,1,2 and
    4. Benoît Pinson1,2
    1. 1 Université Bordeaux, IBGC, UMR 5095, Bordeaux, France
    2. 2 Institut de Biochimie et Génétique Cellulaires, CNRS UMR 5095, Bordeaux, France
    1. *Corresponding author. Institut de Biochimie et Génétique Cellulaires, CNRS UMR 5095, 1, rue Camille Saint Saëns, 33077 Bordeaux Cedex, France. Tel.:+33 556 999 001; Fax:+33 556 999 059; E‐mail: B.Daignan-Fornier{at}ibgc.cnrs.fr

    Pharmaco‐epistasis analyses using drugs mimicking cell size mutations in yeast uncovered a novel pathway in cell size homeostasis regulation. This pathway involves the sirtuin Sir2, the large ribosomal subunit (60S) and the Swi4/Swi6 transcription factors.

    Synopsis

    Pharmaco‐epistasis analyses using drugs mimicking cell size mutations in yeast uncovered a novel pathway in cell size homeostasis regulation. This pathway involves the sirtuin Sir2, the large ribosomal subunit (60S) and the Swi4/Swi6 transcription factors.

    • Drug–gene epistatic interactions with nicotinamide and diazaborine were analyzed using 189 previously identified small and 155 large mutants, showing that cell size homeostasis is the result of signals emanating from several independent pathways.

    • Ribosome biogenesis affects cell size homeostasis in different ways.

    • Modulation of cell size by Sir2 correlates with NAD+ intracellular variation.

    • No simple causal relationship was found between cell size and replicative aging even though both Sir2 and the 60S ribosomal subunit are contributing to these two complex traits.

    • cell size
    • complex quantitative trait
    • epistasis
    • ribosome
    • sirtuin

    Mol Syst Biol. 9: 707

    • Received August 12, 2013.
    • Accepted September 27, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Fabien Moretto, Isabelle Sagot, Bertrand Daignan‐Fornier, Benoît Pinson
  • Metabolic reconstruction identifies strain‐specific regulation of virulence in Toxoplasma gondii
    1. Carl Song1,2,
    2. Melissa A Chiasson3,
    3. Nirvana Nursimulu1,4,
    4. Stacy S Hung1,2,
    5. James Wasmuth1,6,
    6. Michael E Grigg3 and
    7. John Parkinson*,1,2,5
    1. 1 Program in Molecular Structure and Function, The Hospital for Sick Children, Toronto, Ontario, Canada
    2. 2 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
    3. 3 Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda, MD, USA
    4. 4 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
    5. 5 Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada
    6. 6 Department of Ecosystem and Public Health, Faculty of Veterinary Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4Z6, Canada
    1. *Corresponding author. Program in Molecular Structure and Function, The Hospital for Sick Children, 21.9709 Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, Ontario, Canada M5G 0A4. Tel.:+1 416 813 5746; Fax:+1 416 813 5022; E‐mail: john.parkinson{at}utoronto.ca

    The first metabolic reconstruction for Toxoplasma gondiiiCS382’ is presented. Model simulations and drug assays identified strain‐specific differences in growth rates that may reflect an evolutionary strategy, potentiating broad host range.

    Synopsis

    The first metabolic reconstruction for Toxoplasma gondiiiCS382’ is presented. Model simulations and drug assays identified strain‐specific differences in growth rates that may reflect an evolutionary strategy, potentiating broad host range.

    • The first metabolic reconstruction of Toxoplasma gondii was generated, capturing current knowledge of the parasite's metabolic capabilities.

    • Strain‐specific differences in the expression of enzymes in energy production pathways were predicted to impact growth rates and may reflect an underlying evolutionary strategy that allows the parasite to broaden its host range.

    • The validated strain‐specific sensitivity to enzyme knockouts demonstrates the need to consider the diversity of parasite strains during the development of novel therapeutic approaches.

    • flux balance analysis
    • metabolic reconstruction
    • strain differences
    • Toxoplasma gondii

    Mol Syst Biol. 9: 708

    • Received May 10, 2013.
    • Accepted October 10, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Carl Song, Melissa A Chiasson, Nirvana Nursimulu, Stacy S Hung, James Wasmuth, Michael E Grigg, John Parkinson
  • Transcriptional regulation is insufficient to explain substrate‐induced flux changes in Bacillus subtilis
    1. Victor Chubukov1,,
    2. Markus Uhr2,,
    3. Ludovic Le Chat3,,
    4. Roelco J Kleijn1,,
    5. Matthieu Jules3,
    6. Hannes Link1,
    7. Stephane Aymerich3,
    8. Jörg Stelling2 and
    9. Uwe Sauer*,1
    1. 1 Institute of Molecular System Biology, ETH Zurich, Zurich, Switzerland
    2. 2 Department of Biosystems Science and Engineering, SIB Swiss Institute of Bioinformatics, ETH Zurich, Zurich, Switzerland
    3. 3 Micalis Institute, INRA, AgroParisTech, Thiverval‐Grignon, France
    1. *Corresponding author. Institute of Molecular Systems Biology, ETH Zurich, Wolfgang Pauli Strasse 16, Zurich CH‐8093, Switzerland. Tel.:+41 44 633 3672; Fax:+41 44 633 1051; E‐mail: sauer{at}ethz.ch
    1. These authors contributed equally to this work.

    Regulation of enzyme expression is one key mechanism by which cells control their metabolic programs. In this work, a quantitative analysis of metabolism in a model bacterium under different conditions shows that expression alone cannot explain the majority of the observed metabolic changes.

    Synopsis

    Regulation of enzyme expression is one key mechanism by which cells control their metabolic programs. In this work, a quantitative analysis of metabolism in a model bacterium under different conditions shows that expression alone cannot explain the majority of the observed metabolic changes.

    • Most enzymes are indeed highly expressed in conditions where they are more active.

    • Quantitatively, however, the observed changes in expression between conditions do not match the changes in activity for most enzymes.

    • A good quantitative match is only observed for enzymes involved in the TCA cycle.

    • Metabolomics reveals that increased substrate availability explains only a few instances of changes in activity.

    • central carbon metabolism
    • metabolic flux
    • transcriptional regulation

    Mol Syst Biol. 9: 709

    • Received June 15, 2013.
    • Accepted October 23, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Victor Chubukov, Markus Uhr, Ludovic Le Chat, Roelco J Kleijn, Matthieu Jules, Hannes Link, Stephane Aymerich, Jörg Stelling, Uwe Sauer
  • Timescales and bottlenecks in miRNA‐dependent gene regulation
    1. Jean Hausser*,1,[Link],
    2. Afzal Pasha Syed1,
    3. Nathalie Selevsek2,
    4. Erik van Nimwegen1,
    5. Lukasz Jaskiewicz1,
    6. Ruedi Aebersold2 and
    7. Mihaela Zavolan*,1
    1. 1 Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland
    2. 2 Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
    1. *Corresponding authors. Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Klingelbergstrasse 50/70, Basel, 4056, Switzerland. Tel.:+972 8 934 4447; Fax:+972 8 934 4125; E‐mail: jean.hausser{at}weizmann.ac.il or Tel.:+41 61 267 1577; Fax:+41 61 267 1585; E‐mail: mihaela.zavolan{at}unibas.ch
    • Present address: Department of Molecular Cell Biology, Weizmann Institute of Science, Herzl Street 234, 76100 Rehovot, Israel

    Application of a kinetic model of miRNA‐mediated gene regulation to experimental data sets shows that the timescale of regulation is slower than previously assumed, due to bottlenecks imposed by miRNA turnover in the RNA‐induced silencing complex and by slow protein decay.

    Synopsis

    Application of a kinetic model of miRNA‐mediated gene regulation to experimental data sets shows that the timescale of regulation is slower than previously assumed, due to bottlenecks imposed by miRNA turnover in the RNA‐induced silencing complex and by slow protein decay.

    • A mathematical model links the dynamics of miRNA expression and loading into the Argonaute protein to the dynamics of miRNA targets.

    • Loading of miRNAs into Argonaute and the slow decay of proteins impose two bottlenecks on the speed of miRNA‐mediated regulation.

    • Accelerated miRNA turnover is necessary for regulating target expression on the timescale of a day.

    • gene expression regulation
    • kinetics
    • miRNAs
    • modeling
    • protein turnover

    Mol Syst Biol. 9: 711

    • Received May 9, 2013.
    • Accepted October 30, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Jean Hausser, Afzal Pasha Syed, Nathalie Selevsek, Erik van Nimwegen, Lukasz Jaskiewicz, Ruedi Aebersold, Mihaela Zavolan

Review

  • High‐throughput sequencing for biology and medicine
    1. Wendy Weijia Soon1,
    2. Manoj Hariharan1 and
    3. Michael P Snyder*,1
    1. 1 Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
    1. *Corresponding author. Department of Genetics, Stanford University School of Medicine, Alway Building, 300 Pasteur Drive, Stanford, CA 94305, USA. Tel.:+1 650 736 8099; Fax:+1 650 331 7391; E‐mail: mpsnyder{at}stanford.edu

    Genome sequencing technologies have advanced rapidly, dramatically decreasing cost and increasing throughput. But beyond faster and cheaper, these advances have also stimulated the development of innovative new experimental approaches, and are opening new doors in human medicine and health.

    • biology
    • high‐throughput
    • medicine
    • sequencing
    • technologies

    Mol Syst Biol. 9: 640

    • Received July 6, 2012.
    • Accepted October 29, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Wendy Weijia Soon, Manoj Hariharan, Michael P Snyder
  • Genome‐scale engineering for systems and synthetic biology
    1. Kevin M Esvelt*,1 and
    2. Harris H Wang*,1,2,
    1. 1 Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
    2. 2 Department of Systems Biology, Harvard Medical School, Boston, MA, USA
    1. *Corresponding authors. Wyss Institute for Biologically Inspired Engineering, Harvard University, 3 Blackfan Circle, Boston, MA 02115, USA. Tel.: +1 617 955 9575; Fax: +1 617 432 7828; E‐mail: hw2429{at}columbia.edu or Tel.: +1 857 919 3375; Fax: +1 617 432 7828; E‐mail: kevin.esvelt{at}wyss.harvard.edu
    • Present address: Department of Systems Biology, Columbia University Medical Center, 701 West 168th Street, Room 1308‐B, New York, NY 10032, USA

    This review provides an overview of methodologies and technologies enabling genome‐scale engineering, focusing on the design, construction, and testing of modified genomes in a variety of organisms. Future applications for systems and synthetic biology are discussed.

    • directed evolution
    • genome engineering
    • metabolic engineering
    • synthesis
    • synthetic chassis

    Mol Syst Biol. 9: 641

    • Received September 21, 2012.
    • Accepted December 16, 2012.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Kevin M Esvelt, Harris H Wang
  • Basic and applied uses of genome‐scale metabolic network reconstructions of Escherichia coli
    1. Douglas McCloskey1,
    2. Bernhard Ø Palsson1,2 and
    3. Adam M Feist*,1,2
    1. 1 Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
    2. 2 Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
    1. *Corresponding author. Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093‐0412, USA. Tel.:+1 858 822 3181; Fax:+1 858 822 3120; E‐mail: afeist{at}ucsd.edu

    This review summarizes the applications enabled by genome‐scale models of metabolism for the bacterium E. coli. It provides an overview of the applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field.

    • constraint‐based modeling
    • Escherichia coli
    • metabolic engineering
    • metabolism
    • network reconstruction

    Mol Syst Biol. 9: 661

    • Received August 23, 2012.
    • Accepted March 11, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Douglas McCloskey, Bernhard Ø Palsson, Adam M Feist
  • Computational meta'omics for microbial community studies
    Nicola Segata, Daniela Boernigen, Timothy L Tickle, Xochitl C Morgan, Wendy S Garrett, Curtis Huttenhower
  • Biomedically relevant circuit‐design strategies in mammalian synthetic biology
    1. William Bacchus1,
    2. Dominique Aubel1,2 and
    3. Martin Fussenegger*,1,3
    1. 1 Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
    2. 2 IUTA Département Génie Biologique, Université Claude Bernard Lyon 1, Villeurbanne Cedex, France
    3. 3 Faculty of Science, University of Basel, Basel, Switzerland
    1. *Corresponding author. Department of Biosystems Science and Engineering, Faculty of Science, University of Basel, Mattenstrasse 26, Basel 4058, Switzerland. Tel.:+41 61 387 31 69; Fax:+41 61 387 39 88; E‐mail: fussenegger{at}bsse.ethz.ch

    This review covers the burgeoning field of mammalian synthetic biology. New designs for potential clinical applications are discussed with examples of circuits that directly interface with endogenous cellular activities, enable intercellular communication or function as prothetic networks.

    Synopsis

    This review covers the burgeoning field of mammalian synthetic biology. New designs for potential clinical applications are discussed with examples of circuits that directly interface with endogenous cellular activities, enable intercellular communication or function as prothetic networks.

    • gene circuits
    • mammalian designer devices
    • synthetic biology

    Mol Syst Biol. 9: 691

    • Received May 26, 2013.
    • Accepted August 7, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    William Bacchus, Dominique Aubel, Martin Fussenegger

Editorial

Report

  • Epigenetic epistatic interactions constrain the evolution of gene expression
    1. Solip Park1 and
    2. Ben Lehner*,1,2
    1. 1 EMBL‐CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG) and University Pompeu Fabra (UPF), Barcelona, Spain
    2. 2 Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
    1. *Corresponding author. EMBL‐CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG) and University Pompeu Fabra (UPF), Dr Aiguader 88, Barcelona 08003, Spain. Tel.:+34933160194; Fax:+34933160199; E‐mail: ben.lehner{at}crg.eu

    Harmful epistatic (genetic) interactions not only occur between mutations, but also when genes change in expression. Gene expression dynamics in yeast suggests that this ‘epigenetic’ epistasis constrains evolution, with the tight regulation of network hubs promoting a robust, ‘canalized’ phenotype.

    Synopsis

    Harmful epistatic (genetic) interactions not only occur between mutations, but also when genes change in expression. Gene expression dynamics in yeast suggests that this ‘epigenetic’ epistasis constrains evolution, with the tight regulation of network hubs promoting a robust, ‘canalized’ phenotype.

    • Yeast genes with many negative genetic interaction partners tend to have expression that is stable between cells, across conditions, and through evolution.

    • This low expression variation is linked to the use of alternative promoter architectures.

    • The stable expression of genetic interaction network hubs suggests that epigenetic epistasis confers a constraint on evolution.

    • epigenetics
    • epistasis
    • evolution
    • gene expression
    • genetic interaction

    Mol Syst Biol. 9: 645

    • Received September 26, 2012.
    • Accepted January 7, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Solip Park, Ben Lehner
  • A global S. cerevisiae small ubiquitin‐related modifier (SUMO) system interactome
    1. Tharan Srikumar1,
    2. Megan C Lewicki1 and
    3. Brian Raught*,1
    1. 1 Ontario Cancer Institute, University Health Network and Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
    1. *Corresponding author. Ontario Cancer Institute, University Health Network and Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, Ontario, Canada M5G 1L7. Tel.:+1 416 581 7478; Fax:+1 416 581 7278; E‐mail: brian.raught{at}uhnres.utoronto.ca

    A global physical interaction map of the SUMO system was generated to study its functional organization. This resource was used to validate several E3‐specific substrates and uncover novel roles for Ubc9 and Ulp2 in ribosomal DNA maintenance.

    Synopsis

    A global physical interaction map of the SUMO system was generated to study its functional organization. This resource was used to validate several E3‐specific substrates and uncover novel roles for Ubc9 and Ulp2 in ribosomal DNA maintenance.

    • Affinity purification coupled to mass spectrometry was used to construct the first global SUMO interactome in yeast.

    • The analysis identified more than 450 proteins interacting physically with the SUMO E2 Ubc9, the E3 ligases Siz1 and Siz2, and the SUMO‐specific proteases Ulp1 and Ulp2.

    • Several Siz1‐ and Siz2‐specific substrates were validated, such as Cdc12, Sum1, Tup1, Top2, Rpb3 and Spt16.

    • Follow‐up investigations revealed new important roles for Ubc9 and Ulp2 in ribosomal DNA maintenance.

    • affinity purification‐mass spectrometry
    • mass spectrometry
    • ribosomal DNA
    • S. cerevisiae
    • SUMO

    Mol Syst Biol. 9: 668

    • Received October 19, 2012.
    • Accepted April 4, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Tharan Srikumar, Megan C Lewicki, Brian Raught
  • Synthetic mammalian transgene negative autoregulation
    1. Vinay Shimoga1,2,,
    2. Jacob T White1,2,,
    3. Yi Li1,2,
    4. Eduardo Sontag3 and
    5. Leonidas Bleris*,1,2,4
    1. 1 Bioengineering Department, The University of Texas at Dallas, Richardson, TX, USA
    2. 2 Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, USA
    3. 3 Department of Mathematics, Rutgers University, New Brunswick, NJ, USA
    4. 4 Electrical Engineering Department, The University of Texas at Dallas, Richardson, TX, USA
    1. *Corresponding author. Electrical Engineering, Bioengineering Department, The University of Texas at Dallas, 800 West Campbell Road, NSERL 4.708, Richardson, TX 75080, USA. Tel.:+1 972 883 5785; Fax:+1 972 883 5785; E‐mail: bleris{at}utdallas.edu
    1. These authors contributed equally to this work

    The effect of negative feedback on global and local sources of uncertainty is studied with synthetic circuits stably integrated in human cells. Negative feedback is shown to be the most efficient way to mitigate the effects of global fluctuations by introducing a single additional regulatory link.

    Synopsis

    The effect of negative feedback on global and local sources of uncertainty is studied with synthetic circuits stably integrated in human cells. Negative feedback is shown to be the most efficient way to mitigate the effects of global fluctuations by introducing a single additional regulatory link.

    • A method is presented to extract the extrinsic and intrinsic noise contributions from measurements of two reporter proteins controlled by non‐identical promoters.

    • Negative feedback reduces total noise in human transgene integrations.

    • Negative feedback reduces extrinsic noise while it marginally increases intrinsic noise.

    • Negative feedback is the most efficient way to mitigate the effects of extrinsic fluctuations by a single regulatory link.

    • cellular noise
    • human cells
    • negative feedback
    • transgenes

    Mol Syst Biol. 9: 670

    • Received October 15, 2012.
    • Accepted May 3, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Vinay Shimoga, Jacob T White, Yi Li, Eduardo Sontag, Leonidas Bleris
  • In vitro integration of ribosomal RNA synthesis, ribosome assembly, and translation
    1. Michael C Jewett*,1,2,
    2. Brian R Fritz2,
    3. Laura E Timmerman2 and
    4. George M Church*,1
    1. 1 Department of Genetics, Harvard Medical School, Boston, MA, USA
    2. 2 Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA
    1. *Corresponding authors. Department of Chemical and Biological Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA. Tel.:+1 847 467 5007; Fax:+1 847 491 3728; E‐mail: m-jewett{at}northwestern.edu or Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA. Tel.:+1 617 432 1278; Fax:+1 617 432 6513; E‐mail: gchurch{at}genetics.med.harvard.edu

    This report describes an integrated method for in vitro construction of Escherichia coli ribosomes under near‐physiological conditions. This method enables coupling of ribosome synthesis and assembly in a single, integrated system.

    Synopsis

    This report describes an integrated method for in vitro construction of Escherichia coli ribosomes under near‐physiological conditions. This method enables coupling of ribosome synthesis and assembly in a single, integrated system.

    • An integrated synthesis, assembly, and translation technology (termed iSAT) was developed to construct ribosomes in vitro.

    • iSAT mimics co‐transcription of rRNA and ribosome assembly as it occurs in vivo.

    • iSAT makes possible the in vitro construction of modified ribosomes.

    • iSAT is expected to aid studies of ribosome assembly and open new avenues for making ribosomes with altered capabilities.

    • cell‐free synthetic biology
    • in vitro
    • ribosome
    • transcription
    • translation

    Mol Syst Biol. 9: 678

    • Received January 19, 2013.
    • Accepted May 12, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Michael C Jewett, Brian R Fritz, Laura E Timmerman, George M Church
  • Glutamine‐driven oxidative phosphorylation is a major ATP source in transformed mammalian cells in both normoxia and hypoxia
    1. Jing Fan1,
    2. Jurre J Kamphorst1,
    3. Robin Mathew2,3,
    4. Michelle K Chung1,
    5. Eileen White2,3,4,
    6. Tomer Shlomi5, and
    7. Joshua D Rabinowitz*,1,2,6,
    1. 1 Department of Chemistry and Lewis‐Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
    2. 2 The Cancer Institute of New Jersey, New Brunswick, NJ, USA
    3. 3 University of Medicine and Dentistry of New Jersey, Robert Wood Johnson Medical School, Piscataway, NJ, USA
    4. 4 Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ, USA
    5. 5 Department of Computer Science, Technion, Haifa, Israel
    6. 6 Department of Molecular Biology, Princeton University, Princeton, NJ, USA
    1. *Corresponding author. Departments of Chemistry and Integrative Genomics, Princeton University, 241 Carl Icahn Laboratory, Princeton, NJ 08544, USA. Tel.:+1 609 258 8985; Fax:+1 609 258 3565; E‐mail: joshr{at}princeton.edu
    1. These authors contributed equally to this work.

    The impact of oncogene activation and hypoxia on energy metabolism is analyzed by integrating quantitative measurements into a redox‐balanced metabolic flux model. Glutamine‐driven oxidative phosphorylation is found to be a major ATP source even in oncogene‐expressing or hypoxic cells.

    Synopsis

    The impact of oncogene activation and hypoxia on energy metabolism is analyzed by integrating quantitative measurements into a redox‐balanced metabolic flux model. Glutamine‐driven oxidative phosphorylation is found to be a major ATP source even in oncogene‐expressing or hypoxic cells.

    • The integration of oxygen uptake measurements and LC‐MS‐based isotope tracer analyses in a redox‐balanced metabolic flux model enabled quantitative determination of energy generation pathways in cultured cells.

    • In transformed mammalian cells, even in hypoxia (1% oxygen), oxidative phosphorylation produces the majority of ATP.

    • The oncogene Ras simultaneously increases glycolysis and decreases oxidative phosphorylation, thus resulting in no net increase in ATP production.

    • Glutamine is the major source of high‐energy electrons for oxidative phosphorylation, especially upon Ras activation.

    • cancer bioenergetics
    • isotope tracing
    • metabolic flux analysis

    Mol Syst Biol. 9: 712

    • Received May 17, 2013.
    • Accepted October 18, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Jing Fan, Jurre J Kamphorst, Robin Mathew, Michelle K Chung, Eileen White, Tomer Shlomi, Joshua D Rabinowitz

News & Views

Errata

Corrigendum

Articles

  • Interaction proteome of human Hippo signaling: modular control of the co‐activator YAP1
    1. Simon Hauri1,2,
    2. Alexander Wepf3,
    3. Audrey van Drogen1,
    4. Markku Varjosalo1,4,
    5. Nic Tapon5,
    6. Ruedi Aebersold1,2,6 and
    7. Matthias Gstaiger*,1,2
    1. 1 Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
    2. 2 Competence Center for Systems Physiology and Metabolic Diseases, ETH Zürich, Zürich, Switzerland
    3. 3 Analytica Medizinische Laboratorien AG, Zurich, Switzerland
    4. 4 Institute of Biotechnology, University of Helsinki, Helsinki, Finland
    5. 5 Cancer Research UK London Research Institute, London, UK
    6. 6 Faculty of Science, University of Zürich, Zürich, Switzerland
    1. *Corresponding author. Tel: +41 44 633 71 49; Fax: +41 44 633 10 51; E‐mail: gstaiger{at}imsb.biol.ethz.ch

    Systematic characterization of the human Hippo pathway protein interactome by quantitative mass spectrometry generates a high‐resolution network of 480 interactions among 270 proteins and reveals three major modules linked to the transcriptional coactivator YAP1.

    Synopsis

    Systematic characterization of the human Hippo pathway protein interactome by quantitative mass spectrometry generates a high‐resolution network of 480 interactions among 270 proteins and reveals three major modules linked to the transcriptional coactivator YAP1.

    • The interactome of human Hippo signaling provides a rich resource of high confidence protein interactions.

    • Network topology revealed three major modules containing phosphatases, kinases and cell polarity proteins and converging at the transcriptional coactivator YAP1.

    • A subset of protein phosphatase 1 complexes binds and activates YAP1.

    • Cell‐cell contacts control YAP1 transcriptional activity as well as YAP1 complex formation with proteins linked to cell polarity.

    • AP‐MS
    • cell polarity
    • Hippo signaling
    • modularity
    • protein complex analysis

    Mol Syst Biol. 9: 713

    • Received July 31, 2013.
    • Revision received November 13, 2013.
    • Accepted November 20, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Simon Hauri, Alexander Wepf, Audrey van Drogen, Markku Varjosalo, Nic Tapon, Ruedi Aebersold, Matthias Gstaiger

Reviews

  • Evolution and functional cross‐talk of protein post‐translational modifications
    1. Pedro Beltrao*,1,
    2. Peer Bork*,2,3,
    3. Nevan J. Krogan*,4,5,6 and
    4. Vera van Noort*,2
    1. 1 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL‐EBI), Cambridge, UK
    2. 2 Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    3. 3 Max‐Delbruck‐Centre for Molecular Medicine, Berlin‐Buch, Germany
    4. 4 Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
    5. 5 California Institute for Quantitative Biosciences, San Francisco, California, USA
    6. 6 J. David Gladstone Institutes, San Francisco, California, USA
    1. *Corresponding authors. Corresponding authors. E‐mail: pbeltrao{at}ebi.ac.uk or E‐mail: bork{at}embl.de or E‐mail: nevan.krogan{at}ucsf.edu or E‐mail: vera.vannoort{at}embl.de

    Advances in proteomics have opened new avenues for the analysis of the evolution of protein post‐translational modifications (PTMs) and have enabled the large‐scale functional characterization of a range of different modifications types.

    • acetylation
    • evolution
    • phosphorylation
    • post‐translational modifications
    • PTM cross‐talk

    Mol Syst Biol. 9: 714

    • Received April 30, 2013.
    • Revision received November 18, 2013.
    • Accepted November 22, 2013.

    This is an open‐access article distributed under the terms of the Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.

    Pedro Beltrao, Peer Bork, Nevan J. Krogan, Vera van Noort