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  • Phospho‐tyrosine dependent protein–protein interaction network
    Phospho‐tyrosine dependent protein–protein interaction network
    1. Arndt Grossmann1,,
    2. Nouhad Benlasfer1,,
    3. Petra Birth1,
    4. Anna Hegele1,
    5. Franziska Wachsmuth1,
    6. Luise Apelt1 and
    7. Ulrich Stelzl*,1
    1. 1Otto‐Warburg Laboratory, Max‐Planck Institute for Molecular Genetics (MPIMG), Berlin, Germany
    1. *Corresponding author. Tel: +49 30 8413 1264: E‐mail: stelzl{at}molgen.mpg.de
    1. These authors contributed equally to this work

    A modified yeast two‐hybrid approach employed on a large scale generates a network of 292 human phospho‐tyrosine (pY)‐dependent protein–protein interactions. Conditional interactions are validated, and pY‐dependent interaction specificity and network features are assessed.

    Synopsis

    A modified yeast two‐hybrid approach employed on a large scale generates a network of 292 human phospho‐tyrosine (pY)‐dependent protein–protein interactions. Conditional interactions are validated, and pY‐dependent interaction specificity and network features are assessed.

    • A pY‐dependent protein interaction data set is generated using a modified yeast two‐hybrid approach.

    • Network analyses assess the extent of known linear motif‐based pY recognition, pointing toward the importance of context for interaction specificity, and reveal a highly connected pY‐recognition module in the human proteome.

    • A large fraction of PPIs is validated by co‐immunoprecipitation with good success rate.

    • pY‐dependent TSPAN2 interactions are related to cancer phenotypes.

    • cancer signaling
    • network biology
    • post‐translational protein modification
    • yeast two‐hybrid

    Mol Syst Biol. (2015) 11: 794

    • Received December 9, 2014.
    • Revision received February 18, 2015.
    • Accepted February 19, 2015.

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

    Arndt Grossmann, Nouhad Benlasfer, Petra Birth, Anna Hegele, Franziska Wachsmuth, Luise Apelt, Ulrich Stelzl
  • Systematic analysis of BRAFV600E melanomas reveals a role for JNK/c‐Jun pathway in adaptive resistance to drug‐induced apoptosis
    <div xmlns="http://www.w3.org/1999/xhtml">Systematic analysis of BRAF<sup>V</sup><sup>600E</sup> melanomas reveals a role for JNK/c‐Jun pathway in adaptive resistance to drug‐induced apoptosis</div>
    1. Mohammad Fallahi‐Sichani1,
    2. Nathan J Moerke1,
    3. Mario Niepel1,
    4. Tinghu Zhang2,3,
    5. Nathanael S Gray2,3 and
    6. Peter K Sorger*,1
    1. 1HMS LINCS Center, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
    2. 2Department of Cancer Biology, Dana‐Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
    3. 3Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
    1. *Corresponding author. Tel: +1 617 432 6901; E‐mail: peter_sorger{at}hms.harvard.edu

    Adaptive responses to RAF/MEK inhibitors are analyzed systematically across a panel of BRAFV600E melanoma lines to reveal a role for cell‐to‐cell variability induced by the JNK/c‐Jun pathway and other factors in adaptive drug resistance.

    Synopsis

    Adaptive responses to RAF/MEK inhibitors are analyzed systematically across a panel of BRAFV600E melanoma lines to reveal a role for cell‐to‐cell variability induced by the JNK/c‐Jun pathway and other factors in adaptive drug resistance.

    • Adaptive responses are profiled using a combination of multiplex measurements across time, dose, cell line and drug type, statistical modeling and single‐cell analysis.

    • BRAFV600E melanoma lines differ in sensitivity to RAF/MEK inhibition with respect to both IC50 and maximal effect (Emax), reflecting cell‐to‐cell variability in drug response.

    • Adaptive responses to RAF/MEK inhibition are diverse and involve multiple signaling pathways.

    • The JNK/c‐Jun pathway is a common adaptive response that decreases drug maximum effect.

    • JNK inhibition prevents induction of quiescence by RAF inhibition and promotes apoptosis.

    • adaptive responses
    • BRAFV600E melanomas
    • cell‐to‐cell variability
    • RAF and MEK inhibitors
    • submaximal drug effect

    Mol Syst Biol. (2015) 11: 797

    • Received October 27, 2014.
    • Revision received February 28, 2014.
    • Accepted March 4, 2015.

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

    Mohammad Fallahi‐Sichani, Nathan J Moerke, Mario Niepel, Tinghu Zhang, Nathanael S Gray, Peter K Sorger
  • Kinase‐two‐hybrid: towards the conditional interactome
    Kinase‐two‐hybrid: towards the conditional interactome
    1. David Ochoa1 and
    2. Pedro Beltrao (pbeltrao{at}ebi.ac.uk) 1
    1. 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL‐EBI), Cambridge, UK

    The dynamics of the protein–protein interaction network and how it responds to biological perturbations remain difficult to assay by most traditional techniques. A novel kinase‐dependent yeast two‐hybrid framework by Stelzl and colleagues (Grossmann et al, 2015) provides a new prism to study how tyrosine phosphorylation regulates the changes in the interactome under varying conditions.

    See also: A Grossmann et al (March 2015)

    Protein–protein interaction network dynamics remain difficult to assay by most existing techniques. A kinase‐dependent yeast two‐hybrid framework by Stelzl and colleagues (Grossmann et al, 2015) allows analysing how tyrosine phosphorylation controls interactome dynamics under varying conditions.

    Mol Syst Biol. (2015) 11: 798

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

    David Ochoa, Pedro Beltrao
  • Growth‐dependent bacterial susceptibility to ribosome‐targeting antibiotics
    Growth‐dependent bacterial susceptibility to ribosome‐targeting antibiotics
    1. Philip Greulich1,2,,
    2. Matthew Scott3,,
    3. Martin R Evans2 and
    4. Rosalind J Allen*,2
    1. 1Cavendish Laboratory, University of Cambridge, Cambridge, UK
    2. 2SUPA School of Physics and Astronomy University of Edinburgh, Edinburgh, UK
    3. 3Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada
    1. *Corresponding author. Tel: +44 131 6517197; E‐mail: rallen2{at}staffmail.ed.ac.uk
    1. These authors contributed equally to this work

    Fast‐growing E. coli cells are found to be more susceptible to reversibly‐binding ribosome‐targeting antibiotics while the opposite is true for irreversibly‐binding antibiotics. A coarse‐grained model explains this growth‐dependent susceptibility.

    Synopsis

    Fast‐growing E. coli cells are found to be more susceptible to reversibly‐binding ribosome‐targeting antibiotics while the opposite is true for irreversibly‐binding antibiotics. A coarse‐grained model explains this growth‐dependent susceptibility.

    • Fast‐growing cells are more susceptible to reversibly‐binding antibiotics while slow‐growing cells are more susceptible to irreversibly‐binding antibiotics.

    • This can be explained by a simple model, combining drug transport and binding with physiological constraints linking growth rate to ribosome abundance and synthesis.

    • The model highlights a combination of parameters that can be used to characterize growth‐dependent susceptibility and generates non‐trivial predictions for the drug susceptibility of a translation‐mutant strain.

    • The study illustrates how coarse‐grained models can be used to integrate pathogen physiology into drug design and treatment strategies.

    • antibiotic pharmacodynamics
    • bacterial physiology
    • phenomenological growth laws
    • ribosome binding antibiotics

    Mol Syst Biol. (2015) 11: 796

    • Received November 25, 2014.
    • Revision received February 9, 2015.
    • Accepted February 19, 2015.

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

    Philip Greulich, Matthew Scott, Martin R Evans, Rosalind J Allen
  • T160‐phosphorylated CDK2 defines threshold for HGF‐dependent proliferation in primary hepatocytes
    T160‐phosphorylated CDK2 defines threshold for HGF‐dependent proliferation in primary hepatocytes
    1. Stephanie Mueller1,,
    2. Jérémy Huard2,,
    3. Katharina Waldow1,
    4. Xiaoyun Huang1,3,
    5. Lorenza A D'Alessandro1,
    6. Sebastian Bohl1,
    7. Kathleen Börner4,5,
    8. Dirk Grimm4,
    9. Steffen Klamt2,
    10. Ursula Klingmüller*,1,3 and
    11. Marcel Schilling*,1
    1. 1Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany
    2. 2Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
    3. 3Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
    4. 4Centre for Infectious Diseases, Virology, Heidelberg University Hospital, Cluster of Excellence CellNetworks, Heidelberg, Germany
    5. 5German Center for Infection Research (DZIF) Partner Site Heidelberg, Heidelberg, Germany
    1. * Corresponding author. Tel: +49 6221 42 4485; Fax: +49 6221 42 4488; E‐mail: m.schilling{at}dkfz.de

      Corresponding author. Tel: +49 6221 42 4481; Fax: +49 6221 42 4488; E‐mail: u.klingmueller{at}dkfz.de

    1. These authors contributed equally to this work

    Analysis of the mechanisms controlling liver regeneration in response to hepatocyte growth factor (HGF), using single cell and population data combined with mathematical modeling, reveals that CDK2 phosphorylated at T160 acts as gate‐keeper for hepatocyte proliferation.

    Synopsis

    Analysis of the mechanisms controlling liver regeneration in response to hepatocyte growth factor (HGF), using single cell and population data combined with mathematical modeling, reveals that CDK2 phosphorylated at T160 acts as gate‐keeper for hepatocyte proliferation.

    • Primary mouse hepatocytes cross the restriction point after 32 h of HGF stimulation.

    • A hepatocyte‐specific mathematical model of G1/S transition was developed based on time‐resolved quantitative immunoblotting and DNA content data.

    • Single cell experiments demonstrate a linear relationship between CDK2 T160 phosphorylation and hepatocytes in S/G2/M phase.

    • Phosphorylation of CDK2 on T160 constitutes the gate‐keeping mechanism for G1/S transition in hepatocytes.

    • G1/S transition
    • hepatocyte proliferation
    • HGF
    • mathematical model
    • threshold

    Mol Syst Biol. (2015) 11: 795

    • Received January 13, 2015.
    • Revision received February 12, 2015.
    • Accepted February 18, 2015.

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

    Stephanie Mueller, Jérémy Huard, Katharina Waldow, Xiaoyun Huang, Lorenza A D'Alessandro, Sebastian Bohl, Kathleen Börner, Dirk Grimm, Steffen Klamt, Ursula Klingmüller, Marcel Schilling
  • Improving microbial fitness in the mammalian gut by in vivo temporal functional metagenomics
    <div xmlns="http://www.w3.org/1999/xhtml">Improving microbial fitness in the mammalian gut by <em>in vivo</em> temporal functional metagenomics</div>
    1. Stephanie J Yaung1,2,3,
    2. Luxue Deng4,
    3. Ning Li4,
    4. Jonathan L Braff3,
    5. George M Church2,3,
    6. Lynn Bry4,
    7. Harris H Wang*,5,6, and
    8. Georg K Gerber*,4,
    1. 1Program in Medical Engineering and Medical Physics, Harvard‐MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
    2. 2Department of Genetics, Harvard Medical School, Boston, MA, USA
    3. 3Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA
    4. 4Center for Clinical and Translational Metagenomics, Department of Pathology, Brigham & Women's Hospital Harvard Medical School, Boston, MA, USA
    5. 5Department of Systems Biology, Columbia Initiative in Systems Biology, Columbia University, New York, NY, USA
    6. 6Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA
    1. * Corresponding author. Tel: +1 212 305 1697; E‐mail: hw2429{at}columbia.edu

      Corresponding author. Tel: +1 617 278 0468; E‐mail: ggerber{at}partners.org

    1. These authors contributed equally to this work

    A platform for mining metagenomic DNA for genes contributing to fitness of commensal bacteria in vivo is presented. Temporal FUnctional Metagenomics sequencing (TFUMseq) uses shotgun libraries cloned into a recipient bacterial species, tracked over time in gnotobiotic mice by deep sequencing and computational methods.

    Synopsis

    A platform for mining metagenomic DNA for genes contributing to fitness of commensal bacteriain vivo is presented. TFUMseq (Temporal FUnctional Metagenomics sequencing) uses shotgun libraries cloned into a recipient bacterial species, tracked over time in gnotobiotic mice by deep sequencing and computational methods.

    • TFUMseq highlights the utility of functional metagenomics for engineering commensal bacteria with improved properties, including expanded colonization capabilities in vivo.

    • Genes from a donor commensal bacterial species, Bacteroides thetaiotaomicron, confer fitness advantages to E. coli growing in the mouse gut.

    • Analyses of population dynamics of E. coli clones harboring Bacteroides thetaiotaomicron genes reveals a galactokinase central to early colonization of the mouse gut, and subsequent dominance of a glycoside hydrolase enabling sucrose metabolism in E. coli.

    • Co‐evolution of the donor plasmid library and the E. coli genome occurs, driving increased galactose utilization in E. coli.

    • commensal fitness
    • functional metagenomics
    • microbiota
    • next‐generation sequencing
    • synthetic biology

    Mol Syst Biol. (2015) 11: 788

    • Received October 23, 2014.
    • Revision received January 21, 2015.
    • Accepted January 23, 2015.

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

    Stephanie J Yaung, Luxue Deng, Ning Li, Jonathan L Braff, George M Church, Lynn Bry, Harris H Wang, Georg K Gerber
  • Bridging the knowledge gap: from microbiome composition to function
    Bridging the knowledge gap: from microbiome composition to function
    1. Jeremiah J Faith (jeremiah.faith{at}mssm.edu) 1
    1. 1Immunology Institute and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Despite the wealth of metagenomic sequencing data, the functions of most bacterial genes from the mammalian microbiota have remained poorly understood. In their recent study (Yaung et al 2015), Wang, Gerber, and colleagues present a platform which allows functional mining of bacterial genomes for genes that contribute to fitness in vivo and holds great potential for forward engineering microbes with enhanced colonization abilities in the microbiota.

    See also: SJ Yaung et al (March 2015)

    A functional metagenomics platform presented by Wang, Gerber, and colleagues (Yaung et al 2015) allows mining of bacterial genomes for genes that contribute to fitness in vivo and holds great potential for forward engineering microbes with enhanced colonization abilities in the microbiota.

    Mol Syst Biol. (2015) 11: 793

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

    Jeremiah J Faith

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