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  • Cancer type‐dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types
    Cancer type‐dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types
    1. Solip Park1,2 and
    2. Ben Lehner*,1,2,3
    1. 1EMBL‐CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), Barcelona, Spain
    2. 2Universitat Pompeu Fabra, Barcelona, Spain
    3. 3Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
    1. *Corresponding author. Tel: +34 933 160 194; E‐mail: ben.lehner{at}crg.eu

    Analysis of genetic interactions using data from > 3,000 tumors shows that co‐occurrence and mutual exclusivity between cancer driver alterations change extensively in different cancer types, thus indicating plasticity of epistasis across cell types.

    Synopsis

    Analysis of genetic interactions using data from > 3,000 tumors shows that co‐occurrence and mutual exclusivity between cancer driver alterations change extensively in different cancer types, thus indicating plasticity of epistasis across cell types.

    • Co‐occurrence and mutual exclusivity interactions between cancer driver alterations are identified across > 3,000 human tumors.

    • These differences in genetic interactions indicate how genomic alterations co‐operate or act redundantly to driver cancer changes in different cancer types.

    • This plasticity of epistasis across cell types has important implications for cancer therapy, genetic architecture and evolution.

    • cancer
    • epistasis
    • evolution
    • genetic interaction networks
    • tissue specificity

    Mol Syst Biol. (2015) 11: 824

    • Received February 15, 2015.
    • Revision received July 7, 2015.
    • Accepted July 9, 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.

    Solip Park, Ben Lehner
  • Isolated cell behavior drives the evolution of antibiotic resistance
    Isolated cell behavior drives the evolution of antibiotic resistance
    1. Tatiana Artemova1,
    2. Ylaine Gerardin2,
    3. Carmel Dudley1,
    4. Nicole M Vega1 and
    5. Jeff Gore*,1
    1. 1Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
    2. 2Department of Systems Biology, Harvard Medical School, Boston, MA, USA
    1. *Corresponding author. Tel: +1 617 715 4251; E‐mail: gore{at}mit.edu

    Cooperative growth dynamics can influence the minimum inhibitory concentration (MIC) of antibiotics. Quantifying the fitness of single cells (single‐cell MIC) provides a better metric for predicting the evolution of antibiotic resistance.

    Synopsis

    Cooperative growth dynamics can influence the minimum inhibitory concentration (MIC) of antibiotics. Quantifying the fitness of single cells (single‐cell MIC) provides a better metric for predicting the evolution of antibiotic resistance.

    • Cooperative resistance, i.e. enzymatic breakdown of antibiotics, not only helps a given cell to survive but it also decreases the antibiotic concentrations experienced by other cells in the population.

    • The new metric scMIC characterizes individual costs and benefits, while ignoring the cooperative aspects of resistance.

    • The scMIC accurately predicts which strain will be selected for in the presence of an antibiotic and specifies the antibiotic concentration at which selection starts favoring new mutants.

    • The predictive properties of the scMIC are independent of the cell density.

    • antibiotic resistance
    • beta‐lactamase
    • cooperative growth
    • evolution

    Mol Syst Biol. (2015) 11: 822

    • Received November 3, 2014.
    • Revision received June 16, 2015.
    • Accepted July 2, 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.

    Tatiana Artemova, Ylaine Gerardin, Carmel Dudley, Nicole M Vega, Jeff Gore
  • Structure of silent transcription intervals and noise characteristics of mammalian genes
    Structure of silent transcription intervals and noise characteristics of mammalian genes
    1. Benjamin Zoller1,
    2. Damien Nicolas1,
    3. Nacho Molina1 and
    4. Felix Naef*,1
    1. 1The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    1. *Corresponding author. Tel: +41 21 693 16 21; E‐mail: felix.naef{at}epfl.ch

    Analysis of transcriptional bursting from time‐lapse imaging of single alleles in mammalian cells identifies the kinetic structure of promoter cycles underlying refractoriness, and explains noise in mRNA abundance.

    Synopsis

    Analysis of transcriptional bursting from time‐lapse imaging of single alleles in mammalian cells identifies the kinetic structure of promoter cycles underlying refractoriness, and explains noise in mRNA abundance.

    • Quantitative modeling of single allele time‐lapse recordings in mouse cells identifies minimal models of promoter cycles, which inform on the rate‐limiting steps responsible for refractory periods.

    • The structure of promoter cycles is gene specific and independent of genomic location. Typically, five rate‐limiting steps underlie the silent periods of endogenous promoters, while minimal synthetic promoters exhibit only one.

    • Promoter architecture constrains intrinsic noise depending on the structure of the promoter cycles, notably, TATA box genes display increased intrinsic noise in mammals, as confirmed in single‐cell RNA‐seq.

    • noise in mRNA counts
    • promoter cycle
    • single‐cell time‐lapse analysis
    • stochastic gene expression
    • transcriptional bursting

    Mol Syst Biol. (2015) 11: 823

    • Received April 27, 2015.
    • Revision received July 3, 2015.
    • Accepted July 3, 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.

    Benjamin Zoller, Damien Nicolas, Nacho Molina, Felix Naef
  • Extensive mapping of an innate immune network with CRISPR
    Extensive mapping of an innate immune network with CRISPR
    1. Michael Aregger1,
    2. Traver Hart1 and
    3. Jason Moffat (j.moffat{at}utoronto.ca)1,2
    1. 1Donnelly Centre, University of Toronto, Toronto, ON, Canada
    2. 2Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada

    The application of the CRISPR‐Cas9 system marks a major breakthrough for genetic screens, particularly in mammalian cells where high‐throughput targeted gene editing has been lacking. Parnas et al (2015) apply this screening technology to mouse bone marrow‐derived dendritic cells in order to study the regulation of the immune response triggered by PAMPs. Through integrated analysis of gene knockouts in conjunction with changes in protein and mRNA expression, CRISPR screens are facilitating dissection of immune regulatory networks at unprecedented resolution.

    See also: O Parnas et al

    The application of the CRISPR‐Cas9 system marks a major breakthrough for genetic screens in mammalian cells. Parnas et al (2015) apply this technology to primary mouse dendritic cells in order to study immune regulatory networks at unprecedented resolution.

    Mol Syst Biol. (2015) 11: 821

    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.

    Michael Aregger, Traver Hart, Jason Moffat
  • Genetic variation in insulin‐induced kinase signaling
    Genetic variation in insulin‐induced kinase signaling
    1. Isabel Xiaorong Wang*,1,
    2. Girish Ramrattan2 and
    3. Vivian G Cheung*,1,2,3
    1. 1Life Sciences Institute, University of Michigan, Ann Arbor, MI, USA
    2. 2Howard Hughes Medical Institute, Chevy Chase, MD, USA
    3. 3Departments of Pediatrics and Genetics, University of Michigan, Ann Arbor, MI, USA
    1. * Corresponding author. Tel: +1 734 763 2484; Email: ixwang{at}umich.edu

      Corresponding author. Tel: +1 734 763 2484; Email: vgcheung{at}umich.edu

    Genetic variants contribute to individual variation in insulin response, including kinase activation, changes in gene expression and cell growth, suggesting kinase modulators as promising therapeutics for diseases characterized by insulin resistance.

    Synopsis

    Genetic variants contribute to individual variation in insulin response, including kinase activation, changes in gene expression and cell growth, suggesting kinase modulators as promising therapeutics for diseases characterized by insulin resistance.

    • Extensive individual variation is observed in insulin‐induced activation of signal transduction.

    • The variation in signaling response is propagated downstream to influence gene expression and cell growth.

    • There is a genetic component to the individual differences in signaling and gene expression response to insulin.

    • DNA variants
    • individual variation
    • insulin response
    • signal transduction
    • type 2 diabetes

    Mol Syst Biol. (2015) 11: 820

    • Received April 23, 2015.
    • Revision received June 18, 2015.
    • Accepted June 26, 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.

    Isabel Xiaorong Wang, Girish Ramrattan, Vivian G Cheung
  • Data‐driven modelling of a gene regulatory network for cell fate decisions in the growing limb bud
    Data‐driven modelling of a gene regulatory network for cell fate decisions in the growing limb bud
    1. Manu Uzkudun1,
    2. Luciano Marcon1 and
    3. James Sharpe*,1,2
    1. 1EMBL‐CRG Systems Biology Program Centre for Genomic Regulation (CRG) Universitat Pompeu Fabra (UPF), Barcelona Spain
    2. 2Institucio Catalana de Recerca i Estudis Avancats (ICREA), Barcelona, Spain
    1. *Corresponding author. Tel: +34 93 316 0098; E‐mail: james.sharpe{at}crg.eu

    A dynamical 2D computer model of limb development, combining tissue movements and spatially controlled gene regulatory interactions, allows reverse‐engineering the regulatory network controlling cell fate decisions along the main proximodistal (PD) axis.

    Synopsis

    A dynamical 2D computer model of limb development, combining tissue movements and spatially controlled gene regulatory interactions, allows reverse‐engineering the regulatory network controlling cell fate decisions along the main proximodistal (PD) axis.

    • The expression patterns of the PD markers Meis1, Hoxa11 and Hoxa13 are mapped into a dynamic description of the tissue movements that drive limb morphogenesis.

    • Reverse‐engineering is used to test how different gene regulatory networks can interpret the opposing gradient of FGF and RA to pattern the PD markers.

    • Experimental validations reveal a new “crossover model” explaining how Hoxa11 and Hoxa13 are spatially regulated.

    • gene regulatory networks
    • limb development
    • morphogen
    • patterning
    • reverse‐engineering

    Mol Syst Biol. (2015) 11: 815

    • Received October 31, 2014.
    • Revision received May 5, 2015.
    • Accepted May 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.

    Manu Uzkudun, Luciano Marcon, James Sharpe
  • Time‐ and compartment‐resolved proteome profiling of the extracellular niche in lung injury and repair
    Time‐ and compartment‐resolved proteome profiling of the extracellular niche in lung injury and repair
    1. Herbert B Schiller*,1,
    2. Isis E Fernandez2,
    3. Gerald Burgstaller2,
    4. Christoph Schaab1,
    5. Richard A Scheltema1,
    6. Thomas Schwarzmayr3,
    7. Tim M Strom3,
    8. Oliver Eickelberg*,2 and
    9. Matthias Mann*,1
    1. 1Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
    2. 2Comprehensive Pneumology Center, University Hospital of the Ludwig‐Maximilians‐University Munich and Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
    3. 3Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
    1. * Corresponding author. Tel: +49 89 8578 2087; E‐mail: hschille{at}biochem.mpg.de

      Corresponding author. Tel: +49 89 3187 4666; E‐mail: oliver.eickelberg{at}helmholtz-muenchen.de

      Corresponding author. Tel: +49 89 8578 2557; E‐mail: mmann{at}biochem.mpg.de

    A proteome‐wide view of lung injury and repair was elucidated by mass spectrometry analysis of the dynamic composition of lung tissue compartments. In particular, the extracellular matrix proteome uncovers potential factors in stem cell mobilization and fibrosis resolution.

    Synopsis

    A proteome‐wide view of lung injury and repair was elucidated by mass spectrometry analysis of the dynamic composition of lung tissue compartments. In particular, the extracellular matrix proteome uncovers potential factors in stem cell mobilization and fibrosis resolution.

    • Proteomic analysis discovers signatures of consecutive phases of lung injury, fibrosis, and repair.

    • Combined proteomics and transcriptomics define the prevalence of post‐transcriptional events.

    • Compartment proteomics uncovers extracellular matrix and epithelial lining fluid composition.

    • In vivo solubility profiling reveals extracellular matrix interactions with secreted proteins.

    • extracellular matrix
    • fibrosis
    • proteomics
    • regeneration
    • secretome

    Mol Syst Biol. (2015) 11: 819

    • Received February 26, 2015.
    • Revision received May 11, 2015.
    • Accepted May 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.

    Herbert B Schiller, Isis E Fernandez, Gerald Burgstaller, Christoph Schaab, Richard A Scheltema, Thomas Schwarzmayr, Tim M Strom, Oliver Eickelberg, Matthias Mann

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