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  • Article
    Stress‐response balance drives the evolution of a network module and its host genome
    Stress‐response balance drives the evolution of a network module and its host genome
    1. Caleb González1,,
    2. Joe Christian J Ray1,2,,
    3. Michael Manhart3,4,
    4. Rhys M Adams1,
    5. Dmitry Nevozhay1,5,
    6. Alexandre V Morozov3,6 and
    7. Gábor Balázsi*,1,7,8
    1. 1Department of Systems Biology ‐ Unit 950, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
    2. 2Center for Computational Biology & Department of Molecular Biosciences, University of Kansas, Lawrence, KS, USA
    3. 3Department of Physics & Astronomy, Rutgers University, Piscataway, NJ, USA
    4. 4Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
    5. 5School of Biomedicine, Far Eastern Federal University, Vladivostok, Russia
    6. 6BioMaPS Institute for Quantitative Biology, Rutgers University, Piscataway, NJ, USA
    7. 7Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
    8. 8Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
    1. *Corresponding author. Tel: +1 631 632 5414; Fax: +1 631 632 5405; E‐mail: gabor.balazsi{at}stonybrook.edu
    1. These authors contributed equally to this study

    The evolution of a synthetic gene circuit that trades off costly gene expression for drug resistance is analyzed computationally. The predictions are validated experimentally by adjusting gene expression in the absence or presence of environmental stress.

    Synopsis

    The evolution of a synthetic gene circuit that trades off costly gene expression for drug resistance is analyzed computationally. The predictions are validated experimentally by adjusting gene expression in the absence or presence of environmental stress.

    • A synthetic gene circuit is integrated into the yeast genome to model the evolution of drug resistance networks with inherent tradeoff.

    • Computational models are constructed to predict the speed and mechanisms of adaptation for various levels of gene expression and stress.

    • The cell population adapts by mutations eliminating the module quickly when the network gratuitously responds in the absence of stress or by mutations that fine‐tune the module's suboptimal response and establish slowly in the presence of stress.

    • If the module initially fails to respond to stress, the population adapts by mutations that activate gene expression within the module.

    • drug resistance
    • experimental evolution
    • positive feedback
    • synthetic gene circuit
    • tradeoff

    Mol Syst Biol. (2015) 11: 827

    • Received March 21, 2015.
    • Revision received July 31, 2015.
    • Accepted June 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.

    Caleb González, Joe Christian J Ray, Michael Manhart, Rhys M Adams, Dmitry Nevozhay, Alexandre V Morozov, Gábor Balázsi
  • Article
    Extensive allele‐specific translational regulation in hybrid mice
    Extensive allele‐specific translational regulation in hybrid mice
    1. Jingyi Hou1,,
    2. Xi Wang1,,
    3. Erik McShane2,
    4. Henrik Zauber2,
    5. Wei Sun1,
    6. Matthias Selbach2 and
    7. Wei Chen*,1
    1. 1Laboratory for Functional and Medical Genomics, Berlin Institute for Medical Systems Biology, Berlin, Germany
    2. 2Laboratory for Proteome Dynamics, Max‐Delbrück‐Centrum für Molekulare Medizin, Berlin, Germany
    1. *Corresponding author. Tel: +49 30 94062995; E‐mail: wei.chen{at}mdc-berlin.de
    1. These authors contributed equally to this work

    Deep sequencing‐based transcriptome and polysome profiling reveals extensive allele‐specific translational regulation in an F1 hybrid mouse, which enables global analysis of cis‐regulatory elements and the coordination between transcriptional and translational regulation.

    Synopsis

    Deep sequencing‐based transcriptome and polysome profiling reveals extensive allele‐specific translational regulation in an F1 hybrid mouse, which enables global analysis of cis‐regulatory elements and the coordination between transcriptional and translational regulation.

    • Out of 7,156 genes with reliable quantification of both alleles, 1,008 (14.1%) exhibit significant allelic divergence in translational efficiency.

    • Systematic analysis of sequence features reveals that local RNA secondary structure surrounding the start codon and proximal out‐of‐frame upstream AUGs can affect translational efficiency.

    • Cis‐effects in transcriptional and translational regulatory processes are quantitatively comparable and more frequently compensatory, suggesting coordinated regulation at the two levels.

    • allele‐specific gene expression
    • cis‐elements
    • translational regulation

    Mol Syst Biol. (2015) 11: 825

    • Received April 17, 2015.
    • Revision received June 12, 2015.
    • Accepted July 8, 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.

    Jingyi Hou, Xi Wang, Erik McShane, Henrik Zauber, Wei Sun, Matthias Selbach, Wei Chen
  • Article
    Comprehensive assembly of novel transcripts from unmapped human RNA‐Seq data and their association with cancer
    Comprehensive assembly of novel transcripts from unmapped human RNA‐Seq data and their association with cancer
    1. Majid Kazemian1,
    2. Min Ren1,
    3. Jian‐Xin Lin1,
    4. Wei Liao1,
    5. Rosanne Spolski1 and
    6. Warren J Leonard*,1
    1. 1Laboratory of Molecular Immunology and the Immunology Center, National Heart, Lung, and Blood Institute National Institutes of Health, Bethesda, MD, USA
    1. *Corresponding author. Tel: +1 301 496 0098; E‐mail: wjl{at}helix.nih.gov

    Comprehensive analysis of unmapped reads from a large number of cancer and normal RNA‐Seq datasets identifies 2,550 previously unassembled human transcripts. Some of the transcripts are cancer type‐ or tissue‐specific, suggesting context‐dependent functions.

    Synopsis

    Comprehensive analysis of unmapped reads from a large number of cancer and normal RNA‐Seq datasets identifies 2,550 previously unassembled human transcripts. Some of the transcripts are cancer type‐ or tissue‐specific, suggesting context‐dependent functions.

    • RNA‐sequencing reads that could not be mapped to the human reference genome/transcriptome are de novo assembled to obtain long and mainly noncoding transcripts.

    • Alignment to chimp/gorilla genomes enabled predicting the genic neighborhood of these transcripts.

    • Some transcripts associated with specific cancers/tissues are identified, and over‐expression of three selected transcripts is shown to affect the growth of HepG2 cells.

    • cancer‐associated transcripts
    • long noncoding RNAs
    • unmapped sequencing reads

    Mol Syst Biol. (2015) 11: 826

    • Received March 17, 2015.
    • Revision received July 13, 2015.
    • Accepted July 14, 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.

    Majid Kazemian, Min Ren, Jian‐Xin Lin, Wei Liao, Rosanne Spolski, Warren J Leonard
  • Article
    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
  • Article
    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
  • Article
    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
  • News & Views
    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