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  • Tuning noise in gene expression
    Tuning noise in gene expression
    1. Sanjay Tyagi (sanjay.tyagi{at}rutgers.edu) 1
    1. 1Public Health Research Institute, Rutgers University, Newark, NJ, USA

    The relative contribution of promoter architecture and the associated chromatin environment in regulating gene expression noise has remained elusive. In their recent work, Arkin, Schaffer and colleagues (Dey et al, 2015) show that mean expression and noise for a given promoter at different genomic loci are uncorrelated and influenced by the local chromatin environment.

    See also: SS Dey et al (May 2015)

    The relative contribution of promoter architecture and the associated chromatin environment in regulating gene expression noise has remained elusive. Arkin, Schaffer and colleagues (Dey et al, 2015) show that mean expression and noise are uncorrelated and influenced by the chromatin environment.

    Mol Syst Biol. (2015) 11: 805

    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.

    Sanjay Tyagi
  • Orthogonal control of expression mean and variance by epigenetic features at different genomic loci
    Orthogonal control of expression mean and variance by epigenetic features at different genomic loci
    1. Siddharth S Dey1,28,
    2. Jonathan E Foley3,,
    3. Prajit Limsirichai4,
    4. David V Schaffer*,1,2,3,5 and
    5. Adam P Arkin*,3,5,6,7
    1. 1Department of Chemical and Biomolecular Engineering and the Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
    2. 2Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
    3. 3Department of Bioengineering, University of California, Berkeley, CA, USA
    4. 4Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
    5. 5Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
    6. 6Virtual Institute of Microbial Stress and Survival, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
    7. 7DOE, Joint BioEnergy Institute Lawrence Berkeley National Laboratory, Berkeley, CA, USA
    8. 8Hubrecht Institute‐KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, Cancer Genomics Netherlands, CT Utrecht, the Netherlands
    1. * Corresponding author. Tel: +1 510 643 5963; Fax: +1 510 642 4778; Email: schaffer{at}berkeley.edu

      Corresponding author. Tel: +1 510 495 2366; Fax: +1 510 486 6059; E‐mail: aparkin{at}lbl.gov

    1. These authors contributed equally to this work

    Analyses of the molecular basis of gene expression noise by smFISH and flow cytometry show that in mammalian cells, mean expression and noise are uncorrelated across genomic locations and are affected by the local chromatin environment.

    Synopsis

    Analyses of the molecular basis of gene expression noise by smFISH and flow cytometry show that in mammalian cells, mean expression and noise are uncorrelated across genomic locations and are affected by the local chromatin environment.

    • Using a dual‐reporter lentiviral system, the influence of the promoter sequence is deconvolved to systematically study how the chromatin environment regulates gene expression noise.

    • Analysis of 418 single‐integration clones reveals that the mean expression is uncorrelated with the coefficient of variation (CV).

    • Single‐molecule mRNA FISH distributions are fit to a two‐state model of gene expression to show orthogonal control of mean expression by burst size and gene expression noise (CV) by burst frequency.

    • DNase I sensitivity assays reveal that promoters within more repressed chromatin are associated with reduced burst frequency and increased gene expression noise.

    • chromatin environment
    • gene expression noise
    • single‐cell biology
    • single‐molecule RNA FISH

    Mol Syst Biol. (2015) 11: 806

    • Received August 19, 2014.
    • Revision received March 25, 2015.
    • Accepted March 31, 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.

    Siddharth S Dey, Jonathan E Foley, Prajit Limsirichai, David V Schaffer, Adam P Arkin
  • Systematic discovery of drug interaction mechanisms
    Systematic discovery of drug interaction mechanisms
    1. Guillaume Chevereau12 and
    2. Tobias Bollenbach*,1
    1. 1IST Austria, Klosterneuburg, Austria
    2. 2INSA de Strasbourg, Strasbourg, France
    1. *Corresponding author. Tel: +43 2243 9000 4101; E‐mail: tb{at}ist.ac.at

    A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways.

    Synopsis

    A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways.

    • Drug interactions between antibiotics are highly robust to genetic perturbations.

    • A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations.

    • Rare violations of this principle expose cellular functions that control drug interactions.

    • Diverse drug interactions are controlled by recurring cellular functions, including LPS synthesis and ATP synthesis.

    • antibiotics
    • drug combination design
    • drug interaction mechanisms
    • Escherichia coli
    • general principles of biological systems

    Mol Syst Biol. (2015) 11: 807

    • Received February 12, 2015.
    • Revision received April 9, 2015.
    • Accepted April 15, 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.

    Guillaume Chevereau, Tobias Bollenbach
  • Using light to shape chemical gradients for parallel and automated analysis of chemotaxis
    Using light to shape chemical gradients for parallel and automated analysis of chemotaxis
    1. Sean R Collins*,12,
    2. Hee Won Yang1,
    3. Kimberly M Bonger13,
    4. Emmanuel G Guignet14,
    5. Thomas J Wandless1 and
    6. Tobias Meyer*,1
    1. 1Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
    2. 2Department of Microbiology and Molecular Genetics, University of California, Davis, Davis, CA, USA
    3. 3Department of Biomolecular Chemistry, Radboud University Nijmegen, Nijmegen, The Netherlands
    4. 4Bio‐Rad Laboratories, IHD, Cressier, Switzerland
    1. * Corresponding author. Tel: +1 530 752 7497; E‐mail: srcollins{at}ucdavis.edu

      Corresponding author. Tel: +1 650 724 2971; E‐mail: tobias1{at}stanford.edu

    A new strategy, involving optical shaping of gradients, allows systematically analyzing components regulating cell migration speed and directionality. The approach is applied to characterize migration and chemotaxis phenotypes for 285 siRNA perturbations in human neutrophils.

    Synopsis

    A new strategy, involving optical shaping of gradients, allows systematically analyzing components regulating cell migration speed and directionality. The approach is applied to characterize migration and chemotaxis phenotypes for 285 siRNA perturbations in human neutrophils.

    • Automated uncaging of attractants allows systematic live‐cell imaging of chemotaxis.

    • Leukocytes have distinct components specialized for regulating cell speed and cell direction in response to chemoattractant gradients.

    • Specialization in the chemoattractant signaling pathway occurs already at the level of the G‐proteins.

    • chemokinesis
    • chemotaxis
    • Galphai
    • gradients
    • uncaging

    Mol Syst Biol. (2015) 11: 804

    • Received January 8, 2015.
    • Revision received March 25, 2015.
    • Accepted March 27, 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.

    Sean R Collins, Hee Won Yang, Kimberly M Bonger, Emmanuel G Guignet, Thomas J Wandless, Tobias Meyer
  • Differential genetic interactions of yeast stress response MAPK pathways
    Differential genetic interactions of yeast stress response MAPK pathways
    1. Humberto Martin1,
    2. Michael Shales2,
    3. Pablo Fernandez‐Piñar1,
    4. Ping Wei3,
    5. Maria Molina1,
    6. Dorothea Fiedler4,
    7. Kevan M Shokat5,
    8. Pedro Beltrao*,6,7,
    9. Wendell Lim2,8,9 and
    10. Nevan J Krogan*,2,9,10,11
    1. 1Departamento de Microbiología II, Facultad de Farmacia, Universidad Complutense de Madrid and Instituto Ramón y Cajal de Investigaciones Sanitarias (IRYCIS), Madrid, Spain
    2. 2Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA USA
    3. 3Center for Quantitative Biology and Peking‐Tsinghua Center for Life Sciences, Peking University, Beijing, China
    4. 4Department of Chemistry, Princeton University, Princeton, NJ, USA
    5. 5Chemistry and Chemical Biology Graduate Program, University of California, San Francisco, CA, USA
    6. 6European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
    7. 7iBiMED and Department of Health Sciences, University of Aveiro, Aveiro, Portugal
    8. 8Howard Hughes Medical Institute, University of California, San Francisco, CA, USA
    9. 9Center for Systems and Synthetic Biology, University of California, San Francisco, CA, USA
    10. 10California Institute for Quantitative Biosciences, QB3, San Francisco, CA, USA
    11. 11J. David Gladstone Institutes, San Francisco, CA, USA
    1. * Corresponding author. Tel: +44 1223 494 610; E‐mail: pbeltrao{at}ebi.ac.uk

      Corresponding author. Tel: +1 415 476 2980; E‐mail: nevan.krogan{at}ucsf.edu

    A differential genetic interaction screen performed in different stress conditions shows that genetic interactions are often context specific. Conditional genetic interactions recapitulate known signalling interactions and can be used to identify novel conditional functional associations.

    Synopsis

    A differential genetic interaction screen performed in different stress conditions shows that genetic interactions are often context specific. Conditional genetic interactions recapitulate known signalling interactions and can be used to identify novel conditional functional associations.

    • 250,000 measurements of genetic interactions are performed, covering five different stress conditions (e.g. osmotic, oxidative and cell wall‐altering stresses).

    • Genetic interactions tend to be context specific and differential genetic interactions identify condition‐specific functional associations.

    • Osmotic stress conditional genetic interactions suggest that the post‐translational response of the osmotic pathway is more critical and/or specific than the transcriptional response.

    • cell wall integrity
    • genetic interactions
    • osmotic shock
    • stress response

    Mol Syst Biol. (2015) 11: 800

    • Received July 22, 2014.
    • Revision received March 16, 2015.
    • Accepted March 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

    Humberto Martin, Michael Shales, Pablo Fernandez‐Piñar, Ping Wei, Maria Molina, Dorothea Fiedler, Kevan M Shokat, Pedro Beltrao, Wendell Lim, Nevan J Krogan
  • Inferring causal metabolic signals that regulate the dynamic TORC1‐dependent transcriptome
    Inferring causal metabolic signals that regulate the dynamic TORC1‐dependent transcriptome
    1. Ana Paula Oliveira1,,
    2. Sotiris Dimopoulos2,,
    3. Alberto Giovanni Busetto38,
    4. Stefan Christen1,
    5. Reinhard Dechant4,
    6. Laura Falter1,
    7. Morteza Haghir Chehreghani3,
    8. Szymon Jozefczuk1,
    9. Christina Ludwig1,
    10. Florian Rudroff1,
    11. Juliane Caroline Schulz1,
    12. Asier González5,
    13. Alexandre Soulard5,6,
    14. Daniele Stracka5,
    15. Ruedi Aebersold1,7,
    16. Joachim M Buhmann3,
    17. Michael N Hall5,
    18. Matthias Peter4,
    19. Uwe Sauer*,1 and
    20. Jörg Stelling*,2
    1. 1Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
    2. 2Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland
    3. 3Department of Computer Science, ETH Zurich, Zurich, Switzerland
    4. 4Department of Biology, Institute of Biochemistry, ETH Zurich, Zurich, Switzerland
    5. 5Biozentrum, University of Basel, Basel, Switzerland
    6. 6UMR5240 MAP, Université Lyon 1, Villeurbanne, France
    7. 7Faculty of Science, University of Zurich, Zurich, Switzerland
    8. 8Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA
    1. * Corresponding author. Tel: +41 44 633 3672; E‐mail: sauer{at}ethz.ch

      Corresponding author. Tel: +41 61 387 3194; E‐mail: joerg.stelling{at}bsse.ethz.ch

    1. These authors contributed equally to this work

    Dynamic experiments and a computational method are co‐designed to infer causal interactions between metabolism, signaling and transcription. Model‐based data integration suggests new candidates for inputs and targets of yeast nitrogen signaling via TOR complex 1.

    Synopsis

    Dynamic experiments and a computational method are co‐designed to infer causal interactions between metabolism, signaling and transcription. Model‐based data integration suggests new candidates for inputs and targets of yeast nitrogen signaling via TOR complex 1.

    • Dynamic experiments yield a consistent, multi‐omics dataset for yeast responses to shifts in nitrogen quality.

    • The cellular response involves extensive rewiring of metabolism via multiple mechanisms.

    • Our generalizable probabilistic framework infers causal relations from heterogeneous data types and exploits prior knowledge on networks and biological mechanisms.

    • causal inference
    • network motifs
    • nutrient signaling
    • target of rapamycin pathway

    Mol Syst Biol. (2015) 11: 802

    • Received June 3, 2014.
    • Revision received March 19, 2015.
    • Accepted March 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.

    Ana Paula Oliveira, Sotiris Dimopoulos, Alberto Giovanni Busetto, Stefan Christen, Reinhard Dechant, Laura Falter, Morteza Haghir Chehreghani, Szymon Jozefczuk, Christina Ludwig, Florian Rudroff, Juliane Caroline Schulz, Asier González, Alexandre Soulard, Daniele Stracka, Ruedi Aebersold, Joachim M Buhmann, Michael N Hall, Matthias Peter, Uwe Sauer, Jörg Stelling
  • Integrative network analysis reveals molecular mechanisms of blood pressure regulation
    Integrative network analysis reveals molecular mechanisms of blood pressure regulation
    1. Tianxiao Huan1,2,,
    2. Qingying Meng3,
    3. Mohamed A Saleh4,5,
    4. Allison E Norlander4,
    5. Roby Joehanes1,2,6,7,8,,
    6. Jun Zhu9,10,
    7. Brian H Chen1,2,,
    8. Bin Zhang9,10,
    9. Andrew D Johnson1,11,,
    10. Saixia Ying6,,
    11. Paul Courchesne1,2,,
    12. Nalini Raghavachari12,,
    13. Richard Wang13,,
    14. Poching Liu13,,
    15. The International Consortium for Blood Pressure GWAS (ICBP),
    16. Christopher J O'Donnell1,11,,
    17. Ramachandran Vasan1,,
    18. Peter J Munson6,,
    19. Meena S Madhur4,
    20. David G Harrison4,
    21. Xia Yang*,3 and
    22. Daniel Levy*,1,2,
    1. 1The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
    2. 2The Population Sciences Branch and the Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
    3. 3Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
    4. 4Department of Medicine, Division of Clinical Pharmacology, Vanderbilt University, Nashville, TN, USA
    5. 5Department of Pharmacology and Toxicology, Faculty of Pharmacy, Mansoura University, Mansoura, Egypt
    6. 6Mathematical and Statistical Computing Laboratory, Center for Information Technology National Institutes of Health, Bethesda, MD, USA
    7. 7Harvard Medical School, Boston, MA, USA
    8. 8Hebrew SeniorLife, Boston, MA, USA
    9. 9Institute of Genomics and Multiscale Biology, New York, NY, USA
    10. 10Graduate School of Biological Sciences Mount Sinai School of Medicine, New York, NY, USA
    11. 11Cardiovascular Epidemiology and Human Genomics Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, USA
    12. 12Division of Geriatrics and Clinical Gerontology, National Institute on Aging, Bethesda, MD, USA
    13. 13Genomics Core facility Genetics & Developmental Biology Center, The National Heart, Lung and Blood Institute, Bethesda, MD, USA
    1. * Corresponding author. Tel: +1 310 206 1812; Fax: +1 310 206 9184; E‐mail: xyang123{at}ucla.edu

      Corresponding author. Tel: +1 508 935 3458; Fax: +1 508 872 2678; E‐mail: levyd{at}nih.gov

    A systems biology approach integrating genome‐wide genetic variation and transcriptome profiling data from participants of the Framingham Heart Study identifies key regulatory genes and gene networks underlying blood pressure control.

    Synopsis

    A systems biology approach integrating genome‐wide genetic variation and transcriptome profiling data from participants of the Framingham Heart Study identifies key regulatory genes and gene networks underlying blood pressure control.

    • Association analysis of blood pressure (BP) and gene expression levels identified individual genes and coexpression network modules that are correlated with BP.

    • Incorporation of data from BP genome‐wide association studies (GWAS) revealed four BP coexpression network modules (coEMs) that are enriched with eSNPs that demonstrate low P‐values in BP GWAS.

    • Further integration of the BP coEMs with molecular networks uncovered key driver genes that serve as network hubs to interconnect genes within the BP coEMs.

    • One of the top key drivers, SH2B3, was analyzed in a Sh2b3−/− mouse model, which validated its effect on BP regulation and its central role in the BP subnetwork.

    • blood pressure
    • coexpression network
    • gene expression
    • hypertension
    • systems biology

    Mol Syst Biol. (2015) 11: 799

    • Received April 28, 2014.
    • Revision received March 8, 2015.
    • Accepted March 10, 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.

    Tianxiao Huan, Qingying Meng, Mohamed A Saleh, Allison E Norlander, Roby Joehanes, Jun Zhu, Brian H Chen, Bin Zhang, Andrew D Johnson, Saixia Ying, Paul Courchesne, Nalini Raghavachari, Richard Wang, Poching Liu, , Christopher J O'Donnell, Ramachandran Vasan, Peter J Munson, Meena S Madhur, David G Harrison, Xia Yang, Daniel Levy