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  • 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
  • A growth‐rate composition formula for the growth of E. coli on co‐utilized carbon substrates
    <div xmlns="http://www.w3.org/1999/xhtml">A growth‐rate composition formula for the growth of <em>E. coli</em> on co‐utilized carbon substrates</div>
    1. Rutger Hermsen1,2,,
    2. Hiroyuki Okano1,,
    3. Conghui You13,
    4. Nicole Werner1 and
    5. Terence Hwa*,1
    1. 1Department of Physics, University of California at San Diego, La Jolla, CA, USA
    2. 2TBB Group, Department of Biology, Faculty of Science, Utrecht University, Utrecht, the Netherlands
    3. 3Shenzhen Key Laboratory of Microbial Genetic Engineering, College of Life Sciences Shenzhen University, Shenzhen, China
    1. *Corresponding author. Tel: +1 858 534 7263; E‐mail: hwa{at}ucsd.edu
    1. These authors contributed equally to this work

    When cultured in medium containing two carbon substrates, E. coli frequently consumes both simultaneously. A mathematical formula based on simple assumptions accurately predicts the resulting growth rate from the growth rate on each substrate alone.

    Synopsis

    When cultured in medium containing two carbon substrates, E. coli frequently consumes both simultaneously. A mathematical formula based on simple assumptions accurately predicts the resulting growth rate from the growth rate on each substrate alone.

    • Catabolite repression by cAMP‐Crp regulates the total carbon uptake flux through a negative feedback loop.

    • The uptake of one substrate non‐specifically reduces the uptake of a second, co‐utilizable substrate.

    • A growth‐rate composition formula is derived that accurately predicts the growth rate on two co‐utilized substrates based on the growth rate on either substrate alone.

    • bacterial growth
    • catabolite repression
    • metabolic coordination
    • mixed carbon‐substrate growth

    Mol Syst Biol. (2015) 11: 801

    • Received July 1, 2014.
    • Revision received February 27, 2015.
    • Accepted February 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.

    Rutger Hermsen, Hiroyuki Okano, Conghui You, Nicole Werner, Terence Hwa
  • 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

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