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  • Pathway connectivity and signaling coordination in the yeast stress‐activated signaling network
    1. Deborah Chasman1,,
    2. Yi‐Hsuan Ho2,,
    3. David B Berry28,
    4. Corey M Nemec3,
    5. Matthew E MacGilvray2,
    6. James Hose2,
    7. Anna E Merrill4,
    8. M Violet Lee49,
    9. Jessica L Will210,
    10. Joshua J Coon4,5,6,
    11. Aseem Z Ansari*,3,5,
    12. Mark Craven*,1,5,7 and
    13. Audrey P Gasch*,2,5
    1. 1Department of Computer Sciences, University of Wisconsin‐Madison, Madison, WI, USA
    2. 2Laboratory of Genetics, University of Wisconsin‐Madison, Madison, WI, USA
    3. 3Department of Biochemistry, University of Wisconsin‐Madison, Madison, WI, USA
    4. 4Department of Chemistry, University of Wisconsin‐Madison, Madison, WI, USA
    5. 5Genome Center of Wisconsin, University of Wisconsin‐Madison, Madison, WI, USA
    6. 6Department of Biological Chemistry, University of Wisconsin‐Madison, Madison, WI, USA
    7. 7Department of Biostatistics and Medical Informatics, University of Wisconsin‐Madison, Madison, WI, USA
    8. 8Institute for Neurodegenerative Disease University of California‐San Francisco, San Francisco, CA, USA
    9. 9Genentech, South San Francisco, CA, USA
    10. 10University of Georgia, Athens, GA, USA
    1. * Corresponding author. Tel: +1 608 265 4690; E‐mail: ansari{at}biochem.wisc.edu

      Corresponding author. Tel: +1 608 265 6181; E‐mail: craven{at}biostat.wisc.edu

      Corresponding author. Tel: +1 608 265 0859; E‐mail: agasch{at}wisc.edu

    1. These authors share first authorship

    An experimental and computational pipeline was developed to infer the yeast salt‐activated signaling network. The resulting network provides new insights into how cells integrate upstream signals to produce a coordinated transcriptional response to stress.

    Synopsis

    An experimental and computational pipeline was developed to infer the yeast salt‐activated signaling network. The resulting network provides new insights into how cells integrate upstream signals to produce a coordinated transcriptional response to stress.

    • An integer linear programming method for integrating disparate high‐throughput datasets was developed and used to infer the yeast signaling network activated by salt stress.

    • The network shows high connectivity between what are typically considered distinct pathways.

    • The phosphatase Cdc14 coordinates several aspects of the stress response, and RNA Pol II modification is a key regulatory point for the induction of stress‐defense genes with repression of growth‐related genes.

    • The orthologous human network is enriched for cancer‐related genes, underscoring the importance of stress‐responsive signaling networks in human disease biology.

    • environmental stress
    • integer programming
    • proteomics
    • signal transduction
    • transcriptomics

    Mol Syst Biol. (2014) 10: 759

    • Received January 19, 2014.
    • Revision received October 3, 2014.
    • Accepted October 15, 2014.

    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.

    Deborah Chasman, Yi‐Hsuan Ho, David B Berry, Corey M Nemec, Matthew E MacGilvray, James Hose, Anna E Merrill, M Violet Lee, Jessica L Will, Joshua J Coon, Aseem Z Ansari, Mark Craven, Audrey P Gasch
  • Systematic identification of cell size regulators in budding yeast
    1. Ilya Soifer12 and
    2. Naama Barkai*,1
    1. 1Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel
    2. 2NRGENE LTD, Ness Ziona, Israel
    1. *Corresponding author. Tel: +972 8934 4429; E‐mail: naama.barkai{at}weizmann.ac.il

    New regulators of cell size are identified using a mutant screen based on high‐throughput time‐lapse microscopy. A quantitative framework distinguishes direct regulators of size control from mutants whose size is altered due to reduced growth rate.

    Synopsis

    New regulators of cell size are identified using a mutant screen based on high‐throughput time‐lapse microscopy. A quantitative framework distinguishes direct regulators of size control from mutants whose size is altered due to reduced growth rate.

    • High‐throughput time‐lapse microscopy identified 17 negative and dozens positive regulators of cell size at START.

    • Negative regulators form a small genetic network centered around the mitotic exit network, suggesting that G1 length depends on processes occurring prior to cell separation.

    • The small ribosomal subunit affects size control, while the large subunit influences cell growth only, suggesting a role for translation initiation in size control.

    • A backup mode of size control that functions in the budded phase is suggested.

    • cell growth
    • size control
    • START
    • yeast genetics

    Mol Syst Biol. (2014) 10: 761

    • Received April 6, 2014.
    • Revision received October 11, 2014.
    • Accepted October 15, 2014.

    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.

    Ilya Soifer, Naama Barkai
  • Rapid neurogenesis through transcriptional activation in human stem cells
    1. Volker Busskamp1,215,
    2. Nathan E Lewis1,2,3,4,,
    3. Patrick Guye5,,
    4. Alex HM Ng1,2,6,
    5. Seth L Shipman1,2,
    6. Susan M Byrne1,2,
    7. Neville E Sanjana7,8,
    8. Jernej Murn9,10,
    9. Yinqing Li5,
    10. Shangzhong Li11,
    11. Michael Stadler12,13,14,
    12. Ron Weiss5 and
    13. George M Church*,1,2
    1. 1Department of Genetics, Harvard Medical School, Boston, MA, USA
    2. 2Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
    3. 3Department of Biology, Brigham Young University, Provo, UT, USA
    4. 4Department of Pediatrics, University of California, San Diego, CA, USA
    5. 5Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
    6. 6Department of Systems Biology, Harvard Medical School, Boston, MA, USA
    7. 7Broad Institute of MIT and Harvard Cambridge Center, Cambridge, MA, USA
    8. 8McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
    9. 9Department of Cell Biology, Harvard Medical School, Boston, MA, USA
    10. 10Division of Newborn Medicine, Boston Children's Hospital, Boston, MA, USA
    11. 11Department of Bioengineering, University of California, San Diego, CA, USA
    12. 12Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
    13. 13Swiss Institute of Bioinformatics, Basel, Switzerland
    14. 14University of Basel, Basel, Switzerland
    15. 15Center for Regenerative Therapies Dresden (CRTD), Dresden, Germany
    1. *Corresponding author. Tel: +1 617 432 1278; E‐mail: gchurch{at}genetics.med.harvard.edu
    1. These authors contributed equally to this work

    Rapid and homogeneous neuronal differentiation is attained in human stem cells upon overexpression of two Neurogenin transcription factors. mRNA and miRNA expression profiling during differentiation reveals a regulatory network mediating neurogenesis from stem cells.

    Synopsis

    Rapid and homogeneous neuronal differentiation is attained in human stem cells upon overexpression of two Neurogenin transcription factors. mRNA and miRNA expression profiling during differentiation reveals a regulatory network mediating neurogenesis from stem cells.

    • Neurogenin‐1 and ‐2 drive homogeneous differentiation of human stem cells into bipolar neurons in 4 days in defined media.

    • The population homogeneity allowed mRNA and miRNA expression profiling over time during neurogenesis.

    • A network of key transcription factors and miRNAs that promote rapid neurogenesis and loss of pluripotency is identified.

    • Perturbations of key transcription factors affect the homogeneity and phenotypic properties of the resulting neurons.

    • gene regulatory networks
    • microRNAs
    • neurogenesis
    • stem cell differentiation
    • transcriptomics

    Mol Syst Biol. (2014) 10: 760

    • Received June 20, 2014.
    • Revision received October 14, 2014.
    • Accepted October 16, 2014.

    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.

    Volker Busskamp, Nathan E Lewis, Patrick Guye, Alex HM Ng, Seth L Shipman, Susan M Byrne, Neville E Sanjana, Jernej Murn, Yinqing Li, Shangzhong Li, Michael Stadler, Ron Weiss, George M Church
  • Quantification of cytosolic interactions identifies Ede1 oligomers as key organizers of endocytosis
    1. Dominik Boeke1,2,
    2. Susanne Trautmann2,
    3. Matthias Meurer2,
    4. Malte Wachsmuth1,
    5. Camilla Godlee1,
    6. Michael Knop*,2 and
    7. Marko Kaksonen*,1
    1. 1European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
    2. 2Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH) Deutsches Krebsforschungszentrum (DKFZ) DKFZ‐ZMBH‐Allianz, Heidelberg, Germany
    1. * Corresponding author. Tel: +49 6221 544213; Fax: +49 6221 545893; E‐mail: m.knop{at}zmbh.uni-heidelberg.de

      Corresponding author. Tel: +49 6221 3878285; Fax: +49 6221 3878512; E‐mail: kaksonen{at}embl.de

    Cytoplasmic protein–protein interactions between endocytic components are quantitatively analyzed by fluorescence correlation spectroscopy in yeast. Cytoplasmic oligomerization of the scaffold protein Ede1 is shown to be critical for its function in endocytosis.

    Synopsis

    Cytoplasmic protein–protein interactions between endocytic components are quantitatively analyzed by fluorescence correlation spectroscopy in yeast. Cytoplasmic oligomerization of the scaffold protein Ede1 is shown to be critical for its function in endocytosis.

    • Cytoplasmic diffusion coefficients and concentrations of endocytic proteins are analyzed by fluorescence correlation spectroscopy.

    • The endocytic protein–protein interaction network in the cytoplasm is characterized by fluorescence cross‐correlation spectroscopy.

    • Oligomerization of Ede1 is essential for its localization and function as an early endocytic scaffold protein.

    • Ede1
    • endocytosis
    • fluorescence (cross‐)correlation spectroscopy

    Mol Syst Biol. (2014) 10: 756

    • Received May 12, 2014.
    • Revision received September 11, 2014.
    • Accepted October 1, 2014.

    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.

    Dominik Boeke, Susanne Trautmann, Matthias Meurer, Malte Wachsmuth, Camilla Godlee, Michael Knop, Marko Kaksonen
  • An organ boundary‐enriched gene regulatory network uncovers regulatory hierarchies underlying axillary meristem initiation
    1. Caihuan Tian1,
    2. Xiaoni Zhang1,2,
    3. Jun He1,
    4. Haopeng Yu1,3,
    5. Ying Wang1,
    6. Bihai Shi1,3,
    7. Yingying Han1,3,
    8. Guoxun Wang1,3,
    9. Xiaoming Feng1,
    10. Cui Zhang1,
    11. Jin Wang1,3,
    12. Jiyan Qi1,3,
    13. Rong Yu2 and
    14. Yuling Jiao*,1
    1. 1State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and National Center for Plant Gene Research, Beijing, China
    2. 2College of Life Sciences, Capital Normal University, Beijing, China
    3. 3University of Chinese Academy of Sciences, Beijing, China
    1. *Corresponding author. Tel: +86 10 64807656; Fax: +86 10 64806595; E‐mail: yljiao{at}genetics.ac.cn

    The leaf boundary regions separate differentiated organs from undifferentiated stem cells in plants. The gene regulatory network of boundary cells was mapped by combining cell type‐specific genome expression analysis with genomewide yeast one‐hybrid screening.

    Synopsis

    The leaf boundary regions separate differentiated organs from undifferentiated stem cells in plants. The gene regulatory network of boundary cells was mapped by combining cell type‐specific genome expression analysis with genomewide yeast one‐hybrid screening.

    • A leaf boundary cell‐specific gene expression map identifies transcriptional signatures and predicts cellular functions.

    • A genomewide protein–DNA interaction map resolved using a yeast one‐hybrid approach uncovers promoter hubs and predicts new regulating transcription factors (TFs).

    • An intermediate‐scale experimental test determined the regulatory effects of many TFs on their targets and identified new regulators and regulatory relationships in boundary and axillary meristem formation.

    • axillary meristem
    • gene regulatory network
    • organ boundary

    Mol Syst Biol. (2014) 10: 755

    • Received June 3, 2014.
    • Revision received August 14, 2014.
    • Accepted September 24, 2014.

    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.

    Caihuan Tian, Xiaoni Zhang, Jun He, Haopeng Yu, Ying Wang, Bihai Shi, Yingying Han, Guoxun Wang, Xiaoming Feng, Cui Zhang, Jin Wang, Jiyan Qi, Rong Yu, Yuling Jiao
  • Ultrasensitive proteome analysis using paramagnetic bead technology
    1. Christopher S Hughes1,
    2. Sophia Foehr1,
    3. David A Garfield1,
    4. Eileen E Furlong1,
    5. Lars M Steinmetz1 and
    6. Jeroen Krijgsveld*,1
    1. 1European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
    1. *Corresponding author. Tel: +49 6221 3878560; E‐mail: jeroen.krijgsveld{at}embl.de

    A new proteomic sample preparation protocol allows fast, efficient and ultra‐sensitive analyses. The method is illustrated by profiling proteomes from sub‐microgram amounts of material, including the first proteome screen of Drosophila development at a single‐embryo resolution.

    Synopsis

    A new proteomic sample preparation protocol allows fast, efficient and ultra‐sensitive analyses. The method is illustrated by profiling proteomes from sub‐microgram amounts of material, including the first proteome screen of Drosophila development at a single‐embryo resolution.

    • A novel protocol using paramagnetic beads, termed Single‐Pot Solid‐Phase‐enhanced Sample Preparation (SP3) is presented.

    • SP3 enables protein and peptide enrichment, cleanup, digestion, chemical isotope labeling and fractionation in a single tube, without limitations arising from reagent compatibility.

    • SP3 allows unmatched ultra‐sensitive proteome profiling from sub‐microgram amounts of material, as low as 1,000 HeLa cells or a single fly embryo.

    • The first quantitative analysis of early Drosophila development at a single‐embryo resolution reveals dynamic trends in the developmental proteome.

    • mass spectrometry
    • paramagnetic beads
    • proteomics
    • quantification
    • sample preparation

    Mol Syst Biol. (2014) 10: 757

    • Received July 27, 2014.
    • Revision received October 1, 2014.
    • Accepted October 7, 2014.

    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.

    Christopher S Hughes, Sophia Foehr, David A Garfield, Eileen E Furlong, Lars M Steinmetz, Jeroen Krijgsveld
  • Efficient sample processing for proteomics applications—Are we there yet?
    1. Evgeny Kanshin1 and
    2. Pierre Thibault (pierre.thibault{at}umontreal.ca) 1,2
    1. 1Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, QC, Canada
    2. 2Department of Chemistry, Université de Montréal, Montréal, QC, Canada

    The ability to solubilize and digest protein extracts and recover peptides with high efficiency is of paramount importance in proteomics. A novel proteomic sample preparation protocol by Krijgsveld and colleagues (Hughes et al, 2014) provides significant advantages by enabling all sample processing steps to be carried out in a single tube to minimize sample losses, thereby enhancing sensitivity, throughput, and scalability of proteomics analyses.

    See also: Hughes et al (October 2014)

    A new proteomic sample preparation protocol by Krijgsveld and colleagues (Hughes et al, 2014) enables all sample processing steps to be carried out in a single tube to minimize sample losses, thereby enhancing sensitivity, throughput, and scalability of proteomics analyses.

    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.

    Evgeny Kanshin, Pierre Thibault

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