Advertisement

  • The functional diversity of protein lysine methylation
    1. Sylvain Lanouette1,
    2. Vanessa Mongeon1,
    3. Daniel Figeys1 and
    4. Jean‐François Couture*,1
    1. 1Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Canada
    1. *Corresponding author. Tel: +1 613 562 5800 8854; Fax: +1 613 562 5655; E‐mail: jean-francois.couture{at}uottawa.ca

    Lysine methylation is a functionally diverse post‐translational modification found in all living organisms. A comprehensive review of the ~1,000 methylation sites reported to date highlights the involvement of lysine methylation in multiple regulatory networks.

    • lysine demethylation
    • lysine methylation
    • networks
    • proteomics
    • systems biology

    Mol Syst Biol. (2014) 10: 724

    • Received November 8, 2013.
    • Revision received February 17, 2014.
    • Accepted February 18, 2014.

    This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Sylvain Lanouette, Vanessa Mongeon, Daniel Figeys, Jean‐François Couture
  • The three most important things about origins: location, location, location
    1. Nicholas Rhind (nick.rhind{at}umassmed.edu) 1
    1. 1Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA, USA

    The reasons why some DNA replication origins fire earlier than others have remained elusive. New work by Gindin et al suggests that the distribution of replication origins, not their timing per se, is the major determinant of the timing of genome replication in human cells.

    See also: Y Gindin et al (March 2014)

    The reasons why some DNA replication origins fire earlier than others have remained elusive. New work by Gindin et al suggests that the distribution of replication origins, not their timing, per se, is the major determinant of genome replication timing in human cells.

    Mol Syst Biol. (2014) 10: 723

    This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Nicholas Rhind
  • A chromatin structure‐based model accurately predicts DNA replication timing in human cells
    1. Yevgeniy Gindin1,2,
    2. Manuel S Valenzuela3,
    3. Mirit I Aladjem4,
    4. Paul S Meltzer*,1 and
    5. Sven Bilke1
    1. 1Genetics Branch Center for Cancer Research, Bethesda, MD, USA
    2. 2Graduate Program in Bioinformatics, Boston University, Boston, MA, USA
    3. 3Department of Biochemistry and Cancer Biology, School of Medicine, Meharry Medical College, Nashville, TN, USA
    4. 4Laboratory of Molecular Pharmacology, National Cancer Institute, Bethesda, MD, USA
    1. *Corresponding author. Tel: +1 301 496 5266; Fax: +1 301 402 3241; E‐mail: pmeltzer{at}mail.nih.gov

    A mechanistic model predicts cell lineage‐specific DNA replication timing based on the location of DNase‐hypersensitivity data alone. With essentially no parameters to adjust for different cell types, the model is truly predictive even for cells where timing data are not available.

    Synopsis

    A mechanistic model predicts cell lineage‐specific DNA replication timing based on the location of DNase‐hypersensitivity data alone. With essentially no parameters to adjust for different cell types, the model is truly predictive even for cells where timing data are not available.

    • The mechanistic model predicts replication timing in human cells with an accuracy approaching the limit set by experimental noise.

    • In the model, the timing program results from purely time‐stochastic initiation at well‐localized initiation sites and it is determined by the location of initiation sites alone regardless of initiation probabilities.

    • Replication initiation sites are optimally localized by DNase‐hypersensitive sites.

    • computational model
    • DNA replication timing
    • DNase hypersensitivity
    • systems analysis

    Mol Syst Biol. (2014) 10: 722

    • Received September 17, 2013.
    • Revision received February 11, 2014.
    • Accepted February 12, 2014.

    This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Yevgeniy Gindin, Manuel S Valenzuela, Mirit I Aladjem, Paul S Meltzer, Sven Bilke
  • Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling
    1. Rasmus Agren1,,
    2. Adil Mardinoglu1,,
    3. Anna Asplund2,
    4. Caroline Kampf2,
    5. Mathias Uhlen3,4 and
    6. Jens Nielsen*,1,3
    1. 1Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
    2. 2Department of Immunology, Genetics and Pathology Science for Life Laboratory, Uppsala University, Uppsala, Sweden
    3. 3Science for Life Laboratory KTH – Royal Institute of Technology, Stockholm, Sweden
    4. 4Department of Proteomics KTH – Royal Institute of Technology, Stockholm, Sweden
    1. *Corresponding author. Tel: +46 31 772 3804; Fax: +46 31 772 3801; E‐mail: nielsenj{at}chalmers.se
    1. These authors contributed equally to this work.

    Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task‐driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients.

    Synopsis

    Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task‐driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients.

    • The presence of proteins encoded by 15,841 genes in tumors from 27 HCC patients is evaluated by immunohistochemistry.

    • Personalized GEMs for six HCC patients and GEMs for 83 healthy cell types are reconstructed based on HMR 2.0 and the tINIT algorithm for task‐driven model reconstruction.

    • 101 antimetabolites are predicted to inhibit tumor growth in all patients. Antimetabolite toxicity is tested using the 83 cell type‐specific GEMs.

    • An l‐carnitine analog inhibits the proliferation of HepG2 cells.

    • antimetabolites
    • genome‐scale metabolic models
    • hepatocellular carcinoma
    • personalized medicine
    • proteome

    Mol Syst Biol (2014) 10: 721

    • Received January 14, 2014.
    • Revision received February 18, 2014.
    • Accepted February 20, 2014.

    This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Rasmus Agren, Adil Mardinoglu, Anna Asplund, Caroline Kampf, Mathias Uhlen, Jens Nielsen
  • Digital cell quantification identifies global immune cell dynamics during influenza infection
    1. Zeev Altboum1,,
    2. Yael Steuerman2,,
    3. Eyal David2,
    4. Zohar Barnett‐Itzhaki1,
    5. Liran Valadarsky1,
    6. Hadas Keren‐Shaul1,
    7. Tal Meningher3,4,
    8. Ella Mendelson3,5,
    9. Michal Mandelboim*,3,,
    10. Irit Gat‐Viks*,2, and
    11. Ido Amit*,1,
    1. 1Department of Immunology, Weizmann Institute, Rehovot, Israel
    2. 2Cell Research and Immunology Department, Tel Aviv University, Tel Aviv, Israel
    3. 3Central Virology Laboratory, Ministry of Health, Public Health Services, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
    4. 4Faculty of Life Sciences, Bar‐Ilan University, Ramat Gan, Israel
    5. 5Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine, Tel‐Aviv University, Tel‐Aviv, Israel
    1. * Corresponding author. Tel: 972 3 5302455; Fax: 972 3 530 24 57; E‐mail: Michal.Mandelboim{at}sheba.health.gov.il

      Corresponding author. Tel: 972 3 6406945; Fax: 972 3 6422046; E‐mail: iritgv{at}post.tau.ac.il

      Corresponding author. Tel: 972 8 9346974/5; Fax: 972 8 9345176; E‐mail: ido.amit{at}weizmann.ac.il

    1. These authors contributed equally to this work.

    2. These authors contributed equally to this work.

    A method is presented to infer the changes in the quantities of 213 immune cell types within a complex in vivo cell population. High‐resolution temporal analysis during flu infection reveals specific roles of dendritic cell subtypes in early and late disease phases.

    Synopsis

    A method is presented to infer the changes in the quantities of 213 immune cell types within a complex in vivo cell population. High‐resolution temporal analysis during flu infection reveals specific roles of dendritic cell subtypes in early and late disease phases.

    • A systematic approach for exploring in vivo immune cell dynamics is presented.

    • Computational quantification of over 200 immune cell subpopulations is possible.

    • A comprehensive view of influenza infection dynamics uncovers changes in dozens of distinct immune cell subpopulations.

    • Plasmacytoid dendritic cells serve as a cavalry to maintain long‐lasting host defense against influenza infection.

    • cell quantification
    • deconvolution approach
    • dendritic cells
    • immune cell dynamics
    • influenza infection

    Mol Syst Biol. (2014) 10: 720

    • Received October 31, 2013.
    • Revision received January 29, 2014.
    • Accepted January 30, 2014.

    This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Zeev Altboum, Yael Steuerman, Eyal David, Zohar Barnett‐Itzhaki, Liran Valadarsky, Hadas Keren‐Shaul, Tal Meningher, Ella Mendelson, Michal Mandelboim, Irit Gat‐Viks, Ido Amit
  • Alternative polyadenylation diversifies post‐transcriptional regulation by selective RNA–protein interactions
    1. Ishaan Gupta1,
    2. Sandra Clauder‐Münster1,
    3. Bernd Klaus2,
    4. Aino I Järvelin1,
    5. Raeka S Aiyar1,
    6. Vladimir Benes3,
    7. Stefan Wilkening1,4,
    8. Wolfgang Huber1,
    9. Vicent Pelechano*,1 and
    10. Lars M Steinmetz*,1,5,6
    1. 1European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
    2. 2European Molecular Biology Laboratory (EMBL), Centre for Statistical Data Analysis, Heidelberg, Germany
    3. 3European Molecular Biology Laboratory (EMBL), Genomics Core Facility, Heidelberg, Germany
    4. 4Department of Translational Oncology, National Center for Tumor Diseases (NCT), Heidelberg, Germany
    5. 5Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
    6. 6Stanford Genome Technology Center, Palo Alto, CA, USA
    1. * Corresponding author: Tel: +49 6221 387 8389; Fax: +49 6221 387 8518; E‐mail: pelechan{at}embl.de

      Corresponding author: Tel: +49 6221 387 8542; Fax: +49 6221 387 8518; E‐mail: larsms{at}embl.de

    A single gene can give rise to many isoforms via alternative polyadenylation. This study demonstrates that isoforms of each gene can have different molecular phenotypes like RNA stability and interaction with proteins, diversifying the functional potential of the genome.

    Synopsis

    A single gene can give rise to many isoforms via alternative polyadenylation. This study demonstrates that isoforms of each gene can have different molecular phenotypes like RNA stability and interaction with proteins, diversifying the functional potential of the genome.

    • Divergent post‐transcriptional fates of 3′ transcript isoforms are revealed at a genome‐wide level.

    • New techniques are presented that accurately measure isoform‐specific stability and protein binding, thus demonstrating widespread variation in both.

    • Even variations of a few nucleotides are associated with variations in transcript stability.

    • Transcript binding to PUF3 and subsequent destabilization occurs in an isoform‐specific manner.

    • alternative polyadenylation
    • RNA stability
    • RNA‐binding protein
    • transcript isoforms
    • 3′UTR

    Mol Syst Biol. (2014) 10: 719

    • Received December 17, 2013.
    • Revision received January 26, 2014.
    • Accepted January 27, 2014.

    This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Ishaan Gupta, Sandra Clauder‐Münster, Bernd Klaus, Aino I Järvelin, Raeka S Aiyar, Vladimir Benes, Stefan Wilkening, Wolfgang Huber, Vicent Pelechano, Lars M Steinmetz
  • Acetylation dynamics and stoichiometry in Saccharomyces cerevisiae
    1. Brian T. Weinert1,
    2. Vytautas Iesmantavicius1,
    3. Tarek Moustafa2,
    4. Christian Schölz1,
    5. Sebastian A. Wagner1,
    6. Christoph Magnes3,
    7. Rudolf Zechner2 and
    8. Chunaram Choudhary*,1
    1. 1The NNF Center for Protein Research Faculty of Health Sciences University of Copenhagen, Copenhagen, Denmark
    2. 2Institute of Molecular Biosciences University of Graz, Graz, Austria
    3. 3HEALTH – Institute for Biomedicine and Health Sciences Joanneum Research, Graz, Austria
    1. *Corresponding author. Tel: +45 35 32 50 20; Fax: +45 35 32 50 01; E‐mail: chuna.choudhary{at}cpr.ku.dk
    1. BTW and CC designed the project, BTW performed majority of mass spectrometry experiments and data analysis, VI and CS helped with MS analysis and yeast experiments, SAW helped with data analysis, TM and CM analyzed acetyl‐CoA concentrations, BTW, RZ, and CC wrote the manuscript, all authors read and commented on the manuscript.

    Characterization of the basic properties of acetylation in yeast cells by quantitative proteomics reveals distinct acetylation dynamics in different subcellular compartments and provides the first global analysis of acetylation stoichiometry.

    Synopsis

    Characterization of the basic properties of acetylation in yeast cells by quantitative proteomics reveals distinct acetylation dynamics in different subcellular compartments and provides the first global analysis of acetylation stoichiometry.

    • Acetylation is globally affected by metabolism and growth arrest.

    • Mitochondrial proteins are acetylated within mitochondria.

    • Most acetylation occurs at very low stoichiometry.

    • High stoichiometry acetylation occurs on nuclear proteins.

    • acetylation
    • mass spectrometry
    • mitochondria
    • proteomics
    • stoichiometry

    Mol Syst Biol. (2014) 10: 716

    Footnotes

    • The authors declare they have no conflict of interest.

    • Received August 6, 2013.
    • Revision received November 6, 2013.
    • Accepted December 11, 2013.

    This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Brian T. Weinert, Vytautas Iesmantavicius, Tarek Moustafa, Christian Schölz, Sebastian A. Wagner, Christoph Magnes, Rudolf Zechner, Chunaram Choudhary