151
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Jespersen N, Barbar E. Emerging Features of Linear Motif-Binding Hub Proteins. Trends Biochem Sci 2020; 45:375-384. [DOI: 10.1016/j.tibs.2020.01.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 01/05/2020] [Accepted: 01/21/2020] [Indexed: 01/15/2023]
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152
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Grbić M, Matić D, Kartelj A, Vračević S, Filipović V. A three-phase method for identifying functionally related protein groups in weighted PPI networks. Comput Biol Chem 2020; 86:107246. [PMID: 32339914 DOI: 10.1016/j.compbiolchem.2020.107246] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 01/27/2020] [Accepted: 03/03/2020] [Indexed: 01/17/2023]
Abstract
Identifying significant protein groups is of great importance for further understanding protein functions. This paper introduces a novel three-phase heuristic method for identifying such groups in weighted PPI networks. In the first phase a variable neighborhood search (VNS) algorithm is applied on a weighted PPI network, in order to support protein complexes by adding a minimum number of new PPIs. In the second phase proteins from different complexes are merged into larger protein groups. In the third phase these groups are expanded by a number of 2-level neighbor proteins, favoring proteins that have higher average gene co-expression with the base group proteins. Experimental results show that: (i) the proposed VNS algorithm outperforms the existing approach described in literature and (ii) the above-mentioned three-phase method identifies protein groups with very high statistical significance.
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Affiliation(s)
- Milana Grbić
- University of Banjaluka, Faculty of Natural Sciences and Mathematics, Mladena Stojanovića 2, 78000 Banjaluka, Bosnia and Herzegovina.
| | - Dragan Matić
- University of Banjaluka, Faculty of Natural Sciences and Mathematics, Mladena Stojanovića 2, 78000 Banjaluka, Bosnia and Herzegovina.
| | - Aleksandar Kartelj
- University of Belgrade, Faculty of Mathematics, Studentski trg 16/IV 11 000, Belgrade, Serbia.
| | - Savka Vračević
- University of Banjaluka, Faculty of Natural Sciences and Mathematics, Mladena Stojanovića 2, 78000 Banjaluka, Bosnia and Herzegovina.
| | - Vladimir Filipović
- University of Belgrade, Faculty of Mathematics, Studentski trg 16/IV 11 000, Belgrade, Serbia.
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153
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Luck K, Kim DK, Lambourne L, Spirohn K, Begg BE, Bian W, Brignall R, Cafarelli T, Campos-Laborie FJ, Charloteaux B, Choi D, Coté AG, Daley M, Deimling S, Desbuleux A, Dricot A, Gebbia M, Hardy MF, Kishore N, Knapp JJ, Kovács IA, Lemmens I, Mee MW, Mellor JC, Pollis C, Pons C, Richardson AD, Schlabach S, Teeking B, Yadav A, Babor M, Balcha D, Basha O, Bowman-Colin C, Chin SF, Choi SG, Colabella C, Coppin G, D'Amata C, De Ridder D, De Rouck S, Duran-Frigola M, Ennajdaoui H, Goebels F, Goehring L, Gopal A, Haddad G, Hatchi E, Helmy M, Jacob Y, Kassa Y, Landini S, Li R, van Lieshout N, MacWilliams A, Markey D, Paulson JN, Rangarajan S, Rasla J, Rayhan A, Rolland T, San-Miguel A, Shen Y, Sheykhkarimli D, Sheynkman GM, Simonovsky E, Taşan M, Tejeda A, Tropepe V, Twizere JC, Wang Y, Weatheritt RJ, Weile J, Xia Y, Yang X, Yeger-Lotem E, Zhong Q, Aloy P, Bader GD, De Las Rivas J, Gaudet S, Hao T, Rak J, Tavernier J, Hill DE, Vidal M, Roth FP, Calderwood MA. A reference map of the human binary protein interactome. Nature 2020; 580:402-408. [PMID: 32296183 PMCID: PMC7169983 DOI: 10.1038/s41586-020-2188-x] [Citation(s) in RCA: 738] [Impact Index Per Article: 147.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 02/14/2020] [Indexed: 12/14/2022]
Abstract
Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships1,2. Here, we present a human “all-by-all” reference interactome map of human binary protein interactions, or “HuRI”. With ~53,000 high-quality protein-protein interactions (PPIs), HuRI has approximately four times more such interactions than high-quality curated interactions from small-scale studies. Integrating HuRI with genome3, transcriptome4, and proteome5 data enables the study of cellular function within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying specific subcellular roles of PPIs. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian diseases. HuRI represents a systematic proteome-wide reference linking genomic variation to phenotypic outcomes.
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Affiliation(s)
- Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dae-Kyum Kim
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Luke Lambourne
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bridget E Begg
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Wenting Bian
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ruth Brignall
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tiziana Cafarelli
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Francisco J Campos-Laborie
- Cancer Research Center (CiC-IBMCC, CSIC/USAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain.,Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
| | - Benoit Charloteaux
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dongsic Choi
- The Research Institute of the McGill University Health Centre (RI-MUHC), Montreal, Quebec, Canada
| | - Atina G Coté
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Meaghan Daley
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Steven Deimling
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Alice Desbuleux
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA) and Laboratory of Viral Interactomes, University of Liège, Liège, Belgium
| | - Amélie Dricot
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Marinella Gebbia
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Madeleine F Hardy
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nishka Kishore
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Jennifer J Knapp
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - István A Kovács
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Network Science Institute, Northeastern University, Boston, MA, USA.,Wigner Research Centre for Physics, Institute for Solid State Physics and Optics, Budapest, Hungary
| | - Irma Lemmens
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Miles W Mee
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Joseph C Mellor
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada.,seqWell, Beverly, MA, USA
| | - Carl Pollis
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Aaron D Richardson
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sadie Schlabach
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bridget Teeking
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mariana Babor
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Dawit Balcha
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Omer Basha
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,National Institute for Biotechnology in the Negev (NIBN), Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Christian Bowman-Colin
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Suet-Feung Chin
- Cancer Research UK (CRUK) Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Claudia Colabella
- Department of Pharmaceutical Sciences, University of Perugia, Perugia, Italy.,Istituto Zooprofilattico Sperimentale dell'Umbria e delle Marche "Togo Rosati" (IZSUM), Perugia, Italy
| | - Georges Coppin
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA) and Laboratory of Viral Interactomes, University of Liège, Liège, Belgium
| | - Cassandra D'Amata
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - David De Ridder
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Steffi De Rouck
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Miquel Duran-Frigola
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Hanane Ennajdaoui
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Florian Goebels
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Liana Goehring
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Anjali Gopal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Ghazal Haddad
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Elodie Hatchi
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mohamed Helmy
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Yves Jacob
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Paris, France.,Université Paris Diderot, Paris, France
| | - Yoseph Kassa
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Serena Landini
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Roujia Li
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Natascha van Lieshout
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Andrew MacWilliams
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dylan Markey
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Joseph N Paulson
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.,Department of Biostatistics, Product Development, Genentech Inc., South San Francisco, CA, USA
| | - Sudharshan Rangarajan
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - John Rasla
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ashyad Rayhan
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Thomas Rolland
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Adriana San-Miguel
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yun Shen
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Dayag Sheykhkarimli
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada
| | - Gloria M Sheynkman
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Eyal Simonovsky
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,National Institute for Biotechnology in the Negev (NIBN), Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Murat Taşan
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Alexander Tejeda
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Vincent Tropepe
- Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Jean-Claude Twizere
- Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA) and Laboratory of Viral Interactomes, University of Liège, Liège, Belgium
| | - Yang Wang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Jochen Weile
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Yu Xia
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Bioengineering, McGill University, Montreal, Quebec, Canada
| | - Xinping Yang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.,National Institute for Biotechnology in the Negev (NIBN), Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Quan Zhong
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Biological Sciences, Wright State University, Dayton, OH, USA
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Javier De Las Rivas
- Cancer Research Center (CiC-IBMCC, CSIC/USAL), Consejo Superior de Investigaciones Científicas (CSIC) and University of Salamanca (USAL), Salamanca, Spain.,Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
| | - Suzanne Gaudet
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Janusz Rak
- The Research Institute of the McGill University Health Centre (RI-MUHC), Montreal, Quebec, Canada
| | - Jan Tavernier
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA. .,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
| | - Frederick P Roth
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. .,Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, Ontario, Canada. .,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. .,Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, Canada.
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA. .,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
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154
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Nath K, Roy S, Nandi S. InOvIn: A fuzzy-rough approach for detecting overlapping communities with intrinsic structures in evolving networks. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106096] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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155
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Carianopol CS, Chan AL, Dong S, Provart NJ, Lumba S, Gazzarrini S. An abscisic acid-responsive protein interaction network for sucrose non-fermenting related kinase1 in abiotic stress response. Commun Biol 2020; 3:145. [PMID: 32218501 PMCID: PMC7099082 DOI: 10.1038/s42003-020-0866-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 02/24/2020] [Indexed: 12/13/2022] Open
Abstract
Yeast Snf1 (Sucrose non-fermenting1), mammalian AMPK (5′ AMP-activated protein kinase) and plant SnRK1 (Snf1-Related Kinase1) are conserved heterotrimeric kinase complexes that re-establish energy homeostasis following stress. The hormone abscisic acid (ABA) plays a crucial role in plant stress response. Activation of SnRK1 or ABA signaling results in overlapping transcriptional changes, suggesting these stress pathways share common targets. To investigate how SnRK1 and ABA interact during stress response in Arabidopsis thaliana, we screened the SnRK1 complex by yeast two-hybrid against a library of proteins encoded by 258 ABA-regulated genes. Here, we identify 125 SnRK1- interacting proteins (SnIPs). Network analysis indicates that a subset of SnIPs form signaling modules in response to abiotic stress. Functional studies show the involvement of SnRK1 and select SnIPs in abiotic stress responses. This targeted study uncovers the largest set of SnRK1 interactors, which can be used to further characterize SnRK1 role in plant survival under stress. Carianopol et al. construct a detailed protein interaction network for the SnRK1 kinase complex to investigate the interaction of SnRK1 and ABA during stress response. They identify 125 proteins that interact with SnRK1, which can be used further to characterise the role of SnRK1 in plant survival under stress.
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Affiliation(s)
- Carina Steliana Carianopol
- Department of Biological Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON, M1C 1A4, Canada.,Department of Cell and Systems Biology, University of Toronto, 25 Willcocks Street, Toronto, ON, M5S 3B2, Canada
| | - Aaron Lorheed Chan
- Department of Biological Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON, M1C 1A4, Canada.,Department of Cell and Systems Biology, University of Toronto, 25 Willcocks Street, Toronto, ON, M5S 3B2, Canada
| | - Shaowei Dong
- Department of Cell and Systems Biology, University of Toronto, 25 Willcocks Street, Toronto, ON, M5S 3B2, Canada
| | - Nicholas J Provart
- Department of Cell and Systems Biology, University of Toronto, 25 Willcocks Street, Toronto, ON, M5S 3B2, Canada.,Centre for the Analysis of Genome Evolution and Function, 25 Willcocks Street, Toronto, ON, M5S 3B2, Canada
| | - Shelley Lumba
- Department of Cell and Systems Biology, University of Toronto, 25 Willcocks Street, Toronto, ON, M5S 3B2, Canada
| | - Sonia Gazzarrini
- Department of Biological Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON, M1C 1A4, Canada. .,Department of Cell and Systems Biology, University of Toronto, 25 Willcocks Street, Toronto, ON, M5S 3B2, Canada.
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156
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Nomura Y, Montemayor EJ, Virta JM, Hayes SM, Butcher SE. Structural basis for the evolution of cyclic phosphodiesterase activity in the U6 snRNA exoribonuclease Usb1. Nucleic Acids Res 2020; 48:1423-1434. [PMID: 31832688 PMCID: PMC7026655 DOI: 10.1093/nar/gkz1177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 12/02/2019] [Accepted: 12/05/2019] [Indexed: 11/28/2022] Open
Abstract
U6 snRNA undergoes post-transcriptional 3′ end modification prior to incorporation into the active site of spliceosomes. The responsible exoribonuclease is Usb1, which removes nucleotides from the 3′ end of U6 and, in humans, leaves a 2′,3′ cyclic phosphate that is recognized by the Lsm2–8 complex. Saccharomycescerevisiae Usb1 has additional 2′,3′ cyclic phosphodiesterase (CPDase) activity, which converts the cyclic phosphate into a 3′ phosphate group. Here we investigate the molecular basis for the evolution of Usb1 CPDase activity. We examine the structure and function of Usb1 from Kluyveromyces marxianus, which shares 25 and 19% sequence identity to the S. cerevisiae and Homo sapiens orthologs of Usb1, respectively. We show that K. marxianus Usb1 enzyme has CPDase activity and determined its structure, free and bound to the substrate analog uridine 5′-monophosphate. We find that the origin of CPDase activity is related to a loop structure that is conserved in yeast and forms a distinct penultimate (n – 1) nucleotide binding site. These data provide structural and mechanistic insight into the evolutionary divergence of Usb1 catalysis.
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Affiliation(s)
- Yuichiro Nomura
- Department of Biochemistry, University of Wisconsin, Madison, WI 53706, USA
| | - Eric J Montemayor
- Department of Biochemistry, University of Wisconsin, Madison, WI 53706, USA
| | - Johanna M Virta
- Department of Biochemistry, University of Wisconsin, Madison, WI 53706, USA
| | - Samuel M Hayes
- Department of Biochemistry, University of Wisconsin, Madison, WI 53706, USA
| | - Samuel E Butcher
- Department of Biochemistry, University of Wisconsin, Madison, WI 53706, USA
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157
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Yazaki J, Kawashima Y, Ogawa T, Kobayashi A, Okoshi M, Watanabe T, Yoshida S, Kii I, Egami S, Amagai M, Hosoya T, Shiroguchi K, Ohara O. HaloTag-based conjugation of proteins to barcoding-oligonucleotides. Nucleic Acids Res 2020; 48:e8. [PMID: 31752022 PMCID: PMC6954424 DOI: 10.1093/nar/gkz1086] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 10/29/2019] [Accepted: 11/18/2019] [Indexed: 11/12/2022] Open
Abstract
Highly sensitive protein quantification enables the detection of a small number of protein molecules that serve as markers/triggers for various biological phenomena, such as cancer. Here, we describe the development of a highly sensitive protein quantification system called HaloTag protein barcoding. The method involves covalent linking of a target protein to a unique molecule counting oligonucleotide at a 1:1 conjugation ratio based on an azido-cycloalkyne click reaction. The sensitivity of the HaloTag-based barcoding was remarkably higher than that of a conventional luciferase assay. The HaloTag system was successfully validated by analyzing a set of protein-protein interactions, with the identification rate of 44% protein interactions between positive reference pairs reported in the literature. Desmoglein 3, the target antigen of pemphigus vulgaris, an IgG-mediated autoimmune blistering disease, was used in a HaloTag protein barcode assay to detect the anti-DSG3 antibody. The dynamic range of the assay was over 104-times wider than that of a conventional enzyme-linked immunosorbent assay (ELISA). The technology was used to detect anti-DSG3 antibody in patient samples with much higher sensitivity compared to conventional ELISA. Our detection system, with its superior sensitivity, enables earlier detection of diseases possibly allowing the initiation of care/treatment at an early disease stage.
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Affiliation(s)
- Junshi Yazaki
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
| | - Yusuke Kawashima
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
| | - Taisaku Ogawa
- Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Osaka 565-0874, Japan
| | - Atsuo Kobayashi
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
| | - Mayu Okoshi
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
| | - Takashi Watanabe
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
| | - Suguru Yoshida
- Laboratory of Chemical Bioscience, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan
| | - Isao Kii
- Common Facilities Unit, Compass to Healthy Life Research Complex Program, RIKEN Cluster for Science, Technology and Innovation Hub, Kobe 650-0047, Japan
| | - Shohei Egami
- Laboratory for Skin Homeostasis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama 230-0045, Japan.,Department of Dermatology, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Masayuki Amagai
- Laboratory for Skin Homeostasis, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama 230-0045, Japan.,Department of Dermatology, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Takamitsu Hosoya
- Laboratory of Chemical Bioscience, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan.,Laboratory for Chemical Biology, RIKEN Center for Biosystems Dynamics Research (BDR), Kobe 650-0047, Japan
| | - Katsuyuki Shiroguchi
- Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Osaka 565-0874, Japan.,Laboratory for Immunogenetics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama 230-0045, Japan
| | - Osamu Ohara
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences (IMS), Yokohama City 230-0045, Japan
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158
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Yugandhar K, Wang TY, Leung AKY, Lanz MC, Motorykin I, Liang J, Shayhidin EE, Smolka MB, Zhang S, Yu H. MaXLinker: Proteome-wide Cross-link Identifications with High Specificity and Sensitivity. Mol Cell Proteomics 2020; 19:554-568. [PMID: 31839598 PMCID: PMC7050104 DOI: 10.1074/mcp.tir119.001847] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Indexed: 11/06/2022] Open
Abstract
Protein-protein interactions play a vital role in nearly all cellular functions. Hence, understanding their interaction patterns and three-dimensional structural conformations can provide crucial insights about various biological processes and underlying molecular mechanisms for many disease phenotypes. Cross-linking mass spectrometry (XL-MS) has the unique capability to detect protein-protein interactions at a large scale along with spatial constraints between interaction partners. The inception of MS-cleavable cross-linkers enabled the MS2-MS3 XL-MS acquisition strategy that provides cross-link information from both MS2 and MS3 level. However, the current cross-link search algorithm available for MS2-MS3 strategy follows a "MS2-centric" approach and suffers from a high rate of mis-identified cross-links. We demonstrate the problem using two new quality assessment metrics ["fraction of mis-identifications" (FMI) and "fraction of interprotein cross-links from known interactions" (FKI)]. We then address this problem, by designing a novel "MS3-centric" approach for cross-link identification and implementing it as a search engine named MaXLinker. MaXLinker outperforms the currently popular search engine with a lower mis-identification rate, and higher sensitivity and specificity. Moreover, we performed human proteome-wide cross-linking mass spectrometry using K562 cells. Employing MaXLinker, we identified a comprehensive set of 9319 unique cross-links at 1% false discovery rate, comprising 8051 intraprotein and 1268 interprotein cross-links. Finally, we experimentally validated the quality of a large number of novel interactions identified in our study, providing a conclusive evidence for MaXLinker's robust performance.
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Affiliation(s)
- Kumar Yugandhar
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Ting-Yi Wang
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Alden King-Yung Leung
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Michael Charles Lanz
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853; Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853
| | - Ievgen Motorykin
- Mass Spectrometry and Proteomics Facility, Institute of Biotechnology, Cornell University, Ithaca, New York,14853
| | - Jin Liang
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Elnur Elyar Shayhidin
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853
| | - Marcus Bustamante Smolka
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853; Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853
| | - Sheng Zhang
- Mass Spectrometry and Proteomics Facility, Institute of Biotechnology, Cornell University, Ithaca, New York,14853
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, New York,14853; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, 14853.
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159
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Nielsen CP, MacGurn JA. Coupling Conjugation and Deconjugation Activities to Achieve Cellular Ubiquitin Dynamics. Trends Biochem Sci 2020; 45:427-439. [PMID: 32311336 DOI: 10.1016/j.tibs.2020.01.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 01/24/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
In eukaryotic cells, proteome remodeling is mediated by the ubiquitin-proteasome system, which regulates protein degradation, trafficking, and signaling events in the cell. Interplay between the cellular proteome and ubiquitin is complex and dynamic and many regulatory features that support this system have only recently come into focus. An unexpected recurring feature in this system is the physical interaction between E3 ubiquitin ligases and deubiquitylases (DUBs). Recent studies have reported on the regulatory significance of DUB-E3 interactions and it is becoming clear that they play important but complicated roles in the regulation of diverse cellular processes. Here, we summarize the current understanding of interactions between ubiquitin conjugation and deconjugation machineries and we examine the regulatory logic of these enigmatic complexes.
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Affiliation(s)
- Casey P Nielsen
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
| | - Jason A MacGurn
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA.
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160
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Law J, Ng K, Windram OPF. The Phenotype Paradox: Lessons From Natural Transcriptome Evolution on How to Engineer Plants. FRONTIERS IN PLANT SCIENCE 2020; 11:75. [PMID: 32133018 PMCID: PMC7040092 DOI: 10.3389/fpls.2020.00075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 01/20/2020] [Indexed: 06/10/2023]
Abstract
Plants have evolved genome complexity through iterative rounds of single gene and whole genome duplication. This has led to substantial expansion in transcription factor numbers following preferential retention and subsequent functional divergence of these regulatory genes. Here we review how this simple evolutionary network rewiring process, regulatory gene duplication followed by functional divergence, can be used to inspire synthetic biology approaches that seek to develop novel phenotypic variation for future trait based breeding programs in plants.
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Affiliation(s)
- Justin Law
- Grand Challenges in Ecosystems and the Environment, Imperial College London, Ascot, United Kingdom
| | - Kangbo Ng
- The Francis Crick Institute, London, United Kingdom
- Institute for the Physics of Living Systems, University College London, London, United Kingdom
| | - Oliver P. F. Windram
- Grand Challenges in Ecosystems and the Environment, Imperial College London, Ascot, United Kingdom
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161
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Mattiazzi Usaj M, Sahin N, Friesen H, Pons C, Usaj M, Masinas MPD, Shuteriqi E, Shkurin A, Aloy P, Morris Q, Boone C, Andrews BJ. Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability. Mol Syst Biol 2020; 16:e9243. [PMID: 32064787 PMCID: PMC7025093 DOI: 10.15252/msb.20199243] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 12/16/2019] [Accepted: 01/15/2020] [Indexed: 12/13/2022] Open
Abstract
Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments-endocytic patch, actin patch, late endosome and vacuole-identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single-cell level. Our single-cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress.
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Affiliation(s)
| | - Nil Sahin
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | | | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute for Science and TechnologyBarcelona, CataloniaSpain
| | - Matej Usaj
- The Donnelly CentreUniversity of TorontoTorontoONCanada
| | | | | | - Aleksei Shkurin
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona)The Barcelona Institute for Science and TechnologyBarcelona, CataloniaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)Barcelona, CataloniaSpain
| | - Quaid Morris
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
- Computational and Systems Biology ProgramMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
| | - Charles Boone
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
- RIKEN Centre for Sustainable Resource ScienceWakoSaitamaJapan
| | - Brenda J Andrews
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
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162
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Wissberg S, Ronen M, Oren Z, Gerber D, Kalisky B. Sensitive Readout for Microfluidic High-Throughput Applications using Scanning SQUID Microscopy. Sci Rep 2020; 10:1573. [PMID: 32005843 PMCID: PMC6994618 DOI: 10.1038/s41598-020-58307-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 11/29/2019] [Indexed: 11/08/2022] Open
Abstract
Microfluidic chips provide a powerful platform for high-throughput screening of diverse biophysical systems. The most prevalent detection methods are fluorescence based. Developing new readout techniques for microfluidics focusing on quantitative information in the low signal regime is desirable. In this work, we combine the well-established immunoassay approach, with magnetic nanoparticles, with a highly sensitive magnetic imaging technique. We offer to integrate a microfluidic array into a scanning superconducting quantum interference device (SQUID) microscope, to image nanoparticles that were moved through the microfluidic device. We demonstrate the technique on protein-protein interactions (PPI). We compare sensitivity to that of a conventional readout, quantify the amount of interactions, and demonstrate 0.1 atto-mole sensitivity. Our work serves as a proof of concept that will promote the development of a new set of eyes, a stable usable microfluidic-scanning SQUID microscopy.
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Affiliation(s)
- Shai Wissberg
- Department of Physics, Bar-Ilan University, Ramat-Gan, 52900, Israel
- Institute for Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan, 52900, Israel
| | - Maria Ronen
- Institute for Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan, 52900, Israel
- Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, 52900, Israel
| | - Ziv Oren
- Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, 52900, Israel
- Department of Biotechnology, Israel Institute for Biological Research, Nes-Ziona, 7410001, Israel
| | - Doron Gerber
- Institute for Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan, 52900, Israel.
- Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, 52900, Israel.
| | - Beena Kalisky
- Department of Physics, Bar-Ilan University, Ramat-Gan, 52900, Israel.
- Institute for Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan, 52900, Israel.
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163
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Sun J, Feng L, Xie J, Ma X, Wang D, Hu Y. Revealing the predictability of intrinsic structure in complex networks. Nat Commun 2020; 11:574. [PMID: 31996676 PMCID: PMC6989503 DOI: 10.1038/s41467-020-14418-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 01/08/2020] [Indexed: 11/16/2022] Open
Abstract
Structure prediction is an important and widely studied problem in network science and machine learning, finding its applications in various fields. Despite the significant progress in prediction algorithms, the fundamental predictability of structures remains unclear, as networks’ complex underlying formation dynamics are usually unobserved or difficult to describe. As such, there has been a lack of theoretical guidance on the practical development of algorithms for their absolute performances. Here, for the first time, we find that the normalized shortest compression length of a network structure can directly assess the structure predictability. Specifically, shorter binary string length from compression leads to higher structure predictability. We also analytically derive the origin of this linear relationship in artificial random networks. In addition, our finding leads to analytical results quantifying maximum prediction accuracy, and allows the estimation of the network dataset potential values through the size of the compressed network data file. The likelihood of linking within a complex network is of importance to solve real-world problems, but it is challenging to predict. Sun et al. show that the link predictability limit can be well estimated by measuring the shortest compression length of a network without a need of prediction algorithm.
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Affiliation(s)
- Jiachen Sun
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China.,School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510006, China
| | - Ling Feng
- Institute of High Performance Computing, A*STAR, Singapore, 138632, Singapore.,Department of Physics, National University of Singapore, Singapore, 117551, Singapore
| | - Jiarong Xie
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China
| | - Xiao Ma
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China
| | - Dashun Wang
- Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA.,Kellogg School of Management, Northwestern University, Evanston, IL, USA.,McCormick School of Engineering, Northwestern University, Evanston, IL, USA
| | - Yanqing Hu
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510006, China. .,Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, 519082, China.
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164
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Network of Interactions between ZIKA Virus Non-Structural Proteins and Human Host Proteins. Cells 2020; 9:cells9010153. [PMID: 31936331 PMCID: PMC7016862 DOI: 10.3390/cells9010153] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/20/2019] [Accepted: 01/01/2020] [Indexed: 12/16/2022] Open
Abstract
The Zika virus (ZIKV) is a mosquito-borne Flavivirus and can be transmitted through an infected mosquito bite or through human-to-human interaction by sexual activity, blood transfusion, breastfeeding, or perinatal exposure. After the 2015-2016 outbreak in Brazil, a strong link between ZIKV infection and microcephaly emerged. ZIKV specifically targets human neural progenitor cells, suggesting that proteins encoded by ZIKV bind and inactivate host cell proteins, leading to microcephaly. Here, we present a systematic annotation of interactions between human proteins and the seven non-structural ZIKV proteins corresponding to a Brazilian isolate. The interaction network was generated by combining tandem-affinity purification followed by mass spectrometry with yeast two-hybrid screens. We identified 150 human proteins, involved in distinct biological processes, as interactors to ZIKV non-structural proteins. Our interacting network is composed of proteins that have been previously associated with microcephaly in human genetic disorders and/or animal models. Further, we show that the protein inhibitor of activated STAT1 (PIAS1) interacts with NS5 and modulates its stability. This study builds on previously published interacting networks of ZIKV and genes related to autosomal recessive primary microcephaly to generate a catalog of human cellular targets of ZIKV proteins implicated in processes related to microcephaly in humans. Collectively, these data can be used as a resource for future characterization of ZIKV infection biology and help create a basis for the discovery of drugs that may disrupt the interaction and reduce the health damage to the fetus.
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165
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Chasapis CT, Konstantinoudis G. Protein isoelectric point distribution in the interactomes across the domains of life. Biophys Chem 2020; 256:106269. [PMID: 31733408 DOI: 10.1016/j.bpc.2019.106269] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/23/2019] [Accepted: 09/24/2019] [Indexed: 01/19/2023]
Abstract
The distribution of the protein isoelectric point (pI) in the protein-protein interaction (PPI) networks across the domains of life has not been investigated yet. This work attempts to correlate the pI with the number of direct interacting partners in the experimentally supported networks involving 226.085 PPIs from 14 various organisms including human, mouse, yeast, bacteria, viruses and 53.606 virus-host interactions. The results showed that the acidic proteins (pI<3) have the highest average number of interactions in eukaryotes, while in bacteria more neutral proteins. On the contrary, the basic proteins (pI>11) have the lowest average number of interactions in human, mouse, yeast, bacteria and human-viral interactomes and the highest average in intraviral interactomes. We examined the correlation of the pI of the interacting partners by calculating the assortativity index of various PPI networks. We found that the interactions between the acidic, neutral and basic proteins have a fairly random mix, implying weak if any association between the acidic and basic proteins. Furthermore, protein features such as biological function, structurally order and disorder, subcellular localization, and homodimerization were classified according to pI in prokaryote and eukaryote proteomes.
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Affiliation(s)
- Christos T Chasapis
- NMR Center, Instrumental Analysis Laboratory, School of Natural Sciences, University of Patras, Patras, Greece; Institute of Chemical Engineering Sciences, Foundation for Research and Technology, Hellas (FORTH/ICE-HT), Patras, Greece.
| | - Garyfallos Konstantinoudis
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College, St. Mary's Campus, Norfolk Place, London W2 1PG, UK
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166
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Li S, Huang J, Liu J, Huang T, Chen H. Relative-path-based algorithm for link prediction on complex networks using a basic similarity factor. CHAOS (WOODBURY, N.Y.) 2020; 30:013104. [PMID: 32013467 DOI: 10.1063/1.5094448] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 11/12/2019] [Indexed: 06/10/2023]
Abstract
Complex networks have found many applications in various fields. An important problem in theories of complex networks is to find factors that aid link prediction, which is needed for network reconstruction and to study network evolution mechanisms. Though current similarity-based algorithms study factors of common neighbors and local paths connecting a target node pair, they ignore factor information on paths between a node and its neighbors. Therefore, this paper first supposes that paths between nodes and neighbors provide basic similarity features. Accordingly, we propose a so-called relative-path-based method. This method utilizes factor information on paths between nodes and neighbors, besides paths between node pairs, in similarity calculation for link prediction. Furthermore, we solve the problem of determining the parameters in our algorithm as well as in other algorithms after a series of discoveries and validations. Experimental results on six disparate real networks demonstrate that the relative-path-based method can obtain greater prediction accuracy than other methods, as well as performance robustness.
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Affiliation(s)
- Shibao Li
- College of Computer and Communication Engineering, China University of Petroleum, Qing'dao 266555, China
| | - Junwei Huang
- College of Computer and Communication Engineering, China University of Petroleum, Qing'dao 266555, China
| | - Jianhang Liu
- College of Computer and Communication Engineering, China University of Petroleum, Qing'dao 266555, China
| | - Tingpei Huang
- College of Computer and Communication Engineering, China University of Petroleum, Qing'dao 266555, China
| | - Haihua Chen
- College of Computer and Communication Engineering, China University of Petroleum, Qing'dao 266555, China
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167
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Li Z, Nie R, You Z, Cao C, Li J. Using discriminative vector machine model with 2DPCA to predict interactions among proteins. BMC Bioinformatics 2019; 20:694. [PMID: 31874626 PMCID: PMC6929273 DOI: 10.1186/s12859-019-3268-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background The interactions among proteins act as crucial roles in most cellular processes. Despite enormous effort put for identifying protein-protein interactions (PPIs) from a large number of organisms, existing firsthand biological experimental methods are high cost, low efficiency, and high false-positive rate. The application of in silico methods opens new doors for predicting interactions among proteins, and has been attracted a great deal of attention in the last decades. Results Here we present a novelty computational model with the adoption of our proposed Discriminative Vector Machine (DVM) model and a 2-Dimensional Principal Component Analysis (2DPCA) descriptor to identify candidate PPIs only based on protein sequences. To be more specific, a 2DPCA descriptor is employed to capture discriminative feature information from Position-Specific Scoring Matrix (PSSM) of amino acid sequences by the tool of PSI-BLAST. Then, a robust and powerful DVM classifier is employed to infer PPIs. When applied on both gold benchmark datasets of Yeast and H. pylori, our model obtained mean prediction accuracies as high as of 97.06 and 92.89%, respectively, which demonstrates a noticeable improvement than some state-of-the-art methods. Moreover, we constructed Support Vector Machines (SVM) based predictive model and made comparison it with our model on Human benchmark dataset. In addition, to further demonstrate the predictive reliability of our proposed method, we also carried out extensive experiments for identifying cross-species PPIs on five other species datasets. Conclusions All the experimental results indicate that our method is very effective for identifying potential PPIs and could serve as a practical approach to aid bioexperiment in proteomics research.
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Affiliation(s)
- Zhengwei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China.,Mine Digitization Engineering Research Center of Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China.,Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China.,KUNPAND Communications (Kunshan) Co., Ltd., Suzhou, 215300, China
| | - Ru Nie
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China. .,Mine Digitization Engineering Research Center of Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Zhuhong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, 830011, China
| | - Chen Cao
- Departments of Biochemistry & Molecular Biology and Medical Genetics, Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, T2N 4N1, Canada
| | - Jiashu Li
- Mine Digitization Engineering Research Center of Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China.
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168
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Galardini M, Busby BP, Vieitez C, Dunham AS, Typas A, Beltrao P. The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae. Mol Syst Biol 2019; 15:e8831. [PMID: 31885205 PMCID: PMC6901017 DOI: 10.15252/msb.20198831] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 11/13/2019] [Accepted: 11/14/2019] [Indexed: 12/16/2022] Open
Abstract
Loss-of-function (LoF) mutations associated with disease do not manifest equally in different individuals. The impact of the genetic background on the consequences of LoF mutations remains poorly characterized. Here, we systematically assessed the changes in gene deletion phenotypes for 3,786 gene knockouts in four Saccharomyces cerevisiae strains and 38 conditions. We observed 18.5% of deletion phenotypes changing between pairs of strains on average with a small fraction conserved in all four strains. Conditions causing higher wild-type growth differences and the deletion of pleiotropic genes showed above-average changes in phenotypes. In addition, we performed a genome-wide association study (GWAS) for growth under the same conditions for a panel of 925 yeast isolates. Gene-condition associations derived from GWAS were not enriched for genes with deletion phenotypes under the same conditions. However, cases where the results were congruent indicate the most likely mechanism underlying the GWAS signal. Overall, these results show a high degree of genetic background dependencies for LoF phenotypes.
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Affiliation(s)
- Marco Galardini
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteWellcome Trust Genome CampusHinxton, CambridgeUK
| | - Bede P Busby
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteWellcome Trust Genome CampusHinxton, CambridgeUK
- European Molecular Biology LaboratoryGenome Biology UnitHeidelbergGermany
| | - Cristina Vieitez
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteWellcome Trust Genome CampusHinxton, CambridgeUK
- European Molecular Biology LaboratoryGenome Biology UnitHeidelbergGermany
| | - Alistair S Dunham
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteWellcome Trust Genome CampusHinxton, CambridgeUK
| | - Athanasios Typas
- European Molecular Biology LaboratoryGenome Biology UnitHeidelbergGermany
| | - Pedro Beltrao
- European Molecular Biology LaboratoryEuropean Bioinformatics InstituteWellcome Trust Genome CampusHinxton, CambridgeUK
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169
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Kwon D, Lee D, Kim J, Lee J, Sim M, Kim J. Using INTERSPIA to Explore the Dynamics of Protein-Protein Interactions Among Multiple Species. ACTA ACUST UNITED AC 2019; 68:e88. [PMID: 31751498 DOI: 10.1002/cpbi.88] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
INTER-Species Protein Interaction Analysis (INTERSPIA) is a web application for identifying diverse patterns of protein-protein interactions (PPIs) in different species. Given a set of proteins of interest to the user, INTERSPIA first discovers additional proteins that are functionally associated with the input proteins as well as different or common patterns of PPIs among the proteins in multiple species through a server-side pipeline. Second, it visualizes the dynamics of PPIs in multiple species via an easy-to-use web interface. This article contains a basic protocol describing how to visualize diverse patterns of PPIs of input proteins in multiple species, and how to use them for functional analysis in the web interface. INTERSPIA is freely available at http://bioinfo.konkuk.ac.kr/INTERSPIA/. © 2019 by John Wiley & Sons, Inc. Basic Protocol: Running INTERSPIA using a list of input proteins.
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Affiliation(s)
- Daehong Kwon
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Daehwan Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Juyeon Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Jongin Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Mikang Sim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Jaebum Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
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170
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The monothiol glutaredoxin GrxD is essential for sensing iron starvation in Aspergillus fumigatus. PLoS Genet 2019; 15:e1008379. [PMID: 31525190 PMCID: PMC6762210 DOI: 10.1371/journal.pgen.1008379] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 09/26/2019] [Accepted: 08/20/2019] [Indexed: 01/17/2023] Open
Abstract
Efficient adaptation to iron starvation is an essential virulence determinant of the most common human mold pathogen, Aspergillus fumigatus. Here, we demonstrate that the cytosolic monothiol glutaredoxin GrxD plays an essential role in iron sensing in this fungus. Our studies revealed that (i) GrxD is essential for growth; (ii) expression of the encoding gene, grxD, is repressed by the transcription factor SreA in iron replete conditions and upregulated during iron starvation; (iii) during iron starvation but not iron sufficiency, GrxD displays predominant nuclear localization; (iv) downregulation of grxD expression results in de-repression of genes involved in iron-dependent pathways and repression of genes involved in iron acquisition during iron starvation, but did not significantly affect these genes during iron sufficiency; (v) GrxD displays protein-protein interaction with components of the cytosolic iron-sulfur cluster biosynthetic machinery, indicating a role in this process, and with the transcription factors SreA and HapX, which mediate iron regulation of iron acquisition and iron-dependent pathways; (vi) UV-Vis spectra of recombinant HapX or the complex of HapX and GrxD indicate coordination of iron-sulfur clusters; (vii) the cysteine required for iron-sulfur cluster coordination in GrxD is in vitro dispensable for interaction with HapX; and (viii) there is a GrxD-independent mechanism for sensing iron sufficiency by HapX; (ix) inactivation of SreA suppresses the lethal effect caused by GrxD inactivation. Taken together, this study demonstrates that GrxD is crucial for iron homeostasis in A. fumigatus. Aspergillus fumigatus is a ubiquitous saprophytic mold and the major causative pathogen causing life-threatening aspergillosis. To improve therapy, there is an urgent need for a better understanding of the fungal physiology. We have previously shown that adaptation to iron starvation is an essential virulence attribute of A. fumigatus. In the present study, we characterized the mechanism employed by A. fumigatus to sense the cellular iron status, which is essential for iron homeostasis. We demonstrate that the transcription factors SreA and HapX, which coordinate iron acquisition, iron consumption and iron detoxification require physical interaction with the monothiol glutaredoxin GrxD to sense iron starvation. Moreover, we show that there is a GrxD-independent mechanism for sensing excess of iron.
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171
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Lugo-Martinez J, Bar-Joseph Z, Dengjel J, Murphy RF. Integration of Heterogeneous Experimental Data Improves Global Map of Human Protein Complexes. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2019; 2019:144-153. [PMID: 32457940 DOI: 10.1145/3307339.3342150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Protein complexes play a significant role in the core functionality of cells. These complexes are typically identified by detecting densely connected subgraphs in protein-protein interaction (PPI) networks. Recently, multiple large-scale mass spectrometry-based experiments have significantly increased the availability of PPI data in order to further expand the set of known complexes. However, high-throughput experimental data generally are incomplete, show limited agreement between experiments, and show frequent false positive interactions. There is a need for computational approaches that can address these limitations in order to improve the coverage and accuracy of human protein complexes. Here, we present a new method that integrates data from multiple heterogeneous experiments and sources in order to increase the reliability and coverage of predicted protein complexes. We first fused the heterogeneous data into a feature matrix and trained classifiers to score pairwise protein interactions. We next used graph based methods to combine pairwise interactions into predicted protein complexes. Our approach improves the accuracy and coverage of protein pairwise interactions, accurately identifies known complexes, and suggests both novel additions to known complexes and entirely new complexes. Our results suggest that integration of heterogeneous experimental data helps improve the reliability and coverage of diverse high-throughput mass-spectrometry experiments, leading to an improved global map of human protein complexes.
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Affiliation(s)
- Jose Lugo-Martinez
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA
| | - Ziv Bar-Joseph
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA
| | - Jörn Dengjel
- Department of Biology, Université de Fribourg, 1700 Fribourg, Switzerland
| | - Robert F Murphy
- Computational Biology Department, Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA
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172
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Sarkar D, Saha S. Machine-learning techniques for the prediction of protein-protein interactions. J Biosci 2019; 44:104. [PMID: 31502581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein-protein interactions (PPIs) are important for the study of protein functions and pathways involved in different biological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental techniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gap in identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms has been used in conjunction with experimental methods for discovery of novel protein interactions. The two most popular supervised machine-learning techniques used in the prediction of PPIs are support vector machines and random forest classifiers. Bayesian-probabilistic inference has also been used but mainly for the scoring of high-throughput PPI dataset confidence measures. Recently, deep-learning algorithms have been used for sequence-based prediction of PPIs. Several clustering methods such as hierarchical and k-means are useful as unsupervised machine-learning algorithms for the prediction of interacting protein pairs without explicit data labelling. In summary, machine-learning techniques have been widely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.
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173
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Choi SG, Olivet J, Cassonnet P, Vidalain PO, Luck K, Lambourne L, Spirohn K, Lemmens I, Dos Santos M, Demeret C, Jones L, Rangarajan S, Bian W, Coutant EP, Janin YL, van der Werf S, Trepte P, Wanker EE, De Las Rivas J, Tavernier J, Twizere JC, Hao T, Hill DE, Vidal M, Calderwood MA, Jacob Y. Maximizing binary interactome mapping with a minimal number of assays. Nat Commun 2019; 10:3907. [PMID: 31467278 PMCID: PMC6715725 DOI: 10.1038/s41467-019-11809-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 08/02/2019] [Indexed: 02/06/2023] Open
Abstract
Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose a framework for binary protein-protein interaction (PPI) mapping based on optimally combining assays and/or assay versions to maximize detection of true positive interactions, while avoiding detection of random protein pairs. We have engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions, differing by protein expression systems and tagging configurations. The resulting union of N2H versions recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our findings should be applicable to systematic mapping of other biological landscapes.
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Affiliation(s)
- Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Julien Olivet
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.,Laboratory of Viral Interactomes, Unit of Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA Institute), University of Liège, 7 Place du 20 Août, 4000, Liège, Belgium
| | - Patricia Cassonnet
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité, 28 rue du Docteur Roux, 75015, Paris, France
| | - Pierre-Olivier Vidalain
- Équipe Chimie, Biologie, Modélisation et Immunologie pour la Thérapie (CBMIT), Laboratoire de Chimie et Biochimie Pharmacologiques et Toxicologiques (LCBPT), Centre Interdisciplinaire Chimie Biologie-Paris (CICB-Paris), UMR8601, CNRS, Université Paris Descartes, 45 rue des Saints-Pères, 75006, Paris, France
| | - Katja Luck
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Luke Lambourne
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Irma Lemmens
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), 3 Albert Baertsoenkaai, 9000, Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 3 Albert Baertsoenkaai, 9000, Ghent, Belgium
| | - Mélanie Dos Santos
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité, 28 rue du Docteur Roux, 75015, Paris, France
| | - Caroline Demeret
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité, 28 rue du Docteur Roux, 75015, Paris, France
| | - Louis Jones
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, 28 rue du Docteur Roux, 75015, Paris, France
| | - Sudharshan Rangarajan
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Wenting Bian
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Eloi P Coutant
- Département de Biologie Structurale et Chimie, Unité de Chimie et Biocatalyse, Institut Pasteur, UMR3523, CNRS, 28 rue du Docteur Roux, 75015, Paris, France
| | - Yves L Janin
- Département de Biologie Structurale et Chimie, Unité de Chimie et Biocatalyse, Institut Pasteur, UMR3523, CNRS, 28 rue du Docteur Roux, 75015, Paris, France
| | - Sylvie van der Werf
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité, 28 rue du Docteur Roux, 75015, Paris, France
| | - Philipp Trepte
- Neuroproteomics, Max Delbrück Center for Molecular Medicine, 10 Robert-Rössle-Str., 13125, Berlin, Germany.,Brain Development and Disease, Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), 3 Dr. Bohr-Gasse, 1030, Vienna, Austria
| | - Erich E Wanker
- Neuroproteomics, Max Delbrück Center for Molecular Medicine, 10 Robert-Rössle-Str., 13125, Berlin, Germany
| | - Javier De Las Rivas
- Cancer Research Center (CiC-IBMCC, CSIC/USAL), Consejo Superior de Investigaciones Científicas (CSIC), University of Salamanca (USAL), Campus Miguel de Unamuno, 37007, Salamanca, Spain
| | - Jan Tavernier
- Center for Medical Biotechnology, Vlaams Instituut voor Biotechnologie (VIB), 3 Albert Baertsoenkaai, 9000, Ghent, Belgium.,Cytokine Receptor Laboratory (CRL), Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 3 Albert Baertsoenkaai, 9000, Ghent, Belgium
| | - Jean-Claude Twizere
- Laboratory of Viral Interactomes, Unit of Molecular Biology of Diseases, Groupe Interdisciplinaire de Génomique Appliquée (GIGA Institute), University of Liège, 7 Place du 20 Août, 4000, Liège, Belgium
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA.,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA.
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA. .,Department of Genetics, Blavatnik Institute, Harvard Medical School (HMS), 77 Avenue Louis Pasteur, Boston, MA, 02115, USA. .,Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.
| | - Yves Jacob
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute (DFCI), 450 Brookline Avenue, Boston, MA, 02215, USA. .,Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, UMR3569, Centre National de la Recherche Scientifique (CNRS), Université Paris Diderot, Sorbonne Paris Cité, 28 rue du Docteur Roux, 75015, Paris, France.
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174
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He S, Liu YJ, Ye FY, Li RP, Dai RJ. A new grid- and modularity-based layout algorithm for complex biological networks. PLoS One 2019; 14:e0221620. [PMID: 31465473 PMCID: PMC6715240 DOI: 10.1371/journal.pone.0221620] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Accepted: 08/06/2019] [Indexed: 01/23/2023] Open
Abstract
The visualization of biological networks is critically important to aid researchers in understanding complex biological systems and arouses interest in designing efficient layout algorithms to draw biological networks according to their topology structures, especially for those networks with potential modules. The algorithms of grid layout series have an advantage in generating compact layouts with overlap-free nodes compared to force-directed; however, extant grid layout algorithms have difficulty in drawing modular networks and often generate layouts of high visual complexity when applied to networks with dense or clustered connectivity structure. To specifically assist the study of modular networks, we propose a grid- and modularity-based layout algorithm (GML) that consists of three stages: network preprocessing, module layout and grid optimization. The algorithm can draw complex biological networks with or without predefined modules based on the grid layout algorithm. It also outperforms other existing grid-based algorithms in the measurement of computation performance, ratio of edge-edge/node-edge crossings, relative edge lengths, and connectivity F-measures. GML helps users to gain insight into the network global characteristics through module layout, as well as to discern network details with grid optimization. GML has been developed as a VisANT plugin (https://hscz.github.io/Biological-Network-Visualization/) and is freely available to the research community.
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Affiliation(s)
- Sheng He
- School of Computer Engineering, Jiangsu University of Technology, Changzhou, China
| | - Yi-Jun Liu
- School of Computer Engineering, Jiangsu University of Technology, Changzhou, China
| | - Fei-Yue Ye
- School of Computer Engineering, Jiangsu University of Technology, Changzhou, China
- * E-mail:
| | - Ren-Pu Li
- School of Computer Engineering, Jiangsu University of Technology, Changzhou, China
| | - Ren-Jun Dai
- School of Computer Engineering, Jiangsu University of Technology, Changzhou, China
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175
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Sarkar D, Saha S. Machine-learning techniques for the prediction of protein–protein interactions. J Biosci 2019. [DOI: 10.1007/s12038-019-9909-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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176
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Boulgakov AA, Ellington AD, Marcotte EM. Bringing Microscopy-By-Sequencing into View. Trends Biotechnol 2019; 38:154-162. [PMID: 31416630 DOI: 10.1016/j.tibtech.2019.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/10/2019] [Accepted: 06/11/2019] [Indexed: 01/07/2023]
Abstract
The spatial distribution of molecules and cells is fundamental to understanding biological systems. Traditionally, microscopies based on electromagnetic waves such as visible light have been used to localize cellular components by direct visualization. However, these techniques suffer from limitations of transmissibility and throughput. Complementary to optical approaches, biochemical techniques such as crosslinking can colocalize molecules without suffering the same limitations. However, biochemical approaches are often unable to combine individual colocalizations into a map across entire cells or tissues. Microscopy-by-sequencing techniques aim to biochemically colocalize DNA-barcoded molecules and, by tracking their thus unique identities, reconcile all colocalizations into a global spatial map. Here, we review this new field and discuss its enormous potential to answer a broad spectrum of questions.
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Affiliation(s)
- Alexander A Boulgakov
- Center for Systems and Synthetic Biology, Institute of Cellular and Molecular Biology, Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA
| | - Andrew D Ellington
- Center for Systems and Synthetic Biology, Institute of Cellular and Molecular Biology, Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA
| | - Edward M Marcotte
- Center for Systems and Synthetic Biology, Institute of Cellular and Molecular Biology, Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA.
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177
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Kwon D, Lee D, Kim J, Lee J, Sim M, Kim J. INTERSPIA: a web application for exploring the dynamics of protein-protein interactions among multiple species. Nucleic Acids Res 2019; 46:W89-W94. [PMID: 29746660 PMCID: PMC6031021 DOI: 10.1093/nar/gky378] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/27/2018] [Indexed: 02/06/2023] Open
Abstract
Proteins perform biological functions through cascading interactions with each other by forming protein complexes. As a result, interactions among proteins, called protein-protein interactions (PPIs) are not completely free from selection constraint during evolution. Therefore, the identification and analysis of PPI changes during evolution can give us new insight into the evolution of functions. Although many algorithms, databases and websites have been developed to help the study of PPIs, most of them are limited to visualize the structure and features of PPIs in a chosen single species with limited functions in the visualization perspective. This leads to difficulties in the identification of different patterns of PPIs in different species and their functional consequences. To resolve these issues, we developed a web application, called INTER-Species Protein Interaction Analysis (INTERSPIA). Given a set of proteins of user's interest, INTERSPIA first discovers additional proteins that are functionally associated with the input proteins and searches for different patterns of PPIs in multiple species through a server-side pipeline, and second visualizes the dynamics of PPIs in multiple species using an easy-to-use web interface. INTERSPIA is freely available at http://bioinfo.konkuk.ac.kr/INTERSPIA/.
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Affiliation(s)
- Daehong Kwon
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Daehwan Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Juyeon Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Jongin Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Mikang Sim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Jaebum Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
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178
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Schoeters F, Van Dijck P. Protein-Protein Interactions in Candida albicans. Front Microbiol 2019; 10:1792. [PMID: 31440220 PMCID: PMC6693483 DOI: 10.3389/fmicb.2019.01792] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 07/19/2019] [Indexed: 12/27/2022] Open
Abstract
Despite being one of the most important human fungal pathogens, Candida albicans has not been studied extensively at the level of protein-protein interactions (PPIs) and data on PPIs are not readily available in online databases. In January 2018, the database called "Biological General Repository for Interaction Datasets (BioGRID)" that contains the most PPIs for C. albicans, only documented 188 physical or direct PPIs (release 3.4.156) while several more can be found in the literature. Other databases such as the String database, the Molecular INTeraction Database (MINT), and the Database for Interacting Proteins (DIP) database contain even fewer interactions or do not even include C. albicans as a searchable term. Because of the non-canonical codon usage of C. albicans where CUG is translated as serine rather than leucine, it is often problematic to use the yeast two-hybrid system in Saccharomyces cerevisiae to study C. albicans PPIs. However, studying PPIs is crucial to gain a thorough understanding of the function of proteins, biological processes and pathways. PPIs can also be potential drug targets. To aid in creating PPI networks and updating the BioGRID, we performed an exhaustive literature search in order to provide, in an accessible format, a more extensive list of known PPIs in C. albicans.
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Affiliation(s)
- Floris Schoeters
- VIB-KU Leuven Center for Microbiology, Leuven, Belgium
- Laboratory of Molecular Cell Biology, Institute of Botany and Microbiology, KU Leuven, Leuven, Belgium
| | - Patrick Van Dijck
- VIB-KU Leuven Center for Microbiology, Leuven, Belgium
- Laboratory of Molecular Cell Biology, Institute of Botany and Microbiology, KU Leuven, Leuven, Belgium
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179
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Zhang J, Zhong C, Huang Y, Lin HX, Wang M. A method for identifying protein complexes with the features of joint co-localization and joint co-expression in static PPI networks. Comput Biol Med 2019; 111:103333. [PMID: 31376777 DOI: 10.1016/j.compbiomed.2019.103333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 06/01/2019] [Accepted: 06/17/2019] [Indexed: 02/09/2023]
Abstract
Identifying protein complexes in static protein-protein interaction (PPI) networks is essential for understanding the underlying mechanism of biological processes. Proteins in a complex are co-localized at the same place and co-expressed at the same time. We propose a novel method to identify protein complexes with the features of joint co-localization and joint co-expression in static PPI networks. To achieve this goal, we define a joint localization vector to construct a joint co-localization criterion of a protein group, and define a joint gene expression to construct a joint co-expression criterion of a gene group. Moreover, the functional similarity of proteins in a complex is an important characteristic. Thus, we use the CC-based, MF-based, and BP-based protein similarities to devise functional similarity criterion to determine whether a protein is functionally similar to a protein cluster. Based on the core-attachment structure and following to seed expanding strategy, we use four types of biological data including PPI data with reliability score, protein localization data, gene expression data, and gene ontology annotations, to identify protein complexes. The experimental results on yeast data show that comparing with existing methods our proposed method can efficiently and exactly identify more protein complexes, especially more protein complexes of sizes from 2 to 6. Furthermore, the enrichment analysis demonstrates that the protein complexes identified by our method have significant biological meaning.
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Affiliation(s)
- Jinxiong Zhang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China; School of Computer, Electronics and Information, Guangxi University, Nanning, China.
| | - Cheng Zhong
- School of Computer, Electronics and Information, Guangxi University, Nanning, China.
| | - Yiran Huang
- School of Computer, Electronics and Information, Guangxi University, Nanning, China.
| | - Hai Xiang Lin
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands.
| | - Mian Wang
- College of Life Science and Technology, Guangxi University, Nanning, China.
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180
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Su Z, Zheng X, Ai J, Shang L, Shen Y. Link prediction in recommender systems with confidence measures. CHAOS (WOODBURY, N.Y.) 2019; 29:083133. [PMID: 31472512 DOI: 10.1063/1.5099565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 08/09/2019] [Indexed: 06/10/2023]
Abstract
The link prediction aims at predicting missing or future links in networks, which provides theoretical significance and extensive applications in the related field. However, the degree of confidence in the prediction results has not been fully discussed in related works. In this article, we propose a similarity confidence coefficient and a confidence measure for link prediction. The former is used to balance the reliability of similarity calculation results, which might be untrustworthy due to the information asymmetry in the calculation, and also makes it easier to achieve the optimal accuracy with a smaller number of neighbors. The latter is used to quantify our confidence in the prediction results of each prediction. The experimental results based on the Movie-Lens data set show that prediction accuracy is improved when the similarity between the nodes is corrected by the similarity confidence coefficient. Second, the experiments also confirm that the confidence degree of the link prediction results can be measured quantitatively. Our research indicates that the confidence level on each prediction is determined by the amount of data used in the corresponding calculation, which can be measured quantitatively.
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Affiliation(s)
- Zhan Su
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Xiliang Zheng
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Jun Ai
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Lihui Shang
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Yuming Shen
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
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181
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Talkish J, Igel H, Hunter O, Horner SW, Jeffery NN, Leach JR, Jenkins JL, Kielkopf CL, Ares M. Cus2 enforces the first ATP-dependent step of splicing by binding to yeast SF3b1 through a UHM-ULM interaction. RNA (NEW YORK, N.Y.) 2019; 25:1020-1037. [PMID: 31110137 PMCID: PMC6633205 DOI: 10.1261/rna.070649.119] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 05/15/2019] [Indexed: 05/16/2023]
Abstract
Stable recognition of the intron branchpoint (BP) by the U2 snRNP to form the pre-spliceosome is the first ATP-dependent step of splicing. Genetic and biochemical data from yeast indicate that Cus2 aids U2 snRNA folding into the stem IIa conformation prior to pre-spliceosome formation. Cus2 must then be removed by an ATP-dependent function of Prp5 before assembly can progress. However, the location from which Cus2 is displaced and the nature of its binding to the U2 snRNP are unknown. Here, we show that Cus2 contains a conserved UHM (U2AF homology motif) that binds Hsh155, the yeast homolog of human SF3b1, through a conserved ULM (U2AF ligand motif). Mutations in either motif block binding and allow pre-spliceosome formation without ATP. A 2.0 Å resolution structure of the Hsh155 ULM in complex with the UHM of Tat-SF1, the human homolog of Cus2, and complementary binding assays show that the interaction is highly similar between yeast and humans. Furthermore, we show that Tat-SF1 can replace Cus2 function by enforcing ATP dependence of pre-spliceosome formation in yeast extracts. Cus2 is removed before pre-spliceosome formation, and both Cus2 and its Hsh155 ULM binding site are absent from available cryo-EM structure models. However, our data are consistent with the apparent location of the disordered Hsh155 ULM between the U2 stem-loop IIa and the HEAT repeats of Hsh155 that interact with Prp5. We propose a model in which Prp5 uses ATP to remove Cus2 from Hsh155 such that extended base-pairing between U2 snRNA and the intron BP can occur.
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Affiliation(s)
- Jason Talkish
- Center for Molecular Biology of RNA, University of California, Santa Cruz, Santa Cruz, California 95064, USA
| | - Haller Igel
- Center for Molecular Biology of RNA, University of California, Santa Cruz, Santa Cruz, California 95064, USA
| | - Oarteze Hunter
- Center for Molecular Biology of RNA, University of California, Santa Cruz, Santa Cruz, California 95064, USA
| | - Steven W Horner
- Center for RNA Biology, Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, USA
| | - Nazish N Jeffery
- Center for RNA Biology, Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, USA
| | - Justin R Leach
- Center for RNA Biology, Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, USA
| | - Jermaine L Jenkins
- Center for RNA Biology, Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, USA
| | - Clara L Kielkopf
- Center for RNA Biology, Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York 14642, USA
| | - Manuel Ares
- Center for Molecular Biology of RNA, University of California, Santa Cruz, Santa Cruz, California 95064, USA
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182
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Rössler I, Embacher J, Pillet B, Murat G, Liesinger L, Hafner J, Unterluggauer JJ, Birner-Gruenberger R, Kressler D, Pertschy B. Tsr4 and Nap1, two novel members of the ribosomal protein chaperOME. Nucleic Acids Res 2019; 47:6984-7002. [PMID: 31062022 PMCID: PMC6648895 DOI: 10.1093/nar/gkz317] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 04/15/2019] [Accepted: 04/19/2019] [Indexed: 12/12/2022] Open
Abstract
Dedicated chaperones protect newly synthesized ribosomal proteins (r-proteins) from aggregation and accompany them on their way to assembly into nascent ribosomes. Currently, only nine of the ∼80 eukaryotic r-proteins are known to be guarded by such chaperones. In search of new dedicated r-protein chaperones, we performed a tandem-affinity purification based screen and looked for factors co-enriched with individual small subunit r-proteins. We report the identification of Nap1 and Tsr4 as direct binding partners of Rps6 and Rps2, respectively. Both factors promote the solubility of their r-protein clients in vitro. While Tsr4 is specific for Rps2, Nap1 has several interaction partners including Rps6 and two other r-proteins. Tsr4 binds co-translationally to the essential, eukaryote-specific N-terminal extension of Rps2, whereas Nap1 interacts with a large, mostly eukaryote-specific binding surface of Rps6. Mutation of the essential Tsr4 and deletion of the non-essential Nap1 both enhance the 40S synthesis defects of the corresponding r-protein mutants. Our findings highlight that the acquisition of eukaryote-specific domains in r-proteins was accompanied by the co-evolution of proteins specialized to protect these domains and emphasize the critical role of r-protein chaperones for the synthesis of eukaryotic ribosomes.
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Affiliation(s)
- Ingrid Rössler
- Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50, 8010 Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Julia Embacher
- Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50, 8010 Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Benjamin Pillet
- Unit of Biochemistry, Department of Biology, University of Fribourg, Chemin du Musée 10, 1700 Fribourg, Switzerland
| | - Guillaume Murat
- Unit of Biochemistry, Department of Biology, University of Fribourg, Chemin du Musée 10, 1700 Fribourg, Switzerland
| | - Laura Liesinger
- BioTechMed-Graz, Graz, Austria
- Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria
- Omics Center Graz, BioTechMed-Graz, Graz, Austria
| | - Jutta Hafner
- Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50, 8010 Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Julia Judith Unterluggauer
- Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50, 8010 Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Ruth Birner-Gruenberger
- BioTechMed-Graz, Graz, Austria
- Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria
- Omics Center Graz, BioTechMed-Graz, Graz, Austria
| | - Dieter Kressler
- Unit of Biochemistry, Department of Biology, University of Fribourg, Chemin du Musée 10, 1700 Fribourg, Switzerland
| | - Brigitte Pertschy
- Institute of Molecular Biosciences, University of Graz, Humboldtstrasse 50, 8010 Graz, Austria
- BioTechMed-Graz, Graz, Austria
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183
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Corwin T, Woodsmith J, Apelt F, Fontaine JF, Meierhofer D, Helmuth J, Grossmann A, Andrade-Navarro MA, Ballif BA, Stelzl U. Defining Human Tyrosine Kinase Phosphorylation Networks Using Yeast as an In Vivo Model Substrate. Cell Syst 2019; 5:128-139.e4. [PMID: 28837810 DOI: 10.1016/j.cels.2017.08.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Revised: 05/02/2017] [Accepted: 08/03/2017] [Indexed: 12/13/2022]
Abstract
Systematic assessment of tyrosine kinase-substrate relationships is fundamental to a better understanding of cellular signaling and its profound alterations in human diseases such as cancer. In human cells, such assessments are confounded by complex signaling networks, feedback loops, conditional activity, and intra-kinase redundancy. Here we address this challenge by exploiting the yeast proteome as an in vivo model substrate. We individually expressed 16 human non-receptor tyrosine kinases (NRTKs) in Saccharomyces cerevisiae and identified 3,279 kinase-substrate relationships involving 1,351 yeast phosphotyrosine (pY) sites. Based on the yeast data without prior information, we generated a set of linear kinase motifs and assigned ∼1,300 known human pY sites to specific NRTKs. Furthermore, experimentally defined pY sites for each individual kinase were shown to cluster within the yeast interactome network irrespective of linear motif information. We therefore applied a network inference approach to predict kinase-substrate relationships for more than 3,500 human proteins, providing a resource to advance our understanding of kinase biology.
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Affiliation(s)
- Thomas Corwin
- Otto-Warburg Laboratory, Max-Planck Institute for Molecular Genetics (MPIMG), 14195 Berlin, Germany
| | - Jonathan Woodsmith
- Otto-Warburg Laboratory, Max-Planck Institute for Molecular Genetics (MPIMG), 14195 Berlin, Germany; Institute of Pharmaceutical Sciences, University of Graz and BioTechMed-Graz, 8010 Graz, Austria
| | - Federico Apelt
- Otto-Warburg Laboratory, Max-Planck Institute for Molecular Genetics (MPIMG), 14195 Berlin, Germany
| | - Jean-Fred Fontaine
- Genomics and Computational Biology, Kernel Press UG, 55128 Mainz, Germany; Faculty of Biology, Johannes Gutenberg University Mainz and Institute of Molecular Biology, 55128 Mainz, Germany
| | - David Meierhofer
- Otto-Warburg Laboratory, Max-Planck Institute for Molecular Genetics (MPIMG), 14195 Berlin, Germany
| | - Johannes Helmuth
- Otto-Warburg Laboratory, Max-Planck Institute for Molecular Genetics (MPIMG), 14195 Berlin, Germany
| | - Arndt Grossmann
- Otto-Warburg Laboratory, Max-Planck Institute for Molecular Genetics (MPIMG), 14195 Berlin, Germany
| | - Miguel A Andrade-Navarro
- Faculty of Biology, Johannes Gutenberg University Mainz and Institute of Molecular Biology, 55128 Mainz, Germany
| | - Bryan A Ballif
- Department of Biology, University of Vermont, Burlington, VT 05405, USA
| | - Ulrich Stelzl
- Otto-Warburg Laboratory, Max-Planck Institute for Molecular Genetics (MPIMG), 14195 Berlin, Germany; Institute of Pharmaceutical Sciences, University of Graz and BioTechMed-Graz, 8010 Graz, Austria.
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184
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Liu X, Zhu Y, Liu T, Xue Q, Tian F, Yuan Y, Zhao C. Exploring toxicity of perfluorinated compounds through complex network and pathway modeling. J Biomol Struct Dyn 2019; 38:2604-2612. [PMID: 31244379 DOI: 10.1080/07391102.2019.1637281] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Perfluorinated compounds (PFCs) have serious impacts on human health, which could interfere with the body's signal pathways and affect the normal hormone balance of humans. PFCs were reported to bind to many proteins causing a series of biological effects. It was quite possible that the in vivo action of PFCs was not a single target or a single pathway, suggesting the toxic effect was due to the disturbance of protein or gene network, not limited to the modification of a single target protein or gene. Thus, a PFCs-targets interaction network was constructed and the significant differences in the characteristics of complex networks between the branched PFCs and linear PFCs were observed. A molecular dynamics simulation proved that binding ability of the branched PFCs to the target protein was much weaker than that of the linear PFCs, explaining why the branched PFCs presented significantly difference from the linear PFCs in terms of complex network characteristics. In addition, four target genes were identified as the central node genes of the network. The four target genes were proved to present certain influences on some diseases, which suggested a high correlation between PFCs to these diseases, including obesity, hepatocellular carcinoma and diabetes. The present work was helpful to develop new approaches to identify the key toxic targets of compounds and to explore the toxicity effects on pathways. AbbreviationsARandrogen receptorBPAbisphenol AESR1estrogen receptor 1ESR2estrogen receptor 2GLTPglycolipid transfer proteinHbFthe fetal hemoglobinHBG1hemoglobin subunit γ-1hERαhuman ERαHSD17B1hydroxysteroid 17-β dehydrogenase 1KEGGKenya encyclopedia of genes and genomesMDmolecular dynamics simulationPFCsperfluorinated compoundsPFOAperfluorooctanoic acidPFOSperfluorooctane sulfonatePOPspersistent organic pollutantsRMSDroot-mean-square deviationSHBGsex hormone binding globulinSPC/Eextended simple point charge modelTRthyroid hormone receptorCommunicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Xinhe Liu
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Yu Zhu
- Department of Ecology and Environment of Gansu Province, Lanzhou, China
| | - Tingting Liu
- Gansu Provincial Maternity and Child-care Hospital, Lanzhou, China
| | - Qiao Xue
- Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China
| | - Fang Tian
- School of Pharmacy, Lanzhou University, Lanzhou, China
| | - Yongna Yuan
- School of Information Science & Engineering, Lanzhou University, Lanzhou, China
| | - Chunyan Zhao
- School of Pharmacy, Lanzhou University, Lanzhou, China
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185
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Guala D, Ogris C, Müller N, Sonnhammer ELL. Genome-wide functional association networks: background, data & state-of-the-art resources. Brief Bioinform 2019; 21:1224-1237. [PMID: 31281921 PMCID: PMC7373183 DOI: 10.1093/bib/bbz064] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/29/2019] [Accepted: 05/04/2019] [Indexed: 02/06/2023] Open
Abstract
The vast amount of experimental data from recent advances in the field of high-throughput biology begs for integration into more complex data structures such as genome-wide functional association networks. Such networks have been used for elucidation of the interplay of intra-cellular molecules to make advances ranging from the basic science understanding of evolutionary processes to the more translational field of precision medicine. The allure of the field has resulted in rapid growth of the number of available network resources, each with unique attributes exploitable to answer different biological questions. Unfortunately, the high volume of network resources makes it impossible for the intended user to select an appropriate tool for their particular research question. The aim of this paper is to provide an overview of the underlying data and representative network resources as well as to mention methods of integration, allowing a customized approach to resource selection. Additionally, this report will provide a primer for researchers venturing into the field of network integration.
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Affiliation(s)
- Dimitri Guala
- Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden
| | - Christoph Ogris
- Computational Cell Maps, Institute of Computational Biology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Nikola Müller
- Computational Cell Maps, Institute of Computational Biology, Helmholtz Center Munich, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Erik L L Sonnhammer
- Science for Life Laboratory, Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden
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186
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Li M, Ni P, Chen X, Wang J, Wu FX, Pan Y. Construction of Refined Protein Interaction Network for Predicting Essential Proteins. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1386-1397. [PMID: 28186903 DOI: 10.1109/tcbb.2017.2665482] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Identification of essential proteins based on protein interaction network (PIN) is a very important and hot topic in the post genome era. Up to now, a number of network-based essential protein discovery methods have been proposed. Generally, a static protein interaction network was constructed by using the protein-protein interactions obtained from different experiments or databases. Unfortunately, most of the network-based essential protein discovery methods are sensitive to the reliability of the constructed PIN. In this paper, we propose a new method for constructing refined PIN by using gene expression profiles and subcellular location information. The basic idea behind refining the PIN is that two proteins should have higher possibility to physically interact with each other if they appear together at the same subcellular location and are active together at least at a time point in the cell cycle. The original static PIN is denoted by S-PIN while the final PIN refined by our method is denoted by TS-PIN. To evaluate whether the constructed TS-PIN is more suitable to be used in the identification of essential proteins, 10 network-based essential protein discovery methods (DC, EC, SC, BC, CC, IC, LAC, NC, BN, and DMNC) are applied on it to identify essential proteins. A comparison of TS-PIN and two other networks: S-PIN and NF-APIN (a noise-filtered active PIN constructed by using gene expression data and S-PIN) is implemented on the prediction of essential proteins by using these ten network-based methods. The comparison results show that all of the 10 network-based methods achieve better results when being applied on TS-PIN than that being applied on S-PIN and NF-APIN.
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187
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Yugandhar K, Gupta S, Yu H. Inferring Protein-Protein Interaction Networks From Mass Spectrometry-Based Proteomic Approaches: A Mini-Review. Comput Struct Biotechnol J 2019; 17:805-811. [PMID: 31316724 PMCID: PMC6611912 DOI: 10.1016/j.csbj.2019.05.007] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 05/20/2019] [Accepted: 05/26/2019] [Indexed: 01/06/2023] Open
Abstract
Studying protein-protein interaction networks provide key evidence for the underlying molecular mechanisms. Mass spectrometry-based proteomic approaches have been playing a pivotal role in deciphering these interaction networks, along with precise quantification for individual interactions. In this mini-review we discuss the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks. We then summarize the down-stream computational strategies for identification and quantification of interactions from those techniques. Finally, we highlight the challenges and limitations of current computational pipelines in eliminating false positive interactors, followed by a summary of the innovative algorithms to address these issues, along with the scope for future improvements.
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Affiliation(s)
- Kumar Yugandhar
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
| | - Shagun Gupta
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York 14853, USA
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188
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Brant JO, Boatwright JL, Davenport R, Sandoval AGW, Maden M, Barbazuk WB. Comparative transcriptomic analysis of dermal wound healing reveals de novo skeletal muscle regeneration in Acomys cahirinus. PLoS One 2019; 14:e0216228. [PMID: 31141508 PMCID: PMC6541261 DOI: 10.1371/journal.pone.0216228] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 04/16/2019] [Indexed: 01/14/2023] Open
Abstract
The African spiny mouse, Acomys spp., is capable of scar-free dermal wound healing. Here, we have performed a comprehensive analysis of gene expression throughout wound healing following full-thickness excisional dermal wounds in both Acomys cahirinus and Mus musculus. Additionally, we provide an annotated, de novo transcriptome assembly of A. cahirinus skin and skin wounds. Using a novel computational comparative RNA-Seq approach along with pathway and co-expression analyses, we identify enrichment of regeneration associated genes as well as upregulation of genes directly related to muscle development or function. Our RT-qPCR data reveals induction of the myogenic regulatory factors, as well as upregulation of embryonic myosin, starting between days 14 and 18 post-wounding in A. cahirinus. In contrast, the myogenic regulatory factors remain downregulated, embryonic myosin is only modestly upregulated, and no new muscle fibers of the panniculus carnosus are generated in M. musculus wounds. Additionally, we show that Col6a1, a key component of the satellite cell niche, is upregulated in A. cahirinus compared to M. musculus. Our data also demonstrate that the macrophage profile and inflammatory response is different between species, with A. cahirinus expressing significantly higher levels of Il10. We also demonstrate differential expression of the upstream regulators Wnt7a, Wnt2 and Wnt6 during wound healing. Our analyses demonstrate that A. cahirinus is capable of de novo skeletal muscle regeneration of the panniculus carnosus following removal of the extracellular matrix. We believe this study represents the first detailed analysis of de novo skeletal muscle regeneration observed in an adult mammal.
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Affiliation(s)
- Jason O. Brant
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
| | - J. Lucas Boatwright
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
| | - Ruth Davenport
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
| | | | - Malcolm Maden
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
- Genetics Institute, University of Florida, Gainesville, Florida, United States of America
- * E-mail: (WBB); (MM)
| | - W. Brad Barbazuk
- Department of Biology, University of Florida, Gainesville, Florida, United States of America
- Genetics Institute, University of Florida, Gainesville, Florida, United States of America
- * E-mail: (WBB); (MM)
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189
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Rasti S, Vogiatzis C. A survey of computational methods in protein–protein interaction networks. ANNALS OF OPERATIONS RESEARCH 2019; 276:35-87. [DOI: 10.1007/s10479-018-2956-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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190
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Straightforward Protein-Protein Interaction Interface Mapping via Random Mutagenesis and Mammalian Protein Protein Interaction Trap (MAPPIT). Int J Mol Sci 2019; 20:ijms20092058. [PMID: 31027327 PMCID: PMC6539206 DOI: 10.3390/ijms20092058] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 04/08/2019] [Accepted: 04/13/2019] [Indexed: 01/18/2023] Open
Abstract
The MAPPIT (mammalian protein protein interaction trap) method allows high-throughput detection of protein interactions by very simple co-transfection of three plasmids in HEK293T cells, followed by a luciferase readout. MAPPIT detects a large percentage of all protein interactions, including those requiring posttranslational modifications and endogenous or exogenous ligands. Here, we present a straightforward method that allows detailed mapping of interaction interfaces via MAPPIT. The method provides insight into the interaction mechanism and reveals how this is affected by disease-associated mutations. By combining error-prone polymerase chain reaction (PCR) for random mutagenesis, 96-well DNA prepping, Sanger sequencing, and MAPPIT via 384-well transfections, we test the effects of a large number of mutations of a selected protein on its protein interactions. The entire screen takes less than three months and interactions with multiple partners can be studied in parallel. The effect of mutations on the MAPPIT readout is mapped on the protein structure, allowing unbiased identification of all putative interaction sites. We have thus far analysed 6 proteins and mapped their interfaces for 16 different interaction partners. Our method is broadly applicable as the required tools are simple and widely available.
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191
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Liu X, Liu Z, Dziulko AK, Li F, Miller D, Morabito RD, Francois D, Levy SF. iSeq 2.0: A Modular and Interchangeable Toolkit for Interaction Screening in Yeast. Cell Syst 2019; 8:338-344.e8. [PMID: 30954477 PMCID: PMC6483859 DOI: 10.1016/j.cels.2019.03.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 01/10/2019] [Accepted: 03/06/2019] [Indexed: 11/24/2022]
Abstract
We developed a flexible toolkit for combinatorial screening in Saccharomyces cerevisiae, which generates large libraries of cells, each uniquely barcoded to mark a combination of DNA elements. This interaction sequencing platform (iSeq 2.0) includes genomic landing pads that assemble combinations through sequential integration of plasmids or yeast mating, 15 barcoded plasmid libraries containing split selectable markers (URA3AI, KanMXAI, HphMXAI, and NatMXAI), and an array of ∼24,000 "double-barcoder" strains that can make existing yeast libraries iSeq compatible. Various DNA elements are compatible with iSeq: DNA introduced on integrating plasmids, engineered genomic modifications, or entire genetic backgrounds. DNA element libraries are modular and interchangeable, and any two libraries can be combined, making iSeq capable of performing many new combinatorial screens by short-read sequencing.
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Affiliation(s)
- Xianan Liu
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Zhimin Liu
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Adam K Dziulko
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Fangfei Li
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA; Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5215, USA
| | - Darach Miller
- SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA; Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Robert D Morabito
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Danielle Francois
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Sasha F Levy
- Department of Biochemistry, Stony Brook University, Stony Brook, NY 11794-5215, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA; Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5215, USA; Joint Initiative for Metrology in Biology, Stanford, CA 94305-4245, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA; Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA.
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192
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Höpfler M, Kern MJ, Straub T, Prytuliak R, Habermann BH, Pfander B, Jentsch S. Slx5/Slx8-dependent ubiquitin hotspots on chromatin contribute to stress tolerance. EMBO J 2019; 38:embj.2018100368. [PMID: 31015336 PMCID: PMC6545562 DOI: 10.15252/embj.2018100368] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 03/29/2019] [Accepted: 04/03/2019] [Indexed: 12/27/2022] Open
Abstract
Chromatin is a highly regulated environment, and protein association with chromatin is often controlled by post‐translational modifications and the corresponding enzymatic machinery. Specifically, SUMO‐targeted ubiquitin ligases (STUbLs) have emerged as key players in nuclear quality control, genome maintenance, and transcription. However, how STUbLs select specific substrates among myriads of SUMOylated proteins on chromatin remains unclear. Here, we reveal a remarkable co‐localization of the budding yeast STUbL Slx5/Slx8 and ubiquitin at seven genomic loci that we term “ubiquitin hotspots”. Ubiquitylation at these sites depends on Slx5/Slx8 and protein turnover on the Cdc48 segregase. We identify the transcription factor‐like Ymr111c/Euc1 to associate with these sites and to be a critical determinant of ubiquitylation. Euc1 specifically targets Slx5/Slx8 to ubiquitin hotspots via bipartite binding of Slx5 that involves the Slx5 SUMO‐interacting motifs and an additional, novel substrate recognition domain. Interestingly, the Euc1‐ubiquitin hotspot pathway acts redundantly with chromatin modifiers of the H2A.Z and Rpd3L pathways in specific stress responses. Thus, our data suggest that STUbL‐dependent ubiquitin hotspots shape chromatin during stress adaptation.
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Affiliation(s)
- Markus Höpfler
- Max Planck Institute of Biochemistry, Molecular Cell Biology, Martinsried, Germany
| | - Maximilian J Kern
- Max Planck Institute of Biochemistry, Molecular Cell Biology, Martinsried, Germany
| | - Tobias Straub
- Biomedizinisches Centrum, Core Facility Bioinformatics, Ludwig-Maximilians-Universität München, Martinsried, Germany
| | - Roman Prytuliak
- Max Planck Institute of Biochemistry, Computational Biology Group, Martinsried, Germany
| | - Bianca H Habermann
- Max Planck Institute of Biochemistry, Computational Biology Group, Martinsried, Germany.,Aix-Marseille Univ, CNRS, IBDM UMR 7288, Marseille Cedex 9, France
| | - Boris Pfander
- Max Planck Institute of Biochemistry, DNA Replication and Genome Integrity, Martinsried, Germany
| | - Stefan Jentsch
- Max Planck Institute of Biochemistry, Molecular Cell Biology, Martinsried, Germany
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193
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Bing X, Bunea F, Royer M, Das J. Latent Model-Based Clustering for Biological Discovery. iScience 2019; 14:125-135. [PMID: 30954780 PMCID: PMC6449745 DOI: 10.1016/j.isci.2019.03.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 11/26/2018] [Accepted: 03/18/2019] [Indexed: 11/27/2022] Open
Abstract
LOVE, a robust, scalable latent model-based clustering method for biological discovery, can be used across a range of datasets to generate both overlapping and non-overlapping clusters. In our formulation, a cluster comprises variables associated with the same latent factor and is determined from an allocation matrix that indexes our latent model. We prove that the allocation matrix and corresponding clusters are uniquely defined. We apply LOVE to biological datasets (gene expression, serological responses measured from HIV controllers and chronic progressors, vaccine-induced humoral immune responses) resulting in meaningful biological output. For all three datasets, the clusters generated by LOVE remain stable across tuning parameters. Finally, we compared LOVE's performance to that of 13 state-of-the-art methods using previously established benchmarks and found that LOVE outperformed these methods across datasets. Our results demonstrate that LOVE can be broadly used across large-scale biological datasets to generate accurate and meaningful overlapping and non-overlapping clusters. LOVE is a robust, scalable, and versatile latent model-based clustering method Has theoretical guarantees, and can generate overlapping and non-overlapping clusters Generates meaningful clusters from datasets spanning a range of biological domains Using established benchmarks, outperforms 13 state-of-the-art methods across datasets
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194
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195
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Liu H, Pfirrmann T. The Gid-complex: an emerging player in the ubiquitin ligase league. Biol Chem 2019; 400:1429-1441. [DOI: 10.1515/hsz-2019-0139] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 02/20/2019] [Indexed: 12/16/2022]
Abstract
Abstract
The Saccharomyces cerevisiae Gid-complex is a highly evolutionary conserved ubiquitin ligase with at least seven protein subunits. Here, we review our knowledge about the yeast Gid-complex as an important regulator of glucose metabolism, specifically targeting key enzymes of gluconeogenesis for degradation. Furthermore, we summarize existing data about the individual subunits, the topology and possible substrate recognition mechanisms and compare the striking similarities, but also differences, between the yeast complex and its vertebrate counterpart. Present data is summarized to give an overview about cellular processes regulated by the vertebrate GID-complex that range from cell cycle regulation, primary cilia function to the regulation of energy homeostasis. In conclusion, the vertebrate GID-complex evolved as a versatile ubiquitin ligase complex with functions beyond the regulation of glucose metabolism.
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Affiliation(s)
- Huaize Liu
- Martin Luther University Halle-Wittenberg , Institute of Physiological Chemistry , Hollystr. 1 , D-06114 Halle , Germany
| | - Thorsten Pfirrmann
- Martin Luther University Halle-Wittenberg , Institute of Physiological Chemistry , Hollystr. 1 , D-06114 Halle , Germany
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196
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Macossay-Castillo M, Marvelli G, Guharoy M, Jain A, Kihara D, Tompa P, Wodak SJ. The Balancing Act of Intrinsically Disordered Proteins: Enabling Functional Diversity while Minimizing Promiscuity. J Mol Biol 2019; 431:1650-1670. [PMID: 30878482 DOI: 10.1016/j.jmb.2019.03.008] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 02/25/2019] [Accepted: 03/03/2019] [Indexed: 10/27/2022]
Abstract
Intrinsically disordered proteins (IDPs) or regions (IDRs) perform diverse cellular functions, but are also prone to forming promiscuous and potentially deleterious interactions. We investigate the extent to which the properties of, and content in, IDRs have adapted to enable functional diversity while limiting interference from promiscuous interactions in the crowded cellular environment. Information on protein sequences, their predicted intrinsic disorder, and 3D structure contents is related to data on protein cellular concentrations, gene co-expression, and protein-protein interactions in the well-studied yeast Saccharomyces cerevisiae. Results reveal that both the protein IDR content and the frequency of "sticky" amino acids in IDRs (those more frequently involved in protein interfaces) decrease with increasing protein cellular concentration. This implies that the IDR content and the amino acid composition of IDRs experience negative selection as the protein concentration increases. In the S. cerevisiae protein-protein interaction network, the higher a protein's IDR content, the more frequently it interacts with IDR-containing partners, and the more functionally diverse the partners are. Employing a clustering analysis of Gene Ontology terms, we newly identify ~600 putative multifunctional proteins in S. cerevisiae. Strikingly, these proteins are enriched in IDRs and contribute significantly to all the observed trends. In particular, IDRs of multi-functional proteins feature more sticky amino acids than IDRs of their non-multifunctional counterparts, or the surfaces of structured yeast proteins. This property likely affords sufficient binding affinity for the functional interactions, commonly mediated by short IDR segments, thereby counterbalancing the loss in overall IDR conformational entropy upon binding.
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Affiliation(s)
- Mauricio Macossay-Castillo
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium; Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Giulio Marvelli
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium; Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Mainak Guharoy
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium; Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Aashish Jain
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, Hockmeyer Structural Biology Building, 249 S. Martin Jischke Dr West Lafayette, IN 47907, USA
| | - Peter Tompa
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium; Structural Biology Brussels, Department of Bioengineering Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium; Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudosok korutja 2, 1117 Budapest, Hungary
| | - Shoshana J Wodak
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium.
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197
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Abstract
Recent research increasingly shows the relevance of network based approaches for our understanding of biological systems. Analyzing human protein interaction networks, we determined collective influencers (CI), defined as network nodes that damage the integrity of the underlying networks to the utmost degree. We found that CI proteins were enriched with essential, regulatory, signaling and disease genes as well as drug targets, indicating their biological significance. Also by focusing on different organisms, we found that CI proteins had a penchant to be evolutionarily conserved as CI proteins, indicating the fundamental role that collective influencers in protein interaction networks plays for our understanding of regulation, diseases and evolution.
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198
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Nguyen H, Shrestha S, Tran D, Shafi A, Draghici S, Nguyen T. A Comprehensive Survey of Tools and Software for Active Subnetwork Identification. Front Genet 2019; 10:155. [PMID: 30891064 PMCID: PMC6411791 DOI: 10.3389/fgene.2019.00155] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 02/13/2019] [Indexed: 12/13/2022] Open
Abstract
A recent focus of computational biology has been to integrate the complementary information available in molecular profiles as well as in multiple network databases in order to identify connected regions that show significant changes under different conditions. This allows for capturing dynamic and condition-specific mechanisms of the underlying phenomena and disease stages. Here we review 22 such integrative approaches for active module identification published over the last decade. This article only focuses on tools that are currently available for use and are well-maintained. We compare these methods focusing on their primary features, integrative abilities, network structures, mathematical models, and implementations. We also provide real-world scenarios in which these methods have been successfully applied, as well as highlight outstanding challenges in the field that remain to be addressed. The main objective of this review is to help potential users and researchers to choose the best method that is suitable for their data and analysis purpose.
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Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Sangam Shrestha
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Duc Tran
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Adib Shafi
- Department of Computer Science, Wayne State University, Detroit, MI, United States
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, United States
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, United States
| | - Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
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199
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Tian B, Duan Q, Zhao C, Teng B, He Z. Reinforce: An Ensemble Approach for Inferring PPI Network from AP-MS Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:365-376. [PMID: 28534782 DOI: 10.1109/tcbb.2017.2705060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Affinity Purification-Mass Spectrometry (AP-MS) is one of the most important technologies for constructing protein-protein interaction (PPI) networks. In this paper, we propose an ensemble method, Reinforce, for inferring PPI network from AP-MS data set. The new algorithm named Reinforce is based on rank aggregation and false discovery rate control. Under the null hypothesis that the interaction scores from different scoring methods are randomly generated, Reinforce follows three steps to integrate multiple ranking results from different algorithms or different data sets. The experimental results show that Reinforce can get more stable and accurate inference results than existing algorithms. The source codes of Reinforce and data sets used in the experiments are available at: https://sourceforge.net/projects/reinforce/.
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200
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Gay S, Piccini D, Bruhn C, Ricciardi S, Soffientini P, Carotenuto W, Biffo S, Foiani M. A Mad2-Mediated Translational Regulatory Mechanism Promoting S-Phase Cyclin Synthesis Controls Origin Firing and Survival to Replication Stress. Mol Cell 2019; 70:628-638.e5. [PMID: 29775579 PMCID: PMC5972228 DOI: 10.1016/j.molcel.2018.04.020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 02/26/2018] [Accepted: 04/23/2018] [Indexed: 11/03/2022]
Abstract
Cell survival to replication stress depends on the activation of the Mec1ATR-Rad53 checkpoint response that protects the integrity of stalled forks and controls the origin firing program. Here we found that Mad2, a member of the spindle assembly checkpoint (SAC), contributes to efficient origin firing and to cell survival in response to replication stress. We show that Rad53 and Mad2 promote S-phase cyclin expression through different mechanisms: while Rad53 influences Clb5,6 degradation, Mad2 promotes their protein synthesis. We found that Mad2 co-sediments with polysomes and modulates the association of the translation inhibitor Caf204E-BP with the translation machinery and the initiation factor eIF4E. This Mad2-dependent translational regulatory process does not depend on other SAC proteins. Altogether our observations indicate that Mad2 has an additional function outside of mitosis to control DNA synthesis and collaborates with the Mec1-Rad53 regulatory axis to allow cell survival in response to replication stress.
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Affiliation(s)
- Sophie Gay
- IFOM, The FIRC Institute of Molecular Oncology, Via Adamello 16, 20139 Milan, Italy.
| | - Daniele Piccini
- IFOM, The FIRC Institute of Molecular Oncology, Via Adamello 16, 20139 Milan, Italy
| | - Christopher Bruhn
- IFOM, The FIRC Institute of Molecular Oncology, Via Adamello 16, 20139 Milan, Italy
| | - Sara Ricciardi
- Fondazione Istituto Nazionale Genetica Molecolare, Via Francesco Sforza, 32, 20122 Milan, Italy; Università degli Studi di Milano, Via Celoria 26, 20133 Milan, Italy
| | - Paolo Soffientini
- IFOM, The FIRC Institute of Molecular Oncology, Via Adamello 16, 20139 Milan, Italy
| | - Walter Carotenuto
- IFOM, The FIRC Institute of Molecular Oncology, Via Adamello 16, 20139 Milan, Italy
| | - Stefano Biffo
- Fondazione Istituto Nazionale Genetica Molecolare, Via Francesco Sforza, 32, 20122 Milan, Italy; Università degli Studi di Milano, Via Celoria 26, 20133 Milan, Italy
| | - Marco Foiani
- IFOM, The FIRC Institute of Molecular Oncology, Via Adamello 16, 20139 Milan, Italy; Università degli Studi di Milano, Via Celoria 26, 20133 Milan, Italy.
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