Methods & Resources
- Proteome‐wide analysis of phospho‐regulated PDZ domain interactions
- Gustav N Sundell1,
- Roland Arnold (r.arnold.2{at}bham.ac.uk)*,2,
- Muhammad Ali1,
- Piangfan Naksukpaiboon2,
- Julien Orts3,
- Peter Güntert3,4,
- Celestine N Chi (chi.celestine{at}imbim.uu.se)*,5 and
- Ylva Ivarsson (ylva.ivarsson{at}kemi.uu.se)*,1
- 1Department of Chemistry – BMC, Uppsala University, Uppsala, Sweden
- 2Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, UK
- 3Laboratory of Physical Chemistry, ETH Zürich, Zürich, Switzerland
- 4Institute of Biophysical Chemistry, Goethe University, Frankfurt am Main, Germany
- 5Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
- ↵*
Corresponding author. Tel: +44 7936624996; E‐mail: r.arnold.2{at}bham.ac.uk
Corresponding author. Tel: +46 18 471 4557; E‐mail: chi.celestine{at}imbim.uu.se
Corresponding author. Tel: +46 18 471 40 38; E‐mail: ylva.ivarsson{at}kemi.uu.se
The study presents phosphomimetic proteomic peptide phage display, a novel method for exploring phospho‐regulated motif‐based interactions. Application to PDZ domains reveals a site‐specific phospho‐regulation of PDZ‐mediated interactions as a switching mechanism of interaction selectivity.
Synopsis
The study presents phosphomimetic proteomic peptide phage display, a novel method for exploring phospho‐regulated motif‐based interactions. Application to PDZ domains reveals a site‐specific phospho‐regulation of PDZ‐mediated interactions as a switching mechanism of interaction selectivity.
Phosphomimetic proteomic peptide‐phage display (ProP‐PD) is a novel method for simultaneously finding motif‐based interaction and identifying phosphorylation switches.
Site‐specific Ser/Thr phosphorylation events enable or disable PDZ domain interactions as revealed by phosphomimetic ProP‐PD.
The approach can be used to explore potential phospho‐regulation of motif‐based interactions on a large scale.
Mol Syst Biol. (2018) 14: e8129
- Received November 29, 2017.
- Revision received July 24, 2018.
- Accepted July 24, 2018.
- © 2018 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial
- Christina Ludwig (tina.ludwig{at}tum.de)*,1,†,
- Ludovic Gillet2,†,
- George Rosenberger2,3,
- Sabine Amon2,
- Ben C Collins2 and
- Ruedi Aebersold2,4
- 1Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of Munich (TUM), Freising, Germany
- 2Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- 3Department of Systems Biology, Columbia University, New York, NY, USA
- 4Faculty of Science, University of Zurich, Zurich, Switzerland
- ↵*Corresponding author. Tel: +49 8161 71 6130; E‐mail: tina.ludwig{at}tum.de
↵† These authors contributed equally to this work
SWATH‐MS combines deep proteome coverage with quantitative consistency and accuracy and is often the method of choice for personalized medicine, drug screens or systems biology. This tutorial provides guidelines on how to set up SWATH‐MS experiments, perform the mass spectrometric measurements and analyse the data.
Mol Syst Biol. (2018) 14: e8126
- Received November 28, 2017.
- Revision received May 11, 2018.
- Accepted May 15, 2018.
- © 2018 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Unraveling mitotic protein networks by 3D multiplexed epitope drug screening
- Lorenz J Maier1,2,3,†,
- Stefan M Kallenberger1,2,3,†,
- Katharina Jechow2,4,
- Marcel Waschow2,
- Roland Eils (r.eils{at}dkfz.de)*,1,2,4 and
- Christian Conrad (c.conrad{at}dkfz.de)*,1,2
- 1Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), Heidelberg University, Heidelberg, Germany
- 2Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- 3Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB), Heidelberg University, Heidelberg, Germany
- 4BIH Center for Digital Health, Charité Universitätsmedizin Berlin and Berlin Institute of Health, Berlin, Germany
- ↵*
Corresponding author. Tel: +49 30 450 543088; E‐mail: r.eils{at}dkfz.de
Corresponding author. Tel: +49 6221 54 51304; E‐mail: c.conrad{at}dkfz.de
↵† These authors contributed equally to this work
3D SPECS, an automated technology for 3D Spatial characterization of Protein Expression Changes by microscopic Screening combines iterative antibody staining, high‐content 3D imaging, and classification by machine learning. As a proof of concept, it is applied to quantitatively study mitotic stages.
Synopsis
3D SPECS, an automated technology for 3D Spatial characterization of Protein Expression Changes by microscopic Screening combines iterative antibody staining, high‐content 3D imaging, and classification by machine learning. As a proof of concept, it is applied to quantitatively study mitotic stages.
3D SPECS allows to query complex protein networks using microscopic localizations.
Twelve epitopes are mapped and the network effects of 12 mitotic cancer drugs are analyzed in 3D cultured spheroids.
Modelling affinities explores new mitotic protein‐protein interactions.
Mitotic protein networks appear highly connected.
Mol Syst Biol. (2018) 14: e8238
- Received January 23, 2018.
- Revision received July 17, 2018.
- Accepted July 19, 2018.
- © 2018 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Two protein/protein interaction assays in one go
- Mikko Taipale (mikko.taipale{at}utoronto.ca)1
Discovering and characterizing protein–protein interactions (PPIs) that contribute to cellular homeostasis, development, and disease is a key priority in proteomics. Numerous assays for protein–protein interactions have been developed, but each one comes with its own strengths, weaknesses, and false‐positive/false‐negative rates. Therefore, it seems rather intuitive that combining multiple assays is beneficial for robust and reliable discovery of interactions. Along those lines, in their recent study, Wanker and colleagues (Trepte et al, 2018) combined two complementary and quantitative interaction assays in one pot. One assay is luminescence‐based and depends on protein proximity in living cells, while the other relies on formation of more stable complexes detected by co‐precipitation with a luminescence‐based readout, which facilitates confident identification and quantitation of interactions in high throughput.
See also: P Trepte et al (July 2018)
Combining complementary assays is beneficial for identifying protein‐protein interactions. In their recent work, Wanker and colleagues (Trepte et al, 2018) combine a luminescence‐based assay depending on protein proximity in living cells with a co‐precipitation assay that detects more stable complexes.
Mol Syst Biol. (2018) 14: e8485
- © 2018 The Author. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Characterizing meiotic chromosomes' structure and pairing using a designer sequence optimized for Hi‐C
- Héloïse Muller1,2,3,8,†,
- Vittore F Scolari1,2,3,†,
- Nicolas Agier4,
- Aurèle Piazza1,2,3,
- Agnès Thierry1,2,3,
- Guillaume Mercy1,2,3,
- Stéphane Descorps‐Declere3,
- Luciana Lazar‐Stefanita1,2,3,
- Olivier Espeli5,
- Bertrand Llorente6,
- Gilles Fischer4,
- Julien Mozziconacci (mozziconacci{at}lptmc.jussieu.fr)*,7 and
- Romain Koszul (romain.koszul{at}pasteur.fr)*,1,2,3
- 1Department Genomes and Genetics, Groupe Régulation Spatiale des Génomes, Institut Pasteur, Paris, France
- 2CNRS, UMR 3525, Paris, France
- 3Center of Bioinformatics, Biostatistics and Integrative Biology (C3BI), Institut Pasteur, Paris, France
- 4Laboratory of Computational and Quantitative Biology, CNRS, Institut de Biologie Paris‐Seine, Sorbonne Université, Paris, France
- 5Centre Interdisciplinaire de Recherche en Biologie, Collège de France, UMR‐CNRS 7241, INSERM U1050, Paris, France
- 6Cancer Research Center of Marseille, CNRS UMR7258, Inserm U1068, Institut Paoli‐Calmettes, Aix‐Marseille Université UM105, Marseille, France
- 7Theoretical Physics for Condensed Matter Lab, CNRS UMR 7600, Sorbonne Universités, UPMC University Paris 06, Paris, France
- 8Present Address: UMR3664 Dynamique du Noyau, Institut Curie, Paris, France
- ↵*
Corresponding author. Tel: +33 1 44 27 45 40; E‐mail: mozziconacci{at}lptmc.jussieu.fr
Corresponding author. Tel: +33 1 40 61 33 25; E‐mail: romain.koszul{at}pasteur.fr
↵† These authors contributed equally to this work
A 144‐kb region of a yeast chromosome is engineered to display regularly spaced restriction sites to ultimately track homolog pairing during meiotic prophase. The engineered chromosome region (Syn‐HiC) displays a smoother Hi‐C signal at restriction fragment resolution.
Synopsis
A 144‐kb region of a yeast chromosome is engineered to display regularly spaced restriction sites to ultimately track homolog pairing during meiotic prophase. The engineered chromosome region (Syn‐HiC) displays a smoother Hi‐C signal at restriction fragment resolution.
Hi‐C tracks homologous pairing between the Syn‐HiC region and its native homolog during meiotic prophase.
Zygotene and pachytene chromosomes are folded into chromatin loops of various sizes.
Adjacent and non‐adjacent Rec8 cohesin binding sites are enriched at the basis of the loops.
The meiotic prophase and mitotic metaphase chromosomes display very similar cis‐contact patterns, further unifying the two programs.
Mol Syst Biol. (2018) 14: e8293
- Received February 27, 2018.
- Revision received June 18, 2018.
- Accepted June 20, 2018.
- © 2018 Institut Pasteur Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- LuTHy: a double‐readout bioluminescence‐based two‐hybrid technology for quantitative mapping of protein–protein interactions in mammalian cells
- Philipp Trepte1,
- Sabrina Kruse1,
- Simona Kostova1,
- Sheila Hoffmann2,
- Alexander Buntru1,
- Anne Tempelmeier1,
- Christopher Secker1,3,
- Lisa Diez1,
- Aline Schulz1,
- Konrad Klockmeier1,
- Martina Zenkner1,
- Sabrina Golusik1,
- Kirstin Rau1,
- Sigrid Schnoegl1,
- Craig C Garner2 and
- Erich E Wanker (ewanker{at}mdc-berlin.de)*,1
- 1Neuroproteomics, Max Delbrück Center for Molecular Medicine and Berlin Institute of Health, Berlin, Germany
- 2Synaptopathy, German Center for Neurodegenerative Diseases, Berlin, Germany
- 3Department of Neurology, Charité Universitätsmedizin Berlin, Berlin, Germany
- ↵*Corresponding author. Tel: +49 30 9406 2157; E‐mail: ewanker{at}mdc-berlin.de
A new double‐readout bioluminescence‐based two‐hybrid method deepens the coverage of protein interaction maps. It provides two quantitative interaction scores, recovers transient associations and monitors the effects of missense mutations and small molecules on interactions.
Synopsis
A new double‐readout bioluminescence‐based two‐hybrid method deepens the coverage of protein interaction maps. It provides two quantitative interaction scores, recovers transient associations and monitors the effects of missense mutations and small molecules on interactions.
LuTHy is a double‐readout technology that provides two quantitative scores for protein‐protein interactions in mammalian cells.
Low‐ and high‐affinity interactions can be detected with high sensitivity.
Nearly 50% of CSPα interactions with synaptic proteins are affected by ANCL‐disease mutations.
Mol Syst Biol. (2018) 14: e8071
- Received October 25, 2017.
- Revision received June 8, 2018.
- Accepted June 15, 2018.
- © 2018 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Quantifying compartment‐associated variations of protein abundance in proteomics data
- Luca Parca1,5,
- Martin Beck1,2,
- Peer Bork1,3 and
- Alessandro Ori (alessandro.ori{at}leibniz-fli.de)*,4
- 1Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- 2Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- 3Max‐Delbrück‐Centre for Molecular Medicine, Berlin, Germany
- 4Leibniz Institute on Aging‐Fritz Lipmann Institute, Jena, Germany
- 5Present Address: Department of Biology, University of Rome “Tor Vergata”, Rome, Italy
- ↵*Corresponding author. Tel: +49 3641 65 6808; E‐mail: alessandro.ori{at}leibniz-fli.de
Analyses of multiple proteomics datasets shows that distinctive shifts in the fold change estimation for proteins associated to particular cellular compartments are common in proteomics data. A method for revealing changes in the composition of cellular compartments is presented.
Synopsis
Analyses of multiple proteomics datasets shows that distinctive shifts in the fold change estimation for proteins associated to particular cellular compartments are common in proteomics data. A method for revealing changes in the composition of cellular compartments is presented.
Differences in the abundance of organelles and cellular compartments are detectable in proteomics data.
Simple linear models are used to describe changes in protein abundance within a specific compartment.
Compartment‐Normalized Variation (CNV) analysis complements standard differential expression.
Analyses using the CNV approach reveal changes in the composition of mitochondria and extracellular space during C. elegans aging.
Mol Syst Biol. (2018) 14: e8131
- Received November 29, 2017.
- Revision received June 11, 2018.
- Accepted June 11, 2018.
- © 2018 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Genomewide phenotypic analysis of growth, cell morphogenesis, and cell cycle events in Escherichia coli
- Manuel Campos1,2,3,4,
- Sander K Govers1,2,
- Irnov Irnov1,2,6,
- Genevieve S Dobihal1,3,7,
- François Cornet4 and
- Christine Jacobs‐Wagner (christine.jacobs-wagner{at}yale.edu)*,1,2,3,5
- 1Microbial Sciences Institute, Yale University, West Haven, CT, USA
- 2Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA
- 3Howard Hughes Medical Institute, Yale University, New Haven, CT, USA
- 4Laboratoire de Microbiologie et Génétique Moléculaires (LMGM; UMR5100), Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS), Université de Toulouse, UPS, Toulouse, France
- 5Department of Microbial Pathogenesis, Yale School of Medicine, New Haven, CT, USA
- 6Present Address: Department of Microbiology, New York University School of Medicine, New York, NY, USA
- 7Present Address: Department of Microbiology and Immunology, Harvard Medical School, Boston, MA, USA
- ↵*Corresponding author. Tel: +1 203 737 6778; Fax: +1 203 737 6715; E‐mail: christine.jacobs-wagner{at}yale.edu
A quantitative genomewide screen in Escherichia coli identifies genes and pathways associated with growth, cell morphology and the cell cycle, and uncovers a connection between cell morphogenesis and late cell cycle steps through the size of the nucleoid.
Synopsis
A quantitative genomewide screen in Escherichia coli identifies genes and pathways associated with growth, cell morphology and the cell cycle, and uncovers a connection between cell morphogenesis and late cell cycle steps through the size of the nucleoid.
Measurements of growth, cell morphology and late cell cycle features are provided for each gene deletion strain of the E. coli Keio collection.
Multi‐parametric analysis identifies new phenotypic classes of mutants as well as hundreds of novel genes associated with cell morphogenesis and/or the cell cycle.
Correlation analysis across 4,000 genetic perturbations shows that growth rate is not predictive of cell size or of other scored morphological and cell cycle‐related phenotypes.
Information‐theoretic Bayesian network analysis suggests that nucleoid size connects cell size to the relative timings of nucleoid separation and cell constriction.
Mol Syst Biol. (2018) 14: e7573
- Received February 7, 2017.
- Revision received May 28, 2018.
- Accepted May 29, 2018.
- © 2018 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets
- Ricard Argelaguet1,†,
- Britta Velten2,†,
- Damien Arnol1,
- Sascha Dietrich3,
- Thorsten Zenz3,4,5,
- John C Marioni1,6,7,
- Florian Buettner (fbuettner.phys{at}gmail.com)*,1,8,
- Wolfgang Huber (wolfgang.huber{at}embl.de)*,2 and
- Oliver Stegle (oliver.stegle{at}embl.de)*,1,2
- 1European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
- 2European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- 3Heidelberg University Hospital, Heidelberg, Germany
- 4German Cancer Research Center (dkfz) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- 5Germany & Hematology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- 6Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- 7Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK
- 8Helmholtz Zentrum München–German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- ↵*
Corresponding author. Tel: +49 89 23742560; E‐mail: fbuettner.phys{at}gmail.com
Corresponding author. Tel: +49 6221 387 8823; E‐mail: wolfgang.huber{at}embl.de
Corresponding author. Tel: +49 6221 3878190; E‐mail: oliver.stegle{at}embl.de
↵† These authors contributed equally to this work
Multi‐Omics Factor Analysis (MOFA) is a computational framework for unsupervised discovery of the principal axes of biological and technical variation when multiple omics assays are applied to the same samples. MOFA is a broadly applicable approach for multi‐omics data integration.
Synopsis
Multi‐Omics Factor Analysis (MOFA) is a computational framework for unsupervised discovery of the principal axes of biological and technical variation when multiple omics assays are applied to the same samples. MOFA is a broadly applicable approach for multi‐omics data integration.
The inferred latent factors represent the underlying principal axes of heterogeneity across the samples. Factors can be shared by multiple data modalities or can be data‐type specific.
The model flexibly handles missing values and different data types.
In an application to Chronic Lymphocytic Leukaemia, MOFA discovers a low dimensional space spanned by known clinical markers and underappreciated axes of variation such as oxidative stress.
In an application to multi‐omics profiles from single‐cells, MOFA recovers differentiation trajectories and identifies coordinated variation between the transcriptome and the epigenome.
Mol Syst Biol. (2018) 14: e8124
- Received November 27, 2017.
- Revision received May 28, 2018.
- Accepted May 29, 2018.
- © 2018 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
- Mapping DNA damage‐dependent genetic interactions in yeast via party mating and barcode fusion genetics
- J Javier Díaz‐Mejía1,2,3,4,
- Albi Celaj1,2,3,4,
- Joseph C Mellor1,2,3,4,5,9,
- Atina Coté1,2,3,
- Attila Balint1,6,10,
- Brandon Ho1,6,
- Pritpal Bansal1,2,3,
- Fatemeh Shaeri1,2,3,
- Marinella Gebbia1,2,
- Jochen Weile1,2,3,
- Marta Verby1,3,
- Anna Karkhanina1,2,3,
- YiFan Zhang1,2,3,
- Cassandra Wong3,
- Justin Rich1,2,3,
- D'Arcy Prendergast1,2,3,
- Gaurav Gupta1,2,3,
- Sedide Öztürk5,11,
- Daniel Durocher2,3,
- Grant W Brown1,6 and
- Frederick P Roth (fritz.roth{at}utoronto.ca)*,1,2,3,4,5,7,8
- 1Donnelly Centre, University of Toronto, Toronto, ON, Canada
- 2Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- 3Lunenfeld‐Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON, Canada
- 4Department of Computer Science, University of Toronto, Toronto, ON, Canada
- 5Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
- 6Department of Biochemistry, University of Toronto, Toronto, ON, Canada
- 7Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana‐Farber Cancer Institute, Boston, MA, USA
- 8Canadian Institute for Advanced Research, Toronto, ON, Canada
- 9Present Address: SeqWell, Inc., Beverly, MA, USA
- 10Present Address: Department of Cellular and Molecular Medicine, Center for Chromosome Stability, University of Copenhagen, Copenhagen, Denmark
- 11Present Address: Roche Sequencing Solutions, Pleasanton, CA, USA
- ↵*Corresponding author. Tel: +1 416 946 5130; E‐mail: fritz.roth{at}utoronto.ca
A new method, Barcode Fusion Genetics to Map Genetic Interactions (BFG‐GI) allows generating double mutants and measuring condition‐dependent genetic interactions en masse. Application of BFG‐GI to DNA repair genes reveals a new function for the Shu complex.
Synopsis
A new method, Barcode Fusion Genetics to Map Genetic Interactions (BFG‐GI) allows generating double mutants and measuring condition‐dependent genetic interactions en masse. Application of BFG‐GI to DNA repair genes reveals a new function for the Shu complex.
BFG‐GI involves generating double‐mutant‐specific fused barcodes, enabling to measure the abundance of double mutants en masse by next generation sequencing.
Once a double mutant BFG‐GI pool has been generated genetic interactions can be tested in new growth conditions.
BFG‐GI is applied to 26 genes related to DNA damage repair in nine different conditions, including seven DNA‐damaging agents.
A novel relationship is reported between the Shu complex and the checkpoint protein kinase Rad53.
Mol Syst Biol. (2018) 14: e7985
- Received September 7, 2017.
- Revision received April 30, 2018.
- Accepted May 2, 2018.
- © 2018 The Authors. Published under the terms of the CC BY 4.0 license
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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