Methods & Resources
- 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.
- A unified approach for quantifying and interpreting DNA shape readout by transcription factors
- H Tomas Rube1,
- Chaitanya Rastogi1,2,
- Judith F Kribelbauer1,3 and
- Harmen J Bussemaker (hjb2004{at}columbia.edu)*,1,3
- 1Department of Biological Sciences, Columbia University, New York, NY, USA
- 2Program in Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA
- 3Department of Systems Biology, Columbia University Medical Center, New York, NY, USA
- ↵*Corresponding author. Tel: +1 212 854 9932; E‐mail: hjb2004{at}columbia.edu
The sequence dependence of DNA shape parameters is analyzed to clarify the relationship between transcription factor binding specificity, DNA shape readout and scoring matrices. Statistical methods for identifying DNA shape readout are developed.
Synopsis
The sequence dependence of DNA shape parameters is analyzed to clarify the relationship between transcription factor binding specificity, DNA shape readout and scoring matrices. Statistical methods for identifying DNA shape readout are developed.
A unified mathematical representation of the DNA sequence dependence of shape and transcription factor (TF) binding is proposed for analyzing shape readout.
Linear models based on mononucleotide features alone account for 60–70% of the variation of DNA shape.
Most of the residual variance can be explained by adding dinucleotide features as sequence‐to‐shape predictors to the model.
A post hoc analysis method is presented for interpreting any mechanism‐agnostic protein‐DNA binding model in terms of shape readout.
Mol Syst Biol. (2017) 14: e7902
- Received July 28, 2017.
- Revision received January 26, 2018.
- Accepted January 31, 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.
- Large‐scale image‐based profiling of single‐cell phenotypes in arrayed CRISPR‐Cas9 gene perturbation screens
- Reinoud de Groot1,
- Joel Lüthi1,2,
- Helen Lindsay1,
- René Holtackers1 and
- Lucas Pelkmans (lucas.pelkmans{at}imls.uzh.ch)*,1
- 1Institute of Molecular Life Sciences, University of Zürich, Zürich, Switzerland
- 2Systems Biology PhD program, Life Science Zürich Graduate School ETH Zürich and University of Zürich, Zürich, Switzerland
- ↵*Corresponding author. Tel: +41 44 63 53 123; E‐mail: lucas.pelkmans{at}imls.uzh.ch
The CRISPR‐Cas9 system is applied in high‐content image‐based gene perturbation screens. Computational classifiers trained between wild‐type cells and cells expressing Cas9 and gRNA enable the profiling of multivariate single cell phenotypes.
Synopsis
The CRISPR‐Cas9 system is applied in high‐content image‐based gene perturbation screens. Computational classifiers trained between wild‐type cells and cells expressing Cas9 and gRNA enable the profiling of multivariate single cell phenotypes.
CRISPR‐Cas9 mediated gene perturbation by transient transfection of a single targeting plasmid is combined with large‐scale, image‐based profiling.
Methods are developed for the construction of arrayed CRISPR‐Cas9 screening libraries.
Single cell phenotypes are profiled by training computational classifiers between transfected and non‐transfected cells from the same well.
Profiling of a marker of the nuclear pore complex identifies several classes of phenotypic perturbations.
Mol Syst Biol. (2018) 14: e8064
- Received October 23, 2017.
- Revision received December 18, 2017.
- Accepted December 21, 2017.
- © 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.
- Assigning function to natural allelic variation via dynamic modeling of gene network induction
- Magali Richard (magali.richard{at}univ-grenoble-alpes.fr)*,1,2,
- Florent Chuffart1,
- Hélène Duplus‐Bottin1,
- Fanny Pouyet1,
- Martin Spichty1,
- Etienne Fulcrand1,
- Marianne Entrevan1,
- Audrey Barthelaix1,
- Michael Springer3,
- Daniel Jost (daniel.jost{at}univ-grenoble-alpes.fr)*,2 and
- Gaël Yvert (gael.yvert{at}ens-lyon.fr)*,1
- 1Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
- 2Univ. Grenoble Alpes, CNRS CHU Grenoble Alpes Grenoble INP TIMC‐IMAG, Grenoble, France
- 3Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- ↵*
Corresponding author. Tel: +33 4 56 52 00 68; E‐mail: magali.richard{at}univ-grenoble-alpes.fr
Corresponding author. Tel: +33 4 56 52 00 69; E‐mail: daniel.jost{at}univ-grenoble-alpes.fr
Corresponding author. Tel: +33 4 72 72 80 00; E‐mail: gael.yvert{at}ens-lyon.fr
An approach based on genotype‐specific gene regulatory network models is used to examine the functional consequences of yeast GAL3 sequence variants. This framework can be more generally applied to the mechanistic interpretation of genetic variants.
Synopsis
An approach based on genotype‐specific gene regulatory network models is used to examine the functional consequences of yeast GAL3 sequence variants. This framework can be more generally applied to the mechanistic interpretation of genetic variants.
The principle of the proposed approach is linking genetic variation to informative changes of parameter values of a regulatory network model.
Experimental analyses of the yeast GAL network shows that GAL3 natural variation is sufficient to convert a gradual response into a binary switch.
Dynamic network modeling successfully maps alleles to specific locations of the parameter space, allowing functional inference of DNA polymorphisms.
Mol Syst Biol. (2018) 14: e7803
- Received June 7, 2017.
- Revision received December 15, 2017.
- Accepted December 18, 2017.
- © 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.
- Methods to drive systems biology forward
The development of new methodologies has driven the expansion of systems biology over the past decades. Technological breakthroughs in sequencing, in quantitative proteomics, in single‐cell measurements, to name only a few, have each opened up whole new fields of research. To highlight the importance of new experimental and computational methodologies in enabling novel biological discoveries, we are pleased to announce the introduction of a new Methods section in Molecular Systems Biology (http://msb.embopress.org/authorguide#methodsguide).
We are excited to announce our new Methods section, a “new home” for papers focused on new methodological advances in systems and synthetic biology. We look forward to publishing method papers that put forward new concepts and innovative approaches addressing important biological questions.
Mol Syst Biol. (2017) 13: 996
- © 2017 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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