Methods & Resources
- 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.
- A framework for exhaustively mapping functional missense variants
- Jochen Weile1,2,3,4,†,
- Song Sun1,2,3,4,5,†,
- Atina G Cote1,2,3,
- Jennifer Knapp1,2,3,
- Marta Verby1,2,3,
- Joseph C Mellor2,6,
- Yingzhou Wu1,2,3,4,
- Carles Pons7,
- Cassandra Wong1,2,
- Natascha van Lieshout1,
- Fan Yang1,2,3,4,
- Murat Tasan1,2,3,4,
- Guihong Tan2,3,
- Shan Yang8,
- Douglas M Fowler9,
- Robert Nussbaum8,
- Jesse D Bloom10,
- Marc Vidal11,12,
- David E Hill11,
- Patrick Aloy7,13 and
- Frederick P Roth (fritz.roth{at}utoronto.ca)*,1,2,3,4,14
- 1Lunenfeld‐Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
- 2The Donnelly Centre, University of Toronto, Toronto, ON, Canada
- 3Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- 4Department of Computer Science, University of Toronto, Toronto, ON, Canada
- 5Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
- 6SeqWell Inc, Boston, MA, USA
- 7Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
- 8Invitae Corp., San Francisco, CA, USA
- 9Department of Genome Sciences, University of Washington, Seattle, WA, USA
- 10Fred Hutchinson Research Center, Seattle, WA, USA
- 11Center for Cancer Systems Biology (CCSB), Dana‐Farber Cancer Institute, Boston, MA, USA
- 12Department of Genetics, Harvard Medical School, Boston, MA, USA
- 13Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
- 14Canadian Institute for Advanced Research, Toronto, ON, Canada
- ↵*Corresponding author. Tel: +1 416 946 5130; E‐mail: fritz.roth{at}utoronto.ca
↵† These authors contributed equally to this work
A new framework combining random codon‐mutagenesis and multiplexed functional variation assays with computational imputation, allows the comprehensive identification of functional missense variation. The approach is applied to identify pathogenic variation in six human genes.
Synopsis
A new framework combining random codon‐mutagenesis and multiplexed functional variation assays with computational imputation, allows the comprehensive identification of functional missense variation. The approach is applied to identify pathogenic variation in six human genes.
A modular deep mutational scanning (DMS) framework combines random codon‐mutagenesis and multiplexed functional variation assays with computational imputation and refinement.
The framework is applied to four human proteins corresponding to six human genes and generates comprehensive functional variation maps covering > 13,000 missense variants.
These maps confidently identify pathogenic variation.
DMS is a promising approach for generating exhaustive maps of functional variation covering all human genes.
Mol Syst Biol. (2017) 13: 957
- Received August 9, 2017.
- Revision received November 15, 2017.
- Accepted November 18, 2017.
- © 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.
- Natural language processing: put your model where your mouth is
Molecular mechanisms are often described using “word models”—phrases intended to capture the interactions in a biological process. In their recent work, Sorger and colleagues (Gyori et al, 2017) provide a framework for converting word models into computational structures that can be simulated and compared to experimental data. By codifying word‐based descriptions of molecular phenomena, scientific communities can better evaluate, compare, and share mechanistic insights.
See also: BM Gyori et al (November 2017)
Molecular mechanisms are often described using “word models”, phrases intended to capture the interactions in a biological process. In their recent work, Sorger and colleagues (Gyori et al, 2017) provide a framework for converting word models into executable models.
Mol Syst Biol. (2017) 13: 958
- © 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.
- Screening drug effects in patient‐derived cancer cells links organoid responses to genome alterations
- Julia Jabs1,2,3,
- Franziska M Zickgraf4,5,
- Jeongbin Park1,3,
- Steve Wagner4,5,
- Xiaoqi Jiang6,
- Katharina Jechow1,2,
- Kortine Kleinheinz1,2,3,
- Umut H Toprak1,
- Marc A Schneider7,
- Michael Meister7,
- Saskia Spaich8,
- Marc Sütterlin8,
- Matthias Schlesner1,
- Andreas Trumpp4,5,9,
- Martin Sprick4,5,9,
- Roland Eils (r.eils{at}dkfz.de)*,1,2,3,10 and
- Christian Conrad (c.conrad{at}dkfz.de)*,1,2
- 1Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- 2Center for Quantitative Analysis of Molecular and Cellular Biosystems (BioQuant), University of Heidelberg, Heidelberg, Germany
- 3Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg, Germany
- 4Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI‐STEM) gGmbH, Heidelberg, Germany
- 5Division of Stem Cells and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
- 6Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- 7Thoraxklinik at Heidelberg University Hospital, Member of the German Center for Lung Research (DZL), Heidelberg, Germany
- 8Department of Gynaecology and Obstetrics, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
- 9German Cancer Consortium, Heidelberg, Germany
- 10Heidelberg Center for Personalized Oncology, DKFZ‐HIPO, DKFZ, Heidelberg, Germany
- ↵*
Corresponding author. Tel: +49 6221 42 3600; E‐mail: r.eils{at}dkfz.de
Corresponding author. Tel: +49 6221 54 51304; E‐mail: c.conrad{at}dkfz.de
DeathPro, an automated microscopy‐based assay resolves cell death and proliferation inhibition in 2D and 3D cultures. Drug screens using DeathPro provide insights into the impact of culture systems on drug effects and their links to genomic features.
Synopsis
DeathPro, an automated microscopy‐based assay resolves cell death and proliferation inhibition in 2D and 3D cultures. Drug screens using DeathPro provide insights into the impact of culture systems on drug effects and their links to genomic features.
DeathPro resolves cytotoxic and cytostatic effects in drug screens with patient‐derived ovarian and lung cancer cells, organoids and co‐cultures with fibroblasts.
Drug responses in cancer organoids are more diverse than in 2D cultured cells.
Cytostatic drugs depend on culture systems, cytotoxic effects are independent of the culture format.
Genomic analysis of cancer patients links DNA repair deficiency to drug sensitivity in organoids.
Mol Syst Biol. (2017) 13: 955
- Received April 18, 2017.
- Revision received October 26, 2017.
- Accepted October 27, 2017.
- © 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.
- From word models to executable models of signaling networks using automated assembly
- Benjamin M Gyori1,†,
- John A Bachman1,†,
- Kartik Subramanian1,
- Jeremy L Muhlich1,
- Lucian Galescu2 and
- Peter K Sorger (peter_sorger{at}hms.harvard.edu)*,1
- 1Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- 2Institute for Human and Machine Cognition, Pensacola, FL, USA
- ↵*Corresponding author. Tel: +1 617 432 6901/6902; E‐mail: peter_sorger{at}hms.harvard.edu
↵† These authors contributed equally to this work
“Integrated Network and Dynamical Reasoning Assembler” (INDRA) uses natural language processing systems to read descriptions of molecular mechanisms and assembles them into executable models.
Synopsis
“Integrated Network and Dynamical Reasoning Assembler” (INDRA) uses natural language processing systems to read descriptions of molecular mechanisms and assembles them into executable models.
INDRA decouples the curation of knowledge as word models from model implementation.
INDRA is connected to multiple natural language processing systems and can draw on information from curated databases.
INDRA can assemble dynamical models in rule‐based and reaction network formalisms, as well as Boolean networks and visualization formats.
INDRA is used to build models of p53 dynamics, resistance to targeted inhibitors of BRAF in melanoma, and the Ras signaling pathway from natural language.
Mol Syst Biol. (2017) 13: 954
- Received March 23, 2017.
- Revision received October 27, 2017.
- Accepted October 27, 2017.
- © 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.
- Lysine acetylome profiling uncovers novel histone deacetylase substrate proteins in Arabidopsis
- Markus Hartl1,2,3,†,
- Magdalena Füßl1,2,4,†,
- Paul J Boersema5,9,
- Jan‐Oliver Jost6,10,
- Katharina Kramer1,
- Ahmet Bakirbas1,4,11,
- Julia Sindlinger6,
- Magdalena Plöchinger2,
- Dario Leister2,
- Glen Uhrig7,
- Greg BG Moorhead7,
- Jürgen Cox5,
- Michael E Salvucci8,
- Dirk Schwarzer6,
- Matthias Mann5 and
- Iris Finkemeier (iris.finkemeier{at}uni-muenster.de)*,1,2,4
- 1Plant Proteomics, Max Planck Institute for Plant Breeding Research, Cologne, Germany
- 2Plant Molecular Biology, Department Biology I, Ludwig‐Maximilians‐University Munich, Martinsried, Germany
- 3Mass Spectrometry Facility, Max F. Perutz Laboratories (MFPL), Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
- 4Plant Physiology, Institute of Plant Biology and Biotechnology, University of Muenster, Muenster, Germany
- 5Proteomics and Signal Transduction, Max‐Planck Institute of Biochemistry, Martinsried, Germany
- 6Interfaculty Institute of Biochemistry, University of Tübingen, Tübingen, Germany
- 7Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
- 8US Department of Agriculture, Agricultural Research Service, Arid‐Land Agricultural Research Center, Maricopa, AZ, USA
- 9Present Address: Department of Biology, Institute of Biochemistry, ETH Zurich, Zurich, Switzerland
- 10Present Address: Leibniz‐Forschungsinstitut für Molekulare Pharmakologie im Forschungsverbund Berlin e.V. (FMP), Berlin, Germany
- 11Present Address: Plant Biology Graduate Program University of Massachusetts Amherst, Amherst, USA
- ↵*Corresponding author. Tel: +49 251 8323805; E‐mail: iris.finkemeier{at}uni-muenster.de
↵† These authors contributed equally to this work
A comprehensive lysine acetylome profiling identifies new potential substrate proteins of the Arabidopsis RPD3/HDA1‐KDACs with various subcellular localizations. HDA14 is identified as the first RPD3/HDA1‐KDAC, which is active in organelles.
Synopsis
A comprehensive lysine acetylome profiling identifies new potential substrate proteins of the Arabidopsis RPD3/HDA1‐KDACs with various subcellular localizations. HDA14 is identified as the first RPD3/HDA1‐KDAC, which is active in organelles.
2,152 lysine acetylation sites are identified on 1,022 Arabidopsis protein groups.
Analyses with deacetylase inhibitors identify potential target sites of RPD3/HDA1 class‐KDACs of Arabidopsis.
HDA14 is found to be active in Arabidopsis chloroplasts and RuBisCo activase (RCA) Kac‐438 is identified as one of the potential HDA14 substrates.
Lysine acetylation on RCA‐K438 decreases the enzyme's ADP‐sensitivity, which is important for RCA inhibition under low‐light conditions.
Mol Syst Biol. (2017) 13: 949
- Received June 16, 2017.
- Revision received September 22, 2017.
- Accepted September 25, 2017.
- © 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.
- In situ genotyping of a pooled strain library after characterizing complex phenotypes
- Michael J Lawson1,†,
- Daniel Camsund1,†,
- Jimmy Larsson1,
- Özden Baltekin1,
- David Fange1 and
- Johan Elf (johan.elf{at}icm.uu.se)*,1
- 1Department of Cell and Molecular Biology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- ↵*Corresponding author. Tel: +46 18 4714678; E‐mail: johan.elf{at}icm.uu.se
↵† These authors contributed equally to this work
The DuMPLING approach extends the use of advanced live‐cell microscopy to libraries of pool generated genetically diverse strains. The method is illustrated by tracking strains over six generations and precisely quantifying gene expression at the single molecule level.
Synopsis
The DuMPLING approach extends the use of advanced live‐cell microscopy to libraries of pool generated genetically diverse strains. The method is illustrated by tracking strains over six generations and precisely quantifying gene expression at the single molecule level.
A library of plasmids is pool synthesized, expressing a specific RNA barcode and an associated CRISPR interference guide RNA against different chromosomal genes.
Library phenotyping is demonstrated using time‐lapse single‐molecule fluorescence microscopy of bacteria growing for many generations in a microfluidic device.
The strains are genotyped in situ by six rounds of sequential fluorescent in situ hybridization probing in two colors.
Mol Syst Biol. (2017) 13: 947
- Received August 23, 2017.
- Revision received September 11, 2017.
- Accepted September 22, 2017.
- © 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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