Methods & Resources
- Dynamic control of endogenous metabolism with combinatorial logic circuits
- Felix Moser1,
- Amin Espah Borujeni1,
- Amar N. Ghodasara1,
- Ewen Cameron1,
- Yongjin Park1 and
- Christopher A. Voigt (cavoigt{at}gmail.com)*,1
- 1Department of Biological Engineering, Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA
- ↵*Corresponding author. Tel: +1 617 617 4851; E‐mail: cavoigt{at}gmail.com
Genetically encoded sensors responding to feedstock (glucose), growth conditions (oxygen), and byproduct accumulation (acetate) are designed and their response is measured during growth. Sensor integration by combinatorial logic circuits generates different temporal responses and can be used to control metabolism.
Synopsis
Genetically encoded sensors responding to feedstock (glucose), growth conditions (oxygen), and byproduct accumulation (acetate) are designed and their response is measured during growth. Sensor integration by combinatorial logic circuits generates different temporal responses and can be used to control metabolism.
Genetically encoded sensors for glucose and oxygen are built based on synthetic Escherichia coli promoters that respond to native regulators and optimized using oligo synthesis and high‐throughput screening.
As shown computationally, combinatorial logic circuits that implement different truth tables can integrate the three sensors to produce different dynamic responses during a growth experiment.
The circuit output can be used to quickly and strongly repress a target gene by combining CRISPRi (transcriptional control) and a targeted protease (post‐translational control).
Genetic circuits with CRISPRi/protease outputs were able to dynamically repress native target genes and regulate acetate metabolism.
Mol Syst Biol. (2018) 14: e8605
- Received September 10, 2018.
- Revision received October 25, 2018.
- Accepted October 30, 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.
- Deep scanning lysine metabolism in Escherichia coli
- Marcelo C Bassalo1,
- Andrew D Garst2,
- Alaksh Choudhury3,
- William C Grau4,
- Eun J Oh3,
- Eileen Spindler2,
- Tanya Lipscomb2 and
- Ryan T Gill (rygi0567{at}colorado.edu)*,2,3
- 1Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
- 2Inscripta, Inc., Boulder, CO, USA
- 3Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
- 4Department of Chemistry and Biochemistry, University of Colorado Boulder, Boulder, CO, USA
- ↵*Corresponding author. Tel: +1 303 492 2627; E‐mail: rygi0567{at}colorado.edu
Genotype—phenotype mapping is used to allow a high‐resolution interrogation of different routes that alter lysine metabolism and confer resistance to an antimetabolite analog. This approach narrows the search space down to specific targets to guide engineering efforts.
Synopsis
Genotype—phenotype mapping is used to allow a high‐resolution interrogation of different routes that alter lysine metabolism and confer resistance to an antimetabolite analog. This approach narrows the search space down to specific targets to guide engineering efforts.
19 genes affecting lysine metabolism are investigated for their contribution toward resistance to the antimetabolite AEC in E. coli, totaling 16,300 mutations in the pathway.
Loss‐of‐function mutations in the transporter are found to be the dominant selection winner.
CRISPR‐EnAbled Trackable genome Engineering (CREATE) allowed exploring beyond the dominant selection winner, identifying mutations in biosynthetic genes and regulatory proteins that confer resistance by overproduction of lysine.
Mol Syst Biol. (2018) 14: e8371
- Received April 10, 2018.
- Revision received October 26, 2018.
- Accepted October 30, 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 the O‐glycoproteome using site‐specific extraction of O‐linked glycopeptides (EXoO)
- ↵*Corresponding author. Tel: +1 410 502 8149: E‐mail: huizhang{at}jhu.edu
A new method (ExoO) is introduced to define site‐specific glycans for over 3,000 O‐linked glycosylation sites. It reveals conserved and new biology regarding the O‐linked glycoproteome and shows differential expression of O‐linked glycoproteins between human kidney and normal tissues.
Synopsis
A new method (ExoO) is introduced to define site‐specific glycans for over 3,000 O‐linked glycosylation sites. It reveals conserved and new biology regarding the O‐linked glycoproteome and shows differential expression of O‐linked glycoproteins between human kidney and normal tissues.
EXoO enables a large‐scale identification of O‐linked glycosylation sites and definition of their site‐specific glycans from complex samples.
Over 3,000 O‐linked glycosylation sites and their site‐specific glycans from over 1,000 glycoproteins are mapped using EXoO in this study.
EXoO is advantageous for locating O‐linked glycosylation sites and defining site‐specific glycans in mucin‐type glycoproteins.
EXoO reveals expression differences in the O‐linked glycoproteome of tumor and normal kidney tissues.
Mol Syst Biol. (2018) 14: e8486
- Received June 6, 2018.
- Revision received October 16, 2018.
- Accepted October 17, 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.
- 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.
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