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      Proximity labeling of protein complexes and cell-type-specific organellar proteomes in Arabidopsis enabled by TurboID

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          Abstract

          Defining specific protein interactions and spatially or temporally restricted local proteomes improves our understanding of all cellular processes, but obtaining such data is challenging, especially for rare proteins, cell types, or events. Proximity labeling enables discovery of protein neighborhoods defining functional complexes and/or organellar protein compositions. Recent technological improvements, namely two highly active biotin ligase variants (TurboID and miniTurbo), allowed us to address two challenging questions in plants: (1) what are in vivo partners of a low abundant key developmental transcription factor and (2) what is the nuclear proteome of a rare cell type? Proteins identified with FAMA-TurboID include known interactors of this stomatal transcription factor and novel proteins that could facilitate its activator and repressor functions. Directing TurboID to stomatal nuclei enabled purification of cell type- and subcellular compartment-specific proteins. Broad tests of TurboID and miniTurbo in Arabidopsis and Nicotiana benthamiana and versatile vectors enable customization by plant researchers.

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          Cells contain thousands of different proteins that work together to control processes essential for life. To fully understand how these processes work it is important to know which proteins interact with each other, and which proteins are present at specific times or in certain cellular locations. Investigating this is particularly difficult if the proteins of interest are rare, either because they are present only at low levels or because they are unique to a particular type of cell.

          One such protein known as FAMA is only found in young guard cells in plants. Guard cells are rare cells that surround pores on the surface of leaves. They help open or close the pores to allow carbon dioxide and water in and out of the plant. Inside these cells, FAMA regulates the activity of genes in the nucleus, the compartment in the cell that houses the plant’s DNA.

          Two recently developed molecular biology tools, called TurboID and miniTurbo, allow researchers to identify proteins that are in close contact with a protein of interest or are present at a specific place inside living animal cells. These tools use a modified enzyme to add a small chemical tag to proteins that are close to it, or anything to which it is anchored. Mair et al. adapted these tools for use in plants and tested their utility in two species that are commonly used in research: a tobacco relative called Nicotiana benthamiana, and the thale cress Arabidopsis thaliana.

          Their experiments showed that TurboID and miniTurbo can be used to tag proteins in different types of plant cells and organs, as well as at different stages of the plants’ lives. To test whether the tools are suitable for identifying partners of rare proteins, Mair et al. used FAMA as their protein of interest. Using TurboID, they detected several proteins in close proximity to FAMA, including some that FAMA was not previously known to interact with. Mair et al. also found that TurboID could identify a number of proteins that were present in the nuclei of guard cells. This shows that the tool can be used to detect proteins in sub-compartments of rare plant cell types.

          Taken together, these findings show that TurboID and miniTurbo may be customized to study plant protein interactions and to explore local protein ‘neighborhoods’, even for rare proteins or specific cell types. To enable other plant biology researchers to easily access the TurboID and miniTurbo toolset developed in this work, it has been added to the non-profit molecular biology repository Addgene.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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              REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

              Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret. REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                19 September 2019
                2019
                : 8
                : e47864
                Affiliations
                [1 ]deptDepartment of Biology Stanford University StanfordUnited States
                [2 ]Howard Hughes Medical Institute Chevy ChaseUnited States
                [3 ]deptDepartment of Plant Biology Carnegie Institution for Science StanfordUnited States
                [4 ]deptDepartment of Chemistry Massachusetts Institute of Technology CambridgeUnited States
                [5 ]deptDepartment of Genetics Stanford University StanfordUnited States
                [6 ]deptDepartment of Chemistry Stanford University StanfordUnited States
                [7 ]Chan Zuckerberg Biohub San FranciscoUnited States
                The Sainsbury Laboratory United Kingdom
                University of Lausanne Switzerland
                The Sainsbury Laboratory United Kingdom
                Author information
                https://orcid.org/0000-0002-2492-4318
                https://orcid.org/0000-0002-6741-9506
                https://orcid.org/0000-0002-8277-5226
                https://orcid.org/0000-0003-0873-3543
                Article
                47864
                10.7554/eLife.47864
                6791687
                31535972
                38e0e313-8f1a-4dbd-ba0e-b784572a9e51
                © 2019, Mair et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 22 April 2019
                : 15 September 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100002428, Austrian Science Fund;
                Award ID: J4019-B29
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: RO1-CA186568
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100005843, Carnegie Institution of Washington;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006919, Massachusetts Institute of Technology;
                Award ID: Lester Wolfe Fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006919, Massachusetts Institute of Technology;
                Award ID: Dow Graduate Research Fellowship
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Tools and Resources
                Plant Biology
                Custom metadata
                Proximity-labeling using engineered biotin ligases TurboID and miniTurbo enables detection of cell-type-specific and low abundance protein complexes and subcellular proteomes in Arabidopsis and other plants.

                Life sciences
                proximity labeling,nuclear proteome,guard cell,biotin ligase,fama,n. benthamiana,a. thaliana
                Life sciences
                proximity labeling, nuclear proteome, guard cell, biotin ligase, fama, n. benthamiana, a. thaliana

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