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      Expanded roles and divergent regulation of FAMA in Brachypodium and Arabidopsis stomatal development

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          Abstract

          Stomata, cellular valves found on the surfaces of aerial plant tissues, present a paradigm for studying cell fate and patterning in plants. A highly conserved core set of related basic helix-loop-helix (bHLH) transcription factors regulates stomatal development across diverse species. We characterized BdFAMA in the temperate grass Brachypodium distachyon and found this late-acting transcription factor was necessary and sufficient for specifying stomatal guard cell fate, and unexpectedly, could also induce the recruitment of subsidiary cells in the absence of its paralogue, BdMUTE. The overlap in function is paralleled by an overlap in expression pattern and by unique regulatory relationships between BdMUTE and BdFAMA. To better appreciate the relationships among the Brachypodium stomatal bHLHs, we used in vivo proteomics in developing leaves and found evidence for multiple shared interaction partners. We reexamined the roles of these genes in Arabidopsis thaliana by testing genetic sufficiency within and across species, and found that while BdFAMA and AtFAMA can rescue stomatal production in Arabidopsis fama and mute mutants, only AtFAMA can specify Brassica-specific myrosin idioblasts. Taken together, our findings refine the current models of stomatal bHLH function and regulatory feedback among paralogues within grasses as well as across the monocot/dicot divide.

          Abstract

          Comparative expression and functional analysis of the stomatal differentiation gene FAMA in Brachypodium and Arabidopsis reveals variation in cell fate acquisition and maintenance.

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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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            The Perseus computational platform for comprehensive analysis of (prote)omics data.

            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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              The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.

              MaxQuant is one of the most frequently used platforms for mass-spectrometry (MS)-based proteomics data analysis. Since its first release in 2008, it has grown substantially in functionality and can be used in conjunction with more MS platforms. Here we present an updated protocol covering the most important basic computational workflows, including those designed for quantitative label-free proteomics, MS1-level labeling and isobaric labeling techniques. This protocol presents a complete description of the parameters used in MaxQuant, as well as of the configuration options of its integrated search engine, Andromeda. This protocol update describes an adaptation of an existing protocol that substantially modifies the technique. Important concepts of shotgun proteomics and their implementation in MaxQuant are briefly reviewed, including different quantification strategies and the control of false-discovery rates (FDRs), as well as the analysis of post-translational modifications (PTMs). The MaxQuant output tables, which contain information about quantification of proteins and PTMs, are explained in detail. Furthermore, we provide a short version of the workflow that is applicable to data sets with simple and standard experimental designs. The MaxQuant algorithms are efficiently parallelized on multiple processors and scale well from desktop computers to servers with many cores. The software is written in C# and is freely available at http://www.maxquant.org.
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                Author and article information

                Contributors
                Journal
                Plant Cell
                Plant Cell
                plcell
                The Plant Cell
                Oxford University Press (US )
                1040-4651
                1532-298X
                February 2023
                28 November 2022
                28 November 2022
                : 35
                : 2
                : 756-775
                Affiliations
                Department of Genetics, Stanford School of Medicine , Stanford, California 94305, USA
                Biology Department, Stanford University , 371 Jane Stanford Way, Stanford, California 94305, USA
                Biology Department, Stanford University , 371 Jane Stanford Way, Stanford, California 94305, USA
                Howard Hughes Medical Institute, Stanford University , 371 Jane Stanford Way, Stanford, California 94305, USA
                Department of Plant Biology, Carnegie Institution for Science , 260 Panama St., Stanford, California 94305, USA
                Biology Department, Stanford University , 371 Jane Stanford Way, Stanford, California 94305, USA
                Biology Department, Stanford University , 371 Jane Stanford Way, Stanford, California 94305, USA
                Howard Hughes Medical Institute, Stanford University , 371 Jane Stanford Way, Stanford, California 94305, USA
                Author notes
                Author for correspondence: dbergmann@ 123456stanford.edu

                Present address: Department of Plant Biology, University of California, Davis, California, USA.

                Present address: Institute of Plant Sciences, University of Bern, Switzerland.

                The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors ( https://academic.oup.com/plcell) is: Dominique Bergmann ( dbergmann@ 123456stanford.edu ).

                Conflict of interest statement. None declared.

                Author information
                https://orcid.org/0000-0003-3960-332X
                https://orcid.org/0000-0002-9871-4954
                https://orcid.org/0000-0002-2492-4318
                https://orcid.org/0000-0002-6741-9506
                https://orcid.org/0000-0003-3179-9372
                https://orcid.org/0000-0003-0873-3543
                Article
                koac341
                10.1093/plcell/koac341
                9940870
                36440974
                94a8087c-a20f-49cb-a5dd-2e370cc56bfd
                © The Author(s) 2022. Published by Oxford University Press on behalf of American Society of Plant Biologists.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 July 2022
                : 24 November 2022
                : 25 January 2023
                Page count
                Pages: 20
                Funding
                Funded by: NIHGRI;
                Award ID: 5T32HG000044
                Funded by: Stanford University School of Medicine, doi 10.13039/100006521;
                Funded by: Swiss National Science Foundation, doi 10.13039/501100001711;
                Award ID: P2ZHP3_151598
                Funded by: Gordon and Betty Moore Foundation via Life Science Research Foundation;
                Award ID: GMBF2550.05
                Funded by: Carnegie Endowment Fund;
                Funded by: HHMI, doi 10.13039/100000011;
                Categories
                Research Article
                AcademicSubjects/SCI01270
                AcademicSubjects/SCI01280
                AcademicSubjects/SCI02286
                AcademicSubjects/SCI02287
                AcademicSubjects/SCI02288

                Plant science & Botany
                Plant science & Botany

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