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      ABI5–FLZ13 module transcriptionally represses growth-related genes to delay seed germination in response to ABA

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          Abstract

          The bZIP transcription factor ABSCISIC ACID INSENSITIVE5 (ABI5) is a master regulator of seed germination and post-germinative growth in response to abscisic acid (ABA), but the detailed molecular mechanism by which it represses plant growth remains unclear. In this study, we used proximity labeling to map the neighboring proteome of ABI5 and identified FCS-LIKE ZINC FINGER PROTEIN 13 (FLZ13) as a novel ABI5 interaction partner. Phenotypic analysis of flz13 mutants and FLZ13-overexpressing lines demonstrated that FLZ13 acts as a positive regulator of ABA signaling. Transcriptomic analysis revealed that both FLZ13 and ABI5 downregulate the expression of ABA-repressed and growth-related genes involved in chlorophyll biosynthesis, photosynthesis, and cell wall organization, thereby repressing seed germination and seedling establishment in response to ABA. Further genetic analysis showed that FLZ13 and ABI5 function together to regulate seed germination. Collectively, our findings reveal a previously uncharacterized transcriptional regulatory mechanism by which ABA mediates inhibition of seed germination and seedling establishment.

          Abstract

          FLZ13 physically interacts with ABI5 to downregulate the expression of ABA-repressed and growth-related genes, thereby inhibiting seed germination and seedling establishment in response to ABA.

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          Most cited references78

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          Da Huang, Brad T. Sherman, Richard A. Lempicki (2009)
          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            Yingyao Zhou, Bin Zhou, Lars Pache … (2019)
            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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              The Perseus computational platform for comprehensive analysis of (prote)omics data.

              Stefka Tyanova, Tikira Temu, Pavel Sinitcyn … (2016)
              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.
              Article 0 comments Cited 2408 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Chao Yang
                Ming Luo
                Ying Wang
                Caiji Gao
                Journal
                Journal ID (nlm-ta): Plant Commun
                Journal ID (iso-abbrev): Plant Commun
                Title: Plant Communications
                Publisher: Elsevier
                ISSN (Electronic): 2590-3462
                Publication date PMC-release: 09 June 2023
                Publication date Collection: 13 November 2023
                Publication date (Electronic): 09 June 2023
                Volume: 4
                Issue: 6
                Electronic Location Identifier: 100636
                Affiliations
                [1 ]Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement & Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
                [2 ]Guangdong Provincial Key Laboratory of Biotechnology for Plant Development, School of Life Sciences, South China Normal University (SCNU), Guangzhou 510631, China
                [3 ]School of Life Sciences, Centre for Cell and Developmental Biology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
                [4 ]Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas-Universidad Politécnica de Valencia, 46022 Valencia, Spain
                Author notes
                [∗ ]Corresponding author chaoyang@ 123456scbg.ac.cn
                [∗∗ ]Corresponding author luoming@ 123456scbg.ac.cn
                [∗∗∗ ]Corresponding author yingwang@ 123456scib.ac.cn
                [∗∗∗∗ ]Corresponding author gaocaiji@ 123456m.scnu.edu.cn
                Article
                Publisher Item ID: S2590-3462(23)00153-0 Publisher ID: 100636
                DOI: 10.1016/j.xplc.2023.100636
                PMC ID: 10721476
                PubMed ID: 37301981
                SO-VID: cf41bf1b-2bfa-4ce4-ab3f-3481ee4515ce
                Copyright © © 2023 The Author(s)
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 5 December 2022
                Date revision received : 5 May 2023
                Date accepted : 6 June 2023
                Categories
                Subject: Research Article

                Keywords: aba,abi5,flz,gene expression,seed germination
                Data availability:
                Keywords: aba, abi5, flz, gene expression, seed germination

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