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      Integrated Multi-omic Framework of the Plant Response to Jasmonic Acid

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          Abstract

          Understanding the systems-level actions of transcriptional responses to hormones provides insight into how the genome is reprogrammed in response to environmental stimuli. Here, we investigate the signaling pathway of the hormone jasmonic acid (JA), which controls a plethora of critically important processes in plants and is orchestrated by the transcription factor (TF) MYC2 and its closest relatives in Arabidopsis thaliana. We generated an integrated framework of the response to JA that spans from the activity of master and secondary-regulatory TFs, through gene expression outputs and alternative splicing to protein abundance changes, protein phosphorylation and chromatin remodeling. We integrated time series transcriptome analysis with (phospho)proteomic data to reconstruct gene regulatory network models. These enable us to predict previously unknown points of crosstalk from JA to other signaling pathways and to identify new components of the JA regulatory mechanism, which we validated through targeted mutant analysis. These results provide a comprehensive understanding of how a plant hormone remodels cellular functions and plant behavior, the general principles of which provide a framework for analysis of cross-regulation between other hormone and stress signaling pathways.

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          Most cited references70

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          Genome-wide insertional mutagenesis of Arabidopsis thaliana.

          J Alonso (2003)
          Over 225,000 independent Agrobacterium transferred DNA (T-DNA) insertion events in the genome of the reference plant Arabidopsis thaliana have been created that represent near saturation of the gene space. The precise locations were determined for more than 88,000 T-DNA insertions, which resulted in the identification of mutations in more than 21,700 of the approximately 29,454 predicted Arabidopsis genes. Genome-wide analysis of the distribution of integration events revealed the existence of a large integration site bias at both the chromosome and gene levels. Insertion mutations were identified in genes that are regulated in response to the plant hormone ethylene.
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            JAZ repressor proteins are targets of the SCF(COI1) complex during jasmonate signalling.

            Jasmonate and related signalling compounds have a crucial role in both host immunity and development in plants, but the molecular details of the signalling mechanism are poorly understood. Here we identify members of the jasmonate ZIM-domain (JAZ) protein family as key regulators of jasmonate signalling. JAZ1 protein acts to repress transcription of jasmonate-responsive genes. Jasmonate treatment causes JAZ1 degradation and this degradation is dependent on activities of the SCF(COI1) ubiquitin ligase and the 26S proteasome. Furthermore, the jasmonoyl-isoleucine (JA-Ile) conjugate, but not other jasmonate-derivatives such as jasmonate, 12-oxo-phytodienoic acid, or methyl-jasmonate, promotes physical interaction between COI1 and JAZ1 proteins in the absence of other plant proteins. Our results suggest a model in which jasmonate ligands promote the binding of the SCF(COI1) ubiquitin ligase to and subsequent degradation of the JAZ1 repressor protein, and implicate the SCF(COI1)-JAZ1 protein complex as a site of perception of the plant hormone JA-Ile.
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              STEM: a tool for the analysis of short time series gene expression data

              Background Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. Results We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. Conclusion The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at .
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                Author and article information

                Journal
                101651677
                43556
                Nat Plants
                Nat Plants
                Nature plants
                2055-0278
                6 February 2020
                13 March 2020
                March 2020
                13 September 2020
                : 6
                : 3
                : 290-302
                Affiliations
                [1 ]Plant Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
                [2 ]Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
                [3 ]Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
                [4 ]Centre for AgriBioscience, Department of Animal, Plant and Soil Sciences, School of Life Sciences, La Trobe University, Melbourne, VIC 3086, Australia
                [5 ]Australian Research Council Industrial Transformation Research Hub for Medicinal Agriculture, Centre for AgriBioscience, La Trobe University, Bundoora, VIC 3086, Australia
                [6 ]Plant Pathology and Microbiology, Iowa State University, Ames, IA 50011, USA
                [7 ]Present address: School of Natural Sciences, University of California Merced, Merced, CA 95343, USA
                [8 ]Present address: Department of Chemical and Environmental Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
                [9 ]Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
                [10 ]Department of Plant Molecular Genetics, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), 28049 Madrid, Spain
                [11 ]These authors contributed equally
                Author notes

                Contributions: M.Z., M.G.L., R.S. and J.R.E. designed the research. M.Z., M.G.L., A.E.L. and B.J. performed the phenotype screening. M.Z., M.G.L. and J.P.S.G. carried out the RNA-seq and ChIP-seq experiments. M.G.L., E.H. and J.P.S.G. performed the cloning and generation of transgenic constructs. M.G.L., J.R.N., H.C, M.Z. and L.Y. analyzed the sequencing data and performed bioinformatics analyses. A.B. carried out DAP-seq experiments. N.M.C. and J.W.W. analyzed the proteome and phosphoproteome data. N.M.C., J.W.W., A.W. and Z. B-J. performed regulatory network analyses. M.Z., M.G.L and J.R.E. prepared the figures and wrote the manuscript.

                [* ]Authors for correspondence: Joseph R. Ecker ( ecker@ 123456salk.edu ), Mathew G. Lewsey ( m.lewsey@ 123456latrobe.edu.au )
                Article
                HHMIMS1551701
                10.1038/s41477-020-0605-7
                7094030
                32170290
                db3f6810-ce4c-4009-be98-4b589ab2bcc2

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