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      Diffusion and bulk flow of amino acids mediate calcium waves in plants

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          Abstract

          In plants, a variety of stimuli trigger long-range calcium signals that travel rapidly along the vasculature to distal tissues via poorly understood mechanisms. Here, we use quantitative imaging and analysis to demonstrate that traveling calcium waves are mediated by diffusion and bulk flow of amino acid chemical messengers. We propose that wounding triggers release of amino acids that diffuse locally through the apoplast, activating the calcium-permeable channel GLUTAMATE RECEPTOR-LIKE 3.3 as they pass. Over long distances through the vasculature, the wound-triggered dynamics of a fluorescent tracer show that calcium waves are likely driven by bulk flow of a channel-activating chemical. We observed that multiple stimuli trigger calcium waves with similar dynamics, but calcium waves alone cannot initiate all systemic defense responses, suggesting that mobile chemical messengers are a core component of complex systemic signaling in plants.

          Abstract

          Abstract

          Calcium waves are likely driven by chemical messengers that trigger cellular responses as they travel passively through tissues.

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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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            Fitting Linear Mixed-Effects Models Usinglme4

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              Fitting Linear Mixed-Effects Models Using lme4

              Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: ConceptualizationRole: Methodology
                Role: InvestigationRole: ResourcesRole: ValidationRole: Writing - review & editing
                Role: ConceptualizationRole: InvestigationRole: ResourcesRole: ValidationRole: Writing - review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: ResourcesRole: SoftwareRole: Writing - review & editing
                Role: Formal analysisRole: MethodologyRole: SoftwareRole: Writing - review & editing
                Role: ConceptualizationRole: InvestigationRole: Methodology
                Role: MethodologyRole: Resources
                Role: ConceptualizationRole: Methodology
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: Writing - review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Supervision
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing - review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Journal
                Sci Adv
                Sci Adv
                sciadv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                October 2022
                21 October 2022
                : 8
                : 42
                : eabo6693
                Affiliations
                [ 1 ]Crop Genetics, John Innes Centre, Norwich Research Park, Norwich, UK.
                [ 2 ]Biochemistry and Metabolism, John Innes Centre, Norwich Research Park, Norwich, UK.
                [ 3 ]Computational and Systems Biology, John Innes Centre, Norwich Research Park, Norwich, UK.
                Author notes
                [* ]Corresponding author. Email: christine.faulkner@ 123456jic.ac.uk
                [†]

                Present address: Ludwig Institute for Cancer Research, Oxford University, Oxford, UK.

                [‡]

                Present address: Laboratory of Cell Biology, Wageningen University, Droevendaalsesteeg 1, Wageningen, Netherlands.

                [§]

                Present address: Graduate School of Biological Sciences, Nara Institute of Science and Technology, Ikoma 630-0192, Japan.

                Author information
                https://orcid.org/0000-0003-2615-5740
                https://orcid.org/0000-0002-2732-7543
                https://orcid.org/0000-0003-4997-7157
                https://orcid.org/0000-0003-1141-6135
                https://orcid.org/0000-0002-5175-9178
                https://orcid.org/0000-0003-0340-4548
                https://orcid.org/0000-0001-7479-8455
                https://orcid.org/0000-0003-0572-7607
                https://orcid.org/0000-0002-1452-9808
                https://orcid.org/0000-0003-1371-5606
                https://orcid.org/0000-0003-3080-2613
                https://orcid.org/0000-0003-3905-8077
                Article
                abo6693
                10.1126/sciadv.abo6693
                9586480
                36269836
                e7c15763-64a9-43cb-9a47-cd038ee3dbd7
                Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

                This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 February 2022
                : 01 September 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: 725459
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BBS/E/J/000PR9796
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: Doctoral Training Programme Studentship
                Funded by: FundRef http://dx.doi.org/10.13039/501100000324, Gatsby Charitable Foundation;
                Award ID: Studentship
                Funded by: FundRef http://dx.doi.org/10.13039/501100004034, John Innes Foundation;
                Award ID: Rotation Studentship
                Categories
                Research Article
                Biomedicine and Life Sciences
                SciAdv r-articles
                Plant Sciences
                Signal Transduction
                Plant Sciences
                Custom metadata
                Anne Suarez

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