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      Phloem iron remodels root development in response to ammonium as the major nitrogen source

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          Abstract

          Plants use nitrate and ammonium as major nitrogen (N) sources, each affecting root development through different mechanisms. However, the exact signaling pathways involved in root development are poorly understood. Here, we show that, in Arabidopsis thaliana, either disruption of the cell wall-localized ferroxidase LPR2 or a decrease in iron supplementation efficiently alleviates the growth inhibition of primary roots in response to NH 4 + as the N source. Further study revealed that, compared with nitrate, ammonium led to excess iron accumulation in the apoplast of phloem in an LPR2-dependent manner. Such an aberrant iron accumulation subsequently causes massive callose deposition in the phloem from a resulting burst of reactive oxygen species, which impairs the function of the phloem. Therefore, ammonium attenuates primary root development by insufficiently allocating sucrose to the growth zone. Our results link phloem iron to root morphology in response to environmental cues.

          Abstract

          Ammonium affects plant root development through different mechanisms than nitrate. Here, the authors show that the Arabidopsis cell wall-localized ferroxidase LPR2 is required to attenuate root growth in response to ammonium.

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

            High-throughput sequencing of messenger RNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate, and flexible software to reduce the raw read data to comprehensible results. HISAT, StringTie, and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample, and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol’s execution time depends on the computing resources, but typically takes under 45 minutes of computer time. Pertea et al. describe a protocol to analyze RNA-seq data using HISAT, StringTie, and Ballgown (the “new Tuxedo” package). The protocol can be used for assembly of transcripts, quantification of gene expression levels and differential expression analysis.
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              SignalP 5.0 improves signal peptide predictions using deep neural networks

              Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.
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                Author and article information

                Contributors
                jincw@zju.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                28 January 2022
                28 January 2022
                2022
                : 13
                : 561
                Affiliations
                [1 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, State Key Laboratory of Plant Physiology and Biochemistry, College of Natural Resources and Environmental Science, , Zhejiang University, ; Hangzhou, 310058 China
                [2 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, State Key Laboratory of Plant Physiology and Biochemistry, College of Life Science, , Zhejiang University, ; Hangzhou, 310058 China
                Author information
                http://orcid.org/0000-0001-7123-1306
                http://orcid.org/0000-0001-8138-1295
                http://orcid.org/0000-0002-3590-1330
                http://orcid.org/0000-0002-7825-7610
                http://orcid.org/0000-0002-9101-3178
                http://orcid.org/0000-0003-4099-9229
                http://orcid.org/0000-0002-3777-3651
                http://orcid.org/0000-0002-3336-8165
                http://orcid.org/0000-0003-0896-8596
                Article
                28261
                10.1038/s41467-022-28261-4
                8799741
                35091578
                55a71300-7314-4c6e-831d-0a7303367447
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 June 2021
                : 10 January 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 32100232
                Award ID: 31670258
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100004731, Natural Science Foundation of Zhejiang Province (Zhejiang Provincial Natural Science Foundation);
                Award ID: LZ21D010001
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                plant physiology,abiotic,plant development
                Uncategorized
                plant physiology, abiotic, plant development

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