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      Stabilization of dimeric PYR/PYL/RCAR family members relieves abscisic acid-induced inhibition of seed germination

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          Abstract

          Abscisic acid (ABA) is the primary preventing factor of seed germination, which is crucial to plant survival and propagation. ABA-induced seed germination inhibition is mainly mediated by the dimeric PYR/PYL/RCAR (PYLs) family members. However, little is known about the relevance between dimeric stability of PYLs and seed germination. Here, we reveal that stabilization of PYL dimer can relieve ABA-induced inhibition of seed germination using chemical genetic approaches. Di-nitrobensulfamide (DBSA), a computationally designed chemical probe, yields around ten-fold improvement in receptor affinity relative to ABA. DBSA reverses ABA-induced inhibition of seed germination mainly through dimeric receptors and recovers the expression of ABA-responsive genes. DBSA maintains PYR1 in dimeric state during protein oligomeric state experiment. X-ray crystallography shows that DBSA targets a pocket in PYL dimer interface and may stabilize PYL dimer by forming hydrogen networks. Our results illustrate the potential of PYL dimer stabilization in preventing ABA-induced seed germination inhibition.

          Abstract

          The authors reveal that stabilization of PYL dimer can relieve ABA-induced inhibition of seed germination using a computationally designed compound DBSA. Crystal structure and other experiments show that DBSA selectively binds to the dimeric PYL.

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          HTSeq—a Python framework to work with high-throughput sequencing data

          Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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            AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.

            AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user. Copyright 2009 Wiley Periodicals, Inc.
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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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                Author and article information

                Contributors
                zhujk@sustech.edu.cn
                gefei_hao@foxmail.com
                gfyang@mail.ccnu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 September 2024
                14 September 2024
                2024
                : 15
                : 8077
                Affiliations
                [1 ]State Key Laboratory of Green Pesticide, International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, ( https://ror.org/03x1jna21) Wuhan, 430079 China
                [2 ]Institute of Advanced Biotechnology and School of Medicine, Southern University of Science and Technology, ( https://ror.org/049tv2d57) Shenzhen, 518055 China
                [3 ]State Key Laboratory of Crop Genetic Improvement and National Centre of Plant Gene Research, Huazhong Agricultural University, ( https://ror.org/023b72294) Wuhan, 430070 China
                [4 ]GRID grid.443382.a, ISNI 0000 0004 1804 268X, State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals, , Guizhou University, ; Guiyang, 550025 China
                [5 ]GRID grid.410625.4, ISNI 0000 0001 2293 4910, State Key Laboratory of Tree Genetics and Breeding, the Southern Modern Forestry Collaborative Innovation Center, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, , Nanjing Forestry University, ; Nanjing, 210037 China
                [6 ]GRID grid.440622.6, ISNI 0000 0000 9482 4676, State Key Laboratory of Crop Biology, College of Life Science, , Shandong Agricultural University, ; Taian, Shandong 271018 China
                Author information
                http://orcid.org/0000-0001-9748-7246
                http://orcid.org/0000-0003-4538-5533
                http://orcid.org/0000-0001-8001-221X
                http://orcid.org/0000-0001-5134-731X
                http://orcid.org/0000-0003-4090-8411
                http://orcid.org/0000-0003-4384-2593
                Article
                52426
                10.1038/s41467-024-52426-y
                11401921
                39277642
                cd3df5f1-9e4c-4fec-980d-f928f6d5b117
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 26 March 2024
                : 4 September 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 32125033
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                plant hormones,abiotic,small molecules
                Uncategorized
                plant hormones, abiotic, small molecules

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