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      Boosting wheat functional genomics via an indexed EMS mutant library of KN9204

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          Abstract

          A better understanding of wheat functional genomics can improve targeted breeding for better agronomic traits and environmental adaptation. However, the lack of gene-indexed mutants and the low transformation efficiency of wheat limit in-depth gene functional studies and genetic manipulation for breeding. In this study, we created a library for KN9204, a popular wheat variety in northern China, with a reference genome, transcriptome, and epigenome of different tissues, using ethyl methyl sulfonate (EMS) mutagenesis. This library contains a vast developmental diversity of critical tissues and transition stages. Exome capture sequencing of 2090 mutant lines using KN9204 genome–designed probes revealed that 98.79% of coding genes had mutations, and each line had an average of 1383 EMS-type SNPs. We identified new allelic variations for crucial agronomic trait-related genes such as Rht-D1, Q, TaTB1, and WFZP. We tested 100 lines with severe mutations in 80 NAC transcription factors (TFs) under drought and salinity stress and identified 13 lines with altered sensitivity. Further analysis of three lines using transcriptome and chromatin accessibility data revealed hundreds of direct NAC targets with altered transcription patterns under salt or drought stress, including SNAC1, DREB2B, CML16, and ZFP182, factors known to respond to abiotic stress. Thus, we have generated and indexed a KN9204 EMS mutant library that can facilitate functional genomics research and offer resources for genetic manipulation of wheat.

          Abstract

          The lack of gene-indexed mutants limits functional genomics and molecular breeding in wheat. This study reports the generation and characterization of an EMS-mutagenized library by whole-exome capture in the winter wheat variety KN9204. The library contains 2090 lines with mutations covering 98.79% of coding genes and shows various developmental phenotypes; it can facilitate studies of gene function and provide novel resources for genetic manipulation.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                Journal
                Plant Commun
                Plant Commun
                Plant Communications
                Elsevier
                2590-3462
                21 March 2023
                10 July 2023
                21 March 2023
                : 4
                : 4
                : 100593
                Affiliations
                [1 ]Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
                [2 ]Ministry of Education Key Laboratory of Molecular and Cellular Biology, Hebei Key Laboratory of Molecular and Cellular Biology, College of Life Sciences, Hebei Normal University, Hebei Collaboration Innovation Center for Cell Signaling, Shijiazhuang 050024, China
                [3 ]Center for Agricultural Resources Research, Institute of Genetics and Development Biology, Chinese Academy of Sciences, Shijiazhuang 050022, China
                [4 ]University of Chinese Academy of Sciences, Beijing 100049, China
                [5 ]Department of Life Science, Tcuni, Inc, Chengdu 610000, China
                [6 ]State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, School of Life Sciences, Center for Quantitative Biology, Peking University, Beijing 100871, China
                [7 ]State Key Laboratory of Protein and Plant Gene Research, School of Advanced Agricultural Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
                [8 ]Plant Genetic Engineering Center of Hebei Province, Institute of Biotechnology and Food Science, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, China
                [9 ]Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Laboratory of Crop Genetics and Breeding of Hebei, Shijiazhuang 050035, China
                [10 ]Key Laboratory of Molecular Module-Based Breeding of High Yield and Abiotic Resistant Plants in Universities of Shandong, College of Agriculture, Ludong University, Yantai 264025, China
                [11 ]Centre of Excellence for Plant and Microbial Science (CEPAMS), JIC-CAS, Beijing 100101, China
                Author notes
                []Corresponding author ljm@ 123456ms.sjziam.ac.cn
                [∗∗ ]Corresponding author fhe@ 123456genetics.ac.cn
                [∗∗∗ ]Corresponding author xgliu@ 123456hebtu.edu.cn
                [∗∗∗∗ ]Corresponding author jxiao@ 123456genetics.ac.cn
                [12]

                These authors contributed equally to this article.

                Article
                S2590-3462(23)00104-9 100593
                10.1016/j.xplc.2023.100593
                10363553
                ff5833ae-d36b-498c-a723-07b35a450abf
                © 2023 The Author(s)

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

                History
                : 14 December 2022
                : 2 February 2023
                : 17 March 2023
                Categories
                Resource Article

                wheat,exome capture sequencing,ems mutagenesis,functional genomics

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