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      Identification of long noncoding RNAs involved in resistance to downy mildew in Chinese cabbage

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          Abstract

          Brassica downy mildew, a severe disease caused by Hyaloperonospora brassicae, can cause enormous economic losses in Chinese cabbage ( Brassica rapa L. ssp. pekinensis) production. Although some research has been reported recently concerning the underlying resistance to this disease, no studies have identified or characterized long noncoding RNAs involved in this defense response. In this study, using high-throughput RNA sequencing, we analyzed the disease-responding mRNAs and long noncoding RNAs in two resistant lines (T12–19 and 12–85) and one susceptible line (91–112). Clustering and Gene Ontology analysis of differentially expressed genes (DEGs) showed that more DEGs were involved in the defense response in the two resistant lines than in the susceptible line. Different expression patterns and proposed functions of differentially expressed long noncoding RNAs among T12–19, 12–85, and 91–112 indicated that each has a distinct disease response mechanism. There were significantly more cis- and trans-functional long noncoding RNAs in the resistant lines than in the susceptible line, and the genes regulated by these RNAs mostly participated in the disease defense response. Furthermore, we identified a candidate resistance-related long noncoding RNA, MSTRG.19915, which is a long noncoding natural antisense transcript of a MAPK gene, BrMAPK15. Via an agroinfiltration-mediated transient overexpression system and virus-induced gene silencing technology, BrMAPK15 was indicated to have a greater ability to defend against pathogens. MSTRG.19915-silenced seedlings showed enhanced resistance to downy mildew, probably because of the upregulated expression of BrMAPK15. This research identified and characterized long noncoding RNAs involved in resistance to downy mildew, laying a foundation for future in-depth studies of disease resistance mechanisms in Chinese cabbage.

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          MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

          We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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            StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

            Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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              Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms

              High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation 1-3 . We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.
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                Author and article information

                Contributors
                yushuancang@nercv.org
                zhangfenglan@nercv.org
                Journal
                Hortic Res
                Hortic Res
                Horticulture Research
                Nature Publishing Group UK (London )
                2662-6810
                2052-7276
                1 March 2021
                1 March 2021
                2021
                : 8
                : 44
                Affiliations
                [1 ]GRID grid.418260.9, ISNI 0000 0004 0646 9053, Beijing Vegetable Research Center (BVRC), , Beijing Academy of Agriculture and Forestry Sciences (BAAFS), ; 100097 Beijing, China
                [2 ]GRID grid.418524.e, ISNI 0000 0004 0369 6250, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China), , Ministry of Agriculture, ; 100097 Beijing, China
                [3 ]Beijing Key Laboratory of Vegetable Germplasm Improvement, 100097 Beijing, China
                Author information
                http://orcid.org/0000-0002-3411-7341
                Article
                479
                10.1038/s41438-021-00479-1
                7917106
                33642586
                df793bf7-0adc-4c07-b296-0c07bbfaa28f
                © The Author(s) 2021

                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
                : 12 October 2020
                : 14 December 2020
                : 17 December 2020
                Funding
                Funded by: National Natural Science Foundation of China (31772297)
                Funded by: National Natural Science Foundation of China (31872126)
                Funded by: The National Key Research and Development Program of China (2017YFD0101801)
                Funded by: The Key Program of Beijing Municipal Science and Technology Committee (Z191100004019010); The earmarked fund for China Agriculture Research System (CARS-23-A-05)
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                Article
                Custom metadata
                © The Author(s) 2021

                non-coding rnas,plant molecular biology
                non-coding rnas, plant molecular biology

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