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      Integrated Analysis of Metabolome and Transcriptome Reveals Insights for Cold Tolerance in Rapeseed ( Brassica napus L.)

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          Abstract

          Rapeseed ( Brassica napus L.) is an important oilseed crop in the world. Its productivity is significantly influenced by numerous abiotic stresses, including cold stress (CS). Consequently, enhancement in CS tolerance is becoming an important area for agricultural investigation and crop improvement. Therefore, the current study aimed to identify the stress-responsive genes, metabolites, and metabolic pathways based on a combined transcriptome and metabolome analysis to understand the CS responses and tolerance mechanisms in the cold-tolerant (C18) and cold-sensitive (C6) rapeseed varieties. Based on the metabolome analysis, 31 differentially accumulated metabolites (DAMs) were identified between different comparisons of both varieties at the same time points. From the transcriptome analysis, 2,845, 3,358, and 2,819 differentially expressed genes (DEGs) were detected from the comparison of C6-0 vs. C18-0, C6-1 vs. C18-1, and C6-7 vs. C18-7. By combining the transcriptome and metabolome data sets, we found that numerous DAMs were strongly correlated with several differentially expressed genes (DEGs). A functional enrichment analysis of the DAMs and the correlated DEGs specified that most DEGs and DAMs were mainly enriched in diverse carbohydrates and amino acid metabolisms. Among them, starch and sucrose metabolism and phenylalanine metabolism were significantly enriched and played a vital role in the CS adaption of rapeseed. Six candidate genes were selected from the two pathways for controlling the adaption to low temperature. In a further validation, the T-DNA insertion mutants of their Arabidopsis homologous, including 4cl3, cel5, fruct4, ugp1, axs1, and bam2/9, were characterized and six lines differed significantly in levels of freezing tolerance. The outcome of the current study provided new prospects for the understanding of the molecular basis of CS responses and tolerance mechanisms in rapeseed and present a set of candidate genes for use in improving CS adaptability in the same plant.

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            The rapid development of high-throughput sequencing techniques has led biology into the big-data era. Data analyses using various bioinformatics tools rely on programming and command-line environments, which are challenging and time-consuming for most wet-lab biologists. Here, we present TBtools (a Toolkit for Biologists integrating various biological data-handling tools), a stand-alone software with a user-friendly interface. The toolkit incorporates over 130 functions, which are designed to meet the increasing demand for big-data analyses, ranging from bulk sequence processing to interactive data visualization. A wide variety of graphs can be prepared in TBtools using a new plotting engine ("JIGplot") developed to maximize their interactive ability; this engine allows quick point-and-click modification of almost every graphic feature. TBtools is platform-independent software that can be run under all operating systems with Java Runtime Environment 1.6 or newer. It is freely available to non-commercial users at https://github.com/CJ-Chen/TBtools/releases.
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              High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                08 October 2021
                2021
                : 12
                : 721681
                Affiliations
                [1] 1Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS) , Wuhan, China
                [2] 2College of Agriculture, Engineering Research Center of Ecology and Agricultural Use of Wetland of Ministry of Education, Yangtze University , Jingzhou, China
                Author notes

                Edited by: Yingfang Zhu, Henan University, China

                Reviewed by: Wen-Feng Nie, Yangzhou University, China; Xiaoli Sun, Heilongjiang Bayi Agricultural University, China; Mingku Zhu, Jiangsu Normal University, China

                *Correspondence: Yan Lv lvyan01@ 123456caas.cn

                This article was submitted to Plant Physiology, a section of the journal Frontiers in Plant Science

                †ORCID: Ali Raza orcid.org/0000-0002-5120-2791

                Article
                10.3389/fpls.2021.721681
                8532563
                34691103
                9025bc7a-71a1-4f17-88ba-c6d9644879a2
                Copyright © 2021 Raza, Su, Hussain, Mehmood, Zhang, Cheng, Zou and Lv.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 07 June 2021
                : 30 July 2021
                Page count
                Figures: 11, Tables: 1, Equations: 1, References: 115, Pages: 26, Words: 16647
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                abiotic stress,carbohydrate metabolism,metabolites,phenylalanine metabolism,omics

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