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      Metabolic and Transcriptomic Profiling of Lilium Leaves Infected With Botrytis elliptica Reveals Different Stages of Plant Defense Mechanisms

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          Abstract

          Botrytis elliptica, the causal agent of gray mold disease, poses a major threat to commercial Lilium production, limiting its ornamental value and yield. The molecular and metabolic regulation mechanisms of Lilium's defense response to B. elliptica infection have not been completely elucidated. Here, we performed transcriptomic and metabolomic analyses of B. elliptica resistant Lilium oriental hybrid “Sorbonne” to understand the molecular basis of gray mold disease resistance in gray mold disease. A total of 115 differentially accumulated metabolites (DAMs) were detected by comparing the different temporal stages of pathogen infection. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed the differentially expressed genes (DEGs) and DAMs were enriched in the phenylpropanoid and flavonoid pathways at all stages of infection, demonstrating the prominence of these pathways in the defense response of “Sorbonne” to B. elliptica. Network analysis revealed high interconnectivity of the induced defense response. Furthermore, time-course analysis of the transcriptome and a weighted gene coexpression network analysis (WGCNA) led to the identification of a number of hub genes at different stages, revealing that jasmonic acid (JA), salicylic acid (SA), brassinolide (BR), and calcium ions (Ca 2+) play a crucial role in the response of “Sorbonne” to fungal infection. Our work provides a comprehensive perspective on the defense response of Lilium to B. elliptica infection, along with a potential transcriptional regulatory network underlying the defense response, thereby offering gene candidates for resistance breeding and metabolic engineering of Lilium.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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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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              TBtools - an integrative toolkit developed for interactive analyses of big biological data

              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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                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
                22 September 2021
                2021
                : 12
                : 730620
                Affiliations
                [1] 1Chongqing Engineering Research Center for Floriculture, Key Laboratory of Horticulture Science for Southern Mountainous Regions of Ministry of Education, College of Horticulture and Landscape Architecture, Southwest University , Chongqing, China
                [2] 2Key Laboratory of Plant Hormones and Development Regulation of Chongqing, School of Life Sciences, Chongqing University , Chongqing, China
                Author notes

                Edited by: Victoria Pastor, University of Jaume I, Spain

                Reviewed by: Jiancai Li, Shanghai Institutes for Biological Sciences (CAS), China; Mara Quaglia, University of Perugia, Italy

                *Correspondence: Mingyang Li limy@ 123456swu.edu.cn

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

                †These authors have contributed equally to this work

                Article
                10.3389/fpls.2021.730620
                8493297
                8e274fe3-3eda-4560-90b2-2d75d58a5173
                Copyright © 2021 Chai, Xu, Zuo, Sun, Cheng, Sui, Li and Liu.

                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
                : 25 June 2021
                : 27 August 2021
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 86, Pages: 17, Words: 11103
                Funding
                Funded by: National Key Research and Development Program of China, doi 10.13039/501100012166;
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                lilium,botrytis elliptica,gray mold,phenylpropanoid pathway,flavonoid pathway,transcriptome,metabolic profiling

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