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      Integrative metabolome and transcriptome analyses reveal the molecular mechanism underlying variation in floral scent during flower development of Chrysanthemum indicum var. aromaticum

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          Abstract

          Chrysanthemum indicum var. aromaticum (CIA) is an endemic plant that occurs only in the high mountain areas of the Shennongjia Forest District in China. The whole plant, in particular the flowers of CIA, have intense fragrance, making it a novel resource plant for agricultural, medicinal, and industrial applications. However, the volatile metabolite emissions in relation to CIA flower development and the molecular mechanisms underlying the generation of floral scent remain poorly understood. Here, integrative metabolome and transcriptome analyses were performed to investigate floral scent-related volatile compounds and genes in CIA flowers at three different developmental stages. A total of 370 volatile metabolites, mainly terpenoids and esters, were identified, of which 89 key differential metabolites exhibited variable emitting profiles during flower development. Transcriptome analysis further identified 8,945 differentially expressed genes (DEGs) between these samples derived from different flower developmental stages and KEGG enrichment analyses showed that 45, 93, and 101 candidate DEGs associated with the biosynthesis of phenylpropanoids, esters, and terpenes, respectively. Interestingly, significant DEGs involved into the volatile terpenes are only present in the MEP and its downstream pathways, including those genes encoding ISPE, ISPG, FPPS, GPPS, GERD, ND and TPS14 enzymes. Further analysis showed that 20 transcription factors from MYB, bHLH, AP2/EFR, and WRKY families were potentially key regulators affecting the expressions of floral scent-related genes during the CIA flower development. These findings provide insights into the molecular basis of plant floral scent metabolite biosynthesis and serve as an important data resources for molecular breeding and utilization of CIA plants in the future.

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          Most cited references60

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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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              Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

              Massively-parallel cDNA sequencing has opened the way to deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here, we present the Trinity methodology for de novo full-length transcriptome reconstruction, and evaluate it on samples from fission yeast, mouse, and whitefly – an insect whose genome has not yet been sequenced. Trinity fully reconstructs a large fraction of the transcripts present in the data, also reporting alternative splice isoforms and transcripts from recently duplicated genes. In all cases, Trinity performs better than other available de novo transcriptome assembly programs, and its sensitivity is comparable to methods relying on genome alignments. Our approach provides a unified and general solution for transcriptome reconstruction in any sample, especially in the complete absence of a reference genome.
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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
                15 September 2022
                2022
                : 13
                : 919151
                Affiliations
                [1] 1College of Pharmacy, Hubei University of Chinese Medicine , Wuhan, Hubei, China
                [2] 2South China Botanical Garden, Chinese Academy of Sciences , Guangzhou, China
                Author notes

                Edited by: Yunpeng Cao, Wuhan Botanical Garden (CAS), China

                Reviewed by: Wei Sun, China Academy of Chinese Medical Sciences, China; Zhichao Xu, Northeast Forestry University, China; Marcelo Falsarella Carazzolle, State University of Campinas, Brazil; Lihu Wang, Hebei University of Engineering, China

                *Correspondence: Bisheng Huang, hbsh1963@ 123456163.com

                These authors have contributed equally to this work and share first authorship

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

                Article
                10.3389/fpls.2022.919151
                9889088
                36733600
                282b1920-12ff-47f5-a895-1c5428a32220
                Copyright © 2022 Zhu, Liao, Liu, Zhou, Wang, Hu, Huang and Zhang.

                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
                : 13 April 2022
                : 11 August 2022
                Page count
                Figures: 7, Tables: 0, Equations: 0, References: 61, Pages: 16, Words: 8938
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                chrysanthemum indicum var. aromaticum,volatile metabolites,metabolome,transcriptome,floral scent

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