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      The transcription factor complex CmAP3-CmPI-CmUIF1 modulates carotenoid metabolism by directly regulating the carotenogenic gene CmCCD4a-2 in chrysanthemum

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          Abstract

          Carotenoids are one of the most important pigments for the coloring of many plants, fruits, and flowers. Recently, significant progress has been made in carotenoid metabolism. However, our specific understanding of the transcriptional regulation that controls the expression of carotenoid metabolic genes remains extremely limited. Anemone-type chrysanthemums, a special group of chrysanthemum cultivars, contain elongated disc florets in the capitulum that usually differ in color from the ray florets because of their different carotenoid contents. In this study, the carotenoid composition and content of ray and disc florets from the anemone-type chrysanthemum cultivar “Dong Li Fen Gui” were analyzed by high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS), and the key structural gene CmCCD4a-2, whose differential expression resulted in different carotenoid contents in these two types of florets, was identified. The promoter sequence of CmCCD4a-2 was then used as bait to screen a chrysanthemum flower cDNA library, and the transcription factors (TFs) CmAP3 and CmUIF1 were identified. Y2H, BiFC, and Y3H experiments demonstrated that these two TFs were connected by CmPI to form a CmAP3-CmPI-CmUIF1 TF complex. This TF complex regulated carotenoid metabolism by directly activating the expression of CmCCD4a-2. A large number of target genes regulated directly by the CmAP3-CmPI-CmUIF1 TF complex, including carotenoid biosynthetic genes, flavonoid biosynthetic genes, and flower development-related genes, were identified by DNA-affinity purification sequencing (DAP-seq). This result indicated that the CmAP3-CmPI-CmUIF1 TF complex may participate in multiple processes. These findings expand our knowledge of the transcriptional regulation of carotenoid metabolism in plants and will be helpful for manipulating carotenoid accumulation in chrysanthemum.

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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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            KAAS: an automatic genome annotation and pathway reconstruction server

            The number of complete and draft genomes is rapidly growing in recent years, and it has become increasingly important to automate the identification of functional properties and biological roles of genes in these genomes. In the KEGG database, genes in complete genomes are annotated with the KEGG orthology (KO) identifiers, or the K numbers, based on the best hit information using Smith–Waterman scores as well as by the manual curation. Each K number represents an ortholog group of genes, and it is directly linked to an object in the KEGG pathway map or the BRITE functional hierarchy. Here, we have developed a web-based server called KAAS (KEGG Automatic Annotation Server: http://www.genome.jp/kegg/kaas/) i.e. an implementation of a rapid method to automatically assign K numbers to genes in the genome, enabling reconstruction of KEGG pathways and BRITE hierarchies. The method is based on sequence similarities, bi-directional best hit information and some heuristics, and has achieved a high degree of accuracy when compared with the manually curated KEGG GENES database.
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              MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity

              Background MTML-msBayes uses hierarchical approximate Bayesian computation (HABC) under a coalescent model to infer temporal patterns of divergence and gene flow across codistributed taxon-pairs. Under a model of multiple codistributed taxa that diverge into taxon-pairs with subsequent gene flow or isolation, one can estimate hyper-parameters that quantify the mean and variability in divergence times or test models of migration and isolation. The software uses multi-locus DNA sequence data collected from multiple taxon-pairs and allows variation across taxa in demographic parameters as well as heterogeneity in DNA mutation rates across loci. The method also allows a flexible sampling scheme: different numbers of loci of varying length can be sampled from different taxon-pairs. Results Simulation tests reveal increasing power with increasing numbers of loci when attempting to distinguish temporal congruence from incongruence in divergence times across taxon-pairs. These results are robust to DNA mutation rate heterogeneity. Estimating mean divergence times and testing simultaneous divergence was less accurate with migration, but improved if one specified the correct migration model. Simulation validation tests demonstrated that one can detect the correct migration or isolation model with high probability, and that this HABC model testing procedure was greatly improved by incorporating a summary statistic originally developed for this task (Wakeley's ΨW ). The method is applied to an empirical data set of three Australian avian taxon-pairs and a result of simultaneous divergence with some subsequent gene flow is inferred. Conclusions To retain flexibility and compatibility with existing bioinformatics tools, MTML-msBayes is a pipeline software package consisting of Perl, C and R programs that are executed via the command line. Source code and binaries are available for download at http://msbayes.sourceforge.net/ under an open source license (GNU Public License).
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                Author and article information

                Journal
                Hortic Res
                Hortic Res
                hr
                Horticulture Research
                Oxford University Press
                2662-6810
                2052-7276
                2022
                19 February 2022
                19 February 2022
                : 9
                : uhac020
                Affiliations
                [1]Beijing Key Laboratory of Ornamental Plants Germplasm Innovation & Molecular Breeding, National Engineering Research Center for Floriculture, Beijing Laboratory of Urban and Rural Ecological Environment, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Education Ministry, School of Landscape Architecture, Beijing Forestry University , Beijing, 100083, China
                Author notes
                Corresponding authors: E-mail: 101navy@ 123456163.com ; silandai@ 123456sina.com .
                Article
                uhac020
                10.1093/hr/uhac020
                9125392
                35184172
                b52c3360-af78-4d0c-8c1e-f994d41f2023
                © The Author(s) 2022. Published by Oxford University Press on behalf of Nanjing Agricultural University

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 7 October 2021
                : 23 January 2022
                : 25 April 2022
                Page count
                Pages: 16
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