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      An ortholog of LEAFY in Jatropha curcas regulates flowering time and floral organ development

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          Abstract

          Jatropha curcas seeds are an excellent biofuel feedstock, but seed yields of Jatropha are limited by its poor flowering and fruiting ability. Thus, identifying genes controlling flowering is critical for genetic improvement of seed yield. We isolated the JcLFY, a Jatropha ortholog of Arabidopsis thaliana LEAFY ( LFY), and identified JcLFY function by overexpressing it in Arabidopsis and Jatropha. JcLFY is expressed in Jatropha inflorescence buds, flower buds, and carpels, with highest expression in the early developmental stage of flower buds. JcLFY overexpression induced early flowering, solitary flowers, and terminal flowers in Arabidopsis, and also rescued the delayed flowering phenotype of lfy-15, a LFY loss-of-function Arabidopsis mutant. Microarray and qPCR analysis revealed several flower identity and flower organ development genes were upregulated in JcLFY-overexpressing Arabidopsis. JcLFY overexpression in Jatropha also induced early flowering. Significant changes in inflorescence structure, floral organs, and fruit shape occurred in JcLFY co-suppressed plants in which expression of several flower identity and floral organ development genes were changed. This suggests JcLFY is involved in regulating flower identity, floral organ patterns, and fruit shape, although JcLFY function in Jatropha floral meristem determination is not as strong as that of Arabidopsis.

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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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            Floral dip: a simplified method forAgrobacterium-mediated transformation ofArabidopsis thaliana

            The Agrobacterium vacuum infiltration method has made it possible to transform Arabidopsis thaliana without plant tissue culture or regeneration. In the present study, this method was evaluated and a substantially modified transformation method was developed. The labor-intensive vacuum infiltration process was eliminated in favor of simple dipping of developing floral tissues into a solution containing Agrobacterium tumefaciens, 5% sucrose and 500 microliters per litre of surfactant Silwet L-77. Sucrose and surfactant were critical to the success of the floral dip method. Plants inoculated when numerous immature floral buds and few siliques were present produced transformed progeny at the highest rate. Plant tissue culture media, the hormone benzylamino purine and pH adjustment were unnecessary, and Agrobacterium could be applied to plants at a range of cell densities. Repeated application of Agrobacterium improved transformation rates and overall yield of transformants approximately twofold. Covering plants for 1 day to retain humidity after inoculation also raised transformation rates twofold. Multiple ecotypes were transformable by this method. The modified method should facilitate high-throughput transformation of Arabidopsis for efforts such as T-DNA gene tagging, positional cloning, or attempts at targeted gene replacement.
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              MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

              Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                21 November 2016
                2016
                : 6
                : 37306
                Affiliations
                [1 ]Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun , Mengla, Yunnan 666303, China
                [2 ]University of Chinese Academy of Sciences , Beijing 100049, China
                Author notes
                Article
                srep37306
                10.1038/srep37306
                5116762
                27869146
                7806fc03-f01c-4a4f-8b58-12be5b39cb91
                Copyright © 2016, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 25 May 2016
                : 27 October 2016
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