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          Abstract

          Seedling establishment is inhibited on media containing high levels (∼6%) of glucose or fructose. Genetic loci that overcome the inhibition of seedling growth on high sugar have been identified using natural variation analysis and mutant selection, providing insight into sugar signaling pathways. In this study, a quantitative trait locus (QTL) analysis was performed for seedling sensitivity to high sugar in a Col/C24 F 2 population of Arabidopsis thaliana. A glucose and fructose-sensing QTL, GSQ11, was mapped through selective genotyping and confirmed in near-isogenic lines in both Col and C24 backgrounds. Allelism tests and transgenic complementation showed that GSQ11 lies within the ANAC060 gene. The Col ANAC060 allele confers sugar insensitivity and was dominant over the sugar-sensitive C24 allele. Genomic and mRNA analyses showed that a single-nucleotide polymorphism (SNP) in Col ANAC060 affects the splicing patterns of ANAC060 such that 20 additional nucleotides are present in the mRNA. The insertion created a stop codon, resulting in a truncated ANAC60 protein lacking the transmembrane domain (TMD) that is present in the C24 ANAC060 protein. The absence of the TMD results in the nuclear localization of ANAC060. The short version of the ANAC060 protein is found in ∼12% of natural Arabidopsis accessions. Glucose induces GSQ11/ANAC060 expression in a process that requires abscisic acid (ABA) signaling. Chromatin immunoprecipitation-qPCR and transient expression analysis showed that ABI4 directly binds to the GSQ11/ANAC060 promoter to activate transcription. Interestingly, Col ANAC060 reduced ABA sensitivity and Glc-induced ABA accumulation, and ABI4 expression was also reduced in Col ANAC060 lines. Thus, the sugar-ABA signaling cascade induces ANAC060 expression, but the truncated Col ANAC060 protein attenuates ABA induction and ABA signaling. This negative feedback from nuclear ANAC060 on ABA signaling results in sugar insensitivity.

          Author Summary

          In plants, sugars function as signaling molecules that control important processes such as photosynthesis, growth, carbon distribution over different organs and the production of storage compounds. Sugar signaling requires the phytohormone abscisic acid (ABA) and the ABA-induced regulatory transcription factor ABI4. In this study, a genetic analysis identified the transcription factor ANAC060 as an important component in establishing sugar sensitivity. It was found that, in natural Arabidopsis thaliana populations, the ANAC060 protein may occur as a long or a short version due to differential ANAC060 mRNA splicing caused by a single-nucleotide polymorphism (SNP). The long ANAC060 protein with an intact transmembrane domain (TMD) is excluded from the nucleus, whereas the short version lacking the TMD is always present in the nucleus, where it regulates gene expression. Functional analyses indicated that Col ANAC060 is involved in a novel negative feedback loop in the sugar-ABA signaling pathway. In this feedback loop model, ABI4 activates ANAC060 expression, but the nuclear presence of Col ANAC060 suppresses Glc-induced ABA accumulation and ABI4 expression, thereby reducing responsiveness to sugar signals.

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          Transient expression vectors for functional genomics, quantification of promoter activity and RNA silencing in plants

          Background We describe novel plasmid vectors for transient gene expression using Agrobacterium, infiltrated into Nicotiana benthamiana leaves. We have generated a series of pGreenII cloning vectors that are ideally suited to transient gene expression, by removing elements of conventional binary vectors necessary for stable transformation such as transformation selection genes. Results We give an example of expression of heme-thiolate P450 to demonstrate effectiveness of this system. We have also designed vectors that take advantage of a dual luciferase assay system to analyse promoter sequences or post-transcriptional regulation of gene expression. We have demonstrated their utility by co-expression of putative transcription factors and the promoter sequence of potential target genes and show how orthologous promoter sequences respond to these genes. Finally, we have constructed a vector that has allowed us to investigate design features of hairpin constructs related to their ability to initiate RNA silencing, and have used these tools to study cis-regulatory effect of intron-containing gene constructs. Conclusion In developing a series of vectors ideally suited to transient expression analysis we have provided a resource that further advances the application of this technology. These minimal vectors are ideally suited to conventional cloning methods and we have used them to demonstrate their flexibility to investigate enzyme activity, transcription regulation and post-transcriptional regulatory processes in transient assays.
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            Processing of gene expression data generated by quantitative real-time RT-PCR.

            Quantitative real-time PCR represents a highly sensitive and powerful technique for the quantitation of nucleic acids. It has a tremendous potential for the high-throughput analysis of gene expression in research and routine diagnostics. However, the major hurdle is not the practical performance of the experiments themselves but rather the efficient evaluation and the mathematical and statistical analysis of the enormous amount of data gained by this technology, as these functions are not included in the software provided by the manufacturers of the detection systems. In this work, we focus on the mathematical evaluation and analysis of the data generated by quantitative real-time PCR, the calculation of the final results, the propagation of experimental variation of the measured values to the final results, and the statistical analysis. We developed a Microsoft Excel-based software application coded in Visual Basic for Applications, called Q-Gene, which addresses these points. Q-Gene manages and expedites the planning, performance, and evaluation of quantitative real-time PCR experiments, as well as the mathematical and statistical analysis, storage, and graphical presentation of the data. The Q-Gene software application is a tool to cope with complex quantitative real-time PCR experiments at a high-throughput scale and considerably expedites and rationalizes the experimental setup, data analysis, and data management while ensuring highest reproducibility.
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              Q-Gene: processing quantitative real-time RT-PCR data.

              Q-Gene is an application for the processing of quantitative real-time RT-PCR data. It offers the user the possibility to freely choose between two principally different procedures to calculate normalized gene expressions as either means of Normalized Expressions or Mean Normalized Expressions. In this contribution it will be shown that the calculation of Mean Normalized Expressions has to be used for processing simplex PCR data, while multiplex PCR data should preferably be processed by calculating Normalized Expressions. The two procedures, which are currently in widespread use and regarded as more or less equivalent alternatives, should therefore specifically be applied according to the quantification procedure used. Web access to this program is provided at http://www.biotechniques.com/softlib/qgene.html
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                March 2014
                13 March 2014
                : 10
                : 3
                : e1004213
                Affiliations
                [1 ]Laboratory of Photosynthesis and Environmental Biology, Institute of Plant Physiology and Ecology, Shanghai Institutes for Biological Sciences, The Chinese Academy of Sciences, Shanghai, China
                [2 ]Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Stadt Seeland OT Gatersleben, Germany
                [3 ]Department of Molecular Plant Physiology, Utrecht University, Utrecht, The Netherlands
                [4 ]Centre for BioSystems Genomics, Wageningen, The Netherlands
                University of Toronto, Canada
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: ST PL. Performed the experiments: PL HZ XS BY YZ SC YW YP. Analyzed the data: ST PL SCS. Contributed reagents/materials/analysis tools: RCM. Wrote the paper: ST SCS.

                Article
                PGENETICS-D-13-03108
                10.1371/journal.pgen.1004213
                3953025
                24625790
                800b2f3e-e121-497d-9389-2e6acfdeefd6
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 11 November 2013
                : 15 January 2014
                Page count
                Pages: 10
                Funding
                This research was supported by The National Science Foundation of China (31370284, 31100188, 31161130533, 31200150), The Ministry of Agriculture of China for Transgenic Research (2014ZX08009-003, 2014ZX08001-500), The Knowledge Innovation Program of Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences(2012KIP306), The Netherlands Centre for BioSystems Genomics (CBSG), The Netherlands Organization for Scientific Research (NWO-ALW) and the China Exchange Programme of the Royal Netherlands Academy of Sciences (KNAW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Plant Science
                Plant Genetics
                Plant Physiology

                Genetics
                Genetics

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