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      Genome Wide Binding Site Analysis Reveals Transcriptional Coactivation of Cytokinin-Responsive Genes by DELLA Proteins


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          The ability of plants to provide a plastic response to environmental cues relies on the connectivity between signaling pathways. DELLA proteins act as hubs that relay environmental information to the multiple transcriptional circuits that control growth and development through physical interaction with transcription factors from different families. We have analyzed the presence of one DELLA protein at the Arabidopsis genome by chromatin immunoprecipitation coupled to large-scale sequencing and we find that it binds at the promoters of multiple genes. Enrichment analysis shows a strong preference for cis elements recognized by specific transcription factor families. In particular, we demonstrate that DELLA proteins are recruited by type-B ARABIDOPSIS RESPONSE REGULATORS (ARR) to the promoters of cytokinin-regulated genes, where they act as transcriptional co-activators. The biological relevance of this mechanism is underpinned by the necessity of simultaneous presence of DELLAs and ARRs to restrict root meristem growth and to promote photomorphogenesis.

          Author Summary

          Plants respond to environmental cues by modulating transcriptional circuits. One mechanism for such modulation involves DELLA proteins. They are promiscuous interactors of transcription factors and, in most cases, this interaction impairs the recognition of the DNA target sequences. Here we show that DELLA proteins are also recruited to multiple locations of the genome where they act as transcriptional coactivators, and we demonstrate how physical interaction with type-B ARRs is relevant for the regulation of meristem maintenance and photomorphogenesis.

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          Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

          Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.
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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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              Analysis of microarray data using Z score transformation.

              High-throughput cDNA microarray technology allows for the simultaneous analysis of gene expression levels for thousands of genes and as such, rapid, relatively simple methods are needed to store, analyze, and cross-compare basic microarray data. The application of a classical method of data normalization, Z score transformation, provides a way of standardizing data across a wide range of experiments and allows the comparison of microarray data independent of the original hybridization intensities. Data normalized by Z score transformation can be used directly in the calculation of significant changes in gene expression between different samples and conditions. We used Z scores to compare several different methods for predicting significant changes in gene expression including fold changes, Z ratios, Z and t statistical tests. We conclude that the Z score transformation normalization method accompanied by either Z ratios or Z tests for significance estimates offers a useful method for the basic analysis of microarray data. The results provided by these methods can be as rigorous and are no more arbitrary than other test methods, and, in addition, they have the advantage that they can be easily adapted to standard spreadsheet programs.

                Author and article information

                Role: Editor
                PLoS Genet
                PLoS Genet
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                2 July 2015
                July 2015
                : 11
                : 7
                : e1005337
                [1 ]Instituto de Biología Molecular y Celular de Plantas (CSIC-Universidad Politécnica de Valencia), Valencia, Spain
                [2 ]Department of Stem Cell Biology, Centre for Organismal Studies Heidelberg, Heidelberg University, Heidelberg, Germany
                [3 ]School of Biosciences and Centre for Plant Integrative Biology, University of Nottingham, Nottingham, United Kingdom
                [4 ]Department of Forest Genetics and Plant Physiology, Umeå Plant Science Centre, Sveriges Lantbruksuniversitet, Umeå, Sweden
                [5 ]College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
                [6 ]Rothamsted Research, Harpenden, Hertfordshire, United Kingdom
                National University of Singapore and Temasek Life Sciences Laboratory, SINGAPORE
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: NMdlR AP KH RPB SGT MJB JUL MAB DA. Performed the experiments: NMdlR AP KH AL PM ALG AWK MAB DA. Analyzed the data: NMdlR AP KH RPB SGT MJB JUL MAB DA. Wrote the paper: MAB DA.

                Copyright @ 2015

                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

                : 9 December 2014
                : 5 June 2015
                Page count
                Figures: 5, Tables: 0, Pages: 20
                This work was funded by grants BIO2007-60923 and BIO2010-15071 from the Spanish Ministry of Economy and Innovation (MAB); grant ERC-2011-StG_20101109 from the European Research Council (JUL); grants BB/J/00426X/1 and BB/E022618/1 from the Biotechnology and Biological Sciences Research Council (SGT); the Professorial Research Fellowship award BB/G023972/1 from the Biotechnology and Biological Sciences Research Council (KH and MJB); and grant FP7-311929 from the European Union (RPB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Custom metadata
                The ChIP-seq data for RGA-GFP have been deposited in GEO with the code: GSE59187. The microarray data for the influence of DELLAs on ARR1 and cytokinin activities have also been deposited in GEO with the code: GSE69828.



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