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      Cell wall composition and transcriptomics in stem tissues of stinging nettle ( Urtica dioica L.): Spotlight on a neglected fibre crop

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          Stinging nettle ( Urtica dioica L.) produces silky cellulosic fibres, as well as bioactive molecules. To improve the knowledge on nettle and enhance its opportunities of exploitation, a draft transcriptome of the “clone 13” (a fibre clone) is here presented. The transcriptome of whole internodes sampled at the top and middle of the stem is then compared with the core and cortical tissues sampled at the bottom. Young internodes show an enrichment in genes involved in the biosynthesis of phytohormones (auxins and jasmonic acid) and secondary metabolites (flavonoids). The core of internodes collected at the bottom of the stem is enriched in genes partaking in different aspects of secondary cell wall formation (cellulose, hemicellulose, lignin biosynthesis), while the cortical tissues reveal the presence of a C starvation signal probably due to the UDP‐glucose demand necessary for the thickening phase of bast fibres. Cell wall analysis indicates a difference in rhamnogalacturonan structure/composition of mature bast fibres, as evidenced by the higher levels of galactose measured, as well as the occurrence of more water‐soluble pectins in elongating internodes. The targeted quantification of phenolics shows that the middle internode and the cortical tissues at the bottom have higher contents than top internodes. Ultrastructural analyses reveal the presence of a gelatinous layer in bast fibres with a lamellar structure. The data presented will be an important resource and reference for future molecular studies on a neglected fibre crop.

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          Mapping and quantifying mammalian transcriptomes by RNA-Seq.

          We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41-52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3' untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 x 10(5) distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.
            • Record: found
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            • Article: not found

            De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis.

            De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.
              • Record: found
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              • Article: not found

              Cluster analysis and display of genome-wide expression patterns.

              A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.

                Author and article information

                Plant Direct
                Plant Direct
                Plant Direct
                John Wiley and Sons Inc. (Hoboken )
                12 August 2019
                August 2019
                : 3
                : 8 ( doiID: 10.1002/pld3.2019.3.issue-8 )
                [ 1 ] Environmental Research and Innovation (ERIN) Department Luxembourg Institute of Science and Technology (LIST) Esch/Alzette Luxembourg
                [ 2 ] Department of Life Sciences University of Siena Siena Italy
                [ 3 ] Trees and Timber Institute‐National Research Council of Italy (CNR‐IVALSA) Follonica Italy
                [ 4 ] Institute of Biometeorology (IBIMET) National Research Council Bologna Italy
                [ 5 ]Present address: Unité de Recherche Résistance Induite et BioProtection des Plantes UFR Sciences Exactes et Naturelles SFR Condorcet FR CNRS 3417 Université de Reims‐Champagne‐Ardenne Reims Cedex 2 France
                Author notes
                [* ] Correspondence

                Gea Guerriero, Luxembourg Institute of Science and Technology (LIST), Environmental Research and Innovation (ERIN) Department, Esch/Alzette L‐4362, Luxembourg.

                Email: gea.guerriero@

                © 2019 The Authors. Plant Direct published by American Society of Plant Biologists, Society for Experimental Biology and John Wiley & Sons Ltd.

                This is an open access article under the terms of the License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                Page count
                Figures: 5, Tables: 1, Pages: 17, Words: 14857
                Original Research
                Original Research
                Custom metadata
                August 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.7 mode:remove_FC converted:12.08.2019


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