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      De novo transcriptome sequencing and digital gene expression analysis predict biosynthetic pathway of rhynchophylline and isorhynchophylline from Uncaria rhynchophylla, a non-model plant with potent anti-alzheimer’s properties

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          The major medicinal alkaloids isolated from Uncaria rhynchophylla (gouteng in chinese) capsules are rhynchophylline (RIN) and isorhynchophylline (IRN). Extracts containing these terpene indole alkaloids (TIAs) can inhibit the formation and destabilize preformed fibrils of amyloid β protein (a pathological marker of Alzheimer’s disease), and have been shown to improve the cognitive function of mice with Alzheimer-like symptoms. The biosynthetic pathways of RIN and IRN are largely unknown.


          In this study, RNA-sequencing of pooled Uncaria capsules RNA samples taken at three developmental stages that accumulate different amount of RIN and IRN was performed. More than 50 million high-quality reads from a cDNA library were generated and de novo assembled. Sequences for all of the known enzymes involved in TIAs synthesis were identified. Additionally, 193 cytochrome P450 (CYP450), 280 methyltransferase and 144 isomerase genes were identified, that are potential candidates for enzymes involved in RIN and IRN synthesis. Digital gene expression profile (DGE) analysis was performed on the three capsule developmental stages, and based on genes possessing expression profiles consistent with RIN and IRN levels; four CYP450s, three methyltransferases and three isomerases were identified as the candidates most likely to be involved in the later steps of RIN and IRN biosynthesis.


          A combination of de novo transcriptome assembly and DGE analysis was shown to be a powerful method for identifying genes encoding enzymes potentially involved in the biosynthesis of important secondary metabolites in a non-model plant. The transcriptome data from this study provides an important resource for understanding the formation of major bioactive constituents in the capsule extract from Uncaria, and provides information that may aid in metabolic engineering to increase yields of these important alkaloids.

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          The online version of this article (doi:10.1186/1471-2164-15-676) contains supplementary material, which is available to authorized users.

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          Most cited references 85

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          Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

          Massively-parallel cDNA sequencing has opened the way to deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here, we present the Trinity methodology for de novo full-length transcriptome reconstruction, and evaluate it on samples from fission yeast, mouse, and whitefly – an insect whose genome has not yet been sequenced. Trinity fully reconstructs a large fraction of the transcripts present in the data, also reporting alternative splice isoforms and transcripts from recently duplicated genes. In all cases, Trinity performs better than other available de novo transcriptome assembly programs, and its sensitivity is comparable to methods relying on genome alignments. Our approach provides a unified and general solution for transcriptome reconstruction in any sample, especially in the complete absence of a reference genome.
            • Record: found
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            Differential expression analysis for sequence count data

            High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
              • Record: found
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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.

                Author and article information

                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                12 August 2014
                12 August 2014
                : 15
                : 1
                [ ]College of Agriculture, Northeast Agricultural University, Harbin, 150030 China
                [ ]College of Life Science, Agriculture and Forest, Qiqihar University, Qiqihar, 161006 China
                [ ]Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Beijing, 100193 China
                [ ]Yunnan Branch Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Jinghong, 530023 China
                [ ]Guangxi Medicinal Botanical Garden, Nanning, 530023 China
                [ ]State Key Laboratory of Tree Genetics and Breeding, The Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091 China
                [ ]CSIRO Plant Industry, PO Box 1600, Canberra, ACT 2001 Australia
                [ ]Hunan Agricultural University, Changsha, 410128 China
                © Guo et al.; licensee BioMed Central Ltd. 2014

                This article is published under license to BioMed Central Ltd. 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 work is properly credited.

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