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      • Creating Subnetworks from Transcriptomic Data on Central Nervous System Diseases Informed by a Massive Transcriptomic Network

        Feng, Yaping,Syrkin-Nikolau, Judith A.,Wurtele, Eve S. Korean Society for Bioinformatics 2013 Interdisciplinary Bio Central (IBC) Vol.5 No.1

        High quality publicly-available transcriptomic data representing relationships in gene expression across a diverse set of biological conditions is used as a context network to explore transcriptomics of the CNS. The context network, 18367Hu-matrix, contains pairwise Pearson correlations for 22,215 human genes across18,637 human tissue samples1. To do this, we compute a network derived from biological samples from CNS cells and tissues, calculate clusters of co-expressed genes from this network, and compare the significance of these to clusters derived from the larger 18367Hu-matrix network. Sorting and visualization uses the publicly available software, MetaOmGraph (http://www.metnetdb.org/MetNet_MetaOm-Graph.htm). This identifies genes that characterize particular disease conditions. Specifically, differences in gene expression within and between two designations of glial cancer, astrocytoma and glioblastoma, are evaluated in the context of the broader network. Such gene groups, which we term outlier-networks, tease out abnormally expressed genes and the samples in which this expression occurs. This approach distinguishes 48 subnetworks of outlier genes associated with astrocytoma and glioblastoma. As a case study, we investigate the relationships among the genes of a small astrocytoma-only subnetwork. This astrocytoma-only subnetwork consists of SVEP1, IGF1, CHRNA3, and SPAG6. All of these genes are highly coexpressed in a single sample of anaplastic astrocytoma tumor (grade III) and a sample of juvenile pilocytic astrocytoma. Three of these genes are also associated with nicotine. This data lead us to formulate a testable hypothesis that this astrocytoma outlier-network provides a link between some gliomas/astrocytomas and nicotine.

      • Identification and biosynthesis of acylphloroglucinols in <i>Hypericum gentianoides</i>

        Crispin, Matthew C.,Hur, Manhoi,Park, Taeseong,Kim, Young Hwan,Wurtele, Eve Syrkin Blackwell Publishing Ltd 2013 Physiologia Plantarum Vol.148 No.3

        <P>Species of the genus <I>Hypericum</I> contain a rich array of unusual polyketides, however, only a small proportion of the over 450 <I>Hypericum</I> species, other than the popular medicinal supplement St. John's Wort (<I>Hypericum perforatum</I>), have even been chemically characterized. <I>Hypericum gentianoides</I>, a small annual used medicinally by Cherokee Americans, contains bioactive acylphloroglucinols. Here, we identify acylphloroglucinol constituents of <I>H. gentianoides</I> and determine a potential pathway to their synthesis. Liquid chromatography/electrospray ionization‐mass spectrometry (LC/ESI‐MS) and HPLC‐UV indicate that the level of accumulation and profile of acylphloroglucinols in <I>H. gentianoides</I> vary little seasonally when grown in a greenhouse, but do vary with development and are highly dependent on the accession, highlighting the importance of the selection of plant material for study. We identify the chemical structures of the nine prevalent polyketides, based on LC/ESI‐MS and hybrid quadrupole orthogonal time‐of‐flight (Q‐TOF) mass spectrometry; these metabolites include one monomeric phlorisobutyrophenone (PIB) derivative and eight dimeric acylphloroglucinols. Q‐TOF spectrometry was used to identify eight additional PIB derivatives that were not detected by LC/ESI‐MS. These data lead us to propose that diacylphloroglucinols are synthesized via modification of PIB to yield diverse phloroglucinol and filicinic acids moieties, followed by dimerization of a phloroglucinol and a filicinic acid monomer to yield the observed complement of diacylphloroglucinols. The metabolomics data from <I>H. gentianoides</I> are accessible in plant metabolomics resource (PMR) (<url href='http://www.metnetdb.org/pmr'>http://www.metnetdb.org/pmr), a public metabolomics database with analysis software for plants and microbial organisms.</P>

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