20 research outputs found

    The IRE1α/XBP1s Pathway Is Essential for the Glucose Response and Protection of β Cells

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    <div><p>Although glucose uniquely stimulates proinsulin biosynthesis in β cells, surprisingly little is known of the underlying mechanism(s). Here, we demonstrate that glucose activates the unfolded protein response transducer inositol-requiring enzyme 1 alpha (IRE1α) to initiate X-box-binding protein 1 (<i>Xbp1</i>) mRNA splicing in adult primary β cells. Using mRNA sequencing (mRNA-Seq), we show that unconventional <i>Xbp1</i> mRNA splicing is required to increase and decrease the expression of several hundred mRNAs encoding functions that expand the protein secretory capacity for increased insulin production and protect from oxidative damage, respectively. At 2 wk after tamoxifen-mediated <i>Ire1α</i> deletion, mice develop hyperglycemia and hypoinsulinemia, due to defective β cell function that was exacerbated upon feeding and glucose stimulation. Although previous reports suggest IRE1α degrades insulin mRNAs, <i>Ire1α</i> deletion did not alter insulin mRNA expression either in the presence or absence of glucose stimulation. Instead, β cell failure upon <i>Ire1α</i> deletion was primarily due to reduced proinsulin mRNA translation primarily because of defective glucose-stimulated induction of a dozen genes required for the signal recognition particle (SRP), SRP receptors, the translocon, the signal peptidase complex, and over 100 other genes with many other intracellular functions. In contrast, <i>Ire1α</i> deletion in β cells increased the expression of over 300 mRNAs encoding functions that cause inflammation and oxidative stress, yet only a few of these accumulated during high glucose. Antioxidant treatment significantly reduced glucose intolerance and markers of inflammation and oxidative stress in mice with β cell-specific <i>Ire1α</i> deletion. The results demonstrate that glucose activates IRE1α-mediated <i>Xbp1</i> splicing to expand the secretory capacity of the β cell for increased proinsulin synthesis and to limit oxidative stress that leads to β cell failure.</p></div

    Novel Bioinformatics Method for Identification of Genome-Wide Non-Canonical Spliced Regions Using RNA-Seq Data

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    <div><p>Setting</p><p>During endoplasmic reticulum (ER) stress, the endoribonuclease (RNase) <i>Ire1</i>α initiates removal of a 26 nt region from the mRNA encoding the transcription factor <i>Xbp1</i> via an unconventional mechanism (atypically within the cytosol). This causes an open reading frame-shift that leads to altered transcriptional regulation of numerous downstream genes in response to ER stress as part of the unfolded protein response (UPR). Strikingly, other examples of targeted, unconventional splicing of short mRNA regions have yet to be reported.</p><p>Objective</p><p>Our goal was to develop an approach to identify non-canonical, possibly very short, splicing regions using RNA-Seq data and apply it to ER stress-induced <i>Ire1</i>α heterozygous and knockout mouse embryonic fibroblast (MEF) cell lines to identify additional <i>Ire1</i>α targets.</p><p>Results</p><p>We developed a bioinformatics approach called the Read-Split-Walk (RSW) pipeline, and evaluated it using two <i>Ire1</i>α heterozygous and two <i>Ire1</i>α-null samples. The 26 nt non-canonical splice site in <i>Xbp1</i> was detected as the top hit by our RSW pipeline in heterozygous samples but not in the negative control <i>Ire1</i>α knockout samples. We compared the <i>Xbp1</i> results from our approach with results using the alignment program BWA, Bowtie2, STAR, Exonerate and the Unix “<i>grep</i>” command. We then applied our RSW pipeline to RNA-Seq data from the SKBR3 human breast cancer cell line. RSW reported a large number of non-canonical spliced regions for 108 genes in chromosome 17, which were identified by an independent study.</p><p>Conclusions</p><p>We conclude that our RSW pipeline is a practical approach for identifying non-canonical splice junction sites on a genome-wide level. We demonstrate that our pipeline can detect novel splice sites in RNA-Seq data generated under similar conditions for multiple species, in our case mouse and human.</p></div

    Epimedium sempervirens Nakai

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    原著和名: トキハイカリサウ科名: メギ科 = Berberidaceae採集地: 島根県 隠岐 島後 (隠岐 島後)採集日: 1976/5/9採集者: 萩庭丈壽整理番号: JH002351国立科学博物館整理番号: TNS-VS-95235

    Substructure amongst top compounds predicted by QSAR modelling.

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    <p>Major substructure detected by SIMCOMP2 software for the top similar structures from their prediction sets.</p

    Additional file 5: of Read-Split-Run: an improved bioinformatics pipeline for identification of genome-wide non-canonical spliced regions using RNA-Seq data

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    Total number of spliced regions identified by RSR in the human ENCODE RNA-Seq dataset. This file includes all supplementary results for number of supporting reads, splice length, range of supporting reads (spliced regions) identified by RSR in the human ENCODE RNA-Seq dataset. (XLSX 1411 kb

    <i>KO</i> islets exhibit ER stress.

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    <p>(A) qRT-PCR of UPR genes in islets isolated 6 wk post-Tam and incubated in 11 mM glucose 16 h ([<i>n</i> = 5], [<i>p</i> ≤ 0.05]). (B) Immunofluorescence microscopy of pancreas sections stained for KDEL (BIP and GRP94) (green), the plasma membrane protein GLUT2 (red), and nuclei DAPI (blue). Overlap of red/green channels represents defective compartmentalization that was found to be increased in the <i>KO</i><sup><i>Fe/-; Cre</i></sup> as shown in yellow. Scale bars, 400x = 50 μm, 1,000x = 10 μm, 5,180x = 2 μm and 10,500x = 1 μM. Additional examples are shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002277#pbio.1002277.s007" target="_blank">S3B Fig</a>. (C) EM of adult mouse (16 wk old) islets and their β cells from mice 2 wk post-Tam. Scale bars, both panels, 1 μm. Distended mitochondria are outlined with yellow dashes. (D) Conventional PCR flanking the 26 nt intron in <i>Xbp1</i> mRNA spliced by IRE1α from the islet complementary DNAs (cDNAs) used for mRNA-Seq analysis, 6 mM versus 18 mM glucose. Results representative of <i>n</i> = 5 per genotype. (E) Global heatmap for the ~22,000 mRNAs detected by mRNA-Seq for 18 mM <i>KO</i><sup><i>Fe/-; Cre</i></sup> & <i>WT</i><sup><i>Fe/+</i></sup> samples; green and red indicate increased and decreased expression. The blue box indicates genes with inverse expression dependent on IRE1α and high glucose.</p

    Top 10 compounds against S. typhimurium in terms of log(MIC).

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    <p>Top 10 compounds against S. typhimurium in terms of log(MIC).</p

    mRNA sequencing identifies IRE1α- and glucose-dependent mRNAs in islets.

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    <p>(A) mRNA-Seq data on β cell-specific mRNAs. The results show no significant change to INS1 or INS2 in the <i>KO</i><sup><i>Fe/-; Cre</i></sup> samples, while MAFA, GCG, and PC5 are increased by deletion ([<i>n</i> = 5], [18 mM <i>KO</i><sup><i>Fe/-; Cre</i></sup>, <i>p</i>-values ≤ 0.05]). mRNA-Seq expression fold changes were normalized relative to the 6 mM <i>WT</i><sup><i>Fe/+</i></sup> islet context. (B) Four-way Venn diagrams of <i>WT</i><sup><i>Fe/+</i></sup> versus <i>KO</i><sup><i>Fe/-; Cre</i></sup> islets during 6 mM versus 1 8mM glucose exposur<i>e</i> for 72 h. <i>Ire1α</i>-dependent mRNAs are in bold italics, while those also dependent on high glucose are in bold, italicized, and underlined font. At the center, bar graphs representing the <i>Ire1α</i>- and glucose-dependent trends of interest are labeled “Induction” and “Repression.” (C) Combined <b>DAVID</b> (the Database for Annotation, Visualization and Integrated Discovery) and “ConceptGen” GO analysis of <i>Ire1α-</i> and glucose-dependent mRNAs. Categories shown are specifically found in the genotype, while the shared categories have been omitted for simplicity, although no single mRNA was common between the groups. (D) Mass spectrometry of murine islets infected with <i>Ad-IREα-K907A (Ad-ΔR)</i> versus <i>Ad-β-Galactosidase</i> (<i>β-Gal</i>). Proteins with ≥5 unique peptides detected per protein increased or decreased upon infection in triplicate were analyzed for GO using ConceptGen and DAVID web resources (<i>n</i> = 3). The proteins shown (Fig 3D) exhibit the same expression dependence for IRE1α as measured by mRNA-Seq (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002277#pbio.1002277.s002" target="_blank">S2 Data</a>).</p

    Identification of potential antimicrobials against <i>Salmonella typhimurium</i> and <i>Listeria monocytogenes</i> using Quantitative Structure-Activity Relation modeling

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    <div><p>The shelf-life of fresh carcasses and produce depends on the chemical and physical properties of antimicrobials currently used for treatment. For many years the gold standard of these antimicrobials has been Cetylpyridinium Chloride (CPC) a quaternary ammonium compound (QAC). CPC is very effective at removing bacterial pathogens from the surface of chicken but has not been approved for other products due to a toxic residue left behind after treatment. Currently there is also a rising trend in QAC resistant bacteria. In order to find new compounds that can combat both antimicrobial resistance and the toxic residue we have developed two Quantitative Structure-Activity Relationship (QSAR) models for <i>Salmonella typhimurium</i> and <i>Listeria monocytogenes</i>. These models have been shown to be accurate and reliable through multiple internal and external validation techniques. In processing these models we have also identified important descriptors and structures that may be key in producing a viable compound. With these models, development and testing of new compounds should be greatly simplified.</p></div

    Descriptor distribution for top QSAR models.

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    <p>Distribution of chemical descriptors (black) and their respective average absolute coefficient magnitudes (black), in top models for <i>S</i>. <i>typhimurium</i> (A) and <i>L</i>. <i>monocytogenes</i> (B). Descriptor distribution normalized to the total number of models observed for each bacteria. (C) Descriptor distribution (black) and average absolute coefficient magnitudes (grey) based on previously created models against <i>E</i>. <i>coli</i>.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189580#pone.0189580.ref033" target="_blank">33</a>] Most descriptors are in canonical SMILES, using single letters to represent atoms in a molecule, “*” denote any atom, “‘“ represent potential atoms, “()” represent branches in a molecule, numbers represent joining points in ring structures, “=“ represent double bonds, and lower case letters are atoms involved in aromatic strucutres. (D) Distribution of descriptor types across all top models. Descriptors that were pertinent to multiple bins were included in all potential bins.</p
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