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      Two phases of disulfide bond formation have differing requirements for oxygen

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          Abstract

          Disulfide bonds introduced during or shortly after protein synthesis can occur without oxygen, whereas those introduced during post-translational folding or isomerization are oxygen dependent.

          Abstract

          Most proteins destined for the extracellular space require disulfide bonds for folding and stability. Disulfide bonds are introduced co- and post-translationally in endoplasmic reticulum (ER) cargo in a redox relay that requires a terminal electron acceptor. Oxygen can serve as the electron acceptor in vitro, but its role in vivo remains unknown. Hypoxia causes ER stress, suggesting a role for oxygen in protein folding. Here we demonstrate the existence of two phases of disulfide bond formation in living mammalian cells, with differential requirements for oxygen. Disulfide bonds introduced rapidly during protein synthesis can occur without oxygen, whereas those introduced during post-translational folding or isomerization are oxygen dependent. Other protein maturation processes in the secretory pathway, including ER-localized N-linked glycosylation, glycan trimming, Golgi-localized complex glycosylation, and protein transport, occur independently of oxygen availability. These results suggest that an alternative electron acceptor is available transiently during an initial phase of disulfide bond formation and that post-translational oxygen-dependent disulfide bond formation causes hypoxia-induced ER stress.

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          Most cited references61

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          Hypoxia signalling through mTOR and the unfolded protein response in cancer.

          Hypoxia occurs in the majority of tumours, promoting angiogenesis, metastasis and resistance to therapy. Responses to hypoxia are orchestrated in part through activation of the hypoxia-inducible factor family of transcription factors (HIFs). Recently, two additional O(2)-sensitive signalling pathways have also been implicated: signalling through the mammalian target of rapamycin (mTOR) kinase and signalling through activation of the unfolded protein response (UPR). Although they are activated independently, growing evidence suggests that HIF-, mTOR- and UPR-dependent responses to hypoxia act in an integrated way, influencing each other and common downstream pathways that affect gene expression, metabolism, cell survival, tumorigenesis and tumour growth.
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            Protein folding and modification in the mammalian endoplasmic reticulum.

            Analysis of the human genome reveals that approximately a third of all open reading frames code for proteins that enter the endoplasmic reticulum (ER), demonstrating the importance of this organelle for global protein maturation. The path taken by a polypeptide through the secretory pathway starts with its translocation across or into the ER membrane. It then must fold and be modified correctly in the ER before being transported via the Golgi apparatus to the cell surface or another destination. Being physically segregated from the cytosol means that the ER lumen has a distinct folding environment. It contains much of the machinery for fulfilling the task of protein production, including complex pathways for folding, assembly, modification, quality control, and recycling. Importantly, the compartmentalization means that several modifications that do not occur in the cytosol, such as glycosylation and extensive disulfide bond formation, can occur to secreted proteins to enhance their stability before their exposure to the extracellular milieu. How these various machineries interact during the normal pathway of folding and protein secretion is the subject of this review.
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              Model-based variance-stabilizing transformation for Illumina microarray data

              Variance stabilization is a step in the preprocessing of microarray data that can greatly benefit the performance of subsequent statistical modeling and inference. Due to the often limited number of technical replicates for Affymetrix and cDNA arrays, achieving variance stabilization can be difficult. Although the Illumina microarray platform provides a larger number of technical replicates on each array (usually over 30 randomly distributed beads per probe), these replicates have not been leveraged in the current log2 data transformation process. We devised a variance-stabilizing transformation (VST) method that takes advantage of the technical replicates available on an Illumina microarray. We have compared VST with log2 and Variance-stabilizing normalization (VSN) by using the Kruglyak bead-level data (2006) and Barnes titration data (2005). The results of the Kruglyak data suggest that VST stabilizes variances of bead-replicates within an array. The results of the Barnes data show that VST can improve the detection of differentially expressed genes and reduce false-positive identifications. We conclude that although both VST and VSN are built upon the same model of measurement noise, VST stabilizes the variance better and more efficiently for the Illumina platform by leveraging the availability of a larger number of within-array replicates. The algorithms and Supplementary Data are included in the lumi package of Bioconductor, available at: www.bioconductor.org.
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                Author and article information

                Journal
                J Cell Biol
                J. Cell Biol
                jcb
                The Journal of Cell Biology
                The Rockefeller University Press
                0021-9525
                1540-8140
                25 November 2013
                : 203
                : 4
                : 615-627
                Affiliations
                [1 ]Ontario Cancer Institute and Campbell Family Institute for Cancer Research, University Health Network, Toronto, Ontario M5G 2M9, Canada
                [2 ]Departments of Radiation Oncology , [3 ]Medical Biophysics , and [4 ]Institute of Medical Science, University of Toronto, Ontario M5S 1A1, Canada
                [5 ]Department of Radiation Oncology (Maastro Lab), GROW School for Oncology & Developmental Biology, Maastricht University, 6200 MD Maastricht, Netherlands
                [6 ]Informatics and Biocomputing Platform and [7 ]Selective Therapies Program, Ontario Institute for Cancer Research, Ontario M5G 0A3, Canada
                [8 ]Cellular Protein Chemistry, Bijvoet Center for Biomolecular Research, Faculty of Science, Utrecht University, 3584 CH Utrecht, Netherlands
                Author notes
                Correspondence to Marianne Koritzinsky: mkoritzi@ 123456uhnresearch.ca
                Article
                201307185
                10.1083/jcb.201307185
                3840938
                24247433
                c28e58fe-e0a8-4595-ab1c-2b592c299515
                © 2013 Koritzinsky et al.

                This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 3.0 Unported license, as described at http://creativecommons.org/licenses/by-nc-sa/3.0/).

                History
                : 29 July 2013
                : 16 October 2013
                Categories
                Research Articles
                Article

                Cell biology
                Cell biology

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