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      Distinct stress conditions result in aggregation of proteins with similar properties

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          Abstract

          Protein aggregation is the abnormal association of proteins into larger aggregate structures which tend to be insoluble. This occurs during normal physiological conditions and in response to age or stress-induced protein misfolding and denaturation. In this present study we have defined the range of proteins that aggregate in yeast cells during normal growth and after exposure to stress conditions including an oxidative stress (hydrogen peroxide), a heavy metal stress (arsenite) and an amino acid analogue (azetidine-2-carboxylic acid). Our data indicate that these three stress conditions, which work by distinct mechanisms, promote the aggregation of similar types of proteins probably by lowering the threshold of protein aggregation. The proteins that aggregate during physiological conditions and stress share several features; however, stress conditions shift the criteria for protein aggregation propensity. This suggests that the proteins in aggregates are intrinsically aggregation-prone, rather than being proteins which are affected in a stress-specific manner. We additionally identified significant overlaps between stress aggregating yeast proteins and proteins that aggregate during ageing in yeast and C. elegans. We suggest that similar mechanisms may apply in disease- and non-disease settings and that the factors and components that control protein aggregation may be evolutionary conserved.

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          Most cited references34

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          Molecular chaperones in protein folding and proteostasis.

          Most proteins must fold into defined three-dimensional structures to gain functional activity. But in the cellular environment, newly synthesized proteins are at great risk of aberrant folding and aggregation, potentially forming toxic species. To avoid these dangers, cells invest in a complex network of molecular chaperones, which use ingenious mechanisms to prevent aggregation and promote efficient folding. Because protein molecules are highly dynamic, constant chaperone surveillance is required to ensure protein homeostasis (proteostasis). Recent advances suggest that an age-related decline in proteostasis capacity allows the manifestation of various protein-aggregation diseases, including Alzheimer's disease and Parkinson's disease. Interventions in these and numerous other pathological states may spring from a detailed understanding of the pathways underlying proteome maintenance.
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            Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

            We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.
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              Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae.

              We have analyzed the translational status of each mRNA in rapidly growing Saccharomyces cerevisiae. mRNAs were separated by velocity sedimentation on a sucrose gradient, and 14 fractions across the gradient were analyzed by quantitative microarray analysis, providing a profile of ribosome association with mRNAs for thousands of genes. For most genes, the majority of mRNA molecules were associated with ribosomes and presumably engaged in translation. This systematic approach enabled us to recognize genes with unusual behavior. For 43 genes, most mRNA molecules were not associated with ribosomes, suggesting that they may be translationally controlled. For 53 genes, including GCN4, CPA1, and ICY2, three genes for which translational control is known to play a key role in regulation, most mRNA molecules were associated with a single ribosome. The number of ribosomes associated with mRNAs increased with increasing length of the putative protein-coding sequence, consistent with longer transit times for ribosomes translating longer coding sequences. The density at which ribosomes were distributed on each mRNA (i.e., the number of ribosomes per unit ORF length) was well below the maximum packing density for nearly all mRNAs, consistent with initiation as the rate-limiting step in translation. Global analysis revealed an unexpected correlation: Ribosome density decreases with increasing ORF length. Models to account for this surprising observation are discussed.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                18 April 2016
                2016
                : 6
                : 24554
                Affiliations
                [1 ]Faculty of Life Sciences, University of Manchester , Manchester, M13 9PT, UK
                [2 ]Department of Chemistry and Molecular Biology, University of Gothenburg , S-405 30 Gothenburg, Sweden
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                srep24554
                10.1038/srep24554
                4834537
                27086931
                d4676412-9912-4f6a-9937-f220b8ae5099
                Copyright © 2016, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 12 February 2016
                : 23 March 2016
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