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      Global redox proteome and phosphoproteome analysis reveals redox switch in Akt

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          Abstract

          Protein oxidation sits at the intersection of multiple signalling pathways, yet the magnitude and extent of crosstalk between oxidation and other post-translational modifications remains unclear. Here, we delineate global changes in adipocyte signalling networks following acute oxidative stress and reveal considerable crosstalk between cysteine oxidation and phosphorylation-based signalling. Oxidation of key regulatory kinases, including Akt, mTOR and AMPK influences the fidelity rather than their absolute activation state, highlighting an unappreciated interplay between these modifications. Mechanistic analysis of the redox regulation of Akt identified two cysteine residues in the pleckstrin homology domain (C60 and C77) to be reversibly oxidized. Oxidation at these sites affected Akt recruitment to the plasma membrane by stabilizing the PIP 3 binding pocket. Our data provide insights into the interplay between oxidative stress-derived redox signalling and protein phosphorylation networks and serve as a resource for understanding the contribution of cellular oxidation to a range of diseases.

          Abstract

          Crosstalk between protein oxidation and other post-translational modifications remains unexplored. Here, the authors map the phosphoproteome, cysteine redox proteome and total proteome of adipocytes under acute oxidative stress and reveal crosstalk between cysteine oxidation and phosphorylation-based signalling.

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          ROS as signalling molecules: mechanisms that generate specificity in ROS homeostasis.

          Reactive oxygen species (ROS) have been shown to be toxic but also function as signalling molecules. This biological paradox underlies mechanisms that are important for the integrity and fitness of living organisms and their ageing. The pathways that regulate ROS homeostasis are crucial for mitigating the toxicity of ROS and provide strong evidence about specificity in ROS signalling. By taking advantage of the chemistry of ROS, highly specific mechanisms have evolved that form the basis of oxidant scavenging and ROS signalling systems.
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            Reactive oxygen species and mitochondria: A nexus of cellular homeostasis

            Reactive oxygen species (ROS) are integral components of multiple cellular pathways even though excessive or inappropriately localized ROS damage cells. ROS function as anti-microbial effector molecules and as signaling molecules that regulate such processes as NF-kB transcriptional activity, the production of DNA-based neutrophil extracellular traps (NETs), and autophagy. The main sources of cellular ROS are mitochondria and NADPH oxidases (NOXs). In contrast to NOX-generated ROS, ROS produced in the mitochondria (mtROS) were initially considered to be unwanted by-products of oxidative metabolism. Increasing evidence indicates that mtROS have been incorporated into signaling pathways including those regulating immune responses and autophagy. As metabolic hubs, mitochondria facilitate crosstalk between the metabolic state of the cell with these pathways. Mitochondria and ROS are thus a nexus of multiple pathways that determine the response of cells to disruptions in cellular homeostasis such as infection, sterile damage, and metabolic imbalance. In this review, we discuss the roles of mitochondria in the generation of ROS-derived anti-microbial effectors, the interplay of mitochondria and ROS with autophagy and the formation of DNA extracellular traps, and activation of the NLRP3 inflammasome by ROS and mitochondria.
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              Missing value estimation methods for DNA microarrays.

              Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data. We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.
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                Author and article information

                Contributors
                david.james@sydney.edu.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                2 December 2019
                2 December 2019
                2019
                : 10
                : 5486
                Affiliations
                [1 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, Charles Perkins Centre, , The University of Sydney, ; Sydney, NSW 2006 Australia
                [2 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, School of Life and Environmental Sciences, , The University of Sydney, ; Sydney, NSW 2006 Australia
                [3 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, School of Mathematics and Statistics, , The University of Sydney, ; Sydney, NSW 2006 Australia
                [4 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, School of Physics, , The University of Sydney, ; Sydney, NSW 2006 Australia
                [5 ]ISNI 0000 0004 1936 7857, GRID grid.1002.3, Department of Biochemistry and Molecular Biology, School of Biomedical Sciences, , Monash University, ; Clayton, VIC 3800 Australia
                [6 ]ISNI 0000 0004 1936 7857, GRID grid.1002.3, Biomedicine Discovery Institute, , Monash University, ; Clayton, VIC 3800 Australia
                [7 ]ISNI 0000 0001 2191 0423, GRID grid.255364.3, Brody School of Medicine, Physiology Department, , East Carolina University, ; Greenville, NC USA
                [8 ]ISNI 0000 0001 2191 0423, GRID grid.255364.3, East Carolina Diabetes and Obesity Institute, , East Carolina University, ; Greenville, NC USA
                [9 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, The Dr. John and Anne Chong Laboratory for Functional Genomics, Charles Perkins Centre and School of Life & Environmental Sciences, , The University of Sydney, ; Sydney, NSW 2006 Australia
                [10 ]ISNI 0000 0004 0444 7512, GRID grid.248902.5, The Centenary Institute, ; Newtown, NSW 2042 Australia
                [11 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, National Health and Medical Research Council Clinical Trials Centre, , The University of Sydney, ; Sydney, NSW 2006 Australia
                [12 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, Sydney Medical School, , The University of Sydney, ; Sydney, NSW 2006 Australia
                Author information
                http://orcid.org/0000-0002-6609-6151
                http://orcid.org/0000-0003-1098-3138
                http://orcid.org/0000-0002-2666-9744
                http://orcid.org/0000-0002-7521-2417
                http://orcid.org/0000-0001-6486-2863
                http://orcid.org/0000-0001-8241-2903
                Article
                13114
                10.1038/s41467-019-13114-4
                6889415
                31792197
                8c929bcb-ba9f-4406-8cf5-87a6f4bc3597
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 December 2018
                : 18 October 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000925, Department of Health | National Health and Medical Research Council (NHMRC);
                Award ID: GNT1120201
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                proteomics,insulin signalling,post-translational modifications
                Uncategorized
                proteomics, insulin signalling, post-translational modifications

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