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      The PI3K and MAPK/p38 pathways control stress granule assembly in a hierarchical manner

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          Abstract

          PI3K and p38 act in a hierarchical manner to enhance mTORC1 activity and stress granule formation; although PI3K is the main driver, the impact of p38 gets apparent as PI3K activity declines.

          Abstract

          All cells and organisms exhibit stress-coping mechanisms to ensure survival. Cytoplasmic protein-RNA assemblies termed stress granules are increasingly recognized to promote cellular survival under stress. Thus, they might represent tumor vulnerabilities that are currently poorly explored. The translation-inhibitory eIF2α kinases are established as main drivers of stress granule assembly. Using a systems approach, we identify the translation enhancers PI3K and MAPK/p38 as pro-stress-granule-kinases. They act through the metabolic master regulator mammalian target of rapamycin complex 1 (mTORC1) to promote stress granule assembly. When highly active, PI3K is the main driver of stress granules; however, the impact of p38 becomes apparent as PI3K activity declines. PI3K and p38 thus act in a hierarchical manner to drive mTORC1 activity and stress granule assembly. Of note, this signaling hierarchy is also present in human breast cancer tissue. Importantly, only the recognition of the PI3K-p38 hierarchy under stress enabled the discovery of p38’s role in stress granule formation. In summary, we assign a new pro-survival function to the key oncogenic kinases PI3K and p38, as they hierarchically promote stress granule formation.

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          Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood.

          Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis. We suggest an approach that exploits the profile likelihood. It enables to detect structural non-identifiabilities, which manifest in functionally related model parameters. Furthermore, practical non-identifiabilities are detected, that might arise due to limited amount and quality of experimental data. Last but not least confidence intervals can be derived. The results are easy to interpret and can be used for experimental planning and for model reduction. An implementation is freely available for MATLAB and the PottersWheel modeling toolbox at http://web.me.com/andreas.raue/profile/software.html. Supplementary data are available at Bioinformatics online.
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            Phosphatidylinositol-3-OH kinase as a direct target of Ras.

            Ras (p21ras) interacts directly with the catalytic subunit of phosphatidylinositol-3-OH kinase in a GTP-dependent manner through the Ras effector site. In vivo, dominant negative Ras mutant N17 inhibits growth factor induced production of 3' phosphorylated phosphoinositides in PC12 cells, and transfection of Ras, but not Raf, into COS cells results in a large elevation in the level of these lipids. Therefore Ras can probably regulate phosphatidylinositol-3-OH kinase, providing a point of divergence in signalling pathways downstream of Ras.
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              Arsenic toxicity and potential mechanisms of action.

              Exposure to the metalloid arsenic is a daily occurrence because of its environmental pervasiveness. Arsenic, which is found in several different chemical forms and oxidation states, causes acute and chronic adverse health effects, including cancer. The metabolism of arsenic has an important role in its toxicity. The metabolism involves reduction to a trivalent state and oxidative methylation to a pentavalent state. The trivalent arsenicals, including those methylated, have more potent toxic properties than the pentavalent arsenicals. The exact mechanism of the action of arsenic is not known, but several hypotheses have been proposed. At a biochemical level, inorganic arsenic in the pentavalent state may replace phosphate in several reactions. In the trivalent state, inorganic and organic (methylated) arsenic may react with critical thiols in proteins and inhibit their activity. Regarding cancer, potential mechanisms include genotoxicity, altered DNA methylation, oxidative stress, altered cell proliferation, co-carcinogenesis, and tumor promotion. A better understanding of the mechanism(s) of action of arsenic will make a more confident determination of the risks associated with exposure to this chemical.
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                Author and article information

                Journal
                Life Sci Alliance
                Life Sci Alliance
                lsa
                lsa
                Life Science Alliance
                Life Science Alliance LLC
                2575-1077
                28 March 2019
                April 2019
                28 March 2019
                : 2
                : 2
                : e201800257
                Affiliations
                [1 ]Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signaling, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
                [2 ]Department for Neuroscience, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
                [3 ]Laboratory of Molecular Tumor Pathology, Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
                [4 ]German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ), Heidelberg, Germany
                [5 ]Brain Cancer Metabolism Group, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
                [6 ]Faculty of Bioscience, Heidelberg University, Heidelberg, Germany
                [7 ]Jena University Language and Information Engineering Lab, Friedrich-Schiller-University Jena, Jena, Germany
                [8 ]German Federal Institute for Risk Assessment, Strategies for Toxicological Assessment, Experimental Toxicology and ZEBET, German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany
                [9 ]Neurology Clinic and National Center for Tumor Diseases, University Hospital of Heidelberg, Heidelberg, Germany
                [10 ]Faculty of Bioscience, Fisheries and Economics, Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norway
                [11 ]Systems Biology and Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany
                [12 ]Institute of Biochemistry and Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria
                Author notes
                Author information
                https://orcid.org/0000-0002-0328-1990
                https://orcid.org/0000-0003-1193-5103
                https://orcid.org/0000-0001-5575-9821
                https://orcid.org/0000-0002-6219-1514
                https://orcid.org/0000-0002-9124-5112
                https://orcid.org/0000-0003-3862-6546
                https://orcid.org/0000-0002-9069-2930
                Article
                LSA-2018-00257
                10.26508/lsa.201800257
                6441495
                30923191
                e2d98251-050c-4acc-884e-6967b209a3de
                © 2019 Heberle et al.

                This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).

                History
                : 27 November 2018
                : 6 March 2019
                : 7 March 2019
                Funding
                Funded by: Rosalind-Franklin-Fellowship of the University of Groningen;
                Award Recipient :
                Funded by: BMBF e:Med initiative GlioPATH;
                Award ID: 01ZX1402
                Award Recipient :
                Funded by: BMBF e:Med initiative MAPTor-NET;
                Award ID: 031A426A/B
                Award Recipient :
                Funded by: Stichting TSC Fonds;
                Award Recipient :
                Funded by: German Research Foundation;
                Award ID: TH 1358/3-1
                Award Recipient :
                Funded by: MESI-STRAT;
                Award Recipient :
                Funded by: European Union’s Horizon 2020;
                Award ID: 754688
                Funded by: German Bundesministerium für Bildung und Forschung;
                Award ID: 01ZZ1803G
                Funded by: Deutsche Forschungsgemeinschaft;
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