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      Metabolic Adaptation Establishes Disease Tolerance to Sepsis

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          Summary

          Sepsis is an often lethal syndrome resulting from maladaptive immune and metabolic responses to infection, compromising host homeostasis. Disease tolerance is a defense strategy against infection that preserves host homeostasis without exerting a direct negative impact on pathogens. Here, we demonstrate that induction of the iron-sequestering ferritin H chain (FTH) in response to polymicrobial infections is critical to establish disease tolerance to sepsis. The protective effect of FTH is exerted via a mechanism that counters iron-driven oxidative inhibition of the liver glucose-6-phosphatase (G6Pase), and in doing so, sustains endogenous glucose production via liver gluconeogenesis. This is required to prevent the development of hypoglycemia that otherwise compromises disease tolerance to sepsis. FTH overexpression or ferritin administration establish disease tolerance therapeutically. In conclusion, disease tolerance to sepsis relies on a crosstalk between adaptive responses controlling iron and glucose metabolism, required to maintain blood glucose within a physiologic range compatible with host survival.

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          Highlights

          • Ferritin is required to establish disease tolerance to sepsis

          • Iron heme represses liver G6Pase during sepsis

          • Ferritin counters G6Pase repression and sustains blood glucose levels after sepsis

          • Liver gluconeogenesis is required to establish disease tolerance to sepsis

          Abstract

          Disease tolerance to sepsis depends on a crosstalk between iron and glucose- metabolic responses that maintain blood glucose levels within a dynamic range compatible with host survival.

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          Most cited references36

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          Conditional gene targeting in macrophages and granulocytes using LysMcre mice.

          Conditional mutagenesis in mice has recently been made possible through the combination of gene targeting techniques and site-directed mutagenesis, using the bacteriophage P1-derived Cre/loxP recombination system. The versatility of this approach depends on the availability of mouse mutants in which the recombinase Cre is expressed in the appropriate cell lineages or tissues. Here we report the generation of mice that express Cre in myeloid cells due to targeted insertion of the cre cDNA into their endogenous M lysozyme locus. In double mutant mice harboring both the LysMcre allele and one of two different loxP-flanked target genes tested, a deletion efficiency of 83-98% was determined in mature macrophages and near 100% in granulocytes. Partial deletion (16%) could be detected in CD11c+ splenic dendritic cells which are closely related to the monocyte/macrophage lineage. In contrast, no significant deletion was observed in tail DNA or purified T and B cells. Taken together, LysMcre mice allow for both specific and highly efficient Cre-mediated deletion of loxP-flanked target genes in myeloid cells.
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            Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

            Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.
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              The effects of alternate optimal solutions in constraint-based genome-scale metabolic models.

              Genome-scale constraint-based models of several organisms have now been constructed and are being used for model driven research. A key issue that may arise in the use of such models is the existence of alternate optimal solutions wherein the same maximal objective (e.g., growth rate) can be achieved through different flux distributions. Herein, we investigate the effects that alternate optimal solutions may have on the predicted range of flux values calculated using currently practiced linear (LP) and quadratic programming (QP) methods. An efficient LP-based strategy is described to calculate the range of flux variability that can be present in order to achieve optimal as well as suboptimal objective states. Sample results are provided for growth predictions of E. coli using glucose, acetate, and lactate as carbon substrates. These results demonstrate the extent of flux variability to be highly dependent on environmental conditions and network composition. In addition we examined the impact of alternate optima for growth under gene knockout conditions as calculated using QP-based methods. It was observed that calculations using QP-based methods can show significant variation in growth rate if the flux variability among alternate optima is high. The underlying biological significance and general source of such flux variability is further investigated through the identification of redundancies in the network (equivalent reaction sets) that lead to alternate solutions. Collectively, these results illustrate the variability inherent in metabolic flux distributions and the possible implications of this heterogeneity for constraint-based modeling approaches. These methods also provide an efficient and robust method to calculate the range of flux distributions that can be derived from quantitative fermentation data.
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                Author and article information

                Contributors
                Journal
                Cell
                Cell
                Cell
                Cell Press
                0092-8674
                1097-4172
                15 June 2017
                15 June 2017
                : 169
                : 7
                : 1263-1275.e14
                Affiliations
                [1 ]Instituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
                [2 ]Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, 07747 Jena, Germany
                [3 ]Language and Information Engineering Laboratory, Friedrich-Schiller-University, 07743 Jena, Germany
                [4 ]INSERM U1213, Université Claude Bernard Lyon, 69100 Villeurbanne, France
                [5 ]Center for Sepsis Control and Care, Jena University Hospital, 07747 Jena, Germany
                Author notes
                []Corresponding author mpsoares@ 123456igc.gulbenkian.pt
                [6]

                These authors contributed equally

                [7]

                Lead Contact

                Article
                S0092-8674(17)30592-5
                10.1016/j.cell.2017.05.031
                5480394
                28622511
                e3093b96-0a39-4767-b1c6-2d33e52a17e0
                © 2017 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 9 January 2017
                : 10 April 2017
                : 17 May 2017
                Categories
                Article

                Cell biology
                infection,sepsis,disease tolerance,ferritin,iron,heme,gluconeogenesis,glucose-6-phosphatase,inflammation,metabolism

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