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      Ketamine’s antidepressant effect is mediated by energy metabolism and antioxidant defense system

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          Abstract

          Fewer than 50% of all patients with major depressive disorder (MDD) treated with currently available antidepressants (ADs) show full remission. Moreover, about one third of the patients suffering from MDD does not respond to conventional ADs and develop treatment-resistant depression (TRD). Ketamine, a non-competitive, voltage-dependent N-Methyl-D-aspartate receptor (NMDAR) antagonist, has been shown to have a rapid antidepressant effect, especially in patients suffering from TRD. Hippocampi of ketamine-treated mice were analysed by metabolome and proteome profiling to delineate ketamine treatment-affected molecular pathways and biosignatures. Our data implicate mitochondrial energy metabolism and the antioxidant defense system as downstream effectors of the ketamine response. Specifically, ketamine tended to downregulate the adenosine triphosphate (ATP)/adenosine diphosphate (ADP) metabolite ratio which strongly correlated with forced swim test (FST) floating time. Furthermore, we found increased levels of enzymes that are part of the ‘oxidative phosphorylation’ (OXPHOS) pathway. Our study also suggests that ketamine causes less protein damage by rapidly decreasing reactive oxygen species (ROS) production and lend further support to the hypothesis that mitochondria have a critical role for mediating antidepressant action including the rapid ketamine response.

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          In silico prediction of protein-protein interactions in human macrophages

          Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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            Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study

            The Lancet, 349(9064), 1498-1504
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              MetaboAnalyst: a web server for metabolomic data analysis and interpretation

              Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca
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                Author and article information

                Contributors
                Marianne.Mueller@unimedizin-mainz.de
                turck@psych.mpg.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                17 November 2017
                17 November 2017
                2017
                : 7
                : 15788
                Affiliations
                [1 ]ISNI 0000 0000 9497 5095, GRID grid.419548.5, Max Planck Institute of Psychiatry, Department of Translational Research in Psychiatry, ; Munich, Germany
                [2 ]ISNI 0000000121885934, GRID grid.5335.0, Cambridge Centre for Proteomics, Cambridge System Biology Centre, University of Cambridge, ; Cambridge, UK
                [3 ]ISNI 000000041936754X, GRID grid.38142.3c, Division of Signal Transduction, Beth Israel Deaconess Medical Center and Department of Medicine, Harvard Medical School, ; Boston, USA
                [4 ]ISNI 0000 0000 9497 5095, GRID grid.419548.5, Max Planck Institute of Psychiatry, Department of Stress Neurobiology and Neurogenetics, ; Munich, Germany
                [5 ]GRID grid.410607.4, Experimental Psychiatry, Department of Psychiatry and Psychotherapy & Focus Program Translational Neuroscience, Johannes Gutenberg University Medical Center, ; Mainz, Germany
                [6 ]ISNI 0000 0001 1941 7111, GRID grid.5802.f, Present Address: Institute of Pathobiochemistry, Johannes Gutenberg University, Medical School, ; Mainz, Germany
                Article
                16183
                10.1038/s41598-017-16183-x
                5694011
                29150633
                b296fd02-861c-4afd-917b-12399c60aa66
                © The Author(s) 2017

                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
                : 4 August 2017
                : 8 November 2017
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