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      Fluxomics reveals cellular and molecular basis of increased renal ammoniagenesis

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          Abstract

          The kidney plays a critical role in excreting ammonia during metabolic acidosis and liver failure. The mechanisms behind this process have been poorly explored. The present study combines results of in vivo experiments of increased total ammoniagenesis with systems biology modeling, in which eight rats were fed an amino acid-rich diet (HD group) and eight a normal chow diet (AL group). We developed a method based on elementary mode analysis to study changes in amino acid flux occurring across the kidney in increased ammoniagenesis. Elementary modes represent minimal feasible metabolic paths in steady state. The model was used to predict amino acid fluxes in healthy and pre-hyperammonemic conditions, which were compared to experimental fluxes in rats. First, we found that total renal ammoniagenesis increased from 264 ± 68 to 612 ± 87 nmol (100 g body weight) −1 min −1 in the HD group ( P = 0.021) and a concomitated upregulation of NKCC2 ammonia and other transporters in the kidney. In the kidney metabolic model, the best predictions were obtained with ammonia transport as an objective. Other objectives resulting in a fair correlation with the measured fluxes (correlation coefficient >0.5) were growth, protein uptake, urea excretion, and lysine and phenylalanine transport. These predictions were improved when specific gene expression data were considered in HD conditions, suggesting a role for the mitochondrial glycine pathway. Further studies are needed to determine if regulation through the mitochondrial glycine pathway and ammonia transporters can be modulated and how to use the kidney as a therapeutic target in hyperammonemia.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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              Proteomics. Tissue-based map of the human proteome.

              Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
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                Author and article information

                Contributors
                zsoons@ukaachen.de
                Journal
                NPJ Syst Biol Appl
                NPJ Syst Biol Appl
                NPJ Systems Biology and Applications
                Nature Publishing Group UK (London )
                2056-7189
                20 December 2022
                20 December 2022
                2022
                : 8
                : 49
                Affiliations
                [1 ]GRID grid.5012.6, ISNI 0000 0001 0481 6099, Department of Surgery, Maastricht University Medical Centre, and NUTRIM School of Nutrition, Toxicology and Metabolism, , Maastricht University, ; PO Box 616, 6200 MD Maastricht, the Netherlands
                [2 ]GRID grid.83440.3b, ISNI 0000000121901201, Hepato-Pancreato-Biliary and Liver Transplant Surgery, Royal Free Hospital, , University College London, ; Pond Street, London, NW3 2QG UK
                [3 ]GRID grid.83440.3b, ISNI 0000000121901201, Liver Failure Group, UCL Hepatology, Royal Free Hospital, , University College London, ; Pond Street, London, NW3 2QG UK
                [4 ]GRID grid.5379.8, ISNI 0000000121662407, School of Biological Sciences, Faculty of Biology, Medicine and Health, , University of Manchester, ; Manchester, M13 9PT UK
                [5 ]GRID grid.412301.5, ISNI 0000 0000 8653 1507, Department of General, Visceral and Transplantation Surgery, , University Hospital RWTH Aachen, ; Aachen, Germany
                [6 ]GRID grid.412301.5, ISNI 0000 0000 8653 1507, Research Center for Computational Biomedicine, , University Hospital RWTH Aachen, ; Aachen, Germany
                Author information
                http://orcid.org/0000-0003-0913-7428
                http://orcid.org/0000-0001-5033-9537
                http://orcid.org/0000-0002-6472-0184
                http://orcid.org/0000-0002-0767-8856
                Article
                257
                10.1038/s41540-022-00257-2
                9768161
                36539425
                6495da6e-ac17-4c83-90fe-8522efdb3b74
                © The Author(s) 2022

                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
                : 29 October 2018
                : 7 November 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research);
                Award ID: 017-006-106
                Award Recipient :
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                © The Author(s) 2022

                pathogenesis,nephrology
                pathogenesis, nephrology

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