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      Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom

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          Abstract

          Diatoms outcompete other phytoplankton for nitrate, yet little is known about the mechanisms underpinning this ability. Genomes and genome-enabled studies have shown that diatoms possess unique features of nitrogen metabolism however, the implications for nutrient utilization and growth are poorly understood. Using a combination of transcriptomics, proteomics, metabolomics, fluxomics, and flux balance analysis to examine short-term shifts in nitrogen utilization in the model pennate diatom in Phaeodactylum tricornutum, we obtained a systems-level understanding of assimilation and intracellular distribution of nitrogen. Chloroplasts and mitochondria are energetically integrated at the critical intersection of carbon and nitrogen metabolism in diatoms. Pathways involved in this integration are organelle-localized GS-GOGAT cycles, aspartate and alanine systems for amino moiety exchange, and a split-organelle arginine biosynthesis pathway that clarifies the role of the diatom urea cycle. This unique configuration allows diatoms to efficiently adjust to changing nitrogen status, conferring an ecological advantage over other phytoplankton taxa.

          Abstract

          Here, using the diatom Phaeodactylum tricornutum as a model organism, the authors combine functional genomics, phylogenetics, and metabolic modeling to describe how diatoms might have functionally integrated nitrogen metabolism during evolution and how metabolic flux is regulated across cellular compartments

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          COBRApy: COnstraints-Based Reconstruction and Analysis for Python

          Background COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software. Results Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes. Conclusion COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. Availability http://opencobra.sourceforge.net/
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            The role of functional traits and trade-offs in structuring phytoplankton communities: scaling from cellular to ecosystem level.

            Trait-based approaches to community structure are increasingly used in terrestrial ecology. We show that such an approach, augmented by a mechanistic analysis of trade-offs among functional traits, can be successfully used to explain community composition of marine phytoplankton along environmental gradients. Our analysis of literature on major functional traits in phytoplankton, such as parameters of nutrient-dependent growth and uptake, reveals physiological trade-offs in species abilities to acquire and utilize resources. These trade-offs, arising from fundamental relations such as cellular scaling laws and enzyme kinetics, define contrasting ecological strategies of nutrient acquisition. Major groups of marine eukaryotic phytoplankton have adopted distinct strategies with associated traits. These diverse strategies of nutrient utilization can explain the distribution patterns of major functional groups and size classes along nutrient availability gradients.
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              Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells.

              In this study, we evaluated a concatenated low pH (pH 3) and high pH (pH 10) reversed-phase liquid chromatography strategy as a first dimension for two-dimensional liquid chromatography tandem mass spectrometry ("shotgun") proteomic analysis of trypsin-digested human MCF10A cell sample. Compared with the more traditional strong cation exchange method, the use of concatenated high pH reversed-phase liquid chromatography as a first-dimension fractionation strategy resulted in 1.8- and 1.6-fold increases in the number of peptide and protein identifications (with two or more unique peptides), respectively. In addition to broader identifications, advantages of the concatenated high pH fractionation approach include improved protein sequence coverage, simplified sample processing, and reduced sample losses. The results demonstrate that the concatenated high pH reversed-phased strategy is an attractive alternative to strong cation exchange for two-dimensional shotgun proteomic analysis. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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                Author and article information

                Contributors
                aallen@jcvi.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                7 October 2019
                7 October 2019
                2019
                : 10
                : 4552
                Affiliations
                [1 ]GRID grid.469946.0, Microbial and Environmental Genomics, , J. Craig Venter Institute, ; La Jolla, CA 92037 USA
                [2 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Division of Biological Sciences, , University of California, San Diego, ; La Jolla, CA 92093 USA
                [3 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Department of Bioengineering, , University of California, San Diego, ; La Jolla, CA 92093 USA
                [4 ]ISNI 0000 0001 1015 3316, GRID grid.418095.1, Institute of Parasitology, , Biology Centre Czech Academy of Sciences, ; Branišovská 31, 370 05 České Budějovice, Czech Republic
                [5 ]ISNI 0000 0001 2166 4904, GRID grid.14509.39, Faculty of Science, , University of South Bohemia, ; Branišovská 31, 370 05 České Budějovice, Czech Republic
                [6 ]Targenomix, GmbH, Wissenschaftspark Potsdam-Golm, 14476 Potsdam, Germany
                [7 ]ISNI 0000 0001 2218 3491, GRID grid.451303.0, Environmental Molecular Sciences Laboratory, , Pacific Northwest National Laboratory, ; Richland, WA 99352 USA
                [8 ]ISNI 0000 0000 8338 6359, GRID grid.12799.34, Departamento de Biologia Vegetal, , Universidade Federal de Viçosa, ; Viçosa, Minas Gerais 36570-900 Brazil
                [9 ]ISNI 0000 0000 8338 6359, GRID grid.12799.34, Max-Planck Partner Group at the Departamento de Biologia Vegetal, , Universidade Federal de Viçosa, ; Viçosa, Minas Gerais 36570-900 Brazil
                [10 ]Max Planck Institut of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam, Germany
                [11 ]ISNI 0000 0001 0942 1117, GRID grid.11348.3f, Institute of Biochemistry and Biology, , University of Potsdam, ; Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany
                [12 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Scripps Institution of Oceanography, Integrative Oceanography Division, , University of California, San Diego, ; La Jolla, CA 92093 USA
                Author information
                http://orcid.org/0000-0002-1797-0375
                http://orcid.org/0000-0002-0820-6359
                http://orcid.org/0000-0003-3716-0326
                http://orcid.org/0000-0002-4796-2616
                http://orcid.org/0000-0003-2357-6785
                http://orcid.org/0000-0001-5911-6081
                Article
                12407
                10.1038/s41467-019-12407-y
                6779911
                31591397
                fd362587-fac8-4452-a70c-18952992d257
                © 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
                : 11 September 2018
                : 3 September 2019
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                © The Author(s) 2019

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                biochemistry,computational biology and bioinformatics,evolution,microbiology,molecular biology

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