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      Harnessing the intracellular triacylglycerols for titer improvement of polyketides in Streptomyces

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          Abstract

          Pharmaceutically important polyketides such as avermectin are mainly produced as secondary metabolites during the stationary phase of growth of Streptomyces species in fermenters. The source of intracellular metabolites that are funneled into polyketide biosynthesis has proven elusive. We applied multi-omics to reveal that intracellular triacylglycerols (TAGs), which accumulates in primary metabolism, are degraded during stationary phase. This process could channel carbon flux from both intracellular TAGs and extracellular substrates into polyketide biosynthesis. We devised a strategy named 'dynamic degradation of TAG' (ddTAG) to mobilize the TAG pool and increase polyketide biosynthesis. Using ddTAG we increased the titers of actinorhodin, jadomycin B, oxytetracycline and avermectin B1a in Streptomyces coelicolor, Streptomyces venezuelae, Streptomyces rimosus and Streptomyces avermitilis. Application of ddTAG increased the titer of avermectin B1a by 50% to 9.31 g l-1 in a 180-m3 industrial-scale fermentation, which is the highest titer ever reported. Our strategy could improve polyketide titers for pharmaceutical production.

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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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            Molecular regulation of antibiotic biosynthesis in streptomyces.

            Streptomycetes are the most abundant source of antibiotics. Typically, each species produces several antibiotics, with the profile being species specific. Streptomyces coelicolor, the model species, produces at least five different antibiotics. We review the regulation of antibiotic biosynthesis in S. coelicolor and other, nonmodel streptomycetes in the light of recent studies. The biosynthesis of each antibiotic is specified by a large gene cluster, usually including regulatory genes (cluster-situated regulators [CSRs]). These are the main point of connection with a plethora of generally conserved regulatory systems that monitor the organism's physiology, developmental state, population density, and environment to determine the onset and level of production of each antibiotic. Some CSRs may also be sensitive to the levels of different kinds of ligands, including products of the pathway itself, products of other antibiotic pathways in the same organism, and specialized regulatory small molecules such as gamma-butyrolactones. These interactions can result in self-reinforcing feed-forward circuitry and complex cross talk between pathways. The physiological signals and regulatory mechanisms may be of practical importance for the activation of the many cryptic secondary metabolic gene cluster pathways revealed by recent sequencing of numerous Streptomyces genomes.
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              Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli

              To which extent can optimality principles describe the operation of metabolic networks? By explicitly considering experimental errors and in silico alternate optima in flux balance analysis, we systematically evaluate the capacity of 11 objective functions combined with eight adjustable constraints to predict 13C-determined in vivo fluxes in Escherichia coli under six environmental conditions. While no single objective describes the flux states under all conditions, we identified two sets of objectives for biologically meaningful predictions without the need for further, potentially artificial constraints. Unlimited growth on glucose in oxygen or nitrate respiring batch cultures is best described by nonlinear maximization of the ATP yield per flux unit. Under nutrient scarcity in continuous cultures, in contrast, linear maximization of the overall ATP or biomass yields achieved the highest predictive accuracy. Since these particular objectives predict the system behavior without preconditioning of the network structure, the identified optimality principles reflect, to some extent, the evolutionary selection of metabolic network regulation that realizes the various flux states.
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                Author and article information

                Journal
                Nature Biotechnology
                Nat Biotechnol
                Springer Science and Business Media LLC
                1087-0156
                1546-1696
                December 9 2019
                Article
                10.1038/s41587-019-0335-4
                31819261
                30e5f8cf-bb3d-4f4d-a84c-7ddf2361d191
                © 2019

                http://www.springer.com/tdm

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