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      Sulfate limitation increases specific plasmid DNA yield and productivity in E. coli fed-batch processes

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          Abstract

          Plasmid DNA (pDNA) is a key biotechnological product whose importance became apparent in the last years due to its role as a raw material in the messenger ribonucleic acid (mRNA) vaccine manufacturing process. In pharmaceutical production processes, cells need to grow in the defined medium in order to guarantee the highest standards of quality and repeatability. However, often these requirements result in low product titer, productivity, and yield. In this study, we used constraint-based metabolic modeling to optimize the average volumetric productivity of pDNA production in a fed-batch process. We identified a set of 13 nutrients in the growth medium that are essential for cell growth but not for pDNA replication. When these nutrients are depleted in the medium, cell growth is stalled and pDNA production is increased, raising the specific and volumetric yield and productivity. To exploit this effect we designed a three-stage process (1. batch, 2. fed-batch with cell growth, 3. fed-batch without cell growth). The transition between stage 2 and 3 is induced by sulfate starvation. Its onset can be easily controlled via the initial concentration of sulfate in the medium. We validated the decoupling behavior of sulfate and assessed pDNA quality attributes (supercoiled pDNA content) in E. coli with lab-scale bioreactor cultivations. The results showed an increase in supercoiled pDNA to biomass yield by 33% and an increase of supercoiled pDNA volumetric productivity by 13 % upon limitation of sulfate. In conclusion, even for routinely manufactured biotechnological products such as pDNA, simple changes in the growth medium can significantly improve the yield and quality.

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          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12934-023-02248-2.

          Highlights

          • Genome-scale metabolic models predict growth decoupling strategies.

          • Sulfate limitation decouples cell growth from pDNA production.

          • Sulfate limitation increases the specific supercoiled pDNA yield by 33% and the volumetric productivity by 13%.

          • We propose that sulfate limitation improves the biosynthesis of over 25% of naturally secreted products in E. coli.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12934-023-02248-2.

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          Most cited references70

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          SciPy 1.0: fundamental algorithms for scientific computing in Python

          SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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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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              Improved M13 phage cloning vectors and host strains: nucleotide sequences of the M13mp18 and pUC19 vectors.

              Three kinds of improvements have been introduced into the M13-based cloning systems. (1) New Escherichia coli host strains have been constructed for the E. coli bacteriophage M13 and the high-copy-number pUC-plasmid cloning vectors. Mutations introduced into these strains improve cloning of unmodified DNA and of repetitive sequences. A new suppressorless strain facilitates the cloning of selected recombinants. (2) The complete nucleotide sequences of the M13mp and pUC vectors have been compiled from a number of sources, including the sequencing of selected segments. The M13mp18 sequence is revised to include the G-to-T substitution in its gene II at position 6 125 bp (in M13) or 6967 bp in M13mp18. (3) M13 clones suitable for sequencing have been obtained by a new method of generating unidirectional progressive deletions from the polycloning site using exonucleases HI and VII.
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                Author and article information

                Contributors
                juergen.zanghellini@univie.ac.at
                Journal
                Microb Cell Fact
                Microb Cell Fact
                Microbial Cell Factories
                BioMed Central (London )
                1475-2859
                28 November 2023
                28 November 2023
                2023
                : 22
                : 242
                Affiliations
                [1 ]Department of Analytical Chemistry, University of Vienna, ( https://ror.org/03prydq77) Vienna, 1090 Austria
                [2 ]Doctorate School of Chemistry, University of Vienna, ( https://ror.org/03prydq77) Vienna, 1090 Austria
                [3 ]enGenes Biotech GmbH, Vienna, 1190 Austria
                [4 ]GRID grid.507465.5, Baxalta Innovations GmbH, A Part of Takeda Companies, ; Orth an der Donau, 2304 Austria
                Author information
                http://orcid.org/0000-0003-1333-4870
                http://orcid.org/0000-0002-2686-1971
                http://orcid.org/0000-0002-1964-2455
                Article
                2248
                10.1186/s12934-023-02248-2
                10685491
                38017439
                c7cf0f3c-d78a-4759-917a-0daa6bf58bbc
                © The Author(s) 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 21 September 2023
                : 11 November 2023
                Funding
                Funded by: enGenes Biotech GmbH
                Funded by: Baxalta Innovations GmbH, a part of Takeda companies
                Funded by: University of Vienna
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Biotechnology
                growth decoupling,medium optimization,gsmm,fba,dfba,e. coli,plasmid production
                Biotechnology
                growth decoupling, medium optimization, gsmm, fba, dfba, e. coli, plasmid production

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