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Le Grégam L, Guitton Y, Bellvert F, Heux S, Jourdan F, Portais JC, Millard P. PhysioFit: a software to quantify cell growth parameters and extracellular fluxes. Bioinformatics 2024; 40:btae488. [PMID: 39073885 PMCID: PMC11303505 DOI: 10.1093/bioinformatics/btae488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 07/21/2024] [Accepted: 07/29/2024] [Indexed: 07/31/2024] Open
Abstract
SUMMARY Quantification of growth parameters and extracellular uptake and production fluxes is central in systems and synthetic biology. Fluxes can be estimated using various mathematical models by fitting time-course measurements of the concentration of cells and extracellular substrates and products. A single tool is available to non-computational biologists to calculate extracellular fluxes, but it is hardly interoperable and is limited to a single hard-coded growth model. We present our open-source flux calculation software, PhysioFit, which can be used with any growth model and is interoperable by design. PhysioFit includes some of the most common growth models, and advanced users can implement additional models to calculate extracellular fluxes and other growth parameters for metabolic systems or experimental setups that follow alternative kinetics. PhysioFit can be used as a Python library and offers a graphical user interface for intuitive use by end-users and a command-line interface to streamline integration into existing pipelines. AVAILABILITY AND IMPLEMENTATION PhysioFit v3 is implemented in Python 3 and was tested on Windows, Unix, and MacOS platforms. The source code and the documentation are freely distributed under GPL3 license at https://github.com/MetaSys-LISBP/PhysioFit/ and https://physiofit.readthedocs.io/.
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Affiliation(s)
- Loïc Le Grégam
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, 31077, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, 31077, France
| | - Yann Guitton
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, 31077, France
- Oniris, INRAE, LABERCA, Nantes, 44307, France
| | - Floriant Bellvert
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, 31077, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, 31077, France
| | - Stéphanie Heux
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, 31077, France
| | - Fabien Jourdan
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, 31077, France
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, 31027, France
| | - Jean-Charles Portais
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, 31077, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, 31077, France
- RESTORE (Geroscience & Rejuvenation Center), Université de Toulouse, INSERM, CNRS, EFS, Toulouse, 31100, France
| | - Pierre Millard
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, 31077, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, 31077, France
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Bauer J, Klamt S. OptMSP: A toolbox for designing optimal multi-stage (bio)processes. J Biotechnol 2024; 383:94-102. [PMID: 38325658 DOI: 10.1016/j.jbiotec.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/09/2024]
Abstract
One central goal of bioprocess engineering is to maximize the production of specific chemicals using microbial cell factories. Many bioprocesses are one-stage (batch) processes (OSPs), in which growth and product synthesis are coupled. However, OSPs often exhibit low volumetric productivities due to the competition for substrate for biomass and product synthesis implying trade-offs between biomass and product yields. Two-stage or, more generally, multi-stage processes (MSPs) offer the potential to tackle this trade-off for improved efficiency of bioprocesses, for example, by separating growth and production. MSPs have recently gained much attention, also because of a rapidly growing toolbox for the dynamic control of metabolic fluxes. Despite these promising advancements, computational tools specifically tailored for the optimal design of MSPs in the field of biotechnology are still lacking. Here, we present OptMSP, a new Python-based toolbox for identifying optimal MSPs maximizing a user-defined process metrics (such as volumetric productivity, yield, and titer or combinations thereof) under given constraints. In contrast to other methods, our framework starts with a set of well-defined modules representing relevant stages or sub-processes. Experimentally determined parameters (such as growth rates, substrate uptake and product formation rates) are used to build suitable ODE models describing the dynamic behavior of each module. OptMSP finds then the optimal combination of those modules, which, together with the optimal switching time points, maximize a given objective function. We demonstrate the applicability and relevance of the approach with three different case studies, including the example of lactate production by E. coli in a batch setup, where an aerobic growth phase can be combined with anaerobic production phases with or without growth and with or without enhanced ATP turnover.
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Affiliation(s)
- Jasmin Bauer
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, Magdeburg, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, Magdeburg, Germany.
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3
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Mutz M, Brüning V, Brüsseler C, Müller M, Noack S, Marienhagen J. Metabolic engineering of Corynebacterium glutamicum for the production of anthranilate from glucose and xylose. Microb Biotechnol 2024; 17:e14388. [PMID: 38206123 PMCID: PMC10832554 DOI: 10.1111/1751-7915.14388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 11/28/2023] [Accepted: 12/07/2023] [Indexed: 01/12/2024] Open
Abstract
Anthranilate and its derivatives are important basic chemicals for the synthesis of polyurethanes as well as various dyes and food additives. Today, anthranilate is mainly chemically produced from petroleum-derived xylene, but this shikimate pathway intermediate could be also obtained biotechnologically. In this study, Corynebacterium glutamicum was engineered for the microbial production of anthranilate from a carbon source mixture of glucose and xylose. First, a feedback-resistant 3-deoxy-arabinoheptulosonate-7-phosphate synthase from Escherichia coli, catalysing the first step of the shikimate pathway, was functionally introduced into C. glutamicum to enable anthranilate production. Modulation of the translation efficiency of the genes for the shikimate kinase (aroK) and the anthranilate phosphoribosyltransferase (trpD) improved product formation. Deletion of two genes, one for a putative phosphatase (nagD) and one for a quinate/shikimate dehydrogenase (qsuD), abolished by-product formation of glycerol and quinate. However, the introduction of an engineered anthranilate synthase (TrpEG) unresponsive to feedback inhibition by tryptophan had the most pronounced effect on anthranilate production. Component I of this enzyme (TrpE) was engineered using a biosensor-based in vivo screening strategy for identifying variants with increased feedback resistance in a semi-rational library of TrpE muteins. The final strain accumulated up to 5.9 g/L (43 mM) anthranilate in a defined CGXII medium from a mixture of glucose and xylose in bioreactor cultivations. We believe that the constructed C. glutamicum variants are not only limited to anthranilate production but could also be suitable for the synthesis of other biotechnologically interesting shikimate pathway intermediates or any other aromatic compound derived thereof.
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Affiliation(s)
- Mario Mutz
- Institute of Bio‐ and Geosciences, IBG‐1: Biotechnology, Forschungszentrum JülichJülichGermany
- Institute of BiotechnologyRWTH Aachen UniversityAachenGermany
| | - Vincent Brüning
- Institute of Bio‐ and Geosciences, IBG‐1: Biotechnology, Forschungszentrum JülichJülichGermany
| | - Christian Brüsseler
- Institute of Bio‐ and Geosciences, IBG‐1: Biotechnology, Forschungszentrum JülichJülichGermany
| | - Moritz‐Fabian Müller
- Institute of Bio‐ and Geosciences, IBG‐1: Biotechnology, Forschungszentrum JülichJülichGermany
| | - Stephan Noack
- Institute of Bio‐ and Geosciences, IBG‐1: Biotechnology, Forschungszentrum JülichJülichGermany
| | - Jan Marienhagen
- Institute of Bio‐ and Geosciences, IBG‐1: Biotechnology, Forschungszentrum JülichJülichGermany
- Institute of BiotechnologyRWTH Aachen UniversityAachenGermany
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4
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Halle L, Hollmann N, Tenhaef N, Mbengi L, Glitz C, Wiechert W, Polen T, Baumgart M, Bott M, Noack S. Robotic workflows for automated long-term adaptive laboratory evolution: improving ethanol utilization by Corynebacterium glutamicum. Microb Cell Fact 2023; 22:175. [PMID: 37679814 PMCID: PMC10483779 DOI: 10.1186/s12934-023-02180-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/15/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND Adaptive laboratory evolution (ALE) is known as a powerful tool for untargeted engineering of microbial strains and genomics research. It is particularly well suited for the adaptation of microorganisms to new environmental conditions, such as alternative substrate sources. Since the probability of generating beneficial mutations increases with the frequency of DNA replication, ALE experiments are ideally free of constraints on the required duration of cell proliferation. RESULTS Here, we present an extended robotic workflow for performing long-term evolution experiments based on fully automated repetitive batch cultures (rbALE) in a well-controlled microbioreactor environment. Using a microtiter plate recycling approach, the number of batches and thus cell generations is technically unlimited. By applying the validated workflow in three parallel rbALE runs, ethanol utilization by Corynebacterium glutamicum ATCC 13032 (WT) was significantly improved. The evolved mutant strain WT_EtOH-Evo showed a specific ethanol uptake rate of 8.45 ± 0.12 mmolEtOH gCDW-1 h-1 and a growth rate of 0.15 ± 0.01 h-1 in lab-scale bioreactors. Genome sequencing of this strain revealed a striking single nucleotide variation (SNV) upstream of the ald gene (NCgl2698, cg3096) encoding acetaldehyde dehydrogenase (ALDH). The mutated basepair was previously predicted to be part of the binding site for the global transcriptional regulator GlxR, and re-engineering demonstrated that the identified SNV is key for enhanced ethanol assimilation. Decreased binding of GlxR leads to increased synthesis of the rate-limiting enzyme ALDH, which was confirmed by proteomics measurements. CONCLUSIONS The established rbALE technology is generally applicable to any microbial strain and selection pressure that fits the small-scale cultivation format. In addition, our specific results will enable improved production processes with C. glutamicum from ethanol, which is of particular interest for acetyl-CoA-derived products.
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Affiliation(s)
- Lars Halle
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, 52425, Jülich, Germany
- Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - Niels Hollmann
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, 52425, Jülich, Germany
| | - Niklas Tenhaef
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, 52425, Jülich, Germany
| | - Lea Mbengi
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, 52425, Jülich, Germany
| | - Christiane Glitz
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, 52425, Jülich, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, 52425, Jülich, Germany
- Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - Tino Polen
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, 52425, Jülich, Germany
- Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - Meike Baumgart
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, 52425, Jülich, Germany
| | - Michael Bott
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, 52425, Jülich, Germany
- Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - Stephan Noack
- Institute of Bio- and Geosciences, Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, 52425, Jülich, Germany.
- Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.
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5
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Baumann PT, Dal Molin M, Aring H, Krumbach K, Müller MF, Vroling B, van Summeren-Wesenhagen PV, Noack S, Marienhagen J. Beyond rational-biosensor-guided isolation of 100 independently evolved bacterial strain variants and comparative analysis of their genomes. BMC Biol 2023; 21:183. [PMID: 37667306 PMCID: PMC10478468 DOI: 10.1186/s12915-023-01688-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 08/23/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND In contrast to modern rational metabolic engineering, classical strain development strongly relies on random mutagenesis and screening for the desired production phenotype. Nowadays, with the availability of biosensor-based FACS screening strategies, these random approaches are coming back into fashion. In this study, we employ this technology in combination with comparative genome analyses to identify novel mutations contributing to product formation in the genome of a Corynebacterium glutamicum L-histidine producer. Since all known genetic targets contributing to L-histidine production have been already rationally engineered in this strain, identification of novel beneficial mutations can be regarded as challenging, as they might not be intuitively linkable to L-histidine biosynthesis. RESULTS In order to identify 100 improved strain variants that had each arisen independently, we performed > 600 chemical mutagenesis experiments, > 200 biosensor-based FACS screenings, isolated > 50,000 variants with increased fluorescence, and characterized > 4500 variants with regard to biomass formation and L-histidine production. Based on comparative genome analyses of these 100 variants accumulating 10-80% more L-histidine, we discovered several beneficial mutations. Combination of selected genetic modifications allowed for the construction of a strain variant characterized by a doubled L-histidine titer (29 mM) and product yield (0.13 C-mol C-mol-1) in comparison to the starting variant. CONCLUSIONS This study may serve as a blueprint for the identification of novel beneficial mutations in microbial producers in a more systematic manner. This way, also previously unexplored genes or genes with previously unknown contribution to the respective production phenotype can be identified. We believe that this technology has a great potential to push industrial production strains towards maximum performance.
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Affiliation(s)
- Philipp T Baumann
- Institute of Bio- and Geosciences, Forschungszentrum Jülich, IBG-1: Biotechnology, 52425, Jülich, Germany
| | - Michael Dal Molin
- Institute of Bio- and Geosciences, Forschungszentrum Jülich, IBG-1: Biotechnology, 52425, Jülich, Germany
- Department I of Internal Medicine, University of Cologne, 50937, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931, Cologne, Germany
| | - Hannah Aring
- Institute of Bio- and Geosciences, Forschungszentrum Jülich, IBG-1: Biotechnology, 52425, Jülich, Germany
| | - Karin Krumbach
- Institute of Bio- and Geosciences, Forschungszentrum Jülich, IBG-1: Biotechnology, 52425, Jülich, Germany
| | - Moritz-Fabian Müller
- Institute of Bio- and Geosciences, Forschungszentrum Jülich, IBG-1: Biotechnology, 52425, Jülich, Germany
| | - Bas Vroling
- Bioprodict GmbH, Nieuwe Marktstraat 54E, 6511AA, Nijmegen, The Netherlands
| | | | - Stephan Noack
- Institute of Bio- and Geosciences, Forschungszentrum Jülich, IBG-1: Biotechnology, 52425, Jülich, Germany
| | - Jan Marienhagen
- Institute of Bio- and Geosciences, Forschungszentrum Jülich, IBG-1: Biotechnology, 52425, Jülich, Germany.
- Institute of Biotechnology, RWTH Aachen University, Worringer Weg 3, 52074, Aachen, Germany.
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Kim JW, Krausch N, Aizpuru J, Barz T, Lucia S, Neubauer P, Cruz Bournazou MN. Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of E. coli. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Schito S, Zuchowski R, Bergen D, Strohmeier D, Wollenhaupt B, Menke P, Seiffarth J, Nöh K, Kohlheyer D, Bott M, Wiechert W, Baumgart M, Noack S. Communities of Niche-optimized Strains (CoNoS) - Design and creation of stable, genome-reduced co-cultures. Metab Eng 2022; 73:91-103. [PMID: 35750243 DOI: 10.1016/j.ymben.2022.06.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/20/2022] [Accepted: 06/17/2022] [Indexed: 10/18/2022]
Abstract
Current bioprocesses for production of value-added compounds are mainly based on pure cultures that are composed of rationally engineered strains of model organisms with versatile metabolic capacities. However, in the comparably well-defined environment of a bioreactor, metabolic flexibility provided by various highly abundant biosynthetic enzymes is much less required and results in suboptimal use of carbon and energy sources for compound production. In nature, non-model organisms have frequently evolved in communities where genome-reduced, auxotrophic strains cross-feed each other, suggesting that there must be a significant advantage compared to growth without cooperation. To prove this, we started to create and study synthetic communities of niche-optimized strains (CoNoS) that consists of two strains of the same species Corynebacterium glutamicum that are mutually dependent on one amino acid. We used both the wild-type and the genome-reduced C1* chassis for introducing selected amino acid auxotrophies, each based on complete deletion of all required biosynthetic genes. The best candidate strains were used to establish several stably growing CoNoS that were further characterized and optimized by metabolic modelling, microfluidic experiments and rational metabolic engineering to improve amino acid production and exchange. Finally, the engineered CoNoS consisting of an l-leucine and l-arginine auxotroph showed a specific growth rate equivalent to 83% of the wild type in monoculture, making it the fastest co-culture of two auxotrophic C. glutamicum strains to date. Overall, our results are a first promising step towards establishing improved biobased production of value-added compounds using the CoNoS approach.
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Affiliation(s)
- Simone Schito
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Rico Zuchowski
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Daniel Bergen
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Daniel Strohmeier
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Bastian Wollenhaupt
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Philipp Menke
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Johannes Seiffarth
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Katharina Nöh
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Dietrich Kohlheyer
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Michael Bott
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Wolfgang Wiechert
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany; Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, D-52074, Aachen, Germany
| | - Meike Baumgart
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany
| | - Stephan Noack
- Institut für Bio- und Geowissenschaften, IBG-1: Biotechnologie, Forschungszentrum Jülich, Jülich, Germany.
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Nießer J, Müller MF, Kappelmann J, Wiechert W, Noack S. Hot isopropanol quenching procedure for automated microtiter plate scale 13C-labeling experiments. Microb Cell Fact 2022; 21:78. [PMID: 35527247 PMCID: PMC9082905 DOI: 10.1186/s12934-022-01806-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 04/26/2022] [Indexed: 11/12/2022] Open
Abstract
Background Currently, the generation of genetic diversity for microbial cell factories outpaces the screening of strain variants with omics-based phenotyping methods. Especially isotopic labeling experiments, which constitute techniques aimed at elucidating cellular phenotypes and supporting rational strain design by growing microorganisms on substrates enriched with heavy isotopes, suffer from comparably low throughput and the high cost of labeled substrates. Results We present a miniaturized, parallelized, and automated approach to 13C-isotopic labeling experiments by establishing and validating a hot isopropanol quenching method on a robotic platform coupled with a microbioreactor cultivation system. This allows for the first time to conduct automated labeling experiments at a microtiter plate scale in up to 48 parallel batches. A further innovation enabled by the automated quenching method is the analysis of free amino acids instead of proteinogenic ones on said microliter scale. Capitalizing on the latter point and as a proof of concept, we present an isotopically instationary labeling experiment in Corynebacterium glutamicum ATCC 13032, generating dynamic labeling data of free amino acids in the process. Conclusions Our results show that a robotic liquid handler is sufficiently fast to generate informative isotopically transient labeling data. Furthermore, the amount of biomass obtained from a sub-milliliter cultivation in a microbioreactor is adequate for the detection of labeling patterns of free amino acids. Combining the innovations presented in this study, isotopically stationary and instationary automated labeling experiments can be conducted, thus fulfilling the prerequisites for 13C-metabolic flux analyses in high-throughput. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-022-01806-4.
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Osthege M, Tenhaef N, Zyla R, Müller C, Hemmerich J, Wiechert W, Noack S, Oldiges M. bletl - A Python package for integrating BioLector microcultivation devices in the Design-Build-Test-Learn cycle. Eng Life Sci 2022; 22:242-259. [PMID: 35382539 PMCID: PMC8961055 DOI: 10.1002/elsc.202100108] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 01/16/2022] [Accepted: 01/28/2022] [Indexed: 12/13/2022] Open
Abstract
Microbioreactor (MBR) devices have emerged as powerful cultivation tools for tasks of microbial phenotyping and bioprocess characterization and provide a wealth of online process data in a highly parallelized manner. Such datasets are difficult to interpret in short time by manual workflows. In this study, we present the Python package bletl and show how it enables robust data analyses and the application of machine learning techniques without tedious data parsing and preprocessing. bletl reads raw result files from BioLector I, II and Pro devices to make all the contained information available to Python-based data analysis workflows. Together with standard tooling from the Python scientific computing ecosystem, interactive visualizations and spline-based derivative calculations can be performed. Additionally, we present a new method for unbiased quantification of time-variable specific growth rateμ ⃗ t based on unsupervised switchpoint detection with Student-t distributed random walks. With an adequate calibration model, this method enables practitioners to quantify time-variable growth rate with Bayesian uncertainty quantification and automatically detect switch-points that indicate relevant metabolic changes. Finally, we show how time series feature extraction enables the application of machine learning methods to MBR data, resulting in unsupervised phenotype characterization. As an example, Neighbor Embedding (t-SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes.
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Affiliation(s)
- Michael Osthege
- Forschungszentrum Jülich GmbHJülichGermany
- Institute of BiotechnologyRWTH Aachen UniversityAachenGermany
| | | | | | - Carolin Müller
- Forschungszentrum Jülich GmbHJülichGermany
- Institute of BiotechnologyRWTH Aachen UniversityAachenGermany
| | | | - Wolfgang Wiechert
- Forschungszentrum Jülich GmbHJülichGermany
- Computational Systems Biotechnology (AVT.CSB)RWTH Aachen UniversityAachenGermany
| | | | - Marco Oldiges
- Forschungszentrum Jülich GmbHJülichGermany
- Institute of BiotechnologyRWTH Aachen UniversityAachenGermany
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10
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Stella RG, Gertzen CGW, Smits SHJ, Gätgens C, Polen T, Noack S, Frunzke J. Biosensor-based growth-coupling and spatial separation as an evolution strategy to improve small molecule production of Corynebacterium glutamicum. Metab Eng 2021; 68:162-173. [PMID: 34628038 DOI: 10.1016/j.ymben.2021.10.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 09/27/2021] [Accepted: 10/03/2021] [Indexed: 12/13/2022]
Abstract
Evolutionary engineering is a powerful method to improve the performance of microbial cell factories, but can typically not be applied to enhance the production of chemicals due to the lack of an appropriate selection regime. We report here on a new strategy based on transcription factor-based biosensors, which directly couple production to growth. The growth of Corynebacterium glutamicum was coupled to the intracellular concentration of branched-chain amino acids, by integrating a synthetic circuit based on the Lrp biosensor upstream of two growth-regulating genes, pfkA and hisD. Modelling and experimental data highlight spatial separation as key strategy to limit the selection of 'cheater' strains that escaped the evolutionary pressure. This approach facilitated the isolation of strains featuring specific causal mutations enhancing amino acid production. We envision that this strategy can be applied with the plethora of known biosensors in various microbes, unlocking evolution as a feasible strategy to improve production of chemicals.
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Affiliation(s)
- Roberto G Stella
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany
| | - Christoph G W Gertzen
- Center for Structural Studies (CSS), Heinrich Heine University Düsseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany; Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany
| | - Sander H J Smits
- Center for Structural Studies (CSS), Heinrich Heine University Düsseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany; Institute of Biochemistry, Heinrich Heine University Düsseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany
| | - Cornelia Gätgens
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany
| | - Tino Polen
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany
| | - Stephan Noack
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany; Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany
| | - Julia Frunzke
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, Jülich D-52425, Germany.
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11
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Labib M, Görtz J, Brüsseler C, Kallscheuer N, Gätgens J, Jupke A, Marienhagen J, Noack S. Metabolic and process engineering for microbial production of protocatechuate with Corynebacterium glutamicum. Biotechnol Bioeng 2021; 118:4414-4427. [PMID: 34343343 DOI: 10.1002/bit.27909] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/27/2021] [Accepted: 08/01/2021] [Indexed: 11/10/2022]
Abstract
3,4-Dihydroxybenzoate (protocatechuate, PCA) is a phenolic compound naturally found in edible vegetables and medicinal herbs. PCA is of high interest in the chemical industry and has wide potential for pharmaceutical applications. We designed and constructed a novel Corynebacterium glutamicum strain to enable the efficient utilization of d-xylose for microbial production of PCA. Shake flask cultivation of the engineered strain showed a maximum PCA titer of 62.1 ± 12.1 mM (9.6 ± 1.9 g L-1 ) from d-xylose as the primary carbon and energy source. The corresponding yield was 0.33 C-mol PCA per C-mol d-xylose, which corresponds to 38% of the maximum theoretical yield. Under growth-decoupled bioreactor conditions, a comparable PCA titer and a total amount of 16.5 ± 1.1 g PCA could be achieved when d-glucose and d-xylose were combined as orthogonal carbon substrates for biocatalyst provision and product synthesis, respectively. Downstream processing of PCA was realized via electrochemically induced crystallization by taking advantage of the pH-dependent properties of PCA. This resulted in a maximum final purity of 95.4%. The established PCA production process represents a highly sustainable approach, which will serve as a blueprint for the bio-based production of other hydroxybenzoic acids from alternative sugar feedstocks.
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Affiliation(s)
- Mohamed Labib
- Institute of Bio- and Geosciences (IBG-1): Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Jonas Görtz
- Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, Jülich, Germany.,Aachener Verfahrenstechnik - Fluid Process Engineering (AVT.FVT), RWTH Aachen University, Aachen, Germany
| | - Christian Brüsseler
- Institute of Bio- and Geosciences (IBG-1): Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Nicolai Kallscheuer
- Institute of Bio- and Geosciences (IBG-1): Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Jochem Gätgens
- Institute of Bio- and Geosciences (IBG-1): Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Andreas Jupke
- Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, Jülich, Germany.,Aachener Verfahrenstechnik - Fluid Process Engineering (AVT.FVT), RWTH Aachen University, Aachen, Germany
| | - Jan Marienhagen
- Institute of Bio- and Geosciences (IBG-1): Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, Jülich, Germany.,Institute of Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Stephan Noack
- Institute of Bio- and Geosciences (IBG-1): Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Bioeconomy Science Center (BioSC), Forschungszentrum Jülich GmbH, Jülich, Germany
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12
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Morschett H, Tenhaef N, Hemmerich J, Herbst L, Spiertz M, Dogan D, Wiechert W, Noack S, Oldiges M. Robotic integration enables autonomous operation of laboratory scale stirred tank bioreactors with model-driven process analysis. Biotechnol Bioeng 2021; 118:2759-2769. [PMID: 33871051 DOI: 10.1002/bit.27795] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/14/2021] [Accepted: 04/10/2021] [Indexed: 12/19/2022]
Abstract
Given its geometric similarity to large-scale production plants and the excellent possibilities for precise process control and monitoring, the classic stirred tank bioreactor (STR) still represents the gold standard for bioprocess development at a laboratory scale. However, compared to microbioreactor technologies, bioreactors often suffer from a low degree of process automation and deriving key performance indicators (KPIs) such as specific rates or yields often requires manual sampling and sample processing. A widely used parallelized STR setup was automated by connecting it to a liquid handling system and controlling it with a custom-made process control system. This allowed for the setup of a flexible modular platform enabling autonomous operation of the bioreactors without any operator present. Multiple unit operations like automated inoculation, sampling, sample processing and analysis, and decision making, for example for automated induction of protein production were implemented to achieve such functionality. The data gained during application studies was used for fitting of bioprocess models to derive relevant KPIs being in good agreement with literature. By combining the capabilities of STRs with the flexibility of liquid handling systems, this platform technology can be applied to a multitude of different bioprocess development pipelines at laboratory scale.
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Affiliation(s)
- Holger Morschett
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Niklas Tenhaef
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Johannes Hemmerich
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Laura Herbst
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Markus Spiertz
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Deniz Dogan
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Computational Systems Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Stephan Noack
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Marco Oldiges
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Institute of Biotechnology, RWTH Aachen University, Aachen, Germany
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