1
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Hägele L, Pfleger BF, Takors R. Getting the Right Clones in an Automated Manner: An Alternative to Sophisticated Colony-Picking Robotics. Bioengineering (Basel) 2024; 11:892. [PMID: 39329634 PMCID: PMC11429294 DOI: 10.3390/bioengineering11090892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Revised: 08/22/2024] [Accepted: 08/29/2024] [Indexed: 09/28/2024] Open
Abstract
In recent years, the design-build-test-learn (DBTL) cycle has become a key concept in strain engineering. Modern biofoundries enable automated DBTL cycling using robotic devices. However, both highly automated facilities and semi-automated facilities encounter bottlenecks in clone selection and screening. While fully automated biofoundries can take advantage of expensive commercially available colony pickers, semi-automated facilities have to fall back on affordable alternatives. Therefore, our clone selection method is particularly well-suited for academic settings, requiring only the basic infrastructure of a biofoundry. The automated liquid clone selection (ALCS) method represents a straightforward approach for clone selection. Similar to sophisticated colony-picking robots, the ALCS approach aims to achieve high selectivity. Investigating the time analogue of five generations, the model-based set-up reached a selectivity of 98 ± 0.2% for correctly transformed cells. Moreover, the method is robust to variations in cell numbers at the start of ALCS. Beside Escherichia coli, promising chassis organisms, such as Pseudomonas putida and Corynebacterium glutamicum, were successfully applied. In all cases, ALCS enables the immediate use of the selected strains in follow-up applications. In essence, our ALCS approach provides a 'low-tech' method to be implemented in biofoundry settings without requiring additional devices.
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Affiliation(s)
- Lorena Hägele
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany
| | - Brian F Pfleger
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, 70569 Stuttgart, Germany
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2
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Rosch T, Tenhaef J, Stoltmann T, Redeker T, Kösters D, Hollmann N, Krumbach K, Wiechert W, Bott M, Matamouros S, Marienhagen J, Noack S. AutoBioTech─A Versatile Biofoundry for Automated Strain Engineering. ACS Synth Biol 2024; 13:2227-2237. [PMID: 38975718 PMCID: PMC11264319 DOI: 10.1021/acssynbio.4c00298] [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: 04/26/2024] [Revised: 07/02/2024] [Accepted: 07/02/2024] [Indexed: 07/09/2024]
Abstract
The inevitable transition from petrochemical production processes to renewable alternatives has sparked the emergence of biofoundries in recent years. Manual engineering of microbes will not be sufficient to meet the ever-increasing demand for novel producer strains. Here we describe the AutoBioTech platform, a fully automated laboratory system with 14 devices to perform operations for strain construction without human interaction. Using modular workflows, this platform enables automated transformations of Escherichia coli with plasmids assembled via modular cloning. A CRISPR/Cas9 toolbox compatible with existing modular cloning frameworks allows automated and flexible genome editing of E. coli. In addition, novel workflows have been established for the fully automated transformation of the Gram-positive model organism Corynebacterium glutamicum by conjugation and electroporation, with the latter proving to be the more robust technique. Overall, the AutoBioTech platform excels at versatility due to the modularity of workflows and seamless transitions between modules. This will accelerate strain engineering of Gram-negative and Gram-positive bacteria.
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Affiliation(s)
- Tobias
Michael Rosch
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
| | - Julia Tenhaef
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
| | - Tim Stoltmann
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
| | - Till Redeker
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
| | - Dominic Kösters
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
- Institute
of Biotechnology, RWTH Aachen University, Worringer Weg 3, D-52074 Aachen, Germany
| | - Niels Hollmann
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
- Institute
of Biotechnology, RWTH Aachen University, Worringer Weg 3, D-52074 Aachen, Germany
| | - Karin Krumbach
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
| | - Wolfgang Wiechert
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
| | - Michael Bott
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
- The
Bioeconomy Science Center (BioSC), Forschungszentrum
Jülich, D-52425 Jülich, Germany
| | - Susana Matamouros
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
| | - Jan Marienhagen
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
- Institute
of Biotechnology, RWTH Aachen University, Worringer Weg 3, D-52074 Aachen, Germany
| | - Stephan Noack
- Institute
of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, D-52425 Jülich, Germany
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3
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Orsi E, Schada von Borzyskowski L, Noack S, Nikel PI, Lindner SN. Automated in vivo enzyme engineering accelerates biocatalyst optimization. Nat Commun 2024; 15:3447. [PMID: 38658554 PMCID: PMC11043082 DOI: 10.1038/s41467-024-46574-4] [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: 12/21/2023] [Accepted: 03/04/2024] [Indexed: 04/26/2024] Open
Abstract
Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.
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Affiliation(s)
- Enrico Orsi
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | | | - Stephan Noack
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Pablo I Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Steffen N Lindner
- Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam-Golm, Germany.
- Department of Biochemistry, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität, 10117, Berlin, Germany.
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4
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Yi X, Rasor BJ, Boadi N, Louie K, Northen TR, Karim AS, Jewett MC, Alper HS. Establishing a versatile toolkit of flux enhanced strains and cell extracts for pathway prototyping. Metab Eng 2023; 80:241-253. [PMID: 37890611 DOI: 10.1016/j.ymben.2023.10.008] [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: 08/09/2023] [Revised: 10/07/2023] [Accepted: 10/23/2023] [Indexed: 10/29/2023]
Abstract
Building and optimizing biosynthetic pathways in engineered cells holds promise to address societal needs in energy, materials, and medicine, but it is often time-consuming. Cell-free synthetic biology has emerged as a powerful tool to accelerate design-build-test-learn cycles for pathway engineering with increased tolerance to toxic compounds. However, most cell-free pathway prototyping to date has been performed in extracts from wildtype cells which often do not have sufficient flux towards the pathways of interest, which can be enhanced by engineering. Here, to address this gap, we create a set of engineered Escherichia coli and Saccharomyces cerevisiae strains rewired via CRISPR-dCas9 to achieve high-flux toward key metabolic precursors; namely, acetyl-CoA, shikimate, triose-phosphate, oxaloacetate, α-ketoglutarate, and glucose-6-phosphate. Cell-free extracts generated from these strains are used for targeted enzyme screening in vitro. As model systems, we assess in vivo and in vitro production of triacetic acid lactone from acetyl-CoA and muconic acid from the shikimate pathway. The need for these platforms is exemplified by the fact that muconic acid cannot be detected in wildtype extracts provided with the same biosynthetic enzymes. We also perform metabolomic comparison to understand biochemical differences between the cellular and cell-free muconic acid synthesis systems (E. coli and S. cerevisiae cells and cell extracts with and without metabolic rewiring). While any given pathway has different interfaces with metabolism, we anticipate that this set of pre-optimized, flux enhanced cell extracts will enable prototyping efforts for new biosynthetic pathways and the discovery of biochemical functions of enzymes.
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Affiliation(s)
- Xiunan Yi
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, 78712, USA; McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Blake J Rasor
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
| | - Nathalie Boadi
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
| | - Katherine Louie
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Trent R Northen
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Ashty S Karim
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA
| | - Michael C Jewett
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, 60208, USA; Center for Synthetic Biology, Northwestern University, Evanston, IL, 60208, USA; Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
| | - Hal S Alper
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, 78712, USA; McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.
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5
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Patwari P, Pruckner F, Fabris M. Biosensors in microalgae: A roadmap for new opportunities in synthetic biology and biotechnology. Biotechnol Adv 2023; 68:108221. [PMID: 37495181 DOI: 10.1016/j.biotechadv.2023.108221] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 06/22/2023] [Accepted: 07/22/2023] [Indexed: 07/28/2023]
Abstract
Biosensors are powerful tools to investigate, phenotype, improve and prototype microbial strains, both in fundamental research and in industrial contexts. Genetic and biotechnological developments now allow the implementation of synthetic biology approaches to novel different classes of microbial hosts, for example photosynthetic microalgae, which offer unique opportunities. To date, biosensors have not yet been implemented in phototrophic eukaryotic microorganisms, leaving great potential for novel biological and technological advancements untapped. Here, starting from selected biosensor technologies that have successfully been implemented in heterotrophic organisms, we project and define a roadmap on how these could be applied to microalgae research. We highlight novel opportunities for the development of new biosensors, identify critical challenges, and finally provide a perspective on the impact of their eventual implementation to tackle research questions and bioengineering strategies. From studying metabolism at the single-cell level to genome-wide screen approaches, and assisted laboratory evolution experiments, biosensors will greatly impact the pace of progress in understanding and engineering microalgal metabolism. We envision how this could further advance the possibilities for unraveling their ecological role, evolutionary history and accelerate their domestication, to further drive them as resource-efficient production hosts.
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Affiliation(s)
- Payal Patwari
- SDU Biotechnology, Faculty of Engineering, University of Southern Denmark, Odense M DK-5230, Denmark
| | - Florian Pruckner
- SDU Biotechnology, Faculty of Engineering, University of Southern Denmark, Odense M DK-5230, Denmark
| | - Michele Fabris
- SDU Biotechnology, Faculty of Engineering, University of Southern Denmark, Odense M DK-5230, Denmark.
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6
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Tiwari S, Nizet O, Dillon N. Development of a high-throughput minimum inhibitory concentration (HT-MIC) testing workflow. Front Microbiol 2023; 14:1079033. [PMID: 37303796 PMCID: PMC10249070 DOI: 10.3389/fmicb.2023.1079033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 04/24/2023] [Indexed: 06/13/2023] Open
Abstract
The roots of the minimum inhibitory concentration (MIC) determination go back to the early 1900s. Since then, the test has undergone modifications and advancements in an effort to increase its dependability and accuracy. Although biological investigations use an ever-increasing number of samples, complicated processes and human error sometimes result in poor data quality, which makes it challenging to replicate scientific conclusions. Automating manual steps using protocols decipherable by machine can ease procedural difficulties. Originally relying on manual pipetting and human vision to determine the results, modern broth dilution MIC testing procedures have incorporated microplate readers to enhance sample analysis. However, current MIC testing procedures are unable to simultaneously evaluate a large number of samples efficiently. Here, we have created a proof-of-concept workflow using the Opentrons OT-2 robot to enable high-throughput MIC testing. We have further optimized the analysis by incorporating Python programming for MIC assignment to streamline the automation. In this workflow, we performed MIC tests on four different strains, three replicates per strain, and analyzed a total of 1,152 wells. Comparing our workflow to a conventional plate MIC procedure, we find that the HT-MIC method is 800% faster while simultaneously boasting a 100% accuracy. Our high-throughput MIC workflow can be adapted in both academic and clinical settings since it is faster, more efficient, and as accurate than many conventional methods.
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Affiliation(s)
- Suman Tiwari
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, United States
| | - Oliver Nizet
- La Jolla Country Day School, La Jolla, CA, United States
| | - Nicholas Dillon
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, United States
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7
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Tellechea-Luzardo J, Stiebritz MT, Carbonell P. Transcription factor-based biosensors for screening and dynamic regulation. Front Bioeng Biotechnol 2023; 11:1118702. [PMID: 36814719 PMCID: PMC9939652 DOI: 10.3389/fbioe.2023.1118702] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/26/2023] [Indexed: 02/09/2023] Open
Abstract
Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.
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Affiliation(s)
- Jonathan Tellechea-Luzardo
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Valencia, Spain
| | - Martin T. Stiebritz
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Valencia, Spain
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Valencia, Spain
- Institute for Integrative Systems Biology I2SysBio, Universitat de València-CSIC, Paterna, Spain
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8
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Kranz A, Polen T, Kotulla C, Arndt A, Bosco G, Bussmann M, Chattopadhyay A, Cramer A, Davoudi CF, Degner U, Diesveld R, Freiherr von Boeselager R, Gärtner K, Gätgens C, Georgi T, Geraths C, Haas S, Heyer A, Hünnefeld M, Ishige T, Kabus A, Kallscheuer N, Kever L, Klaffl S, Kleine B, Kočan M, Koch-Koerfges A, Kraxner KJ, Krug A, Krüger A, Küberl A, Labib M, Lange C, Mack C, Maeda T, Mahr R, Majda S, Michel A, Morosov X, Müller O, Nanda AM, Nickel J, Pahlke J, Pfeifer E, Platzen L, Ramp P, Rittmann D, Schaffer S, Scheele S, Spelberg S, Schulte J, Schweitzer JE, Sindelar G, Sorger-Herrmann U, Spelberg M, Stansen C, Tharmasothirajan A, Ooyen JV, van Summeren-Wesenhagen P, Vogt M, Witthoff S, Zhu L, Eikmanns BJ, Oldiges M, Schaumann G, Baumgart M, Brocker M, Eggeling L, Freudl R, Frunzke J, Marienhagen J, Wendisch VF, Bott M. A manually curated compendium of expression profiles for the microbial cell factory Corynebacterium glutamicum. Sci Data 2022; 9:594. [PMID: 36182956 PMCID: PMC9526701 DOI: 10.1038/s41597-022-01706-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 08/18/2022] [Indexed: 11/12/2022] Open
Abstract
Corynebacterium glutamicum is the major host for the industrial production of amino acids and has become one of the best studied model organisms in microbial biotechnology. Rational strain construction has led to an improvement of producer strains and to a variety of novel producer strains with a broad substrate and product spectrum. A key factor for the success of these approaches is detailed knowledge of transcriptional regulation in C. glutamicum. Here, we present a large compendium of 927 manually curated microarray-based transcriptional profiles for wild-type and engineered strains detecting genome-wide expression changes of the 3,047 annotated genes in response to various environmental conditions or in response to genetic modifications. The replicates within the 927 experiments were combined to 304 microarray sets ordered into six categories that were used for differential gene expression analysis. Hierarchical clustering confirmed that no outliers were present in the sets. The compendium provides a valuable resource for future fundamental and applied research with C. glutamicum and contributes to a systemic understanding of this microbial cell factory. Measurement(s) Gene Expression Analysis Technology Type(s) Two Color Microarray Factor Type(s) WT condition A vs. WT condition B • Plasmid-based gene overexpression in parental strain vs. parental strain with empty vector control • Deletion mutant vs. parental strain Sample Characteristic - Organism Corynebacterium glutamicum Sample Characteristic - Environment laboratory environment Sample Characteristic - Location Germany.
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Affiliation(s)
- Angela Kranz
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany.
- IBG-4: Bioinformatics, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany.
| | - Tino Polen
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Christian Kotulla
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Annette Arndt
- Institute of Microbiology and Biotechnology, University of Ulm, D-89069, Ulm, Germany
| | - Graziella Bosco
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Michael Bussmann
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Ava Chattopadhyay
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Annette Cramer
- Institute of Microbiology and Biotechnology, University of Ulm, D-89069, Ulm, Germany
| | - Cedric-Farhad Davoudi
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Ursula Degner
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Ramon Diesveld
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | | | - Kim Gärtner
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Cornelia Gätgens
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Tobias Georgi
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Christian Geraths
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Sabine Haas
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Antonia Heyer
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Max Hünnefeld
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Takeru Ishige
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Armin Kabus
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Nicolai Kallscheuer
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Larissa Kever
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Simon Klaffl
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Britta Kleine
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Martina Kočan
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Abigail Koch-Koerfges
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Kim J Kraxner
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Andreas Krug
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Aileen Krüger
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Andreas Küberl
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Mohamed Labib
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Christian Lange
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Christina Mack
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Tomoya Maeda
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Regina Mahr
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Stephan Majda
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Andrea Michel
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Xenia Morosov
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Olga Müller
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Arun M Nanda
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Jens Nickel
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Jennifer Pahlke
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Eugen Pfeifer
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Laura Platzen
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Paul Ramp
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Doris Rittmann
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Steffen Schaffer
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Sandra Scheele
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Stephanie Spelberg
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Julia Schulte
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Jens-Eric Schweitzer
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Georg Sindelar
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Ulrike Sorger-Herrmann
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Markus Spelberg
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Corinna Stansen
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Apilaasha Tharmasothirajan
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Jan van Ooyen
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | | | - Michael Vogt
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Sabrina Witthoff
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Lingfeng Zhu
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Bernhard J Eikmanns
- Institute of Microbiology and Biotechnology, University of Ulm, D-89069, Ulm, Germany
| | - Marco Oldiges
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Georg Schaumann
- SenseUp GmbH, c/o Campus Forschungszentrum, Wilhelm-Johnen-Strasse, D-52425, Jülich, Germany
| | - Meike Baumgart
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Melanie Brocker
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Lothar Eggeling
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Roland Freudl
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Julia Frunzke
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Jan Marienhagen
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany
| | - Volker F Wendisch
- Genetics of Prokaryotes, Biology & CeBiTec, Bielefeld University, Universitaetsstr. 25, D-33615, Bielefeld, Germany
| | - Michael Bott
- IBG-1: Biotechnology, Institute of Bio- and Geosciences, Forschungszentrum Jülich, D-52425, Jülich, Germany.
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9
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Sridhar S, Ajo-Franklin CM, Masiello CA. A Framework for the Systematic Selection of Biosensor Chassis for Environmental Synthetic Biology. ACS Synth Biol 2022; 11:2909-2916. [PMID: 35961652 PMCID: PMC9486965 DOI: 10.1021/acssynbio.2c00079] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Indexed: 01/24/2023]
Abstract
Microbial biosensors sense and report exposures to stimuli, thereby facilitating our understanding of environmental processes. Successful design and deployment of biosensors hinge on the persistence of the microbial host of the genetic circuit, termed the chassis. However, model chassis organisms may persist poorly in environmental conditions. In contrast, non-model organisms persist better in environmental conditions but are limited by other challenges, such as genetic intractability and part unavailability. Here we identify ecological, metabolic, and genetic constraints for chassis development and propose a conceptual framework for the systematic selection of environmental biosensor chassis. We identify key challenges with using current model chassis and delineate major points of conflict in choosing the most suitable organisms as chassis for environmental biosensing. This framework provides a way forward in the selection of biosensor chassis for environmental synthetic biology.
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Affiliation(s)
- Swetha Sridhar
- Systems,
Synthetic, and Physical Biology Graduate Program, Rice University, 6100 Main Street, MS-180, Houston, Texas 77005, United
States
| | - Caroline M. Ajo-Franklin
- Department
of BioSciences, Rice University, 6100 Main Street, MS-140, Houston, Texas 77005, United States
| | - Caroline A. Masiello
- Department
of BioSciences, Rice University, 6100 Main Street, MS-140, Houston, Texas 77005, United States
- Department
of Earth, Environmental, and Planetary Sciences, Rice University, 6100 Main St, MS-126, Houston, Texas 77005, United
States
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10
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Jonguitud-Borrego N, Malcı K, Anand M, Baluku E, Webb C, Liang L, Barba-Ostria C, Guaman LP, Hui L, Rios-Solis L. High—throughput and automated screening for COVID-19. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:969203. [PMID: 36188187 PMCID: PMC9521367 DOI: 10.3389/fmedt.2022.969203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
The COVID-19 pandemic has become a global challenge for the healthcare systems of many countries with 6 million people having lost their lives and 530 million more having tested positive for the virus. Robust testing and a comprehensive track and trace process for positive patients are essential for effective pandemic control, leading to high demand for diagnostic testing. In order to comply with demand and increase testing capacity worldwide, automated workflows have come into prominence as they enable high-throughput screening, faster processing, exclusion of human error, repeatability, reproducibility and diagnostic precision. The gold standard for COVID-19 testing so far has been RT-qPCR, however, different SARS-CoV-2 testing methods have been developed to be combined with high throughput testing to improve diagnosis. Case studies in China, Spain and the United Kingdom have been reviewed and automation has been proven to be promising for mass testing. Free and Open Source scientific and medical Hardware (FOSH) plays a vital role in this matter but there are some challenges to be overcome before automation can be fully implemented. This review discusses the importance of automated high-throughput testing, the different equipment available, the bottlenecks of its implementation and key selected case studies that due to their high effectiveness are already in use in hospitals and research centres.
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Affiliation(s)
- Nestor Jonguitud-Borrego
- Institute for Bioengineering, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom
- Centre for Synthetic and Systems Biology (SynthSys), The University of Edinburgh, Edinburgh, United Kingdom
| | - Koray Malcı
- Institute for Bioengineering, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom
- Centre for Synthetic and Systems Biology (SynthSys), The University of Edinburgh, Edinburgh, United Kingdom
| | - Mihir Anand
- School of Biochemical Engineering, Indian Institute of Technology BHU, Varanasi, India
| | - Erikan Baluku
- School of Bio-Security, Biotechnical and Laboratory Sciences Makerere University, Kampala, Uganda
| | - Calum Webb
- Institute for Bioengineering, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom
| | - Lungang Liang
- BGI Clinical Laboratories, BGI-Shenzhen, Shenzhen, China
| | - Carlos Barba-Ostria
- Escuela de Medicina, Colegio de Ciencias de la Salud Quito, Universidad San Francisco de Quito USFQ, Quito, Ecuador
| | - Linda P. Guaman
- Centro de Investigación Biomédica (CENBIO), Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador
| | - Liu Hui
- BGI Clinical Laboratories, BGI-Shenzhen, Shenzhen, China
| | - Leonardo Rios-Solis
- Institute for Bioengineering, School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom
- Centre for Synthetic and Systems Biology (SynthSys), The University of Edinburgh, Edinburgh, United Kingdom
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Correspondence: Leonardo Rios-Solis
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11
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Müller C, Bakkes PJ, Lenz P, Waffenschmidt V, Helleckes LM, Jaeger KE, Wiechert W, Knapp A, Freudl R, Oldiges M. Accelerated strain construction and characterization of C. glutamicum protein secretion by laboratory automation. Appl Microbiol Biotechnol 2022; 106:4481-4497. [PMID: 35759036 PMCID: PMC9259529 DOI: 10.1007/s00253-022-12017-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/01/2022] [Accepted: 06/04/2022] [Indexed: 11/02/2022]
Abstract
Secretion of bacterial proteins into the culture medium simplifies downstream processing by avoiding cell disruption for target protein purification. However, a suitable signal peptide for efficient secretion needs to be identified, and currently, there are no tools available to predict optimal combinations of signal peptides and target proteins. The selection of such a combination is influenced by several factors, including protein biosynthesis efficiency and cultivation conditions, which both can have a significant impact on secretion performance. As a result, a large number of combinations must be tested. Therefore, we have developed automated workflows allowing for targeted strain construction and secretion screening using two platforms. Key advantages of this experimental setup include lowered hands-on time and increased throughput. In this study, the automated workflows were established for the heterologous production of Fusarium solani f. sp. pisi cutinase in Corynebacterium glutamicum. The target protein was monitored in culture supernatants via enzymatic activity and split GFP assay. Varying spacer lengths between the Shine-Dalgarno sequence and the start codon of Bacillus subtilis signal peptides were tested. Consistent with previous work on the secretory cutinase production in B. subtilis, a ribosome binding site with extended spacer length to up to 12 nt, which likely slows down translation initiation, does not necessarily lead to poorer cutinase secretion by C. glutamicum. The best performing signal peptides for cutinase secretion with a standard spacer length were identified in a signal peptide screening. Additional insights into the secretion process were gained by monitoring secretion stress using the C. glutamicum K9 biosensor strain. KEY POINTS: • Automated workflows for strain construction and screening of protein secretion • Comparison of spacer, signal peptide, and host combinations for cutinase secretion • Signal peptide screening for secretion by C. glutamicum using the split GFP assay.
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Affiliation(s)
- Carolin Müller
- Institute of Bio- and Geosciences IBG-1, Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.,Institute of Biotechnology, RWTH Aachen University, 52062, Aachen, Germany
| | - Patrick J Bakkes
- Institute of Bio- and Geosciences IBG-1, Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - Patrick Lenz
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Vera Waffenschmidt
- Institute of Bio- and Geosciences IBG-1, Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - Laura M Helleckes
- Institute of Bio- and Geosciences IBG-1, Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.,Institute of Biotechnology, RWTH Aachen University, 52062, Aachen, Germany
| | - Karl-Erich Jaeger
- Institute of Bio- and Geosciences IBG-1, Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.,Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences IBG-1, Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany.,Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52062, Aachen, Germany
| | - Andreas Knapp
- Institute of Molecular Enzyme Technology, Heinrich Heine University Düsseldorf, Forschungszentrum Jülich, 52425, Jülich, Germany.,Castrol Germany GmbH, 41179, Mönchengladbach, Germany
| | - Roland Freudl
- Institute of Bio- and Geosciences IBG-1, Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - Marco Oldiges
- Institute of Bio- and Geosciences IBG-1, Biotechnology, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany. .,Institute of Biotechnology, RWTH Aachen University, 52062, Aachen, Germany.
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12
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Yang Y, Mao Y, Wang R, Li H, Liu Y, Cheng H, Shi Z, Wang Y, Wang M, Zheng P, Liao X, Ma H. AutoESD: a web tool for automatic editing sequence design for genetic manipulation of microorganisms. Nucleic Acids Res 2022; 50:W75-W82. [PMID: 35639727 PMCID: PMC9252779 DOI: 10.1093/nar/gkac417] [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: 02/27/2022] [Revised: 04/20/2022] [Accepted: 05/09/2022] [Indexed: 11/30/2022] Open
Abstract
Advances in genetic manipulation and genome engineering techniques have enabled on-demand targeted deletion, insertion, and substitution of DNA sequences. One important step in these techniques is the design of editing sequences (e.g. primers, homologous arms) to precisely target and manipulate DNA sequences of interest. Experimental biologists can employ multiple tools in a stepwise manner to assist editing sequence design (ESD), but this requires various software involving non-standardized data exchange and input/output formats. Moreover, necessary quality control steps might be overlooked by non-expert users. This approach is low-throughput and can be error-prone, which illustrates the need for an automated ESD system. In this paper, we introduce AutoESD (https://autoesd.biodesign.ac.cn/), which designs editing sequences for all steps of genetic manipulation of many common homologous-recombination techniques based on screening-markers. Notably, multiple types of manipulations for different targets (CDS or intergenic region) can be processed in one submission. Moreover, AutoESD has an entirely cloud-based serverless architecture, offering high reliability, robustness and scalability which is capable of parallelly processing hundreds of design tasks each having thousands of targets in minutes. To our knowledge, AutoESD is the first cloud platform enabling precise, automated, and high-throughput ESD across species, at any genomic locus for all manipulation types.
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Affiliation(s)
- Yi Yang
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yufeng Mao
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Ruoyu Wang
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Haoran Li
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Ye Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Haijiao Cheng
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Zhenkun Shi
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Yu Wang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Meng Wang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Ping Zheng
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Xiaoping Liao
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
| | - Hongwu Ma
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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13
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Navarrete C, Estrada M, Martínez JL. Debaryomyces hansenii: an old acquaintance for a fresh start in the era of the green biotechnology. World J Microbiol Biotechnol 2022; 38:99. [PMID: 35482161 PMCID: PMC9050785 DOI: 10.1007/s11274-022-03280-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022]
Abstract
The halophilic yeast Debaryomyces hansenii has been studied for several decades, serving as eukaryotic model for understanding salt and osmotic tolerance. Nevertheless, lack of consensus among different studies is found and, sometimes, contradictory information derived from studies performed in very diverse conditions. These two factors hampered its establishment as the key biotechnological player that was called to be in the past decade. On top of that, very limited (often deficient) engineering tools are available for this yeast. Fortunately Debaryomyces is again gaining momentum and recent advances using highly instrumented lab scale bioreactors, together with advanced –omics and HT-robotics, have revealed a new set of interesting results. Those forecast a very promising future for D. hansenii in the era of the so-called green biotechnology. Moreover, novel genetic tools enabling precise gene editing on this yeast are now available. In this review, we highlight the most recent developments, which include the identification of a novel gene implicated in salt tolerance, a newly proposed survival mechanism for D. hansenii at very high salt and limiting nutrient concentrations, and its utilization as production host in biotechnological processes.
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Affiliation(s)
- Clara Navarrete
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads Building 223, 2800, Kgs. Lyngby, Denmark
| | - Mònica Estrada
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads Building 223, 2800, Kgs. Lyngby, Denmark
| | - José L Martínez
- Section of Synthetic Biology (DTU Bioengineering), Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads Building 223, 2800, Kgs. Lyngby, Denmark.
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14
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Kang DH, Ko SC, Heo YB, Lee HJ, Woo HM. RoboMoClo: A Robotics-Assisted Modular Cloning Framework for Multiple Gene Assembly in Biofoundry. ACS Synth Biol 2022; 11:1336-1348. [PMID: 35167276 DOI: 10.1021/acssynbio.1c00628] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Efficient and versatile DNA assembly frameworks have had an impact on promoting synthetic biology to build complex biological systems. To accelerate system development, laboratory automation (or biofoundry) provides an opportunity to construct organisms and DNA assemblies via computer-aided design. However, a modular cloning (MoClo) system for multiple DNA assemblies limits the biofoundry workflow in terms of simplicity and feasibility by preparing the number of cloning materials such as destination vectors prior to the automation process. Herein, we propose robot-assisted MoClo (RoboMoClo) to accelerate a synthetic biology project with multiple gene expressions at the biofoundry. The architecture of the RoboMoClo framework provides a hybrid strategy of hierarchical gene assembly and iterative gene assembly, and fewer destination vectors compared with other MoClo systems. An industrial bacterium, Corynebacterium glutamicum, was used as a model host for RoboMoClo. After building a biopart library (promoter and terminator; level 0) and evaluating its features (level 1), various transcriptional directions in multiple gene assemblies (level 2) were studied using the RoboMoClo vectors. Among the constructs, the convergent construct exhibited potential transcriptional interference through the collision of RNA polymerases. To study design of experiment-guided lycopene biosynthesis in C. glutamicum (levels 1, 2, and 3), the biofoundry-assisted multiple gene assembly was demonstrated as a proof-of-concept by constructing various sub-pathway units (level 2) and pathway units (level 3) for C. glutamicum. The RoboMoClo framework provides an improved MoClo toolkit for laboratory automation in a synthetic biology application.
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Affiliation(s)
- Dong Hun Kang
- Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
- Biofoundry Research Center, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| | - Sung Cheon Ko
- Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
- Biofoundry Research Center, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| | - Yu Been Heo
- Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
- Biofoundry Research Center, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| | - Hyun Jeong Lee
- Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
- Biofoundry Research Center, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
| | - Han Min Woo
- Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
- Biofoundry Research Center, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
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15
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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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