1
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Ngo ACR, Haarmann M, Weindorf N, Guanzon DAV, Linke V, Smitka J, Tischler D. Golden Gate Cloning in Actinobacteria: Opportunities and Challenges. Methods Mol Biol 2025; 2850:377-386. [PMID: 39363083 DOI: 10.1007/978-1-0716-4220-7_21] [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] [Indexed: 10/05/2024]
Abstract
As we exploit biological machineries and circuits to redesign nature, it is just important to use efficient cloning strategies and methods to heterologously express the resulting DNA constructs. Golden Gate cloning allows the assembly of multiple fragments in a single reaction, making the process efficient and seamless. Although Golden Gate strategies have already been employed for different organisms, it is still not well-established for Actinobacteria. Here, we describe methods for Golden Gate cloning and how it can be utilized for Actinobacteria.
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Affiliation(s)
| | - Melody Haarmann
- Microbial Biotechnology, Ruhr University Bochum, Bochum, Germany
| | - Nils Weindorf
- Microbial Biotechnology, Ruhr University Bochum, Bochum, Germany
| | | | - Vivian Linke
- Microbial Biotechnology, Ruhr University Bochum, Bochum, Germany
| | - Joe Smitka
- Microbial Biotechnology, Ruhr University Bochum, Bochum, Germany
| | - Dirk Tischler
- Microbial Biotechnology, Ruhr University Bochum, Bochum, Germany
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2
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Bryant JA, Wright RC. Biofoundry-Assisted Golden Gate Cloning with AssemblyTron. Methods Mol Biol 2025; 2850:133-147. [PMID: 39363070 DOI: 10.1007/978-1-0716-4220-7_8] [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] [Indexed: 10/05/2024]
Abstract
Golden Gate assembly is a requisite method in synthetic biology that facilitates critical conventions such as genetic part abstraction and rapid prototyping. However, compared to robotic implementation, manual Golden Gate implementation is cumbersome, error-prone, and inconsistent for complex assembly designs. AssemblyTron is an open-source python package that provides an affordable automation solution using open-source OpenTrons OT-2 lab robots. Automating Golden Gate assembly with AssemblyTron can reduce failure-rate, resource consumption, and training requirements for building complex DNA constructs, as well as indexed and combinatorial libraries. Here, we dissect a panel of upgrades to AssemblyTron's Golden Gate assembly capabilities, which include Golden Gate assembly into modular cloning part vectors, error-prone polymerase chain reaction (PCR) combinatorial mutant library assembly, and modular cloning indexed plasmid library assembly. These upgrades enable a broad pool of users with varying levels of experience to readily implement advanced Golden Gate applications using low-cost, open-source lab robotics.
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Affiliation(s)
- John A Bryant
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - R Clay Wright
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
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3
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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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4
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Woo HM, Keasling J. Measuring the economic efficiency of laboratory automation in biotechnology. Trends Biotechnol 2024; 42:1076-1080. [PMID: 38402137 DOI: 10.1016/j.tibtech.2024.02.001] [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: 01/14/2024] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 02/26/2024]
Abstract
Laboratory automation with robot-assisted processes enhances synthetic biology, but its economic impact on projects is uncertain. We have proposed an experiment price index (EPI) for a quantitative comparison of factors in time, cost, and sample numbers, helping measure the efficiency of laboratory automation in synthetic biology and biomolecular engineering.
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Affiliation(s)
- 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; Department of MetaBioHealth, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, USA.
| | - Jay Keasling
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; Synthetic Biochemistry Center, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen, China
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5
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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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6
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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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7
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Ko SC, Woo HM. CRISPR-dCas13a system for programmable small RNAs and polycistronic mRNA repression in bacteria. Nucleic Acids Res 2024; 52:492-506. [PMID: 38015471 PMCID: PMC10783499 DOI: 10.1093/nar/gkad1130] [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: 10/05/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/29/2023] Open
Abstract
Bacterial small RNAs (sRNAs) function in post-transcriptional regulatory responses to environmental changes. However, the lack of eukaryotic RNA interference-like machinery in bacteria has limited the systematic engineering of RNA repression. Here, we report the development of clustered regularly interspaced short palindromic repeats (CRISPR)-guided dead CRIPSR-associated protein 13a (dCas13a) ribonucleoprotein that utilizes programmable CRISPR RNAs (crRNAs) to repress trans-acting and cis-acting sRNA as the target, altering regulatory mechanisms and stress-related phenotypes. In addition, we implemented a modular loop engineering of the crRNA to promote modular repression of the target gene with 92% knockdown efficiency and a single base-pair mismatch specificity. With the engineered crRNAs, we achieved targetable single-gene repression in the polycistronic operon. For metabolic application, 102 crRNAs were constructed in the biofoundry and used for screening novel knockdown sRNA targets to improve lycopene (colored antioxidant) production in Escherichia coli. The CRISPR-dCas13a system will assist as a valuable systematic tool for the discovery of novel sRNAs and the fine-tuning of bacterial RNA repression in both scientific and industrial applications.
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Affiliation(s)
- 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, Institute of Biotechnology and Bioengineering, 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, Institute of Biotechnology and Bioengineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
- Department of MetaBioHealth, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
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8
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Nava A, Fear AL, Lee N, Mellinger P, Lan G, McCauley J, Tan S, Kaplan N, Goyal G, Coates RC, Roberts J, Johnson Z, Hu R, Wu B, Ahn J, Kim WE, Wan Y, Yin K, Hillson N, Haushalter RW, Keasling JD. Automated Platform for the Plasmid Construction Process. ACS Synth Biol 2023; 12:3506-3513. [PMID: 37948662 PMCID: PMC10729297 DOI: 10.1021/acssynbio.3c00292] [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: 05/07/2023] [Indexed: 11/12/2023]
Abstract
There is a growing need for applications capable of handling large synthesis biology experiments. At the core of synthetic biology is the process of cloning and manipulating DNA as plasmids. Here, we report the development of an application named DNAda capable of writing automation instructions for any given DNA construct design generated by the J5 DNA assembly program. We also describe the automation pipeline and several useful features. The pipeline is particularly useful for the construction of combinatorial DNA assemblies. Furthermore, we demonstrate the platform by constructing a library of polyketide synthase parts, which includes 120 plasmids ranging in size from 7 to 14 kb from 4 to 7 DNA fragments.
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Affiliation(s)
- Alberto
A. Nava
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, United States
| | - Anna Lisa Fear
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Namil Lee
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Peter Mellinger
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Guangxu Lan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Joshua McCauley
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Stephen Tan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Nurgul Kaplan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Garima Goyal
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - R. Cameron Coates
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Jacob Roberts
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Zahmiria Johnson
- Department
of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, United States
| | - Romina Hu
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Bryan Wu
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Jared Ahn
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
| | - Woojoo E. Kim
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Yao Wan
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Kevin Yin
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Plant and Microbial Biology, University
of California, Berkeley, Berkeley, California 94720, United States
| | - Nathan Hillson
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE
Agile BioFoundry, Emeryville, California 94608, United States
| | - Robert W. Haushalter
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Jay D. Keasling
- Joint
BioEnergy Institute, Lawrence Berkeley National
Laboratory, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, United States
- Department
of Bioengineering, University of California,
Berkeley, Berkeley, California 94720, United States
- Center
for Synthetic Biochemistry, Shenzhen Institutes
for Advanced Technologies, Shenzhen 518055, P.R. China
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University Denmark, Kemitorvet, Building 220, Kongens Lyngby 2800, Denmark
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9
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Stephenson A, Lastra L, Nguyen B, Chen YJ, Nivala J, Ceze L, Strauss K. Physical Laboratory Automation in Synthetic Biology. ACS Synth Biol 2023; 12:3156-3169. [PMID: 37935025 DOI: 10.1021/acssynbio.3c00345] [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] [Indexed: 11/09/2023]
Abstract
Synthetic Biology has overcome many of the early challenges facing the field and is entering a systems era characterized by adoption of Design-Build-Test-Learn (DBTL) approaches. The need for automation and standardization to enable reproducible, scalable, and translatable research has become increasingly accepted in recent years, and many of the hardware and software tools needed to address these challenges are now in place or under development. However, the lack of connectivity between DBTL modules and barriers to access and adoption remain significant challenges to realizing the full potential of lab automation. In this review, we characterize and classify the state of automation in synthetic biology with a focus on the physical automation of experimental workflows. Though fully autonomous scientific discovery is likely a long way off, impressive progress has been made toward automating critical elements of experimentation by combining intelligent hardware and software tools. It is worth questioning whether total automation that removes humans entirely from the loop should be the ultimate goal, and considerations for appropriate automation versus total automation are discussed in this light while emphasizing areas where further development is needed in both contexts.
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Affiliation(s)
- Ashley Stephenson
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
- Microsoft Research, Redmond, Washington 98052, United States
| | - Lauren Lastra
- Microsoft Research, Redmond, Washington 98052, United States
| | - Bichlien Nguyen
- Microsoft Research, Redmond, Washington 98052, United States
| | - Yuan-Jyue Chen
- Microsoft Research, Redmond, Washington 98052, United States
| | - Jeff Nivala
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Luis Ceze
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Karin Strauss
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
- Microsoft Research, Redmond, Washington 98052, United States
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10
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Occhialini A, Lenaghan SC. Plastid engineering using episomal DNA. PLANT CELL REPORTS 2023:10.1007/s00299-023-03020-x. [PMID: 37127835 DOI: 10.1007/s00299-023-03020-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 04/11/2023] [Indexed: 05/03/2023]
Abstract
KEY MESSAGE Novel episomal systems have the potential to accelerate plastid genetic engineering for application in plant synthetic biology. Plastids represent valuable subcellular compartments for genetic engineering of plants with intrinsic advantages to engineering the nucleus. The ability to perform site-specific transgene integration by homologous recombination (HR), coordination of transgene expression in operons, and high production of heterologous proteins, all make plastids an attractive target for synthetic biology. Typically, plastid engineering is performed by homologous recombination; however, episomal-replicating vectors have the potential to accelerate the design/build/test cycles for plastid engineering. By accelerating the timeline from design to validation, it will be possible to generate translational breakthroughs in fields ranging from agriculture to biopharmaceuticals. Episomal-based plastid engineering will allow precise single step metabolic engineering in plants enabling the installation of complex synthetic circuits with the ambitious goal of reaching similar efficiency and flexibility of to the state-of-the-art genetic engineering of prokaryotic systems. The prospect to design novel episomal systems for production of transplastomic marker-free plants will also improve biosafety for eventual release in agriculture.
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Affiliation(s)
- Alessandro Occhialini
- Department of Plant Sciences, University of Tennessee, 112 Plant Biotechnology Building 2505 E J Chapman Drive, Knoxville, TN, 37996, USA.
- Center for Agricultural Synthetic Biology (CASB), University of Tennessee, 2640 Morgan Circle Drive, Knoxville, TN, 37996, USA.
| | - Scott C Lenaghan
- Center for Agricultural Synthetic Biology (CASB), University of Tennessee, 2640 Morgan Circle Drive, Knoxville, TN, 37996, USA.
- Department of Food Science, University of Tennessee, 102 Food Safety and Processing Building 2600 River Dr., Knoxville, TN, 37996, USA.
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11
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Xu S, Gao S, An Y. Research progress of engineering microbial cell factories for pigment production. Biotechnol Adv 2023; 65:108150. [PMID: 37044266 DOI: 10.1016/j.biotechadv.2023.108150] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 03/14/2023] [Accepted: 04/06/2023] [Indexed: 04/14/2023]
Abstract
Pigments are widely used in people's daily life, such as food additives, cosmetics, pharmaceuticals, textiles, etc. In recent years, the natural pigments produced by microorganisms have attracted increased attention because these processes cannot be affected by seasons like the plant extraction methods, and can also avoid the environmental pollution problems caused by chemical synthesis. Synthetic biology and metabolic engineering have been used to construct and optimize metabolic pathways for production of natural pigments in cellular factories. Building microbial cell factories for synthesis of natural pigments has many advantages, including well-defined genetic background of the strains, high-density and rapid culture of cells, etc. Until now, the technical means about engineering microbial cell factories for pigment production and metabolic regulation processes have not been systematically analyzed and summarized. Therefore, the studies about construction, modification and regulation of synthetic pathways for microbial synthesis of pigments in recent years have been reviewed, aiming to provide an up-to-date summary of engineering strategies for microbial synthesis of natural pigments including carotenoids, melanins, riboflavins, azomycetes and quinones. This review should provide new ideas for further improving microbial production of natural pigments in the future.
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Affiliation(s)
- Shumin Xu
- College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang, China; College of Food Science, Shenyang Agricultural University, Shenyang, China
| | - Song Gao
- College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang, China
| | - Yingfeng An
- College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang, China; College of Food Science, Shenyang Agricultural University, Shenyang, China; Shenyang Key Laboratory of Microbial Resources Mining and Molecular Breeding, Shenyang, China; Liaoning Provincial Key Laboratory of Agricultural Biotechnology, Shenyang, China.
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Ko SC, Cho M, Lee HJ, Woo HM. Biofoundry Palette: Planning-Assistant Software for Liquid Handler-Based Experimentation and Operation in the Biofoundry Workflow. ACS Synth Biol 2022; 11:3538-3543. [PMID: 36173735 DOI: 10.1021/acssynbio.2c00390] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Lab automation has facilitated synthetic biology applications in an automated workflow, and biofoundry facilities have enabled automated high-throughput experiments of gene cloning and genome engineering to be conducted following a precise experimental design and protocol. However, before-experiment procedures in biofoundry applications have been underdetermined. We aimed to develop a Python-based planning-assistant software, namely Biofoundry Palette, for liquid handler-based experimentation and operation in the biofoundry workflow. Depending on the synthetic biology project, variable information and content information may vary; the Biofoundry Palette provides precise information for the before-experiment units for each process module in the biofoundry workflow. As a demonstration, more than 200 unique information sets, generated by Biofoundry Palette, were used in automated gene cloning or pathway construction. The information on planning and management can potentially help the operator faithfully execute the biofoundry workflow after securing the before-experiment unit, thereby lowering the risk of human errors and performing successful biofoundry operations for synthetic biology applications.
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Affiliation(s)
- 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
| | - Mingu Cho
- Department of Food Science and Biotechnology, 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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