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Møller TA, Booth TJ, Shaw S, Møller VK, Frandsen RJ, Weber T. ActinoMation: A literate programming approach for medium-throughput robotic conjugation of Streptomyces spp. Synth Syst Biotechnol 2025; 10:667-676. [PMID: 40235855 PMCID: PMC11999424 DOI: 10.1016/j.synbio.2025.03.005] [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: 12/19/2024] [Revised: 02/10/2025] [Accepted: 03/08/2025] [Indexed: 04/17/2025] Open
Abstract
The genus Streptomyces are valuable producers of antibiotics and other pharmaceutically important bioactive compounds. Advances in molecular engineering tools, such as CRISPR, have provided some access to the metabolic potential of Streptomyces, but efficient genetic engineering of strains is hindered by laborious and slow manual transformation protocols. In this paper, we present a semi-automated medium-throughput workflow for the introduction of recombinant DNA into Streptomyces spp. using the affordable and open-sourced Opentrons (OT-2) robotics platform. To increase the accessibility of the workflow we provide an open-source protocol-creator, ActinoMation. ActinoMation is a literate programming environment using Python in Jupyter Notebook. We validated the method by transforming Streptomyces coelicolor (M1152 and M1146), S. albidoflavus (J1047), and S. venezuelae (DSM40230) with the plasmids pSETGUS and pIJ12551. We demonstrate conjugation efficiencies of 3.33∗10-3/0.33 % for M1152 with pSETGUS and pIJ12551; 2.96∗10-3/0.29 % for M1146 with pSETGUS and pIJ12551; 1.21∗10-5/0.0012 % for J1047 with pSETGUS and 4.70∗10-4/0.047 % with pIJ12551, and 4.97∗10-2/4.97 % for DSM40230 with pSETGUS and 6.13∗10-2/6.13 % with pIJ12551 with a false positive rate between 8.33 % and 54.54 %. Automation of the conjugation workflow facilitates a streamlined workflow on a larger scale without any evident loss of conjugation efficiency.
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Affiliation(s)
- Tenna A. Møller
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Thomas J. Booth
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Simon Shaw
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Vilhelm K. Møller
- DTU Bioengineering, Technical University of Denmark, Kgs. Lyngby, Denmark
| | | | - Tilmann Weber
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
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2
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Hägele L, Trachtmann N, Takors R. The knowledge driven DBTL cycle provides mechanistic insights while optimising dopamine production in Escherichia coli. Microb Cell Fact 2025; 24:111. [PMID: 40380156 DOI: 10.1186/s12934-025-02729-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2025] [Accepted: 04/24/2025] [Indexed: 05/19/2025] Open
Abstract
BACKGROUND Dopamine is a promising organic compound with several key applications in emergency medicine, diagnosis and treatment of cancer, production of lithium anodes, and wastewater treatment. Since studies on in vivo dopamine production are limited, this study demonstrates the development and optimisation of a dopamine production strain by the help of the knowledge driven design-build-test-learn (DBTL) cycle for rational strain engineering. RESULTS The knowledge driven DBTL cycle, involving upstream in vitro investigation, is an automated workflow that enables both mechanistic understanding and efficient DBTL cycling. Following the in vitro cell lysate studies, the results were translated to the in vivo environment through high-throughput ribosome binding site (RBS) engineering. As a result, we developed a dopamine production strain capable of producing dopamine at concentrations of 69.03 ± 1.2 mg/L which equals 34.34 ± 0.59 mg/gbiomass. Compared to state-of-the-art in vivo dopamine production, our approach improved performance by 2.6 and 6.6-fold, respectively. CONCLUSION In essence, a highly efficient dopamine production strain was developed by implementing the knowledge driven DBTL cycle involving upstream in vitro investigation. The fine-tuning of the dopamine pathway by high-throughput RBS engineering clearly demonstrated the impact of GC content in the Shine-Dalgarno sequence on the RBS strength.
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Affiliation(s)
- Lorena Hägele
- Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Natalia Trachtmann
- Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
- Laboratory of Molecular Genetics and Microbiology Methods, Kazan Scientific Center of Russian Academy of Sciences, 420111, Kazan, Russia
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany.
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3
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Annese D, Romani F, Grandellis C, Ives L, Frangedakis E, Buson FX, Molloy JC, Haseloff J. Semi-automated workflow for high-throughput Agrobacterium-mediated plant transformation. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2025; 122:e70118. [PMID: 40220013 PMCID: PMC11993085 DOI: 10.1111/tpj.70118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2024] [Revised: 03/03/2025] [Accepted: 03/10/2025] [Indexed: 04/14/2025]
Abstract
High-throughput experiments in plants are hindered by long generation times and high costs. To address these challenges, we present an optimized pipeline for Agrobacterium tumefaciens transformation and a simplified a protocol to obtain stable transgenic lines of the model liverwort Marchantia polymorpha, paving the way for efficient high-throughput experiments for plant synthetic biology and other applications. Our protocol involves a freeze-thaw Agrobacterium transformation method in six-well plates that can be adapted to robotic automation. Using the Opentrons open-source platform, we implemented a semi-automated protocol showing similar efficiency compared to manual manipulation. Additionally, we have streamlined and simplified the process of stable transformation and selection of M. polymorpha, reducing cost, time, and manual labor without compromising transformation efficiency. The addition of sucrose in the selection media significantly enhances the production of gemmae, accelerating the generation of isogenic plants. We believe these protocols have the potential to facilitate high-throughput screenings in diverse plant species and represent a significant step towards the full automation of plant transformation pipelines. This approach allows testing ~100 constructs per month, using conventional plant tissue culture facilities. We recently demonstrated the successful implementation of this protocol for screening hundreds of fluorescent reporters in Marchantia gemmae.
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Affiliation(s)
- Davide Annese
- Department of Plant SciencesUniversity of CambridgeCambridgeUK
| | - Facundo Romani
- Department of Plant SciencesUniversity of CambridgeCambridgeUK
| | | | | | | | - Felipe X. Buson
- Department of Chemical Engineering and BiotechnologyUniversity of CambridgeCambridgeUK
| | - Jennifer C. Molloy
- Department of Chemical Engineering and BiotechnologyUniversity of CambridgeCambridgeUK
| | - Jim Haseloff
- Department of Plant SciencesUniversity of CambridgeCambridgeUK
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4
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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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5
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Vidal G, Vitalis C, Guillén J. Standardized Golden Gate Assembly Metadata Representation Using SBOL. Methods Mol Biol 2025; 2850:89-104. [PMID: 39363068 DOI: 10.1007/978-1-0716-4220-7_6] [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
Synthetic biology, also known as engineering biology, is an interdisciplinary field that applies engineering principles to biological systems. One way to engineer biological systems is by modifying their DNA. A common workflow involves creating new DNA parts through synthesis and then using them in combination with other parts through assembly. Assembly standards such as MoClo, Phytobricks, and Loop are based on Golden Gate, and provide a framework for combining parts. The Synthetic Biology Open Language (SBOL) has implemented a best practice for representing build plans to communicate them to other practitioners through whiteboard designs and in a machine-readable format for communication with lab automation tools. Here we present a software tool for creating SBOL representations of build plans to simulate type IIS-mediated assembly reactions and store relevant metadata.
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Affiliation(s)
- Gonzalo Vidal
- Interdisciplinary Computing and Complex Biosystems, School of Computing, Newcastle University, Newcastle upon Tyne, UK.
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA.
| | - Carolus Vitalis
- Department of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, Boulder, CO, USA
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6
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Moukarzel G, Wang Y, Xin W, Hofmann C, Joshi A, Loughney JW, Bowman A. Automation of biochemical assays using an open-sourced, inexpensive robotic liquid handler. SLAS Technol 2024; 29:100205. [PMID: 39396729 DOI: 10.1016/j.slast.2024.100205] [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: 06/17/2024] [Revised: 08/30/2024] [Accepted: 10/10/2024] [Indexed: 10/15/2024]
Abstract
High Throughput Screening is crucial in pharmaceutical companies for efficient testing in drug discovery and development. Our Vaccines Analytical Research and Development (V-AR&D) department extensively uses robotic liquid handlers in their High Throughput Analytics (HTA) group for assay development and sample screening. However, these instruments are expensive and require extensive training. Opentrons' OT-2 liquid handler offers a more affordable option (<∼ $10,000) with Python programming language and open-source flexibility, reducing training requirements. OT-2 allows broadening of testing capabilities and method transfer without significant capital investments. Two biochemical assays were conducted to assess OT-2's performance, and it demonstrated accurate pipetting with low covariance compared to Tecan EVO liquid handlers. Though OT-2 has some limitations such as lack of a crash detection system and limited deck space, it is a cost-effective, medium-throughput, and accurate liquid handling tool suitable for early-stage development and method transfer.
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Affiliation(s)
- George Moukarzel
- High Throughput Analytics, Analytical Research & Development, Merck & Co. Inc., Rahway, NJ, United States
| | - Yi Wang
- High Throughput Analytics, Analytical Research & Development, Merck & Co. Inc., Rahway, NJ, United States
| | - Weiyue Xin
- High Throughput Analytics, Analytical Research & Development, Merck & Co. Inc., Rahway, NJ, United States
| | - Carl Hofmann
- High Throughput Analytics, Analytical Research & Development, Merck & Co. Inc., Rahway, NJ, United States
| | - Anjali Joshi
- High Throughput Analytics, Analytical Research & Development, Merck & Co. Inc., Rahway, NJ, United States
| | - John W Loughney
- High Throughput Analytics, Analytical Research & Development, Merck & Co. Inc., Rahway, NJ, United States
| | - Amy Bowman
- High Throughput Analytics, Analytical Research & Development, Merck & Co. Inc., Rahway, NJ, United States.
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7
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Bultelle M, Casas A, Kitney R. Engineering biology and automation-Replicability as a design principle. ENGINEERING BIOLOGY 2024; 8:53-68. [PMID: 39734660 PMCID: PMC11681252 DOI: 10.1049/enb2.12035] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 06/24/2024] [Accepted: 07/07/2024] [Indexed: 12/31/2024] Open
Abstract
Applications in engineering biology increasingly share the need to run operations on very large numbers of biological samples. This is a direct consequence of the application of good engineering practices, the limited predictive power of current computational models and the desire to investigate very large design spaces in order to solve the hard, important problems the discipline promises to solve. Automation has been proposed as a key component for running large numbers of operations on biological samples. This is because it is strongly associated with higher throughput, and with higher replicability (thanks to the reduction of human input). The authors focus on replicability and make the point that, far from being an additional burden for automation efforts, replicability should be considered central to the design of the automated pipelines processing biological samples at scale-as trialled in biofoundries. There cannot be successful automation without effective error control. Design principles for an IT infrastructure that supports replicability are presented. Finally, the authors conclude with some perspectives regarding the evolution of automation in engineering biology. In particular, they speculate that the integration of hardware and software will show rapid progress, and offer users a degree of control and abstraction of the robotic infrastructure on a level significantly greater than experienced today.
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Affiliation(s)
| | - Alexis Casas
- Department of BioengineeringImperial College LondonLondonUK
| | - Richard Kitney
- Department of BioengineeringImperial College LondonLondonUK
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8
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Thieme A, Renwick S, Marschmann M, Guimaraes PI, Weissenborn S, Clifton J. Deep integration of low-cost liquid handling robots in an industrial pharmaceutical development environment. SLAS Technol 2024; 29:100180. [PMID: 39222913 DOI: 10.1016/j.slast.2024.100180] [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: 03/12/2024] [Revised: 08/02/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024]
Abstract
The pharmaceutical industry is increasingly embracing laboratory automation to enhance experimental efficiency and operational resilience, particularly through the integration of automated liquid handlers (ALHs). This paper explores the integration of the low-cost Opentrons OT-2 liquid handling robot with F. Hoffmann-La Roche AG's in-house workflow orchestration software, AutoLab, to overcome barriers to lab automation. By leveraging the OT-2's development-oriented interfaces and AutoLab's modular architecture, we achieved a user-friendly, cost-efficient, and flexible automation solution that aligns with FAIR (findable, accessible, interoperable, reusable) data principles. We demonstrate an advanced workflow development methodology, utilizing the software architecture, that facilitates the creation of two flexible pipetting protocols and medium complexity assays. This deep integration approach diminishes the learning curve for novice users while simultaneously enhancing the overall efficiency and reliability of the experimental workflow. Our findings suggest that such integrations can significantly mitigate the challenges associated with lab automation, including cost, complexity, and adaptability, paving the way for more accessible and robust automated systems in pharmaceutical research.
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9
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Chaisupa P, Rahman MM, Hildreth SB, Moseley S, Gatling C, Bryant MR, Helm RF, Wright RC. Genetically Encoded, Noise-Tolerant, Auxin Biosensors in Yeast. ACS Synth Biol 2024; 13:2804-2819. [PMID: 39197086 PMCID: PMC11421217 DOI: 10.1021/acssynbio.4c00186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 08/07/2024] [Accepted: 08/08/2024] [Indexed: 08/30/2024]
Abstract
Auxins are crucial signaling molecules that regulate the growth, metabolism, and behavior of various organisms, most notably plants but also bacteria, fungi, and animals. Many microbes synthesize and perceive auxins, primarily indole-3-acetic acid (IAA, referred to as auxin herein), the most prevalent natural auxin, which influences their ability to colonize plants and animals. Understanding auxin biosynthesis and signaling in fungi may allow us to better control interkingdom relationships and microbiomes from agricultural soils to the human gut. Despite this importance, a biological tool for measuring auxin with high spatial and temporal resolution has not been engineered in fungi. In this study, we present a suite of genetically encoded, ratiometric, protein-based auxin biosensors designed for the model yeast Saccharomyces cerevisiae. Inspired by auxin signaling in plants, the ratiometric nature of these biosensors enhances the precision of auxin concentration measurements by minimizing clonal and growth phase variation. We used these biosensors to measure auxin production across diverse growth conditions and phases in yeast cultures and calibrated their responses to physiologically relevant levels of auxin. Future work will aim to improve the fold change and reversibility of these biosensors. These genetically encoded auxin biosensors are valuable tools for investigating auxin biosynthesis and signaling in S. cerevisiae and potentially other yeast and fungi and will also advance quantitative functional studies of the plant auxin perception machinery, from which they are built.
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Affiliation(s)
- Patarasuda Chaisupa
- Department
of Biological Systems Engineering, Virginia
Tech, Blacksburg, Virginia 24061, United States
| | - Md Mahbubur Rahman
- Department
of Biological Systems Engineering, Virginia
Tech, Blacksburg, Virginia 24061, United States
| | - Sherry B. Hildreth
- Fralin
Life Sciences Institute, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Saede Moseley
- Department
of Biological Systems Engineering, Virginia
Tech, Blacksburg, Virginia 24061, United States
| | - Chauncey Gatling
- Department
of Biological Systems Engineering, Virginia
Tech, Blacksburg, Virginia 24061, United States
| | - Matthew R. Bryant
- Department
of Biological Systems Engineering, Virginia
Tech, Blacksburg, Virginia 24061, United States
| | - Richard F. Helm
- Fralin
Life Sciences Institute, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department
of Biochemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - R. Clay Wright
- Department
of Biological Systems Engineering, Virginia
Tech, Blacksburg, Virginia 24061, United States
- Fralin
Life Sciences Institute, Virginia Tech, Blacksburg, Virginia 24061, United States
- The
Translational Plant Sciences Center (TPSC), Virginia Tech, Blacksburg, Virginia 24061, United States
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10
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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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11
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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: 20] [Impact Index Per Article: 20.0] [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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12
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Bryant JA, Longmire C, Sridhar S, Janousek S, Kellinger M, Wright RC. TidyTron: Reducing lab waste using validated wash-and-reuse protocols for common plasticware in Opentrons OT-2 lab robots. SLAS Technol 2024; 29:100107. [PMID: 37696493 DOI: 10.1016/j.slast.2023.08.007] [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: 05/31/2023] [Revised: 08/15/2023] [Accepted: 08/26/2023] [Indexed: 09/13/2023]
Abstract
Every year biotechnology labs generate a combined total of ∼5.5 million tons of plastic waste. As the global bioeconomy expands, biofoundries will inevitably increase plastic consumption in-step with synthetic biology scaling. Decontamination and reuse of single-use plastics could increase sustainability and reduce recurring costs of biological research. However, throughput and variable cleaning quality make manual decontamination impractical in most instances. Automating single-use plastic cleaning with liquid handling robots makes decontamination more practical by offering higher throughput and consistent cleaning quality. However, open-source, validated protocols using low-cost lab robotics for effective decontamination of plasticware-facilitating safe reuse-have not yet been developed. Here we introduce and validate TidyTron: a library of protocols for cleaning micropipette tips and microtiter plates that are contaminated with DNA, E. coli, and S. cerevisiae. We tested a variety of cleaning solutions, contact times, and agitation methods with the aim of minimizing time and cost, while maximizing cleaning stringency and sustainability. We tested and validated these cleaning procedures by comparing fresh (first-time usage) versus cleaned tips and plates for contamination with cells, DNA, or cleaning solutions. We assessed contamination by measuring colony forming units by plating, PCR efficiency and DNA concentration by qPCR, and event counts and debris by flow cytometry. Open source cleaning protocols are available at https://github.com/PlantSynBioLab/TidyTron and hosted on a graphical user interface at https://jbryantvt.github.io/TidyTron/.
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Affiliation(s)
- John A Bryant
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States
| | - Cameron Longmire
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States
| | - Sriya Sridhar
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States
| | - Samuel Janousek
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States
| | - Mason Kellinger
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States
| | - R Clay Wright
- Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States.
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