1
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Zuo Y, Zhao M, Gou Y, Huang L, Xu Z, Lian J. Transportation engineering for enhanced production of plant natural products in microbial cell factories. Synth Syst Biotechnol 2024; 9:742-751. [PMID: 38974023 PMCID: PMC11224930 DOI: 10.1016/j.synbio.2024.05.014] [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: 04/09/2024] [Revised: 05/21/2024] [Accepted: 05/30/2024] [Indexed: 07/09/2024] Open
Abstract
Plant natural products (PNPs) exhibit a wide range of biological activities and have essential applications in various fields such as medicine, agriculture, and flavors. Given their natural limitations, the production of high-value PNPs using microbial cell factories has become an effective alternative in recent years. However, host metabolic burden caused by its massive accumulation has become one of the main challenges for efficient PNP production. Therefore, it is necessary to strengthen the transmembrane transport process of PNPs. This review introduces the discovery and mining of PNP transporters to directly mediate PNP transmembrane transportation both intracellularly and extracellularly. In addition to transporter engineering, this review also summarizes several auxiliary strategies (such as small molecules, environmental changes, and vesicles assisted transport) for strengthening PNP transportation. Finally, this review is concluded with the applications and future perspectives of transportation engineering in the construction and optimization of PNP microbial cell factories.
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Affiliation(s)
- Yimeng Zuo
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education & National Key Laboratory of Biobased Transportation Fuel Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310000, China
| | - Minghui Zhao
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education & National Key Laboratory of Biobased Transportation Fuel Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310000, China
| | - Yuanwei Gou
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education & National Key Laboratory of Biobased Transportation Fuel Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310000, China
| | - Lei Huang
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education & National Key Laboratory of Biobased Transportation Fuel Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310000, China
| | - Zhinan Xu
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education & National Key Laboratory of Biobased Transportation Fuel Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Jiazhang Lian
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education & National Key Laboratory of Biobased Transportation Fuel Technology, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310000, China
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2
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Bultelle M, Casas A, Kitney R. Construction of a Calibration Curve for Lycopene on a Liquid-Handling Platform─Wider Lessons for the Development of Automated Dilution Protocols. ACS Synth Biol 2024; 13:2357-2375. [PMID: 39096303 PMCID: PMC11334188 DOI: 10.1021/acssynbio.4c00031] [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: 01/17/2024] [Revised: 07/04/2024] [Accepted: 07/23/2024] [Indexed: 08/05/2024]
Abstract
Liquid-handling is a fundamental operation in synthetic biology─all protocols involve one or more liquid-handling operations. It is, therefore, crucial that this step be carefully automated in order to unlock the benefits of automation (e.g., higher throughput, higher replicability). In the paper, we present a study, conducted at the London Biofoundry at SynbiCITE, that approaches liquid-handling and its reliable automation from the standpoint of the construction of the calibration curve for lycopene in dimethyl sulfoxide (DMSO). The study has important practical industrial applications (e.g., lycopene is a carotenoid of industrial interest, DMSO is a popular extractant). The study was also an effective testbed for the automation of liquid-handling. It necessitated the development of flexible liquid-handling methods, which can be generalizable to other automated applications. In addition, because lycopene/DMSO is a difficult mix, it was capable of revealing issues with automated liquid-handling protocols and stress-testing them. An important component of the study is the constraint that, due to the omnipresence of liquid-handling steps, errors should be controlled to a high standard. It is important to avoid such errors propagating to other parts of the protocol. To achieve this, a practical framework based on regression was developed and utilized throughout the study to identify, assess, and monitor transfer errors. The paper concludes with recommendations regarding automation of liquid-handling, which are applicable to a large set of applications (not just to complex liquids such as lycopene in DMSO or calibration curves).
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Affiliation(s)
- Matthieu Bultelle
- Department
of Bioengineering, Imperial College London, London SW7 2BX, United Kingdom
| | - Alexis Casas
- Department
of Bioengineering, Imperial College London, London SW7 2BX, United Kingdom
| | - Richard Kitney
- Department
of Bioengineering, Imperial College London, London SW7 2BX, United Kingdom
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3
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Tom G, Schmid SP, Baird SG, Cao Y, Darvish K, Hao H, Lo S, Pablo-García S, Rajaonson EM, Skreta M, Yoshikawa N, Corapi S, Akkoc GD, Strieth-Kalthoff F, Seifrid M, Aspuru-Guzik A. Self-Driving Laboratories for Chemistry and Materials Science. Chem Rev 2024. [PMID: 39137296 DOI: 10.1021/acs.chemrev.4c00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2024]
Abstract
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.
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Affiliation(s)
- Gary Tom
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Stefan P Schmid
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, CH-8093 Zurich, Switzerland
| | - Sterling G Baird
- Acceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Yang Cao
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Kourosh Darvish
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Han Hao
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Acceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Stanley Lo
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Sergio Pablo-García
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
| | - Ella M Rajaonson
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Marta Skreta
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Naruki Yoshikawa
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
| | - Samantha Corapi
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
| | - Gun Deniz Akkoc
- Forschungszentrum Jülich GmbH, Helmholtz Institute for Renewable Energy Erlangen-Nürnberg, Cauerstr. 1, 91058 Erlangen, Germany
- Department of Chemical and Biological Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Egerlandstr. 3, 91058 Erlangen, Germany
| | - Felix Strieth-Kalthoff
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- School of Mathematics and Natural Sciences, University of Wuppertal, Gaußstraße 20, 42119 Wuppertal, Germany
| | - Martin Seifrid
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Department of Materials Science and Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States of America
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George St, Toronto, Ontario M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave Suite 710, Toronto, Ontario M5G 1M1, Canada
- Acceleration Consortium, 80 St. George St, Toronto, Ontario M5S 3H6, Canada
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
- Department of Materials Science & Engineering, University of Toronto, Toronto, Ontario M5S 3E4, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 661 University Ave, Toronto, Ontario M5G 1M1, Canada
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4
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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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5
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Omar MN, Minggu MM, Nor Muhammad NA, Abdul PM, Zhang Y, Ramzi AB. Towards consolidated bioprocessing of biomass and plastic substrates for semi-synthetic production of bio-poly(ethylene furanoate) (PEF) polymer using omics-guided construction of artificial microbial consortia. Enzyme Microb Technol 2024; 177:110429. [PMID: 38537325 DOI: 10.1016/j.enzmictec.2024.110429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/20/2024] [Accepted: 03/14/2024] [Indexed: 04/29/2024]
Abstract
Poly(ethylene furanoate) (PEF) plastic is a 100% renewable polyester that is currently being pursued for commercialization as the next-generation bio-based plastic. This is in line with growing demand for circular bioeconomy and new plastics economy that is aimed at minimizing plastic waste mismanagement and lowering carbon footprint of plastics. However, the current catalytic route for the synthesis of PEF is impeded with technical challenges including high cost of pretreatment and catalyst refurbishment. On the other hand, the semi-biosynthetic route of PEF plastic production is of increased biotechnological interest. In particular, the PEF monomers (Furan dicarboxylic acid and ethylene glycol) can be synthesized via microbial-based biorefinery and purified for subsequent catalyst-mediated polycondensation into PEF. Several bioengineering and bioprocessing issues such as efficient substrate utilization and pathway optimization need to be addressed prior to establishing industrial-scale production of the monomers. This review highlights current advances in semi-biosynthetic production of PEF monomers using consolidated waste biorefinery strategies, with an emphasis on the employment of omics-driven systems biology approaches in enzyme discovery and pathway construction. The roles of microbial protein transporters will be discussed, especially in terms of improving substrate uptake and utilization from lignocellulosic biomass, as well as from depolymerized plastic waste as potential bio-feedstock. The employment of artificial bioengineered microbial consortia will also be highlighted to provide streamlined systems and synthetic biology strategies for bio-based PEF monomer production using both plant biomass and plastic-derived substrates, which are important for circular and new plastics economy advances.
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Affiliation(s)
- Mohd Norfikri Omar
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), UKM, Bangi, Selangor 43600, Malaysia
| | - Matthlessa Matthew Minggu
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), UKM, Bangi, Selangor 43600, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), UKM, Bangi, Selangor 43600, Malaysia
| | - Peer Mohamed Abdul
- Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia; Centre for Sustainable Process Technology (CESPRO), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
| | - Ying Zhang
- BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Ahmad Bazli Ramzi
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), UKM, Bangi, Selangor 43600, Malaysia.
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6
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Simon CG, Bozenhardt EH, Celluzzi CM, Dobnik D, Grant ML, Lakshmipathy U, Nebel T, Peltier L, Ratcliffe A, Sherley JL, Stacey GN, Taghizadeh RR, Tan EHP, Vessillier S. Mechanism of action, potency and efficacy: considerations for cell therapies. J Transl Med 2024; 22:416. [PMID: 38698408 PMCID: PMC11067168 DOI: 10.1186/s12967-024-05179-7] [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: 03/15/2024] [Accepted: 04/05/2024] [Indexed: 05/05/2024] Open
Abstract
One of the most challenging aspects of developing advanced cell therapy products (CTPs) is defining the mechanism of action (MOA), potency and efficacy of the product. This perspective examines these concepts and presents helpful ways to think about them through the lens of metrology. A logical framework for thinking about MOA, potency and efficacy is presented that is consistent with the existing regulatory guidelines, but also accommodates what has been learned from the 27 US FDA-approved CTPs. Available information regarding MOA, potency and efficacy for the 27 FDA-approved CTPs is reviewed to provide background and perspective. Potency process and efficacy process charts are introduced to clarify and illustrate the relationships between six key concepts: MOA, potency, potency test, efficacy, efficacy endpoint and efficacy endpoint test. Careful consideration of the meaning of these terms makes it easier to discuss the challenges of correlating potency test results with clinical outcomes and to understand how the relationships between the concepts can be misunderstood during development and clinical trials. Examples of how a product can be "potent but not efficacious" or "not potent but efficacious" are presented. Two example applications of the framework compare how MOA is assessed in cell cultures, animal models and human clinical trials and reveals the challenge of establishing MOA in humans. Lastly, important considerations for the development of potency tests for a CTP are discussed. These perspectives can help product developers set appropriate expectations for understanding a product's MOA and potency, avoid unrealistic assumptions and improve communication among team members during the development of CTPs.
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Affiliation(s)
- Carl G Simon
- Biosystems and Biomaterials Division, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA.
| | - Erich H Bozenhardt
- United Therapeutics Corporation, Regenerative Medicine Operations, Research Triangle Park, NC, USA
| | - Christina M Celluzzi
- Association for the Advancement of Blood and Biotherapies (AABB), Bethesda, MD, USA
| | - David Dobnik
- Niba Labs, Ljubljana, Slovenia
- National Institute of Biology, Ljubljana, Slovenia
| | - Melanie L Grant
- Department of Pediatrics, Children's Healthcare of Atlanta, Marcus Center for Cellular and Gene Therapies, Correlative Studies Laboratory, Emory University School of Medicine, Atlanta, GA, USA
| | - Uma Lakshmipathy
- Pharma Services, Science and Technology, Thermo Fisher Scientific, San Diego, CA, USA
| | - Thiana Nebel
- Medical Education, Sports Medicine and Orthobiologics, Medical Sales Institute, San Diego, CA, USA
| | - Linda Peltier
- Cellular Therapy Lab, Research Institute of McGill University Health Center, Montreal, QC, Canada
| | | | | | - Glyn N Stacey
- International Stem Cell Banking Initiative, Barley, Herts, UK
- National Stem Cell Resource Centre, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cells and Regenerative Medicine, Chinese Academy of Sciences, Beijing, China
| | | | - Eddie H P Tan
- Cell and Gene Therapy Facility, Health Sciences Authority, Singapore, Singapore
| | - Sandrine Vessillier
- Science, Research and Innovation Group, Biotherapeutics and Advanced Therapies Division, Medicines and Healthcare Products Regulatory Agency, South Mimms, Hertfordshire, UK
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7
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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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8
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Billerbeck S, Walker RSK, Pretorius IS. Killer yeasts: expanding frontiers in the age of synthetic biology. Trends Biotechnol 2024:S0167-7799(24)00067-2. [PMID: 38575438 DOI: 10.1016/j.tibtech.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/07/2024] [Accepted: 03/07/2024] [Indexed: 04/06/2024]
Abstract
Killer yeasts secrete protein toxins that are selectively lethal to other yeast and filamentous fungi. These exhibit exceptional genetic and functional diversity, and have several biotechnological applications. However, despite decades of research, several limitations hinder their widespread adoption. In this perspective we contend that technical advances in synthetic biology present an unprecedented opportunity to unlock the full potential of yeast killer systems across a spectrum of applications. By leveraging these new technologies, engineered killer toxins may emerge as a pivotal new tool to address antifungal resistance and food security. Finally, we speculate on the biotechnological potential of re-engineering host double-stranded (ds) RNA mycoviruses, from which many toxins derive, as a safe and noninfectious system to produce designer RNA.
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Affiliation(s)
- Sonja Billerbeck
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology institute, University of Groningen, Groningen 9747, AG, The Netherlands
| | - Roy S K Walker
- Department of Molecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia; ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Isak S Pretorius
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia.
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9
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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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10
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Ma Y, Zhang Z, Jia B, Yuan Y. Automated high-throughput DNA synthesis and assembly. Heliyon 2024; 10:e26967. [PMID: 38500977 PMCID: PMC10945133 DOI: 10.1016/j.heliyon.2024.e26967] [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: 04/23/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 03/20/2024] Open
Abstract
DNA synthesis and assembly primarily revolve around the innovation and refinement of tools that facilitate the creation of specific genes and the manipulation of entire genomes. This multifaceted process encompasses two fundamental steps: the synthesis of lengthy oligonucleotides and the seamless assembly of numerous DNA fragments. With the advent of automated pipetting workstations and integrated experimental equipment, a substantial portion of repetitive tasks in the field of synthetic biology can now be efficiently accomplished through integrated liquid handling workstations. This not only reduces the need for manual labor but also enhances overall efficiency. This review explores the ongoing advancements in the oligonucleotide synthesis platform, automated DNA assembly techniques, and biofoundries. The development of accurate and high-throughput DNA synthesis and assembly technologies presents both challenges and opportunities.
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Affiliation(s)
- Yuxin Ma
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Zhaoyang Zhang
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Bin Jia
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
| | - Yingjin Yuan
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
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11
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Goold HD, Moseley JL, Lauersen KJ. The synthetic future of algal genomes. CELL GENOMICS 2024; 4:100505. [PMID: 38395701 PMCID: PMC10943592 DOI: 10.1016/j.xgen.2024.100505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/18/2023] [Accepted: 01/24/2024] [Indexed: 02/25/2024]
Abstract
Algae are diverse organisms with significant biotechnological potential for resource circularity. Taking inspiration from fermentative microbes, engineering algal genomes holds promise to broadly expand their application ranges. Advances in genome sequencing with improvements in DNA synthesis and delivery techniques are enabling customized molecular tool development to confer advanced traits to algae. Efforts to redesign and rebuild entire genomes to create fit-for-purpose organisms currently being explored in heterotrophic prokaryotes and eukaryotic microbes could also be applied to photosynthetic algae. Future algal genome engineering will enhance yields of native products and permit the expression of complex biochemical pathways to produce novel metabolites from sustainable inputs. We present a historical perspective on advances in engineering algae, discuss the requisite genetic traits to enable algal genome optimization, take inspiration from whole-genome engineering efforts in other microbes for algal systems, and present candidate algal species in the context of these engineering goals.
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Affiliation(s)
- Hugh D Goold
- New South Wales Department of Primary Industries, Orange, NSW 2800, Australia; ARC Center of Excellence in Synthetic Biology, Macquarie University, Sydney, NSW 2109, Australia; School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Jeffrey L Moseley
- California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA 94720, USA; Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Phycoil Biotechnology International, Inc., Fremont, CA 94538, USA
| | - Kyle J Lauersen
- Bioengineering Program, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
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12
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Mock M, Langmead CJ, Grandsard P, Edavettal S, Russell A. Recent advances in generative biology for biotherapeutic discovery. Trends Pharmacol Sci 2024; 45:255-267. [PMID: 38378385 DOI: 10.1016/j.tips.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/22/2023] [Accepted: 01/05/2024] [Indexed: 02/22/2024]
Abstract
Generative biology combines artificial intelligence (AI), advanced life sciences technologies, and automation to revolutionize the process of designing novel biomolecules with prescribed properties, giving drug discoverers the ability to escape the limitations of biology during the design of next-generation protein therapeutics. Significant hurdles remain, namely: (i) the inherently complex nature of drug discovery, (ii) the bewildering number of promising computational and experimental techniques that have emerged in the past several years, and (iii) the limited availability of relevant protein sequence-function data for drug-like molecules. There is a need to focus on computational methods that will be most practically effective for protein drug discovery and on building experimental platforms to generate the data most appropriate for these methods. Here, we discuss recent advances in computational and experimental life sciences that are most crucial for impacting the pace and success of protein drug discovery.
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Affiliation(s)
- Marissa Mock
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | | | - Peter Grandsard
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Suzanne Edavettal
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA
| | - Alan Russell
- Amgen Research, Amgen Inc., One Amgen Center Drive, Thousand Oaks, CA 91320, USA.
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13
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Woo HM, Keasling J. Measuring the economic efficiency of laboratory automation in biotechnology. Trends Biotechnol 2024:S0167-7799(24)00030-1. [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] [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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14
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Teng F, Cui T, Zhou L, Gao Q, Zhou Q, Li W. Programmable synthetic receptors: the next-generation of cell and gene therapies. Signal Transduct Target Ther 2024; 9:7. [PMID: 38167329 PMCID: PMC10761793 DOI: 10.1038/s41392-023-01680-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/22/2023] [Accepted: 10/11/2023] [Indexed: 01/05/2024] Open
Abstract
Cell and gene therapies hold tremendous promise for treating a range of difficult-to-treat diseases. However, concerns over the safety and efficacy require to be further addressed in order to realize their full potential. Synthetic receptors, a synthetic biology tool that can precisely control the function of therapeutic cells and genetic modules, have been rapidly developed and applied as a powerful solution. Delicately designed and engineered, they can be applied to finetune the therapeutic activities, i.e., to regulate production of dosed, bioactive payloads by sensing and processing user-defined signals or biomarkers. This review provides an overview of diverse synthetic receptor systems being used to reprogram therapeutic cells and their wide applications in biomedical research. With a special focus on four synthetic receptor systems at the forefront, including chimeric antigen receptors (CARs) and synthetic Notch (synNotch) receptors, we address the generalized strategies to design, construct and improve synthetic receptors. Meanwhile, we also highlight the expanding landscape of therapeutic applications of the synthetic receptor systems as well as current challenges in their clinical translation.
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Affiliation(s)
- Fei Teng
- University of Chinese Academy of Sciences, Beijing, 101408, China.
| | - Tongtong Cui
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
| | - Li Zhou
- University of Chinese Academy of Sciences, Beijing, 101408, China
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
| | - Qingqin Gao
- University of Chinese Academy of Sciences, Beijing, 101408, China
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
| | - Qi Zhou
- University of Chinese Academy of Sciences, Beijing, 101408, China.
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Wei Li
- University of Chinese Academy of Sciences, Beijing, 101408, China.
- State Key Laboratory of Stem Cell and Regenerative Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
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15
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Grob A, Enrico Bena C, Di Blasi R, Pessina D, Sood M, Yunyue Z, Bosia C, Isalan M, Ceroni F. Mammalian cell growth characterisation by a non-invasive plate reader assay. Nat Commun 2024; 15:57. [PMID: 38167870 PMCID: PMC10761699 DOI: 10.1038/s41467-023-44396-4] [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: 04/05/2023] [Accepted: 12/12/2023] [Indexed: 01/05/2024] Open
Abstract
Automated and non-invasive mammalian cell analysis is currently lagging behind due to a lack of methods suitable for a variety of cell lines and applications. Here, we report the development of a high throughput non-invasive method for tracking mammalian cell growth and performance based on plate reader measurements. We show the method to be suitable for both suspension and adhesion cell lines, and we demonstrate it can be adopted when cells are grown under different environmental conditions. We establish that the method is suitable to inform on effective drug treatments to be used depending on the cell line considered, and that it can support characterisation of engineered mammalian cells over time. This work provides the scientific community with an innovative approach to mammalian cell screening, also contributing to the current efforts towards high throughput and automated mammalian cell engineering.
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Affiliation(s)
- Alice Grob
- Department of Chemical Engineering, Imperial College London, London, UK
- Imperial College Centre for Synthetic Biology, Imperial College London, London, UK
| | - Chiara Enrico Bena
- Italian Institute for Genomic Medicine, Torino, Italy
- Université Paris-Saclay (INRAE), AgroParisTech, Micalis Institute, 78350, Jouy-en-Josas, France
| | - Roberto Di Blasi
- Department of Chemical Engineering, Imperial College London, London, UK
- Imperial College Centre for Synthetic Biology, Imperial College London, London, UK
| | - Daniele Pessina
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Matthew Sood
- Department of Chemical Engineering, Imperial College London, London, UK
| | - Zhou Yunyue
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Carla Bosia
- Italian Institute for Genomic Medicine, Torino, Italy.
- Department of Applied Science and Technology, Politecnico di Torino, Torino, Italy.
| | - Mark Isalan
- Imperial College Centre for Synthetic Biology, Imperial College London, London, UK.
- Department of Life Sciences, Imperial College London, London, United Kingdom.
| | - Francesca Ceroni
- Department of Chemical Engineering, Imperial College London, London, UK.
- Imperial College Centre for Synthetic Biology, Imperial College London, London, UK.
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16
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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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17
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Hillson NJ. A Procedural Framework for Benchmarking Biofoundry Capabilities. ACS Synth Biol 2023; 12:3778-3782. [PMID: 37943942 PMCID: PMC10729010 DOI: 10.1021/acssynbio.3c00491] [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: 08/10/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023]
Abstract
Benchmarking compares the performance of a product or service with a competitor. In a biofoundry context, capability benchmarking enables more effective use of development resources and furthering business development efforts. Biofoundries considering benchmarking activities are immediately faced with many implementation questions and decisions. While differing circumstances between biofoundries may lead to different answers to those same questions, a common framework for the benchmarking process is desirable. Perhaps the framework described here, and developed for the United States Department of Energy Agile BioFoundry, will be useful to other biofoundries around the world.
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Affiliation(s)
- Nathan J. Hillson
- United
States Department of Energy Agile BioFoundry, Emeryville, California 94608, United States
- Biological
Systems and Engineering Division, Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
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18
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Yu H, Deng H, He J, Keasling JD, Luo X. UniKP: a unified framework for the prediction of enzyme kinetic parameters. Nat Commun 2023; 14:8211. [PMID: 38081905 PMCID: PMC10713628 DOI: 10.1038/s41467-023-44113-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023] Open
Abstract
Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce UniKP, a unified framework based on pretrained language models for the prediction of enzyme kinetic parameters, including enzyme turnover number (kcat), Michaelis constant (Km), and catalytic efficiency (kcat / Km), from protein sequences and substrate structures. A two-layer framework derived from UniKP (EF-UniKP) has also been proposed to allow robust kcat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods are systematically explored to successfully reduce the prediction error in high-value prediction tasks. We have demonstrated the application of UniKP and EF-UniKP in several enzyme discovery and directed evolution tasks, leading to the identification of new enzymes and enzyme mutants with higher activity. UniKP is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications.
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Affiliation(s)
- Han Yu
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Huaxiang Deng
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jiahui He
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jay D Keasling
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Joint BioEnergy Institute, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Department of Chemical and Biomolecular Engineering & Department of Bioengineering, University of California, Berkeley, CA, 94720, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kgs, Lyngby, Denmark
| | - Xiaozhou Luo
- Shenzhen Key Laboratory for the Intelligent Microbial Manufacturing of Medicines, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- Center for Synthetic Biochemistry, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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19
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Meier F, Dixon T, Williams T, Paulsen I. Navigating the Frontier of Synthetic Biology: An AI-Driven Analytics Platform for Exploring Research Trends and Relationships. ACS Synth Biol 2023; 12:3229-3241. [PMID: 37648657 DOI: 10.1021/acssynbio.3c00192] [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: 09/01/2023]
Abstract
The field of synthetic biology has experienced rapid growth in recent years, leading to an overwhelming amount of literature that can make it difficult to comprehend the scope and trends of the discipline. In this study, we employ topic modeling to comprehensively map research topics within synthetic biology, revealing subtopics and their relationships, as well as trends over time. We utilize metadata to identify the most significant journals and countries in the field and discuss potential policy impact on the research output. In addition, we investigate co-authorship networks to analyze collaborations among authors, institutions, and countries. We believe that our findings could serve as a valuable resource for gaining a deeper understanding of synthetic biology and provide a foundation for analyzing other disciplines.
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Affiliation(s)
- Felix Meier
- School of Natural Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
- ARC Centre of Excellence in Synthetic Biology, Sydney, New South Wales 2109, Australia
| | - Thom Dixon
- ARC Centre of Excellence in Synthetic Biology, Sydney, New South Wales 2109, Australia
- School of Social Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Tom Williams
- School of Natural Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
- ARC Centre of Excellence in Synthetic Biology, Sydney, New South Wales 2109, Australia
| | - Ian Paulsen
- School of Natural Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
- ARC Centre of Excellence in Synthetic Biology, Sydney, New South Wales 2109, Australia
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20
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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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21
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Merzbacher C, Oyarzún DA. Applications of artificial intelligence and machine learning in dynamic pathway engineering. Biochem Soc Trans 2023; 51:1871-1879. [PMID: 37656433 PMCID: PMC10657174 DOI: 10.1042/bst20221542] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023]
Abstract
Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.
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Affiliation(s)
| | - Diego A. Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, U.K
- The Alan Turing Institute, London, U.K
- School of Biological Sciences, University of Edinburgh, Edinburgh, U.K
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22
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Dixon TA, Walker RSK, Pretorius IS. Visioning synthetic futures for yeast research within the context of current global techno-political trends. Yeast 2023; 40:443-456. [PMID: 37653687 DOI: 10.1002/yea.3897] [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: 06/22/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023] Open
Abstract
Yeast research is entering into a new period of scholarship, with new scientific tools, new questions to ask and new issues to consider. The politics of emerging and critical technology can no longer be separated from the pursuit of basic science in fields, such as synthetic biology and engineering biology. Given the intensifying race for technological leadership, yeast research is likely to attract significant investment from government, and that it offers huge opportunities to the curious minded from a basic research standpoint. This article provides an overview of new directions in yeast research with a focus on Saccharomyces cerevisiae, and places these trends in their geopolitical context. At the highest level, yeast research is situated within the ongoing convergence of the life sciences with the information sciences. This convergent effect is most strongly pronounced in areas of AI-enabled tools for the life sciences, and the creation of synthetic genomes, minimal genomes, pan-genomes, neochromosomes and metagenomes using computer-assisted design tools and methodologies. Synthetic yeast futures encompass basic and applied science questions that will be of intense interest to government and nongovernment funding sources. It is essential for the yeast research community to map and understand the context of their research to ensure their collaborations turn global challenges into research opportunities.
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Affiliation(s)
- Thomas A Dixon
- School of Social Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Roy S K Walker
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales, Australia
| | - Isak S Pretorius
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales, Australia
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23
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Dykstra CB, Pyne ME, Martin VJJ. CRAPS: Chromosomal-Repair-Assisted Pathway Shuffling in Yeast. ACS Synth Biol 2023; 12:2578-2587. [PMID: 37584634 DOI: 10.1021/acssynbio.3c00170] [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: 08/17/2023]
Abstract
A fundamental challenge of metabolic engineering involves assembling and screening vast combinations of orthologous enzymes across a multistep biochemical pathway. Current pathway assembly workflows involve combining genetic parts ex vivo and assembling one pathway configuration per tube or well. Here, we present CRAPS, Chromosomal-Repair-Assisted Pathway Shuffling, an in vivo pathway engineering technique that enables the self-assembly of one pathway configuration per cell. CRAPS leverages the yeast chromosomal repair pathway and utilizes a pool of inactive, chromosomally integrated orthologous gene variants corresponding to a target multistep pathway. Supplying gRNAs to the CRAPS host activates the expression of one gene variant per pathway step, resulting in a unique pathway configuration in each cell. We deployed CRAPS to build more than 1000 theoretical combinations of a four-step carotenoid biosynthesis network. Sampling the CRAPS pathway space yielded strains with distinct color phenotypes and carotenoid product profiles. We anticipate that CRAPS will expedite strain engineering campaigns by enabling the generation and sampling of vast biochemical spaces.
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Affiliation(s)
- Christien B Dykstra
- Department of Biology, Concordia University, Montréal, Quebec, Canada H4B 1R6
- Centre for Applied Synthetic Biology, Concordia University, Montréal, Quebec, Canada H4B 1R6
| | - Michael E Pyne
- Department of Biology, Concordia University, Montréal, Quebec, Canada H4B 1R6
- Centre for Applied Synthetic Biology, Concordia University, Montréal, Quebec, Canada H4B 1R6
| | - Vincent J J Martin
- Department of Biology, Concordia University, Montréal, Quebec, Canada H4B 1R6
- Centre for Applied Synthetic Biology, Concordia University, Montréal, Quebec, Canada H4B 1R6
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Zhang XE, Liu C, Dai J, Yuan Y, Gao C, Feng Y, Wu B, Wei P, You C, Wang X, Si T. Enabling technology and core theory of synthetic biology. SCIENCE CHINA. LIFE SCIENCES 2023; 66:1742-1785. [PMID: 36753021 PMCID: PMC9907219 DOI: 10.1007/s11427-022-2214-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/04/2022] [Indexed: 02/09/2023]
Abstract
Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.
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Affiliation(s)
- Xian-En Zhang
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Chenli Liu
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Junbiao Dai
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Yingjin Yuan
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
| | - Caixia Gao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Bian Wu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ping Wei
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Chun You
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Tong Si
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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25
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Milito A, Aschern M, McQuillan JL, Yang JS. Challenges and advances towards the rational design of microalgal synthetic promoters in Chlamydomonas reinhardtii. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:3833-3850. [PMID: 37025006 DOI: 10.1093/jxb/erad100] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
Microalgae hold enormous potential to provide a safe and sustainable source of high-value compounds, acting as carbon-fixing biofactories that could help to mitigate rapidly progressing climate change. Bioengineering microalgal strains will be key to optimizing and modifying their metabolic outputs, and to render them competitive with established industrial biotechnology hosts, such as bacteria or yeast. To achieve this, precise and tuneable control over transgene expression will be essential, which would require the development and rational design of synthetic promoters as a key strategy. Among green microalgae, Chlamydomonas reinhardtii represents the reference species for bioengineering and synthetic biology; however, the repertoire of functional synthetic promoters for this species, and for microalgae generally, is limited in comparison to other commercial chassis, emphasizing the need to expand the current microalgal gene expression toolbox. Here, we discuss state-of-the-art promoter analyses, and highlight areas of research required to advance synthetic promoter development in C. reinhardtii. In particular, we exemplify high-throughput studies performed in other model systems that could be applicable to microalgae, and propose novel approaches to interrogating algal promoters. We lastly outline the major limitations hindering microalgal promoter development, while providing novel suggestions and perspectives for how to overcome them.
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Affiliation(s)
- Alfonsina Milito
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
| | - Moritz Aschern
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
| | - Josie L McQuillan
- Department of Chemical and Biological Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK
| | - Jae-Seong Yang
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Bellaterra, Barcelona, Spain
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26
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del Olmo Lianes I, Yubero P, Gómez-Luengo Á, Nogales J, Espeso DR. Technical upgrade of an open-source liquid handler to support bacterial colony screening. Front Bioeng Biotechnol 2023; 11:1202836. [PMID: 37404684 PMCID: PMC10315574 DOI: 10.3389/fbioe.2023.1202836] [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: 04/09/2023] [Accepted: 06/07/2023] [Indexed: 07/06/2023] Open
Abstract
The optimization of genetically engineered biological constructs is a key step to deliver high-impact biotechnological applications. The use of high-throughput DNA assembly methods allows the construction of enough genotypic variants to successfully cover the target design space. This, however, entails extra workload for researchers during the screening stage of candidate variants. Despite the existence of commercial colony pickers, their high price excludes small research laboratories and budget-adjusted institutions from accessing such extensive screening capability. In this work we present COPICK, a technical solution to automatize colony picking in an open-source liquid handler Opentrons OT-2. COPICK relies on a mounted camera to capture images of regular Petri dishes and detect microbial colonies for automated screening. COPICK's software can then automatically select the best colonies according to different criteria (size, color and fluorescence) and execute a protocol to pick them for further analysis. Benchmark tests performed for E. coli and P. putida colonies delivers a raw picking performance over pickable colonies of 82% with an accuracy of 73.4% at an estimated rate of 240 colonies/h. These results validate the utility of COPICK, and highlight the importance of ongoing technical improvements in open-source laboratory equipment to support smaller research teams.
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Affiliation(s)
- Irene del Olmo Lianes
- Department of Systems Biology, Centro Nacional de Biotecnología—Consejo Superior de Investigaciones Científicas, Madrid, Spain
| | - Pablo Yubero
- Department of Systems Biology, Centro Nacional de Biotecnología—Consejo Superior de Investigaciones Científicas, Madrid, Spain
| | - Álvaro Gómez-Luengo
- Department of Systems Biology, Centro Nacional de Biotecnología—Consejo Superior de Investigaciones Científicas, Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics Towards a Circular Economy—Consejo Superior de Investigaciones Científicas, SusPlast-CSIC, Madrid, Spain
| | - Juan Nogales
- Department of Systems Biology, Centro Nacional de Biotecnología—Consejo Superior de Investigaciones Científicas, Madrid, Spain
- Interdisciplinary Platform for Sustainable Plastics Towards a Circular Economy—Consejo Superior de Investigaciones Científicas, SusPlast-CSIC, Madrid, Spain
| | - David R. Espeso
- Department of Systems Biology, Centro Nacional de Biotecnología—Consejo Superior de Investigaciones Científicas, Madrid, Spain
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27
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Gurdo N, Volke DC, McCloskey D, Nikel PI. Automating the design-build-test-learn cycle towards next-generation bacterial cell factories. N Biotechnol 2023; 74:1-15. [PMID: 36736693 DOI: 10.1016/j.nbt.2023.01.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/15/2023] [Accepted: 01/22/2023] [Indexed: 02/04/2023]
Abstract
Automation is playing an increasingly significant role in synthetic biology. Groundbreaking technologies, developed over the past 20 years, have enormously accelerated the construction of efficient microbial cell factories. Integrating state-of-the-art tools (e.g. for genome engineering and analytical techniques) into the design-build-test-learn cycle (DBTLc) will shift the metabolic engineering paradigm from an almost artisanal labor towards a fully automated workflow. Here, we provide a perspective on how a fully automated DBTLc could be harnessed to construct the next-generation bacterial cell factories in a fast, high-throughput fashion. Innovative toolsets and approaches that pushed the boundaries in each segment of the cycle are reviewed to this end. We also present the most recent efforts on automation of the DBTLc, which heralds a fully autonomous pipeline for synthetic biology in the near future.
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Affiliation(s)
- Nicolás Gurdo
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark
| | - Daniel C Volke
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark
| | - Douglas McCloskey
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark
| | - Pablo Iván Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kongens, Lyngby, Denmark.
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28
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Bilbao A, Munoz N, Kim J, Orton DJ, Gao Y, Poorey K, Pomraning KR, Weitz K, Burnet M, Nicora CD, Wilton R, Deng S, Dai Z, Oksen E, Gee A, Fasani RA, Tsalenko A, Tanjore D, Gardner J, Smith RD, Michener JK, Gladden JM, Baker ES, Petzold CJ, Kim YM, Apffel A, Magnuson JK, Burnum-Johnson KE. PeakDecoder enables machine learning-based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements. Nat Commun 2023; 14:2461. [PMID: 37117207 PMCID: PMC10147702 DOI: 10.1038/s41467-023-37031-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 02/24/2023] [Indexed: 04/30/2023] Open
Abstract
Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.
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Affiliation(s)
- Aivett Bilbao
- Pacific Northwest National Laboratory, Richland, WA, USA.
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA.
| | - Nathalie Munoz
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Joonhoon Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Daniel J Orton
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yuqian Gao
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | | | - Kyle R Pomraning
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Karl Weitz
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Meagan Burnet
- Pacific Northwest National Laboratory, Richland, WA, USA
| | | | - Rosemarie Wilton
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Argonne National Laboratory, Lemont, IL, USA
| | - Shuang Deng
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Ziyu Dai
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Ethan Oksen
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Aaron Gee
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Rick A Fasani
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Anya Tsalenko
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Deepti Tanjore
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - James Gardner
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Joshua K Michener
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - John M Gladden
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Sandia National Laboratory, Livermore, CA, USA
| | - Erin S Baker
- Department of Chemistry, University of North Carolina, Chapel Hill, NC, USA
| | - Christopher J Petzold
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
- Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Young-Mo Kim
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Alex Apffel
- Agilent Research Laboratories, Agilent Technologies, Santa Clara, CA, USA
| | - Jon K Magnuson
- Pacific Northwest National Laboratory, Richland, WA, USA
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA
| | - Kristin E Burnum-Johnson
- Pacific Northwest National Laboratory, Richland, WA, USA.
- US Department of Energy, Agile BioFoundry, Emeryville, CA, USA.
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29
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Naseri G. A roadmap to establish a comprehensive platform for sustainable manufacturing of natural products in yeast. Nat Commun 2023; 14:1916. [PMID: 37024483 PMCID: PMC10079933 DOI: 10.1038/s41467-023-37627-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/24/2023] [Indexed: 04/08/2023] Open
Abstract
Secondary natural products (NPs) are a rich source for drug discovery. However, the low abundance of NPs makes their extraction from nature inefficient, while chemical synthesis is challenging and unsustainable. Saccharomyces cerevisiae and Pichia pastoris are excellent manufacturing systems for the production of NPs. This Perspective discusses a comprehensive platform for sustainable production of NPs in the two yeasts through system-associated optimization at four levels: genetics, temporal controllers, productivity screening, and scalability. Additionally, it is pointed out critical metabolic building blocks in NP bioengineering can be identified through connecting multilevel data of the optimized system using deep learning.
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Affiliation(s)
- Gita Naseri
- Max Planck Unit for the Science of Pathogens, Charitéplatz 1, 10117, Berlin, Germany.
- Institut für Biologie, Humboldt-Universität zu Berlin, Philippstrasse 13, 10115, Berlin, Germany.
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30
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Sanders LM, Scott RT, Yang JH, Qutub AA, Garcia Martin H, Berrios DC, Hastings JJA, Rask J, Mackintosh G, Hoarfrost AL, Chalk S, Kalantari J, Khezeli K, Antonsen EL, Babdor J, Barker R, Baranzini SE, Beheshti A, Delgado-Aparicio GM, Glicksberg BS, Greene CS, Haendel M, Hamid AA, Heller P, Jamieson D, Jarvis KJ, Komarova SV, Komorowski M, Kothiyal P, Mahabal A, Manor U, Mason CE, Matar M, Mias GI, Miller J, Myers JG, Nelson C, Oribello J, Park SM, Parsons-Wingerter P, Prabhu RK, Reynolds RJ, Saravia-Butler A, Saria S, Sawyer A, Singh NK, Snyder M, Soboczenski F, Soman K, Theriot CA, Van Valen D, Venkateswaran K, Warren L, Worthey L, Zitnik M, Costes SV. Biological research and self-driving labs in deep space supported by artificial intelligence. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00618-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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31
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Hoffmann SA, Diggans J, Densmore D, Dai J, Knight T, Leproust E, Boeke JD, Wheeler N, Cai Y. Safety by design: Biosafety and biosecurity in the age of synthetic genomics. iScience 2023; 26:106165. [PMID: 36895643 PMCID: PMC9988571 DOI: 10.1016/j.isci.2023.106165] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023] Open
Abstract
Technologies to profoundly engineer biology are becoming increasingly affordable, powerful, and accessible to a widening group of actors. While offering tremendous potential to fuel biological research and the bioeconomy, this development also increases the risk of inadvertent or deliberate creation and dissemination of pathogens. Effective regulatory and technological frameworks need to be developed and deployed to manage these emerging biosafety and biosecurity risks. Here, we review digital and biological approaches of a range of technology readiness levels suited to address these challenges. Digital sequence screening technologies already are used to control access to synthetic DNA of concern. We examine the current state of the art of sequence screening, challenges and future directions, and environmental surveillance for the presence of engineered organisms. As biosafety layer on the organism level, we discuss genetic biocontainment systems that can be used to created host organisms with an intrinsic barrier against unchecked environmental proliferation.
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Affiliation(s)
- Stefan A Hoffmann
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
| | - James Diggans
- Twist Bioscience, 681 Gateway Boulevard, South San Francisco, CA 9408, USA
| | - Douglas Densmore
- Department of Electrical and Computer Engineering, Boston University, 610 Commonwealth Avenue, Boston, MA 02215, USA
| | - Junbiao Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Tom Knight
- Ginkgo Bioworks, 27 Drydock Avenue, Boston, MA 02210, USA
| | - Emily Leproust
- Twist Bioscience, 681 Gateway Boulevard, South San Francisco, CA 9408, USA
| | - Jef D Boeke
- Institute for Systems Genetics, and Department of Biochemistry & Molecular Pharmacology, NYU Langone Health, 435 East 30th Street, New York, NY 10016, USA.,Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY 11201, USA
| | - Nicole Wheeler
- Institute of Microbiology and Infection, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Yizhi Cai
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
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32
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Voiniciuc C. It's time to go glyco in cell wall bioengineering. CURRENT OPINION IN PLANT BIOLOGY 2023; 71:102313. [PMID: 36411187 DOI: 10.1016/j.pbi.2022.102313] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/22/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Tailoring the structure of cellulose, hemicellulose or pectin in plant cell walls can modulate growth, disease resistance, biomass yield and other important agronomic traits. Recent advances in the biosynthesis of microfibrils and matrix polysaccharides force us to re-examine old assumptions about the assembly and functions of cell wall components. The engineering of living or hybrid materials in microorganisms could be adapted to plant biopolymers or to inspire the development of new plant-based composites. High-throughput cellular factories and synthetic biology toolkits could unveil the biological roles and biotechnological potential of the large, unexplored space of carbohydrate-active enzymes. Increasing automation and enhanced carbohydrate detection methods are unlocking new routes to design plant glycans for a sustainable bioeconomy.
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Affiliation(s)
- Cătălin Voiniciuc
- Horticultural Sciences Department, University of Florida, Gainesville, FL, 32611, USA.
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33
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Holub M, Agena E. Biofoundries and citizen science can accelerate disease surveillance and environmental monitoring. Front Bioeng Biotechnol 2023; 10:1110376. [PMID: 36714630 PMCID: PMC9877229 DOI: 10.3389/fbioe.2022.1110376] [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: 11/28/2022] [Accepted: 12/23/2022] [Indexed: 01/15/2023] Open
Abstract
A biofoundry is a highly automated facility for processing of biological samples. In that capacity it has a major role in accelerating innovation and product development in engineering biology by implementing design, build, test and learn (DBTL) cycles. Biofoundries bring public and private stakeholders together to share resources, develop standards and forge collaborations on national and international levels. In this paper we argue for expanding the scope of applications for biofoundries towards roles in biosurveillance and biosecurity. Reviewing literature on these topics, we conclude that this could be achieved in multiple ways including developing measurement standards and protocols, engaging citizens in data collection, closer collaborations with biorefineries, and processing of samples. Here we provide an overview of these roles that despite their potential utility have not yet been commonly considered by policymakers and funding agencies and identify roadblocks to their realization. This document should prove useful to policymakers and other stakeholders who wish to strengthen biosecurity programs in ways that synergize with bioeconomy.
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Affiliation(s)
- Martin Holub
- Department of Bionanoscience, Delft University of Technology, Delft, Netherlands,*Correspondence: Martin Holub,
| | - Ethan Agena
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
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34
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Abstract
Synthetic DNA is of increasing demand across many sectors of research and commercial activities. Engineering biology, therapy, data storage and nanotechnology are set for rapid developments if DNA can be provided at scale and low cost. Stimulated by successes in next generation sequencing and gene editing technologies, DNA synthesis is already a burgeoning industry. However, the synthesis of >200 bp sequences remains unaffordable. To overcome these limitations and start writing DNA as effectively as it is read, alternative technologies have been developed including molecular assembly and cloning methods, template-independent enzymatic synthesis, microarray and rolling circle amplification techniques. Here, we review the progress in developing and commercializing these technologies, which are exemplified by innovations from leading companies. We discuss pros and cons of each technology, the need for oversight and regulatory policies for DNA synthesis as a whole and give an overview of DNA synthesis business models.
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35
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Volk MJ, Tran VG, Tan SI, Mishra S, Fatma Z, Boob A, Li H, Xue P, Martin TA, Zhao H. Metabolic Engineering: Methodologies and Applications. Chem Rev 2022; 123:5521-5570. [PMID: 36584306 DOI: 10.1021/acs.chemrev.2c00403] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.
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Affiliation(s)
- Michael J Volk
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shih-I Tan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hongxiang Li
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Teresa A Martin
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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36
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Guo E, Fu L, Fang X, Xie W, Li K, Zhang Z, Hong Z, Si T. Robotic Construction and Screening of Lanthipeptide Variant Libraries in Escherichia coli. ACS Synth Biol 2022; 11:3900-3911. [PMID: 36379012 DOI: 10.1021/acssynbio.2c00344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Lanthipeptides are a major class of ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by thioether cross-links called lanthionine (Lan) and methyllanthionine (MeLan). Previously, we developed a method to produce mature lanthipeptides in recombinant Escherichia coli, but manual steps hinder large-scale analogue screening. Here we devised an automated workflow for creating and screening variant libraries of haloduracin, a two-component class II lanthipeptide. An integrated work cell of a synthetic biology foundry was programmed to robotically execute DNA library construction, host transformation, peptide production, mass spectrometry analysis, and activity screening by agar diffusion assay. For recombinantly produced Halα peptides, the sequence-activity relationship of 380 single-residue variants and >1300 triple-residue combinatorial variants were rapidly analyzed in microplates within weeks. The peptide expression levels in E. coli were also visualized via robotic creation and analysis of GFP-lanthipeptide fusions for select peptide mutants. Following shake-flask fermentation and purification, one Halα mutant was confirmed with enhanced specific antimicrobial activity relative to the wild-type peptide. Overall, this approach may be generally applicable for the high-throughput characterization and engineering of RiPP natural products.
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Affiliation(s)
- Erpeng Guo
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,BGI-Shenzhen, Shenzhen 518083, China
| | - Lihao Fu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoting Fang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Wenhao Xie
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Keyi Li
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhiyu Zhang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhilai Hong
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,BGI-Shenzhen, Shenzhen 518083, China
| | - Tong Si
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,BGI-Shenzhen, Shenzhen 518083, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Shenzhen 518055, China
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37
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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: 0] [Impact Index Per Article: 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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38
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Ramírez Rojas AA, Swidah R, Schindler D. Microbes of traditional fermentation processes as synthetic biology chassis to tackle future food challenges. Front Bioeng Biotechnol 2022; 10:982975. [PMID: 36185425 PMCID: PMC9523148 DOI: 10.3389/fbioe.2022.982975] [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: 06/30/2022] [Accepted: 08/10/2022] [Indexed: 11/23/2022] Open
Abstract
Microbial diversity is magnificent and essential to almost all life on Earth. Microbes are an essential part of every human, allowing us to utilize otherwise inaccessible resources. It is no surprise that humans started, initially unconsciously, domesticating microbes for food production: one may call this microbial domestication 1.0. Sourdough bread is just one of the miracles performed by microbial fermentation, allowing extraction of more nutrients from flour and at the same time creating a fluffy and delicious loaf. There are a broad range of products the production of which requires fermentation such as chocolate, cheese, coffee and vinegar. Eventually, with the rise of microscopy, humans became aware of microbial life. Today our knowledge and technological advances allow us to genetically engineer microbes - one may call this microbial domestication 2.0. Synthetic biology and microbial chassis adaptation allow us to tackle current and future food challenges. One of the most apparent challenges is the limited space on Earth available for agriculture and its major tolls on the environment through use of pesticides and the replacement of ecosystems with monocultures. Further challenges include transport and packaging, exacerbated by the 24/7 on-demand mentality of many customers. Synthetic biology already tackles multiple food challenges and will be able to tackle many future food challenges. In this perspective article, we highlight recent microbial synthetic biology research to address future food challenges. We further give a perspective on how synthetic biology tools may teach old microbes new tricks, and what standardized microbial domestication could look like.
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39
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Oliveira SMD, Densmore D. Hardware, Software, and Wetware Codesign Environment for Synthetic Biology. BIODESIGN RESEARCH 2022; 2022:9794510. [PMID: 37850136 PMCID: PMC10521664 DOI: 10.34133/2022/9794510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 08/10/2022] [Indexed: 10/19/2023] Open
Abstract
Synthetic biology is the process of forward engineering living systems. These systems can be used to produce biobased materials, agriculture, medicine, and energy. One approach to designing these systems is to employ techniques from the design of embedded electronics. These techniques include abstraction, standards, modularity, automated design, and formal semantic models of computation. Together, these elements form the foundation of "biodesign automation," where software, robotics, and microfluidic devices combine to create exciting biological systems of the future. This paper describes a "hardware, software, wetware" codesign vision where software tools can be made to act as "genetic compilers" that transform high-level specifications into engineered "genetic circuits" (wetware). This is followed by a process where automation equipment, well-defined experimental workflows, and microfluidic devices are explicitly designed to house, execute, and test these circuits (hardware). These systems can be used as either massively parallel experimental platforms or distributed bioremediation and biosensing devices. Next, scheduling and control algorithms (software) manage these systems' actual execution and data analysis tasks. A distinguishing feature of this approach is how all three of these aspects (hardware, software, and wetware) may be derived from the same basic specification in parallel and generated to fulfill specific cost, performance, and structural requirements.
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Affiliation(s)
- Samuel M. D. Oliveira
- Department of Electrical and Computer Engineering, Boston University, MA 02215, USA
- Biological Design Center, Boston University, MA 02215, USA
| | - Douglas Densmore
- Department of Electrical and Computer Engineering, Boston University, MA 02215, USA
- Biological Design Center, Boston University, MA 02215, USA
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40
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Carruthers DN, Lee TS. Translating advances in microbial bioproduction to sustainable biotechnology. Front Bioeng Biotechnol 2022; 10:968437. [PMID: 36082166 PMCID: PMC9445250 DOI: 10.3389/fbioe.2022.968437] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 07/21/2022] [Indexed: 11/24/2022] Open
Abstract
Advances in synthetic biology have radically changed our ability to rewire microorganisms and significantly improved the scalable production of a vast array of drop-in biopolymers and biofuels. The success of a drop-in bioproduct is contingent on market competition with petrochemical analogues and weighted upon relative economic and environmental metrics. While the quantification of comparative trade-offs is critical for accurate process-level decision making, the translation of industrial ecology to synthetic biology is often ambiguous and assessment accuracy has proven challenging. In this review, we explore strategies for evaluating industrial biotechnology through life cycle and techno-economic assessment, then contextualize how recent developments in synthetic biology have improved process viability by expanding feedstock availability and the productivity of microbes. By juxtaposing biological and industrial constraints, we highlight major obstacles between the disparate disciplines that hinder accurate process evaluation. The convergence of these disciplines is crucial in shifting towards carbon neutrality and a circular bioeconomy.
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Affiliation(s)
- David N. Carruthers
- Joint BioEnergy Institute, Emeryville, CA, United States
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Taek Soon Lee
- Joint BioEnergy Institute, Emeryville, CA, United States
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- *Correspondence: Taek Soon Lee,
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41
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Cho JS, Kim GB, Eun H, Moon CW, Lee SY. Designing Microbial Cell Factories for the Production of Chemicals. JACS AU 2022; 2:1781-1799. [PMID: 36032533 PMCID: PMC9400054 DOI: 10.1021/jacsau.2c00344] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/26/2022] [Accepted: 07/26/2022] [Indexed: 05/24/2023]
Abstract
The sustainable production of chemicals from renewable, nonedible biomass has emerged as an essential alternative to address pressing environmental issues arising from our heavy dependence on fossil resources. Microbial cell factories are engineered microorganisms harboring biosynthetic pathways streamlined to produce chemicals of interests from renewable carbon sources. The biosynthetic pathways for the production of chemicals can be defined into three categories with reference to the microbial host selected for engineering: native-existing pathways, nonnative-existing pathways, and nonnative-created pathways. Recent trends in leveraging native-existing pathways, discovering nonnative-existing pathways, and designing de novo pathways (as nonnative-created pathways) are discussed in this Perspective. We highlight key approaches and successful case studies that exemplify these concepts. Once these pathways are designed and constructed in the microbial cell factory, systems metabolic engineering strategies can be used to improve the performance of the strain to meet industrial production standards. In the second part of the Perspective, current trends in design tools and strategies for systems metabolic engineering are discussed with an eye toward the future. Finally, we survey current and future challenges that need to be addressed to advance microbial cell factories for the sustainable production of chemicals.
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Affiliation(s)
- Jae Sung Cho
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
- BioProcess
Engineering Research Center and BioInformatics Research Center, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Gi Bae Kim
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Hyunmin Eun
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Cheon Woo Moon
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic
and Biomolecular Engineering National Research Laboratory and Systems
Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative
Laboratory, Department of Chemical and Biomolecular Engineering (BK21
four), Korea Advanced Institute of Science
and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST
Institute for the BioCentury and KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
- BioProcess
Engineering Research Center and BioInformatics Research Center, Korea Advanced Institute of Science and Technology
(KAIST), Daejeon 34141, Republic of Korea
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42
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Romantseva E, Alperovich N, Ross D, Lund SP, Strychalski EA. Effects of DNA template preparation on variability in cell-free protein production. Synth Biol (Oxf) 2022; 7:ysac015. [PMID: 36046152 PMCID: PMC9425043 DOI: 10.1093/synbio/ysac015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 07/01/2022] [Accepted: 08/12/2022] [Indexed: 08/08/2023] Open
Abstract
DNA templates for protein production remain an unexplored source of variability in the performance of cell-free expression (CFE) systems. To characterize this variability, we investigated the effects of two common DNA extraction methodologies, a postprocessing step and manual versus automated preparation on protein production using CFE. We assess the concentration of the DNA template, the quality of the DNA template in terms of physical damage and the quality of the DNA solution in terms of purity resulting from eight DNA preparation workflows. We measure the variance in protein titer and rate of protein production in CFE reactions associated with the biological replicate of the DNA template, the technical replicate DNA solution prepared with the same workflow and the measurement replicate of nominally identical CFE reactions. We offer practical guidance for preparing and characterizing DNA templates to achieve acceptable variability in CFE performance.
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Affiliation(s)
| | - Nina Alperovich
- National Institute of Standards and Technology, Gaithersburg, MD USA
| | - David Ross
- National Institute of Standards and Technology, Gaithersburg, MD USA
| | - Steven P Lund
- National Institute of Standards and Technology, Gaithersburg, MD USA
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43
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Zhang S, Zhu J, Fan S, Xie W, Yang Z, Si T. Directed evolution of a cyclodipeptide synthase with new activities via label-free mass spectrometric screening. Chem Sci 2022; 13:7581-7586. [PMID: 35872818 PMCID: PMC9241961 DOI: 10.1039/d2sc01637k] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/01/2022] [Indexed: 12/12/2022] Open
Abstract
Directed evolution is a powerful approach to engineer enzymes via iterative creation and screening of variant libraries. However, assay development for high-throughput mutant screening remains challenging, particularly for new catalytic activities. Mass spectrometry (MS) analysis is label-free and well suited for untargeted discovery of new enzyme products but is traditionally limited by slow speed. Here we report an automated workflow for directed evolution of new enzymatic activities via high-throughput library creation and label-free MS screening. For a proof of concept, we chose to engineer a cyclodipeptide synthase (CDPS) that synthesizes diketopiperazine (DKP) compounds with therapeutic potential. In recombinant Escherichia coli, site-saturation mutagenesis (SSM) and error-prone PCR (epPCR) libraries expressing CDPS mutants were automatically created and cultivated on an integrated work cell. Culture supernatants were then robotically processed for matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS analysis at a rate of 5 s per sample. The resulting mass spectral data were processed via custom computational algorithms, which performed a multivariant analysis of 108 theoretical mass-to-charge (m/z) values of 190 possible DKP molecules within a mass window of 115–373 Da. An F186L CDPS mutant was isolated to produce cyclo(l-Phe–l-Val), which is undetectable in the product profile of the wild-type enzyme. This robotic, label-free MS screening approach may be generally applicable to engineering other enzymes with new activities in high throughput. A robotic workflow for directed evolution of new enzymatic activities via high-throughput library creation and label-free MS screening.![]()
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Affiliation(s)
- Songya Zhang
- CAS Key Lib Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences Shenzhen 518055 China
| | - Jing Zhu
- CAS Key Lib Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences Shenzhen 518055 China
| | - Shuai Fan
- The Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 1000050 China
| | - Wenhao Xie
- CAS Key Lib Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences Shenzhen 518055 China
| | - Zhaoyong Yang
- The Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 1000050 China
| | - Tong Si
- CAS Key Lib Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences Shenzhen 518055 China
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44
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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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A framework to efficiently describe and share reproducible DNA materials and construction protocols. Nat Commun 2022; 13:2894. [PMID: 35610233 PMCID: PMC9130275 DOI: 10.1038/s41467-022-30588-x] [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: 11/11/2021] [Accepted: 05/10/2022] [Indexed: 12/02/2022] Open
Abstract
DNA constructs and their annotated sequence maps have been rapidly accumulating with the advancement of DNA cloning, synthesis, and assembly methods. Such resources have also been utilized in designing and building new DNA materials. However, as commonly seen in the life sciences, no framework exists to describe reproducible DNA construction processes. Furthermore, the use of previously developed DNA materials and building protocols is usually not appropriately credited. Here, we report a framework QUEEN (framework to generate quinable and efficiently editable nucleotide sequence resources) to resolve these issues and accelerate the building of DNA. QUEEN enables the flexible design of new DNA by using existing DNA material resource files and recording its construction process in an output file (GenBank file format). A GenBank file generated by QUEEN can regenerate the process code such that it perfectly clones itself and bequeaths the same process code to its successive GenBank files, recycling its partial DNA resources. QUEEN-generated GenBank files are compatible with existing DNA repository services and software. We propose QUEEN as a solution to start significantly advancing the material and protocol sharing of DNA resources. DNA constructs and their annotated sequence maps have been rapidly accumulating with the advancement of DNA cloning, synthesis, and assembly methods. Here the authors introduce QUEEN, a framework to describe and share DNA materials and construction protocols.
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Shirai T, Kondo A. In Silico Design Strategies for the Production of Target Chemical Compounds Using Iterative Single-Level Linear Programming Problems. Biomolecules 2022; 12:620. [PMID: 35625545 PMCID: PMC9138359 DOI: 10.3390/biom12050620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 02/05/2023] Open
Abstract
The optimization of metabolic reaction modifications for the production of target compounds is a complex computational problem whose execution time increases exponentially with the number of metabolic reactions. Therefore, practical technologies are needed to identify reaction deletion combinations to minimize computing times and promote the production of target compounds by modifying intracellular metabolism. In this paper, a practical metabolic design technology named AERITH is proposed for high-throughput target compound production. This method can optimize the production of compounds of interest while maximizing cell growth. With this approach, an appropriate combination of metabolic reaction deletions can be identified by solving a simple linear programming problem. Using a standard CPU, the computation time could be as low as 1 min per compound, and the system can even handle large metabolic models. AERITH was implemented in MATLAB and is freely available for non-profit use.
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Affiliation(s)
- Tomokazu Shirai
- Cell Factory Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan;
| | - Akihiko Kondo
- Cell Factory Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan;
- Department of Chemical Science and Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan
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Metabolic Engineering Strategies for Improved Lipid Production and Cellular Physiological Responses in Yeast Saccharomyces cerevisiae. J Fungi (Basel) 2022; 8:jof8050427. [PMID: 35628683 PMCID: PMC9144191 DOI: 10.3390/jof8050427] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/13/2022] [Accepted: 04/18/2022] [Indexed: 02/01/2023] Open
Abstract
Microbial lipids have been a hot topic in the field of metabolic engineering and synthetic biology due to their increased market and important applications in biofuels, oleochemicals, cosmetics, etc. This review first compares the popular hosts for lipid production and explains the four modules for lipid synthesis in yeast, including the fatty acid biosynthesis module, lipid accumulation module, lipid sequestration module, and fatty acid modification module. This is followed by a summary of metabolic engineering strategies that could be used for enhancing each module for lipid production. In addition, the efforts being invested in improving the production of value-added fatty acids in engineered yeast, such as cyclopropane fatty acid, ricinoleic acid, gamma linoleic acid, EPA, and DHA, are included. A discussion is further made on the potential relationships between lipid pathway engineering and consequential changes in cellular physiological properties, such as cell membrane integrity, intracellular reactive oxygen species level, and mitochondrial membrane potential. Finally, with the rapid development of synthetic biology tools, such as CRISPR genome editing tools and machine learning models, this review proposes some future trends that could be employed to engineer yeast with enhanced intracellular lipid production while not compromising much of its cellular health.
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Nielsen J, Tillegreen CB, Petranovic D. Innovation trends in industrial biotechnology. Trends Biotechnol 2022; 40:1160-1172. [PMID: 35459568 DOI: 10.1016/j.tibtech.2022.03.007] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/14/2022] [Accepted: 03/21/2022] [Indexed: 11/27/2022]
Abstract
Microbial fermentations are used for the sustainable production of a range of products. Due to increasing trends in the food sector toward plant-based foods and meat and dairy product substitutes, microbial fermentation will have an increasing role in this sector, as it will enable a sustainable and scalable production of valuable foods and food ingredients. Microbial fermentation will also be used to advance and expand the production of sustainable chemicals and natural products. Much of this market expansion will come from new start-ups that translate academic research into novel processes and products using state-of-the art technologies. Here, we discuss the trends in innovation and technology and provide recommendations for how to successfully start and grow companies in industrial biotechnology.
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Affiliation(s)
- Jens Nielsen
- BioInnovation Institute, Ole Maaløes Vej 3, DK2200 Copenhagen N, Denmark; Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296 Gothenburg, Sweden.
| | | | - Dina Petranovic
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Kgs. Lyngby, Denmark
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Chakraborty D, Rengaswamy R, Raman K. Designing Biological Circuits: From Principles to Applications. ACS Synth Biol 2022; 11:1377-1388. [PMID: 35320676 DOI: 10.1021/acssynbio.1c00557] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits─the repressilator and the toggle switch─were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domain by employing the versatile framework of generalized morphological analysis. Literature in the area has been mapped on the basis of (a) the design methodologies used, ranging from brute-force searches to control-theoretic approaches, (b) the modeling techniques employed, (c) various circuit functionalities implemented, (d) key design characteristics, and (e) the strategies used for the robust design of genetic circuits. We conclude our review with an outlook on multiple exciting areas for future research, based on the systematic assessment of key research gaps that have been readily unravelled by our analysis framework.
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Affiliation(s)
- Debomita Chakraborty
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
| | - Raghunathan Rengaswamy
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Department of Chemical Engineering, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
| | - Karthik Raman
- Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Centre for Integrative Biology and Systems medicinE (IBSE), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
- Robert Bosch Centre for Data Science and Articial Intelligence (RBCDSAI), Indian Institute of Technology (IIT) Madras, Chennai 600 036, India
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50
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A Phage Foundry Framework to Systematically Develop Viral Countermeasures to Combat Antibiotic-Resistant Bacterial Pathogens. iScience 2022; 25:104121. [PMID: 35402883 PMCID: PMC8983348 DOI: 10.1016/j.isci.2022.104121] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
At its current rate, the rise of antimicrobial-resistant (AMR) infections is predicted to paralyze our industries and healthcare facilities while becoming the leading global cause of loss of human life. With limited new antibiotics on the horizon, we need to invest in alternative solutions. Bacteriophages (phages)—viruses targeting bacteria—offer a powerful alternative approach to tackle bacterial infections. Despite recent advances in using phages to treat recalcitrant AMR infections, the field lacks systematic development of phage therapies scalable to different applications. We propose a Phage Foundry framework to establish metrics for phage characterization and to fill the knowledge and technological gaps in phage therapeutics. Coordinated investment in AMR surveillance, sampling, characterization, and data sharing procedures will enable rational exploitation of phages for treatments. A fully realized Phage Foundry will enhance the sharing of knowledge, technology, and viral reagents in an equitable manner and will accelerate the biobased economy.
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