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Moreno-Paz S, Schmitz J, Suarez-Diez M. In silico analysis of design of experiment methods for metabolic pathway optimization. Comput Struct Biotechnol J 2024; 23:1959-1967. [PMID: 38736694 PMCID: PMC11087228 DOI: 10.1016/j.csbj.2024.04.062] [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: 03/12/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/14/2024] Open
Abstract
Microbial cell factories allow the production of chemicals presenting an alternative to traditional fossil fuel-dependent production. However, finding the optimal expression of production pathway genes is crucial for the development of efficient production strains. Unlike sequential experimentation, combinatorial optimization captures the relationships between pathway genes and production, albeit at the cost of conducting multiple experiments. Fractional factorial designs followed by linear modeling and statistical analysis reduce the experimental workload while maximizing the information gained during experimentation. Although tools to perform and analyze these designs are available, guidelines for selecting appropriate factorial designs for pathway optimization are missing. In this study, we leverage a kinetic model of a seven-genes pathway to simulate the performance of a full factorial strain library. We compare this approach to resolution V, IV, III, and Plackett Burman (PB) designs. Additionally, we evaluate the performance of these designs as training sets for a random forest algorithm aimed at identifying best-producing strains. Evaluating the robustness of these designs to noise and missing data, traits inherent to biological datasets, we find that while resolution V designs capture most information present in full factorial data, they necessitate the construction of a large number of strains. On the other hand, resolution III and PB designs fall short in identifying optimal strains and miss relevant information. Besides, given the small number of experiments required for the optimization of a pathway with seven genes, linear models outperform random forest. Consequently, we propose the use of resolution IV designs followed by linear modeling in Design-Build-Test-Learn (DBTL) cycles targeting the screening of multiple factors. These designs enable the identification of optimal strains and provide valuable guidance for subsequent optimization cycles.
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Affiliation(s)
- Sara Moreno-Paz
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708WE Wageningen, the Netherlands
| | - Joep Schmitz
- Department of Science and Research - dsm-firmenich, Science & Research, 2600 MA Delft, the Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708WE Wageningen, the Netherlands
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2
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Zhou HY, Ding WQ, Zhang X, Zhang HY, Hu ZC, Liu ZQ, Zheng YG. Fine and combinatorial regulation of key metabolic pathway for enhanced β-alanine biosynthesis with non-inducible Escherichia coli. Biotechnol Bioeng 2024. [PMID: 38978393 DOI: 10.1002/bit.28799] [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: 11/24/2023] [Revised: 06/22/2024] [Accepted: 06/27/2024] [Indexed: 07/10/2024]
Abstract
β-Alanine is the only β-amino acid in nature and one of the most important three-carbon chemicals. This work was aimed to construct a non-inducible β-alanine producer with enhanced metabolic flux towards β-alanine biosynthesis in Escherichia coli. First of all, the assembled E. coli endogenous promoters and 5'-untranslated regions (PUTR) were screened to finely regulate the combinatorial expression of genes panDBS and aspBCG for an optimal flux match between two key pathways. Subsequently, additional copies of key genes (panDBS K104S and ppc) were chromosomally introduced into the host A1. On these bases, dynamical regulation of the gene thrA was performed to reduce the carbon flux directed in the competitive pathway. Finally, the β-alanine titer reached 10.25 g/L by strain A14-R15, 361.7% higher than that of the original strain. Under fed-batch fermentation in a 5-L fermentor, a titer of 57.13 g/L β-alanine was achieved at 80 h. This is the highest titer of β-alanine production ever reported using non-inducible engineered E. coli. This metabolic modification strategy for optimal carbon flux distribution developed in this work could also be used for the production of various metabolic products.
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Affiliation(s)
- Hai-Yan Zhou
- National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China
| | - Wen-Qing Ding
- National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China
| | - Xi Zhang
- National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China
| | - Hong-Yu Zhang
- National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China
| | - Zhong-Ce Hu
- National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China
| | - Zhi-Qiang Liu
- National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China
| | - Yu-Guo Zheng
- National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, China
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology, Hangzhou, China
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3
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Moreno-Paz S, van der Hoek R, Eliana E, Martins Dos Santos VAP, Schmitz J, Suarez-Diez M. Combinatorial optimization of pathway, process and media for the production of p-coumaric acid by Saccharomyces cerevisiae. Microb Biotechnol 2024; 17:e14424. [PMID: 38528768 DOI: 10.1111/1751-7915.14424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/22/2024] [Accepted: 01/25/2024] [Indexed: 03/27/2024] Open
Abstract
Microbial cell factories are instrumental in transitioning towards a sustainable bio-based economy, offering alternatives to conventional chemical processes. However, fulfilling their potential requires simultaneous screening for optimal media composition, process and genetic factors, acknowledging the complex interplay between the organism's genotype and its environment. This study employs statistical design of experiments to systematically explore these relationships and optimize the production of p-coumaric acid (pCA) in Saccharomyces cerevisiae. Two rounds of fractional factorial designs were used to identify factors with a significant effect on pCA production, which resulted in a 168-fold variation in pCA titre. Moreover, a significant interaction between the culture temperature and expression of ARO4 highlighted the importance of simultaneous process and strain optimization. The presented approach leverages the strengths of experimental design and statistical analysis and could be systematically applied during strain and bioprocess design efforts to unlock the full potential of microbial cell factories.
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Affiliation(s)
- Sara Moreno-Paz
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, The Netherlands
| | - Rianne van der Hoek
- Department of Science and Research-dsm-firmenich, Science & Research, Delft, The Netherlands
| | - Elif Eliana
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, The Netherlands
| | | | - Joep Schmitz
- Department of Science and Research-dsm-firmenich, Science & Research, Delft, The Netherlands
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, The Netherlands
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4
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Moon S, Saboe A, Smanski MJ. Using design of experiments to guide genetic optimization of engineered metabolic pathways. J Ind Microbiol Biotechnol 2024; 51:kuae010. [PMID: 38490746 PMCID: PMC10981448 DOI: 10.1093/jimb/kuae010] [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: 12/12/2023] [Accepted: 03/14/2024] [Indexed: 03/17/2024]
Abstract
Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges. ONE-SENTENCE SUMMARY This is a review of literature related to applying Design of Experiments for genetic optimization.
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Affiliation(s)
- Seonyun Moon
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, St Paul, MN 55108, USA
- Biotechnology Institute, University of Minnesota, St Paul, MN 55108, USA
| | - Anna Saboe
- Biotechnology Institute, University of Minnesota, St Paul, MN 55108, USA
| | - Michael J Smanski
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, St Paul, MN 55108, USA
- Biotechnology Institute, University of Minnesota, St Paul, MN 55108, USA
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5
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Park JH, Bassalo MC, Lin GM, Chen Y, Doosthosseini H, Schmitz J, Roubos JA, Voigt CA. Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis. ACS Synth Biol 2023; 12:1119-1132. [PMID: 36943773 PMCID: PMC10127285 DOI: 10.1021/acssynbio.2c00607] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.
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Affiliation(s)
- Jong Hyun Park
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Marcelo C Bassalo
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Geng-Min Lin
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Ye Chen
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Hamid Doosthosseini
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
| | - Joep Schmitz
- DSM Science & Innovation, Biodata & Translational Sciences, P.O. Box 1, 2600 MA Delft, The Netherlands
| | - Johannes A Roubos
- DSM Science & Innovation, Biodata & Translational Sciences, P.O. Box 1, 2600 MA Delft, The Netherlands
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, 500 Technology Square, Cambridge, Massachusetts 02139, United States
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6
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Hsu SY, Lee J, Sychla A, Smanski MJ. Rational search of genetic design space for a heterologous terpene metabolic pathway in Streptomyces. Metab Eng 2023; 77:1-11. [PMID: 36863605 DOI: 10.1016/j.ymben.2023.02.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 01/05/2023] [Accepted: 02/22/2023] [Indexed: 03/04/2023]
Abstract
Modern tools in DNA synthesis and assembly give genetic engineers control over the nucleotide-level design of complex, multi-gene systems. Systematic approaches to explore genetic design space and optimize the performance of genetic constructs are lacking. Here we explore the application of a five-level Plackett-Burman fractional factorial design to improve the titer of a heterologous terpene biosynthetic pathway in Streptomyces. A library of 125 engineered gene clusters encoding the production of diterpenoid ent-atiserenoic acid (eAA) via the methylerythritol phosphate pathway was constructed and introduced into Streptomyces albidoflavus J1047 for heterologous expression. The eAA production titer varied within the library by over two orders of magnitude and host strains showed unexpected and reproducible colony morphology phenotypes. Analysis of Plackett-Burman design identified expression of dxs, the gene encoding the first and the flux-controlling enzyme, having the strongest impact on eAA titer, but with a counter-intuitive negative correlation between dxs expression and eAA production. Finally, simulation modeling was performed to determine how several plausible sources of experimental error/noise and non-linearity impact the utility of Plackett-Burman analyses.
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Affiliation(s)
- Szu-Yi Hsu
- Department of Biochemistry, Molecular Biology, and Biophysics, USA; Biotechnology Institute, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Jihaeng Lee
- Department of Biochemistry, Molecular Biology, and Biophysics, USA; Biotechnology Institute, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Adam Sychla
- Department of Biochemistry, Molecular Biology, and Biophysics, USA; Biotechnology Institute, University of Minnesota, Saint Paul, MN, 55108, USA
| | - Michael J Smanski
- Department of Biochemistry, Molecular Biology, and Biophysics, USA; Biotechnology Institute, University of Minnesota, Saint Paul, MN, 55108, USA.
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Gao Q, Yang H, Wang C, Xie XY, Liu KX, Lin Y, Han SY, Zhu M, Neureiter M, Lin Y, Ye JW. Advances and trends in microbial production of polyhydroxyalkanoates and their building blocks. Front Bioeng Biotechnol 2022; 10:966598. [PMID: 35928942 PMCID: PMC9343942 DOI: 10.3389/fbioe.2022.966598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 07/01/2022] [Indexed: 11/13/2022] Open
Abstract
With the rapid development of synthetic biology, a variety of biopolymers can be obtained by recombinant microorganisms. Polyhydroxyalkanoates (PHA) is one of the most popular one with promising material properties, such as biodegradability and biocompatibility against the petrol-based plastics. This study reviews the recent studies focusing on the microbial synthesis of PHA, including chassis engineering, pathways engineering for various substrates utilization and PHA monomer synthesis, and PHA synthase modification. In particular, advances in metabolic engineering of dominant workhorses, for example Halomonas, Ralstonia eutropha, Escherichia coli and Pseudomonas, with outstanding PHA accumulation capability, were summarized and discussed, providing a full landscape of diverse PHA biosynthesis. Meanwhile, we also introduced the recent efforts focusing on structural analysis and mutagenesis of PHA synthase, which significantly determines the polymerization activity of varied monomer structures and PHA molecular weight. Besides, perspectives and solutions were thus proposed for achieving scale-up PHA of low cost with customized material property in the coming future.
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Affiliation(s)
- Qiang Gao
- Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining, QH, China
| | - Hao Yang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Chi Wang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Xin-Ying Xie
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Kai-Xuan Liu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Ying Lin
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Shuang-Yan Han
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Mingjun Zhu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Markus Neureiter
- Institute for Environmental Biotechnology, Department of Agrobiotechnology, University of Natural Resources and Life Sciences, Tulln, Austria
- *Correspondence: Markus Neureiter, ; Yina Lin, ; Jian-Wen Ye,
| | - Yina Lin
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
- *Correspondence: Markus Neureiter, ; Yina Lin, ; Jian-Wen Ye,
| | - Jian-Wen Ye
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
- *Correspondence: Markus Neureiter, ; Yina Lin, ; Jian-Wen Ye,
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8
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Van Brempt M, Peeters AI, Duchi D, De Wannemaeker L, Maertens J, De Paepe B, De Mey M. Biosensor-driven, model-based optimization of the orthogonally expressed naringenin biosynthesis pathway. Microb Cell Fact 2022; 21:49. [PMID: 35346204 PMCID: PMC8962593 DOI: 10.1186/s12934-022-01775-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/15/2022] [Indexed: 12/30/2022] Open
Abstract
Background The rapidly expanding synthetic biology toolbox allows engineers to develop smarter strategies to tackle the optimization of complex biosynthetic pathways. In such a strategy, multi-gene pathways are subdivided in several modules which are each dynamically controlled to fine-tune their expression in response to a changing cellular environment. To fine-tune separate modules without interference between modules or from the host regulatory machinery, a sigma factor (σ) toolbox was developed in previous work for tunable orthogonal gene expression. Here, this toolbox is implemented in E. coli to orthogonally express and fine-tune a pathway for the heterologous biosynthesis of the industrially relevant plant metabolite, naringenin. To optimize the production of this pathway, a practical workflow is still imperative to balance all steps of the pathway. This is tackled here by the biosensor-driven screening, subsequent genotyping of combinatorially engineered libraries and finally the training of three different computer models to predict the optimal pathway configuration. Results The efficiency and knowledge gained through this workflow is demonstrated here by improving the naringenin production titer by 32% with respect to a random pathway library screen. Our best strain was cultured in a batch bioreactor experiment and was able to produce 286 mg/L naringenin from glycerol in approximately 26 h. This is the highest reported naringenin production titer in E. coli without the supplementation of pathway precursors to the medium or any precursor pathway engineering. In addition, valuable pathway configuration preferences were identified in the statistical learning process, such as specific enzyme variant preferences and significant correlations between promoter strength at specific steps in the pathway and titer. Conclusions An efficient strategy, powered by orthogonal expression, was applied to successfully optimize a biosynthetic pathway for microbial production of flavonoids in E. coli up to high, competitive levels. Within this strategy, statistical learning techniques were combined with combinatorial pathway optimization techniques and an in vivo high-throughput screening method to efficiently determine the optimal operon configuration of the pathway. This “pathway architecture designer” workflow can be applied for the fast and efficient development of new microbial cell factories for different types of molecules of interest while also providing additional insights into the underlying pathway characteristics. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-022-01775-8.
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Affiliation(s)
- Maarten Van Brempt
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Andries Ivo Peeters
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Dries Duchi
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Lien De Wannemaeker
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Jo Maertens
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Brecht De Paepe
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Marjan De Mey
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium.
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Currin A, Parker S, Robinson CJ, Takano E, Scrutton NS, Breitling R. The evolving art of creating genetic diversity: From directed evolution to synthetic biology. Biotechnol Adv 2021; 50:107762. [PMID: 34000294 PMCID: PMC8299547 DOI: 10.1016/j.biotechadv.2021.107762] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 04/21/2021] [Accepted: 04/25/2021] [Indexed: 12/31/2022]
Abstract
The ability to engineer biological systems, whether to introduce novel functionality or improved performance, is a cornerstone of biotechnology and synthetic biology. Typically, this requires the generation of genetic diversity to explore variations in phenotype, a process that can be performed at many levels, from single molecule targets (i.e., in directed evolution of enzymes) to whole organisms (e.g., in chassis engineering). Recent advances in DNA synthesis technology and automation have enhanced our ability to create variant libraries with greater control and throughput. This review highlights the latest developments in approaches to create such a hierarchy of diversity from the enzyme level to entire pathways in vitro, with a focus on the creation of combinatorial libraries that are required to navigate a target's vast design space successfully to uncover significant improvements in function.
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Affiliation(s)
- Andrew Currin
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom.
| | - Steven Parker
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
| | - Christopher J Robinson
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
| | - Eriko Takano
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
| | - Nigel S Scrutton
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom
| | - Rainer Breitling
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, United Kingdom.
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10
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Halomonas as a chassis. Essays Biochem 2021; 65:393-403. [PMID: 33885142 PMCID: PMC8314019 DOI: 10.1042/ebc20200159] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/24/2021] [Accepted: 03/26/2021] [Indexed: 01/04/2023]
Abstract
With the rapid development of systems and synthetic biology, the non-model bacteria, Halomonas spp., have been developed recently to become a cost-competitive platform for producing a variety of products including polyesters, chemicals and proteins owing to their contamination resistance and ability of high cell density growth at alkaline pH and high salt concentration. These salt-loving microbes can partially solve the challenges of current industrial biotechnology (CIB) which requires high energy-consuming sterilization to prevent contamination as CIB is based on traditional chassis, typically, Escherichia coli, Bacillus subtilis, Pseudomonas putida and Corynebacterium glutamicum. The advantages and current status of Halomonas spp. including their molecular biology and metabolic engineering approaches as well as their applications are reviewed here. Moreover, a systematic strain engineering streamline, including product-based host development, genetic parts mining, static and dynamic optimization of modularized pathways and bioprocess-inspired cell engineering are summarized. All of these developments result in the term called next-generation industrial biotechnology (NGIB). Increasing efforts are made to develop their versatile cell factories powered by synthetic biology to demonstrate a new biomanufacturing strategy under open and continuous processes with significant cost-reduction on process complexity, energy, substrates and fresh water consumption.
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11
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Gilman J, Walls L, Bandiera L, Menolascina F. Statistical Design of Experiments for Synthetic Biology. ACS Synth Biol 2021; 10:1-18. [PMID: 33406821 DOI: 10.1021/acssynbio.0c00385] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.
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Affiliation(s)
- James Gilman
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Laura Walls
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Lucia Bandiera
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
| | - Filippo Menolascina
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, U.K
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12
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Babaei M, Borja Zamfir GM, Chen X, Christensen HB, Kristensen M, Nielsen J, Borodina I. Metabolic Engineering of Saccharomyces cerevisiae for Rosmarinic Acid Production. ACS Synth Biol 2020; 9:1978-1988. [PMID: 32589831 PMCID: PMC8961883 DOI: 10.1021/acssynbio.0c00048] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Indexed: 02/08/2023]
Abstract
Rosmarinic acid is a hydroxycinnamic acid ester commonly found in the Boraginaceae and Lamiaceae plant families. It exhibits various biological activities, including antioxidant, anti-inflammatory, antibacterial, antiallergic, and antiviral properties. Rosmarinic acid is used as a food and cosmetic ingredient, and several pharmaceutical applications have been suggested as well. Rosmarinic acid is currently produced by extraction from plants or chemical synthesis; however, due to limited availability of the plant sources and the complexity of the chemical synthesis method, there is an increasing interest in producing this compound by microbial fermentation. In this study, we aimed to produce rosmarinic acid by engineered baker's yeast Saccharomyces cerevisiae. Multiple biosynthetic pathway variants, carrying only plant genes or a combination of plant and Escherichia coli genes, were implemented using a full factorial design of experiment. Through analysis of variances, the effect of each enzyme variant (factors), together with possible interactions between these factors, was assessed. The best pathway variant produced 2.95 ± 0.08 mg/L rosmarinic acid in mineral medium with glucose as the sole carbon source. Increasing the copy number of rosmarinic acid biosynthetic genes increased the titer to 5.93 ± 0.06 mg/L. The study shows the feasibility of producing rosmarinic acid by yeast fermentation.
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Affiliation(s)
- Mahsa Babaei
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, DK-2800 Kgs. Lyngby, Denmark
| | - Gheorghe M. Borja Zamfir
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, DK-2800 Kgs. Lyngby, Denmark
| | - Xiao Chen
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, DK-2800 Kgs. Lyngby, Denmark
| | - Hanne Bjerre Christensen
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, DK-2800 Kgs. Lyngby, Denmark
| | - Mette Kristensen
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, DK-2800 Kgs. Lyngby, Denmark
| | - Jens Nielsen
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, DK-2800 Kgs. Lyngby, Denmark
- Department
of Biology and Biological Engineering, Chalmers
University of Technology, 412 96, Gothenburg, Sweden
- BioInnovation
Institute, Ole Måløes
Vej 3, 2200, Copenhagen
N, Denmark
| | - Irina Borodina
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, DK-2800 Kgs. Lyngby, Denmark
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13
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Berepiki A, Kent R, Machado LFM, Dixon N. Development of High-Performance Whole Cell Biosensors Aided by Statistical Modeling. ACS Synth Biol 2020; 9:576-589. [PMID: 32023410 PMCID: PMC7146887 DOI: 10.1021/acssynbio.9b00448] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Whole cell biosensors are genetic systems that link the presence of a chemical, or other stimulus, to a user-defined gene expression output for applications in sensing and control. However, the gene expression level of biosensor regulatory components required for optimal performance is nonintuitive, and classical iterative approaches do not efficiently explore multidimensional experimental space. To overcome these challenges, we used a design of experiments (DoE) methodology to efficiently map gene expression levels and provide biosensors with enhanced performance. This methodology was applied to two biosensors that respond to catabolic breakdown products of lignin biomass, protocatechuic acid and ferulic acid. Utilizing DoE we systematically modified biosensor dose-response behavior by increasing the maximum signal output (up to 30-fold increase), improving dynamic range (>500-fold), expanding the sensing range (∼4-orders of magnitude), increasing sensitivity (by >1500-fold), and modulated the slope of the curve to afford biosensors designs with both digital and analogue dose-response behavior. This DoE method shows promise for the optimization of regulatory systems and metabolic pathways constructed from novel, poorly characterized parts.
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Affiliation(s)
- Adokiye Berepiki
- †Manchester
Institute of Biotechnology (MIB), ‡SYNBIOCHEM, Department of Chemistry, University of Manchester, Manchester M1 7DN, U.K.
| | - Ross Kent
- †Manchester
Institute of Biotechnology (MIB), ‡SYNBIOCHEM, Department of Chemistry, University of Manchester, Manchester M1 7DN, U.K.
| | - Leopoldo F. M. Machado
- †Manchester
Institute of Biotechnology (MIB), ‡SYNBIOCHEM, Department of Chemistry, University of Manchester, Manchester M1 7DN, U.K.
| | - Neil Dixon
- †Manchester
Institute of Biotechnology (MIB), ‡SYNBIOCHEM, Department of Chemistry, University of Manchester, Manchester M1 7DN, U.K.,E-mail:
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14
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Systems biology based metabolic engineering for non-natural chemicals. Biotechnol Adv 2019; 37:107379. [DOI: 10.1016/j.biotechadv.2019.04.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 02/23/2019] [Accepted: 04/01/2019] [Indexed: 12/17/2022]
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15
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Opgenorth P, Costello Z, Okada T, Goyal G, Chen Y, Gin J, Benites V, de Raad M, Northen TR, Deng K, Deutsch S, Baidoo EEK, Petzold CJ, Hillson NJ, Garcia Martin H, Beller HR. Lessons from Two Design-Build-Test-Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning. ACS Synth Biol 2019; 8:1337-1351. [PMID: 31072100 DOI: 10.1021/acssynbio.9b00020] [Citation(s) in RCA: 82] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The Design-Build-Test-Learn (DBTL) cycle, facilitated by exponentially improving capabilities in synthetic biology, is an increasingly adopted metabolic engineering framework that represents a more systematic and efficient approach to strain development than historical efforts in biofuels and biobased products. Here, we report on implementation of two DBTL cycles to optimize 1-dodecanol production from glucose using 60 engineered Escherichia coli MG1655 strains. The first DBTL cycle employed a simple strategy to learn efficiently from a relatively small number of strains (36), wherein only the choice of ribosome-binding sites and an acyl-ACP/acyl-CoA reductase were modulated in a single pathway operon including genes encoding a thioesterase (UcFatB1), an acyl-ACP/acyl-CoA reductase (Maqu_2507, Maqu_2220, or Acr1), and an acyl-CoA synthetase (FadD). Measured variables included concentrations of dodecanol and all proteins in the engineered pathway. We used the data produced in the first DBTL cycle to train several machine-learning algorithms and to suggest protein profiles for the second DBTL cycle that would increase production. These strategies resulted in a 21% increase in dodecanol titer in Cycle 2 (up to 0.83 g/L, which is more than 6-fold greater than previously reported batch values for minimal medium). Beyond specific lessons learned about optimizing dodecanol titer in E. coli, this study had findings of broader relevance across synthetic biology applications, such as the importance of sequencing checks on plasmids in production strains as well as in cloning strains, and the critical need for more accurate protein expression predictive tools.
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Affiliation(s)
- Paul Opgenorth
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Zak Costello
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE Agile BioFoundry, Emeryville, California 94608, United States
| | - Takuya Okada
- Research Institute for Bioscience Product & Fine Chemicals, Ajinomoto Co., Inc., Kawasaki 210-8680, Japan
| | - Garima Goyal
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE Agile BioFoundry, Emeryville, California 94608, United States
| | - Yan Chen
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE Agile BioFoundry, Emeryville, California 94608, United States
| | - Jennifer Gin
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE Agile BioFoundry, Emeryville, California 94608, United States
| | - Veronica Benites
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE Agile BioFoundry, Emeryville, California 94608, United States
| | - Markus de Raad
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE Joint Genome Institute, Walnut Creek, California 94598, United States
| | - Trent R. Northen
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE Joint Genome Institute, Walnut Creek, California 94598, United States
| | - Kai Deng
- Sandia National Laboratories, Livermore, California 94550, United States
| | - Samuel Deutsch
- DOE Joint Genome Institute, Walnut Creek, California 94598, United States
| | - Edward E. K. Baidoo
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE Agile BioFoundry, Emeryville, California 94608, United States
| | - Christopher J. Petzold
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE Agile BioFoundry, Emeryville, California 94608, United States
| | - Nathan J. Hillson
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE Agile BioFoundry, Emeryville, California 94608, United States
- DOE Joint Genome Institute, Walnut Creek, California 94598, United States
| | - Hector Garcia Martin
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- DOE Agile BioFoundry, Emeryville, California 94608, United States
- BCAM, Basque Center for Applied Mathematics, 48009 Bilbao, Spain
| | - Harry R. Beller
- Joint BioEnergy Institute (JBEI), Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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16
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Jang S, Jang S, Im DK, Kang TJ, Oh MK, Jung GY. Artificial Caprolactam-Specific Riboswitch as an Intracellular Metabolite Sensor. ACS Synth Biol 2019; 8:1276-1283. [PMID: 31074964 DOI: 10.1021/acssynbio.8b00452] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Caprolactam is a monomer used for the synthesis of nylon-6, and a recombinant microbial strain for biobased production of nylon-6 was recently developed. An intracellular biosensor for caprolactam can facilitate high-throughput metabolic engineering of recombinant microbial strains. Because of the mixed production of caprolactam and valerolactam in the recombinant strain, a caprolactam biosensor should be highly specific for caprolactam. However, a highly specific caprolactam sensor has not been reported. Here, we developed an artificial riboswitch that specifically responds to caprolactam. This riboswitch was prepared using a coupled in vitro- in vivo selection strategy with a heterogeneous pool of RNA aptamers obtained from in vitro selection to construct a riboswitch library used in in vivo selection. The caprolactam riboswitch successfully discriminated caprolactam from valerolactam. Moreover, the riboswitch was activated by 3.36-fold in the presence of 50 mM caprolactam. This riboswitch enabled caprolactam-dependent control of cell growth, which will be useful for improving caprolactam production and is a valuable tool for metabolic engineering.
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Affiliation(s)
- Sungyeon Jang
- Department of Chemical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Korea
| | - Sungho Jang
- Department of Chemical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Korea
| | - Dae-Kyun Im
- Department of Chemical and Biological Engineering, Korea University, 145 Anam-Ro, Seongbuk-Gu, Seoul 02841, Korea
| | - Taek Jin Kang
- Department of Chemical and Biochemical Engineering, Dongguk University-Seoul, 30 Pildong-Ro 1-Gil, Jung-Gu, Seoul 04620, Korea
| | - Min-Kyu Oh
- Department of Chemical and Biological Engineering, Korea University, 145 Anam-Ro, Seongbuk-Gu, Seoul 02841, Korea
| | - Gyoo Yeol Jung
- Department of Chemical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Korea
- School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk 37673, Korea
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17
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Expanding lysine industry: industrial biomanufacturing of lysine and its derivatives. ACTA ACUST UNITED AC 2018; 45:719-734. [DOI: 10.1007/s10295-018-2030-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 03/22/2018] [Indexed: 12/12/2022]
Abstract
Abstract
l-Lysine is widely used as a nutrition supplement in feed, food, and beverage industries as well as a chemical intermediate. At present, great efforts are made to further decrease the cost of lysine to make it more competitive in the markets. Furthermore, lysine also shows potential as a feedstock to produce other high-value chemicals for active pharmaceutical ingredients, drugs, or materials. In this review, the current biomanufacturing of lysine is first presented. Second, the production of novel derivatives from lysine is discussed. Some chemicals like l-pipecolic acid, cadaverine, and 5-aminovalerate already have been obtained at a lab scale. Others like 6-aminocaproic acid, valerolactam, and caprolactam could be produced through a biological and chemical coupling pathway or be synthesized by a hypothetical pathway. This review demonstrates an active and expansive lysine industry, and these green biomanufacturing strategies could also be applied to enhance the competitiveness of other amino acid industry.
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18
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de Frias UA, Pereira GKB, Guazzaroni ME, Silva-Rocha R. Boosting Secondary Metabolite Production and Discovery through the Engineering of Novel Microbial Biosensors. BIOMED RESEARCH INTERNATIONAL 2018; 2018:7021826. [PMID: 30079350 PMCID: PMC6069586 DOI: 10.1155/2018/7021826] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 02/11/2018] [Indexed: 01/05/2023]
Abstract
Bacteria are a source of a large number of secondary metabolites with several biomedical and biotechnological applications. In recent years, there has been tremendous progress in the development of novel synthetic biology approaches both to increase the production rate of secondary metabolites of interest in native producers and to mine and reconstruct novel biosynthetic gene clusters in heterologous hosts. Here, we present the recent advances toward the engineering of novel microbial biosensors to detect the synthesis of secondary metabolites in bacteria and in the development of synthetic promoters and expression systems aiming at the construction of microbial cell factories for the production of these compounds. We place special focus on the potential of Gram-negative bacteria as a source of biosynthetic gene clusters and hosts for pathway assembly, on the construction and characterization of novel promoters for native hosts, and on the use of computer-aided design of novel pathways and expression systems for secondary metabolite production. Finally, we discuss some of the potentials and limitations of the approaches that are currently being developed and we highlight new directions that could be addressed in the field.
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Affiliation(s)
| | | | - María-Eugenia Guazzaroni
- Faculty of Philosophy, Science and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Rafael Silva-Rocha
- Medical School of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
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19
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An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. Commun Biol 2018; 1:66. [PMID: 30271948 PMCID: PMC6123781 DOI: 10.1038/s42003-018-0076-9] [Citation(s) in RCA: 127] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 05/10/2018] [Indexed: 12/15/2022] Open
Abstract
The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design–Build-Test–Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2S)-pinocembrin in Escherichia coli, to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L−1. The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest. Pablo Carbonell et al. present an automated pipeline for the discovery and optimization of biosynthetic pathways for microbial production of fine chemicals. They apply their pipeline to the production of the flavonoid (2S)-pinocembrin in Escherichia coli and show improvement of the pathway by 500-fold.
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20
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Abstract
Genetically engineered bacteria have the potential to diagnose and treat a wide range of diseases linked to the gastrointestinal tract, or gut. Such engineered microbes will be less expensive and invasive than current diagnostics and more effective and safe than current therapeutics. Recent advances in synthetic biology have dramatically improved the reliability with which bacteria can be engineered with the sensors, genetic circuits, and output (actuator) genes necessary for diagnostic and therapeutic functions. However, to deploy such bacteria in vivo, researchers must identify appropriate gut-adapted strains and consider performance metrics such as sensor detection thresholds, circuit computation speed, growth rate effects, and the evolutionary stability of engineered genetic systems. Other recent reviews have focused on engineering bacteria to target cancer or genetically modifying the endogenous gut microbiota in situ. Here, we develop a standard approach for engineering "smart probiotics," which both diagnose and treat disease, as well as "diagnostic gut bacteria" and "drug factory probiotics," which perform only the former and latter function, respectively. We focus on the use of cutting-edge synthetic biology tools, gut-specific design considerations, and current and future engineering challenges.
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21
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Multidimensional heuristic process for high-yield production of astaxanthin and fragrance molecules in Escherichia coli. Nat Commun 2018; 9:1858. [PMID: 29752432 PMCID: PMC5948211 DOI: 10.1038/s41467-018-04211-x] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 04/06/2018] [Indexed: 01/09/2023] Open
Abstract
Optimization of metabolic pathways consisting of large number of genes is challenging. Multivariate modular methods (MMMs) are currently available solutions, in which reduced regulatory complexities are achieved by grouping multiple genes into modules. However, these methods work well for balancing the inter-modules but not intra-modules. In addition, application of MMMs to the 15-step heterologous route of astaxanthin biosynthesis has met with limited success. Here, we expand the solution space of MMMs and develop a multidimensional heuristic process (MHP). MHP can simultaneously balance different modules by varying promoter strength and coordinating intra-module activities by using ribosome binding sites (RBSs) and enzyme variants. Consequently, MHP increases enantiopure 3S,3′S-astaxanthin production to 184 mg l−1 day−1 or 320 mg l−1. Similarly, MHP improves the yields of nerolidol and linalool. MHP may be useful for optimizing other complex biochemical pathways. Achieving high titer yield and productivity of target chemicals in industrial organism depends on multidimensional pathway optimization. Here, the authors use a refined modular method called multidimensional heuristic process to improve production of astaxanthin, nerolidol and linalool in E. coli.
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22
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Heinsch SC, Das SR, Smanski MJ. Simulation Modeling to Compare High-Throughput, Low-Iteration Optimization Strategies for Metabolic Engineering. Front Microbiol 2018. [PMID: 29535690 PMCID: PMC5835107 DOI: 10.3389/fmicb.2018.00313] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Increasing the final titer of a multi-gene metabolic pathway can be viewed as a multivariate optimization problem. While numerous multivariate optimization algorithms exist, few are specifically designed to accommodate the constraints posed by genetic engineering workflows. We present a strategy for optimizing expression levels across an arbitrary number of genes that requires few design-build-test iterations. We compare the performance of several optimization algorithms on a series of simulated expression landscapes. We show that optimal experimental design parameters depend on the degree of landscape ruggedness. This work provides a theoretical framework for designing and executing numerical optimization on multi-gene systems.
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Affiliation(s)
- Stephen C. Heinsch
- BioTechnology Institute, University of Minnesota, Twin-Cities, Saint Paul, MN, United States
- Bioinformatics and Computational Biology Program, University of Minnesota, Twin-Cities, Saint Paul, MN, United States
| | - Siba R. Das
- BioTechnology Institute, University of Minnesota, Twin-Cities, Saint Paul, MN, United States
| | - Michael J. Smanski
- BioTechnology Institute, University of Minnesota, Twin-Cities, Saint Paul, MN, United States
- Bioinformatics and Computational Biology Program, University of Minnesota, Twin-Cities, Saint Paul, MN, United States
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Twin-Cities, Saint Paul, MN, United States
- *Correspondence: Michael J. Smanski,
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23
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Casini A, Chang FY, Eluere R, King AM, Young EM, Dudley QM, Karim A, Pratt K, Bristol C, Forget A, Ghodasara A, Warden-Rothman R, Gan R, Cristofaro A, Borujeni AE, Ryu MH, Li J, Kwon YC, Wang H, Tatsis E, Rodriguez-Lopez C, O’Connor S, Medema MH, Fischbach MA, Jewett MC, Voigt C, Gordon DB. A Pressure Test to Make 10 Molecules in 90 Days: External Evaluation of Methods to Engineer Biology. J Am Chem Soc 2018; 140:4302-4316. [DOI: 10.1021/jacs.7b13292] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Arturo Casini
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Fang-Yuan Chang
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States,
| | - Raissa Eluere
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Andrew M. King
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States,
| | - Eric M. Young
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States,
| | - Quentin M. Dudley
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Department of Chemical and Biological Engineering, Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
| | - Ashty Karim
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Department of Chemical and Biological Engineering, Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
| | - Katelin Pratt
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Cassandra Bristol
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Anthony Forget
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States,
| | - Amar Ghodasara
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States,
| | - Robert Warden-Rothman
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States,
| | - Rui Gan
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Department of Chemical and Biological Engineering, Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
| | - Alexander Cristofaro
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States,
| | - Amin Espah Borujeni
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States,
| | - Min-Hyung Ryu
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States,
| | - Jian Li
- Department of Chemical and Biological Engineering, Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
| | - Yong-Chan Kwon
- Department of Chemical and Biological Engineering, Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
| | - He Wang
- Department of Chemical and Biological Engineering, Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
| | - Evangelos Tatsis
- Department of Biological Chemistry, John Innes Centre, Norwich NR4 7UH, United Kingdom
| | | | - Sarah O’Connor
- Department of Biological Chemistry, John Innes Centre, Norwich NR4 7UH, United Kingdom
| | - Marnix H. Medema
- Bioinformatics Group, Wageningen University, Wageningen 6708 PB, The Netherlands
| | - Michael A. Fischbach
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Department of Bioengineering and Chemistry, Engineering & Medicine for Human Health, Stanford University, Stanford, California 94305, United States
| | - Michael C. Jewett
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Department of Chemical and Biological Engineering, Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
| | - Christopher Voigt
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States,
| | - D. Benjamin Gordon
- The Foundry, 75 Ames Street, Cambridge, Massachusetts 02142, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States,
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24
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Jin E, Wong L, Jiao Y, Engel J, Holdridge B, Xu P. Rapid evolution of regulatory element libraries for tunable transcriptional and translational control of gene expression. Synth Syst Biotechnol 2017; 2:295-301. [PMID: 29552654 PMCID: PMC5851936 DOI: 10.1016/j.synbio.2017.10.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2017] [Revised: 10/12/2017] [Accepted: 10/12/2017] [Indexed: 12/16/2022] Open
Abstract
Engineering cell factories for producing biofuels and pharmaceuticals has spurred great interests to develop rapid and efficient synthetic biology tools customized for modular pathway engineering. Along the way, combinatorial gene expression control through modification of regulatory element offered tremendous opportunity for fine-tuning gene expression and generating digital-like genetic circuits. In this report, we present an efficient evolutionary approach to build a range of regulatory control elements. The reported method allows for rapid construction of promoter, 5'UTR, terminator and trans-activating RNA libraries. Synthetic overlapping oligos with high portion of degenerate nucleotides flanking the regulatory element could be efficiently assembled to a vector expressing fluorescence reporter. This approach combines high mutation rate of the synthetic DNA with the high assembly efficiency of Gibson Mix. Our constructed library demonstrates broad range of transcriptional or translational gene expression dynamics. Specifically, both the promoter library and 5'UTR library exhibits gene expression dynamics spanning across three order of magnitude. The terminator library and trans-activating RNA library displays relatively narrowed gene expression pattern. The reported study provides a versatile toolbox for rapidly constructing a large family of prokaryotic regulatory elements. These libraries also facilitate the implementation of combinatorial pathway engineering principles and the engineering of more efficient microbial cell factory for various biomanufacturing applications.
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Affiliation(s)
- Erqing Jin
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, United States.,Department of Food Science and Engineering, Jinan University, 601 West Huangpu Road, Guangzhou 510632, China
| | - Lynn Wong
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, United States
| | - Yun Jiao
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, United States
| | - Jake Engel
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, United States
| | - Benjamin Holdridge
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, United States
| | - Peng Xu
- Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, United States
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25
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Ghodasara A, Voigt CA. Balancing gene expression without library construction via a reusable sRNA pool. Nucleic Acids Res 2017; 45:8116-8127. [PMID: 28609783 PMCID: PMC5737548 DOI: 10.1093/nar/gkx530] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 06/07/2017] [Indexed: 01/06/2023] Open
Abstract
Balancing protein expression is critical when optimizing genetic systems. Typically, this requires library construction to vary the genetic parts controlling each gene, which can be expensive and time-consuming. Here, we develop sRNAs corresponding to 15nt ‘target’ sequences that can be inserted upstream of a gene. The targeted gene can be repressed from 1.6- to 87-fold by controlling sRNA expression using promoters of different strength. A pool is built where six sRNAs are placed under the control of 16 promoters that span a ∼103-fold range of strengths, yielding ∼107 combinations. This pool can simultaneously optimize up to six genes in a system. This requires building only a single system-specific construct by placing a target sequence upstream of each gene and transforming it with the pre-built sRNA pool. The resulting library is screened and the top clone is sequenced to determine the promoter controlling each sRNA, from which the fold-repression of the genes can be inferred. The system is then rebuilt by rationally selecting parts that implement the optimal expression of each gene. We demonstrate the versatility of this approach by using the same pool to optimize a metabolic pathway (β-carotene) and genetic circuit (XNOR logic gate).
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Affiliation(s)
- Amar Ghodasara
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Christopher A Voigt
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
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26
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Espah Borujeni A, Cetnar D, Farasat I, Smith A, Lundgren N, Salis HM. Precise quantification of translation inhibition by mRNA structures that overlap with the ribosomal footprint in N-terminal coding sequences. Nucleic Acids Res 2017; 45:5437-5448. [PMID: 28158713 PMCID: PMC5435973 DOI: 10.1093/nar/gkx061] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 01/24/2017] [Indexed: 02/06/2023] Open
Abstract
A mRNA's translation rate is controlled by several sequence determinants, including the presence of RNA structures within the N-terminal regions of its coding sequences. However, the physical rules that govern when such mRNA structures will inhibit translation remain unclear. Here, we introduced systematically designed RNA hairpins into the N-terminal coding region of a reporter protein with steadily increasing distances from the start codon, followed by characterization of their mRNA and expression levels in Escherichia coli. We found that the mRNAs' translation rates were repressed, by up to 530-fold, when mRNA structures overlapped with the ribosome's footprint. In contrast, when the mRNA structure was located outside the ribosome's footprint, translation was repressed by <2-fold. By combining our measurements with biophysical modeling, we determined that the ribosomal footprint extends 13 nucleotides into the N-terminal coding region and, when a mRNA structure overlaps or partially overlaps with the ribosomal footprint, the free energy to unfold only the overlapping structure controlled the extent of translation repression. Overall, our results provide precise quantification of the rules governing translation initiation at N-terminal coding regions, improving the predictive design of post-transcriptional regulatory elements that regulate translation rate.
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Affiliation(s)
- Amin Espah Borujeni
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Daniel Cetnar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Iman Farasat
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Ashlee Smith
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Natasha Lundgren
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Howard M Salis
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.,Department of Biological Engineering, The Pennsylvania State University, University Park, PA 16802, USA
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27
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Chao R, Mishra S, Si T, Zhao H. Engineering biological systems using automated biofoundries. Metab Eng 2017; 42:98-108. [PMID: 28602523 PMCID: PMC5544601 DOI: 10.1016/j.ymben.2017.06.003] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 05/22/2017] [Accepted: 06/05/2017] [Indexed: 11/19/2022]
Abstract
Engineered biological systems such as genetic circuits and microbial cell factories have promised to solve many challenges in the modern society. However, the artisanal processes of research and development are slow, expensive, and inconsistent, representing a major obstacle in biotechnology and bioengineering. In recent years, biological foundries or biofoundries have been developed to automate design-build-test engineering cycles in an effort to accelerate these processes. This review summarizes the enabling technologies for such biofoundries as well as their early successes and remaining challenges.
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Affiliation(s)
- Ran Chao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Shekhar Mishra
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Tong Si
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States; Departments of Chemistry, Biochemistry, Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States.
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28
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Woodruff LBA, Gorochowski TE, Roehner N, Mikkelsen TS, Densmore D, Gordon DB, Nicol R, Voigt CA. Registry in a tube: multiplexed pools of retrievable parts for genetic design space exploration. Nucleic Acids Res 2017; 45:1553-1565. [PMID: 28007941 PMCID: PMC5388403 DOI: 10.1093/nar/gkw1226] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 11/22/2016] [Indexed: 11/14/2022] Open
Abstract
Genetic designs can consist of dozens of genes and hundreds of genetic parts. After evaluating a design, it is desirable to implement changes without the cost and burden of starting the construction process from scratch. Here, we report a two-step process where a large design space is divided into deep pools of composite parts, from which individuals are retrieved and assembled to build a final construct. The pools are built via multiplexed assembly and sequenced using next-generation sequencing. Each pool consists of ∼20 Mb of up to 5000 unique and sequence-verified composite parts that are barcoded for retrieval by PCR. This approach is applied to a 16-gene nitrogen fixation pathway, which is broken into pools containing a total of 55 848 composite parts (71.0 Mb). The pools encompass an enormous design space (1043 possible 23 kb constructs), from which an algorithm-guided 192-member 4.5 Mb library is built. Next, all 1030 possible genetic circuits based on 10 repressors (NOR/NOT gates) are encoded in pools where each repressor is fused to all permutations of input promoters. These demonstrate that multiplexing can be applied to encompass entire design spaces from which individuals can be accessed and evaluated.
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Affiliation(s)
- Lauren B A Woodruff
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Thomas E Gorochowski
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicholas Roehner
- Biological Design Center, Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Tarjei S Mikkelsen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Douglas Densmore
- Biological Design Center, Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - D Benjamin Gordon
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Robert Nicol
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Christopher A Voigt
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.,Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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29
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Zhang J, Barajas JF, Burdu M, Wang G, Baidoo EE, Keasling JD. Application of an Acyl-CoA Ligase from Streptomyces aizunensis for Lactam Biosynthesis. ACS Synth Biol 2017; 6:884-890. [PMID: 28414905 DOI: 10.1021/acssynbio.6b00372] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
ε-Caprolactam and δ-valerolactam are important commodity chemicals used in the manufacture of nylons, with millions of tons produced annually. Biological production of these highly valued chemicals has been limited due to a lack of enzymes that cyclize ω-amino fatty acid precursors to corresponding lactams under ambient conditions. In this study, we demonstrated production of these chemicals using ORF26, an acyl-CoA ligase involved in the biosynthesis of ECO-02301 in Streptomyces aizunensis. This enzyme has a broad substrate spectrum and can cyclize 4-aminobutyric acid into γ-butyrolactam, 5-aminovaleric acid into δ-valerolactam and 6-aminocaproic acid into ε-caprolactam. Recombinant E. coli expressing ORF26 produced valerolactam and caprolactam when 5-aminovaleric acid and 6-aminocaproic acid were added to the culture medium. Upon coexpressing ORF26 with a metabolic pathway that produced 5-aminovaleric acid from lysine, we were able to demonstrate production of δ-valerolactam from lysine.
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Affiliation(s)
- Jingwei Zhang
- UCSF-UCB
Joint Graduate Group in Bioengineering, University of California, Berkeley, California 94720, United States
- Joint BioEnergy Institute, Emeryville, California 94608, United States
- Synthetic
Biology Engineering Research Center, University of California, Berkeley, California 94720, United States
| | - Jesus F. Barajas
- Joint BioEnergy Institute, Emeryville, California 94608, United States
| | - Mehmet Burdu
- Joint BioEnergy Institute, Emeryville, California 94608, United States
| | - George Wang
- Joint BioEnergy Institute, Emeryville, California 94608, United States
| | - Edward E. Baidoo
- Joint BioEnergy Institute, Emeryville, California 94608, United States
| | - Jay D. Keasling
- UCSF-UCB
Joint Graduate Group in Bioengineering, University of California, Berkeley, California 94720, United States
- Joint BioEnergy Institute, Emeryville, California 94608, United States
- Synthetic
Biology Engineering Research Center, University of California, Berkeley, California 94720, United States
- Department of Chemical & Biomolecular Engineering, University of California, Berkeley, California 94720, United States
- California
Institute for Quantitative Biosciences, University of California, Berkeley, California 94720, United States
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30
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Chae TU, Ko YS, Hwang KS, Lee SY. Metabolic engineering of Escherichia coli for the production of four-, five- and six-carbon lactams. Metab Eng 2017; 41:82-91. [DOI: 10.1016/j.ymben.2017.04.001] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 03/31/2017] [Accepted: 04/01/2017] [Indexed: 11/16/2022]
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31
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Zhang J, Barajas JF, Burdu M, Ruegg TL, Dias B, Keasling JD. Development of a Transcription Factor-Based Lactam Biosensor. ACS Synth Biol 2017; 6:439-445. [PMID: 27997130 DOI: 10.1021/acssynbio.6b00136] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Lactams are an important class of commodity chemicals used in the manufacture of nylons, with millions of tons produced every year. Biological production of lactams could be greatly improved by high-throughput sensors for lactam biosynthesis. To identify biosensors of lactams, we applied a chemoinformatic approach inspired by small molecule drug discovery. We define this approach as analogue generation toward catabolizable chemicals or AGTC. We discovered a lactam biosensor based on the ChnR/Pb transcription factor-promoter pair. The microbial biosensor is capable of sensing ε-caprolactam, δ-valerolactam, and butyrolactam in a dose-dependent manner. The biosensor has sufficient specificity to discriminate against lactam biosynthetic intermediates and therefore could potentially be applied for high-throughput metabolic engineering for industrially important high titer lactam biosynthesis.
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Affiliation(s)
- Jingwei Zhang
- Joint BioEnergy Institute, Emeryville, California United States
| | | | - Mehmet Burdu
- Joint BioEnergy Institute, Emeryville, California United States
| | - Thomas L. Ruegg
- Joint BioEnergy Institute, Emeryville, California United States
| | - Bryton Dias
- Joint BioEnergy Institute, Emeryville, California United States
| | - Jay D. Keasling
- Joint BioEnergy Institute, Emeryville, California United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California United States
- The
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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32
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Xu P, Rizzoni EA, Sul SY, Stephanopoulos G. Improving Metabolic Pathway Efficiency by Statistical Model-Based Multivariate Regulatory Metabolic Engineering. ACS Synth Biol 2017; 6:148-158. [PMID: 27490704 DOI: 10.1021/acssynbio.6b00187] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Metabolic engineering entails target modification of cell metabolism to maximize the production of a specific compound. For empowering combinatorial optimization in strain engineering, tools and algorithms are needed to efficiently sample the multidimensional gene expression space and locate the desirable overproduction phenotype. We addressed this challenge by employing design of experiment (DoE) models to quantitatively correlate gene expression with strain performance. By fractionally sampling the gene expression landscape, we statistically screened the dominant enzyme targets that determine metabolic pathway efficiency. An empirical quadratic regression model was subsequently used to identify the optimal gene expression patterns of the investigated pathway. As a proof of concept, our approach yielded the natural product violacein at 525.4 mg/L in shake flasks, a 3.2-fold increase from the baseline strain. Violacein production was further increased to 1.31 g/L in a controlled benchtop bioreactor. We found that formulating discretized gene expression levels into logarithmic variables (Linlog transformation) was essential for implementing this DoE-based optimization procedure. The reported methodology can aid multivariate combinatorial pathway engineering and may be generalized as a standard procedure for accelerating strain engineering and improving metabolic pathway efficiency.
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Affiliation(s)
- Peng Xu
- Department
of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Elizabeth Anne Rizzoni
- Department
of Chemistry, Wellesley College, 106 Central Street, Wellesley, Massachusetts 02481, United States
| | - Se-Yeong Sul
- Department
of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Gregory Stephanopoulos
- Department
of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
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33
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Roehner N, Young EM, Voigt CA, Gordon DB, Densmore D. Double Dutch: A Tool for Designing Combinatorial Libraries of Biological Systems. ACS Synth Biol 2016; 5:507-17. [PMID: 27110633 DOI: 10.1021/acssynbio.5b00232] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Recently, semirational approaches that rely on combinatorial assembly of characterized DNA components have been used to engineer biosynthetic pathways. In practice, however, it is not practical to assemble and test millions of pathway variants in order to elucidate how different DNA components affect the behavior of a pathway. To address this challenge, we apply a rigorous mathematical approach known as design of experiments (DOE) that can be used to construct empirical models of system behavior without testing all variants. To support this approach, we have developed a tool named Double Dutch, which uses a formal grammar and heuristic algorithms to automate the process of DOE library design. Compared to designing by hand, Double Dutch enables users to more efficiently and scalably design libraries of pathway variants that can be used in a DOE framework and uniquely provides a means to flexibly balance design considerations of statistical analysis, construction cost, and risk of homologous recombination, thereby demonstrating the utility of automating decision making when faced with complex design trade-offs.
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Affiliation(s)
- Nicholas Roehner
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Eric M. Young
- Department
of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Christopher A. Voigt
- Department
of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - D. Benjamin Gordon
- Department
of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Douglas Densmore
- Department
of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, United States
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34
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Sadowski MI, Grant C, Fell TS. Harnessing QbD, Programming Languages, and Automation for Reproducible Biology. Trends Biotechnol 2015; 34:214-227. [PMID: 26708960 DOI: 10.1016/j.tibtech.2015.11.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 11/16/2015] [Accepted: 11/19/2015] [Indexed: 12/18/2022]
Abstract
Building robust manufacturing processes from biological components is a task that is highly complex and requires sophisticated tools to describe processes, inputs, and measurements and administrate management of knowledge, data, and materials. We argue that for bioengineering to fully access biological potential, it will require application of statistically designed experiments to derive detailed empirical models of underlying systems. This requires execution of large-scale structured experimentation for which laboratory automation is necessary. This requires development of expressive, high-level languages that allow reusability of protocols, characterization of their reliability, and a change in focus from implementation details to functional properties. We review recent developments in these areas and identify what we believe is an exciting trend that promises to revolutionize biotechnology.
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Affiliation(s)
- Michael I Sadowski
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK
| | - Chris Grant
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK
| | - Tim S Fell
- Synthace Limited, London Bioscience Innovation Centre, 2 Royal College St, London NW1 0NH, UK.
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