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Hartline CJ, Zhang F. The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors. ACS Synth Biol 2022; 11:2247-2258. [PMID: 35700119 PMCID: PMC9994378 DOI: 10.1021/acssynbio.2c00143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.
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Affiliation(s)
- Christopher J Hartline
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, Missouri 63130, United States
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, Missouri 63130, United States.,Division of Biology & Biomedical Sciences, Washington University in St. Louis, Saint Louis, Missouri 63130, United States.,Institute of Materials Science & Engineering, Washington University in St. Louis, Saint Louis, Missouri 63130, United States
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Abstract
Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to its ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase the titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy, and pharmaceutical sectors.
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Affiliation(s)
- Babita K. Verma
- School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, U.K
| | - Ahmad A. Mannan
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry CV4 7AL, U.K
| | - Fuzhong Zhang
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Diego A. Oyarzún
- School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, U.K
- School of Informatics, The University of Edinburgh, Edinburgh EH8 9AB, U.K
- The Alan Turing Institute, London, NW1 2DB, U.K
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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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Abstract
Metabolite biosensors are central to current efforts toward precision engineering of metabolism. Although most research has focused on building new biosensors, their tunability remains poorly understood and is fundamental for their broad applicability. Here we asked how genetic modifications shape the dose-response curve of biosensors based on metabolite-responsive transcription factors. Using the lac system in Escherichia coli as a model system, we built promoter libraries with variable operator sites that reveal interdependencies between biosensor dynamic range and response threshold. We developed a phenomenological theory to quantify such design constraints in biosensors with various architectures and tunable parameters. Our theory reveals a maximal achievable dynamic range and exposes tunable parameters for orthogonal control of dynamic range and response threshold. Our work sheds light on fundamental limits of synthetic biology designs and provides quantitative guidelines for biosensor design in applications such as dynamic pathway control, strain optimization, and real-time monitoring of metabolism.
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Affiliation(s)
- Ahmad A. Mannan
- Department of Mathematics, Imperial College London, London SW7 2AZ, U.K
| | - Di Liu
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Fuzhong Zhang
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Diego A. Oyarzún
- Department of Mathematics, Imperial College London, London SW7 2AZ, U.K
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Younger AKD, Dalvie NC, Rottinghaus AG, Leonard JN. Engineering Modular Biosensors to Confer Metabolite-Responsive Regulation of Transcription. ACS Synth Biol 2017; 6:311-325. [PMID: 27744683 DOI: 10.1021/acssynbio.6b00184] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Efforts to engineer microbial factories have benefitted from mining biological diversity and high throughput synthesis of novel enzymatic pathways, yet screening and optimizing metabolic pathways remain rate-limiting steps. Metabolite-responsive biosensors may help to address these persistent challenges by enabling the monitoring of metabolite levels in individual cells and metabolite-responsive feedback control. We are currently limited to naturally evolved biosensors, which are insufficient for monitoring many metabolites of interest. Thus, a method for engineering novel biosensors would be powerful, yet we lack a generalizable approach that enables the construction of a wide range of biosensors. As a step toward this goal, we here explore several strategies for converting a metabolite-binding protein into a metabolite-responsive transcriptional regulator. By pairing a modular protein design approach with a library of synthetic promoters and applying robust statistical analyses, we identified strategies for engineering biosensor-regulated bacterial promoters and for achieving design-driven improvements of biosensor performance. We demonstrated the feasibility of this strategy by fusing a programmable DNA binding motif (zinc finger module) with a model ligand binding protein (maltose binding protein), to generate a novel biosensor conferring maltose-regulated gene expression. This systematic investigation provides insights that may guide the development of additional novel biosensors for diverse synthetic biology applications.
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Affiliation(s)
- Andrew K. D. Younger
- Interdisciplinary
Biological Sciences Graduate Program, Northwestern University, Evanston, Illinois 60208, United States
| | - Neil C. Dalvie
- Department
of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Austin G. Rottinghaus
- Department
of Chemical and Biological Engineering, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
| | - Joshua N. Leonard
- Department
of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Center
for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
- Chemistry
of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Member, Robert
H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, Illinois 60208, United States
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