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Ferreira SS, Antunes MS. Genetically encoded Boolean logic operators to sense and integrate phenylpropanoid metabolite levels in plants. THE NEW PHYTOLOGIST 2024; 243:674-687. [PMID: 38752334 DOI: 10.1111/nph.19823] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 04/30/2024] [Indexed: 06/21/2024]
Abstract
Synthetic biology has the potential to revolutionize biotechnology, public health, and agriculture. Recent studies have shown the enormous potential of plants as chassis for synthetic biology applications. However, tools to precisely manipulate metabolic pathways for bioproduction in plants are still needed. We used bacterial allosteric transcription factors (aTFs) that control gene expression in a ligand-specific manner and tested their ability to repress semi-synthetic promoters in plants. We also tested the modulation of their repression activity in response to specific plant metabolites, especially phenylpropanoid-related molecules. Using these aTFs, we also designed synthetic genetic circuits capable of computing Boolean logic operations. Three aTFs, CouR, FapR, and TtgR, achieved c. 95% repression of their respective target promoters. For TtgR, a sixfold de-repression could be triggered by inducing its ligand accumulation, showing its use as biosensor. Moreover, we designed synthetic genetic circuits that use AND, NAND, IMPLY, and NIMPLY Boolean logic operations and integrate metabolite levels as input to the circuit. We showed that biosensors can be implemented in plants to detect phenylpropanoid-related metabolites and activate a genetic circuit that follows a predefined logic, demonstrating their potential as tools for exerting control over plant metabolic pathways and facilitating the bioproduction of natural products.
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Affiliation(s)
- Savio S Ferreira
- Department of Biological Sciences, University of North Texas, Denton, TX, 76203, USA
- BioDiscovery Institute, University of North Texas, Denton, TX, 76203, USA
| | - Mauricio S Antunes
- Department of Biological Sciences, University of North Texas, Denton, TX, 76203, USA
- BioDiscovery Institute, University of North Texas, Denton, TX, 76203, USA
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2
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Joshi SHN, Jenkins C, Ulaeto D, Gorochowski TE. Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0037. [PMID: 38919711 PMCID: PMC11197468 DOI: 10.34133/bdr.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 06/27/2024] Open
Abstract
Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.
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Affiliation(s)
| | - Christopher Jenkins
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - David Ulaeto
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK
- BrisEngBio,
School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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3
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Pham C, Nasr MA, Skarina T, Di Leo R, Kwan DH, Martin VJJ, Stogios PJ, Mahadevan R, Savchenko A. Functional and structural characterization of an IclR family transcription factor for the development of dicarboxylic acid biosensors. FEBS J 2024. [PMID: 38696354 DOI: 10.1111/febs.17149] [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: 12/15/2023] [Revised: 03/15/2024] [Accepted: 04/17/2024] [Indexed: 05/04/2024]
Abstract
Prokaryotic transcription factors (TFs) regulate gene expression in response to small molecules, thus representing promising candidates as versatile small molecule-detecting biosensors valuable for synthetic biology applications. The engineering of such biosensors requires thorough in vitro and in vivo characterization of TF ligand response as well as detailed molecular structure information. In this work, we functionally and structurally characterize the Pca regulon regulatory protein (PcaR) transcription factor belonging to the IclR transcription factor family. Here, we present in vitro functional analysis of the ligand profile of PcaR and the construction of genetic circuits for the characterization of PcaR as an in vivo biosensor in the model eukaryote Saccharomyces cerevisiae. We report the crystal structures of PcaR in the apo state and in complex with one of its ligands, succinate, which suggests the mechanism of dicarboxylic acid recognition by this transcription factor. This work contributes key structural and functional insights enabling the engineering of PcaR for dicarboxylic acid biosensors, in addition to providing more insights into the IclR family of regulators.
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Affiliation(s)
- Chester Pham
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
| | - Mohamed A Nasr
- Centre for Applied Synthetic Biology, Concordia University, Montreal, Canada
- Department of Biology, Concordia University, Montreal, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Canada
| | - Tatiana Skarina
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
| | - Rosa Di Leo
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
| | - David H Kwan
- Centre for Applied Synthetic Biology, Concordia University, Montreal, Canada
- Department of Biology, Concordia University, Montreal, Canada
- PROTEO, Quebec Network for Research on Protein Function, Structure, and Engineering, Canada
- Department of Chemistry and Biochemistry, Concordia University, Montreal, Canada
| | - Vincent J J Martin
- Centre for Applied Synthetic Biology, Concordia University, Montreal, Canada
- Department of Biology, Concordia University, Montreal, Canada
| | - Peter J Stogios
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
- The Institute of Biomedical Engineering, University of Toronto, Canada
| | - Alexei Savchenko
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Canada
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Canada
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d'Oelsnitz S, Stofel SK, Love JD, Ellington AD. Snowprint: a predictive tool for genetic biosensor discovery. Commun Biol 2024; 7:163. [PMID: 38336860 PMCID: PMC10858194 DOI: 10.1038/s42003-024-05849-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:operator interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio .
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Affiliation(s)
- Simon d'Oelsnitz
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
- Synthetic Biology HIVE, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.
| | - Sarah K Stofel
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
| | - Joshua D Love
- Independent Web Developer, Bentonville, AR, 72712, USA
| | - Andrew D Ellington
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
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Merzbacher C, Oyarzún DA. Applications of artificial intelligence and machine learning in dynamic pathway engineering. Biochem Soc Trans 2023; 51:1871-1879. [PMID: 37656433 PMCID: PMC10657174 DOI: 10.1042/bst20221542] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/07/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023]
Abstract
Dynamic pathway engineering aims to build metabolic production systems embedded with intracellular control mechanisms for improved performance. These control systems enable host cells to self-regulate the temporal activity of a production pathway in response to perturbations, using a combination of biosensors and feedback circuits for controlling expression of heterologous enzymes. Pathway design, however, requires assembling together multiple biological parts into suitable circuit architectures, as well as careful calibration of the function of each component. This results in a large design space that is costly to navigate through experimentation alone. Methods from artificial intelligence (AI) and machine learning are gaining increasing attention as tools to accelerate the design cycle, owing to their ability to identify hidden patterns in data and rapidly screen through large collections of designs. In this review, we discuss recent developments in the application of machine learning methods to the design of dynamic pathways and their components. We cover recent successes and offer perspectives for future developments in the field. The integration of AI into metabolic engineering pipelines offers great opportunities to streamline design and discover control systems for improved production of high-value chemicals.
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Affiliation(s)
| | - Diego A. Oyarzún
- School of Informatics, University of Edinburgh, Edinburgh, U.K
- The Alan Turing Institute, London, U.K
- School of Biological Sciences, University of Edinburgh, Edinburgh, U.K
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Li S, Li Z, Tan GY, Xin Z, Wang W. In vitro allosteric transcription factor-based biosensing. Trends Biotechnol 2023; 41:1080-1095. [PMID: 36967257 DOI: 10.1016/j.tibtech.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/15/2023] [Accepted: 03/01/2023] [Indexed: 06/18/2023]
Abstract
A biosensor is an analytical device that converts a biological response into a measurable output signal. Bacterial allosteric transcription factors (aTFs) have been utilized as a novel class of recognition elements for in vitro biosensing, which circumvents the limitations of aTF-based whole-cell biosensors (WCBs) and helps to meet the increasing requirement of small-molecule biosensors for diverse applications. In this review, we summarize the recent advances related to the configuration of aTF-based biosensors in vitro. Particularly, we evaluate the advantages of aTFs for in vitro biosensing and highlight their great potential for the establishment of robust and easy-to-implement biosensing strategies. We argue that key technical innovations and generalizable workflows will enhance the pipeline for facile construction of diverse aTF-based small-molecule biosensors.
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Affiliation(s)
- Shanshan Li
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Zilong Li
- State Key Laboratory of Microbial Resources, Institute of Microbiology, CAS, Beijing 100101, PR China
| | - Gao-Yi Tan
- State Key Laboratory of Bioreactor Engineering and School of Biotechnology, East China University of Science and Technology, Shanghai 200237, PR China
| | - Zhenguo Xin
- State Key Laboratory of Microbial Resources, Institute of Microbiology, CAS, Beijing 100101, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Weishan Wang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, CAS, Beijing 100101, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China.
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Demeester W, De Baets J, Duchi D, De Mey M, De Paepe B. MoBioS: Modular Platform Technology for High-Throughput Construction and Characterization of Tunable Transcriptional Biological Sensors. BIOSENSORS 2023; 13:590. [PMID: 37366955 DOI: 10.3390/bios13060590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/16/2023] [Accepted: 05/26/2023] [Indexed: 06/28/2023]
Abstract
All living organisms have evolved and fine-tuned specialized mechanisms to precisely monitor a vast array of different types of molecules. These natural mechanisms can be sourced by researchers to build Biological Sensors (BioS) by combining them with an easily measurable output, such as fluorescence. Because they are genetically encoded, BioS are cheap, fast, sustainable, portable, self-generating and highly sensitive and specific. Therefore, BioS hold the potential to become key enabling tools that stimulate innovation and scientific exploration in various disciplines. However, the main bottleneck in unlocking the full potential of BioS is the fact that there is no standardized, efficient and tunable platform available for the high-throughput construction and characterization of biosensors. Therefore, a modular, Golden Gate-based construction platform, called MoBioS, is introduced in this article. It allows for the fast and easy creation of transcription factor-based biosensor plasmids. As a proof of concept, its potential is demonstrated by creating eight different, functional and standardized biosensors that detect eight diverse molecules of industrial interest. In addition, the platform contains novel built-in features to facilitate fast and efficient biosensor engineering and response curve tuning.
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Affiliation(s)
- Wouter Demeester
- Centre for Synthetic Biology (CSB), Ghent University, 9000 Ghent, Belgium
| | - Jasmine De Baets
- Centre for Synthetic Biology (CSB), Ghent University, 9000 Ghent, Belgium
| | - Dries Duchi
- Centre for Synthetic Biology (CSB), Ghent University, 9000 Ghent, Belgium
| | - Marjan De Mey
- Centre for Synthetic Biology (CSB), Ghent University, 9000 Ghent, Belgium
| | - Brecht De Paepe
- Centre for Synthetic Biology (CSB), Ghent University, 9000 Ghent, Belgium
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