1
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Martín Lázaro H, Marín Bautista R, Carbonell P. DetSpace: a web server for engineering detectable pathways for bio-based chemical production. Nucleic Acids Res 2024; 52:W476-W480. [PMID: 38634809 PMCID: PMC11223873 DOI: 10.1093/nar/gkae287] [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: 01/29/2024] [Revised: 03/18/2024] [Accepted: 04/16/2024] [Indexed: 04/19/2024] Open
Abstract
Tackling climate change challenges requires replacing current chemical industrial processes through the rational and sustainable use of biodiversity resources. To that end, production routes to key bio-based chemicals for the bioeconomy have been identified. However, their production still remains inefficient in terms of titers, rates, and yields; because of the hurdles found when scaling up. In order to make production more efficient, strategies like automated screening and dynamic pathway regulation through biosensors have been applied as part of strain optimization. However, to date, no systematic way exists to design a genetic circuit that is responsive to concentrations of a given target compound. Here, the DetSpace web server provides a set of integrated tools that allows a user to select and design a biological circuit that performs the sensing of a molecule of interest by its enzymatic conversion to a detectable molecule through a transcription factor. In that way, the DetSpace web server allows synthetic biologists to easily design biosensing routes for the dynamic regulation of metabolic pathways in applications ranging from genetic circuits design, screening, production, and bioremediation of bio-based chemicals, to diagnostics and drug delivery.
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Affiliation(s)
- Hèctor Martín Lázaro
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Camí de Vera s/n, 46022 València, Spain
| | - Ricardo Marín Bautista
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Camí de Vera s/n, 46022 València, Spain
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Camí de Vera s/n, 46022 València, Spain
- Institute for Integrative Systems Biology I2SysBio, Universitat de València-CSIC, Escardino Street 9, Paterna, 46980 València, Spain
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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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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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4
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Hanko EKR, Joosab Noor Mahomed TA, Stoney RA, Breitling R. TFBMiner: A User-Friendly Command Line Tool for the Rapid Mining of Transcription Factor-Based Biosensors. ACS Synth Biol 2023; 12:1497-1507. [PMID: 37053505 DOI: 10.1021/acssynbio.2c00679] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Transcription factors responsive to small molecules are essential elements in synthetic biology designs. They are often used as genetically encoded biosensors with applications ranging from the detection of environmental contaminants and biomarkers to microbial strain engineering. Despite our efforts to expand the space of compounds that can be detected using biosensors, the identification and characterization of transcription factors and their corresponding inducer molecules remain labor- and time-intensive tasks. Here, we introduce TFBMiner, a new data mining and analysis pipeline that enables the automated and rapid identification of putative metabolite-responsive transcription factor-based biosensors (TFBs). This user-friendly command line tool harnesses a heuristic rule-based model of gene organization to identify both gene clusters involved in the catabolism of user-defined molecules and their associated transcriptional regulators. Ultimately, biosensors are scored based on how well they fit the model, providing wet-lab scientists with a ranked list of candidates that can be experimentally tested. We validated the pipeline using a set of molecules for which TFBs have been reported previously, including sensors responding to sugars, amino acids, and aromatic compounds, among others. We further demonstrated the utility of TFBMiner by identifying a biosensor for S-mandelic acid, an aromatic compound for which a responsive transcription factor had not been found previously. Using a combinatorial library of mandelate-producing microbial strains, the newly identified biosensor was able to distinguish between low- and high-producing strain candidates. This work will aid in the unraveling of metabolite-responsive microbial gene regulatory networks and expand the synthetic biology toolbox to allow for the construction of more sophisticated self-regulating biosynthetic pathways.
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Affiliation(s)
- Erik K R Hanko
- Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
| | - Tariq A Joosab Noor Mahomed
- Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
| | - Ruth A Stoney
- Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
| | - Rainer Breitling
- Manchester Institute of Biotechnology, Faculty of Science and Engineering, University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
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5
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Tellechea-Luzardo J, Martín Lázaro H, Moreno López R, Carbonell P. Sensbio: an online server for biosensor design. BMC Bioinformatics 2023; 24:71. [PMID: 36855083 PMCID: PMC9972687 DOI: 10.1186/s12859-023-05201-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
Allosteric transcription factor (aTF) based biosensors can be used to engineer genetic circuits for a wide range of applications. The literature and online databases contain hundreds of experimentally validated molecule-TF pairs; however, the knowledge is scattered and often incomplete. Additionally, compared to the number of compounds that can be produced in living systems, those with known associated TF-compound interactions are low. For these reasons, new tools that help researchers find new possible TF-ligand pairs are called for. In this work, we present Sensbio, a computational tool that through similarity comparison against a TF-ligand reference database, is able to identify putative transcription factors that can be activated by a given input molecule. In addition to the collection of algorithms, an online application has also been developed, together with a predictive model created to find new possible matches based on machine learning.
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Affiliation(s)
- Jonathan Tellechea-Luzardo
- grid.157927.f0000 0004 1770 5832Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), 46022 Valencia, Spain
| | - Hèctor Martín Lázaro
- grid.157927.f0000 0004 1770 5832Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), 46022 Valencia, Spain
| | - Raúl Moreno López
- grid.157927.f0000 0004 1770 5832Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), 46022 Valencia, Spain
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), 46022, Valencia, Spain. .,Institute for Integrative Systems Biology I2SysBio, Universitat de València-CSIC, 46980, Paterna, Spain.
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6
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Tellechea-Luzardo J, Stiebritz MT, Carbonell P. Transcription factor-based biosensors for screening and dynamic regulation. Front Bioeng Biotechnol 2023; 11:1118702. [PMID: 36814719 PMCID: PMC9939652 DOI: 10.3389/fbioe.2023.1118702] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/26/2023] [Indexed: 02/09/2023] Open
Abstract
Advances in synthetic biology and genetic engineering are bringing into the spotlight a wide range of bio-based applications that demand better sensing and control of biological behaviours. Transcription factor (TF)-based biosensors are promising tools that can be used to detect several types of chemical compounds and elicit a response according to the desired application. However, the wider use of this type of device is still hindered by several challenges, which can be addressed by increasing the current metabolite-activated transcription factor knowledge base, developing better methods to identify new transcription factors, and improving the overall workflow for the design of novel biosensor circuits. These improvements are particularly important in the bioproduction field, where researchers need better biosensor-based approaches for screening production-strains and precise dynamic regulation strategies. In this work, we summarize what is currently known about transcription factor-based biosensors, discuss recent experimental and computational approaches targeted at their modification and improvement, and suggest possible future research directions based on two applications: bioproduction screening and dynamic regulation of genetic circuits.
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Affiliation(s)
- Jonathan Tellechea-Luzardo
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Valencia, Spain
| | - Martin T. Stiebritz
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Valencia, Spain
| | - Pablo Carbonell
- Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), Valencia, Spain,Institute for Integrative Systems Biology I2SysBio, Universitat de València-CSIC, Paterna, Spain,*Correspondence: Pablo Carbonell,
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7
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Soudier P, Zúñiga A, Duigou T, Voyvodic PL, Bazi-Kabbaj K, Kushwaha M, Vendrell JA, Solassol J, Bonnet J, Faulon JL. PeroxiHUB: A Modular Cell-Free Biosensing Platform Using H 2O 2 as Signal Integrator. ACS Synth Biol 2022; 11:2578-2588. [PMID: 35913043 DOI: 10.1021/acssynbio.2c00138] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Cell-free systems have great potential for delivering robust, inexpensive, and field-deployable biosensors. Many cell-free biosensors rely on transcription factors responding to small molecules, but their discovery and implementation still remain challenging. Here we report the engineering of PeroxiHUB, an optimized H2O2-centered sensing platform supporting cell-free detection of different metabolites. H2O2 is a central metabolite and a byproduct of numerous enzymatic reactions. PeroxiHUB uses enzymatic transducers to convert metabolites of interest into H2O2, enabling rapid reprogramming of sensor specificity using alternative transducers. We first screen several transcription factors and optimize OxyR for the transcriptional response to H2O2 in a cell-free system, highlighting the need for preincubation steps to obtain suitable signal-to-noise ratios. We then demonstrate modular detection of metabolites of clinical interest─lactate, sarcosine, and choline─using different transducers mined via a custom retrosynthesis workflow publicly available on the SynBioCAD Galaxy portal. We find that expressing the transducer during the preincubation step is crucial for optimal sensor operation. We then show that different reporters can be connected to PeroxiHUB, providing high adaptability for various applications. Finally, we demonstrate that a peroxiHUB lactate biosensor can detect endogenous levels of this metabolite in clinical samples. Given the wide range of enzymatic reactions producing H2O2, the PeroxiHUB platform will support cell-free detection of a large number of metabolites in a modular and scalable fashion.
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Affiliation(s)
- Paul Soudier
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France.,Université de Montpellier, INSERM, CNRS, Centre de Biologie Structurale, 34090 Montpellier, France
| | - Ana Zúñiga
- Université de Montpellier, INSERM, CNRS, Centre de Biologie Structurale, 34090 Montpellier, France
| | - Thomas Duigou
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
| | - Peter L Voyvodic
- Université de Montpellier, INSERM, CNRS, Centre de Biologie Structurale, 34090 Montpellier, France
| | - Kenza Bazi-Kabbaj
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
| | - Manish Kushwaha
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
| | - Julie A Vendrell
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France
| | - Jerome Solassol
- Laboratoire de Biologie des Tumeurs Solides, Département de Pathologie et Oncobiologie, CHU Montpellier, Université de Montpellier, 34295 Montpellier, France.,IRCM, INSERM, Univ Montpellier, ICM, 34298 Montpellier, France
| | - Jerome Bonnet
- Université de Montpellier, INSERM, CNRS, Centre de Biologie Structurale, 34090 Montpellier, France
| | - Jean-Loup Faulon
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
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8
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Pham C, Stogios PJ, Savchenko A, Mahadevan R. Advances in engineering and optimization of transcription factor-based biosensors for plug-and-play small molecule detection. Curr Opin Biotechnol 2022; 76:102753. [PMID: 35872379 DOI: 10.1016/j.copbio.2022.102753] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/30/2022]
Abstract
Transcription factor (TF)-based biosensors have been applied in biotechnology for a variety of functions, including protein engineering, dynamic control, environmental detection, and point-of-care diagnostics. Such biosensors are promising analytical tools due to their wide range of detectable ligands and modular nature. However, designing biosensors tailored for applications of interest with the desired performance parameters, including ligand specificity, remains challenging. Biosensors often require significant engineering and tuning to meet desired specificity, sensitivity, dynamic range, and operating range parameters. Another limitation is the orthogonality of biosensors across hosts, given the role of the cellular context. Here, we describe recent advances and examples in the engineering and optimization of TF-based biosensors for plug-and-play small molecule detection. We highlight novel developments in TF discovery and biosensor design, TF specificity engineering, and biosensor tuning, with emphasis on emerging computational methods.
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Affiliation(s)
- Chester Pham
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, ON, Canada
| | - Peter J Stogios
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, ON, Canada
| | - Alexei Savchenko
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, ON, Canada; Department of Microbiology, Immunology and Infectious Disease, University of Calgary, AB, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, ON, Canada; The Institute of Biomedical Engineering, University of Toronto, ON, Canada.
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9
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Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods. NPJ Syst Biol Appl 2022; 8:24. [PMID: 35831427 PMCID: PMC9279433 DOI: 10.1038/s41540-022-00234-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 06/20/2022] [Indexed: 11/08/2022] Open
Abstract
The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism is not well represented in public databases and existing reconstructions. In a previous work, using different sources of knowledge, bioinformatic and modelling tools, we developed AGREDA, an extended metabolic network more amenable to analyze the interaction of the human gut microbiota with diet. Despite the substantial improvement achieved by AGREDA, it was not sufficient to represent the diverse metabolic space of phenolic compounds. In this article, we make use of an enzyme promiscuity approach to complete further the metabolism of phenolic compounds in the human gut microbiota. In particular, we apply RetroPath RL, a previously developed approach based on Monte Carlo Tree Search strategy reinforcement learning, in order to predict the degradation pathways of compounds present in Phenol-Explorer, the largest database of phenolic compounds in the literature. Reactions predicted by RetroPath RL were integrated with AGREDA, leading to a more complete version of the human gut microbiota metabolic network. We assess the impact of our improvements in the metabolic processing of various foods, finding previously undetected connections with output microbial metabolites. By means of untargeted metabolomics data, we present in vitro experimental validation for output microbial metabolites released in the fermentation of lentils with feces of children representing different clinical conditions.
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10
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Huang C, Wang C, Luo Y. Research progress of pathway and genome evolution in microbes. Synth Syst Biotechnol 2022; 7:648-656. [PMID: 35224232 PMCID: PMC8857405 DOI: 10.1016/j.synbio.2022.01.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 12/23/2021] [Accepted: 01/06/2022] [Indexed: 12/16/2022] Open
Abstract
Microbes can produce valuable natural products widely applied in medicine, food and other important fields. Nevertheless, it is usually challenging to achieve ideal industrial yields due to low production rate and poor toxicity tolerance. Evolution is a constant mutation and adaptation process used to improve strain performance. Generally speaking, the synthesis of natural products in microbes is often intricate, involving multiple enzymes or multiple pathways. Individual evolution of a certain enzyme often fails to achieve the desired results, and may lead to new rate-limiting nodes that affect the growth of microbes. Therefore, it is inevitable to evolve the biosynthetic pathways or the whole genome. Here, we reviewed the pathway-level evolution including multi-enzyme evolution, regulatory elements engineering, and computer-aided engineering, as well as the genome-level evolution based on several tools, such as genome shuffling and CRISPR/Cas systems. Finally, we also discussed the major challenges faced by in vivo evolution strategies and proposed some potential solutions.
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Affiliation(s)
- Chaoqun Huang
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China
| | - Chang Wang
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China
| | - Yunzi Luo
- Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China
- Georgia Tech Shenzhen Institute, Tianjin University, Tangxing Road 133, Nanshan District, Shenzhen, 518071, China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin University, Tianjin, 300072, China
- Corresponding author. Frontier Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
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11
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Batista AC, Levrier A, Soudier P, Voyvodic PL, Achmedov T, Reif-Trauttmansdorff T, DeVisch A, Cohen-Gonsaud M, Faulon JL, Beisel CL, Bonnet J, Kushwaha M. Differentially Optimized Cell-Free Buffer Enables Robust Expression from Unprotected Linear DNA in Exonuclease-Deficient Extracts. ACS Synth Biol 2022; 11:732-746. [PMID: 35034449 DOI: 10.1021/acssynbio.1c00448] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The use of linear DNA templates in cell-free systems promises to accelerate the prototyping and engineering of synthetic gene circuits. A key challenge is that linear templates are rapidly degraded by exonucleases present in cell extracts. Current approaches tackle the problem by adding exonuclease inhibitors and DNA-binding proteins to protect the linear DNA, requiring additional time- and resource-intensive steps. Here, we delete the recBCD exonuclease gene cluster from the Escherichia coli BL21 genome. We show that the resulting cell-free systems, with buffers optimized specifically for linear DNA, enable near-plasmid levels of expression from σ70 promoters in linear DNA templates without employing additional protection strategies. When using linear or plasmid DNA templates at the buffer calibration step, the optimal potassium glutamate concentrations obtained when using linear DNA were consistently lower than those obtained when using plasmid DNA for the same extract. We demonstrate the robustness of the exonuclease deficient extracts across seven different batches and a wide range of experimental conditions across two different laboratories. Finally, we illustrate the use of the ΔrecBCD extracts for two applications: toehold switch characterization and enzyme screening. Our work provides a simple, efficient, and cost-effective solution for using linear DNA templates in cell-free systems and highlights the importance of specifically tailoring buffer composition for the final experimental setup. Our data also suggest that similar exonuclease deletion strategies can be applied to other species suitable for cell-free synthetic biology.
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Affiliation(s)
- Angelo Cardoso Batista
- Université Paris-Saclay, INRAe, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
| | - Antoine Levrier
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Paul Soudier
- Université Paris-Saclay, INRAe, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Peter L. Voyvodic
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Tatjana Achmedov
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Centre for Infection Research (HZI), 97080 Würzburg, Germany
| | | | - Angelique DeVisch
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Martin Cohen-Gonsaud
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Jean-Loup Faulon
- Université Paris-Saclay, INRAe, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
| | - Chase L. Beisel
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Centre for Infection Research (HZI), 97080 Würzburg, Germany
- Medical Faculty, University of Würzburg, 97080 Würzburg, Germany
| | - Jerome Bonnet
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, 34090 Montpellier, France
| | - Manish Kushwaha
- Université Paris-Saclay, INRAe, AgroParisTech, Micalis Institute, 78352 Jouy-en-Josas, France
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12
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Bansal P, Morgat A, Axelsen KB, Muthukrishnan V, Coudert E, Aimo L, Hyka-Nouspikel N, Gasteiger E, Kerhornou A, Neto TB, Pozzato M, Blatter MC, Ignatchenko A, Redaschi N, Bridge A. Rhea, the reaction knowledgebase in 2022. Nucleic Acids Res 2022; 50:D693-D700. [PMID: 34755880 PMCID: PMC8728268 DOI: 10.1093/nar/gkab1016] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/08/2021] [Accepted: 11/09/2021] [Indexed: 12/15/2022] Open
Abstract
Rhea (https://www.rhea-db.org) is an expert-curated knowledgebase of biochemical reactions based on the chemical ontology ChEBI (Chemical Entities of Biological Interest) (https://www.ebi.ac.uk/chebi). In this paper, we describe a number of key developments in Rhea since our last report in the database issue of Nucleic Acids Research in 2019. These include improved reaction coverage in Rhea, the adoption of Rhea as the reference vocabulary for enzyme annotation in the UniProt knowledgebase UniProtKB (https://www.uniprot.org), the development of a new Rhea website, and the designation of Rhea as an ELIXIR Core Data Resource. We hope that these and other developments will enhance the utility of Rhea as a reference resource to study and engineer enzymes and the metabolic systems in which they function.
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Affiliation(s)
- Parit Bansal
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Anne Morgat
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Kristian B Axelsen
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Venkatesh Muthukrishnan
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Elisabeth Coudert
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Lucila Aimo
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Nevila Hyka-Nouspikel
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Elisabeth Gasteiger
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Arnaud Kerhornou
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Teresa Batista Neto
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Monica Pozzato
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Marie-Claude Blatter
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Alex Ignatchenko
- EMBL-EBI European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nicole Redaschi
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
| | - Alan Bridge
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, CH-1211 Geneva 4, Switzerland
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13
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Soudier P, Faure L, Kushwaha M, Faulon JL. Cell-Free Biosensors and AI Integration. Methods Mol Biol 2022; 2433:303-323. [PMID: 34985753 DOI: 10.1007/978-1-0716-1998-8_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Cell-free biosensors hold a great potential as alternatives for traditional analytical chemistry methods providing low-cost low-resource measurement of specific chemicals. However, their large-scale use is limited by the complexity of their development.In this chapter, we present a standard methodology based on computer-aided design (CAD ) tools that enables fast development of new cell-free biosensors based on target molecule information transduction and reporting through metabolic and genetic layers, respectively. Such systems can then be repurposed to represent complex computational problems, allowing defined multiplex sensing of various inputs and integration of artificial intelligence in synthetic biological systems.
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Affiliation(s)
- Paul Soudier
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France
| | - Léon Faure
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France
| | - Manish Kushwaha
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France
| | - Jean-Loup Faulon
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France. .,Université Paris-Saclay, Systems & Synthetic Biology Lab (iSSB), UMR, Evry, France. .,Manchester Institute of Biotechnology, SYNBIOCHEM Center, School of Chemistry, The University of Manchester, Manchester, UK.
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14
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Shenshin VA, Lescanne C, Gines G, Rondelez Y. A small-molecule chemical interface for molecular programs. Nucleic Acids Res 2021; 49:7765-7774. [PMID: 34223901 PMCID: PMC8287923 DOI: 10.1093/nar/gkab470] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 05/03/2021] [Accepted: 06/29/2021] [Indexed: 12/17/2022] Open
Abstract
In vitro molecular circuits, based on DNA-programmable chemistries, can perform an increasing range of high-level functions, such as molecular level computation, image or chemical pattern recognition and pattern generation. Most reported demonstrations, however, can only accept nucleic acids as input signals. Real-world applications of these programmable chemistries critically depend on strategies to interface them with a variety of non-DNA inputs, in particular small biologically relevant chemicals. We introduce here a general strategy to interface DNA-based circuits with non-DNA signals, based on input-translating modules. These translating modules contain a DNA response part and an allosteric protein sensing part, and use a simple design that renders them fully tunable and modular. They can be repurposed to either transmit or invert the response associated with the presence of a given input. By combining these translating-modules with robust and leak-free amplification motifs, we build sensing circuits that provide a fluorescent quantitative time-response to the concentration of their small-molecule input, with good specificity and sensitivity. The programmability of the DNA layer can be leveraged to perform DNA based signal processing operations, which we demonstrate here with logical inversion, signal modulation and a classification task on two inputs. The DNA circuits are also compatible with standard biochemical conditions, and we show the one-pot detection of an enzyme through its native metabolic activity. We anticipate that this sensitive small-molecule-to-DNA conversion strategy will play a critical role in the future applications of molecular-level circuitry.
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Affiliation(s)
- Vasily A Shenshin
- Laboratoire Gulliver, CNRS, ESPCI Paris, PSL Research University, 10 rue Vauquelin 75005 Paris, France
| | - Camille Lescanne
- Laboratoire Gulliver, CNRS, ESPCI Paris, PSL Research University, 10 rue Vauquelin 75005 Paris, France
| | - Guillaume Gines
- Laboratoire Gulliver, CNRS, ESPCI Paris, PSL Research University, 10 rue Vauquelin 75005 Paris, France
| | - Yannick Rondelez
- Laboratoire Gulliver, CNRS, ESPCI Paris, PSL Research University, 10 rue Vauquelin 75005 Paris, France
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15
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Yi D, Bayer T, Badenhorst CPS, Wu S, Doerr M, Höhne M, Bornscheuer UT. Recent trends in biocatalysis. Chem Soc Rev 2021; 50:8003-8049. [PMID: 34142684 PMCID: PMC8288269 DOI: 10.1039/d0cs01575j] [Citation(s) in RCA: 122] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Indexed: 12/13/2022]
Abstract
Biocatalysis has undergone revolutionary progress in the past century. Benefited by the integration of multidisciplinary technologies, natural enzymatic reactions are constantly being explored. Protein engineering gives birth to robust biocatalysts that are widely used in industrial production. These research achievements have gradually constructed a network containing natural enzymatic synthesis pathways and artificially designed enzymatic cascades. Nowadays, the development of artificial intelligence, automation, and ultra-high-throughput technology provides infinite possibilities for the discovery of novel enzymes, enzymatic mechanisms and enzymatic cascades, and gradually complements the lack of remaining key steps in the pathway design of enzymatic total synthesis. Therefore, the research of biocatalysis is gradually moving towards the era of novel technology integration, intelligent manufacturing and enzymatic total synthesis.
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Affiliation(s)
- Dong Yi
- Department of Biotechnology & Enzyme Catalysis, Institute of Biochemistry, University GreifswaldFelix-Hausdorff-Str. 4D-17487 GreifswaldGermany
| | - Thomas Bayer
- Department of Biotechnology & Enzyme Catalysis, Institute of Biochemistry, University GreifswaldFelix-Hausdorff-Str. 4D-17487 GreifswaldGermany
| | - Christoffel P. S. Badenhorst
- Department of Biotechnology & Enzyme Catalysis, Institute of Biochemistry, University GreifswaldFelix-Hausdorff-Str. 4D-17487 GreifswaldGermany
| | - Shuke Wu
- Department of Biotechnology & Enzyme Catalysis, Institute of Biochemistry, University GreifswaldFelix-Hausdorff-Str. 4D-17487 GreifswaldGermany
| | - Mark Doerr
- Department of Biotechnology & Enzyme Catalysis, Institute of Biochemistry, University GreifswaldFelix-Hausdorff-Str. 4D-17487 GreifswaldGermany
| | - Matthias Höhne
- Department of Biotechnology & Enzyme Catalysis, Institute of Biochemistry, University GreifswaldFelix-Hausdorff-Str. 4D-17487 GreifswaldGermany
| | - Uwe T. Bornscheuer
- Department of Biotechnology & Enzyme Catalysis, Institute of Biochemistry, University GreifswaldFelix-Hausdorff-Str. 4D-17487 GreifswaldGermany
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16
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Boada Y, Vignoni A, Picó J, Carbonell P. Extended Metabolic Biosensor Design for Dynamic Pathway Regulation of Cell Factories. iScience 2020; 23:101305. [PMID: 32629420 PMCID: PMC7334618 DOI: 10.1016/j.isci.2020.101305] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 05/05/2020] [Accepted: 06/18/2020] [Indexed: 12/17/2022] Open
Abstract
Transcription factor-based biosensors naturally occur in metabolic pathways to maintain cell growth and to provide a robust response to environmental fluctuations. Extended metabolic biosensors, i.e., the cascading of a bio-conversion pathway and a transcription factor (TF) responsive to the downstream effector metabolite, provide sensing capabilities beyond natural effectors for implementing context-aware synthetic genetic circuits and bio-observers. However, the engineering of such multi-step circuits is challenged by stability and robustness issues. In order to streamline the design of TF-based biosensors in metabolic pathways, here we investigate the response of a genetic circuit combining a TF-based extended metabolic biosensor with an antithetic integral circuit, a feedback controller that achieves robustness against environmental fluctuations. The dynamic response of an extended biosensor-based regulated flavonoid pathway is analyzed in order to address the issues of biosensor tuning of the regulated pathway under industrial biomanufacturing operating constraints.
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Affiliation(s)
- Yadira Boada
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain; Centro Universitario EDEM, Escuela de Empresarios, Muelle de la Aduana s/n, La Marina de València, 46024 Valencia, Spain
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain
| | - Jesús Picó
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain
| | - Pablo Carbonell
- Synthetic Biology and Biosystems Control Lab, I.U. de Automática e Informática Industrial (ai2), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain.
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17
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Duigou T, du Lac M, Carbonell P, Faulon JL. RetroRules: a database of reaction rules for engineering biology. Nucleic Acids Res 2020; 47:D1229-D1235. [PMID: 30321422 PMCID: PMC6323975 DOI: 10.1093/nar/gky940] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 10/09/2018] [Indexed: 01/03/2023] Open
Abstract
RetroRules is a database of reaction rules for metabolic engineering (https://retrorules.org). Reaction rules are generic descriptions of chemical reactions that can be used in retrosynthesis workflows in order to enumerate all possible biosynthetic routes connecting a target molecule to its precursors. The use of such rules is becoming increasingly important in the context of synthetic biology applied to de novo pathway discovery and in systems biology to discover underground metabolism due to enzyme promiscuity. Here, we provide for the first time a complete set containing >400 000 stereochemistry-aware reaction rules extracted from public databases and expressed in the community-standard SMARTS (SMIRKS) format, augmented by a rule representation at different levels of specificity (the atomic environment around the reaction center). Such numerous representations of reactions expand natural chemical diversity by predicting de novo reactions of promiscuous enzymes.
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Affiliation(s)
- Thomas Duigou
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Melchior du Lac
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Pablo Carbonell
- SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK
| | - Jean-Loup Faulon
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France.,SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK.,CNRS-UMR8030/Laboratoire iSSB, Université Paris-Saclay, Évry 91000, France
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18
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Genetic Biosensor Design for Natural Product Biosynthesis in Microorganisms. Trends Biotechnol 2020; 38:797-810. [PMID: 32359951 DOI: 10.1016/j.tibtech.2020.03.013] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 03/26/2020] [Accepted: 03/26/2020] [Indexed: 12/28/2022]
Abstract
Low yield and low titer of natural products are common issues in natural product biosynthesis through microbial cell factories. One effective way to resolve such bottlenecks is to design genetic biosensors to monitor and regulate the biosynthesis of target natural products. In this review, we evaluate the most recent advances in the design of genetic biosensors for natural product biosynthesis in microorganisms. In particular, we examine strategies for selection of genetic parts and construction principles for the design and evaluation of genetic biosensors. We also review the latest applications of transcription factor- and riboswitch-based genetic biosensors in natural product biosynthesis. Lastly, we discuss challenges and solutions in designing genetic biosensors for the biosynthesis of natural products in microorganisms.
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19
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Snoek T, Chaberski EK, Ambri F, Kol S, Bjørn SP, Pang B, Barajas JF, Welner DH, Jensen MK, Keasling JD. Evolution-guided engineering of small-molecule biosensors. Nucleic Acids Res 2020; 48:e3. [PMID: 31777933 PMCID: PMC6943132 DOI: 10.1093/nar/gkz954] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 10/06/2019] [Accepted: 10/24/2019] [Indexed: 11/14/2022] Open
Abstract
Allosteric transcription factors (aTFs) have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and regulation of cellular metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs. Here, we present a versatile and high-throughput method to evolve prokaryotic aTF specificity and transfer functions in a eukaryote chassis, namely baker's yeast Saccharomyces cerevisiae. From a single round of mutagenesis of the effector-binding domain (EBD) coupled with various toggled selection regimes, we robustly select aTF variants of the cis,cis-muconic acid-inducible transcription factor BenM evolved for change in ligand specificity, increased dynamic output range, shifts in operational range, and a complete inversion-of-function from activation to repression. Importantly, by targeting only the EBD, the evolved biosensors display DNA-binding affinities similar to BenM, and are functional when ported back into a prokaryotic chassis. The developed platform technology thus leverages aTF evolvability for the development of new host-agnostic biosensors with user-defined small-molecule specificities and transfer functions.
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Affiliation(s)
- Tim Snoek
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Evan K Chaberski
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Francesca Ambri
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Stefan Kol
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Sara P Bjørn
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Bo Pang
- Joint BioEnergy Institute, Emeryville, CA, USA
| | | | - Ditte H Welner
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Michael K Jensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Jay D Keasling
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark.,Joint BioEnergy Institute, Emeryville, CA, USA.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Department of Chemical and Biomolecular Engineering & Department of Bioengineering, University of California, Berkeley, CA, USA.,Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institutes of Advanced Technologies, Shenzhen, China
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20
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Voyvodic PL, Bonnet J. Cell-free biosensors for biomedical applications. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2020. [DOI: 10.1016/j.cobme.2019.08.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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21
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Abstract
Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are required to explore the chemical biosynthetic space. Here, we propose to explore the bioretrosynthesis space using an artificial intelligence based approach relying on the Monte Carlo Tree Search reinforcement learning method, guided by chemical similarity. We implement this method in RetroPath RL, an open-source and modular command line tool. We validate it on a golden data set of 20 manually curated experimental pathways as well as on a larger data set of 152 successful metabolic engineering projects. Moreover, we provide a novel feature that suggests potential media supplements to complement the enzymatic synthesis plan.
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Affiliation(s)
- Mathilde Koch
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Thomas Duigou
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Jean-Loup Faulon
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
- iSSB Laboratory, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057 Evry, France
- SYNBIOCHEM Center, School of Chemistry, University of Manchester, Manchester M13 9PL, U.K
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22
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Armetta J, Berthome R, Cros A, Pophillat C, Colombo BM, Pandi A, Grigoras I. Biosensor-based enzyme engineering approach applied to psicose biosynthesis. Synth Biol (Oxf) 2019; 4:ysz028. [PMID: 32995548 PMCID: PMC7445875 DOI: 10.1093/synbio/ysz028] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 10/11/2019] [Accepted: 10/25/2019] [Indexed: 11/16/2022] Open
Abstract
Bioproduction of chemical compounds is of great interest for modern industries, as it reduces their production costs and ecological impact. With the use of synthetic biology, metabolic engineering and enzyme engineering tools, the yield of production can be improved to reach mass production and cost-effectiveness expectations. In this study, we explore the bioproduction of D-psicose, also known as D-allulose, a rare non-toxic sugar and a sweetener present in nature in low amounts. D-psicose has interesting properties and seemingly the ability to fight against obesity and type 2 diabetes. We developed a biosensor-based enzyme screening approach as a tool for enzyme selection that we benchmarked with the Clostridium cellulolyticum D-psicose 3-epimerase for the production of D-psicose from D-fructose. For this purpose, we constructed and characterized seven psicose responsive biosensors based on previously uncharacterized transcription factors and either their predicted promoters or an engineered promoter. In order to standardize our system, we created the Universal Biosensor Chassis, a construct with a highly modular architecture that allows rapid engineering of any transcription factor-based biosensor. Among the seven biosensors, we chose the one displaying the most linear behavior and the highest increase in fluorescence fold change. Next, we generated a library of D-psicose 3-epimerase mutants by error-prone PCR and screened it using the biosensor to select gain of function enzyme mutants, thus demonstrating the framework's efficiency.
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Affiliation(s)
- Jeremy Armetta
- iSSB, UMR8030 Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Genopole Campus 1, Bât. 6, 5 rue Henri Desbruères, 91030 Evry, France
| | - Rose Berthome
- iSSB, UMR8030 Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Genopole Campus 1, Bât. 6, 5 rue Henri Desbruères, 91030 Evry, France
| | - Antonin Cros
- iSSB, UMR8030 Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Genopole Campus 1, Bât. 6, 5 rue Henri Desbruères, 91030 Evry, France
| | - Celine Pophillat
- iSSB, UMR8030 Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Genopole Campus 1, Bât. 6, 5 rue Henri Desbruères, 91030 Evry, France
| | - Bruno Maria Colombo
- iSSB, UMR8030 Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Genopole Campus 1, Bât. 6, 5 rue Henri Desbruères, 91030 Evry, France
| | - Amir Pandi
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Ioana Grigoras
- iSSB, UMR8030 Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Genopole Campus 1, Bât. 6, 5 rue Henri Desbruères, 91030 Evry, France
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23
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Abstract
Synthetic biology uses living cells as the substrate for performing human-defined computations. Many current implementations of cellular computing are based on the “genetic circuit” metaphor, an approximation of the operation of silicon-based computers. Although this conceptual mapping has been relatively successful, we argue that it fundamentally limits the types of computation that may be engineered inside the cell, and fails to exploit the rich and diverse functionality available in natural living systems. We propose the notion of “cellular supremacy” to focus attention on domains in which biocomputing might offer superior performance over traditional computers. We consider potential pathways toward cellular supremacy, and suggest application areas in which it may be found. Synthetic biology uses cells as its computing substrate, often based on the genetic circuit concept. In this Perspective, the authors argue that existing synthetic biology approaches based on classical models of computation limit the potential of biocomputing, and propose that living organisms have under-exploited capabilities.
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24
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Custom-made transcriptional biosensors for metabolic engineering. Curr Opin Biotechnol 2019; 59:78-84. [DOI: 10.1016/j.copbio.2019.02.016] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 01/31/2019] [Accepted: 02/19/2019] [Indexed: 01/20/2023]
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25
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Pandi A, Koch M, Voyvodic PL, Soudier P, Bonnet J, Kushwaha M, Faulon JL. Metabolic perceptrons for neural computing in biological systems. Nat Commun 2019; 10:3880. [PMID: 31462649 PMCID: PMC6713752 DOI: 10.1038/s41467-019-11889-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Accepted: 08/08/2019] [Indexed: 12/30/2022] Open
Abstract
Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a different approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model fitted on experimental data, we finally implement two four-input perceptrons for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing. So far, synthetic genetic circuits have relied on digital logic for information processing. Here the authors present metabolic perceptrons that use analog weighted adders to vary the contributions of multiple inputs, resulting in different classification functions.
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Affiliation(s)
- Amir Pandi
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Mathilde Koch
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Peter L Voyvodic
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, Montpellier, France
| | - Paul Soudier
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.,iSSB Laboratory, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Jerome Bonnet
- Centre de Biochimie Structurale, INSERM U1054, CNRS UMR 5048, University of Montpellier, Montpellier, France
| | - Manish Kushwaha
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.
| | - Jean-Loup Faulon
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France. .,iSSB Laboratory, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France. .,SYNBIOCHEM Center, School of Chemistry, University of Manchester, Manchester, UK.
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26
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Plug-and-play metabolic transducers expand the chemical detection space of cell-free biosensors. Nat Commun 2019; 10:1697. [PMID: 30979906 PMCID: PMC6461607 DOI: 10.1038/s41467-019-09722-9] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 03/21/2019] [Indexed: 11/14/2022] Open
Abstract
Cell-free transcription–translation systems have great potential for biosensing, yet the range of detectable chemicals is limited. Here we provide a workflow to expand the range of molecules detectable by cell-free biosensors through combining synthetic metabolic cascades with transcription factor-based networks. These hybrid cell-free biosensors have a fast response time, strong signal response, and a high dynamic range. In addition, they are capable of functioning in a variety of complex media, including commercial beverages and human urine, in which they can be used to detect clinically relevant concentrations of small molecules. This work provides a foundation to engineer modular cell-free biosensors tailored for many applications. The range of chemicals detectable by cell-free systems is still limited. Here the authors combine metabolic cascades with transcription factor networks to detect small molecules in complex environments.
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27
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In vivo biosensors: mechanisms, development, and applications. ACTA ACUST UNITED AC 2018; 45:491-516. [DOI: 10.1007/s10295-018-2004-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 12/30/2017] [Indexed: 01/09/2023]
Abstract
Abstract
In vivo biosensors can recognize and respond to specific cellular stimuli. In recent years, biosensors have been increasingly used in metabolic engineering and synthetic biology, because they can be implemented in synthetic circuits to control the expression of reporter genes in response to specific cellular stimuli, such as a certain metabolite or a change in pH. There are many types of natural sensing devices, which can be generally divided into two main categories: protein-based and nucleic acid-based. Both can be obtained either by directly mining from natural genetic components or by engineering the existing genetic components for novel specificity or improved characteristics. A wide range of new technologies have enabled rapid engineering and discovery of new biosensors, which are paving the way for a new era of biotechnological progress. Here, we review recent advances in the design, optimization, and applications of in vivo biosensors in the field of metabolic engineering and synthetic biology.
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28
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Noda-Garcia L, Liebermeister W, Tawfik DS. Metabolite–Enzyme Coevolution: From Single Enzymes to Metabolic Pathways and Networks. Annu Rev Biochem 2018; 87:187-216. [DOI: 10.1146/annurev-biochem-062917-012023] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
How individual enzymes evolved is relatively well understood. However, individual enzymes rarely confer a physiological advantage on their own. Judging by its current state, the emergence of metabolism seemingly demanded the simultaneous emergence of many enzymes. Indeed, how multicomponent interlocked systems, like metabolic pathways, evolved is largely an open question. This complexity can be unlocked if we assume that survival of the fittest applies not only to genes and enzymes but also to the metabolites they produce. This review develops our current knowledge of enzyme evolution into a wider hypothesis of pathway and network evolution. We describe the current models for pathway evolution and offer an integrative metabolite–enzyme coevolution hypothesis. Our hypothesis addresses the origins of new metabolites and of new enzymes and the order of their recruitment. We aim to not only survey established knowledge but also present open questions and potential ways of addressing them.
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Affiliation(s)
- Lianet Noda-Garcia
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel;,
| | - Wolfram Liebermeister
- INRA, Unité MaIAGE, 78352 Jouy en Josas, France
- Institute of Biochemistry, Charité Universitätsmedizin, Berlin, 10117 Berlin, Germany
| | - Dan S. Tawfik
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel;,
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D'Ambrosio V, Jensen MK. Lighting up yeast cell factories by transcription factor-based biosensors. FEMS Yeast Res 2018; 17:4157790. [PMID: 28961766 PMCID: PMC5812511 DOI: 10.1093/femsyr/fox076] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Accepted: 09/12/2017] [Indexed: 12/17/2022] Open
Abstract
Our ability to rewire cellular metabolism for the sustainable production of chemicals, fuels and therapeutics based on microbial cell factories has advanced rapidly during the last two decades. Especially the speed and precision by which microbial genomes can be engineered now allow for more advanced designs to be implemented and tested. However, compared to the methods developed for engineering cell factories, the methods developed for testing the performance of newly engineered cell factories in high throughput are lagging far behind, which consequently impacts the overall biomanufacturing process. For this purpose, there is a need to develop new techniques for screening and selection of best-performing cell factory designs in multiplex. Here we review the current status of the sourcing, design and engineering of biosensors derived from allosterically regulated transcription factors applied to the biotechnology work-horse budding yeast Saccharomyces cerevisiae. We conclude by providing a perspective on the most important challenges and opportunities lying ahead in order to harness the full potential of biosensor development for increasing both the throughput of cell factory development and robustness of overall bioprocesses.
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Affiliation(s)
- Vasil D'Ambrosio
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Michael K Jensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
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Trabelsi H, Koch M, Faulon JL. Building a minimal and generalizable model of transcription factor-based biosensors: Showcasing flavonoids. Biotechnol Bioeng 2018; 115:2292-2304. [PMID: 29733444 PMCID: PMC6548992 DOI: 10.1002/bit.26726] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Revised: 04/20/2018] [Accepted: 04/30/2018] [Indexed: 01/05/2023]
Abstract
Progress in synthetic biology tools has transformed the way we engineer living cells. Applications of circuit design have reached a new level, offering solutions for metabolic engineering challenges that include developing screening approaches for libraries of pathway variants. The use of transcription‐factor‐based biosensors for screening has shown promising results, but the quantitative relationship between the sensors and the sensed molecules still needs more rational understanding. Herein, we have successfully developed a novel biosensor to detect pinocembrin based on a transcriptional regulator. The FdeR transcription factor (TF), known to respond to naringenin, was combined with a fluorescent reporter protein. By varying the copy number of its plasmid and the concentration of the biosensor TF through a combinatorial library, different responses have been recorded and modeled. The fitted model provides a tool to understand the impact of these parameters on the biosensor behavior in terms of dose–response and time curves and offers guidelines to build constructs oriented to increased sensitivity and or ability of linear detection at higher titers. Our model, the first to explicitly take into account the impact of plasmid copy number on biosensor sensitivity using Hill‐based formalism, is able to explain uncharacterized systems without extensive knowledge of the properties of the TF. Moreover, it can be used to model the response of the biosensor to different compounds (here naringenin and pinocembrin) with minimal parameter refitting.
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Affiliation(s)
- Heykel Trabelsi
- Micalis Institute, INRA, AgroParisTech, University of Paris-Saclay, Jouy-en-Josas, France.,Systems and Synthetic Biology Lab, CEA, CNRS, UMR 8030, Genomics Metabolics, University Paris-Saclay, Évry, France
| | - Mathilde Koch
- Micalis Institute, INRA, AgroParisTech, University of Paris-Saclay, Jouy-en-Josas, France
| | - Jean-Loup Faulon
- Micalis Institute, INRA, AgroParisTech, University of Paris-Saclay, Jouy-en-Josas, France.,Systems and Synthetic Biology Lab, CEA, CNRS, UMR 8030, Genomics Metabolics, University Paris-Saclay, Évry, France.,SYNBIOCHEM Center, School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK
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31
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Courbet A, Amar P, Fages F, Renard E, Molina F. Computer-aided biochemical programming of synthetic microreactors as diagnostic devices. Mol Syst Biol 2018; 14:e7845. [PMID: 29700076 PMCID: PMC5917673 DOI: 10.15252/msb.20177845] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Revised: 02/26/2018] [Accepted: 03/21/2018] [Indexed: 12/14/2022] Open
Abstract
Biological systems have evolved efficient sensing and decision-making mechanisms to maximize fitness in changing molecular environments. Synthetic biologists have exploited these capabilities to engineer control on information and energy processing in living cells. While engineered organisms pose important technological and ethical challenges, de novo assembly of non-living biomolecular devices could offer promising avenues toward various real-world applications. However, assembling biochemical parts into functional information processing systems has remained challenging due to extensive multidimensional parameter spaces that must be sampled comprehensively in order to identify robust, specification compliant molecular implementations. We introduce a systematic methodology based on automated computational design and microfluidics enabling the programming of synthetic cell-like microreactors embedding biochemical logic circuits, or protosensors, to perform accurate biosensing and biocomputing operations in vitro according to temporal logic specifications. We show that proof-of-concept protosensors integrating diagnostic algorithms detect specific patterns of biomarkers in human clinical samples. Protosensors may enable novel approaches to medicine and represent a step toward autonomous micromachines capable of precise interfacing of human physiology or other complex biological environments, ecosystems, or industrial bioprocesses.
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Affiliation(s)
- Alexis Courbet
- Sys2diag UMR9005 CNRS/ALCEDIAG, Montpellier, France
- Department of Endocrinology, Diabetes, Nutrition and INSERM 1411 Clinical Investigation Center, University Hospital of Montpellier, Montpellier Cedex 5, France
| | - Patrick Amar
- Sys2diag UMR9005 CNRS/ALCEDIAG, Montpellier, France
- LRI, Université Paris Sud - UMR CNRS 8623, Orsay Cedex, France
| | | | - Eric Renard
- Department of Endocrinology, Diabetes, Nutrition and INSERM 1411 Clinical Investigation Center, University Hospital of Montpellier, Montpellier Cedex 5, France
- Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of Montpellier, Montpellier Cedex 5, France
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32
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Cheng F, Tang XL, Kardashliev T. Transcription Factor-Based Biosensors in High-Throughput Screening: Advances and Applications. Biotechnol J 2018; 13:e1700648. [DOI: 10.1002/biot.201700648] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 01/17/2018] [Indexed: 12/13/2022]
Affiliation(s)
- Feng Cheng
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology; Hangzhou 310014 P. R. China
| | - Xiao-Ling Tang
- Key Laboratory of Bioorganic Synthesis of Zhejiang Province, College of Biotechnology and Bioengineering, Zhejiang University of Technology; Hangzhou 310014 P. R. China
| | - Tsvetan Kardashliev
- Bioprocess Laboratory, Department of Biosystems Science and Engineering, ETH Zürich; Mattenstrasse 26 4058 Basel Switzerland
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33
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Mori Y, Shirai T. Designing artificial metabolic pathways, construction of target enzymes, and analysis of their function. Curr Opin Biotechnol 2018; 54:41-44. [PMID: 29452926 DOI: 10.1016/j.copbio.2018.01.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 12/26/2017] [Accepted: 01/22/2018] [Indexed: 11/24/2022]
Abstract
Artificial design of metabolic pathways is essential for the production of useful compounds using microbes. Based on this design, heterogeneous genes are introduced into the host, and then various analysis and evaluation methods are conducted to ensure that the target enzyme reactions are functionalized within the cell. In this chapter, we list successful examples of useful compounds produced by designing artificial metabolic pathways, and describe the methods involved in analyzing, evaluating, and optimizing the target enzyme reaction.
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Affiliation(s)
- Yutaro Mori
- Biomass Engineering Research Division, Center for Sustainable Resource Science, RIKEN, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Tomokazu Shirai
- Biomass Engineering Research Division, Center for Sustainable Resource Science, RIKEN, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan.
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34
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Carbonell P, Koch M, Duigou T, Faulon JL. Enzyme Discovery: Enzyme Selection and Pathway Design. Methods Enzymol 2018; 608:3-27. [PMID: 30173766 DOI: 10.1016/bs.mie.2018.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
In this protocol, we describe in silico design methods that can assist in the engineering of production pathways that are based on enzymatic transformations. The described protocols are the basis for automated processes to be integrated into an iterative Design-Build-Test-Learn cycle in synthetic biology for chemical production. Selecting the right enzyme sequence for a desired biocatalytic activity from the extensive catalogue of sequences available in databases is challenging and can dramatically influence the success of bioproducing chemical compounds. A method for enzyme selection is presented that helps identifying candidate enzyme sequences through a scoring approach that considers not only sequence homology but also reaction similarity. Selecting a viable biochemical pathway for compound production requires screening large sets of reactions in a process involving combinatorial complexity. A method for pathway design using retrosynthesis is presented. The protocol allows the discovery of alternative chemical pathways leading to the final product by using reaction rules of selectable degree of specificity. The protocols can be reversed through clustering discovery and product identification processes. The integration of these protocols into a general pipeline provides a toolbox for enhanced automated synthetic biology design and metabolic engineering.
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Affiliation(s)
- Pablo Carbonell
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom
| | - Mathilde Koch
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Thomas Duigou
- Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
| | - Jean-Loup Faulon
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom; Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France; School of Chemistry, The University of Manchester, Manchester, United Kingdom.
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35
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Delépine B, Duigou T, Carbonell P, Faulon JL. RetroPath2.0: A retrosynthesis workflow for metabolic engineers. Metab Eng 2018; 45:158-170. [DOI: 10.1016/j.ymben.2017.12.002] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 11/03/2017] [Accepted: 12/05/2017] [Indexed: 12/01/2022]
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36
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Abstract
Determining the fraction of the chemical space that can be processed in vivo by using natural and synthetic biology devices is crucial for the development of advanced synthetic biology applications. The extended metabolic space is a coding system based on molecular signatures that enables the derivation of reaction rules for metabolic reactions and the enumeration of all possible substrates and products corresponding to the rules. The extended metabolic space expands capabilities for controlling the production, processing, sensing, and the release of specific molecules in chassis organisms.
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37
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Dvořák P, Nikel PI, Damborský J, de Lorenzo V. Bioremediation 3 . 0 : Engineering pollutant-removing bacteria in the times of systemic biology. Biotechnol Adv 2017; 35:845-866. [DOI: 10.1016/j.biotechadv.2017.08.001] [Citation(s) in RCA: 126] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Revised: 08/01/2017] [Accepted: 08/04/2017] [Indexed: 01/07/2023]
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38
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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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39
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De Paepe B, Peters G, Coussement P, Maertens J, De Mey M. Tailor-made transcriptional biosensors for optimizing microbial cell factories. J Ind Microbiol Biotechnol 2016; 44:623-645. [PMID: 27837353 DOI: 10.1007/s10295-016-1862-3] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 10/30/2016] [Indexed: 12/24/2022]
Abstract
Monitoring cellular behavior and eventually properly adapting cellular processes is key to handle the enormous complexity of today's metabolic engineering questions. Hence, transcriptional biosensors bear the potential to augment and accelerate current metabolic engineering strategies, catalyzing vital advances in industrial biotechnology. The development of such transcriptional biosensors typically starts with exploring nature's richness. Hence, in a first part, the transcriptional biosensor architecture and the various modi operandi are briefly discussed, as well as experimental and computational methods and relevant ontologies to search for natural transcription factors and their corresponding binding sites. In the second part of this review, various engineering approaches are reviewed to tune the main characteristics of these (natural) transcriptional biosensors, i.e., the response curve and ligand specificity, in view of specific industrial biotechnology applications, which is illustrated using success stories of transcriptional biosensor engineering.
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Affiliation(s)
- Brecht De Paepe
- Department of Biochemical and Microbial Technology, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
| | - Gert Peters
- Department of Biochemical and Microbial Technology, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
| | - Pieter Coussement
- Department of Biochemical and Microbial Technology, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
| | - Jo Maertens
- Department of Biochemical and Microbial Technology, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
| | - Marjan De Mey
- Department of Biochemical and Microbial Technology, Ghent University, Coupure Links 653, 9000, Ghent, Belgium.
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40
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Biofuel metabolic engineering with biosensors. Curr Opin Chem Biol 2016; 35:150-158. [PMID: 27768949 DOI: 10.1016/j.cbpa.2016.09.020] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Revised: 09/15/2016] [Accepted: 09/22/2016] [Indexed: 11/21/2022]
Abstract
Metabolic engineering offers the potential to renewably produce important classes of chemicals, particularly biofuels, at an industrial scale. DNA synthesis and editing techniques can generate large pathway libraries, yet identifying the best variants is slow and cumbersome. Traditionally, analytical methods like chromatography and mass spectrometry have been used to evaluate pathway variants, but such techniques cannot be performed with high throughput. Biosensors - genetically encoded components that actuate a cellular output in response to a change in metabolite concentration - are therefore a promising tool for rapid and high-throughput evaluation of candidate pathway variants. Applying biosensors can also dynamically tune pathways in response to metabolic changes, improving balance and productivity. Here, we describe the major classes of biosensors and briefly highlight recent progress in applying them to biofuel-related metabolic pathway engineering.
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SYNBIOCHEM Synthetic Biology Research Centre, Manchester - A UK foundry for fine and speciality chemicals production. Synth Syst Biotechnol 2016; 1:271-275. [PMID: 29062953 PMCID: PMC5625740 DOI: 10.1016/j.synbio.2016.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Revised: 07/08/2016] [Accepted: 07/11/2016] [Indexed: 11/21/2022] Open
Abstract
The UK Synthetic Biology Research Centre, SYNBIOCHEM, hosted by the Manchester Institute of Biotechnology at the University of Manchester is delivering innovative technology platforms to facilitate the predictable engineering of microbial bio-factories for fine and speciality chemicals production. We provide an overview of our foundry activities that are being applied to grand challenge projects to deliver innovation in bio-based chemicals production for industrial biotechnology.
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Libis V, Delépine B, Faulon JL. Sensing new chemicals with bacterial transcription factors. Curr Opin Microbiol 2016; 33:105-112. [PMID: 27472026 DOI: 10.1016/j.mib.2016.07.006] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Revised: 06/15/2016] [Accepted: 07/06/2016] [Indexed: 11/30/2022]
Abstract
Bacteria rely on allosteric transcription factors (aTFs) to sense a wide range of chemicals. The variety of effectors has contributed in making aTFs the most used input system in synthetic biological circuits. Considering their enabling role in biotechnology, an important question concerns the size of the chemical space that can potentially be detected by these biosensors. From digging into the ever changing repertoire of natural regulatory circuits, to advances in aTF engineering, we review here different strategies that are pushing the boundaries of this chemical space. We also review natural and synthetic cases of indirect sensing, where aTFs work in combination with metabolism to enable detection of new molecules.
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Affiliation(s)
- Vincent Libis
- iSSB, Genopole, CNRS, UEVE, Université Paris Saclay, 91000 Évry, France; Micalis Institute, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy-en-Josas, France
| | - Baudoin Delépine
- iSSB, Genopole, CNRS, UEVE, Université Paris Saclay, 91000 Évry, France; Micalis Institute, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy-en-Josas, France
| | - Jean-Loup Faulon
- iSSB, Genopole, CNRS, UEVE, Université Paris Saclay, 91000 Évry, France; Micalis Institute, INRA, AgroParisTech, Université Paris Saclay, 78350 Jouy-en-Josas, France; SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK.
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