1
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Liu A, Ellis D, Mhatre A, Brahmankar S, Seto J, Nielsen DR, Varman AM. Biomanufacturing of value-added chemicals from lignin. Curr Opin Biotechnol 2024; 89:103178. [PMID: 39098292 DOI: 10.1016/j.copbio.2024.103178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 07/16/2024] [Accepted: 07/22/2024] [Indexed: 08/06/2024]
Abstract
Lignin valorization faces persistent biomanufacturing challenges due to the heterogeneous and toxic carbon substrates derived from lignin depolymerization. To address the heterogeneous nature of aromatic feedstocks, plant cell wall engineering and 'lignin first' pretreatment methods have recently emerged. Next, to convert the resulting aromatic substrates into value-added chemicals, diverse microbial host systems also continue to be developed. This includes microbes that (1) lack aromatic metabolism, (2) metabolize aromatics but not sugars, and (3) co-metabolize both aromatics and sugars, each system presenting unique pros and cons. Considering the intrinsic complexity of lignin-derived substrate mixtures, emerging and non-model microbes with native metabolism for aromatics appear poised to provide the greatest impacts on lignin valorization via biomanufacturing.
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Affiliation(s)
- Arren Liu
- Biological Design Program, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA
| | - Dylan Ellis
- Chemical Engineering Program, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA
| | - Apurv Mhatre
- Chemical Engineering Program, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA
| | - Sumant Brahmankar
- Chemical Engineering Program, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA
| | - Jong Seto
- Chemical Engineering Program, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA
| | - David R Nielsen
- Biological Design Program, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA; Chemical Engineering Program, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA
| | - Arul M Varman
- Biological Design Program, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA; Chemical Engineering Program, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA.
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2
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Zeng M, Sarker B, Rondthaler SN, Vu V, Andrews LB. Identifying LasR Quorum Sensors with Improved Signal Specificity by Mapping the Sequence-Function Landscape. ACS Synth Biol 2024; 13:568-589. [PMID: 38206199 DOI: 10.1021/acssynbio.3c00543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Programmable intercellular signaling using components of naturally occurring quorum sensing can allow for coordinated functions to be engineered in microbial consortia. LuxR-type transcriptional regulators are widely used for this purpose and are activated by homoserine lactone (HSL) signals. However, they often suffer from imperfect molecular discrimination of structurally similar HSLs, causing misregulation within engineered consortia containing multiple HSL signals. Here, we studied one such example, the regulator LasR from Pseudomonas aeruginosa. We elucidated its sequence-function relationship for ligand specificity using targeted protein engineering and multiplexed high-throughput biosensor screening. A pooled combinatorial saturation mutagenesis library (9,486 LasR DNA sequences) was created by mutating six residues in LasR's β5 sheet with single, double, or triple amino acid substitutions. Sort-seq assays were performed in parallel using cognate and noncognate HSLs to quantify each corresponding sensor's response to each HSL signal, which identified hundreds of highly specific variants. Sensor variants identified were individually assayed and exhibited up to 60.6-fold (p = 0.0013) improved relative activation by the cognate signal compared to the wildtype. Interestingly, we uncovered prevalent mutational epistasis and previously unidentified residues contributing to signal specificity. The resulting sensors with negligible signal crosstalk could be broadly applied to engineer bacteria consortia.
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Affiliation(s)
- Min Zeng
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Biprodev Sarker
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Stephen N Rondthaler
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Vanessa Vu
- Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Lauren B Andrews
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Molecular and Cellular Biology Graduate Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Biotechnology Training Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
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3
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Pearson AN, Incha MR, Ho CN, Schmidt M, Roberts JB, Nava AA, Keasling JD. Characterization and Diversification of AraC/XylS Family Regulators Guided by Transposon Sequencing. ACS Synth Biol 2024; 13:206-219. [PMID: 38113125 DOI: 10.1021/acssynbio.3c00441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
In this study, we explored the development of engineered inducible systems. Publicly available data from previous transposon sequencing assays were used to identify regulators of metabolism in Pseudomonas putida KT2440. For AraC family regulators (AFRs) represented in these data, we posited AFR/promoter/inducer groupings. Twelve promoters were characterized for a response to their proposed inducers in P. putida, and the resultant data were used to create and test nine two-plasmid sensor systems in Escherichia coli. Several of these were further developed into a palette of single-plasmid inducible systems. From these experiments, we observed an unreported inducer response from a previously characterized AFR, demonstrated that the addition of a P. putida transporter improved the sensor dynamics of an AFR in E. coli, and identified an uncharacterized AFR with a novel potential inducer specificity. Finally, targeted mutations in an AFR, informed by structural predictions, enabled the further diversification of these inducible plasmids.
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Affiliation(s)
- Allison N Pearson
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, United States
| | - Matthew R Incha
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, United States
| | - Cindy N Ho
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Matthias Schmidt
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Institute of Applied Microbiology-iAMB, Aachen Biology and Biotechnology-ABBt, RWTH Aachen University, Aachen 52062, Germany
| | - Jacob B Roberts
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Joint Program in Bioengineering, University of California, Berkeley/San Francisco, California 94720, United States
| | - Alberto A Nava
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States
| | - Jay D Keasling
- Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, California 94608, United States
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Joint Program in Bioengineering, University of California, Berkeley/San Francisco, California 94720, United States
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States
- Institute for Quantitative Biosciences, University of California, Berkeley, California 94720, United States
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby 2800, Denmark
- Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen 518055, China
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4
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Chiang AJ, Hasty J. Design of synthetic bacterial biosensors. Curr Opin Microbiol 2023; 76:102380. [PMID: 37703812 DOI: 10.1016/j.mib.2023.102380] [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: 03/13/2023] [Revised: 07/19/2023] [Accepted: 08/15/2023] [Indexed: 09/15/2023]
Abstract
Novel whole-cell bacterial biosensor designs require an emphasis on moving toward field deployment. Many current sensors are characterized under specified laboratory conditions, which frequently do not represent actual deployment conditions. To this end, recent developments such as toolkits for probing new host chassis that are more robust to environments of interest, have paved the way for improved designs. Strategies for rational tuning of genetic components or tools such as genetic amplifiers or designs that allow post hoc tuning are essential in optimizing existing biosensors for practical application. Furthermore, recent work has seen a rise in directed evolution techniques, which can be immensely valuable in both tuning existing sensors and developing sensors for new analytes that lack characterized sensors. Combined with advancements in bioinformatics and capabilities in rewiring two-component systems, many new sensors can be established, broadening biosensor use cases. Last, recent work in CRISPR-based dynamic regulation and memory mechanisms, as well as kill-switches for biosafety and innovative output integration concepts, represents promising steps toward designing bacterial biosensors for deployment in dynamic and heterogeneous conditions.
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Affiliation(s)
- Alyssa J Chiang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
| | - Jeff Hasty
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA; Molecular Biology Section, Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA; Synthetic Biology Institute, University of California San Diego, La Jolla, CA, USA
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5
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Lebovich M, Zeng M, Andrews LB. Algorithmic Programming of Sequential Logic and Genetic Circuits for Recording Biochemical Concentration in a Probiotic Bacterium. ACS Synth Biol 2023; 12:2632-2649. [PMID: 37581922 PMCID: PMC10510703 DOI: 10.1021/acssynbio.3c00232] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Indexed: 08/16/2023]
Abstract
Through the implementation of designable genetic circuits, engineered probiotic microorganisms could be used as noninvasive diagnostic tools for the gastrointestinal tract. For these living cells to report detected biomarkers or signals after exiting the gut, the genetic circuits must be able to record these signals by using genetically encoded memory. Complex memory register circuits could enable multiplex interrogation of biomarkers and signals. A theory-based approach to create genetic circuits containing memory, known as sequential logic circuits, was previously established for a model laboratory strain of Escherichia coli, yet how circuit component performance varies for nonmodel and clinically relevant bacterial strains is poorly understood. Here, we develop a scalable computational approach to design robust sequential logic circuits in probiotic strain Escherichia coli Nissle 1917 (EcN). In this work, we used TetR-family transcriptional repressors to build genetic logic gates that can be composed into sequential logic circuits, along with a set of engineered sensors relevant for use in the gut environment. Using standard methods, 16 genetic NOT gates and nine sensors were experimentally characterized in EcN. These data were used to design and predict the performance of circuit designs. We present a set of genetic circuits encoding both combinational logic and sequential logic and show that the circuit outputs are in close agreement with our quantitative predictions from the design algorithm. Furthermore, we demonstrate an analog-like concentration recording circuit that detects and reports three input concentration ranges of a biochemical signal using sequential logic.
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Affiliation(s)
- Matthew Lebovich
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
- Biotechnology
Training Program, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Min Zeng
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Lauren B. Andrews
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
- Biotechnology
Training Program, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
- Molecular
and Cellular Biology Graduate Program, University
of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
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6
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Li S, Li Z, Tan GY, Xin Z, Wang W. In vitro allosteric transcription factor-based biosensing. Trends Biotechnol 2023; 41:1080-1095. [PMID: 36967257 DOI: 10.1016/j.tibtech.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/15/2023] [Accepted: 03/01/2023] [Indexed: 06/18/2023]
Abstract
A biosensor is an analytical device that converts a biological response into a measurable output signal. Bacterial allosteric transcription factors (aTFs) have been utilized as a novel class of recognition elements for in vitro biosensing, which circumvents the limitations of aTF-based whole-cell biosensors (WCBs) and helps to meet the increasing requirement of small-molecule biosensors for diverse applications. In this review, we summarize the recent advances related to the configuration of aTF-based biosensors in vitro. Particularly, we evaluate the advantages of aTFs for in vitro biosensing and highlight their great potential for the establishment of robust and easy-to-implement biosensing strategies. We argue that key technical innovations and generalizable workflows will enhance the pipeline for facile construction of diverse aTF-based small-molecule biosensors.
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Affiliation(s)
- Shanshan Li
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, PR China
| | - Zilong Li
- State Key Laboratory of Microbial Resources, Institute of Microbiology, CAS, Beijing 100101, PR China
| | - Gao-Yi Tan
- State Key Laboratory of Bioreactor Engineering and School of Biotechnology, East China University of Science and Technology, Shanghai 200237, PR China
| | - Zhenguo Xin
- State Key Laboratory of Microbial Resources, Institute of Microbiology, CAS, Beijing 100101, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Weishan Wang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, CAS, Beijing 100101, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China.
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7
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Di Rienzo L, Miotto M, Milanetti E, Ruocco G. Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of biosensors. Comput Struct Biotechnol J 2023; 21:3002-3009. [PMID: 37249971 PMCID: PMC10220229 DOI: 10.1016/j.csbj.2023.05.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 04/17/2023] [Accepted: 05/04/2023] [Indexed: 05/31/2023] Open
Abstract
Organisms have developed effective mechanisms to sense the external environment. Human-designed biosensors exploit this natural optimization, where different biological machinery have been adapted to detect the presence of user-defined molecules. Specifically, the pheromone pathway in the model organism Saccharomyces cerevisiae represents a suitable candidate as a synthetic signaling system. Indeed, it expresses just one G-Protein Coupled Receptor (GPCR), Ste2, able to recognize pheromone and initiate the expression of pheromone-dependent genes. To date, the standard procedure to engineer this system relies on the substitution of the yeast GPCR with another one and on the modification of the yeast G-protein to bind the inserted receptor. Here, we propose an innovative computational procedure, based on geometrical and chemical optimization of protein binding pockets, to select the amino acid substitutions required to make the native yeast GPCR able to recognize a user-defined ligand. This procedure would allow the yeast to recognize a wide range of ligands, without a-priori knowledge about a GPCR recognizing them or the corresponding G protein. We used Monte Carlo simulations to design on Ste2 a binding pocket able to recognize epinephrine, selected as a test ligand. We validated Ste2 mutants via molecular docking and molecular dynamics. We verified that the amino acid substitutions we identified make Ste2 able to accommodate and remain firmly bound to epinephrine. Our results indicate that we sampled efficiently the huge space of possible mutants, proposing such a strategy as a promising starting point for the development of a new kind of S.cerevisiae-based biosensors.
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Affiliation(s)
- Lorenzo Di Rienzo
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
| | - Mattia Miotto
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
| | - Edoardo Milanetti
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
- Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Giancarlo Ruocco
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
- Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
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8
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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: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [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
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9
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Aggarwal N, Kitano S, Puah GRY, Kittelmann S, Hwang IY, Chang MW. Microbiome and Human Health: Current Understanding, Engineering, and Enabling Technologies. Chem Rev 2023; 123:31-72. [PMID: 36317983 PMCID: PMC9837825 DOI: 10.1021/acs.chemrev.2c00431] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Indexed: 01/12/2023]
Abstract
The human microbiome is composed of a collection of dynamic microbial communities that inhabit various anatomical locations in the body. Accordingly, the coevolution of the microbiome with the host has resulted in these communities playing a profound role in promoting human health. Consequently, perturbations in the human microbiome can cause or exacerbate several diseases. In this Review, we present our current understanding of the relationship between human health and disease development, focusing on the microbiomes found across the digestive, respiratory, urinary, and reproductive systems as well as the skin. We further discuss various strategies by which the composition and function of the human microbiome can be modulated to exert a therapeutic effect on the host. Finally, we examine technologies such as multiomics approaches and cellular reprogramming of microbes that can enable significant advancements in microbiome research and engineering.
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Affiliation(s)
- Nikhil Aggarwal
- NUS
Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456, Singapore
- Synthetic
Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Shohei Kitano
- NUS
Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456, Singapore
- Synthetic
Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Ginette Ru Ying Puah
- NUS
Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456, Singapore
- Synthetic
Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Wilmar-NUS
(WIL@NUS) Corporate Laboratory, National
University of Singapore, Singapore 117599, Singapore
- Wilmar
International Limited, Singapore 138568, Singapore
| | - Sandra Kittelmann
- Wilmar-NUS
(WIL@NUS) Corporate Laboratory, National
University of Singapore, Singapore 117599, Singapore
- Wilmar
International Limited, Singapore 138568, Singapore
| | - In Young Hwang
- NUS
Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456, Singapore
- Synthetic
Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Department
of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- Singapore
Institute of Technology, Singapore 138683, Singapore
| | - Matthew Wook Chang
- NUS
Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore 117456, Singapore
- Synthetic
Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Wilmar-NUS
(WIL@NUS) Corporate Laboratory, National
University of Singapore, Singapore 117599, Singapore
- Department
of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
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10
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Rottinghaus AG, Vo S, Moon TS. Computational design of CRISPR guide RNAs to enable strain-specific control of microbial consortia. Proc Natl Acad Sci U S A 2023; 120:e2213154120. [PMID: 36574681 PMCID: PMC9910470 DOI: 10.1073/pnas.2213154120] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 11/29/2022] [Indexed: 12/28/2022] Open
Abstract
Microbes naturally coexist in complex, multistrain communities. However, extracting individual microbes from and specifically manipulating the composition of these consortia remain challenging. The sequence-specific nature of CRISPR guide RNAs can be leveraged to accurately differentiate microorganisms and facilitate the creation of tools that can achieve these tasks. We developed a computational program, ssCRISPR, which designs strain-specific CRISPR guide RNA sequences with user-specified target strains, protected strains, and guide RNA properties. We experimentally verify the accuracy of the strain specificity predictions in both Escherichia coli and Pseudomonas spp. and show that up to three nucleotide mismatches are often required to ensure perfect specificity. To demonstrate the functionality of ssCRISPR, we apply computationally designed CRISPR-Cas9 guide RNAs to two applications: the purification of specific microbes through one- and two-plasmid transformation workflows and the targeted removal of specific microbes using DNA-loaded liposomes. For strain purification, we utilize gRNAs designed to target and kill all microbes in a consortium except the specific microbe to be isolated. For strain elimination, we utilize gRNAs designed to target only the unwanted microbe while protecting all other strains in the community. ssCRISPR will be of use in diverse microbiota engineering applications.
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Affiliation(s)
- Austin G. Rottinghaus
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130
| | - Steven Vo
- Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63110
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130
- Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63110
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11
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Leonard AC, Whitehead TA. Design and engineering of genetically encoded protein biosensors for small molecules. Curr Opin Biotechnol 2022; 78:102787. [PMID: 36058141 DOI: 10.1016/j.copbio.2022.102787] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/27/2022] [Accepted: 08/02/2022] [Indexed: 12/14/2022]
Abstract
Genetically encoded protein biosensors controlled by small organic molecules are valuable tools for many biotechnology applications, including control of cellular decisions in living cells. Here, we review recent advances in protein biosensor design and engineering for binding to novel ligands. We categorize sensor architecture as either integrated or portable, where portable biosensors uncouple molecular recognition from signal transduction. Proposed advances to improve portable biosensor development include standardizing a limited set of protein scaffolds, and automating ligand-compatibility screening and ligand-protein-interface design.
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Affiliation(s)
- Alison C Leonard
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80305, USA
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80305, USA.
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12
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Beltrán J, Steiner PJ, Bedewitz M, Wei S, Peterson FC, Li Z, Hughes BE, Hartley Z, Robertson NR, Medina-Cucurella AV, Baumer ZT, Leonard AC, Park SY, Volkman BF, Nusinow DA, Zhong W, Wheeldon I, Cutler SR, Whitehead TA. Rapid biosensor development using plant hormone receptors as reprogrammable scaffolds. Nat Biotechnol 2022; 40:1855-1861. [PMID: 35726092 PMCID: PMC9750858 DOI: 10.1038/s41587-022-01364-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 05/17/2022] [Indexed: 01/14/2023]
Abstract
A general method to generate biosensors for user-defined molecules could provide detection tools for a wide range of biological applications. Here, we describe an approach for the rapid engineering of biosensors using PYR1 (Pyrabactin Resistance 1), a plant abscisic acid (ABA) receptor with a malleable ligand-binding pocket and a requirement for ligand-induced heterodimerization, which facilitates the construction of sense-response functions. We applied this platform to evolve 21 sensors with nanomolar to micromolar sensitivities for a range of small molecules, including structurally diverse natural and synthetic cannabinoids and several organophosphates. X-ray crystallography analysis revealed the mechanistic basis for new ligand recognition by an evolved cannabinoid receptor. We demonstrate that PYR1-derived receptors are readily ported to various ligand-responsive outputs, including enzyme-linked immunosorbent assay (ELISA)-like assays, luminescence by protein-fragment complementation and transcriptional circuits, all with picomolar to nanomolar sensitivity. PYR1 provides a scaffold for rapidly evolving new biosensors for diverse sense-response applications.
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Affiliation(s)
- Jesús Beltrán
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA
| | - Paul J Steiner
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Matthew Bedewitz
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Shuang Wei
- Department of Biochemistry, University of California, Riverside, Riverside, CA, USA
| | - Francis C Peterson
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Zongbo Li
- Department of Chemistry, University of California, Riverside, Riverside, CA, USA
| | - Brigid E Hughes
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Zachary Hartley
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA
| | - Nicholas R Robertson
- Department of Bioengineering, University of California, Riverside, Riverside, USA
| | | | - Zachary T Baumer
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Alison C Leonard
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Sang-Youl Park
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA
| | - Brian F Volkman
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Wenwan Zhong
- Department of Chemistry, University of California, Riverside, Riverside, CA, USA
| | - Ian Wheeldon
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA.
- Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, CA, USA.
| | - Sean R Cutler
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, USA.
- Institute for Integrative Genome Biology, University of California, Riverside, Riverside, CA, USA.
- Center for Plant Cell Biology, University of California, Riverside, Riverside, CA, USA.
| | - Timothy A Whitehead
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA.
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13
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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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14
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Rottinghaus AG, Ferreiro A, Fishbein SRS, Dantas G, Moon TS. Genetically stable CRISPR-based kill switches for engineered microbes. Nat Commun 2022; 13:672. [PMID: 35115506 PMCID: PMC8813983 DOI: 10.1038/s41467-022-28163-5] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 01/13/2022] [Indexed: 12/13/2022] Open
Abstract
Microbial biocontainment is an essential goal for engineering safe, next-generation living therapeutics. However, the genetic stability of biocontainment circuits, including kill switches, is a challenge that must be addressed. Kill switches are among the most difficult circuits to maintain due to the strong selection pressure they impart, leading to high potential for evolution of escape mutant populations. Here we engineer two CRISPR-based kill switches in the probiotic Escherichia coli Nissle 1917, a single-input chemical-responsive switch and a 2-input chemical- and temperature-responsive switch. We employ parallel strategies to address kill switch stability, including functional redundancy within the circuit, modulation of the SOS response, antibiotic-independent plasmid maintenance, and provision of intra-niche competition by a closely related strain. We demonstrate that strains harboring either kill switch can be selectively and efficiently killed inside the murine gut, while strains harboring the 2-input switch are additionally killed upon excretion. Leveraging redundant strategies, we demonstrate robust biocontainment of our kill switch strains and provide a template for future kill switch development.
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Affiliation(s)
- Austin G Rottinghaus
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Aura Ferreiro
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Skye R S Fishbein
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Gautam Dantas
- The Edison Family Center for Genome Sciences & Systems Biology, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA.
- Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA.
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
- Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA.
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