1
|
Pisera AO, Yu Y, Williams RL, Liu CC. Ultra-efficient Integration of Gene Libraries onto Yeast Cytosolic Plasmids. ACS Synth Biol 2025; 14:1002-1008. [PMID: 40127237 DOI: 10.1021/acssynbio.4c00786] [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: 03/26/2025]
Abstract
Efficient methods for diversifying genes of interest (GOIs) are essential in protein engineering. For example, OrthoRep, a yeast-based orthogonal DNA replication system that achieves the rapid in vivo diversification of GOIs encoded on a cytosolic plasmid (p1), has been successfully used to drive numerous protein engineering campaigns. However, OrthoRep-based GOI evolution has almost always started from single GOI sequences, limiting the number of locations on a fitness landscape from where evolutionary search begins. Here, we present a simple approach for the high-efficiency integration of GOI libraries onto OrthoRep. By leveraging integrases, we demonstrate recombination of donor DNA onto the cytosolic p1 plasmid at exceptionally high transformation efficiencies, even surpassing the transformation efficiency of standard circular plasmids and linearized plasmid fragments into yeast. We demonstrate our method's utility through the straightforward construction of mock nanobody libraries encoded on OrthoRep, from which rare binders were reliably enriched. Overall, integrase-assisted manipulation of yeast cytosolic plasmids should enhance the versatility of OrthoRep in continuous evolution experiments and support the routine construction of large GOI libraries in yeast, in general.
Collapse
Affiliation(s)
- Alexander Olek Pisera
- Department of Biomedical Engineering, University of California, Irvine, California 92617, United States
- Center for Synthetic Biology, University of California, Irvine, California 92617, United States
| | - Yutong Yu
- Department of Biomedical Engineering, University of California, Irvine, California 92617, United States
- Center for Synthetic Biology, University of California, Irvine, California 92617, United States
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92617, United States
| | - Rory L Williams
- Department of Biomedical Engineering, University of California, Irvine, California 92617, United States
- Center for Synthetic Biology, University of California, Irvine, California 92617, United States
| | - Chang C Liu
- Department of Biomedical Engineering, University of California, Irvine, California 92617, United States
- Center for Synthetic Biology, University of California, Irvine, California 92617, United States
- Department of Pharmaceutical Sciences, University of California, Irvine, California 92617, United States
- Department of Chemistry, University of California, Irvine, California 92617, United States
- Department of Molecular Biology & Biochemistry, University of California, Irvine, California 92617, United States
| |
Collapse
|
2
|
Chu W, Guo Y, Wu Y, Lv X, Li J, Liu L, Du G, Chen J, Liu Y. Enhancing Cellular and Enzymatic Properties Through In Vivo Continuous Evolution. Chembiochem 2024; 25:e202400564. [PMID: 39248206 DOI: 10.1002/cbic.202400564] [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: 06/29/2024] [Revised: 09/08/2024] [Accepted: 09/09/2024] [Indexed: 09/10/2024]
Abstract
Directed evolution seeks to evolve target genes at a rate far exceeding the natural mutation rate, thereby endowing cellular and enzymatic properties with desired traits. In vivo continuous directed evolution achieves these purposes by generating libraries within living cells, enabling a continuous cycle of mutant generation and selection, enhancing the exploration of gene variants. Continuous evolution has become powerful tools for unraveling evolution mechanism and improving cellular and enzymatic properties. This review categorizes current continuous evolution into three distinct classes: non-targeted chromosomal, targeted chromosomal, and extra-chromosomal hypermutation approaches. It also compares various continuous evolution strategies based on different principles, providing a reference for selecting suitable methods for specific evolutionary goals. Furthermore, this review discusses the two primary limitations for further widespread application of in vivo continuous evolution, which are lack of general applicability and insufficient mutagenic capability. We envision that developing generally applicable mutagenic components and methods to enhance mutation rates for in vivo continuous evolution are promising future directions for wide range applications of continuous evolution.
Collapse
Affiliation(s)
- Weiran Chu
- School of Biotechnology and Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
| | - Yaxin Guo
- School of Biotechnology and Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, China
| | - Yaokang Wu
- School of Biotechnology and Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
| | - Xueqin Lv
- School of Biotechnology and Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
| | - Jianghua Li
- School of Biotechnology and Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
| | - Long Liu
- School of Biotechnology and Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
| | - Guocheng Du
- School of Biotechnology and Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
| | - Jian Chen
- School of Biotechnology and Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
| | - Yanfeng Liu
- School of Biotechnology and Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, 214122, China
| |
Collapse
|
3
|
Pham C, Stogios PJ, Savchenko A, Mahadevan R. Computation-guided transcription factor biosensor specificity engineering for adipic acid detection. Comput Struct Biotechnol J 2024; 23:2211-2219. [PMID: 38817964 PMCID: PMC11137364 DOI: 10.1016/j.csbj.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 06/01/2024] Open
Abstract
Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity. Using this method, we engineer the LysR family BenM TF to alter its specificity from its cognate ligand cis,cis-muconic acid to adipic acid through a single amino acid substitution identified by our computational workflow. When implemented in a cell-free system, the engineered biosensor shows higher ligand sensitivity, expanding the potential applications of this circuit. We further investigate ligand binding through molecular dynamics to analyze the substitution, elucidating the impact of modulating a single amino acid position on the mechanism of BenM ligand binding. This study represents the first application of biomolecular modeling methods for altering BenM specificity and for gaining insights into how mutations influence the structural dynamics of BenM. Such methods can potentially be applied to other TFs to alter specificity and analyze the dynamics responsible for these changes, highlighting the applicability of computational tools for informing experiments. In addition, our developed adipic acid biosensor can be applied for the identification and engineering of enzymes to produce adipic acid.
Collapse
Affiliation(s)
- Chester Pham
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
| | - Peter J. Stogios
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
| | - Alexei Savchenko
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, Alberta, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Ontario, Canada
- The Institute of Biomedical Engineering, University of Toronto, Ontario, Canada
| |
Collapse
|
4
|
Chacón M, Dixon N. Genetically encoded biosensors for the circular plastics bioeconomy. Metab Eng Commun 2024; 19:e00255. [PMID: 39737114 PMCID: PMC11683335 DOI: 10.1016/j.mec.2024.e00255] [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: 06/27/2024] [Revised: 10/21/2024] [Accepted: 11/27/2024] [Indexed: 01/01/2025] Open
Abstract
Current plastic production and consumption routes are unsustainable due to impact upon climate change and pollution, and therefore reform across the entire value chain is required. Biotechnology offers solutions for production from renewable feedstocks, and to aid end of life recycling/upcycling of plastics. Biology sequence/design space is complex requiring high-throughput analytical methods to facilitate the iterative optimisation, design-build, test-learn (DBTL), cycle of Synthetic Biology. Furthermore, genetic regulatory tools can enable harmonisation between biotechnological demands and the physiological constraints of the selected production host. Genetically encoded biosensors offer a solution for both requirements to facilitate the circular plastic bioeconomy. In this review we present a summary of biosensors developed to date reported to be responsive to plastic precursors/monomers. In addition, we provide a summary of the demonstrated and prospective applications of these biosensors for the construction and deconstruction of plastics. Collectively, this review provides a valuable resource of biosensor tools and enabled applications to support the development of the circular plastics bioeconomy.
Collapse
Affiliation(s)
- Micaela Chacón
- Manchester Institute of Biotechnology (MIB), Department of Chemistry, University of Manchester, Manchester, M1 7DN, UK
| | - Neil Dixon
- Manchester Institute of Biotechnology (MIB), Department of Chemistry, University of Manchester, Manchester, M1 7DN, UK
| |
Collapse
|
5
|
Pisera A, Yu Y, Williams RL, Liu CC. Ultra-Efficient Integration of Gene Libraries onto Yeast Cytosolic Plasmids. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.11.29.626108. [PMID: 39651169 PMCID: PMC11623697 DOI: 10.1101/2024.11.29.626108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2024]
Abstract
Efficient methods for diversifying genes of interest (GOIs) are essential in protein engineering. For example, OrthoRep, a yeast-based orthogonal DNA replication system that achieves the rapid in vivo diversification of GOIs encoded on a cytosolic plasmid (p1), has been successfully used to drive numerous protein engineering campaigns. However, OrthoRep-based GOI evolution has almost always started from single GOI sequences, limiting the number of locations on a fitness landscape from where evolutionary search begins. Here, we present a simple approach for the high-efficiency integration of GOI libraries onto OrthoRep. By leveraging integrases, we demonstrate recombination of donor DNA onto the cytosolic p1 plasmid at exceptionally high transformation efficiencies, even surpassing the transformation efficiency of standard circular plasmids into yeast. We demonstrate our method's utility through the straightforward construction of mock nanobody libraries encoded on OrthoRep, from which rare binders were reliably enriched. Overall, integrase-assisted manipulation of yeast cytosolic plasmids should enhance the versatility of OrthoRep in continuous evolution experiments and support the routine construction of large GOI libraries in yeast in general.
Collapse
|
6
|
Rix G, Williams RL, Hu VJ, Spinner H, Pisera A(O, Marks DS, Liu CC. Continuous evolution of user-defined genes at 1 million times the genomic mutation rate. Science 2024; 386:eadm9073. [PMID: 39509492 PMCID: PMC11750425 DOI: 10.1126/science.adm9073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 09/10/2024] [Indexed: 11/15/2024]
Abstract
When nature evolves a gene over eons at scale, it produces a diversity of homologous sequences with patterns of conservation and change that contain rich structural, functional, and historical information about the gene. However, natural gene diversity accumulates slowly and likely excludes large regions of functional sequence space, limiting the information that is encoded and extractable. We introduce upgraded orthogonal DNA replication (OrthoRep) systems that radically accelerate the evolution of chosen genes under selection in yeast. When applied to a maladapted biosynthetic enzyme, we obtained collections of extensively diverged sequences with patterns that revealed structural and environmental constraints shaping the enzyme's activity. Our upgraded OrthoRep systems should support the discovery of factors influencing gene evolution, uncover previously unknown regions of fitness landscapes, and find broad applications in biomolecular engineering.
Collapse
Affiliation(s)
- Gordon Rix
- Department of Molecular Biology and Biochemistry, University of California; Irvine, CA, 92617, USA
| | - Rory L. Williams
- Department of Biomedical Engineering, University of California; Irvine, CA, 92617, USA
| | - Vincent J. Hu
- Department of Biomedical Engineering, University of California; Irvine, CA, 92617, USA
| | - Han Spinner
- Department of Systems Biology, Harvard Medical School; Boston, MA, 02115, USA
| | | | - Debora S. Marks
- Department of Systems Biology, Harvard Medical School; Boston, MA, 02115, USA
- Broad Institute of Harvard and MIT; Cambridge, MA, 02142, USA
| | - Chang C. Liu
- Department of Molecular Biology and Biochemistry, University of California; Irvine, CA, 92617, USA
- Department of Biomedical Engineering, University of California; Irvine, CA, 92617, USA
- Department of Chemistry, University of California; Irvine, CA, 92617, USA
- Center for Synthetic Biology, University of California; Irvine, CA, 92617, USA
| |
Collapse
|
7
|
Mengiste AA, McDonald JL, Nguyen Tran MT, Plank AV, Wilson RH, Butty VL, Shoulders MD. MutaT7 GDE: A Single Chimera for the Targeted, Balanced, Efficient, and Processive Installation of All Possible Transition Mutations In Vivo. ACS Synth Biol 2024; 13:2693-2701. [PMID: 39190860 DOI: 10.1021/acssynbio.4c00316] [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] [Indexed: 08/29/2024]
Abstract
Deaminase-T7 RNA polymerase fusion (MutaT7) proteins are a growing class of synthetic biology tools used to diversify target genes during in vivo laboratory evolution. To date, MutaT7 chimeras comprise either a deoxyadenosine or deoxycytidine deaminase fused to a T7 RNA polymerase. Their expression drives targeted deoxyadenosine-to-deoxyguanosine or deoxycytidine-to-deoxythymidine mutagenesis, respectively. Here, we repurpose recently engineered substrate-promiscuous general deaminases (GDEs) to establish a substantially simplified system based on a single chimeric enzyme capable of targeting both deoxyadenosine and deoxycytidine. We assess on- and off-target mutagenesis, strand and context preference, and parity of deamination for four different MutaT7GDE constructs. We identify a single chimera that installs all possible transition mutations more efficiently than preexisting, more cumbersome MutaT7 tools. The optimized MutaT7GDE chimera reported herein is a next-generation hypermutator capable of mediating efficient and uniform target-gene diversification during in vivo directed evolution.
Collapse
Affiliation(s)
- Amanuella A Mengiste
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Julie L McDonald
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Minh Thuan Nguyen Tran
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Anna V Plank
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Brain and Cognitive Sciences, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Robert H Wilson
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Vincent L Butty
- BioMicroCenter, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Matthew D Shoulders
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02139, United States
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| |
Collapse
|
8
|
Van Gelder K, Lindner SN, Hanson AD, Zhou J. Strangers in a foreign land: 'Yeastizing' plant enzymes. Microb Biotechnol 2024; 17:e14525. [PMID: 39222378 PMCID: PMC11368087 DOI: 10.1111/1751-7915.14525] [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: 03/03/2024] [Accepted: 07/02/2024] [Indexed: 09/04/2024] Open
Abstract
Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test cycle.
Collapse
Affiliation(s)
- Kristen Van Gelder
- Horticultural Sciences DepartmentUniversity of FloridaGainesvilleFloridaUSA
| | - Steffen N. Lindner
- Department of Systems and Synthetic MetabolismMax Planck Institute of Molecular Plant PhysiologyPotsdamGermany
- Department of BiochemistryCharité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt‐UniversitätBerlinGermany
| | - Andrew D. Hanson
- Horticultural Sciences DepartmentUniversity of FloridaGainesvilleFloridaUSA
| | - Juannan Zhou
- Department of BiologyUniversity of FloridaGainesvilleFloridaUSA
| |
Collapse
|
9
|
Guan A, He Z, Wang X, Jia ZJ, Qin J. Engineering the next-generation synthetic cell factory driven by protein engineering. Biotechnol Adv 2024; 73:108366. [PMID: 38663492 DOI: 10.1016/j.biotechadv.2024.108366] [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: 11/02/2023] [Revised: 03/21/2024] [Accepted: 04/22/2024] [Indexed: 05/09/2024]
Abstract
Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.
Collapse
Affiliation(s)
- Ailin Guan
- College of Biomass Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Zixi He
- College of Biomass Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Xin Wang
- West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Zhi-Jun Jia
- West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Jiufu Qin
- College of Biomass Science and Engineering, Sichuan University, Chengdu 610065, China.
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Peng Q, Bao W, Geng B, Yang S. Biosensor-assisted CRISPRi high-throughput screening to identify genetic targets in Zymomonas mobilis for high d-lactate production. Synth Syst Biotechnol 2024; 9:242-249. [PMID: 38390372 PMCID: PMC10883783 DOI: 10.1016/j.synbio.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/04/2024] [Accepted: 02/04/2024] [Indexed: 02/24/2024] Open
Abstract
Lactate is an important monomer for the synthesis of poly-lactate (PLA), which is a substitute for the petrochemical plastics. To achieve the goal of high lactate titer, rate, and yield for commercial production, efficient lactate production pathway is needed as well as genetic targets that affect high lactate production and tolerance. In this study, an LldR-based d-lactate biosensor with a broad dynamic range was first applied into Zymomonas mobilis to select mutant strains with strong GFP fluorescence, which could be the mutant strains with increased d-lactate production. Then, LldR-based d-lactate biosensor was combined with a genome-wide CRISPR interference (CRISPRi) library targeting the entire genome to generate thousands of mutants with gRNA targeting different genetic targets across the whole genome. Specifically, two mutant libraries were selected containing 105 and 104 mutants with different interference sites from two rounds of fluorescence-activated cell sorting (FACS), respectively. Two genetic targets of ZMO1323 and ZMO1530 were characterized and confirmed to be associated with the increased d-lactate production, further knockout of ZMO1323 and ZMO1530 resulted in a 15% and 21% increase of d-lactate production, respectively. This work thus not only established a high-throughput approach that combines genome-scale CRISPRi and biosensor-assisted screening to identify genetic targets associated with d-lactate production in Z. mobilis, but also provided a feasible high-throughput screening approach for rapid identification of genetic targets associated with strain performance for other industrial microorganisms.
Collapse
Affiliation(s)
- Qiqun Peng
- State Key Laboratory of Biocatalysis and Enzyme Engineering, and School of Life Sciences, Hubei University, Wuhan, 430062, China
| | - Weiwei Bao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, and School of Life Sciences, Hubei University, Wuhan, 430062, China
| | - Binan Geng
- State Key Laboratory of Biocatalysis and Enzyme Engineering, and School of Life Sciences, Hubei University, Wuhan, 430062, China
| | - Shihui Yang
- State Key Laboratory of Biocatalysis and Enzyme Engineering, and School of Life Sciences, Hubei University, Wuhan, 430062, China
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Xi C, Diao J, Moon TS. Advances in ligand-specific biosensing for structurally similar molecules. Cell Syst 2023; 14:1024-1043. [PMID: 38128482 PMCID: PMC10751988 DOI: 10.1016/j.cels.2023.10.009] [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: 05/21/2023] [Revised: 08/23/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.
Collapse
Affiliation(s)
- Chenggang Xi
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jinjin Diao
- Department of Energy, Environmental and Chemical Engineering, 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.
| |
Collapse
|
14
|
Tian R, Zhao R, Guo H, Yan K, Wang C, Lu C, Lv X, Li J, Liu L, Du G, Chen J, Liu Y. Engineered bacterial orthogonal DNA replication system for continuous evolution. Nat Chem Biol 2023; 19:1504-1512. [PMID: 37443393 DOI: 10.1038/s41589-023-01387-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 06/16/2023] [Indexed: 07/15/2023]
Abstract
Continuous evolution can generate biomolecules for synthetic biology and enable evolutionary investigation. The orthogonal DNA replication system (OrthoRep) in yeast can efficiently mutate long DNA fragments in an easy-to-operate manner. However, such a system is lacking in bacteria. Therefore, we developed a bacterial orthogonal DNA replication system (BacORep) for continuous evolution. We achieved this by harnessing the temperate phage GIL16 DNA replication machinery in Bacillus thuringiensis with an engineered error-prone orthogonal DNA polymerase. BacORep introduces all 12 types of nucleotide substitution in 15-kilobase genes on orthogonally replicating linear plasmids with a 6,700-fold higher mutation rate than that of the host genome, the mutation rate of which is unchanged. Here we demonstrate the utility of BacORep-based continuous evolution by generating strong promoters applicable to model bacteria, Bacillus subtilis and Escherichia coli, and achieving a 7.4-fold methanol assimilation increase in B. thuringiensis. BacORep is a powerful tool for continuous evolution in prokaryotic cells.
Collapse
Affiliation(s)
- Rongzhen Tian
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
- Science Center for Future Foods, Jiangnan University, Wuxi, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
| | - Runzhi Zhao
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
- Science Center for Future Foods, Jiangnan University, Wuxi, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
| | - Haoyu Guo
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
- Science Center for Future Foods, Jiangnan University, Wuxi, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
| | - Kun Yan
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
- Science Center for Future Foods, Jiangnan University, Wuxi, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
| | - Chenyun Wang
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
- Science Center for Future Foods, Jiangnan University, Wuxi, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
| | - Cheng Lu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
- Science Center for Future Foods, Jiangnan University, Wuxi, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
- Science Center for Future Foods, Jiangnan University, Wuxi, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
- Science Center for Future Foods, Jiangnan University, Wuxi, China
| | - Guocheng Du
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China
- Science Center for Future Foods, Jiangnan University, Wuxi, China
| | - Jian Chen
- Science Center for Future Foods, Jiangnan University, Wuxi, China
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, China.
- Science Center for Future Foods, Jiangnan University, Wuxi, China.
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology, Jiangnan University, Wuxi, China.
- Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, Wuxi, China.
| |
Collapse
|
15
|
Rix G, Williams RL, Spinner H, Hu VJ, Marks DS, Liu CC. Continuous evolution of user-defined genes at 1-million-times the genomic mutation rate. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566922. [PMID: 38014077 PMCID: PMC10680746 DOI: 10.1101/2023.11.13.566922] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
When nature maintains or evolves a gene's function over millions of years at scale, it produces a diversity of homologous sequences whose patterns of conservation and change contain rich structural, functional, and historical information about the gene. However, natural gene diversity likely excludes vast regions of functional sequence space and includes phylogenetic and evolutionary eccentricities, limiting what information we can extract. We introduce an accessible experimental approach for compressing long-term gene evolution to laboratory timescales, allowing for the direct observation of extensive adaptation and divergence followed by inference of structural, functional, and environmental constraints for any selectable gene. To enable this approach, we developed a new orthogonal DNA replication (OrthoRep) system that durably hypermutates chosen genes at a rate of >10 -4 substitutions per base in vivo . When OrthoRep was used to evolve a conditionally essential maladapted enzyme, we obtained thousands of unique multi-mutation sequences with many pairs >60 amino acids apart (>15% divergence), revealing known and new factors influencing enzyme adaptation. The fitness of evolved sequences was not predictable by advanced machine learning models trained on natural variation. We suggest that OrthoRep supports the prospective and systematic discovery of constraints shaping gene evolution, uncovering of new regions in fitness landscapes, and general applications in biomolecular engineering.
Collapse
|
16
|
Baugh AC, Momany C, Neidle EL. Versatility and Complexity: Common and Uncommon Facets of LysR-Type Transcriptional Regulators. Annu Rev Microbiol 2023; 77:317-339. [PMID: 37285554 DOI: 10.1146/annurev-micro-050323-040543] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
LysR-type transcriptional regulators (LTTRs) form one of the largest families of bacterial regulators. They are widely distributed and contribute to all aspects of metabolism and physiology. Most are homotetramers, with each subunit composed of an N-terminal DNA-binding domain followed by a long helix connecting to an effector-binding domain. LTTRs typically bind DNA in the presence or absence of a small-molecule ligand (effector). In response to cellular signals, conformational changes alter DNA interactions, contact with RNA polymerase, and sometimes contact with other proteins. Many are dual-function repressor-activators, although different modes of regulation may occur at multiple promoters. This review presents an update on the molecular basis of regulation, the complexity of regulatory schemes, and applications in biotechnology and medicine. The abundance of LTTRs reflects their versatility and importance. While a single regulatory model cannot describe all family members, a comparison of similarities and differences provides a framework for future study.
Collapse
Affiliation(s)
- Alyssa C Baugh
- Department of Microbiology, University of Georgia, Athens, Georgia, USA;
| | - Cory Momany
- Department of Pharmaceutical and Biomedical Sciences, University of Georgia, Athens, Georgia, USA
| | - Ellen L Neidle
- Department of Microbiology, University of Georgia, Athens, Georgia, USA;
| |
Collapse
|
17
|
Martin HG, Radivojevic T, Zucker J, Bouchard K, Sustarich J, Peisert S, Arnold D, Hillson N, Babnigg G, Marti JM, Mungall CJ, Beckham GT, Waldburger L, Carothers J, Sundaram S, Agarwal D, Simmons BA, Backman T, Banerjee D, Tanjore D, Ramakrishnan L, Singh A. Perspectives for self-driving labs in synthetic biology. Curr Opin Biotechnol 2023; 79:102881. [PMID: 36603501 DOI: 10.1016/j.copbio.2022.102881] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/23/2022] [Accepted: 12/07/2022] [Indexed: 01/04/2023]
Abstract
Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.
Collapse
Affiliation(s)
- Hector G Martin
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States; BCAM, Basque Center for Applied Mathematics, Bilbao, Spain.
| | - Tijana Radivojevic
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States
| | - Jeremy Zucker
- Earth and Biological Sciences Division, Pacific Northwest National Laboratories, Richland, WA, United States
| | - Kristofer Bouchard
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Lawrence Berkeley National Laboratory, Scientific Data Division, Berkeley, CA, United States; Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience, Berkeley, CA, United States
| | - Jess Sustarich
- Joint BioEnergy Institute, Emeryville, CA, United States; Biomaterials and Biomanufacturing Division, Sandia National Laboratories, Livermore, CA, United States
| | - Sean Peisert
- Lawrence Berkeley National Laboratory, Scientific Data Division, Berkeley, CA, United States; University of California, Davis, Department of Computer Science, Davis, CA, United States
| | - Dan Arnold
- Lawrence Berkeley National Laboratory, Energy Storage and Distributed Resources Division, Berkeley, CA, United States
| | - Nathan Hillson
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States
| | - Gyorgy Babnigg
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Biosciences Division, Argonne National Laboratory, Argonne, IL, United States
| | - Jose M Marti
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States; Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Christopher J Mungall
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States
| | - Gregg T Beckham
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO 80401, United States
| | - Lucas Waldburger
- Department of Bioengineering, University of California, Berkeley, CA, United States
| | - James Carothers
- Department of Chemical Engineering, Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, United States
| | - ShivShankar Sundaram
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States; Center for Bioengineering, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Deb Agarwal
- Lawrence Berkeley National Laboratory, Scientific Data Division, Berkeley, CA, United States
| | - Blake A Simmons
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States
| | - Tyler Backman
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States
| | - Deepanwita Banerjee
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States
| | - Deepti Tanjore
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Advanced Biofuels and Bioproducts Process Development Unit, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Lavanya Ramakrishnan
- Lawrence Berkeley National Laboratory, Scientific Data Division, Berkeley, CA, United States
| | - Anup Singh
- Joint BioEnergy Institute, Emeryville, CA, United States; Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| |
Collapse
|
18
|
Volk MJ, Tran VG, Tan SI, Mishra S, Fatma Z, Boob A, Li H, Xue P, Martin TA, Zhao H. Metabolic Engineering: Methodologies and Applications. Chem Rev 2022; 123:5521-5570. [PMID: 36584306 DOI: 10.1021/acs.chemrev.2c00403] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.
Collapse
Affiliation(s)
- Michael J Volk
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shih-I Tan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hongxiang Li
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Teresa A Martin
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| |
Collapse
|
19
|
Holland K, Blazeck J. High throughput mutagenesis and screening for yeast engineering. J Biol Eng 2022; 16:37. [PMID: 36575525 PMCID: PMC9793380 DOI: 10.1186/s13036-022-00315-7] [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: 09/27/2022] [Accepted: 12/03/2022] [Indexed: 12/28/2022] Open
Abstract
The eukaryotic yeast Saccharomyces cerevisiae is a model host utilized for whole cell biocatalytic conversions, protein evolution, and scientific inquiries into the pathogenesis of human disease. Over the past decade, the scale and pace of such studies has drastically increased alongside the advent of novel tools for both genome-wide studies and targeted genetic mutagenesis. In this review, we will detail past and present (e.g., CRISPR/Cas) genome-scale screening platforms, typically employed in the context of growth-based selections for improved whole cell phenotype or for mechanistic interrogations. We will further highlight recent advances that enable the rapid and often continuous evolution of biomolecules with improved function. Additionally, we will detail the corresponding advances in high throughput selection and screening strategies that are essential for assessing or isolating cellular and protein improvements. Finally, we will describe how future developments can continue to advance yeast high throughput engineering.
Collapse
Affiliation(s)
- Kendreze Holland
- grid.213917.f0000 0001 2097 4943Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia USA ,grid.213917.f0000 0001 2097 4943Bioengineering Program, Georgia Institute of Technology, Atlanta, Georgia USA
| | - John Blazeck
- grid.213917.f0000 0001 2097 4943Bioengineering Program, Georgia Institute of Technology, Atlanta, Georgia USA ,grid.213917.f0000 0001 2097 4943School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia USA
| |
Collapse
|
20
|
Shin SM, Jha RK, Dale T. Tackling the Catch-22 Situation of Optimizing a Sensor and a Transporter System in a Whole-Cell Microbial Biosensor Design for an Anthropogenic Small Molecule. ACS Synth Biol 2022; 11:3996-4008. [PMID: 36472954 DOI: 10.1021/acssynbio.2c00364] [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: 12/12/2022]
Abstract
Whole-cell biosensors provide a convenient detection tool for the high-throughput screening of genetically engineered biocatalytic activity. But establishing a biosensor for an anthropogenic molecule requires both a custom transporter and a transcription factor. This results in an unavoidable "Catch-22" situation in which transporter activity cannot be easily confirmed without a biosensor and a biosensor cannot be established without a functional transporter in a host organism. We overcame this type of circular problem while developing an adipic acid (ADA) sensor. First, leveraging an established cis,cis-muconic acid (ccMA) sensor, an annotated ccMA transporter MucK, which is expected to be broadly responsive to dicarboxylates, was stably expressed in the genome of Pseudomonas putida to function as a transporter for ADA, and then a PcaR transcription factor (endogenous to the strain and naturally induced by β-ketoadipic acid, BKA) was diversified and selected to detect the ADA molecule. While MucK expression is otherwise very unstable in P. putida under strong promoter expression, our optimized mucK expression was functional for over 70 generations without loss of function, and we selected an ADA sensor that showed a specificity switch of over 35-fold from BKA at low concentrations (typically <0.1 mM of inducers). Our ADA and BKA biosensors show high sensitivity (low detection concentration <10 μM) and dynamic range (∼50-fold) in an industrially relevant organism and will open new avenues for high throughput discovery and optimization of enzymes and metabolic pathways for the biomanufacture of these molecules. In particular, the novel ADA sensor will aid the discovery and evolution of efficient biocatalysts for the biological recycling of ADA from the degradation of nylon-6,6 waste.
Collapse
Affiliation(s)
- Sang-Min Shin
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico87545, United States.,BOTTLE Consortium, Golden, Colorado80401, United States
| | - Ramesh K Jha
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico87545, United States.,BOTTLE Consortium, Golden, Colorado80401, United States.,Agile BioFoundry, Emeryville, California94608, United States
| | - Taraka Dale
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico87545, United States.,BOTTLE Consortium, Golden, Colorado80401, United States.,Agile BioFoundry, Emeryville, California94608, United States
| |
Collapse
|
21
|
d'Oelsnitz S, Love JD, Diaz DJ, Ellington AD. GroovDB: A Database of Ligand-Inducible Transcription Factors. ACS Synth Biol 2022; 11:3534-3537. [PMID: 36178800 DOI: 10.1021/acssynbio.2c00382] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Genetic biosensors are integral to synthetic biology. In particular, ligand-inducible prokaryotic transcription factors are frequently used in high-throughput screening, for dynamic feedback regulation, as multilayer logic gates, and in diagnostic applications. In order to provide a curated source that users can rely on for engineering applications, we have developed GroovDB (available at https://groov.bio), a Web-accessible database of ligand-inducible transcription factors that contains all information necessary to build chemically responsive genetic circuits, including biosensor sequence, ligand, and operator data. Ligand and DNA interaction data have been verified against the literature, while an automated data curation pipeline is used to programmatically fetch metadata, structural information, and references for every entry. A custom tool to visualize the natural genetic context of biosensor entries provides potential insights into alternative ligands and systems biology.
Collapse
Affiliation(s)
- Simon d'Oelsnitz
- Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas 78712, United States
| | - Joshua D Love
- Independent Web Developer, Bentonville, Arizona 72712, United States
| | - Daniel J Diaz
- Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, United States
| | - Andrew D Ellington
- Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas 78712, United States
| |
Collapse
|
22
|
Zou ZP, Yang Y, Wang J, Zhou Y, Ye BC. Coupling split-lux cassette with a toggle switch in bacteria for ultrasensitive blood markers detection in feces and urine. Biosens Bioelectron 2022; 214:114520. [DOI: 10.1016/j.bios.2022.114520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/24/2022] [Accepted: 06/26/2022] [Indexed: 11/29/2022]
|
23
|
Andon JS, Lee B, Wang T. Enzyme directed evolution using genetically encodable biosensors. Org Biomol Chem 2022; 20:5891-5906. [PMID: 35437559 DOI: 10.1039/d2ob00443g] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Directed evolution has been remarkably successful in identifying enzyme variants with new or improved properties, such as altered substrate scope or novel reactivity. Genetically encodable biosensors (GEBs), which convert the concentration of a small molecule ligand into an easily detectable output signal, have seen increasing application to enzyme directed evolution in the last decade. GEBs enable the use of high-throughput methods to assess enzyme activity of very large libraries, which can accelerate the search for variants with desirable activity. Here, we review different classes of GEBs and their properties in the context of enzyme evolution, how GEBs have been integrated into directed evolution workflows, and recent examples of enzyme evolution efforts utilizing GEBs. Finally, we discuss the advantages, challenges, and opportunities for using GEBs in the directed evolution of enzymes.
Collapse
Affiliation(s)
- James S Andon
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - ByungUk Lee
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.
| | - Tina Wang
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53706, USA.
| |
Collapse
|
24
|
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: 19] [Impact Index Per Article: 6.3] [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.
Collapse
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.
| |
Collapse
|
25
|
Molina RS, Rix G, Mengiste AA, Alvarez B, Seo D, Chen H, Hurtado J, Zhang Q, Donato García-García J, Heins ZJ, Almhjell PJ, Arnold FH, Khalil AS, Hanson AD, Dueber JE, Schaffer DV, Chen F, Kim S, Ángel Fernández L, Shoulders MD, Liu CC. In vivo hypermutation and continuous evolution. NATURE REVIEWS. METHODS PRIMERS 2022; 2:37. [PMID: 37073402 PMCID: PMC10108624 DOI: 10.1038/s43586-022-00130-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Rosana S. Molina
- Department of Biomedical Engineering, University of California, Irvine, CA 92617, USA
| | - Gordon Rix
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA
| | - Amanuella A. Mengiste
- Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - Beatriz Alvarez
- Department of Microbial Biotechnology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Darwin 3, Campus UAM Cantoblanco, 28049 Madrid, Spain
| | - Daeje Seo
- Department of Chemistry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea
| | - Haiqi Chen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Juan Hurtado
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Qiong Zhang
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
| | - Jorge Donato García-García
- Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Av. General Ramon Corona 2514, Nuevo Mexico, C.P. 45138, Zapopan, Jalisco, Mexico
| | - Zachary J. Heins
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
| | - Patrick J. Almhjell
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Frances H. Arnold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Ahmad S. Khalil
- Biological Design Center, Boston University, Boston, Massachusetts, USA
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA
| | - Andrew D. Hanson
- Horticultural Sciences Department, University of Florida, Gainesville, FL 32611, USA
| | - John E. Dueber
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- Innovative Genomics Institute, University of California Berkeley and San Francisco, Berkeley, CA, USA
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - David V. Schaffer
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- Innovative Genomics Institute, University of California Berkeley and San Francisco, Berkeley, CA, USA
- Department of Chemical and Biomolecular Engineering, University of California Berkeley, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Fei Chen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seokhee Kim
- Department of Chemistry, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea
| | - Luis Ángel Fernández
- Department of Microbial Biotechnology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Darwin 3, Campus UAM Cantoblanco, 28049 Madrid, Spain
| | - Matthew D. Shoulders
- Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA
| | - Chang C. Liu
- Department of Biomedical Engineering, University of California, Irvine, CA 92617, USA
- Department of Molecular Biology and Biochemistry, University of California, Irvine, CA 92697, USA
- Department of Chemistry, University of California, Irvine, CA 92617, USA
| |
Collapse
|
26
|
McLure RJ, Radford SE, Brockwell DJ. High-throughput directed evolution: a golden era for protein science. TRENDS IN CHEMISTRY 2022. [DOI: 10.1016/j.trechm.2022.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|