1
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Yuan Y, Miao J. Agrochemical control of gene expression using evolved split RNA polymerase. II. PeerJ 2024; 12:e18042. [PMID: 39247540 PMCID: PMC11380473 DOI: 10.7717/peerj.18042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 08/13/2024] [Indexed: 09/10/2024] Open
Abstract
Agrochemical inducible gene expression system provides cost-effective and orthogonal control of energy and information flow in bacterial cells. However, the previous version of Mandipropamid inducible gene expression system (Mandi-T7) became constitutively active at room temperature. We moved the split site of the eRNAP from position LYS179 to position ILE109. This new eRNAP showed proximity dependence at 23 °C, but not at 37 °C. We built Mandi-T7-v2 system based on the new eRNAP and it worked in both Escherichia coli and Agrobacterium tumefaciens. We also induced GFP expression in Agrobacterium cells in a semi-in vivo system. The modified eRNAP when combined with the leucine zipper-based dimerization system, behaved as a cold inducible gene expression system. Our new system provides a means to broaden the application of agrochemicals for both research and agricultural application. Portions of this text were previously published as part of a preprint (https://www.biorxiv.org/content/10.1101/2024.04.02.587689v1).
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Affiliation(s)
- Yuan Yuan
- Department of Neurophysiology and Neuropharmacology, Institute of Special Environmental Medicine and Co-innovation Center of Neuroregeneration, Nantong University, Nantong, China
| | - Jin Miao
- Duke Kunshan University, Kunshan, Jiangsu Province, China
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2
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Ding N, Yuan Z, Ma Z, Wu Y, Yin L. AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors. Molecules 2024; 29:3512. [PMID: 39124917 PMCID: PMC11313831 DOI: 10.3390/molecules29153512] [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: 06/27/2024] [Revised: 07/22/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.
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Affiliation(s)
- Nana Ding
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China;
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
| | - Zenan Yuan
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China;
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
| | - Zheng Ma
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Sciences, China Jiliang University, Hangzhou 310018, China;
| | - Yefei Wu
- Zhejiang Qianjiang Biochemical Co., Ltd., Haining 314400, China;
| | - Lianghong Yin
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China;
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
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3
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Joshi SHN, Jenkins C, Ulaeto D, Gorochowski TE. Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0037. [PMID: 38919711 PMCID: PMC11197468 DOI: 10.34133/bdr.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 06/27/2024] Open
Abstract
Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.
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Affiliation(s)
| | - Christopher Jenkins
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - David Ulaeto
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK
- BrisEngBio,
School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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4
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Gao Y, Wang L, Wang B. Customizing cellular signal processing by synthetic multi-level regulatory circuits. Nat Commun 2023; 14:8415. [PMID: 38110405 PMCID: PMC10728147 DOI: 10.1038/s41467-023-44256-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 12/05/2023] [Indexed: 12/20/2023] Open
Abstract
As synthetic biology permeates society, the signal processing circuits in engineered living systems must be customized to meet practical demands. Towards this mission, novel regulatory mechanisms and genetic circuits with unprecedented complexity have been implemented over the past decade. These regulatory mechanisms, such as transcription and translation control, could be integrated into hybrid circuits termed "multi-level circuits". The multi-level circuit design will tremendously benefit the current genetic circuit design paradigm, from modifying basic circuit dynamics to facilitating real-world applications, unleashing our capabilities to customize cellular signal processing and address global challenges through synthetic biology.
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Affiliation(s)
- Yuanli Gao
- College of Chemical and Biological Engineering & ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310058, China
- School of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FF, UK
| | - Lei Wang
- Center of Synthetic Biology and Integrated Bioengineering & School of Engineering, Westlake University, Hangzhou, 310030, China.
| | - Baojun Wang
- College of Chemical and Biological Engineering & ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 310058, China.
- Research Center for Biological Computation, Zhejiang Lab, Hangzhou, 311100, China.
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5
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Tarnowski MJ, Varliero G, Scown J, Phelps E, Gorochowski TE. Soil as a transdisciplinary research catalyst: from bioprospecting to biorespecting. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230963. [PMID: 38026022 PMCID: PMC10646459 DOI: 10.1098/rsos.230963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023]
Abstract
The vast microbial biodiversity of soils is beginning to be observed and understood by applying modern DNA sequencing techniques. However, ensuring this potentially valuable information is used in a fair and equitable way remains a challenge. Here, we present a public engagement project that explores this topic through collaborative research of soil microbiomes at six urban locations using nanopore-based DNA sequencing. The project brought together researchers from the disciplines of synthetic biology, environmental humanities and microbial ecology, as well as school students aged 14-16 years old, to gain a broader understanding of views on the use of data from the environment. Discussions led to the transformation of 'bioprospecting', a metaphor with extractive connotations which is often used to frame environmental DNA sequencing studies, towards a more collaborative approach-'biorespecting'. This shift in terminology acknowledges that genetic information contained in soil arises as a result of entire ecosystems, including the people involved in its creation. Therefore, any use of sequence information should be accountable to the ecosystems from which it arose. As knowledge can arise from ecosystems and communities, science and technology should acknowledge this link and reciprocate with care and benefit-sharing to help improve the wellbeing of future generations.
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Affiliation(s)
- Matthew J. Tarnowski
- School of Biological Science, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
- School of Biosciences, Geography and Physics, Swansea University, Swansea SA2 8PP, UK
| | - Gilda Varliero
- Rhizosphere Processes Group, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
| | - Jim Scown
- Humanities and Social Sciences, University of Exeter, Cornwall TR10 9FE, UK
| | - Emily Phelps
- School of Biological Science, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
| | - Thomas E. Gorochowski
- School of Biological Science, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
- BrisEngBio, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK
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6
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Zhang C, Li Z, Liu J, Liu C, Zhang H, Lee WG, Yao C, Guo H, Xu F. Synthetic Gene Circuit-Based Assay with Multilevel Switch Enables Background-Free and Absolute Quantification of Circulating Tumor DNA. RESEARCH (WASHINGTON, D.C.) 2023; 6:0217. [PMID: 37789988 PMCID: PMC10543738 DOI: 10.34133/research.0217] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 08/02/2023] [Indexed: 10/05/2023]
Abstract
Circulating tumor DNA (ctDNA) detection has found widespread applications in tumor diagnostics and treatment, where the key is to obtain accurate quantification of ctDNA. However, this remains challenging due to the issue of background noise associated with existing assays. In this work, we developed a synthetic gene circuit-based assay with multilevel switch (termed CATCH) for background-free and absolute quantification of ctDNA. The multilevel switch combining a small transcription activating RNA and a toehold switch was designed to simultaneously regulate transcription and translation processes in gene circuit to eliminate background noise. Moreover, such a multilevel switch-based gene circuit was integrated with a Cas9 nickase H840A (Cas9n) recognizer and a molecular beacon reporter to form CATCH for ctDNA detection. The CATCH can be implemented in one-pot reaction at 35 °C with virtually no background noise, and achieve robust absolute quantification of ctDNA when integrated with a digital chip (i.e., digital CATCH). Finally, we validated the clinical capability of CATCH by detecting drug-resistant ctDNA mutations from the plasma of 76 non-small cell lung cancer (NSCLC) patients, showing satisfying clinical sensitivity and specificity. We envision that the simple and robust CATCH would be a powerful tool for next-generation ctDNA detection.
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Affiliation(s)
- Chao Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education,
School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, P.R. China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi’an 710049, P.R. China
| | - Zedong Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education,
School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, P.R. China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi’an 710049, P.R. China
- TFX Group-Xi'an Jiaotong University Institute of Life Health, Xi'an 710049, P.R. China
| | - Jie Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education,
School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, P.R. China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi’an 710049, P.R. China
| | - Chang Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education,
School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, P.R. China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi’an 710049, P.R. China
| | - Haoqing Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education,
School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, P.R. China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi’an 710049, P.R. China
| | - Won Gu Lee
- Department of Mechanical Engineering,
Kyung Hee University, Yongin 17104, Republic of Korea
| | - Chunyan Yao
- Department of Transfusion Medicine, Southwest Hospital,
Third Military Medical University (Army Medical University), Chongqing 400038, P.R. China
| | - Hui Guo
- Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, P.R. China
| | - Feng Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education,
School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, P.R. China
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi’an 710049, P.R. China
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7
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Alexis E, Schulte CCM, Cardelli L, Papachristodoulou A. Regulation strategies for two-output biomolecular networks. J R Soc Interface 2023; 20:20230174. [PMID: 37528680 PMCID: PMC10394417 DOI: 10.1098/rsif.2023.0174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 07/06/2023] [Indexed: 08/03/2023] Open
Abstract
Feedback control theory facilitates the development of self-regulating systems with desired performance which are predictable and insensitive to disturbances. Feedback regulatory topologies are found in many natural systems and have been of key importance in the design of reliable synthetic bio-devices operating in complex biological environments. Here, we study control schemes for biomolecular processes with two outputs of interest, expanding previously described concepts based on single-output systems. Regulation of such processes may unlock new design possibilities but can be challenging due to coupling interactions; also potential disturbances applied on one of the outputs may affect both. We therefore propose architectures for robustly manipulating the ratio/product and linear combinations of the outputs as well as each of the outputs independently. To demonstrate their characteristics, we apply these architectures to a simple process of two mutually activated biomolecular species. We also highlight the potential for experimental implementation by exploring synthetic realizations both in vivo and in vitro. This work presents an important step forward in building bio-devices capable of sophisticated functions.
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Affiliation(s)
- Emmanouil Alexis
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| | - Carolin C. M. Schulte
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
- Department of Biology, University of Oxford, Oxford OX1 3RB, UK
| | - Luca Cardelli
- Department of Computer Science, University of Oxford, Oxford OX1 3QD, UK
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8
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Zhang C, Liu H, Li X, Xu F, Li Z. Modularized synthetic biology enabled intelligent biosensors. Trends Biotechnol 2023; 41:1055-1065. [PMID: 36967259 DOI: 10.1016/j.tibtech.2023.03.005] [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: 12/29/2022] [Revised: 02/27/2023] [Accepted: 03/06/2023] [Indexed: 03/29/2023]
Abstract
Biosensors that sense the concentration of a specified target and produce a specific signal output have become important technology for biological analysis. Recently, intelligent biosensors have received great interest due to their adaptability to meet sophisticated demands. Advances in developing standard modules and carriers in synthetic biology have shed light on intelligent biosensors that can implement advanced analytical processing to better accommodate practical applications. This review focuses on intelligent synthetic biology-enabled biosensors (SBBs). First, we illustrate recent progress in intelligent SBBs with the capability of computation, memory storage, and self-calibration. Then, we discuss emerging applications of SBBs in point-of-care testing (POCT) and wearable monitoring. Finally, future perspectives on intelligent SBBs are proposed.
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Affiliation(s)
- Chao Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Hao Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Xiujun Li
- Department of Chemistry and Biochemistry, University of Texas at El Paso, 500 West University Ave, El Paso, TX 79968, USA
| | - Feng Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China.
| | - Zedong Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China; Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China; TFX Group-Xi'an Jiaotong University Institute of Life Health, Xi'an 710049, P.R. China.
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9
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O'Connor LM, O'Connor BA, Lim SB, Zeng J, Lo CH. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective. J Pharm Anal 2023; 13:836-850. [PMID: 37719197 PMCID: PMC10499660 DOI: 10.1016/j.jpha.2023.06.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 09/19/2023] Open
Abstract
Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
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Affiliation(s)
- Lance M. O'Connor
- College of Biological Sciences, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Blake A. O'Connor
- School of Pharmacy, University of Wisconsin, Madison, WI, 53705, USA
| | - Su Bin Lim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
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10
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Gyorgy A, Menezes A, Arcak M. A blueprint for a synthetic genetic feedback optimizer. Nat Commun 2023; 14:2554. [PMID: 37137895 PMCID: PMC10156725 DOI: 10.1038/s41467-023-37903-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 04/05/2023] [Indexed: 05/05/2023] Open
Abstract
Biomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.
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Affiliation(s)
- Andras Gyorgy
- Division of Engineering, New York University Abu Dhabi, Abu Dhabi, UAE.
| | - Amor Menezes
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA
| | - Murat Arcak
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
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11
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Zhou GJ, Zhang F. Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors. BIOSENSORS 2023; 13:428. [PMID: 37185503 PMCID: PMC10136082 DOI: 10.3390/bios13040428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 05/17/2023]
Abstract
Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.
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Affiliation(s)
- Gloria J. Zhou
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA;
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA;
- Division of Biology & Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA
- Institute of Materials Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
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12
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Sheets MB, Tague N, Dunlop MJ. An optogenetic toolkit for light-inducible antibiotic resistance. Nat Commun 2023; 14:1034. [PMID: 36823420 PMCID: PMC9950086 DOI: 10.1038/s41467-023-36670-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 02/13/2023] [Indexed: 02/25/2023] Open
Abstract
Antibiotics are a key control mechanism for synthetic biology and microbiology. Resistance genes are used to select desired cells and regulate bacterial populations, however their use to-date has been largely static. Precise spatiotemporal control of antibiotic resistance could enable a wide variety of applications that require dynamic control of susceptibility and survival. Here, we use light-inducible Cre recombinase to activate expression of drug resistance genes in Escherichia coli. We demonstrate light-activated resistance to four antibiotics: carbenicillin, kanamycin, chloramphenicol, and tetracycline. Cells exposed to blue light survive in the presence of lethal antibiotic concentrations, while those kept in the dark do not. To optimize resistance induction, we vary promoter, ribosome binding site, and enzyme variant strength using chromosome and plasmid-based constructs. We then link inducible resistance to expression of a heterologous fatty acid enzyme to increase production of octanoic acid. These optogenetic resistance tools pave the way for spatiotemporal control of cell survival.
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Affiliation(s)
- Michael B Sheets
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.,Biological Design Center, Boston University, Boston, MA, 02215, USA
| | - Nathan Tague
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.,Biological Design Center, Boston University, Boston, MA, 02215, USA
| | - Mary J Dunlop
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA. .,Biological Design Center, Boston University, Boston, MA, 02215, USA.
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13
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Siddiqui M, Tous C, Wong WW. Small molecule-inducible gene regulatory systems in mammalian cells: progress and design principles. Curr Opin Biotechnol 2022; 78:102823. [PMID: 36332343 PMCID: PMC9951109 DOI: 10.1016/j.copbio.2022.102823] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/06/2022] [Accepted: 09/15/2022] [Indexed: 12/14/2022]
Abstract
Small molecule-inducible gene circuits are some of the most important tools in biology because they provide a convenient way to exert precise regulation of biological systems. These systems typically are designed to govern gene activation, repression, or disruption at multiple levels, such as through genome modification, transcription, translation, or post-translational regulation of protein activity. Due to their importance, many new systems have been created in the past few years to address different needs or afford orthogonality. They can be broadly characterized based on the inducer used, the mode of regulation, and the effector protein enabling the regulation. Furthermore, each synthetic circuit has varying performance metrics and design considerations. Here, we provide a concise comparison of recently developed tools and recommend standardized metrics for evaluating their performance and potential as biological interrogators or therapeutics.
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Affiliation(s)
- Menna Siddiqui
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215, USA
| | - Cristina Tous
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215, USA
| | - Wilson W Wong
- Department of Biomedical Engineering and Biological Design Center, Boston University, Boston, MA 02215, USA.
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14
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Joshi SHN, Yong C, Gyorgy A. Inducible plasmid copy number control for synthetic biology in commonly used E. coli strains. Nat Commun 2022; 13:6691. [PMID: 36335103 PMCID: PMC9637173 DOI: 10.1038/s41467-022-34390-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Abstract
The ability to externally control gene expression has been paradigm shifting for all areas of biological research, especially for synthetic biology. Such control typically occurs at the transcriptional and translational level, while technologies enabling control at the DNA copy level are limited by either (i) relying on a handful of plasmids with fixed and arbitrary copy numbers; or (ii) require multiple plasmids for replication control; or (iii) are restricted to specialized strains. To overcome these limitations, we present TULIP (TUnable Ligand Inducible Plasmid): a self-contained plasmid with inducible copy number control, designed for portability across various Escherichia coli strains commonly used for cloning, protein expression, and metabolic engineering. Using TULIP, we demonstrate through multiple application examples that flexible plasmid copy number control accelerates the design and optimization of gene circuits, enables efficient probing of metabolic burden, and facilitates the prototyping and recycling of modules in different genetic contexts.
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Affiliation(s)
- Shivang Hina-Nilesh Joshi
- grid.440573.10000 0004 1755 5934Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Chentao Yong
- grid.440573.10000 0004 1755 5934Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates ,grid.137628.90000 0004 1936 8753Department of Chemical and Biomolecular Engineering, New York University, New York, NY USA
| | - Andras Gyorgy
- grid.440573.10000 0004 1755 5934Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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15
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Moschner C, Wedd C, Bakshi S. The context matrix: Navigating biological complexity for advanced biodesign. Front Bioeng Biotechnol 2022; 10:954707. [PMID: 36082163 PMCID: PMC9445834 DOI: 10.3389/fbioe.2022.954707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 06/29/2022] [Indexed: 12/05/2022] Open
Abstract
Synthetic biology offers many solutions in healthcare, production, sensing and agriculture. However, the ability to rationally engineer synthetic biosystems with predictable and robust functionality remains a challenge. A major reason is the complex interplay between the synthetic genetic construct, its host, and the environment. Each of these contexts contains a number of input factors which together can create unpredictable behaviours in the engineered biosystem. It has become apparent that for the accurate assessment of these contextual effects a more holistic approach to design and characterisation is required. In this perspective article, we present the context matrix, a conceptual framework to categorise and explore these contexts and their net effect on the designed synthetic biosystem. We propose the use and community-development of the context matrix as an aid for experimental design that simplifies navigation through the complex design space in synthetic biology.
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16
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Pandi A, Diehl C, Yazdizadeh Kharrazi A, Scholz SA, Bobkova E, Faure L, Nattermann M, Adam D, Chapin N, Foroughijabbari Y, Moritz C, Paczia N, Cortina NS, Faulon JL, Erb TJ. A versatile active learning workflow for optimization of genetic and metabolic networks. Nat Commun 2022; 13:3876. [PMID: 35790733 PMCID: PMC9256728 DOI: 10.1038/s41467-022-31245-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 06/10/2022] [Indexed: 11/13/2022] Open
Abstract
Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO2-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 1025 conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities. Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, aimed at democratization and standardization, the authors describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets.
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Affiliation(s)
- Amir Pandi
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
| | - Christoph Diehl
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | | | - Scott A Scholz
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Elizaveta Bobkova
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Léon Faure
- Micalis Institute, INRAE, AgroParisTech, University of Paris-Saclay, Jouy-en-Josas, France
| | - Maren Nattermann
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - David Adam
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Nils Chapin
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Yeganeh Foroughijabbari
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Charles Moritz
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Nicole Paczia
- Core Facility for Metabolomics and Small Molecule Mass Spectrometry, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Niña Socorro Cortina
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.,LiVeritas Biosciences, Inc., 432N Canal St.; Ste. 20, South San Francisco, CA, 94080, USA
| | - Jean-Loup Faulon
- Micalis Institute, INRAE, AgroParisTech, University of Paris-Saclay, Jouy-en-Josas, France.,Genomique Metabolique, Genoscope, Institut Francois Jacob, CEA, CNRS, Univ Evry, University of Paris-Saclay, Evry, France.,Manchester Institute of Biotechnology, SYNBIOCHEM center, School of Chemistry, The University of Manchester, Manchester, UK
| | - Tobias J Erb
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany. .,SYNMIKRO Center of Synthetic Microbiology, Marburg, Germany.
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17
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Liu B, Cuba Samaniego C, Bennett M, Chappell J, Franco E. RNA Compensation: A Positive Feedback Insulation Strategy for RNA-Based Transcription Networks. ACS Synth Biol 2022; 11:1240-1250. [PMID: 35244392 DOI: 10.1021/acssynbio.1c00540] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The lack of signaling modularity of biomolecular systems poses major challenges toward engineering complex networks. Directional signaling between an upstream and a downstream circuit requires the presence of binding events, which result in the consumption of regulatory molecules and can compromise the operation of the upstream circuit. This issue has been previously addressed by introducing insulation strategies that include high-gain negative feedback and activation-deactivation reaction cycles. In this paper, we focus on RNA-based circuits and propose a new positive-feedback strategy to mitigate signal consumption that we propose occurs for each regulatory event due to irreversible binding of the RNA input to the RNA target. To mitigate this, an extra RNA input is added in tandem with transcription output to compensate the RNA consumption, leading to concentration robustness of the input RNA molecule regardless of the amount of downstream modules. We term this strategy RNA compensation, and it can be applied to systems that have a stringent input-output gain, such as Small Transcription Activating RNAs (STARs). Our theoretical analysis shows that RNA compensation not only eliminates the signaling consumption in individual STAR-based regulators, but also improves the composability of STAR cascades and the modularity of RNA bistable systems.
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Affiliation(s)
- Baiyang Liu
- Graduate Program in Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, United States
| | - Christian Cuba Samaniego
- Department of Mechanical and Aerospace Engineering, Bioengineering, and Molecular Biology Institute, University of California at Los Angeles, Los Angeles, California 90095, United States
| | - Matthew Bennett
- Department of Biosciences, Rice University, Houston, Texas 77005, United States
- Department of Bioengineering, Rice University, Houston, Texas 77005, United States
| | - James Chappell
- Department of Biosciences, Rice University, Houston, Texas 77005, United States
- Department of Bioengineering, Rice University, Houston, Texas 77005, United States
| | - Elisa Franco
- Department of Mechanical and Aerospace Engineering, Bioengineering, and Molecular Biology Institute, University of California at Los Angeles, Los Angeles, California 90095, United States
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18
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Chee WKD, Yeoh JW, Dao VL, Poh CL. Highly Reversible Tunable Thermal-Repressible Split-T7 RNA Polymerases (Thermal-T7RNAPs) for Dynamic Gene Regulation. ACS Synth Biol 2022; 11:921-937. [PMID: 35089710 DOI: 10.1021/acssynbio.1c00545] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Temperature is a physical cue that is easy to apply, allowing cellular behaviors to be controlled in a contactless and dynamic manner via heat-inducible/repressible systems. However, existing heat-repressible systems are limited in number, rely on thermal sensitive mRNA or transcription factors that function at low temperatures, lack tunability, suffer delays, and are overly complex. To provide an alternative mode of thermal regulation, we developed a library of compact, reversible, and tunable thermal-repressible split-T7 RNA polymerase systems (Thermal-T7RNAPs), which fused temperature-sensitive domains of Tlpa protein with split-T7RNAP to enable direct thermal control of the T7RNAP activity between 30 and 42 °C. We generated a large mutant library with varying thermal performances via an automated screening framework to extend temperature tunability. Lastly, using the mutants, novel thermal logic circuitry was implemented to regulate cell growth and achieve active thermal control of the cell proportions within co-cultures. Overall, this technology expanded avenues for thermal control in biotechnology applications.
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Affiliation(s)
- Wai Kit David Chee
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, 117583 Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, 28 Medical Drive, 117456 Singapore
| | - Jing Wui Yeoh
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, 117583 Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, 28 Medical Drive, 117456 Singapore
| | - Viet Linh Dao
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, 117583 Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, 28 Medical Drive, 117456 Singapore
| | - Chueh Loo Poh
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, 117583 Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, 28 Medical Drive, 117456 Singapore
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19
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Miller CA, Ho JML, Bennett MR. Strategies for Improving Small-Molecule Biosensors in Bacteria. BIOSENSORS 2022; 12:bios12020064. [PMID: 35200325 PMCID: PMC8869690 DOI: 10.3390/bios12020064] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 05/03/2023]
Abstract
In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.
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Affiliation(s)
- Corwin A. Miller
- Department of Biosciences, Rice University MS-140, 6100 Main St., Houston, TX 77005, USA; (C.A.M.); (J.M.L.H.)
| | - Joanne M. L. Ho
- Department of Biosciences, Rice University MS-140, 6100 Main St., Houston, TX 77005, USA; (C.A.M.); (J.M.L.H.)
| | - Matthew R. Bennett
- Department of Biosciences, Rice University MS-140, 6100 Main St., Houston, TX 77005, USA; (C.A.M.); (J.M.L.H.)
- Department of Bioengineering, Rice University MS-140, 6100 Main St., Houston, TX 77005, USA
- Correspondence:
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20
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Greco FV, Irvine T, Grierson CS, Gorochowski TE. Design and Assembly of Multilevel Transcriptional and Translational Regulators for Stringent Control of Gene Expression. Methods Mol Biol 2022; 2518:99-110. [PMID: 35666441 DOI: 10.1007/978-1-0716-2421-0_6] [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: 06/15/2023]
Abstract
Precise control of gene expression is crucial when reprogramming the behavior of living cells. However, common inducible systems often lack the ability to stringently control gene expression due to the use of a single type of regulator that can be susceptible to unavoidable biomolecular fluctuations. In contrast, multilevel controllers (MLCs) employ several forms of regulation simultaneously to overcome this issue, ensuring a reduced basal expression while minimally affecting the maximum induced expression level that can be achieved. Here, we show how our publicly available genetic toolkit can be used to simplify the assembly of MLCs for the stringent control of gene expression. We demonstrate how new compatible parts can be designed and explain the rapid end-to-end assembly procedure.
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Affiliation(s)
- F Veronica Greco
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - Thea Irvine
- School of Biological Sciences, University of Bristol, Bristol, UK
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21
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Moore JC, Ramos I, Van Dien S. OUP accepted manuscript. J Ind Microbiol Biotechnol 2022; 49:6520437. [PMID: 35108392 PMCID: PMC9118995 DOI: 10.1093/jimb/kuab088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 12/20/2021] [Indexed: 11/13/2022]
Abstract
Optimization of metabolism to maximize production of bio-based chemicals must consistently balance cellular resources for biocatalyst growth and desired compound synthesis. This mini-review discusses synthetic biology strategies for dynamically controlling expression of genes to enable dual-phase fermentations in which growth and production are separated into dedicated phases. Emphasis is placed on practical examples which can be reliably scaled to commercial production with the current state of technology. Recent case studies are presented, and recommendations are provided for environmental signals and genetic control circuits.
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Affiliation(s)
| | - Itzel Ramos
- BP Biosciences Center, San Diego, CA 92121, USA
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22
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Li C, Jiang T, Li M, Zou Y, Yan Y. Fine-tuning gene expression for improved biosynthesis of natural products: From transcriptional to post-translational regulation. Biotechnol Adv 2022; 54:107853. [PMID: 34637919 PMCID: PMC8724446 DOI: 10.1016/j.biotechadv.2021.107853] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 02/08/2023]
Abstract
Microbial production of natural compounds has attracted extensive attention due to their high value in pharmaceutical, cosmetic, and food industries. Constructing efficient microbial cell factories for biosynthesis of natural products requires the fine-tuning of gene expressions to minimize the accumulation of toxic metabolites, reduce the competition between cell growth and product generation, as well as achieve the balance of redox or co-factors. In this review, we focus on recent advances in fine-tuning gene expression at the DNA, RNA, and protein levels to improve the microbial biosynthesis of natural products. Commonly used regulatory toolsets in each level are discussed, and perspectives for future direction in this area are provided.
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Affiliation(s)
- Chenyi Li
- School of Chemical, Materials and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Tian Jiang
- School of Chemical, Materials and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Michelle Li
- North Oconee High School, Bogart, GA 30622, USA
| | - Yusong Zou
- School of Chemical, Materials and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Yajun Yan
- School of Chemical, Materials and Biomedical Engineering, College of Engineering, The University of Georgia, Athens, GA 30602, USA.
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23
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Dey A, Barik D. Emergent Bistable Switches from the Incoherent Feed-Forward Signaling of a Positive Feedback Loop. ACS Synth Biol 2021; 10:3117-3128. [PMID: 34694110 DOI: 10.1021/acssynbio.1c00373] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Bistability is intrinsically connected to various decision making processes in living systems. The operating principles of a bistable switch, generated from a positive feedback loop, are well understood both in natural and synthetic settings. However, the fate of dynamic modularity of a positive feedback loop is unknown when it is connected to another dynamically modular signaling motif. In order to address this, here we investigate feed-forward signaling of a positive feedback loop to determine the fate of a bistable switch under such signaling. Using the potential energy based high-throughput bifurcation analysis method, we uncover that in addition to the conventional bistability the hybrid motifs generate various emergent bistable switches, namely mushroom and isola switches, which are not produced by the individual motifs. Using random parameter sampling, network perturbation, and phase plane analysis, we establish the design principles of such emergent behaviors. Incoherent feed-forward signaling of a positive feedback loop with distinct regulatory thresholds of the two arms of the feed-forward loop are the key requirements for such emergent behaviors. Our calculations show that the specific types of atypical bistable responses depend on the logic gate configuration of the signals. However, the emergent bistable behaviors of the hybrid networks do not depend on the nature of the positive feedback loop.
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Affiliation(s)
- Anupam Dey
- School of Chemistry, University of Hyderabad, Central University P.O., Hyderabad, 500046, Telangana, India
| | - Debashis Barik
- School of Chemistry, University of Hyderabad, Central University P.O., Hyderabad, 500046, Telangana, India
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24
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Gyorgy A. Context-Dependent Stability and Robustness of Genetic Toggle Switches with Leaky Promoters. Life (Basel) 2021; 11:life11111150. [PMID: 34833026 PMCID: PMC8624834 DOI: 10.3390/life11111150] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/21/2021] [Accepted: 10/26/2021] [Indexed: 01/22/2023] Open
Abstract
Multistable switches are ubiquitous building blocks in both systems and synthetic biology. Given their central role, it is thus imperative to understand how their fundamental properties depend not only on the tunable biophysical properties of the switches themselves, but also on their genetic context. To this end, we reveal in this article how these factors shape the essential characteristics of toggle switches implemented using leaky promoters such as their stability and robustness to noise, both at single-cell and population levels. In particular, our results expose the roles that competition for scarce transcriptional and translational resources, promoter leakiness, and cell-to-cell heterogeneity collectively play. For instance, the interplay between protein expression from leaky promoters and the associated cost of relying on shared cellular resources can give rise to tristable dynamics even in the absence of positive feedback. Similarly, we demonstrate that while promoter leakiness always acts against multistability, resource competition can be leveraged to counteract this undesirable phenomenon. Underpinned by a mechanistic model, our results thus enable the context-aware rational design of multistable genetic switches that are directly translatable to experimental considerations, and can be further leveraged during the synthesis of large-scale genetic systems using computer-aided biodesign automation platforms.
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Affiliation(s)
- Andras Gyorgy
- Division of Engineering, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates
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25
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Bayer CN, Rennig M, Ehrmann AK, Nørholm MHH. A standardized genome architecture for bacterial synthetic biology (SEGA). Nat Commun 2021; 12:5876. [PMID: 34620865 PMCID: PMC8497626 DOI: 10.1038/s41467-021-26155-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 09/21/2021] [Indexed: 02/08/2023] Open
Abstract
Chromosomal recombinant gene expression offers a number of advantages over plasmid-based synthetic biology. However, the methods applied for bacterial genome engineering are still challenging and far from being standardized. Here, in an attempt to realize the simplest recombinant genome technology imaginable and facilitate the transition from recombinant plasmids to genomes, we create a simplistic methodology and a comprehensive strain collection called the Standardized Genome Architecture (SEGA). In its simplest form, SEGA enables genome engineering by combining only two reagents: a DNA fragment that can be ordered from a commercial vendor and a stock solution of bacterial cells followed by incubation on agar plates. Recombinant genomes are identified by visual inspection using green-white colony screening akin to classical blue-white screening for recombinant plasmids. The modular nature of SEGA allows precise multi-level control of transcriptional, translational, and post-translational regulation. The SEGA architecture simultaneously supports increased standardization of genetic designs and a broad application range by utilizing well-characterized parts optimized for robust performance in the context of the bacterial genome. Ultimately, its adaption and expansion by the scientific community should improve predictability and comparability of experimental outcomes across different laboratories.
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Affiliation(s)
- Carolyn N Bayer
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Maja Rennig
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark.
| | - Anja K Ehrmann
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Morten H H Nørholm
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark.
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26
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Castle SD, Grierson CS, Gorochowski TE. Towards an engineering theory of evolution. Nat Commun 2021; 12:3326. [PMID: 34099656 PMCID: PMC8185075 DOI: 10.1038/s41467-021-23573-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 05/04/2021] [Indexed: 02/07/2023] Open
Abstract
Biological technologies are fundamentally unlike any other because biology evolves. Bioengineering therefore requires novel design methodologies with evolution at their core. Knowledge about evolution is currently applied to the design of biosystems ad hoc. Unless we have an engineering theory of evolution, we will neither be able to meet evolution's potential as an engineering tool, nor understand or limit its unintended consequences for our biological designs. Here, we propose the evotype as a helpful concept for engineering the evolutionary potential of biosystems, or other self-adaptive technologies, potentially beyond the realm of biology.
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Affiliation(s)
- Simeon D Castle
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - Claire S Grierson
- School of Biological Sciences, University of Bristol, Bristol, UK
- BrisSynBio, University of Bristol, Bristol, UK
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, Bristol, UK.
- BrisSynBio, University of Bristol, Bristol, UK.
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