1
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Chen F, Fang H, Zhao J, Jiang P, Dong H, Zhao Y, Wang H, Zhang T, Zhang D. Multivariate modular metabolic engineering and medium optimization for vitamin B 12 production by Escherichia coli. Synth Syst Biotechnol 2024; 9:453-461. [PMID: 38634001 PMCID: PMC11021867 DOI: 10.1016/j.synbio.2024.03.017] [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: 01/21/2024] [Revised: 03/24/2024] [Accepted: 03/29/2024] [Indexed: 04/19/2024] Open
Abstract
Vitamin B12 is a complex compound synthesized by microorganisms. The industrial production of vitamin B12 relies on specific microbial fermentation processes. E. coli has been utilized as a host for the de novo biosynthesis of vitamin B12, incorporating approximately 30 heterologous genes. However, a metabolic imbalance in the intricate pathway significantly limits vitamin B12 production. In this study, we employed multivariate modular metabolic engineering to enhance vitamin B12 production in E. coli by manipulating two modules comprising a total of 10 genes within the vitamin B12 biosynthetic pathway. These two modules were integrated into the chromosome of a chassis cell, regulated by T7, J23119, and J23106 promoters to achieve combinatorial pathway optimization. The highest vitamin B12 titer was attained by engineering the two modules controlled by J23119 and T7 promoters. The inclusion of yeast powder to the fermentation medium increased the vitamin B12 titer to 1.52 mg/L. This enhancement was attributed to the effect of yeast powder on elevating the oxygen transfer rate and augmenting the strain's isopropyl-β-d-1-thiogalactopyranoside (IPTG) tolerance. Ultimately, vitamin B12 titer of 2.89 mg/L was achieved through scaled-up fermentation in a 5-liter fermenter. The strategies reported herein will expedite the development of industry-scale vitamin B12 production utilizing E. coli.
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Affiliation(s)
- Feitao Chen
- College of Biotechnology, Tianjin University of Science & Technology, Tianjin, 300457, China
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Huan Fang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- University of Chinese Academy of Science, Beijing, 100049, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
| | - Jianghua Zhao
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- University of Chinese Academy of Science, Beijing, 100049, China
| | - Pingtao Jiang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Huina Dong
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- University of Chinese Academy of Science, Beijing, 100049, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
| | - Ying Zhao
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Huiying Wang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Tongcun Zhang
- College of Biotechnology, Tianjin University of Science & Technology, Tianjin, 300457, China
| | - Dawei Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
- University of Chinese Academy of Science, Beijing, 100049, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, China
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Joshi SHN, Jenkins C, Ulaeto D, Gorochowski TE. Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0037. [PMID: 38919711 PMCID: PMC11197468 DOI: 10.34133/bdr.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 06/27/2024] Open
Abstract
Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.
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Affiliation(s)
| | - Christopher Jenkins
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - David Ulaeto
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK
- BrisEngBio,
School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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3
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Alcantar MA, English MA, Valeri JA, Collins JJ. A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation. Mol Cell 2024; 84:2382-2396.e9. [PMID: 38906116 DOI: 10.1016/j.molcel.2024.05.025] [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: 06/05/2023] [Revised: 04/11/2024] [Accepted: 05/24/2024] [Indexed: 06/23/2024]
Abstract
The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.
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Affiliation(s)
- Miguel A Alcantar
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA
| | - Max A English
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA
| | - Jacqueline A Valeri
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - James J Collins
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Institute for Medical Engineering and Science, MIT, Cambridge, MA 02139, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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4
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Brasino M, Wagnell E, Ozdemir ES, Ranganathan S, Merritt J. Mutation of the peptide-regulated transcription factor ComR for amidated peptide specificity and heterologous function in Lactiplantibacillus plantarum WCFS1. Microbiol Spectr 2024; 12:e0051724. [PMID: 38687019 DOI: 10.1128/spectrum.00517-24] [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: 03/11/2024] [Accepted: 03/28/2024] [Indexed: 05/02/2024] Open
Abstract
There is a growing interest in the use of probiotic bacteria as biosensors for the detection of disease. However, there is a lack of bacterial receptors developed for specific disease biomarkers. Here, we have investigated the use of the peptide-regulated transcription factor ComR from Streptococcus spp. for specific peptide biomarker detection. ComR exhibits a number of attractive features that are potentially exploitable to create a biomolecular switch for engineered biosensor circuitry within the probiotic organism Lactiplantibacillus plantarum WCFS1. Through iterative design-build-test cycles, we developed a genomically integrated, ComR-based biosensor circuit that allowed WCFS1 to detect low nanomolar concentrations of ComR's cognate peptide XIP. By screening a library of ComR proteins with mutant residues substituted at the K100 position, we identified mutations that increased the specificity of ComR toward an amidated version of its cognate peptide, demonstrating the potential for ComR to detect this important class of biomarker.IMPORTANCEUsing bacteria to detect disease is an exciting possibility under active study. Detecting extracellular peptides with specific amino acid sequences would be particularly useful as these are important markers of health and disease (biomarkers). In this work, we show that a probiotic bacteria (Lactiplantibacillus plantarum) can be genetically engineered to detect specific extracellular peptides using the protein ComR from Streptococcus bacteria. In its natural form, ComR allowed the probiotic bacteria to detect a specific peptide, XIP. We then modified XIP to be more like the peptide biomarkers found in humans and engineered ComR so that it activated with this modified XIP and not the original XIP. This newly engineered ComR also worked in the probiotic bacteria, as expected. This suggests that with additional engineering, ComR might be able to activate with human peptide biomarkers and be used by genetically engineered probiotic bacteria to better detect disease.
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Affiliation(s)
- Michael Brasino
- Cancer Early Detection Advanced Research (CEDAR) Center, Knight Cancer Institute, School of Medicine, Oregon Health and Science University, Portland, Oregon, USA
| | - Eli Wagnell
- Cancer Early Detection Advanced Research (CEDAR) Center, Knight Cancer Institute, School of Medicine, Oregon Health and Science University, Portland, Oregon, USA
| | - E Sila Ozdemir
- Cancer Early Detection Advanced Research (CEDAR) Center, Knight Cancer Institute, School of Medicine, Oregon Health and Science University, Portland, Oregon, USA
| | - Srivathsan Ranganathan
- Cancer Early Detection Advanced Research (CEDAR) Center, Knight Cancer Institute, School of Medicine, Oregon Health and Science University, Portland, Oregon, USA
| | - Justin Merritt
- Department of Biomaterial and Biomedical Sciences, School of Dentistry, Oregon Health and Science University, Portland, Oregon, USA
- Department of Molecular Microbiology and Immunology, School of Medicine, Oregon Health and Science University, Portland, Oregon, USA
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5
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Madhu S, Sengupta A, Sarnaik AP, Wangikar PP. Expanding the synthetic biology repertoire of a fast-growing cyanobacterium Synechococcus elongatus PCC 11801. Biotechnol Bioeng 2024. [PMID: 38773863 DOI: 10.1002/bit.28740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 05/04/2024] [Accepted: 05/07/2024] [Indexed: 05/24/2024]
Abstract
Synechococcus elongatus PCC 11801 is a fast-growing cyanobacterium, exhibiting high tolerance to environmental stresses. We have earlier characterized its genome and analysed its transcriptome and proteome. However, to deploy it as a potential cell factory, it is necessary to expand its synthetic biology toolbox, including promoter elements and ribosome binding sites (RBSs). Here, based on the global transcriptome analysis, 48 native promoters of the genes with high transcript count were characterized using a fluorescent reporter system. The promoters PcpcB, PpsbA1, and P11770 exhibited consistently high fluorescence under all the cultivation conditions. Similarly, from the genome data and proteome analysis, 534 operons were identified. Fifteen intergenic regions exhibiting higher protein expression from the downstream gene were systematically characterized for identifying RBSs, using an operon construct comprising fluorescent protein genes eyfp and mTurq under PcpcB (PcpcB:eyfp:RBS:mTurq:TrrnB). Overall, the work presents promoter and RBS sequence libraries, with varying strengths, to expedite bioengineering of PCC 11801.
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Affiliation(s)
- Swati Madhu
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Annesha Sengupta
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Aditya P Sarnaik
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Pramod P Wangikar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
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6
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Zheng L, Shen J, Chen R, Hu Y, Zhao W, Leung ELH, Dai L. Genome engineering of the human gut microbiome. J Genet Genomics 2024; 51:479-491. [PMID: 38218395 DOI: 10.1016/j.jgg.2024.01.002] [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: 10/08/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 01/15/2024]
Abstract
The human gut microbiome, a complex ecosystem, significantly influences host health, impacting crucial aspects such as metabolism and immunity. To enhance our comprehension and control of the molecular mechanisms orchestrating the intricate interplay between gut commensal bacteria and human health, the exploration of genome engineering for gut microbes is a promising frontier. Nevertheless, the complexities and diversities inherent in the gut microbiome pose substantial challenges to the development of effective genome engineering tools for human gut microbes. In this comprehensive review, we provide an overview of the current progress and challenges in genome engineering of human gut commensal bacteria, whether executed in vitro or in situ. A specific focus is directed towards the advancements and prospects in cargo DNA delivery and high-throughput techniques. Additionally, we elucidate the immense potential of genome engineering methods to enhance our understanding of the human gut microbiome and engineer the microorganisms to enhance human health.
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Affiliation(s)
- Linggang Zheng
- Dr Neher's Biophysics Laboratory for Innovative Drug Discovery/State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China; CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Juntao Shen
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruiyue Chen
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yucan Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Zhao
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Elaine Lai-Han Leung
- Cancer Center, Faculty of Health Science, University of Macau, Macau 999078, China; MOE Frontiers Science Center for Precision Oncology, University of Macau, Macau 999078, China.
| | - Lei Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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7
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uz-Zaman MH, D’Alton S, Barrick JE, Ochman H. Promoter recruitment drives the emergence of proto-genes in a long-term evolution experiment with Escherichia coli. PLoS Biol 2024; 22:e3002418. [PMID: 38713714 PMCID: PMC11101190 DOI: 10.1371/journal.pbio.3002418] [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: 10/18/2023] [Revised: 05/17/2024] [Accepted: 04/18/2024] [Indexed: 05/09/2024] Open
Abstract
The phenomenon of de novo gene birth-the emergence of genes from non-genic sequences-has received considerable attention due to the widespread occurrence of genes that are unique to particular species or genomes. Most instances of de novo gene birth have been recognized through comparative analyses of genome sequences in eukaryotes, despite the abundance of novel, lineage-specific genes in bacteria and the relative ease with which bacteria can be studied in an experimental context. Here, we explore the genetic record of the Escherichia coli long-term evolution experiment (LTEE) for changes indicative of "proto-genic" phases of new gene birth in which non-genic sequences evolve stable transcription and/or translation. Over the time span of the LTEE, non-genic regions are frequently transcribed, translated and differentially expressed, with levels of transcription across low-expressed regions increasing in later generations of the experiment. Proto-genes formed downstream of new mutations result either from insertion element activity or chromosomal translocations that fused preexisting regulatory sequences to regions that were not expressed in the LTEE ancestor. Additionally, we identified instances of proto-gene emergence in which a previously unexpressed sequence was transcribed after formation of an upstream promoter, although such cases were rare compared to those caused by recruitment of preexisting promoters. Tracing the origin of the causative mutations, we discovered that most occurred early in the history of the LTEE, often within the first 20,000 generations, and became fixed soon after emergence. Our findings show that proto-genes emerge frequently within evolving populations, can persist stably, and can serve as potential substrates for new gene formation.
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Affiliation(s)
- Md. Hassan uz-Zaman
- Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas, United States of America
| | - Simon D’Alton
- Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas, United States of America
| | - Jeffrey E. Barrick
- Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas, United States of America
| | - Howard Ochman
- Department of Molecular Biosciences, University of Texas at Austin, Austin, Texas, United States of America
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Ji CH, Je HW, Kim H, Kang HS. Promoter engineering of natural product biosynthetic gene clusters in actinomycetes: concepts and applications. Nat Prod Rep 2024; 41:672-699. [PMID: 38259139 DOI: 10.1039/d3np00049d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Covering 2011 to 2022Low titers of natural products in laboratory culture or fermentation conditions have been one of the challenging issues in natural products research. Many natural product biosynthetic gene clusters (BGCs) are also transcriptionally silent in laboratory culture conditions, making it challenging to characterize the structures and activities of their metabolites. Promoter engineering offers a potential solution to this problem by providing tools for transcriptional activation or optimization of biosynthetic genes. In this review, we summarize the 10 years of progress in promoter engineering approaches in natural products research focusing on the most metabolically talented group of bacteria actinomycetes.
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Affiliation(s)
- Chang-Hun Ji
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea.
| | - Hyun-Woo Je
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea.
| | - Hiyoung Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea.
| | - Hahk-Soo Kang
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea.
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9
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Park S, Kim M, Lee JW. Optimizing Nucleic Acid Delivery Systems through Barcode Technology. ACS Synth Biol 2024; 13:1006-1018. [PMID: 38526308 DOI: 10.1021/acssynbio.3c00602] [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: 03/26/2024]
Abstract
Conventional biological experiments often focus on in vitro assays because of the inherent limitations when handling multiple variables in vivo, including labor-intensive and time-consuming procedures. Often only a subset of samples demonstrating significant efficacy in the in vitro assays can be evaluated in vivo. Nonetheless, because of the low correlation between the in vitro and in vivo tests, evaluation of the variables under examination in vivo and not solely in vitro is critical. An emerging approach to achieve high-throughput in vivo tests involves using a barcode system consisting of various nucleotide combinations. Unique barcodes for each variant enable the simultaneous testing of multiple entities, eliminating the need for separate individual tests. Subsequently, to identify crucial parameters, samples were collected and analyzed using barcode sequencing. This review explores the development of barcode design and its applications, including the evaluation of nucleic acid delivery systems and the optimization of gene expression in vivo.
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Affiliation(s)
- Soan Park
- Department of Chemical Engineering, Pohang University of Science and Technology, 77 CheongamRo, Gyeongbuk, 37673 NamGu, Pohang, Republic of Korea
| | - Mibang Kim
- Department of Chemical Engineering, Pohang University of Science and Technology, 77 CheongamRo, Gyeongbuk, 37673 NamGu, Pohang, Republic of Korea
| | - Jeong Wook Lee
- Department of Chemical Engineering, Pohang University of Science and Technology, 77 CheongamRo, Gyeongbuk, 37673 NamGu, Pohang, Republic of Korea
- School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, 77 CheongamRo, Gyeongbuk, 37673 NamGu, Pohang, Republic of Korea
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10
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Blanco P, Hipólito A, García-Pastor L, Trigo da Roza F, Toribio-Celestino L, Ortega A, Vergara E, San Millán Á, Escudero J. Identification of promoter activity in gene-less cassettes from Vibrionaceae superintegrons. Nucleic Acids Res 2024; 52:2961-2976. [PMID: 38214222 PMCID: PMC11014356 DOI: 10.1093/nar/gkad1252] [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: 04/18/2023] [Revised: 12/18/2023] [Accepted: 12/26/2023] [Indexed: 01/13/2024] Open
Abstract
Integrons are genetic platforms that acquire new genes encoded in integron cassettes (ICs), building arrays of adaptive functions. ICs generally encode promoterless genes, whose expression relies on the platform-associated Pc promoter, with the cassette array functioning as an operon-like structure regulated by the distance to the Pc. This is relevant in large sedentary chromosomal integrons (SCIs) carrying hundreds of ICs, like those in Vibrio species. We selected 29 gene-less cassettes in four Vibrio SCIs, and explored whether their function could be related to the transcription regulation of adjacent ICs. We show that most gene-less cassettes have promoter activity on the sense strand, enhancing the expression of downstream cassettes. Additionally, we identified the transcription start sites of gene-less ICs through 5'-RACE. Accordingly, we found that most of the superintegron in Vibrio cholerae is not silent. These promoter cassettes can trigger the expression of a silent dfrB9 cassette downstream, increasing trimethoprim resistance >512-fold in V. cholerae and Escherichia coli. Furthermore, one cassette with an antisense promoter can reduce trimethoprim resistance when cloned downstream. Our findings highlight the regulatory role of gene-less cassettes in the expression of adjacent cassettes, emphasizing their significance in SCIs and their clinical importance if captured by mobile integrons.
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Affiliation(s)
- Paula Blanco
- Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid 28040, Spain
- VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Alberto Hipólito
- Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid 28040, Spain
- VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Lucía García-Pastor
- Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid 28040, Spain
- VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Filipa Trigo da Roza
- Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid 28040, Spain
- VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Laura Toribio-Celestino
- Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid 28049, Spain
| | - Alba Cristina Ortega
- Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Ester Vergara
- Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid 28040, Spain
- VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid 28040, Spain
| | - Álvaro San Millán
- Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid 28049, Spain
- Centro de Investigación Biológica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid 28222, Spain
| | - José Antonio Escudero
- Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid 28040, Spain
- VISAVET Health Surveillance Centre, Universidad Complutense de Madrid, Madrid 28040, Spain
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11
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Lee SH, Hu Y, Chou A, Chen J, Gonzalez R. Metabolic flux optimization of iterative pathways through orthogonal gene expression control: Application to the β-oxidation reversal. Metab Eng 2024; 82:262-273. [PMID: 38387675 DOI: 10.1016/j.ymben.2024.02.007] [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: 08/27/2023] [Revised: 02/02/2024] [Accepted: 02/13/2024] [Indexed: 02/24/2024]
Abstract
Balancing relative expression of pathway genes to minimize flux bottlenecks and metabolic burden is one of the key challenges in metabolic engineering. This is especially relevant for iterative pathways, such as reverse β-oxidation (rBOX) pathway, which require control of flux partition at multiple nodes to achieve efficient synthesis of target products. Here, we develop a plasmid-based inducible system for orthogonal control of gene expression (referred to as the TriO system) and demonstrate its utility in the rBOX pathway. Leveraging effortless construction of TriO vectors in a plug-and-play manner, we simultaneously explored the solution space for enzyme choice and relative expression levels. Remarkably, varying individual expression levels led to substantial change in product specificity ranging from no production to optimal performance of about 90% of the theoretical yield of the desired products. We obtained titers of 6.3 g/L butyrate, 2.2 g/L butanol and 4.0 g/L hexanoate from glycerol in E. coli, which exceed the best titers previously reported using equivalent enzyme combinations. Since a similar system behavior was observed with alternative termination routes and higher-order iterations, we envision our approach to be broadly applicable to other iterative pathways besides the rBOX. Considering that high throughput, automated strain construction using combinatorial promoter and RBS libraries remain out of reach for many researchers, especially in academia, tools like the TriO system could democratize the testing and evaluation of pathway designs by reducing cost, time and infrastructure requirements.
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Affiliation(s)
- Seung Hwan Lee
- Department of Chemical, Biological, and Materials Engineering, University of South Florida, Tampa, FL, USA
| | - Yang Hu
- Department of Chemical, Biological, and Materials Engineering, University of South Florida, Tampa, FL, USA
| | - Alexander Chou
- Department of Chemical, Biological, and Materials Engineering, University of South Florida, Tampa, FL, USA
| | - Jing Chen
- Department of Chemical, Biological, and Materials Engineering, University of South Florida, Tampa, FL, USA
| | - Ramon Gonzalez
- Department of Chemical, Biological, and Materials Engineering, University of South Florida, Tampa, FL, USA
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12
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Deal C, De Wannemaeker L, De Mey M. Towards a rational approach to promoter engineering: understanding the complexity of transcription initiation in prokaryotes. FEMS Microbiol Rev 2024; 48:fuae004. [PMID: 38383636 PMCID: PMC10911233 DOI: 10.1093/femsre/fuae004] [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: 08/31/2023] [Revised: 01/29/2024] [Accepted: 02/20/2024] [Indexed: 02/23/2024] Open
Abstract
Promoter sequences are important genetic control elements. Through their interaction with RNA polymerase they determine transcription strength and specificity, thereby regulating the first step in gene expression. Consequently, they can be targeted as elements to control predictability and tuneability of a genetic circuit, which is essential in applications such as the development of robust microbial cell factories. This review considers the promoter elements implicated in the three stages of transcription initiation, detailing the complex interplay of sequence-specific interactions that are involved, and highlighting that DNA sequence features beyond the core promoter elements work in a combinatorial manner to determine transcriptional strength. In particular, we emphasize that, aside from promoter recognition, transcription initiation is also defined by the kinetics of open complex formation and promoter escape, which are also known to be highly sequence specific. Significantly, we focus on how insights into these interactions can be manipulated to lay the foundation for a more rational approach to promoter engineering.
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Affiliation(s)
- Cara Deal
- Centre for Synthetic Biology, Ghent University. Coupure Links 653, BE-9000 Ghent, Belgium
| | - Lien De Wannemaeker
- Centre for Synthetic Biology, Ghent University. Coupure Links 653, BE-9000 Ghent, Belgium
| | - Marjan De Mey
- Centre for Synthetic Biology, Ghent University. Coupure Links 653, BE-9000 Ghent, Belgium
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13
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Cautereels C, Smets J, Bircham P, De Ruysscher D, Zimmermann A, De Rijk P, Steensels J, Gorkovskiy A, Masschelein J, Verstrepen KJ. Combinatorial optimization of gene expression through recombinase-mediated promoter and terminator shuffling in yeast. Nat Commun 2024; 15:1112. [PMID: 38326309 PMCID: PMC10850122 DOI: 10.1038/s41467-024-44997-7] [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: 08/10/2023] [Accepted: 01/12/2024] [Indexed: 02/09/2024] Open
Abstract
Microbes are increasingly employed as cell factories to produce biomolecules. This often involves the expression of complex heterologous biosynthesis pathways in host strains. Achieving maximal product yields and avoiding build-up of (toxic) intermediates requires balanced expression of every pathway gene. However, despite progress in metabolic modeling, the optimization of gene expression still heavily relies on trial-and-error. Here, we report an approach for in vivo, multiplexed Gene Expression Modification by LoxPsym-Cre Recombination (GEMbLeR). GEMbLeR exploits orthogonal LoxPsym sites to independently shuffle promoter and terminator modules at distinct genomic loci. This approach facilitates creation of large strain libraries, in which expression of every pathway gene ranges over 120-fold and each strain harbors a unique expression profile. When applied to the biosynthetic pathway of astaxanthin, an industrially relevant antioxidant, a single round of GEMbLeR improved pathway flux and doubled production titers. Together, this shows that GEMbLeR allows rapid and efficient gene expression optimization in heterologous biosynthetic pathways, offering possibilities for enhancing the performance of microbial cell factories.
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Affiliation(s)
- Charlotte Cautereels
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Jolien Smets
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Peter Bircham
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Dries De Ruysscher
- Molecular Biotechnology of Plants and Micro-organisms, Department of Biology, KU Leuven, Kasteelpark Arenberg 31, box 2438, Leuven, 3001, Belgium
- Laboratory for Biomolecular Discovery & Engineering, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
| | - Anna Zimmermann
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Peter De Rijk
- Neuromics Support Facility, VIB Center for Molecular Neurology, VIB, Antwerp, 2610, Belgium
- Neuromics Support Facility, Department of Biomedical Sciences, University of Antwerp, Antwerp, 2610, Belgium
| | - Jan Steensels
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Anton Gorkovskiy
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium
| | - Joleen Masschelein
- Molecular Biotechnology of Plants and Micro-organisms, Department of Biology, KU Leuven, Kasteelpark Arenberg 31, box 2438, Leuven, 3001, Belgium
- Laboratory for Biomolecular Discovery & Engineering, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium
| | - Kevin J Verstrepen
- VIB Laboratory for Systems Biology, VIB-KU Leuven Center for Microbiology, Leuven, 3001, Belgium.
- Laboratory of Genetics and Genomics, Center of Microbial and Plant Genetics, Department M2S, KU Leuven, Gaston Geenslaan 1, Leuven, 3001, Belgium.
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14
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Stone A, Youssef A, Rijal S, Zhang R, Tian XJ. Context-dependent redesign of robust synthetic gene circuits. Trends Biotechnol 2024:S0167-7799(24)00003-9. [PMID: 38320912 DOI: 10.1016/j.tibtech.2024.01.003] [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: 11/02/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 02/08/2024]
Abstract
Cells provide dynamic platforms for executing exogenous genetic programs in synthetic biology, resulting in highly context-dependent circuit performance. Recent years have seen an increasing interest in understanding the intricacies of circuit-host relationships, their influence on the synthetic bioengineering workflow, and in devising strategies to alleviate undesired effects. We provide an overview of how emerging circuit-host interactions, such as growth feedback and resource competition, impact both deterministic and stochastic circuit behaviors. We also emphasize control strategies for mitigating these unwanted effects. This review summarizes the latest advances and the current state of host-aware and resource-aware design of synthetic gene circuits.
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Affiliation(s)
- Austin Stone
- School of Biological and Health System Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Abdelrahaman Youssef
- School of Biological and Health System Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Sadikshya Rijal
- School of Biological and Health System Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Rong Zhang
- School of Biological and Health System Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Xiao-Jun Tian
- School of Biological and Health System Engineering, Arizona State University, Tempe, AZ 85281, USA.
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15
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Andreani V, South EJ, Dunlop MJ. Generating information-dense promoter sequences with optimal string packing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565124. [PMID: 37961203 PMCID: PMC10635063 DOI: 10.1101/2023.11.01.565124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs libraries of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts. Author Summary The way protein binding sites are arranged on DNA can control the regulation and transcription of downstream genes. Areas with a high concentration of binding sites can enable complex interplay between transcription factors, a feature that is exploited by natural promoters. However, designing synthetic promoters that contain dense arrangements of binding sites is a challenge. The task involves overlapping many binding sites, each typically about 10 nucleotides long, within a constrained sequence area, which becomes increasingly difficult as sequence length decreases, and binding site variety increases. We introduce an approach to design nucleotide sequences with optimally packed protein binding sites, which we call the nucleotide String Packing Problem (SPP). We show that the SPP can be solved efficiently using integer linear programming to identify the densest arrangements of binding sites for a specified sequence length. We show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The presented approach enables the rapid design and study of nucleotide sequences with complex, dense binding site architectures.
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16
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de Lorenzo V. The principle of uncertainty in biology: Will machine learning/artificial intelligence lead to the end of mechanistic studies? PLoS Biol 2024; 22:e3002495. [PMID: 38329935 PMCID: PMC10852237 DOI: 10.1371/journal.pbio.3002495] [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] [Indexed: 02/10/2024] Open
Abstract
Molecular Biology has long tried to discover mechanisms, considering that unless we understand the principles, we cannot develop applications. Now machine learning and artificial intelligence enable direct leaps to application without understanding the principles. Will this herald a decline in mechanistic studies?
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Affiliation(s)
- Victor de Lorenzo
- Systems Biology Department, Centro Nacional de Biotecnología, CSIC, C/ Darwin, Madrid, Spain
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17
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Greiss F, Lardon N, Schütz L, Barak Y, Daube SS, Weinhold E, Noireaux V, Bar-Ziv R. A genetic circuit on a single DNA molecule as an autonomous dissipative nanodevice. Nat Commun 2024; 15:883. [PMID: 38287055 PMCID: PMC10825189 DOI: 10.1038/s41467-024-45186-2] [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: 11/03/2023] [Accepted: 01/16/2024] [Indexed: 01/31/2024] Open
Abstract
Realizing genetic circuits on single DNA molecules as self-encoded dissipative nanodevices is a major step toward miniaturization of autonomous biological systems. A circuit operating on a single DNA implies that genetically encoded proteins localize during coupled transcription-translation to DNA, but a single-molecule measurement demonstrating this has remained a challenge. Here, we use a genetically encoded fluorescent reporter system with improved temporal resolution and observe the synthesis of individual proteins tethered to a DNA molecule by transient complexes of RNA polymerase, messenger RNA, and ribosome. Against expectations in dilute cell-free conditions where equilibrium considerations favor dispersion, these nascent proteins linger long enough to regulate cascaded reactions on the same DNA. We rationally design a pulsatile genetic circuit by encoding an activator and repressor in feedback on the same DNA molecule. Driven by the local synthesis of only several proteins per hour and gene, the circuit dynamics exhibit enhanced variability between individual DNA molecules, and fluctuations with a broad power spectrum. Our results demonstrate that co-expressional localization, as a nonequilibrium process, facilitates single-DNA genetic circuits as dissipative nanodevices, with implications for nanobiotechnology applications and artificial cell design.
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Affiliation(s)
- Ferdinand Greiss
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
| | - Nicolas Lardon
- Department of Chemical Biology, Max Planck Institute for Medical Research, 69120, Heidelberg, Germany
| | - Leonie Schütz
- Institute of Organic Chemistry, RWTH Aachen University, 52056, Aachen, Germany
| | - Yoav Barak
- Department of Chemical Research Support, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Shirley S Daube
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Elmar Weinhold
- Institute of Organic Chemistry, RWTH Aachen University, 52056, Aachen, Germany
| | - Vincent Noireaux
- School of Physics and Astronomy, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Roy Bar-Ziv
- Department of Chemical and Biological Physics, Weizmann Institute of Science, Rehovot, 7610001, Israel.
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18
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Rondthaler S, Sarker B, Howitz N, Shah I, Andrews LB. Toolbox of Characterized Genetic Parts for Staphylococcus aureus. ACS Synth Biol 2024; 13:103-118. [PMID: 38064657 PMCID: PMC10805105 DOI: 10.1021/acssynbio.3c00325] [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: 05/24/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 01/23/2024]
Abstract
Staphylococcus aureus is an important clinical bacterium prevalent in human-associated microbiomes and the cause of many diseases. However, S. aureus has been intractable to synthetic biology approaches due to limited characterized genetic parts for this nonmodel Gram-positive bacterium. Moreover, genetic manipulation of S. aureus has relied on cumbersome and inefficient cloning strategies. Here, we report the first standardized genetic parts toolbox for S. aureus, which includes characterized promoters, ribosome binding sites, terminators, and plasmid replicons from a variety of bacteria for precise control of gene expression. We established a standard relative expression unit (REU) for S. aureus using a plasmid reference and characterized genetic parts in standardized REUs using S. aureus ATCC 12600. We constructed promoter and terminator part plasmids that are compatible with an efficient Type IIS DNA assembly strategy to effectively build multipart DNA constructs. A library of 24 constitutive promoters was built and characterized in S. aureus, which showed a 380-fold activity range. This promoter library was also assayed in Bacillus subtilis (122-fold activity range) to demonstrate the transferability of the constitutive promoters between these Gram-positive bacteria. By applying an iterative design-build-test-learn cycle, we demonstrated the use of our toolbox for the rational design and engineering of a tetracycline sensor in S. aureus using the PXyl-TetO aTc-inducible promoter that achieved 25.8-fold induction. This toolbox greatly expands the growing number of genetic parts for Gram-positive bacteria and will allow researchers to leverage synthetic biology approaches to study and engineer cellular processes in S. aureus.
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Affiliation(s)
- Stephen
N. Rondthaler
- 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
| | - Nathaniel Howitz
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Ishita Shah
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Lauren B. Andrews
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
- Molecular
and Cellular Biology Graduate Program, University
of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Biotechnology
Training Program, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
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19
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Steinkühler J, Peruzzi JA, Krüger A, Villaseñor CG, Jacobs ML, Jewett MC, Kamat NP. Improving Cell-Free Expression of Model Membrane Proteins by Tuning Ribosome Cotranslational Membrane Association and Nascent Chain Aggregation. ACS Synth Biol 2024; 13:129-140. [PMID: 38150067 DOI: 10.1021/acssynbio.3c00357] [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: 12/28/2023]
Abstract
Cell-free gene expression (CFE) systems are powerful tools for transcribing and translating genes outside of a living cell. Synthesis of membrane proteins is of particular interest, but their yield in CFE is substantially lower than that for soluble proteins. In this paper, we study the CFE of membrane proteins and develop a quantitative kinetic model. We identify that ribosome stalling during the translation of membrane proteins is a strong predictor of membrane protein synthesis due to aggregation between the ribosome nascent chains. Synthesis can be improved by the addition of lipid membranes, which incorporate protein nascent chains and, therefore, kinetically compete with aggregation. We show that the balance between peptide-membrane association and peptide aggregation rates determines the yield of the synthesized membrane protein. We define a membrane protein expression score that can be used to rationalize the engineering of lipid composition and the N-terminal domain of a native and computationally designed membrane proteins produced through CFE.
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Affiliation(s)
- Jan Steinkühler
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Bio-Inspired Computation, Kiel University, Kaiserstraße 2, 24143 Kiel, Germany
- Kiel Nano, Surface and Interface Science KiNSIS, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
| | - Justin A Peruzzi
- Department of Chemical and Biological Engineering, Center for Synthetic Biology, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
| | - Antje Krüger
- Department of Chemical and Biological Engineering, Center for Synthetic Biology, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
| | - Citlayi G Villaseñor
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Miranda L Jacobs
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Michael C Jewett
- Department of Chemical and Biological Engineering, Center for Synthetic Biology, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
- Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Department of Bioengineering, Stanford University, Stanford, California 94305, United States
| | - Neha P Kamat
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, United States
- Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
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20
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Zeng M, Sarker B, Howitz N, Shah I, Andrews LB. Synthetic Homoserine Lactone Sensors for Gram-Positive Bacillus subtilis Using LuxR-Type Regulators. ACS Synth Biol 2024; 13:282-299. [PMID: 38079538 PMCID: PMC10805106 DOI: 10.1021/acssynbio.3c00504] [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: 08/18/2023] [Revised: 10/11/2023] [Accepted: 10/18/2023] [Indexed: 01/23/2024]
Abstract
A universal biochemical signal for bacterial cell-cell communication could facilitate programming dynamic responses in diverse bacterial consortia. However, the classical quorum sensing paradigm is that Gram-negative and Gram-positive bacteria generally communicate via homoserine lactones (HSLs) or oligopeptide molecular signals, respectively, to elicit population responses. Here, we create synthetic HSL sensors for Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and CinR) and synthetic promoters. Promoters were combinatorially designed from different sequence elements (-35, -16, -10, and transcriptional start regions). We quantified the effects of these combinatorial promoters on sensor activity and determined how regulator expression affects its activation, achieving up to 293-fold activation. Using the statistical design of experiments, we identified significant effects of promoter regions and pairwise interactions on sensor activity, which helped to understand the sequence-function relationships for synthetic promoter design. We present the first known set of functional HSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p-coumaroyl-HSL, 3-oxohexanoyl-HSL, n-butyryl-HSL, and n-(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors for a Gram-positive bacterium can pave the way for designable interspecies communication within microbial consortia.
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Affiliation(s)
- Min Zeng
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Biprodev Sarker
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Nathaniel Howitz
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Ishita Shah
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Lauren B. Andrews
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
- Molecular
and Cellular Biology Graduate Program, University
of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Biotechnology
Training Program, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
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21
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Moon S, Saboe A, Smanski MJ. Using design of experiments to guide genetic optimization of engineered metabolic pathways. J Ind Microbiol Biotechnol 2024; 51:kuae010. [PMID: 38490746 PMCID: PMC10981448 DOI: 10.1093/jimb/kuae010] [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: 12/12/2023] [Accepted: 03/14/2024] [Indexed: 03/17/2024]
Abstract
Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges. ONE-SENTENCE SUMMARY This is a review of literature related to applying Design of Experiments for genetic optimization.
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Affiliation(s)
- Seonyun Moon
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, St Paul, MN 55108, USA
- Biotechnology Institute, University of Minnesota, St Paul, MN 55108, USA
| | - Anna Saboe
- Biotechnology Institute, University of Minnesota, St Paul, MN 55108, USA
| | - Michael J Smanski
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, St Paul, MN 55108, USA
- Biotechnology Institute, University of Minnesota, St Paul, MN 55108, USA
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22
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Trofimova E, Logel DY, Jaschke PR. An Improved Method for Eliminating or Creating Intragenic Bacterial Promoters. Methods Mol Biol 2024; 2760:199-207. [PMID: 38468090 DOI: 10.1007/978-1-0716-3658-9_12] [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: 03/13/2024]
Abstract
Recent advances in genomic refactoring have been hindered by the ever-present complication of internal or cryptic transcriptional regulation. Typical approaches to these features have been to randomize or perform mass alterations to the gene sequences thought to contain the regulatory motifs; however, this approach can cause problems by altering translational speeds, introducing long distance DNA-DNA interaction effects, and inducing RNA toxicity. Previously, we developed a rational design approach named COdon Restrained Promoter SilEncing (CORPSE) which takes externally identified promoter sequences and uses position-specific scoring matrices as proxy promoter strengths to make minimal changes to promoter sequences to disable their activity. Additionally, through inverting our system we were also able to modify weak internal promoters to increase their activity. In this chapter, we augment our previous process with the biophysical model Promoter Calculator v1.0 developed by LaFleur et al. to combine promoter identification and activity prediction, with our algorithm to silently modify promoter sequences, to provide more robust promoter elimination and creation.
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Affiliation(s)
- Ellina Trofimova
- School of Natural Sciences, Macquarie University, Sydney, NSW, Australia
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, NSW, Australia
| | - Dominic Y Logel
- School of Natural Sciences, Macquarie University, Sydney, NSW, Australia
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, NSW, Australia
| | - Paul R Jaschke
- School of Natural Sciences, Macquarie University, Sydney, NSW, Australia.
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, NSW, Australia.
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23
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Boob AG, Chen J, Zhao H. Enabling pathway design by multiplex experimentation and machine learning. Metab Eng 2024; 81:70-87. [PMID: 38040110 DOI: 10.1016/j.ymben.2023.11.006] [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: 09/14/2023] [Revised: 11/01/2023] [Accepted: 11/25/2023] [Indexed: 12/03/2023]
Abstract
The remarkable metabolic diversity observed in nature has provided a foundation for sustainable production of a wide array of valuable molecules. However, transferring the biosynthetic pathway to the desired host often runs into inherent failures that arise from intermediate accumulation and reduced flux resulting from competing pathways within the host cell. Moreover, the conventional trial and error methods utilized in pathway optimization struggle to fully grasp the intricacies of installed pathways, leading to time-consuming and labor-intensive experiments, ultimately resulting in suboptimal yields. Considering these obstacles, there is a pressing need to explore the enzyme expression landscape and identify the optimal pathway configuration for enhanced production of molecules. This review delves into recent advancements in pathway engineering, with a focus on multiplex experimentation and machine learning techniques. These approaches play a pivotal role in overcoming the limitations of traditional methods, enabling exploration of a broader design space and increasing the likelihood of discovering optimal pathway configurations for enhanced production of molecules. We discuss several tools and strategies for pathway design, construction, and optimization for sustainable and cost-effective microbial production of molecules ranging from bulk to fine chemicals. We also highlight major successes in academia and industry through compelling case studies.
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Affiliation(s)
- Aashutosh Girish Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Junyu Chen
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
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24
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Cornuault JK. CRISPRpi: Inducing and Curing Prophage Using the CRISPR Interference. Methods Mol Biol 2024; 2793:257-271. [PMID: 38526735 DOI: 10.1007/978-1-0716-3798-2_16] [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: 03/27/2024]
Abstract
We present here a CRISPR-interference-based protocol to trigger prophage induction, even for non-inducible prophages. This method can also be used to cure the prophage from the bacterial host. The method is based on silencing of the phage's repressor transcription, thanks to CRISPR interference. Plasmid electroporation is used to bring the CRISPRi system into the bacteria, specifically on a plasmid carrying spacers targeting the prophage repressor. This method enables prophage induction and curation in a week or two with a high efficiency.
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Affiliation(s)
- Jeffrey K Cornuault
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
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25
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Peng P, Yang J, DiSpirito AA, Semrau JD. MmoD regulates soluble methane monooxygenase and methanobactin production in Methylosinus trichosporium OB3b. Appl Environ Microbiol 2023; 89:e0160123. [PMID: 38014956 PMCID: PMC10734442 DOI: 10.1128/aem.01601-23] [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: 09/12/2023] [Accepted: 10/10/2023] [Indexed: 11/29/2023] Open
Abstract
IMPORTANCE Aerobic methanotrophs play a critical role in the global carbon cycle, particularly in controlling net emissions of methane to the atmosphere. As methane is a much more potent greenhouse gas than carbon dioxide, there is increasing interest in utilizing these microbes to mitigate future climate change by increasing their ability to consume methane. Any such efforts, however, require a detailed understanding of how to manipulate methanotrophic activity. Herein, we show that methanotrophic activity is strongly controlled by MmoD, i.e., MmoD regulates methanotrophy through the post-transcriptional regulation of the soluble methane monooxygenase and controls the ability of methanotrophs to collect copper. Such data are likely to prove quite useful in future strategies to enhance the use of methanotrophs to not only reduce methane emissions but also remove methane from the atmosphere.
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Affiliation(s)
- Peng Peng
- Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Junwon Yang
- Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
| | - Alan A. DiSpirito
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa, USA
| | - Jeremy D. Semrau
- Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA
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26
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Wang X, Xu K, Tan Y, Yu S, Zhao X, Zhou J. Deep Learning-Assisted Design of Novel Promoters in Escherichia coli. ADVANCED GENETICS (HOBOKEN, N.J.) 2023; 4:2300184. [PMID: 38099247 PMCID: PMC10716054 DOI: 10.1002/ggn2.202300184] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/09/2023] [Indexed: 12/17/2023]
Abstract
Deep learning (DL) approaches have the ability to accurately recognize promoter regions and predict their strength. Here, the potential for controllably designing active Escherichia coli promoter is explored by combining multiple deep learning models. First, "DRSAdesign," which relies on a diffusion model to generate different types of novel promoters is created, followed by predicting whether they are real or fake and strength. Experimental validation showed that 45 out of 50 generated promoters are active with high diversity, but most promoters have relatively low activity. Next, "Ndesign," which relies on generating random sequences carrying functional -35 and -10 motifs of the sigma70 promoter is introduced, and their strength is predicted using the designed DL model. The DL model is trained and validated using 200 and 50 generated promoters, and displays Pearson correlation coefficients of 0.49 and 0.43, respectively. Taking advantage of the DL models developed in this work, possible 6-mers are predicted as key functional motifs of the sigma70 promoter, suggesting that promoter recognition and strength prediction mainly rely on the accommodation of functional motifs. This work provides DL tools to design promoters and assess their functions, paving the way for DL-assisted metabolic engineering.
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Affiliation(s)
- Xinglong Wang
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
| | - Kangjie Xu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
| | - Yameng Tan
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
| | - Shangyang Yu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
| | - Xinyi Zhao
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
| | - Jingwen Zhou
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Jiangsu Province Engineering Research Center of Food Synthetic BiotechnologyJiangnan UniversityWuxi214122China
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27
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Fu ZH, He SZ, Wu Y, Zhao GR. Design and deep learning of synthetic B-cell-specific promoters. Nucleic Acids Res 2023; 51:11967-11979. [PMID: 37889080 PMCID: PMC10681721 DOI: 10.1093/nar/gkad930] [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: 05/27/2023] [Revised: 09/20/2023] [Accepted: 10/10/2023] [Indexed: 10/28/2023] Open
Abstract
Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synthetic elements is important for B-cell engineering. Here, we designed and pooled synthesized 23 640 B-cell-specific promoters that exhibit larger sequence space, B-cell-specific expression, and enable diverse transcriptional patterns in B-cells. By MPRA (Massively parallel reporter assays), we deciphered the sequence features that regulate promoter transcriptional, including motifs and motif syntax (their combination and distance). Finally, we built and trained a deep learning model capable of predicting the transcriptional strength of the immunoglobulin V gene promoter directly from sequence. Prediction of thousands of promoter variants identified in the global human population shows that polymorphisms in promoters influence the transcription of immunoglobulin V genes, which may contribute to individual differences in adaptive humoral immune responses. Our work helps to decipher the transcription mechanism in immunoglobulin genes and offers thousands of non-similar promoters for B-cell engineering.
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Affiliation(s)
- Zong-Heng Fu
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
| | - Si-Zhe He
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
| | - Yi Wu
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
| | - Guang-Rong Zhao
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
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28
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Hyun JC, Monk JM, Szubin R, Hefner Y, Palsson BO. Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species. Nat Commun 2023; 14:7690. [PMID: 38001096 PMCID: PMC10673929 DOI: 10.1038/s41467-023-43549-9] [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: 12/11/2022] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. When applied to 12 species, 27,155 genomes, and 69 drugs, we 1) find AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrate that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identify 142 AMR gene candidates. Validation of two candidates in E. coli BW25113 reveals cases of conditional resistance: ΔcycA confers ciprofloxacin resistance in minimal media with D-serine, and frdD V111D confers ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.
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Affiliation(s)
- Jason C Hyun
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Jonathan M Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Richard Szubin
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Ying Hefner
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Bernhard O Palsson
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, 2800, Kongens, Lyngby, Denmark.
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29
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Parthiban S, Vijeesh T, Gayathri T, Shanmugaraj B, Sharma A, Sathishkumar R. Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals. FRONTIERS IN PLANT SCIENCE 2023; 14:1252166. [PMID: 38034587 PMCID: PMC10684705 DOI: 10.3389/fpls.2023.1252166] [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: 07/03/2023] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.
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Affiliation(s)
- Subramanian Parthiban
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thandarvalli Vijeesh
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Thashanamoorthi Gayathri
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Balamurugan Shanmugaraj
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, Mexico
| | - Ramalingam Sathishkumar
- Plant Genetic Engineering Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, India
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30
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Yang X, Rocks JW, Jiang K, Walters AJ, Rai K, Liu J, Nguyen J, Olson SD, Mehta P, Collins JJ, Daringer NM, Bashor CJ. Engineering synthetic phosphorylation signaling networks in human cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557100. [PMID: 37745327 PMCID: PMC10515791 DOI: 10.1101/2023.09.11.557100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Protein phosphorylation signaling networks play a central role in how cells sense and respond to their environment. Here, we describe the engineering of artificial phosphorylation networks in which "push-pull" motifs-reversible enzymatic phosphorylation cycles consisting of opposing kinase and phosphatase activities-are assembled from modular protein domain parts and then wired together to create synthetic phosphorylation circuits in human cells. We demonstrate that the composability of our design scheme enables model-guided tuning of circuit function and the ability to make diverse network connections; synthetic phosphorylation circuits can be coupled to upstream cell surface receptors to enable fast-timescale sensing of extracellular ligands, while downstream connections can regulate gene expression. We leverage these capabilities to engineer cell-based cytokine controllers that dynamically sense and suppress activated T cells. Our work introduces a generalizable approach for designing and building phosphorylation signaling circuits that enable user-defined sense-and-respond function for diverse biosensing and therapeutic applications.
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Affiliation(s)
- Xiaoyu Yang
- Department of Bioengineering, Rice University; Houston, TX 77030, USA
- Graduate Program in Systems, Synthetic and Physical Biology, Rice University; Houston, TX 77030, USA
| | - Jason W. Rocks
- Department of Physics, Boston University; Boston, MA 02215, USA
| | - Kaiyi Jiang
- Department of Bioengineering, Rice University; Houston, TX 77030, USA
| | - Andrew J. Walters
- Department of Bioengineering, Rice University; Houston, TX 77030, USA
- Graduate Program in Bioengineering, Rice University; Houston, TX 77030, USA
- Department of Pediatric Surgery, McGovern Medical School, University of Texas Health Science Center at Houston; Houston, TX 77030, USA
| | - Kshitij Rai
- Graduate Program in Systems, Synthetic and Physical Biology, Rice University; Houston, TX 77030, USA
| | - Jing Liu
- Department of Bioengineering, Rice University; Houston, TX 77030, USA
| | - Jason Nguyen
- Department of Bioengineering, Rice University; Houston, TX 77030, USA
| | - Scott D. Olson
- Department of Pediatric Surgery, McGovern Medical School, University of Texas Health Science Center at Houston; Houston, TX 77030, USA
| | - Pankaj Mehta
- Department of Physics, Boston University; Boston, MA 02215, USA
- Biological Design Center, Boston University; Boston, MA 02215, USA
- Faculty of Computing and Data Science, Boston University; Boston, MA 02215, USA
| | - James J. Collins
- Institute for Medical Engineering and Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology; Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard; Cambridge, MA 02142, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University; Boston, MA 02115, USA
| | - Nichole M Daringer
- Department of Biomedical Engineering, Rowan University; Glassboro, NJ 08028, USA
| | - Caleb J. Bashor
- Department of Bioengineering, Rice University; Houston, TX 77030, USA
- Department of Biosciences, Rice University; Houston, TX 77030, USA
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31
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Diebold PJ, Rhee MW, Shi Q, Trung NV, Umrani F, Ahmed S, Kulkarni V, Deshpande P, Alexander M, Thi Hoa N, Christakis NA, Iqbal NT, Ali SA, Mathad JS, Brito IL. Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide. Nat Commun 2023; 14:7366. [PMID: 37963868 PMCID: PMC10645880 DOI: 10.1038/s41467-023-42998-6] [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] [Received: 08/10/2023] [Accepted: 10/27/2023] [Indexed: 11/16/2023] Open
Abstract
The acquisition of antimicrobial resistance (AR) genes has rendered important pathogens nearly or fully unresponsive to antibiotics. It has been suggested that pathogens acquire AR traits from the gut microbiota, which collectively serve as a global reservoir for AR genes conferring resistance to all classes of antibiotics. However, only a subset of AR genes confers resistance to clinically relevant antibiotics, and, although these AR gene profiles are well-characterized for common pathogens, less is known about their taxonomic associations and transfer potential within diverse members of the gut microbiota. We examined a collection of 14,850 human metagenomes and 1666 environmental metagenomes from 33 countries, in addition to nearly 600,000 isolate genomes, to gain insight into the global prevalence and taxonomic range of clinically relevant AR genes. We find that several of the most concerning AR genes, such as those encoding the cephalosporinase CTX-M and carbapenemases KPC, IMP, NDM, and VIM, remain taxonomically restricted to Proteobacteria. Even cfiA, the most common carbapenemase gene within the human gut microbiome, remains tightly restricted to Bacteroides, despite being found on a mobilizable plasmid. We confirmed these findings in gut microbiome samples from India, Honduras, Pakistan, and Vietnam, using a high-sensitivity single-cell fusion PCR approach. Focusing on a set of genes encoding carbapenemases and cephalosporinases, thus far restricted to Bacteroides species, we find that few mutations are required for efficacy in a different phylum, raising the question of why these genes have not spread more widely. Overall, these data suggest that globally prevalent, clinically relevant AR genes have not yet established themselves across diverse commensal gut microbiota.
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Affiliation(s)
- Peter J Diebold
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Matthew W Rhee
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Qiaojuan Shi
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Nguyen Vinh Trung
- Oxford University Clinical Research Unit (OUCRU) in Ho Chi Minh City, Ho Chi Minh city, Viet Nam
| | | | | | - Vandana Kulkarni
- Johns Hopkins University Clinical Trials Unit, Byramjee Jeejeebhoy Government Medical College, Pune, Maharashtra, India
| | - Prasad Deshpande
- Johns Hopkins University Clinical Trials Unit, Byramjee Jeejeebhoy Government Medical College, Pune, Maharashtra, India
| | - Mallika Alexander
- Johns Hopkins University Clinical Trials Unit, Byramjee Jeejeebhoy Government Medical College, Pune, Maharashtra, India
| | - Ngo Thi Hoa
- Oxford University Clinical Research Unit (OUCRU) in Ho Chi Minh City, Ho Chi Minh city, Viet Nam
- Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Microbiology Department and Center for Tropical Medicine Research, Ngoc Thach University of Medicine, Ho Chi Minh city, Vietnam
| | | | | | | | | | - Ilana L Brito
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
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32
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Lux MW, Strychalski EA, Vora GJ. Advancing reproducibility can ease the 'hard truths' of synthetic biology. Synth Biol (Oxf) 2023; 8:ysad014. [PMID: 38022744 PMCID: PMC10640854 DOI: 10.1093/synbio/ysad014] [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: 03/07/2023] [Revised: 07/26/2023] [Accepted: 10/04/2023] [Indexed: 12/01/2023] Open
Abstract
Reproducibility has been identified as an outstanding challenge in science, and the field of synthetic biology is no exception. Meeting this challenge is critical to allow the transformative technological capabilities emerging from this field to reach their full potential to benefit the society. We discuss the current state of reproducibility in synthetic biology and how improvements can address some of the central shortcomings in the field. We argue that the successful adoption of reproducibility as a routine aspect of research and development requires commitment spanning researchers and relevant institutions via education, incentivization and investment in related infrastructure. The urgency of this topic pervades synthetic biology as it strives to advance fundamental insights and unlock new capabilities for safe, secure and scalable applications of biotechnology. Graphical Abstract.
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Affiliation(s)
- Matthew W Lux
- Research & Operations Directorate, U.S. Army Combat Capabilities Development Command Chemical Biological Center, APG, MD 21010, USA
| | - Elizabeth A Strychalski
- Cellular Engineering Group, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Gary J Vora
- Center for Bio/Molecular Science & Engineering, U.S. Naval Research Laboratory, Washington, DC 20375, USA
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33
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Alba Burbano D, Cardiff RAL, Tickman BI, Kiattisewee C, Maranas CJ, Zalatan JG, Carothers JM. Engineering activatable promoters for scalable and multi-input CRISPRa/i circuits. Proc Natl Acad Sci U S A 2023; 120:e2220358120. [PMID: 37463216 PMCID: PMC10374173 DOI: 10.1073/pnas.2220358120] [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: 11/30/2022] [Accepted: 06/13/2023] [Indexed: 07/20/2023] Open
Abstract
Dynamic, multi-input gene regulatory networks (GRNs) are ubiquitous in nature. Multilayer CRISPR-based genetic circuits hold great promise for building GRNs akin to those found in naturally occurring biological systems. We develop an approach for creating high-performing activatable promoters that can be assembled into deep, wide, and multi-input CRISPR-activation and -interference (CRISPRa/i) GRNs. By integrating sequence-based design and in vivo screening, we engineer activatable promoters that achieve up to 1,000-fold dynamic range in an Escherichia coli-based cell-free system. These components enable CRISPRa GRNs that are six layers deep and four branches wide. We show the generalizability of the promoter engineering workflow by improving the dynamic range of the light-dependent EL222 optogenetic system from 6-fold to 34-fold. Additionally, high dynamic range promoters enable CRISPRa systems mediated by small molecules and protein-protein interactions. We apply these tools to build input-responsive CRISPRa/i GRNs, including feedback loops, logic gates, multilayer cascades, and dynamic pulse modulators. Our work provides a generalizable approach for the design of high dynamic range activatable promoters and enables classes of gene regulatory functions in cell-free systems.
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Affiliation(s)
- Diego Alba Burbano
- Department of Chemical Engineering, University of Washington, Seattle, WA98195
- Center for Synthetic Biology, University of Washington, Seattle, WA98195
| | - Ryan A. L. Cardiff
- Center for Synthetic Biology, University of Washington, Seattle, WA98195
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA98195
| | - Benjamin I. Tickman
- Center for Synthetic Biology, University of Washington, Seattle, WA98195
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA98195
| | - Cholpisit Kiattisewee
- Center for Synthetic Biology, University of Washington, Seattle, WA98195
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA98195
| | - Cassandra J. Maranas
- Center for Synthetic Biology, University of Washington, Seattle, WA98195
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA98195
| | - Jesse G. Zalatan
- Center for Synthetic Biology, University of Washington, Seattle, WA98195
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA98195
- Department of Chemistry, University of Washington, Seattle, WA98195
| | - James M. Carothers
- Department of Chemical Engineering, University of Washington, Seattle, WA98195
- Center for Synthetic Biology, University of Washington, Seattle, WA98195
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA98195
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34
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Huttanus HM, Triola EKH, Velasquez-Guzman JC, Shin SM, Granja-Travez RS, Singh A, Dale T, Jha RK. Targeted mutagenesis and high-throughput screening of diversified gene and promoter libraries for isolating gain-of-function mutations. Front Bioeng Biotechnol 2023; 11:1202388. [PMID: 37545889 PMCID: PMC10400447 DOI: 10.3389/fbioe.2023.1202388] [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: 04/08/2023] [Accepted: 06/25/2023] [Indexed: 08/08/2023] Open
Abstract
Targeted mutagenesis of a promoter or gene is essential for attaining new functions in microbial and protein engineering efforts. In the burgeoning field of synthetic biology, heterologous genes are expressed in new host organisms. Similarly, natural or designed proteins are mutagenized at targeted positions and screened for gain-of-function mutations. Here, we describe methods to attain complete randomization or controlled mutations in promoters or genes. Combinatorial libraries of one hundred thousands to tens of millions of variants can be created using commercially synthesized oligonucleotides, simply by performing two rounds of polymerase chain reactions. With a suitably engineered reporter in a whole cell, these libraries can be screened rapidly by performing fluorescence-activated cell sorting (FACS). Within a few rounds of positive and negative sorting based on the response from the reporter, the library can rapidly converge to a few optimal or extremely rare variants with desired phenotypes. Library construction, transformation and sequence verification takes 6-9 days and requires only basic molecular biology lab experience. Screening the library by FACS takes 3-5 days and requires training for the specific cytometer used. Further steps after sorting, including colony picking, sequencing, verification, and characterization of individual clones may take longer, depending on number of clones and required experiments.
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Affiliation(s)
- Herbert M. Huttanus
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- Agile BioFoundry, Emeryville, CA, United States
| | - Ellin-Kristina H. Triola
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- Agile BioFoundry, Emeryville, CA, United States
| | - Jeanette C. Velasquez-Guzman
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- Agile BioFoundry, Emeryville, CA, United States
| | - Sang-Min Shin
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- BOTTLE Consortium, Golden, CO, United States
| | - Rommel S. Granja-Travez
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- BOTTLE Consortium, Golden, CO, United States
| | - Anmoldeep Singh
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
| | - Taraka Dale
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- Agile BioFoundry, Emeryville, CA, United States
- BOTTLE Consortium, Golden, CO, United States
| | - Ramesh K. Jha
- Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States
- Agile BioFoundry, Emeryville, CA, United States
- BOTTLE Consortium, Golden, CO, United States
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35
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Chen JP, Gong JS, Su C, Li H, Xu ZH, Shi JS. Improving the soluble expression of difficult-to-express proteins in prokaryotic expression system via protein engineering and synthetic biology strategies. Metab Eng 2023; 78:99-114. [PMID: 37244368 DOI: 10.1016/j.ymben.2023.05.007] [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: 04/09/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023]
Abstract
Solubility and folding stability are key concerns for difficult-to-express proteins (DEPs) restricted by amino acid sequences and superarchitecture, resolved by the precise distribution of amino acids and molecular interactions as well as the assistance of the expression system. Therefore, an increasing number of tools are available to achieve efficient expression of DEPs, including directed evolution, solubilization partners, chaperones, and affluent expression hosts, among others. Furthermore, genome editing tools, such as transposons and CRISPR Cas9/dCas9, have been developed and expanded to construct engineered expression hosts capable of efficient expression ability of soluble proteins. Accounting for the accumulated knowledge of the pivotal factors in the solubility and folding stability of proteins, this review focuses on advanced technologies and tools of protein engineering, protein quality control systems, and the redesign of expression platforms in prokaryotic expression systems, as well as advances of the cell-free expression technologies for membrane proteins production.
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Affiliation(s)
- Jin-Ping Chen
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
| | - Jin-Song Gong
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China.
| | - Chang Su
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China
| | - Heng Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China
| | - Zheng-Hong Xu
- National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, School of Biotechnology, Jiangnan University, Wuxi, 214122, PR China; Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
| | - Jin-Song Shi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
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36
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Wang X, Liu D, Luo J, Kong D, Zhang Y. Exploring the Role of Enhancer-Mediated Transcriptional Regulation in Precision Biology. Int J Mol Sci 2023; 24:10843. [PMID: 37446021 PMCID: PMC10342031 DOI: 10.3390/ijms241310843] [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: 05/10/2023] [Revised: 06/18/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023] Open
Abstract
The emergence of precision biology has been driven by the development of advanced technologies and techniques in high-resolution biological research systems. Enhancer-mediated transcriptional regulation, a complex network of gene expression and regulation in eukaryotes, has attracted significant attention as a promising avenue for investigating the underlying mechanisms of biological processes and diseases. To address biological problems with precision, large amounts of data, functional information, and research on the mechanisms of action of biological molecules is required to address biological problems with precision. Enhancers, including typical enhancers and super enhancers, play a crucial role in gene expression and regulation within this network. The identification and targeting of disease-associated enhancers hold the potential to advance precision medicine. In this review, we present the concepts, progress, importance, and challenges in precision biology, transcription regulation, and enhancers. Furthermore, we propose a model of transcriptional regulation for multi-enhancers and provide examples of their mechanisms in mammalian cells, thereby enhancing our understanding of how enhancers achieve precise regulation of gene expression in life processes. Precision biology holds promise in providing new tools and platforms for discovering insights into gene expression and disease occurrence, ultimately benefiting individuals and society as a whole.
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Affiliation(s)
- Xueyan Wang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Danli Liu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Jing Luo
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Dashuai Kong
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
| | - Yubo Zhang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China;
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; (D.L.); (J.L.); (D.K.)
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37
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Amrofell MB, Moon TS. Characterizing a Propionate Sensor in E. coli Nissle 1917. ACS Synth Biol 2023; 12:1868-1873. [PMID: 37220256 PMCID: PMC10865894 DOI: 10.1021/acssynbio.3c00138] [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: 05/25/2023]
Abstract
Short-chain fatty acids (SCFAs) are commonly found in the large intestine, but generally not in the small intestine, and influence microbiome composition and host physiology. Thus, synthetic biologists are interested in developing engineered probiotics capable of in situ detection of SCFAs as biogeography or disease sensors. One SCFA, propionate, is both sensed and consumed by E. coli. Here, we utilize the E. coli transcription factor PrpR, sensitive to the propionate-derived metabolite (2S,3S)-2-methylcitrate, and its cognate promoter PprpBCDE to detect extracellular propionate with the probiotic chassis bacterium E. coli Nissle 1917. We identify that PrpR-PprpBCDE displays stationary phase leakiness and transient bimodality, and we explain these observations through evolutionary rationales and deterministic modeling, respectively. Our results will help researchers build biogeographically sensitive genetic circuits.
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Affiliation(s)
- Matthew B. Amrofell
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Lous, MO, USA 63130
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Lous, MO, USA 63130
- Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri, 63130, USA
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38
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Nikolados EM, Oyarzún DA. Deep learning for optimization of protein expression. Curr Opin Biotechnol 2023; 81:102941. [PMID: 37087839 DOI: 10.1016/j.copbio.2023.102941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/02/2023] [Accepted: 03/17/2023] [Indexed: 04/25/2023]
Abstract
Recent progress in high-throughput DNA synthesis and sequencing has enabled the development of massively parallel reporter assays for strain characterization. These datasets map a large number of DNA sequences to protein expression levels, sparking increased interest in data-driven methods for sequence-to-expression modeling. Here, we highlight advances in deep learning models of protein expression and their potential for optimizing strains engineered to produce recombinant proteins. We review recent works that built highly accurate models and discuss challenges that hinder adoption by end users. There is a need to better align this technology with the constraints encountered in strain engineering, particularly the cost of acquiring large amounts of data and the requirement for interpretable models that generalize beyond the training data. Overcoming these barriers will help to incentivize academic and industrial laboratories to tap into a new era of data-centric strain engineering.
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Affiliation(s)
| | - Diego A Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JH, UK; School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK; The Alan Turing Institute, London NW1 2DB, UK.
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39
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Kumar A, Das SK, Emdad L, Fisher PB. Applications of tissue-specific and cancer-selective gene promoters for cancer diagnosis and therapy. Adv Cancer Res 2023; 160:253-315. [PMID: 37704290 DOI: 10.1016/bs.acr.2023.03.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
Current treatment of solid tumors with standard of care chemotherapies, radiation therapy and/or immunotherapies are often limited by severe adverse toxic effects, resulting in a narrow therapeutic index. Cancer gene therapy represents a targeted approach that in principle could significantly reduce undesirable side effects in normal tissues while significantly inhibiting tumor growth and progression. To be effective, this strategy requires a clear understanding of the molecular biology of cancer development and evolution and developing biological vectors that can serve as vehicles to target cancer cells. The advent and fine tuning of omics technologies that permit the collective and spatial recognition of genes (genomics), mRNAs (transcriptomics), proteins (proteomics), metabolites (metabolomics), epiomics (epigenomics, epitranscriptomics, and epiproteomics), and their interactomics in defined complex biological samples provide a roadmap for identifying crucial targets of relevance to the cancer paradigm. Combining these strategies with identified genetic elements that control target gene expression uncovers significant opportunities for developing guided gene-based therapeutics for cancer. The purpose of this review is to overview the current state and potential limitations in developing gene promoter-directed targeted expression of key genes and highlights their potential applications in cancer gene therapy.
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Affiliation(s)
- Amit Kumar
- Department of Human and Molecular Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States; VCU Institute of Molecular Medicine, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States
| | - Swadesh K Das
- Department of Human and Molecular Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States; VCU Institute of Molecular Medicine, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States; VCU Massey Comprehensive Cancer Center, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States
| | - Luni Emdad
- Department of Human and Molecular Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States; VCU Institute of Molecular Medicine, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States; VCU Massey Comprehensive Cancer Center, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States
| | - Paul B Fisher
- Department of Human and Molecular Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States; VCU Institute of Molecular Medicine, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States; VCU Massey Comprehensive Cancer Center, Virginia Commonwealth University, School of Medicine, Richmond, VA, United States.
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40
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Das M, Hossain A, Banerjee D, Praul CA, Girirajan S. Challenges and considerations for reproducibility of STARR-seq assays. Genome Res 2023; 33:479-495. [PMID: 37130797 PMCID: PMC10234304 DOI: 10.1101/gr.277204.122] [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: 08/14/2022] [Accepted: 03/15/2023] [Indexed: 05/04/2023]
Abstract
High-throughput methods such as RNA-seq, ChIP-seq, and ATAC-seq have well-established guidelines, commercial kits, and analysis pipelines that enable consistency and wider adoption for understanding genome function and regulation. STARR-seq, a popular assay for directly quantifying the activities of thousands of enhancer sequences simultaneously, has seen limited standardization across studies. The assay is long, with more than 250 steps, and frequent customization of the protocol and variations in bioinformatics methods raise concerns for reproducibility of STARR-seq studies. Here, we assess each step of the protocol and analysis pipelines from published sources and in-house assays, and identify critical steps and quality control (QC) checkpoints necessary for reproducibility of the assay. We also provide guidelines for experimental design, protocol scaling, customization, and analysis pipelines for better adoption of the assay. These resources will allow better optimization of STARR-seq for specific research needs, enable comparisons and integration across studies, and improve the reproducibility of results.
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Affiliation(s)
- Maitreya Das
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania 16802, USA;
- Molecular and Cellular Integrative Biosciences Graduate Program, Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Ayaan Hossain
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Bioinformatics and Genomics Graduate Program, Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Deepro Banerjee
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Bioinformatics and Genomics Graduate Program, Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Craig Alan Praul
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Santhosh Girirajan
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania 16802, USA;
- Molecular and Cellular Integrative Biosciences Graduate Program, Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Bioinformatics and Genomics Graduate Program, Pennsylvania State University, University Park, Pennsylvania 16802, USA
- Department of Anthropology, Pennsylvania State University, University Park, Pennsylvania 16802, USA
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41
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Caringella G, Bandiera L, Menolascina F. Recent advances, opportunities and challenges in cybergenetic identification and control of biomolecular networks. Curr Opin Biotechnol 2023; 80:102893. [PMID: 36706519 DOI: 10.1016/j.copbio.2023.102893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 01/26/2023]
Abstract
Cybergenetics is a new area of research aimed at developing digital and biological controllers for living systems. Synthetic biologists have begun exploiting cybergenetic tools and platforms to both accelerate the development of mathematical models and develop control strategies for complex biological phenomena. Here, we review the state of the art in cybergenetic identification and control. Our aim is to lower the entry barrier to this field and foster the adoption of methods and technologies that will accelerate the pace at which Synthetic Biology progresses toward applications.
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Affiliation(s)
- Gianpio Caringella
- School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh EH9 3DW, UK
| | - Lucia Bandiera
- School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh EH9 3DW, UK; Centre for Engineering Biology, The University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Filippo Menolascina
- School of Engineering, Institute for Bioengineering, The University of Edinburgh, Edinburgh EH9 3DW, UK; Centre for Engineering Biology, The University of Edinburgh, Edinburgh EH9 3BF, UK.
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42
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O'Connell RW, Rai K, Piepergerdes TC, Samra KD, Wilson JA, Lin S, Zhang TH, Ramos EM, Sun A, Kille B, Curry KD, Rocks JW, Treangen TJ, Mehta P, Bashor CJ. Ultra-high throughput mapping of genetic design space. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.532704. [PMID: 36993481 PMCID: PMC10055055 DOI: 10.1101/2023.03.16.532704] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements. However, because these approaches only interrogate short sequences, it remains challenging to perform high throughput (HT) assays on constructs containing combinations of sequence elements arranged across multi-kb length scales. Overcoming this barrier could accelerate synthetic biology; by screening diverse gene circuit designs, "composition-to-function" mappings could be created that reveal genetic part composability rules and enable rapid identification of behavior-optimized variants. Here, we introduce CLASSIC, a generalizable genetic screening platform that combines long- and short-read next-generation sequencing (NGS) modalities to quantitatively assess pooled libraries of DNA constructs of arbitrary length. We show that CLASSIC can measure expression profiles of >10 5 drug-inducible gene circuit designs (ranging from 6-9 kb) in a single experiment in human cells. Using statistical inference and machine learning (ML) approaches, we demonstrate that data obtained with CLASSIC enables predictive modeling of an entire circuit design landscape, offering critical insight into underlying design principles. Our work shows that by expanding the throughput and understanding gained with each design-build-test-learn (DBTL) cycle, CLASSIC dramatically augments the pace and scale of synthetic biology and establishes an experimental basis for data-driven design of complex genetic systems.
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43
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Li H, Zhang J, Zhao Y, Yang W. Predicting Corynebacterium glutamicum promoters based on novel feature descriptor and feature selection technique. Front Microbiol 2023; 14:1141227. [PMID: 36937275 PMCID: PMC10018189 DOI: 10.3389/fmicb.2023.1141227] [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: 01/10/2023] [Accepted: 02/10/2023] [Indexed: 03/06/2023] Open
Abstract
The promoter is an important noncoding DNA regulatory element, which combines with RNA polymerase to activate the expression of downstream genes. In industry, artificial arginine is mainly synthesized by Corynebacterium glutamicum. Replication of specific promoter regions can increase arginine production. Therefore, it is necessary to accurately locate the promoter in C. glutamicum. In the wet experiment, promoter identification depends on sigma factors and DNA splicing technology, this is a laborious job. To quickly and conveniently identify the promoters in C. glutamicum, we have developed a method based on novel feature representation and feature selection to complete this task, describing the DNA sequences through statistical parameters of multiple physicochemical properties, filtering redundant features by combining analysis of variance and hierarchical clustering, the prediction accuracy of the which is as high as 91.6%, the sensitivity of 91.9% can effectively identify promoters, and the specificity of 91.2% can accurately identify non-promoters. In addition, our model can correctly identify 181 promoters and 174 non-promoters among 400 independent samples, which proves that the developed prediction model has excellent robustness.
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Affiliation(s)
- HongFei Li
- College of Life Science, Northeast Forestry University, Harbin, China
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Jingyu Zhang
- Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yuming Zhao
- College of Life Science, Northeast Forestry University, Harbin, China
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
- *Correspondence: Yuming Zhao, ; Wen Yang,
| | - Wen Yang
- International Medical Center, Shenzhen University General Hospital, Shenzhen, China
- *Correspondence: Yuming Zhao, ; Wen Yang,
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44
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Tack DS, Tonner PD, Pressman A, Olson ND, Levy SF, Romantseva EF, Alperovich N, Vasilyeva O, Ross D. Precision engineering of biological function with large-scale measurements and machine learning. PLoS One 2023; 18:e0283548. [PMID: 36989327 PMCID: PMC10057847 DOI: 10.1371/journal.pone.0283548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/11/2023] [Indexed: 03/30/2023] Open
Abstract
As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor's sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.
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Affiliation(s)
- Drew S Tack
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Peter D Tonner
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Abe Pressman
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Nathan D Olson
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Sasha F Levy
- SLAC National Accelerator Laboratory, Menlo Park, CA, United States of America
- Joint Initiative for Metrology in Biology, Stanford, CA, United States of America
| | - Eugenia F Romantseva
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Nina Alperovich
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Olga Vasilyeva
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - David Ross
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
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45
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Scholz SA, Lindeboom CD, Freddolino PL. Genetic context effects can override canonical cis regulatory elements in Escherichia coli. Nucleic Acids Res 2022; 50:10360-10375. [PMID: 36134716 PMCID: PMC9561378 DOI: 10.1093/nar/gkac787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/10/2022] [Accepted: 09/02/2022] [Indexed: 11/12/2022] Open
Abstract
Recent experiments have shown that in addition to control by cis regulatory elements, the local chromosomal context of a gene also has a profound impact on its transcription. Although this chromosome-position dependent expression variation has been empirically mapped at high-resolution, the underlying causes of the variation have not been elucidated. Here, we demonstrate that 1 kb of flanking, non-coding synthetic sequences with a low frequency of guanosine and cytosine (GC) can dramatically reduce reporter expression compared to neutral and high GC-content flanks in Escherichia coli. Natural and artificial genetic context can have a similarly strong effect on reporter expression, regardless of cell growth phase or medium. Despite the strong reduction in the maximal expression level from the fully-induced reporter, low GC synthetic flanks do not affect the time required to reach the maximal expression level after induction. Overall, we demonstrate key determinants of transcriptional propensity that appear to act as tunable modulators of transcription, independent of regulatory sequences such as the promoter. These findings provide insight into the regulation of naturally occurring genes and an independent control for optimizing expression of synthetic biology constructs.
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Affiliation(s)
- Scott A Scholz
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Chase D Lindeboom
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Peter L Freddolino
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
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