1
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Shao M, Xu F, Ke X, Huang M, Chu J. Enhancing erythromycin production in Saccharopolyspora erythraea through rational engineering and fermentation refinement: A Design-Build-Test-Learn approach. Biotechnol J 2024; 19:e2400039. [PMID: 38797723 DOI: 10.1002/biot.202400039] [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: 01/16/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 05/29/2024]
Abstract
Industrial production of bioactive compounds from actinobacteria, such as erythromycin and its derivatives, faces challenges in achieving optimal yields. To this end, the Design-Build-Test-Learn (DBTL) framework, a systematic metabolic engineering approach, was employed to enhance erythromycin production in Saccharopolyspora erythraea (S. erythraea) E3 strain. A genetically modified strain, S. erythraea E3-CymRP21-dcas9-sucC (S. erythraea CS), was developed by suppressing the sucC gene using an inducible promoter and dcas9 protein. The strain exhibited improved erythromycin synthesis, attributed to enhanced precursor synthesis and increased NADPH availability. Transcriptomic and metabolomic analyses revealed altered central carbon metabolism, amino acid metabolism, energy metabolism, and co-factor/vitamin metabolism in CS. Augmented amino acid metabolism led to nitrogen depletion, potentially causing cellular autolysis during later fermentation stages. By refining the fermentation process through ammonium sulfate supplementation, erythromycin yield reached 1125.66 mg L-1, a 43.5% increase. The results demonstrate the power of the DBTL methodology in optimizing erythromycin production, shedding light on its potential for revolutionizing antibiotic manufacturing in response to the global challenge of antibiotic resistance.
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Affiliation(s)
- Minghao Shao
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Feng Xu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Xiang Ke
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Mingzhi Huang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
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2
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Stock M, Gorochowski TE. Open-endedness in synthetic biology: A route to continual innovation for biological design. SCIENCE ADVANCES 2024; 10:eadi3621. [PMID: 38241375 DOI: 10.1126/sciadv.adi3621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024]
Abstract
Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.
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Affiliation(s)
- Michiel Stock
- KERMIT & Biobix, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, Life Sciences Building, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
- BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK
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3
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Vogeleer P, Millard P, Arbulú ASO, Pflüger-Grau K, Kremling A, Létisse F. Metabolic impact of heterologous protein production in Pseudomonas putida: Insights into carbon and energy flux control. Metab Eng 2024; 81:26-37. [PMID: 37918614 DOI: 10.1016/j.ymben.2023.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/05/2023] [Accepted: 10/22/2023] [Indexed: 11/04/2023]
Abstract
For engineered microorganisms, the production of heterologous proteins that are often useless to host cells represents a burden on resources, which have to be shared with normal cellular processes. Within a certain metabolic leeway, this competitive process has no impact on growth. However, once this leeway, or free capacity, is fully utilized, the extra load becomes a metabolic burden that inhibits cellular processes and triggers a broad cellular response, reducing cell growth and often hindering the production of heterologous proteins. In this study, we sought to characterize the metabolic rearrangements occurring in the central metabolism of Pseudomonas putida at different levels of metabolic load. To this end, we constructed a P. putida KT2440 strain that expressed two genes encoding fluorescent proteins, one in the genome under constitutive expression to monitor the free capacity, and the other on an inducible plasmid to probe heterologous protein production. We found that metabolic fluxes are considerably reshuffled, especially at the level of periplasmic pathways, as soon as the metabolic load exceeds the free capacity. Heterologous protein production leads to the decoupling of anabolism and catabolism, resulting in large excess energy production relative to the requirements of protein biosynthesis. Finally, heterologous protein production was found to exert a stronger control on carbon fluxes than on energy fluxes, indicating that the flexible nature of P. putida's central metabolic network is solicited to sustain energy production.
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Affiliation(s)
- Philippe Vogeleer
- Toulouse Biotechnology Institute, Université de Toulouse, INSA, UPS, Toulouse, France
| | - Pierre Millard
- Toulouse Biotechnology Institute, Université de Toulouse, INSA, UPS, Toulouse, France; MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Ana-Sofia Ortega Arbulú
- Technical University Munich, TUM School of Engineering and Design, Department of Energy and Process Engineering, Systems Biotechnology, Germany
| | - Katharina Pflüger-Grau
- Technical University Munich, TUM School of Engineering and Design, Department of Energy and Process Engineering, Systems Biotechnology, Germany
| | - Andreas Kremling
- Technical University Munich, TUM School of Engineering and Design, Department of Energy and Process Engineering, Systems Biotechnology, Germany
| | - Fabien Létisse
- Toulouse Biotechnology Institute, Université de Toulouse, INSA, UPS, Toulouse, France.
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4
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Feng H, Li F, Wang T, Xing XH, Zeng AP, Zhang C. Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision. SCIENCE ADVANCES 2023; 9:eadg5296. [PMID: 37939173 PMCID: PMC10631719 DOI: 10.1126/sciadv.adg5296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023]
Abstract
Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.
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Affiliation(s)
- Huibao Feng
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Fan Li
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Tianmin Wang
- Tsinghua-Peking Center for Life Sciences, School of Medicine, Tsinghua University, Beijing 100084, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xin-hui Xing
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - An-ping Zeng
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg 21073, Germany
- Center of Synthetic Biology and Integrated Bioengineering, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Chong Zhang
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
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5
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Yeom J, Park JS, Jung SW, Lee S, Kwon H, Yoo SM. High-throughput genetic engineering tools for regulating gene expression in a microbial cell factory. Crit Rev Biotechnol 2023; 43:82-99. [PMID: 34957867 DOI: 10.1080/07388551.2021.2007351] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
With the rapid advances in biotechnological tools and strategies, microbial cell factory-constructing strategies have been established for the production of value-added compounds. However, optimizing the tradeoff between the biomass, yield, and titer remains a challenge in microbial production. Gene regulation is necessary to optimize and control metabolic fluxes in microorganisms for high-production performance. Various high-throughput genetic engineering tools have been developed for achieving rational gene regulation and genetic perturbation, diversifying the cellular phenotype and enhancing bioproduction performance. In this paper, we review the current high-throughput genetic engineering tools for gene regulation. In particular, technological approaches used in a diverse range of genetic tools for constructing microbial cell factories are introduced, and representative applications of these tools are presented. Finally, the prospects for high-throughput genetic engineering tools for gene regulation are discussed.
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Affiliation(s)
- Jinho Yeom
- School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Jong Seong Park
- School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Seung-Woon Jung
- School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Sumin Lee
- School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Hyukjin Kwon
- School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea
| | - Seung Min Yoo
- School of Integrative Engineering, Chung-Ang University, Seoul, Republic of Korea
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Duong-Trung N, Born S, Kim JW, Schermeyer MT, Paulick K, Borisyak M, Cruz-Bournazou MN, Werner T, Scholz R, Schmidt-Thieme L, Neubauer P, Martinez E. When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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7
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Zhao Z, Cheng JF, Yoshikuni Y. Chromosomal integration of complex DNA constructs using CRAGE and CRAGE-Duet systems. STAR Protoc 2022; 3:101546. [PMID: 35842866 PMCID: PMC9294251 DOI: 10.1016/j.xpro.2022.101546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/11/2022] [Accepted: 06/16/2022] [Indexed: 11/18/2022] Open
Abstract
Our recent development of the CRAGE (chassis-independent recombinase-assisted genome engineering) system enables single-step integration of large, complex DNA constructs directly into bacteria genomes across multiple phyla. This protocol describes the details of the experimental design and procedures of CRAGE and extended CRAGE-Duet systems. It also describes a strategy that combines CRISPR with CRAGE, which allows implementation of CRISPR-Cas9, CRISPRa, and CRISPRi in diverse bacteria, overcoming major limitations to broaden the application of CRISPR in non-model bacterial genome engineering. For complete details on the use and execution of this protocol, please refer to Wang et al. (2019), Wang et al. (2020), and Liu et al. (2020). Domestication of non-model bacteria using CRAGE and CRAGE-Duet systems Single-step chromosomal integration of complex DNA constructs (payloads) Use of CRISPR-Cas9, CRISPRa, and CRISPRi tools in non-model bacteria
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
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Affiliation(s)
- Zhiying Zhao
- The US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
| | - Jan-Fang Cheng
- The US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Yasuo Yoshikuni
- The US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Center for Advanced Bioenergy and Bioproducts Innovation, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Global Center for Food, Land, and Water Resources, Hokkaido University, Hokkaido 060-8589, Japan.
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8
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Van Brempt M, Peeters AI, Duchi D, De Wannemaeker L, Maertens J, De Paepe B, De Mey M. Biosensor-driven, model-based optimization of the orthogonally expressed naringenin biosynthesis pathway. Microb Cell Fact 2022; 21:49. [PMID: 35346204 PMCID: PMC8962593 DOI: 10.1186/s12934-022-01775-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/15/2022] [Indexed: 12/30/2022] Open
Abstract
Background The rapidly expanding synthetic biology toolbox allows engineers to develop smarter strategies to tackle the optimization of complex biosynthetic pathways. In such a strategy, multi-gene pathways are subdivided in several modules which are each dynamically controlled to fine-tune their expression in response to a changing cellular environment. To fine-tune separate modules without interference between modules or from the host regulatory machinery, a sigma factor (σ) toolbox was developed in previous work for tunable orthogonal gene expression. Here, this toolbox is implemented in E. coli to orthogonally express and fine-tune a pathway for the heterologous biosynthesis of the industrially relevant plant metabolite, naringenin. To optimize the production of this pathway, a practical workflow is still imperative to balance all steps of the pathway. This is tackled here by the biosensor-driven screening, subsequent genotyping of combinatorially engineered libraries and finally the training of three different computer models to predict the optimal pathway configuration. Results The efficiency and knowledge gained through this workflow is demonstrated here by improving the naringenin production titer by 32% with respect to a random pathway library screen. Our best strain was cultured in a batch bioreactor experiment and was able to produce 286 mg/L naringenin from glycerol in approximately 26 h. This is the highest reported naringenin production titer in E. coli without the supplementation of pathway precursors to the medium or any precursor pathway engineering. In addition, valuable pathway configuration preferences were identified in the statistical learning process, such as specific enzyme variant preferences and significant correlations between promoter strength at specific steps in the pathway and titer. Conclusions An efficient strategy, powered by orthogonal expression, was applied to successfully optimize a biosynthetic pathway for microbial production of flavonoids in E. coli up to high, competitive levels. Within this strategy, statistical learning techniques were combined with combinatorial pathway optimization techniques and an in vivo high-throughput screening method to efficiently determine the optimal operon configuration of the pathway. This “pathway architecture designer” workflow can be applied for the fast and efficient development of new microbial cell factories for different types of molecules of interest while also providing additional insights into the underlying pathway characteristics. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-022-01775-8.
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Affiliation(s)
- Maarten Van Brempt
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Andries Ivo Peeters
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Dries Duchi
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Lien De Wannemaeker
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Jo Maertens
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Brecht De Paepe
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium
| | - Marjan De Mey
- Centre For Synthetic Biology, Ghent University, Coupure Links 653, B-9000, Ghent, Belgium.
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9
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Kratzl F, Kremling A, Pflüger‐Grau K. Streamlining of a synthetic co-culture towards an individually controllable one-pot process for polyhydroxyalkanoate production from light and CO 2. Eng Life Sci 2022; 23:e2100156. [PMID: 36619884 PMCID: PMC9815089 DOI: 10.1002/elsc.202100156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 12/23/2021] [Accepted: 02/01/2022] [Indexed: 01/11/2023] Open
Abstract
Rationally designed synthetic microbial consortia carry a vast potential for biotechnological applications. The application of such a consortium in a bioprocess, however, requires tight and individual controllability of the involved microbes. Here, we present the streamlining of a co-cultivation process consisting of Synechococcus elongatus cscB and Pseudomonas putida for the production of polyhydroxyalkanoates (PHA) from light and CO2. First, the process was improved by employing P. putida cscRABY, a strain with a higher metabolic activity towards sucrose. Next, the individual controllability of the co-culture partners was addressed by providing different nitrogen sources, each exclusively available for one strain. By this, the growth rate of the co-culture partners could be regulated individually, and defined conditions could be set. The molC/molN ratio, a key value for PHA accumulation, was estimated from the experimental data, and the necessary feeding rates to obtain a specific ratio could be predicted. This information was then implemented in the co-cultivation process, following the concept of a DBTL-cycle. In total, the streamlining of the process resulted in an increased maximal PHA titer of 393 mg/L and a PHA production rate of 42.1 mg/(L•day).
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Affiliation(s)
- Franziska Kratzl
- Professorship of Systems BiotechnologyTechnical University of MunichGarchingGermany
| | - Andreas Kremling
- Professorship of Systems BiotechnologyTechnical University of MunichGarchingGermany
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10
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Shokravi H, Shokravi Z, Heidarrezaei M, Ong HC, Rahimian Koloor SS, Petrů M, Lau WJ, Ismail AF. Fourth generation biofuel from genetically modified algal biomass: Challenges and future directions. CHEMOSPHERE 2021; 285:131535. [PMID: 34329137 DOI: 10.1016/j.chemosphere.2021.131535] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/27/2021] [Accepted: 07/09/2021] [Indexed: 06/13/2023]
Abstract
Genetic engineering applications in the field of biofuel are rapidly expanding due to their potential to boost biomass productivity while lowering its cost and enhancing its quality. Recently, fourth-generation biofuel (FGB), which is biofuel obtained from genetically modified (GM) algae biomass, has gained considerable attention from academic and industrial communities. However, replacing fossil resources with FGB is still beset with many challenges. Most notably, technical aspects of genetic modification operations need to be more fully articulated and elaborated. However, relatively little attention has been paid to GM algal biomass. There is a limited number of reviews on the progress and challenges faced in the algal genetics of FGB. Therefore, the present review aims to fill this gap in the literature by recapitulating the findings of recent studies and achievements on safe and efficient genetic manipulation in the production of FGB. Then, the essential issues and parameters related to genome editing in algal strains are highlighted. Finally, the main challenges to FGB pertaining to the diffusion risk and regulatory frameworks are addressed. This review concluded that the technical and biosafety aspects of FGB, as well as the complexity and diversity of the related regulations, legitimacy concerns, and health and environmental risks, are among the most important challenges that require a strong commitment at the national/international levels to reach a global consensus.
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Affiliation(s)
- Hoofar Shokravi
- School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor Bahru, Johor, Malaysia
| | - Zahra Shokravi
- Department of Microbiology, Faculty of Basic Science, Islamic Azad University, Science and Research Branch of Tehran, Markazi, Iran
| | - Mahshid Heidarrezaei
- School of Chemical & Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor Bahru, Johor, Malaysia; Institute of Bioproduct Development (IBD), Universiti Teknologi Malaysia, Johor Bahru, 81310, Malaysia
| | - Hwai Chyuan Ong
- Centre for Green Technology, Faculty of Engineering and Information Technology, University of Technology Sydney, NSW, 2007, Australia.
| | - Seyed Saeid Rahimian Koloor
- Institute for Nanomaterials, Advanced Technologies, and Innovation (CXI), Technical University of Liberec (TUL), Studentska 2, 461 17, Liberec, Czech Republic
| | - Michal Petrů
- Institute for Nanomaterials, Advanced Technologies, and Innovation (CXI), Technical University of Liberec (TUL), Studentska 2, 461 17, Liberec, Czech Republic
| | - Woei Jye Lau
- School of Chemical & Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor Bahru, Johor, Malaysia; Advanced Membrane Technology Research Centre (AMTEC), Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
| | - Ahmad Fauzi Ismail
- School of Chemical & Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor Bahru, Johor, Malaysia; Advanced Membrane Technology Research Centre (AMTEC), Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
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11
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Synthetic Biology Advanced Natural Product Discovery. Metabolites 2021; 11:metabo11110785. [PMID: 34822443 PMCID: PMC8617713 DOI: 10.3390/metabo11110785] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/10/2021] [Accepted: 11/12/2021] [Indexed: 01/16/2023] Open
Abstract
A wide variety of bacteria, fungi and plants can produce bioactive secondary metabolites, which are often referred to as natural products. With the rapid development of DNA sequencing technology and bioinformatics, a large number of putative biosynthetic gene clusters have been reported. However, only a limited number of natural products have been discovered, as most biosynthetic gene clusters are not expressed or are expressed at extremely low levels under conventional laboratory conditions. With the rapid development of synthetic biology, advanced genome mining and engineering strategies have been reported and they provide new opportunities for discovery of natural products. This review discusses advances in recent years that can accelerate the design, build, test, and learn (DBTL) cycle of natural product discovery, and prospects trends and key challenges for future research directions.
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12
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Mey F, Clauwaert J, Van Huffel K, Waegeman W, De Mey M. Improving the performance of machine learning models for biotechnology: The quest for deus ex machina. Biotechnol Adv 2021; 53:107858. [PMID: 34695560 DOI: 10.1016/j.biotechadv.2021.107858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/13/2021] [Accepted: 10/14/2021] [Indexed: 11/24/2022]
Abstract
Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements and predictive tools in the field. Robust prediction of the behavior of microbial cell factories and production processes not only greatly increases our understanding of the function of such systems, but also provides significant savings of development time. However, many pitfalls when modeling biological data - bad fit, noisy data, model instability, low data quantity and imbalances in the data - cause models to suffer in their performance. Here we provide an accessible, in-depth analysis on the problems created by these pitfalls, as well as means of their detection and mediation, with a focus on supervised learning. Assessing the state of the art, we show that, currently, in-depth analyses of model performance are often absent and must be improved. This review provides a toolbox for the analysis of model robustness and performance, and simultaneously proposes a standard for the community to facilitate future work. It is further accompanied by an interactive online tutorial on the discussed issues.
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Affiliation(s)
- Friederike Mey
- Centre for Synthetic Biology (CSB), Department of Biotechnology, Ghent University, 9000 Ghent, Belgium
| | - Jim Clauwaert
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Ghent, Belgium
| | - Kirsten Van Huffel
- Centre for Synthetic Biology (CSB), Department of Biotechnology, Ghent University, 9000 Ghent, Belgium
| | - Willem Waegeman
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Ghent, Belgium
| | - Marjan De Mey
- Centre for Synthetic Biology (CSB), Department of Biotechnology, Ghent University, 9000 Ghent, Belgium.
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13
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Nakazawa S, Imaichi O, Kogure T, Kubota T, Toyoda K, Suda M, Inui M, Ito K, Shirai T, Araki M. History-Driven Genetic Modification Design Technique Using a Domain-Specific Lexical Model for the Acceleration of DBTL Cycles for Microbial Cell Factories. ACS Synth Biol 2021; 10:2308-2317. [PMID: 34351735 DOI: 10.1021/acssynbio.1c00234] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The development of microbes for conducting bioprocessing via synthetic biology involves design-build-test-learn (DBTL) cycles. To aid the designing step, we developed a computational technique that suggests next genetic modifications on the basis of relatedness to the user's design history of genetic modifications accumulated through former DBTL cycles conducted by the user. This technique, which comprehensively retrieves well-known designs related to the history, involves searching text for previous literature and then mining genes that frequently co-occur in the literature with those modified genes. We further developed a domain-specific lexical model that weights literature that is more related to the domain of metabolic engineering to emphasize genes modified for bioprocessing. Our technique made a suggestion by using a history of creating a Corynebacterium glutamicum strain producing shikimic acid that had 18 genetic modifications. Inspired by the suggestion, eight genes were considered by biologists for further modification, and modifying four of these genes proved experimentally efficient in increasing the production of shikimic acid. These results indicated that our proposed technique successfully utilized the former cycles to suggest relevant designs that biologists considered worth testing. Comprehensive retrieval of well-tested designs will help less-experienced researchers overcome the entry barrier as well as inspire experienced researchers to formulate design concepts that have been overlooked or suspended. This technique will aid DBTL cycles by feeding histories back to the next genetic design, thereby complementing the designing step.
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Affiliation(s)
- Shiori Nakazawa
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., 1-280, Higashi-Koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Osamu Imaichi
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., 1-280, Higashi-Koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Takahisa Kogure
- Research Institute of Innovative Technology for Earth, 9-2, Kizugawadai, Kizugawa-shi, Kyoto 619-0292, Japan
| | - Takeshi Kubota
- Research Institute of Innovative Technology for Earth, 9-2, Kizugawadai, Kizugawa-shi, Kyoto 619-0292, Japan
| | - Koichi Toyoda
- Research Institute of Innovative Technology for Earth, 9-2, Kizugawadai, Kizugawa-shi, Kyoto 619-0292, Japan
| | - Masako Suda
- Research Institute of Innovative Technology for Earth, 9-2, Kizugawadai, Kizugawa-shi, Kyoto 619-0292, Japan
| | - Masayuki Inui
- Research Institute of Innovative Technology for Earth, 9-2, Kizugawadai, Kizugawa-shi, Kyoto 619-0292, Japan
| | - Kiyoto Ito
- Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., 1-280, Higashi-Koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Tomokazu Shirai
- Riken, 1-6 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 240-0035, Japan
| | - Michihiro Araki
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
- Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan
- National Institutes of Biomedical Innovation, Health and Nutrition, 1-23-1 Toyama, Shinjuku-ku, Tokyo 162-8638, Japan
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14
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la-Rosa JDPD, García-Ramírez MA, Gschaedler-Mathis AC, Gómez-Guzmán AI, Solís-Pacheco JR, González-Reynoso O. Estimation of metabolic fluxes distribution in Saccharomyces cerevisiae during the production of volatile compounds of Tequila. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:5094-5113. [PMID: 34517479 DOI: 10.3934/mbe.2021259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
A stoichiometric model for Saccharomyces cerevisiae is reconstructed to analyze the continuous fermentation process of agave juice in Tequila production. The metabolic model contains 94 metabolites and 117 biochemical reactions. From the above set of reactions, 93 of them are linked to internal biochemical reactions and 24 are related to transport fluxes between the medium and the cell. The central metabolism of S. cerevisiae includes the synthesis for 20 amino-acids, carbohydrates, lipids, DNA and RNA. Using flux balance analysis (FBA), different physiological states of S. cerevisiae are shown during the fermentative process; these states are compared with experimental data under different dilution rates (0.04-0.12 h$ ^{-1} $). Moreover, the model performs anabolic and catabolic biochemical reactions for the production of higher alcohols. The importance of the Saccharomyces cerevisiae genomic model in the area of alcoholic beverage fermentation is due to the fact that it allows to estimate the metabolic fluxes during the beverage fermentation process and a physiology state of the microorganism.
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Affiliation(s)
| | - Mario Alberto García-Ramírez
- Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. M. García Barragán # 1451, C.P. 44430, Guadalajara, Jalisco, México
| | | | | | - Josué R Solís-Pacheco
- Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. M. García Barragán # 1451, C.P. 44430, Guadalajara, Jalisco, México
| | - Orfil González-Reynoso
- Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. M. García Barragán # 1451, C.P. 44430, Guadalajara, Jalisco, México
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15
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Goris T, Pérez‐Valero Á, Martínez I, Yi D, Fernández‐Calleja L, San León D, Bornscheuer UT, Magadán‐Corpas P, Lombó F, Nogales J. Repositioning microbial biotechnology against COVID-19: the case of microbial production of flavonoids. Microb Biotechnol 2021; 14:94-110. [PMID: 33047877 PMCID: PMC7675739 DOI: 10.1111/1751-7915.13675] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 12/19/2022] Open
Abstract
Coronavirus-related disease 2019 (COVID-19) became a pandemic in February 2020, and worldwide researchers try to tackle the disease with approved drugs of all kinds, or to develop novel compounds inhibiting viral spreading. Flavonoids, already investigated as antivirals in general, also might bear activities specific for the viral agent causing COVID-19, SARS-CoV-2. Microbial biotechnology and especially synthetic biology may help to produce flavonoids, which are exclusive plant secondary metabolites, at a larger scale or indeed to find novel pharmaceutically active flavonoids. Here, we review the state of the art in (i) antiviral activity of flavonoids specific for coronaviruses and (ii) results derived from computational studies, mostly docking studies mainly inhibiting specific coronaviral proteins such as the 3CL (main) protease, the spike protein or the RNA-dependent RNA polymerase. In the end, we strive towards a synthetic biology pipeline making the fast and tailored production of valuable antiviral flavonoids possible by applying the last concepts of division of labour through co-cultivation/microbial community approaches to the DBTL (Design, Build, Test, Learn) principle.
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Affiliation(s)
- Tobias Goris
- Department of Molecular Toxicology, Research Group Intestinal MicrobiologyGerman Institute of Human Nutrition Potsdam‐RehbrueckeArthur‐Scheunert‐Allee 114‐116NuthetalBrandenburg14558Germany
| | - Álvaro Pérez‐Valero
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - Igor Martínez
- Department of Systems BiologyCentro Nacional de BiotecnologíaCSICMadridSpain
| | - Dong Yi
- Department of Biotechnology & Enzyme CatalysisInstitute of BiochemistryUniversity GreifswaldFelix‐Hausdorff‐Str. 4GreifswaldD‐17487Germany
| | - Luis Fernández‐Calleja
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - David San León
- Department of Systems BiologyCentro Nacional de BiotecnologíaCSICMadridSpain
| | - Uwe T. Bornscheuer
- Department of Biotechnology & Enzyme CatalysisInstitute of BiochemistryUniversity GreifswaldFelix‐Hausdorff‐Str. 4GreifswaldD‐17487Germany
| | - Patricia Magadán‐Corpas
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - Felipe Lombó
- Research Unit “Biotechnology in Nutraceuticals and Bioactive Compounds‐BIONUC”Departamento de Biología Funcional, Área de MicrobiologíaUniversidad de OviedoOviedoSpain
- Instituto Universitario de Oncología del Principado de AsturiasOviedoSpain
- Instituto de Investigación Sanitaria del Principado de AsturiasOviedoSpain
| | - Juan Nogales
- Department of Systems BiologyCentro Nacional de BiotecnologíaCSICMadridSpain
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy‐Spanish National Research Council (SusPlast‐CSIC)MadridSpain
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16
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Meng J, Qiu Y, Shi S. CRISPR/Cas9 Systems for the Development of Saccharomyces cerevisiae Cell Factories. Front Bioeng Biotechnol 2020; 8:594347. [PMID: 33330425 PMCID: PMC7710542 DOI: 10.3389/fbioe.2020.594347] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 10/19/2020] [Indexed: 01/09/2023] Open
Abstract
Synthetic yeast cell factories provide a remarkable solution for the sustainable supply of a range of products, ranging from large-scale industrial chemicals to high-value pharmaceutical compounds. Synthetic biology is a field in which metabolic pathways are intensively studied and engineered. The clustered, regularly interspaced, short, palindromic repeat-associated (CRISPR)/CRISPR-associated protein 9 (Cas9) technology has emerged as the state-of-the-art gene editing technique for synthetic biology. Recently, the use of different CRISPR/Cas9 systems has been extended to the field of yeast engineering for single-nucleotide resolution editing, multiple-gene editing, transcriptional regulation, and genome-scale modifications. Such advancing systems have led to accelerated microbial engineering involving less labor and time and also enhanced the understanding of cellular genetics and physiology. This review provides a brief overview of the latest research progress and the use of CRISPR/Cas9 systems in genetic manipulation, with a focus on the applications of Saccharomyces cerevisiae cell factory engineering.
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Affiliation(s)
- Jie Meng
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Yue Qiu
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
| | - Shuobo Shi
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China
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17
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Lawson CE, Martí JM, Radivojevic T, Jonnalagadda SVR, Gentz R, Hillson NJ, Peisert S, Kim J, Simmons BA, Petzold CJ, Singer SW, Mukhopadhyay A, Tanjore D, Dunn JG, Garcia Martin H. Machine learning for metabolic engineering: A review. Metab Eng 2020; 63:34-60. [PMID: 33221420 DOI: 10.1016/j.ymben.2020.10.005] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/22/2020] [Accepted: 10/31/2020] [Indexed: 12/14/2022]
Abstract
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.
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Affiliation(s)
- Christopher E Lawson
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA
| | - Jose Manuel Martí
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Tijana Radivojevic
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Sai Vamshi R Jonnalagadda
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Reinhard Gentz
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Nathan J Hillson
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Sean Peisert
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; University of California Davis, Davis, CA, 95616, USA
| | - Joonhoon Kim
- Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Pacific Northwest National Laboratory, Richland, 99354, WA, USA
| | - Blake A Simmons
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Christopher J Petzold
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA
| | - Steven W Singer
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA
| | - Aindrila Mukhopadhyay
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, USA
| | - Deepti Tanjore
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Advanced Biofuels and Bioproducts Process Development Unit, Emeryville, CA, 94608, USA
| | | | - Hector Garcia Martin
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Joint BioEnergy Institute, Emeryville, CA, 94608, USA; DOE Agile BioFoundry, Emeryville, CA, 94608, USA; Basque Center for Applied Mathematics, 48009, Bilbao, Spain; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, USA.
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18
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Mezzina MP, Manoli MT, Prieto MA, Nikel PI. Engineering Native and Synthetic Pathways in Pseudomonas putida for the Production of Tailored Polyhydroxyalkanoates. Biotechnol J 2020; 16:e2000165. [PMID: 33085217 DOI: 10.1002/biot.202000165] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/16/2020] [Indexed: 12/16/2022]
Abstract
Growing environmental concern sparks renewed interest in the sustainable production of (bio)materials that can replace oil-derived goods. Polyhydroxyalkanoates (PHAs) are isotactic polymers that play a critical role in the central metabolism of producer bacteria, as they act as dynamic reservoirs of carbon and reducing equivalents. PHAs continue to attract industrial attention as a starting point toward renewable, biodegradable, biocompatible, and versatile thermoplastic and elastomeric materials. Pseudomonas species have been known for long as efficient biopolymer producers, especially for medium-chain-length PHAs. The surge of synthetic biology and metabolic engineering approaches in recent years offers the possibility of exploiting the untapped potential of Pseudomonas cell factories for the production of tailored PHAs. In this article, an overview of the metabolic and regulatory circuits that rule PHA accumulation in Pseudomonas putida is provided, and approaches leading to the biosynthesis of novel polymers (e.g., PHAs including nonbiological chemical elements in their structures) are discussed. The potential of novel PHAs to disrupt existing and future market segments is closer to realization than ever before. The review is concluded by pinpointing challenges that currently hinder the wide adoption of bio-based PHAs, and strategies toward programmable polymer biosynthesis from alternative substrates in engineered P. putida strains are proposed.
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Affiliation(s)
- Mariela P Mezzina
- Systems Environmental Microbiology Group, The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs Lyngby, 2800, Denmark
| | - María Tsampika Manoli
- Microbial and Plant Biotechnology Department, Centro de Investigaciones Biológicas «Margarita Salas» (CIB-CSIC), Polymer Biotechnology Group, Madrid, 28040, Spain.,Spanish National Research Council (SusPlast-CSIC), Interdisciplinary Platform for Sustainable Plastics Toward a Circular Economy, Madrid, 28040, Spain
| | - M Auxiliadora Prieto
- Microbial and Plant Biotechnology Department, Centro de Investigaciones Biológicas «Margarita Salas» (CIB-CSIC), Polymer Biotechnology Group, Madrid, 28040, Spain.,Spanish National Research Council (SusPlast-CSIC), Interdisciplinary Platform for Sustainable Plastics Toward a Circular Economy, Madrid, 28040, Spain
| | - Pablo I Nikel
- Systems Environmental Microbiology Group, The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs Lyngby, 2800, Denmark
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19
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O-Succinyl-l-homoserine overproduction with enhancement of the precursor succinyl-CoA supply by engineered Escherichia coli. J Biotechnol 2020; 325:164-172. [PMID: 33157196 DOI: 10.1016/j.jbiotec.2020.11.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 09/22/2020] [Accepted: 11/01/2020] [Indexed: 11/22/2022]
Abstract
O-Succinyl-l-homoserine (OSH) is an important platform chemical in production of C4 chemicals such as succinic acid, homoserine lactone, γ‑butyrolactone, and 1,4‑butanediol. The production of OSH through chemical method or the current engineering strain is difficult and not optimal, and thereby there remains a need to develop new engineering strategy. Here, we engineered an OSH overproducing Escherichia coli strain through deleting the degradation and competitive pathways, overexpressing thrA and metL to enhance the metabolic flux from l-asparate to l-homoserine. Additionally, increasing the precursor succinyl-CoA supply through simultaneously knocking out sucD and overexpressing sucA further increased the yield of OSH. The engineered strain OSH9/pTrc-metA11-yjeH with above strategies produced OSH at the concentration of 24.1 g/L (0.609 g/g glucose) in batch fermentation. To gain detailed insight into metabolism of the engineered strain, comparative metabolic profiling was performed between the engineered and wide-type strain. The metabolomics data deciphered that the carbon was directed toward the OSH biosynthesis resulting in less flexibility of the genetically modified strain than the wide-type strain.
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20
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Wu Y, Liu Y, Lv X, Li J, Du G, Liu L. Applications of CRISPR in a Microbial Cell Factory: From Genome Reconstruction to Metabolic Network Reprogramming. ACS Synth Biol 2020; 9:2228-2238. [PMID: 32794766 DOI: 10.1021/acssynbio.0c00349] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The well-designed microbial cell factory finds wide applications in chemical, pharmaceutical, and food industries due to its sustainable and environmentally friendly features. Recently, the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (CRISPR-Cas) systems have been developed into powerful tools to perform genome editing and transcriptional regulation in prokaryotic and eukaryotic cells. Accordingly, these tools are useful to build microbial cell factories not only by reconstructing the genome but also by reprogramming the metabolic network. In this review, we summarize the recent significant headway and potential uses of the CRISPR technology in the construction of efficient microbial cell factories. Moreover, the future perspectives on the improvement and upgradation of CRISPR-based tools are also discussed.
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Affiliation(s)
- Yaokang Wu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
| | - Yanfeng Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
| | - Xueqin Lv
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
| | - Jianghua Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
| | - Guocheng Du
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
| | - Long Liu
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi, 214122, China
- Science Center for Future Foods, Jiangnan University, Wuxi, 214122, China
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21
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22
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Fontana J, Sparkman-Yager D, Zalatan JG, Carothers JM. Challenges and opportunities with CRISPR activation in bacteria for data-driven metabolic engineering. Curr Opin Biotechnol 2020; 64:190-198. [PMID: 32599515 DOI: 10.1016/j.copbio.2020.04.005] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 01/22/2020] [Accepted: 04/20/2020] [Indexed: 12/26/2022]
Abstract
Creating CRISPR gene activation (CRISPRa) technologies in industrially promising bacteria could be transformative for accelerating data-driven metabolic engineering and strain design. CRISPRa has been widely used in eukaryotes, but applications in bacterial systems have remained limited. Recent work shows that multiple features of bacterial promoters impose stringent requirements on CRISPRa-mediated gene activation. However, by systematically defining rules for effective bacterial CRISPRa sites and developing new approaches for encoding complex functions in engineered guide RNAs, there are now clear routes to generalize synthetic gene regulation in bacteria. When combined with multi-omics data collection and machine learning, the full development of bacterial CRISPRa will dramatically improve the ability to rapidly engineer bacteria for bioproduction through accelerated design-build-test-learn cycles.
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Affiliation(s)
- Jason Fontana
- Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington. Seattle, WA 98195, United States
| | - David Sparkman-Yager
- Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington. Seattle, WA 98195, United States
| | - Jesse G Zalatan
- Department of Chemistry, University of Washington. Seattle, WA 98195, United States.
| | - James M Carothers
- Department of Chemical Engineering, University of Washington. Seattle, WA 98195, United States.
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23
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Pines G, Fankhauser RG, Eckert CA. Predicting Drug Resistance Using Deep Mutational Scanning. Molecules 2020; 25:E2265. [PMID: 32403408 PMCID: PMC7248951 DOI: 10.3390/molecules25092265] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 05/05/2020] [Accepted: 05/05/2020] [Indexed: 12/12/2022] Open
Abstract
Drug resistance is a major healthcare challenge, resulting in a continuous need to develop new inhibitors. The development of these inhibitors requires an understanding of the mechanisms of resistance for a critical mass of occurrences. Recent genome editing technologies based on high-throughput DNA synthesis and sequencing may help to predict mutations resulting in resistance by testing large mutagenesis libraries. Here we describe the rationale of this approach, with examples and relevance to drug development and resistance in malaria.
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Affiliation(s)
- Gur Pines
- Department of Entomology, Agricultural Research Organization, Volcani Center, P.O.B 15159, Rishon LeZion 7505101, Israel
| | - Reilly G. Fankhauser
- Department of Dermatology, Oregon Health & Science University, Baird Hall 3225 SW Pavilion Loop, Portland, OR 97239, USA;
| | - Carrie A. Eckert
- Renewable and Sustainable Energy Institute, University of Colorado Boulder, 027 UCB, Boulder, CO 80309, USA
- Biosciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA
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24
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mSphere of Influence: Synthetic Biology of Natural Product Biosynthesis. mSphere 2020; 5:5/1/e00954-19. [PMID: 31915225 PMCID: PMC6952210 DOI: 10.1128/msphere.00954-19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Mark Walker studies the biosynthesis and engineering of bacterial natural products with the long-term goal of identifying new antibiotic compounds. In this mSphere of Influence, he reflects on how “Direct cloning and refactoring of a silent lipopeptide biosynthetic gene cluster yields the antibiotic taromycin A” by K. Yamanaka, K. A. Reynolds, R. D. Kersten, K. S. Mark Walker studies the biosynthesis and engineering of bacterial natural products with the long-term goal of identifying new antibiotic compounds. In this mSphere of Influence, he reflects on how “Direct cloning and refactoring of a silent lipopeptide biosynthetic gene cluster yields the antibiotic taromycin A” by K. Yamanaka, K. A. Reynolds, R. D. Kersten, K. S. Ryan, et al. (Proc Natl Acad Sci USA 111:1957–1962, 2014, https://doi.org/10.1073/pnas.1319584111) impacted his thinking on using synthetic biology approaches to study natural product biosynthesis.
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25
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Ren J, Lee J, Na D. Recent advances in genetic engineering tools based on synthetic biology. J Microbiol 2020; 58:1-10. [PMID: 31898252 DOI: 10.1007/s12275-020-9334-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 08/19/2019] [Accepted: 11/05/2019] [Indexed: 12/26/2022]
Abstract
Genome-scale engineering is a crucial methodology to rationally regulate microbiological system operations, leading to expected biological behaviors or enhanced bioproduct yields. Over the past decade, innovative genome modification technologies have been developed for effectively regulating and manipulating genes at the genome level. Here, we discuss the current genome-scale engineering technologies used for microbial engineering. Recently developed strategies, such as clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9, multiplex automated genome engineering (MAGE), promoter engineering, CRISPR-based regulations, and synthetic small regulatory RNA (sRNA)-based knockdown, are considered as powerful tools for genome-scale engineering in microbiological systems. MAGE, which modifies specific nucleotides of the genome sequence, is utilized as a genome-editing tool. Contrastingly, synthetic sRNA, CRISPRi, and CRISPRa are mainly used to regulate gene expression without modifying the genome sequence. This review introduces the recent genome-scale editing and regulating technologies and their applications in metabolic engineering.
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Affiliation(s)
- Jun Ren
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Jingyu Lee
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Dokyun Na
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea.
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26
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Motamedian E, Sarmadi M, Derakhshan E. Development of a regulatory defined medium using a system-oriented strategy to reduce the intracellular constraints. Process Biochem 2019. [DOI: 10.1016/j.procbio.2019.09.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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27
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Venayak N, Raj K, Mahadevan R. Impact framework: A python package for writing data analysis workflows to interpret microbial physiology. Metab Eng Commun 2019; 9:e00089. [PMID: 31011536 PMCID: PMC6462781 DOI: 10.1016/j.mec.2019.e00089] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 03/19/2019] [Accepted: 03/19/2019] [Indexed: 12/26/2022] Open
Abstract
Microorganisms can be genetically engineered to solve a range of challenges in diverse including health, environmental protection and sustainability. The natural complexity of biological systems makes this an iterative cycle, perturbing metabolism and making stepwise progress toward a desired phenotype through four major stages: design, build, test, and data interpretation. This cycle has been accelerated by advances in molecular biology (e.g. robust DNA synthesis and assembly techniques), liquid handling automation and scale-down characterization platforms, generating large heterogeneous data sets. Here, we present an extensible Python package for scientists and engineers working with large biological data sets to interpret, model, and visualize data: the IMPACT (Integrated Microbial Physiology: Analysis, Characterization and Translation) framework. Impact aims to ease the development of Python-based data analysis workflows for a range of stakeholders in the bioengineering process, offering open-source tools for data analysis, physiology characterization and translation to visualization. Using this framework, biologists and engineers can opt for reproducible and extensible programmatic data analysis workflows, mediating a bottleneck limiting the throughput of microbial engineering. The Impact framework is available at https://github.com/lmse/impact.
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Affiliation(s)
- Naveen Venayak
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, M5S 3E5, Canada
| | - Kaushik Raj
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, M5S 3E5, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, M5S 3E5, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, M5S 3G9, Canada
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Sandberg TE, Salazar MJ, Weng LL, Palsson BO, Feist AM. The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology. Metab Eng 2019; 56:1-16. [PMID: 31401242 DOI: 10.1016/j.ymben.2019.08.004] [Citation(s) in RCA: 247] [Impact Index Per Article: 49.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 08/01/2019] [Accepted: 08/05/2019] [Indexed: 12/21/2022]
Abstract
Harnessing the process of natural selection to obtain and understand new microbial phenotypes has become increasingly possible due to advances in culturing techniques, DNA sequencing, bioinformatics, and genetic engineering. Accordingly, Adaptive Laboratory Evolution (ALE) experiments represent a powerful approach both to investigate the evolutionary forces influencing strain phenotypes, performance, and stability, and to acquire production strains that contain beneficial mutations. In this review, we summarize and categorize the applications of ALE to various aspects of microbial physiology pertinent to industrial bioproduction by collecting case studies that highlight the multitude of ways in which evolution can facilitate the strain construction process. Further, we discuss principles that inform experimental design, complementary approaches such as computational modeling that help maximize utility, and the future of ALE as an efficient strain design and build tool driven by growing adoption and improvements in automation.
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Affiliation(s)
- Troy E Sandberg
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Michael J Salazar
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Liam L Weng
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Adam M Feist
- Department of Bioengineering, University of California, San Diego, CA, 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Lyngby, Denmark.
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29
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St. John PC, Bomble YJ. Approaches to Computational Strain Design in the Multiomics Era. Front Microbiol 2019; 10:597. [PMID: 31024467 PMCID: PMC6461008 DOI: 10.3389/fmicb.2019.00597] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/08/2019] [Indexed: 01/29/2023] Open
Abstract
Modern omics analyses are able to effectively characterize the genetic, regulatory, and metabolic phenotypes of engineered microbes, yet designing genetic interventions to achieve a desired phenotype remains challenging. With recent developments in genetic engineering techniques, timelines associated with building and testing strain designs have been greatly reduced, allowing for the first time an efficient closed loop iteration between experiment and analysis. However, the scale and complexity associated with multi-omics datasets complicates manual biological reasoning about the mechanisms driving phenotypic changes. Computational techniques therefore form a critical part of the Design-Build-Test-Learn (DBTL) cycle in metabolic engineering. Traditional statistical approaches can reduce the dimensionality of these datasets and identify common motifs among high-performing strains. While successful in many studies, these methods do not take full advantage of known connections between genes, proteins, and metabolic networks. There is therefore a growing interest in model-aided design, in which modeling frameworks from systems biology are used to integrate experimental data and generate effective and non-intuitive design predictions. In this mini-review, we discuss recent progress and challenges in this field. In particular, we compare methods augmenting flux balance analysis with additional constraints from fluxomic, genomic, and metabolomic datasets and methods employing kinetic representations of individual metabolic reactions, and machine learning. We conclude with a discussion of potential future directions for improving strain design predictions in the omics era and remaining experimental and computational hurdles.
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30
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García-Granados R, Lerma-Escalera JA, Morones-Ramírez JR. Metabolic Engineering and Synthetic Biology: Synergies, Future, and Challenges. Front Bioeng Biotechnol 2019; 7:36. [PMID: 30886847 PMCID: PMC6409320 DOI: 10.3389/fbioe.2019.00036] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 02/13/2019] [Indexed: 12/21/2022] Open
Abstract
The “-omics” era has brought a new set of tools and methods that have created a significant impact on the development of Metabolic Engineering and Synthetic Biology. These fields, rather than working separately, depend on each other to prosper and achieve their individual goals. Synthetic Biology aims to design libraries of genetic components (promoters, coding sequences, terminators, transcriptional factors and their binding sequences, and more), the assembly of devices, genetic circuits and even organism; in addition to obtaining quantitative information for the creation of models that can predict the behavior of biological systems (Cameron et al., 2014). Metabolic engineering seeks for the optimization of cellular processes, endemic to a specific organism, to produce a compound of interest from a substrate, preferably cheap and simple. It uses different databases, libraries of components and conditions to generate the maximum production rate of a desired chemical compound and avoiding inhibitors and conditions that affect the growth rate and other vital functions in the specific organism to achieve these goals; metabolic fluxes manipulation represents an important alternative (Stephanopoulos, 2012).
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Affiliation(s)
- Raúl García-Granados
- Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Mexico.,Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León, Apodaca, Mexico
| | - Jordy Alexis Lerma-Escalera
- Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Mexico.,Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León, Apodaca, Mexico.,Facultad de Ciencias Biológicas, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Mexico
| | - José R Morones-Ramírez
- Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Mexico.,Centro de Investigación en Biotecnología y Nanotecnología, Facultad de Ciencias Químicas, Universidad Autónoma de Nuevo León, Apodaca, Mexico
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31
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Recent advances in CRISPR/Cas9 mediated genome editing in Bacillus subtilis. World J Microbiol Biotechnol 2018; 34:153. [DOI: 10.1007/s11274-018-2537-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 09/20/2018] [Indexed: 12/20/2022]
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32
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An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. Commun Biol 2018; 1:66. [PMID: 30271948 PMCID: PMC6123781 DOI: 10.1038/s42003-018-0076-9] [Citation(s) in RCA: 127] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 05/10/2018] [Indexed: 12/15/2022] Open
Abstract
The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design–Build-Test–Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2S)-pinocembrin in Escherichia coli, to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L−1. The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest. Pablo Carbonell et al. present an automated pipeline for the discovery and optimization of biosynthetic pathways for microbial production of fine chemicals. They apply their pipeline to the production of the flavonoid (2S)-pinocembrin in Escherichia coli and show improvement of the pathway by 500-fold.
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33
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Kuivanen J, Holmström S, Lehtinen B, Penttilä M, Jäntti J. A High-throughput workflow for CRISPR/Cas9 mediated combinatorial promoter replacements and phenotype characterization in yeast. Biotechnol J 2018; 13:e1700593. [PMID: 29729128 DOI: 10.1002/biot.201700593] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 04/05/2018] [Accepted: 04/19/2018] [Indexed: 01/24/2023]
Abstract
Due to the rapidly increasing sequence information on gene variants generated by evolution and our improved abilities to engineer novel biological activities, microbial cells can be evolved for the production of a growing spectrum of compounds. For high productivity, efficient carbon channeling towards the end product is a key element. In large scale production systems the genetic modifications that ensure optimal performance cannot be dependent on plasmid-based regulators, but need to be engineered stably into the host genome. Here we describe a CRISPR/Cas9 mediated high-throughput workflow for combinatorial and multiplexed replacement of native promoters with synthetic promoters and the following high-throughput phenotype characterization in the yeast Saccharomyces cerevisiae. The workflow is demonstrated with three central metabolic genes, ZWF1, PGI1 and TKL1 encoding a glucose-6-phosphate dehydrogenase, phosphoglucose isomerase and transketolase, respectively. The synthetic promoter donor DNA libraries were generated by PCR and transformed to yeast cells. A 50% efficiency was achieved for simultaneous replacement at three individual loci using short 60-bp flanking homology sequences in the donor promoters. Phenotypic strain characterization was validated and demonstrated using liquid handling automation and 150 µl cultivation volume in 96-well plate format. The established workflow offers a robust platform for automated engineering and improvement of yeast strains.
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Affiliation(s)
- Joosu Kuivanen
- VTT Technical Research Centre of Finland Ltd, Espoo, Finland
| | - Sami Holmström
- VTT Technical Research Centre of Finland Ltd, Espoo, Finland
| | - Birgitta Lehtinen
- VTT Technical Research Centre of Finland Ltd, Espoo, Finland
- Current address: Life Science Graduate School Zurich, Institute of Molecular Systems biology (IMSB), Department of Biology (D-BIOL), ETH Zurich
| | - Merja Penttilä
- VTT Technical Research Centre of Finland Ltd, Espoo, Finland
| | - Jussi Jäntti
- VTT Technical Research Centre of Finland Ltd, Espoo, Finland
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34
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Sardi M, Gasch AP. Incorporating comparative genomics into the design-test-learn cycle of microbial strain engineering. FEMS Yeast Res 2018. [PMID: 28637316 DOI: 10.1093/femsyr/fox042] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Engineering microbes with new properties is an important goal in industrial engineering, to establish biological factories for production of biofuels, commodity chemicals and pharmaceutics. But engineering microbes to produce new compounds with high yield remains a major challenge toward economically viable production. Incorporating several modern approaches, including synthetic and systems biology, metabolic modeling and regulatory rewiring, has proven to significantly advance industrial strain engineering. This review highlights how comparative genomics can also facilitate strain engineering, by identifying novel genes and pathways, regulatory mechanisms and genetic background effects for engineering. We discuss how incorporating comparative genomics into the design-test-learn cycle of strain engineering can provide novel information that complements other engineering strategies.
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Affiliation(s)
- Maria Sardi
- Great Lakes Bioenergy Research Center, Madison, WI 53706, USA
| | - Audrey P Gasch
- Great Lakes Bioenergy Research Center, Madison, WI 53706, USA.,Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA
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35
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Wang X, He Q, Yang Y, Wang J, Haning K, Hu Y, Wu B, He M, Zhang Y, Bao J, Contreras LM, Yang S. Advances and prospects in metabolic engineering of Zymomonas mobilis. Metab Eng 2018; 50:57-73. [PMID: 29627506 DOI: 10.1016/j.ymben.2018.04.001] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 03/31/2018] [Accepted: 04/01/2018] [Indexed: 12/22/2022]
Abstract
Biorefinery of biomass-based biofuels and biochemicals by microorganisms is a competitive alternative of traditional petroleum refineries. Zymomonas mobilis is a natural ethanologen with many desirable characteristics, which makes it an ideal industrial microbial biocatalyst for commercial production of desirable bioproducts through metabolic engineering. In this review, we summarize the metabolic engineering progress achieved in Z. mobilis to expand its substrate and product ranges as well as to enhance its robustness against stressful conditions such as inhibitory compounds within the lignocellulosic hydrolysates and slurries. We also discuss a few metabolic engineering strategies that can be applied in Z. mobilis to further develop it as a robust workhorse for economic lignocellulosic bioproducts. In addition, we briefly review the progress of metabolic engineering in Z. mobilis related to the classical synthetic biology cycle of "Design-Build-Test-Learn", as well as the progress and potential to develop Z. mobilis as a model chassis for biorefinery practices in the synthetic biology era.
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Affiliation(s)
- Xia Wang
- Hubei Collaborative Innovation Center for Green Transformation of Bio-resources, Environmental Microbial Technology Center of Hubei Province, Hubei Key Laboratory of Industrial Biotechnology, College of Life Sciences, Hubei University, Wuhan 430062, China.
| | - Qiaoning He
- Hubei Collaborative Innovation Center for Green Transformation of Bio-resources, Environmental Microbial Technology Center of Hubei Province, Hubei Key Laboratory of Industrial Biotechnology, College of Life Sciences, Hubei University, Wuhan 430062, China.
| | - Yongfu Yang
- Hubei Collaborative Innovation Center for Green Transformation of Bio-resources, Environmental Microbial Technology Center of Hubei Province, Hubei Key Laboratory of Industrial Biotechnology, College of Life Sciences, Hubei University, Wuhan 430062, China.
| | - Jingwen Wang
- Hubei Collaborative Innovation Center for Green Transformation of Bio-resources, Environmental Microbial Technology Center of Hubei Province, Hubei Key Laboratory of Industrial Biotechnology, College of Life Sciences, Hubei University, Wuhan 430062, China.
| | - Katie Haning
- Institute for Cellular and Molecular Biology, Department of Chemical Engineering, Cockrell School of Engineering, University of Texas at Austin, Austin, TX, United States.
| | - Yun Hu
- Hubei Collaborative Innovation Center for Green Transformation of Bio-resources, Environmental Microbial Technology Center of Hubei Province, Hubei Key Laboratory of Industrial Biotechnology, College of Life Sciences, Hubei University, Wuhan 430062, China.
| | - Bo Wu
- Key Laboratory of Development and Application of Rural Renewable Energy, Biomass Energy Technology Research Centre, Biogas Institute of Ministry of Agriculture, South Renmin Road, Chengdu 610041, China.
| | - Mingxiong He
- Key Laboratory of Development and Application of Rural Renewable Energy, Biomass Energy Technology Research Centre, Biogas Institute of Ministry of Agriculture, South Renmin Road, Chengdu 610041, China.
| | - Yaoping Zhang
- DOE-Great Lakes Bioenergy Research Center (GLBRC), University of Wisconsin-Madison, Madison, WI, United States.
| | - Jie Bao
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
| | - Lydia M Contreras
- Institute for Cellular and Molecular Biology, Department of Chemical Engineering, Cockrell School of Engineering, University of Texas at Austin, Austin, TX, United States.
| | - Shihui Yang
- Hubei Collaborative Innovation Center for Green Transformation of Bio-resources, Environmental Microbial Technology Center of Hubei Province, Hubei Key Laboratory of Industrial Biotechnology, College of Life Sciences, Hubei University, Wuhan 430062, China.
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36
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In situ biomolecule production by bacteria; a synthetic biology approach to medicine. J Control Release 2018; 275:217-228. [DOI: 10.1016/j.jconrel.2018.02.023] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 02/14/2018] [Accepted: 02/15/2018] [Indexed: 02/06/2023]
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37
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Park SY, Yang D, Ha SH, Lee SY. Metabolic Engineering of Microorganisms for the Production of Natural Compounds. ACTA ACUST UNITED AC 2017. [DOI: 10.1002/adbi.201700190] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Seon Young Park
- Metabolic and Biomolecular Engineering National Research Laboratory; Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Institute for the BioCentury; Korea Advanced Institute of Science and Technology (KAIST); Daejeon 34141 Republic of Korea
| | - Dongsoo Yang
- Metabolic and Biomolecular Engineering National Research Laboratory; Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Institute for the BioCentury; Korea Advanced Institute of Science and Technology (KAIST); Daejeon 34141 Republic of Korea
| | - Shin Hee Ha
- Metabolic and Biomolecular Engineering National Research Laboratory; Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Institute for the BioCentury; Korea Advanced Institute of Science and Technology (KAIST); Daejeon 34141 Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory; Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Institute for the BioCentury; Korea Advanced Institute of Science and Technology (KAIST); Daejeon 34141 Republic of Korea
- BioProcess Engineering Research Center; KAIST; Daejeon 34141 Republic of Korea
- BioInformatics Research Center; KAIST; Daejeon 34141 Republic of Korea
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38
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Chen X, Gao C, Guo L, Hu G, Luo Q, Liu J, Nielsen J, Chen J, Liu L. DCEO Biotechnology: Tools To Design, Construct, Evaluate, and Optimize the Metabolic Pathway for Biosynthesis of Chemicals. Chem Rev 2017; 118:4-72. [DOI: 10.1021/acs.chemrev.6b00804] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Xiulai Chen
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Cong Gao
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Liang Guo
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guipeng Hu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Qiuling Luo
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jia Liu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jens Nielsen
- Department
of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden
- Novo
Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800 Lyngby, Denmark
| | - Jian Chen
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Liming Liu
- State
Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi 214122, China
- Department
of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden
- Key
Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
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Lahiry A, Stimple SD, Wood DW, Lease RA. Retargeting a Dual-Acting sRNA for Multiple mRNA Transcript Regulation. ACS Synth Biol 2017; 6:648-658. [PMID: 28067500 DOI: 10.1021/acssynbio.6b00261] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Multitargeting small regulatory RNAs (sRNAs) represent a potentially useful tool for metabolic engineering applications. Natural multitargeting sRNAs govern bacterial gene expression by binding to the translation initiation regions of protein-coding mRNAs through base pairing. We designed an Escherichia coli based genetic system to create and assay dual-acting retargeted-sRNA variants. The variants can be assayed for coordinate translational regulation of two alternate mRNA leaders fused to independent reporter genes. Accordingly, we began with the well-characterized E. coli native DsrA sRNA. The merits of using DsrA include its well-characterized separation of function into two independently folded stem-loop domains, wherein alterations at one stem do not necessarily abolish activity at the other stem. Expression of the sRNA and each reporter mRNA was independently controlled by small inducer molecules, allowing precise quantification of the regulatory effects of each sRNA:mRNA interaction in vivo with a microtiter plate assay. Using this system, we semirationally designed DsrA variants screened in E. coli for their ability to regulate key mRNA leader sequences from the Clostridium acetobutylicum n-butanol synthesis pathway. To coordinate intervention at two points in a metabolic pathway, we created bifunctional sRNA prototypes by combining sequences from two singly retargeted DsrA variants. This approach constitutes a platform for designing sRNAs to specifically target arbitrary mRNA transcript sequences, and thus provides a generalizable tool for retargeting and characterizing multitarget sRNAs for metabolic engineering.
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Affiliation(s)
- Ashwin Lahiry
- Department
of Microbiology, The Ohio State University, 484 W. 12th Avenue, Columbus, Ohio 43210, United States
| | - Samuel D. Stimple
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Avenue, Columbus, Ohio 43210, United States
| | - David W. Wood
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Avenue, Columbus, Ohio 43210, United States
- Department
of Microbiology, The Ohio State University, 484 W. 12th Avenue, Columbus, Ohio 43210, United States
| | - Richard A. Lease
- Department
of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruff Avenue, Columbus, Ohio 43210, United States
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40
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Waseem H, Williams MR, Stedtfeld T, Chai B, Stedtfeld RD, Cole JR, Tiedje JM, Hashsham SA. Virulence factor activity relationships (VFARs): a bioinformatics perspective. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2017; 19:247-260. [PMID: 28261716 PMCID: PMC5897045 DOI: 10.1039/c6em00689b] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Virulence factor activity relationships (VFARs) - a concept loosely based on quantitative structure-activity relationships (QSARs) for chemicals was proposed as a predictive tool for ranking risks due to microorganisms relevant to water safety. A rapid increase in sequencing capabilities and bioinformatics tools has significantly increased the potential for VFAR-based analyses. This review summarizes more than 20 bioinformatics databases and tools, developed over the last decade, along with their virulence and antimicrobial resistance prediction capabilities. With the number of bacterial whole genome sequences exceeding 241 000 and metagenomic analysis projects exceeding 13 000 and the ability to add additional genome sequences for few hundred dollars, it is evident that further development of VFARs is not limited by the availability of information at least at the genomic level. However, additional information related to co-occurrence, treatment response, modulation of virulence due to environmental and other factors, and economic impact must be gathered and incorporated in a manner that also addresses the associated uncertainties. Of the bioinformatics tools, a majority are either designed exclusively for virulence/resistance determination or equipped with a dedicated module. The remaining have the potential to be employed for evaluating virulence. This review focusing broadly on omics technologies and tools supports the notion that these tools are now sufficiently developed to allow the application of VFAR approaches combined with additional engineering and economic analyses to rank and prioritize organisms important to a given niche. Knowledge gaps do exist but can be filled with focused experimental and theoretical analyses that were unimaginable a decade ago. Further developments should consider the integration of the measurement of activity, risk, and uncertainty to improve the current capabilities.
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Affiliation(s)
- Hassan Waseem
- Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA.
| | - Maggie R Williams
- Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA.
| | - Tiffany Stedtfeld
- Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA.
| | - Benli Chai
- Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA
| | - Robert D Stedtfeld
- Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA.
| | - James R Cole
- Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA
| | - James M Tiedje
- Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA and Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Syed A Hashsham
- Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA. and Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA and Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
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41
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Liu Y, Li J, Du G, Chen J, Liu L. Metabolic engineering of Bacillus subtilis fueled by systems biology: Recent advances and future directions. Biotechnol Adv 2017; 35:20-30. [DOI: 10.1016/j.biotechadv.2016.11.003] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 10/21/2016] [Accepted: 11/16/2016] [Indexed: 12/25/2022]
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Recent applications of metabolomics to advance microbial biofuel production. Curr Opin Biotechnol 2016; 43:118-126. [PMID: 27883952 DOI: 10.1016/j.copbio.2016.11.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Revised: 10/31/2016] [Accepted: 11/01/2016] [Indexed: 12/26/2022]
Abstract
Biofuel production from plant biomass is a promising source of renewable energy [1]. However, efficient biofuel production involves the complex task of engineering high-performance microorganisms, which requires detailed knowledge of metabolic function and regulation. This review highlights the potential of mass-spectrometry-based metabolomic analysis to guide rational engineering of biofuel-producing microbes. We discuss recent studies that apply knowledge gained from metabolomic analyses to increase the productivity of engineered pathways, characterize the metabolism of emerging biofuel producers, generate novel bioproducts, enable utilization of lignocellulosic feedstock, and improve the stress tolerance of biofuel producers.
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Biofuel metabolic engineering with biosensors. Curr Opin Chem Biol 2016; 35:150-158. [PMID: 27768949 DOI: 10.1016/j.cbpa.2016.09.020] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Revised: 09/15/2016] [Accepted: 09/22/2016] [Indexed: 11/21/2022]
Abstract
Metabolic engineering offers the potential to renewably produce important classes of chemicals, particularly biofuels, at an industrial scale. DNA synthesis and editing techniques can generate large pathway libraries, yet identifying the best variants is slow and cumbersome. Traditionally, analytical methods like chromatography and mass spectrometry have been used to evaluate pathway variants, but such techniques cannot be performed with high throughput. Biosensors - genetically encoded components that actuate a cellular output in response to a change in metabolite concentration - are therefore a promising tool for rapid and high-throughput evaluation of candidate pathway variants. Applying biosensors can also dynamically tune pathways in response to metabolic changes, improving balance and productivity. Here, we describe the major classes of biosensors and briefly highlight recent progress in applying them to biofuel-related metabolic pathway engineering.
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