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Synthetic Biology for Natural Compounds Xinrui Zhao,†,‡ Seon Young Park,†,‡ Dongsoo Yang,†,‡ and 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), 34141 Daejeon, Republic of Korea ‡ Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea § BioProcess Engineering Research Center and BioInformatics Research Center, Korea Advanced Institute of Science and Technology (KAIST), 34141 Daejeon, Republic of Korea
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key intermediates. Furthermore, optimal gene expression can be achieved by testing different combinations of regulatory elements (e.g., promoter and 5′ untranslated region) libraries. In addition to construction and optimization of key heterologous biosynthetic pathways through synthetic biology, systems approaches aiming to optimize the entire cellular metabolism are required. By employing powerful tools and strategies employed for systems metabolic engineering, such as the recently developed synthetic small regulatory RNAs and various genome engineering tools such as CRISPR−Cas9, multiplex manipulation of the cellular fluxes for the overproduction of target natural compounds can be performed. Because many biosynthetic pathways of natural compounds are long and complex, expression of all the enzymes in a single host usually results in poor cell growth and a lower titer of the desired product. If the pathway is very long, splitting the pathway to develop multiple (usually two) strains followed by stepwise fermentation and/or co-culture can be a practical option for alleviating metabolic burden. For example, applying a stepwise culture of four engineered E. coli strains significantly increased the yields of opiates. As for co-culture, the highest titer of oxygenated taxanes could be obtained by the utilization of an E. coli and S. cerevisiae dual-microbe system.2 If we think about the natural compounds present in many medicinal plants that have been used in the Orient as “traditional Oriental medicine”, a large number of pharmaceutical or nutraceutical natural compounds are yet to be discovered. To produce such natural compounds that have not been identified or biosynthesized, various strategies have been developed (Figure 1B). On the basis of huge amounts of omics data (genomics, transcriptomics, proteomics, and metabolomics), putative genes involved in unidentified pathways for the biosynthesis of natural compounds can sometimes be discovered and annotated. Also, a great number of silent genes that encode cryptic biosynthetic pathways of unknown high-value natural compounds can be discovered by some methods, such as the reporter-guided mutant selection (RGMS) and the yeast homologous recombination-based promoter engineering techniques. Furthermore, many in silico tools, including antibiotics and secondary metabolites analysis
large number of natural compounds derived from plants, fungi, and microorganisms possess broad pharmaceutical and medical applications. However, conventional methods, including chemical synthesis and extraction from natural sources, are complicated, costly, and time-consuming. Thus, there has been increasing demand for their industrial-scale production by microbial fermentation. Because many natural products of plant origin are not synthesized by microorganisms, synthetic biology has attracted much attention in terms of the development of engineered microorganisms capable of producing these natural compounds. Over the past several decades, an increasing number of natural compounds and their precursors, including the recently biosynthesized opioids and Taxol precursors, have been produced by microorganisms developed through applying synthetic biology.1 Commercialization of natural compounds through microbial fermentation is still limited to only a few cases, such as artemisinin and resveratrol. One of the reasons for the lack of many commercial products is the low titer and productivity achieved by engineered microorganisms. Also, microbial production of many natural products of interest even cannot be attempted because key catalytic enzymes in biosynthetic pathways of these compounds remain unidentified. Major bottlenecks for overproducing natural compounds by industrially preferred microorganisms, such as Escherichia coli, Corynebacteria, Pseudomonas, Saccharomyces cerevisiae, include low activities of heterologous enzymes involved in natural product biosynthesis, unbalanced metabolic fluxes, and excessive metabolic burden caused by long and complex reaction steps (Figure 1A). To overcome these obstacles, many novel synthetic biology tools and strategies have been developed. First, engineering and reprogramming of key heterologous enzymes, including polyketide synthase (PKS) and cytochrome P450, can enhance their activities. On the basis of simulation software programs (e.g., ClusterCAD), metabolite biosensors (e.g., L-dihydroxyphenylalanine dioxygenase-coupled biosensor), and high-throughput screening technologies (e.g., droplet-based microfluidic platform), enzyme variants having improved activities can be obtained. In addition, it is important to eliminate metabolic flux imbalance by dynamical control of the expression of target genes. Through recent development of tools, including various metabolic toggle switches (e.g., riboswitches) and metabolite-responsive promoters (e.g., farnesyl pyrophosphate-responsive promoters), metabolic flux can be regulated in situ based on intracellular concentrations of © XXXX American Chemical Society
Special Issue: The Chemistry of Synthetic Biology Received: May 20, 2018
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DOI: 10.1021/acs.biochem.8b00569 Biochemistry XXXX, XXX, XXX−XXX
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Biochemistry
Figure 1. Summary of the current synthetic biology strategies and tools for the production of natural compounds. (A) Strategies for optimizing the biosynthetic pathways of known natural compounds. For enzyme reprogramming, (i) in silico simulations can be utilized to identify active sites of the enzymes. (ii) High-throughput screening through sensitive biosensor cells and (iii) droplet-based microfluidics are useful for sorting beneficial enzyme variants among large libraries. For dynamic balancing of the metabolic flux within the target biosynthetic pathways, (iv) diverse riboswitches and (v) regulatory promoters can be employed. In addition, (vi) an optimal combination of promoters and 5′ untranslated region (5′ UTR) sequences can be selected by screening the variants. For long and complex biosynthetic pathways, (vii) the genes can be partitioned into separate strains, and stepwise cultivation or (viii) a co-culture method can be employed. (B) Strategies for discovering unknown or silent genes and compounds. (i) High-throughput analysis of genomics, transcriptomics, and metabolomics can be used to uncover the genes and compounds. (ii) Bioinformatics tools also can be utilized to mine biosynthetic gene clusters in genomic databases. (iii) Chimeric megasynthases or (iv) novel enzymes designed ab initio can be employed to produce desirable natural compounds.
heterologous expression (HEx) in S. cerevisiae can be employed to validate the products of predicted biosynthetic genes.4 Furthermore, novel natural compounds can be produced by modular reconstruction of natural or modified megasynthases such as PKS and nonribosomal peptide synthase (NRPS). When better strategies for structure-based protein design become available in the future, completely new enzymes can
shell (anti-SMASH), can be used to efficiently identify biosynthetic gene clusters for novel natural compounds. The latest updated version of antiSMASH 4.0 can even predict gene clusters that are responsible for the biosynthesis of posttranslationally modified products and accurate boundaries of biosynthetic gene clusters.3 Once the biosynthetic gene clusters are identified, experimental platforms such as scalable B
DOI: 10.1021/acs.biochem.8b00569 Biochemistry XXXX, XXX, XXX−XXX
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Biochemistry
(2) Zhou, K., Qiao, K. J., Edgar, S., and Stephanopoulos, G. (2015) Distributing a metabolic pathway among a microbial consortium enhances production of natural products. Nat. Biotechnol. 33, 377− 383. (3) Blin, K., Wolf, T., Chevrette, M. G., Lu, X., Schwalen, C. J., Kautsar, S. A., Suarez Duran, H. G., De Los Santos, E. L. C., Kim, H. U., Nave, M., Dickschat, J. S., Mitchell, D. A., Shelest, E., Breitling, R., Takano, E., Lee, S. Y., Weber, T., and Medema, M. H. (2017) antiSMASH 4.0improvements in chemistry prediction and gene cluster boundary identification. Nucleic Acids Res. 45, W36−W41. (4) Harvey, C. J. B., Tang, M., Schlecht, U., Horecka, J., Fischer, C. R., Lin, H. C., Li, J., Naughton, B., Cherry, J., Miranda, M., Li, Y. F., Chu, A. M., Hennessy, J. R., Vandova, G. A., Inglis, D., Aiyar, R. S., Steinmetz, L. M., Davis, R. W., Medema, M. H., Sattely, E., Khosla, C., St, St. Onge, R. P., Tang, Y., and Hillenmeyer, M. E. (2018) HEx: A heterologous expression platform for the discovery of fungal natural products. Sci. Adv. 4, eaar5459. (5) Shen, Y., Wang, Y., Chen, T., Gao, F., Gong, J. H., Abramczyk, D., Walker, R., Zhao, H. C., Chen, S. H., Liu, W., Luo, Y. S., Muller, C. A., Paul-Dubois-Taine, A., Alver, B., Stracquadanio, G., Mitchell, L. A., Luo, Z. Q., Fan, Y. Q., Zhou, B. J., Wen, B., Tan, F. J., Wang, Y. J., Zi, J., Xie, Z. X., Li, B. Z., Yang, K., Richardson, S. M., Jiang, H., French, C. E., Nieduszynski, C. A., Koszul, R., Marston, A. L., Yuan, Y. J., Wang, J., Bader, J. S., Dai, J. B., Boeke, J. D., Xu, X., Cai, Y. Z., and Yang, H. M. (2017) Deep functional analysis of synII, a 770-kilobase synthetic yeast chromosome. Science 355, eaaf4791.
be designed or created ab initio to perform desired reactions and consequently produce novel natural compounds. Although synthetic biology tools are being developed at an unprecedented speed, synergetic outcomes between different fields of study such as systems biology, metabolic engineering, evolutionary engineering, and computer science are expected to further enhance the production of complex or novel natural compounds. Integration of these technologies is expected to aid in fully understanding complex gene regulatory networks and metabolic networks. For example, the recently re-emerged machine learning technology can be employed to better interpret a large volume of chemical data, protein structure data, and also data from genomic, transcriptomic, proteomic, metabolomic, and fluxomic analyses. As recently demonstrated, machine learning can perform a synthetic chemist’s job to design new synthesis reactions. Such strategies can be employed to design artificial biosynthetic pathways synthesizing natural products that have not yet been produced. This will open up a new era in synthetic biology for the production of natural compounds. Although synthetic biology and metabolic engineering of natural microorganisms will be major strategies for the enhanced production of natural products, synthetic microorganisms can also be employed in the era of genome writing.5 Taken together, synthetic biology will play increasingly important roles in the production of diverse natural compounds by fermentation of engineered microorganisms. These engineered microorganisms equipped with heterologous biosynthetic pathways will be optimized through various strategies, including enzyme engineering, pathway engineering, regulatory circuit rewiring, metabolic flux tuning, and chassis (host) modification, to name a few. In an aging society, it is expected that the demand for natural compounds will continue to increase because of the benefits of enhanced nutrition, better health, and better well-being. Development of microbial cell factories efficiently producing diverse natural compounds will allow us to better access these valuable compounds for their use in pharmaceutical, food, cosmetic, and chemical industries.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Sang Yup Lee: 0000-0003-0599-3091 Funding
This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors thank Jae Sung Cho for valuable advice. REFERENCES
(1) Park, S. Y., Yang, D., Ha, S. H., and Lee, S. Y. (2018) Metabolic engineering of microorganisms for the production of natural compounds. Adv. Biosyst. 2, 1700190. C
DOI: 10.1021/acs.biochem.8b00569 Biochemistry XXXX, XXX, XXX−XXX
